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1.  Home Telehealth for Patients With Chronic Obstructive Pulmonary Disease (COPD) 
Executive Summary
In July 2010, the Medical Advisory Secretariat (MAS) began work on a Chronic Obstructive Pulmonary Disease (COPD) evidentiary framework, an evidence-based review of the literature surrounding treatment strategies for patients with COPD. This project emerged from a request by the Health System Strategy Division of the Ministry of Health and Long-Term Care that MAS provide them with an evidentiary platform on the effectiveness and cost-effectiveness of COPD interventions.
After an initial review of health technology assessments and systematic reviews of COPD literature, and consultation with experts, MAS identified the following topics for analysis: vaccinations (influenza and pneumococcal), smoking cessation, multidisciplinary care, pulmonary rehabilitation, long-term oxygen therapy, noninvasive positive pressure ventilation for acute and chronic respiratory failure, hospital-at-home for acute exacerbations of COPD, and telehealth (including telemonitoring and telephone support). Evidence-based analyses were prepared for each of these topics. For each technology, an economic analysis was also completed where appropriate. In addition, a review of the qualitative literature on patient, caregiver, and provider perspectives on living and dying with COPD was conducted, as were reviews of the qualitative literature on each of the technologies included in these analyses.
The Chronic Obstructive Pulmonary Disease Mega-Analysis series is made up of the following reports, which can be publicly accessed at the MAS website at:
Chronic Obstructive Pulmonary Disease (COPD) Evidentiary Framework
Influenza and Pneumococcal Vaccinations for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Smoking Cessation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Community-Based Multidisciplinary Care for Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Pulmonary Rehabilitation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Long-term Oxygen Therapy for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Acute Respiratory Failure Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Chronic Respiratory Failure Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Hospital-at-Home Programs for Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Home Telehealth for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Cost-Effectiveness of Interventions for Chronic Obstructive Pulmonary Disease Using an Ontario Policy Model
Experiences of Living and Dying With COPD: A Systematic Review and Synthesis of the Qualitative Empirical Literature
For more information on the qualitative review, please contact Mita Giacomini at:
For more information on the economic analysis, please visit the PATH website:
The Toronto Health Economics and Technology Assessment (THETA) collaborative has produced an associated report on patient preference for mechanical ventilation. For more information, please visit the THETA website:
The objective of this analysis was to conduct an evidence-based assessment of home telehealth technologies for patients with chronic obstructive pulmonary disease (COPD) in order to inform recommendations regarding the access and provision of these services in Ontario. This analysis was one of several analyses undertaken to evaluate interventions for COPD. The perspective of this assessment was that of the Ontario Ministry of Health and Long-Term Care, a provincial payer of medically necessary health care services.
Clinical Need: Condition and Target Population
Canada is facing an increase in chronic respiratory diseases due in part to its aging demographic. The projected increase in COPD will put a strain on health care payers and providers. There is therefore an increasing demand for telehealth services that improve access to health care services while maintaining or improving quality and equality of care. Many telehealth technologies however are in the early stages of development or diffusion and thus require study to define their application and potential harms or benefits. The Medical Advisory Secretariat (MAS) therefore sought to evaluate telehealth technologies for COPD.
Telemedicine (or telehealth) refers to using advanced information and communication technologies and electronic medical devices to support the delivery of clinical care, professional education, and health-related administrative services.
Generally there are 4 broad functions of home telehealth interventions for COPD:
to monitor vital signs or biological health data (e.g., oxygen saturation),
to monitor symptoms, medication, or other non-biologic endpoints (e.g., exercise adherence),
to provide information (education) and/or other support services (such as reminders to exercise or positive reinforcement), and
to establish a communication link between patient and provider.
These functions often require distinct technologies, although some devices can perform a number of these diverse functions. For the purposes of this review, MAS focused on home telemonitoring and telephone only support technologies.
Telemonitoring (or remote monitoring) refers to the use of medical devices to remotely collect a patient’s vital signs and/or other biologic health data and the transmission of those data to a monitoring station for interpretation by a health care provider.
Telephone only support refers to disease/disorder management support provided by a health care provider to a patient who is at home via telephone or videoconferencing technology in the absence of transmission of patient biologic data.
Research Questions
What is the effectiveness, cost-effectiveness, and safety of home telemonitoring compared with usual care for patients with COPD?
What is the effectiveness, cost-effectiveness, and safety of telephone only support programs compared with usual care for patients with COPD?
Research Methods
Literature Search
Search Strategy
A literature search was performed on November 3, 2010 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from January 1, 2000 until November 3, 2010. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria, full-text articles were obtained. Reference lists were also examined for any additional relevant studies not identified through the search. Articles with unknown eligibility were reviewed with a second clinical epidemiologist, and then a group of epidemiologists until consensus was established. The quality of evidence was assessed as high, moderate, low, or very low according to GRADE methodology.
Inclusion Criteria – Question #1
frequent transmission of a patient’s physiological data collected at home and without a health care professional physically present to health care professionals for routine monitoring through the use of a communication technology;
monitoring combined with a coordinated management and feedback system based on transmitted data;
telemonitoring as a key component of the intervention (subjective determination);
usual care as provided by the usual care provider for the control group;
randomized controlled trials (RCTs), controlled clinical trials (CCTs), systematic reviews, and/or meta-analyses;
published between January 1, 2000 and November 3, 2010.
Inclusion Criteria – Question #2
scheduled or frequent contact between patient and a health care professional via telephone or videoconferencing technology in the absence of transmission of patient physiological data;
monitoring combined with a coordinated management and feedback system based on transmitted data;
telephone support as a key component of the intervention (subjective determination);
usual care as provided by the usual care provider for the control group;
RCTs, CCTs, systematic reviews, and/or meta-analyses;
published between January 1, 2000 and November 3, 2010.
Exclusion Criteria
published in a language other than English;
intervention group (and not control) receiving some form of home visits by a medical professional, typically a nurse (i.e., telenursing) beyond initial technology set-up and education, to collect physiological data, or to somehow manage or treat the patient;
not recording patient or health system outcomes (e.g., technical reports testing accuracy, reliability or other development-related outcomes of a device, acceptability/feasibility studies, etc.);
not using an independent control group that received usual care (e.g., studies employing historical or periodic controls).
Outcomes of Interest
hospitalizations (primary outcome)
emergency department visits
length of stay
quality of life
other […]
Subgroup Analyses (a priori)
length of intervention (primary)
severity of COPD (primary)
Quality of Evidence
The quality of evidence assigned to individual studies was determined using a modified CONSORT Statement Checklist for Randomized Controlled Trials. (1) The CONSORT Statement was adapted to include 3 additional quality measures: the adequacy of control group description, significant differential loss to follow-up between groups, and greater than or equal to 30% study attrition. Individual study quality was defined based on total scores according to the CONSORT Statement checklist: very low (0 to < 40%), low (≥ 40 to < 60%), moderate (≥ 60 to < 80%), and high (≥ 80 to 100%).
The quality of the body of evidence was assessed as high, moderate, low, or very low according to the GRADE Working Group criteria. The following definitions of quality were used in grading the quality of the evidence:
Summary of Findings
Six publications, representing 5 independent trials, met the eligibility criteria for Research Question #1. Three trials were RCTs reported across 4 publications, whereby patients were randomized to home telemonitoring or usual care, and 2 trials were CCTs, whereby patients or health care centers were nonrandomly assigned to intervention or usual care.
A total of 310 participants were studied across the 5 included trials. The mean age of study participants in the included trials ranged from 61.2 to 74.5 years for the intervention group and 61.1 to 74.5 years for the usual care group. The percentage of men ranged from 40% to 64% in the intervention group and 46% to 72% in the control group.
All 5 trials were performed in a moderate to severe COPD patient population. Three trials initiated the intervention following discharge from hospital. One trial initiated the intervention following a pulmonary rehabilitation program. The final trial initiated the intervention during management of patients at an outpatient clinic.
Four of the 5 trials included oxygen saturation (i.e., pulse oximetry) as one of the biological patient parameters being monitored. Additional parameters monitored included forced expiratory volume in one second, peak expiratory flow, and temperature.
There was considerable clinical heterogeneity between trials in study design, methods, and intervention/control. In relation to the telemonitoring intervention, 3 of the 5 included studies used an electronic health hub that performed multiple functions beyond the monitoring of biological parameters. One study used only a pulse oximeter device alone with modem capabilities. Finally, in 1 study, patients measured and then forwarded biological data to a nurse during a televideo consultation. Usual care varied considerably between studies.
Only one trial met the eligibility criteria for Research Question #2. The included trial was an RCT that randomized 60 patients to nurse telephone follow-up or usual care (no telephone follow-up). Participants were recruited from the medical department of an acute-care hospital in Hong Kong and began receiving follow-up after discharge from the hospital with a diagnosis of COPD (no severity restriction). The intervention itself consisted of only two 10-to 20-minute telephone calls, once between days 3 to 7 and once between days 14 to 20, involving a structured, individualized educational and supportive programme led by a nurse that focused on 3 components: assessment, management options, and evaluation.
Regarding Research Question #1:
Low to very low quality evidence (according to GRADE) finds non-significant effects or conflicting effects (of significant or non-significant benefit) for all outcomes examined when comparing home telemonitoring to usual care.
There is a trend towards significant increase in time free of hospitalization and use of other health care services with home telemonitoring, but these findings need to be confirmed further in randomized trials of high quality.
There is severe clinical heterogeneity between studies that limits summary conclusions.
The economic impact of home telemonitoring is uncertain and requires further study.
Home telemonitoring is largely dependent on local information technologies, infrastructure, and personnel, and thus the generalizability of external findings may be low. Jurisdictions wishing to replicate home telemonitoring interventions should likely test those interventions within their jurisdictional framework before adoption, or should focus on home-grown interventions that are subjected to appropriate evaluation and proven effective.
Regarding Research Question #2:
Low quality evidence finds significant benefit in favour of telephone-only support for self-efficacy and emergency department visits when compared to usual care, but non-significant results for hospitalizations and hospital length of stay.
There are very serious issues with the generalizability of the evidence and thus additional research is required.
PMCID: PMC3384362  PMID: 23074421
2.  What Is eHealth (4): A Scoping Exercise to Map the Field 
Lack of consensus on the meaning of eHealth has led to uncertainty among academics, policymakers, providers and consumers. This project was commissioned in light of the rising profile of eHealth on the international policy agenda and the emerging UK National Programme for Information Technology (now called Connecting for Health) and related developments in the UK National Health Service.
To map the emergence and scope of eHealth as a topic and to identify its place within the wider health informatics field, as part of a larger review of research and expert analysis pertaining to current evidence, best practice and future trends.
Multiple databases of scientific abstracts were explored in a nonsystematic fashion to assess the presence of eHealth or conceptually related terms within their taxonomies, to identify journals in which articles explicitly referring to eHealth are contained and the topics covered, and to identify published definitions of the concept. The databases were Medline (PubMed), the Cumulative Index of Nursing and Allied Health Literature (CINAHL), the Science Citation Index (SCI), the Social Science Citation Index (SSCI), the Cochrane Database (including Dare, Central, NHS Economic Evaluation Database [NHS EED], Health Technology Assessment [HTA] database, NHS EED bibliographic) and ISTP (now known as ISI proceedings).We used the search query, “Ehealth OR e-health OR e*health”. The timeframe searched was 1997-2003, although some analyses contain data emerging subsequent to this period. This was supplemented by iterative searches of Web-based sources, such as commercial and policy reports, research commissioning programmes and electronic news pages. Definitions extracted from both searches were thematically analyzed and compared in order to assess conceptual heterogeneity.
The term eHealth only came into use in the year 2000, but has since become widely prevalent. The scope of the topic was not immediately discernable from that of the wider health informatics field, for which over 320000 publications are listed in Medline alone, and it is not explicitly represented within the existing Medical Subject Headings (MeSH) taxonomy. Applying eHealth as narrative search term to multiple databases yielded 387 relevant articles, distributed across 154 different journals, most commonly related to information technology and telemedicine, but extending to such areas as law. Most eHealth articles are represented on Medline. Definitions of eHealth vary with respect to the functions, stakeholders, contexts and theoretical issues targeted. Most encompass a broad range of medical informatics applications either specified (eg, decision support, consumer health information) or presented in more general terms (eg, to manage, arrange or deliver health care). However the majority emphasize the communicative functions of eHealth and specify the use of networked digital technologies, primarily the Internet, thus differentiating eHealth from the field of medical informatics. While some definitions explicitly target health professionals or patients, most encompass applications for all stakeholder groups. The nature of the scientific and broader literature pertaining to eHealth closely reflects these conceptualizations.
We surmise that the field – as it stands today – may be characterized by the global definitions suggested by Eysenbach and Eng.
PMCID: PMC1550637  PMID: 15829481
eHealth; Internet; telemedicine; medical informatics
3.  SYMBIOmatics: Synergies in Medical Informatics and Bioinformatics – exploring current scientific literature for emerging topics 
BMC Bioinformatics  2007;8(Suppl 1):S18.
The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to ). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics.
This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000–2005 ("recent") and 1990–1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays.
We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science.
PMCID: PMC1885847  PMID: 17430562
4.  Ecologies, outreach, and the evolution of medical libraries* 
Journal of the Medical Library Association  2005;93(4 Suppl):S86-S92.
Question: What are some of the forces shaping the evolution of medical libraries, and where might they lead?
Data Sources: Published literature in the fields of library and information sciences, technology, health services research, and business was consulted.
Main Results: Medical libraries currently have a modest footprint in most consumers' personal health ecologies, the network of resources and activities they use to improve their health. They also occupy a relatively small space in the health care, information, and business ecologies of which they are a part. Several trends in knowledge discovery, technology, and social organizations point to ways in which the roles of medical libraries might grow and become more complex.
Conclusion: As medical libraries evolve and reach out to previously underserved communities, an ecological approach can serve as a useful organizing framework for the forces shaping this evolution.
PMCID: PMC1255758  PMID: 16239963
5.  Medicus Deus: a review of factors affecting hospital library services to patients between 1790–1950 
Question: What are some of the historical societal, medical, and public health trends leading to today's provision of hospital library services to patients?
Data Sources: Literature from the archives of the Bulletin of the Medical Library Association and other library sources, medical journals, primary historical documents, and texts from the history of medicine form the core of this review.
Study Selection: The period of review extends from about 1790 through 1950 and focuses solely on trends in the United States. Of primary concern are explicitly documented examples that appear to illustrate the patient-physician relationship and those between librarians and their patient-patrons during the earliest years of the profession's development.
Data Extraction: An historical timeline was created to allow the identification of major trends that may have affected library services. Multiple literature searches were conducted using library, medical, and health anthropology resources. When possible, primary sources were preferred over reviews.
Main Results: Juxtapositioning historical events allows the reader to obtain an overview of the roots of consumer health services in medical libraries and to consider their potential legacy in today's health care libraries.
Conclusion: This review article highlights early developments in hospital library service to patients. Further research is needed to verify a preliminary conclusion that in some medical library settings, services to the general public are shaped by the broader health care environment as it has evolved.
PMCID: PMC1525305  PMID: 16888658
6.  Growing a Professional Network to Over 3000 Members in Less Than 4 Years: Evaluation of InspireNet, British Columbia’s Virtual Nursing Health Services Research Network 
Use of Web 2.0 and social media technologies has become a new area of research among health professionals. Much of this work has focused on the use of technologies for health self-management and the ways technologies support communication between care providers and consumers. This paper addresses a new use of technology in providing a platform for health professionals to support professional development, increase knowledge utilization, and promote formal/informal professional communication. Specifically, we report on factors necessary to attract and sustain health professionals’ use of a network designed to increase nurses’ interest in and use of health services research and to support knowledge utilization activities in British Columbia, Canada.
“InspireNet”, a virtual professional network for health professionals, is a living laboratory permitting documentation of when and how professionals take up Web 2.0 and social media. Ongoing evaluation documents our experiences in establishing, operating, and evaluating this network.
Overall evaluation methods included (1) tracking website use, (2) conducting two member surveys, and (3) soliciting member feedback through focus groups and interviews with those who participated in electronic communities of practice (eCoPs) and other stakeholders. These data have been used to learn about the types of support that seem relevant to network growth.
Network growth exceeded all expectations. Members engaged with varying aspects of the network’s virtual technologies, such as teams of professionals sharing a common interest, research teams conducting their work, and instructional webinars open to network members. Members used wikis, blogs, and discussion groups to support professional work, as well as a members’ database with contact information and areas of interest. The database is accessed approximately 10 times per day. InspireNet public blog posts are accessed roughly 500 times each. At the time of writing, 21 research teams conduct their work virtually using the InspireNet platform; 10 topic-based Action Teams meet to address issues of mutual concern. Nursing and other health professionals, even those who rated themselves as computer literate, required significant mentoring and support in their efforts to adopt their practice to a virtual environment. There was a steep learning curve for professionals to learn to work in a virtual environment and to benefit from the available technologies.
Virtual professional networks can be positioned to make a significant contribution to ongoing professional practice and to creating environments supportive of information sharing, mentoring, and learning across geographical boundaries. Nonetheless, creation of a Web 2.0 and social media platform is not sufficient, in and of itself, to attract or sustain a vibrant community of professionals interested in improving their practice. Essential support includes instruction in the use of Web-based activities and time management, a biweekly e-Newsletter, regular communication from leaders, and an annual face-to-face conference.
PMCID: PMC3961696  PMID: 24566806
social networking; social media; nursing; health services; research; education
7.  Metropolis redux: the unique importance of library skills in informatics 
Objectives: The objective is to highlight the important role that librarians have in teaching within a successful medical informatics program. Librarians regularly utilize skills that, although not technology dependent, are essential to conducting computer-based research. The Metropolis analogy is used to introduce the part librarians play as informatics partners. Science fiction is a modern mythology that, beyond a technical exterior, has lasting value in its ability to reflect the human condition. The teaching of medical informatics, an intersection of technology and knowledge, is also most relevant when it transcends the operation of databases and systems. Librarians can teach students to understand, research, and utilize information beyond specific technologies.
Methods: A survey of twenty-six informatics programs was conducted during 2002, with specific emphasis on the role of the library service.
Results: The survey demonstrated that librarians currently do have a central role in informatics instruction, and that library-focused skills form a significant part of the curriculum in many of those programs. In addition, librarians have creative opportunities to enhance their involvement in informatics training. As a sample program in the study, the development of the informatics course at the Massachusetts College of Pharmacy and Health Sciences is included.
Conclusions: Medical informatics training is a wonderful opportunity for librarians to collaborate with professionals from the sciences and other information disciplines. Librarians' unique combination of human research and technology skills provides a valuable contribution to any program.
PMCID: PMC385302  PMID: 15098050
8.  Tobacco Company Efforts to Influence the Food and Drug Administration-Commissioned Institute of Medicine Report Clearing the Smoke: An Analysis of Documents Released through Litigation 
PLoS Medicine  2013;10(5):e1001450.
Stanton Glantz and colleagues investigate efforts by tobacco companies to influence Clearing the Smoke, a 2001 Institute of Medicine report on harm reduction tobacco products.
Please see later in the article for the Editors' Summary
Spurred by the creation of potential modified risk tobacco products, the US Food and Drug Administration (FDA) commissioned the Institute of Medicine (IOM) to assess the science base for tobacco “harm reduction,” leading to the 2001 IOM report Clearing the Smoke. The objective of this study was to determine how the tobacco industry organized to try to influence the IOM committee that prepared the report.
Methods and Findings
We analyzed previously secret tobacco industry documents in the University of California, San Francisco Legacy Tobacco Documents Library, and IOM public access files. (A limitation of this method includes the fact that the tobacco companies have withheld some possibly relevant documents.) Tobacco companies considered the IOM report to have high-stakes regulatory implications. They developed and implemented strategies with consulting and legal firms to access the IOM proceedings. When the IOM study staff invited the companies to provide information on exposure and disease markers, clinical trial design for safety and efficacy, and implications for initiation and cessation, tobacco company lawyers, consultants, and in-house regulatory staff shaped presentations from company scientists. Although the available evidence does not permit drawing cause-and-effect conclusions, and the IOM may have come to the same conclusions without the influence of the tobacco industry, the companies were pleased with the final report, particularly the recommendations for a tiered claims system (with separate tiers for exposure and risk, which they believed would ease the process of qualifying for a claim) and license to sell products comparable to existing conventional cigarettes (“substantial equivalence”) without prior regulatory approval. Some principles from the IOM report, including elements of the substantial equivalence recommendation, appear in the 2009 Family Smoking Prevention and Tobacco Control Act.
Tobacco companies strategically interacted with the IOM to win several favored scientific and regulatory recommendations.
Please see later in the article for the Editors' Summary
Editors' Summary
Up to half of tobacco users will die of cancer, lung disease, heart disease, stroke, or another tobacco-related disease. Cigarettes and other tobacco products cause disease because they expose their users to nicotine and numerous other toxic chemicals. Tobacco companies have been working to develop a “safe” cigarette for more than half a century. Initially, their attention focused on cigarettes that produced lower tar and nicotine yields in machine-smoking tests. These products were perceived as “safer” products by the public and scientists for many years, but it is now known that the use of low-yield cigarettes can actually expose smokers to higher levels of toxins than standard cigarettes. More recently, the tobacco companies have developed other products (for example, products that heat aerosols of nicotine, rather than burning the tobacco) that claim to reduce harm and the risk of tobacco-related disease, but they can only market these modified risk tobacco products in the US after obtaining Food and Drug Administration (FDA) approval. In 1999, the FDA commissioned the US Institute of Medicine (IOM, an influential source of independent expert advice on medical issues) to assess the science base for tobacco “harm reduction.” In 2001, the IOM published its report Clearing the Smoke: Assessing the Science Base for Tobacco Harm and Reduction, which, although controversial, set the tone for the development and regulation of tobacco products in the US, particularly those claiming to be less dangerous, in subsequent years.
Why Was This Study Done?
Tobacco companies have a long history of working to shape scientific discussions and agendas. For example, they have produced research results designed to “create controversy” about the dangers of smoking and secondhand smoke. In this study, the researchers investigate how tobacco companies organized to try to influence the IOM committee that prepared the Clearing the Smoke report on modified risk tobacco products by analyzing tobacco industry and IOM documents.
What Did the Researchers Do and Find?
The researchers searched the Legacy Tobacco Documents Library (a collection of internal tobacco industry documents released as a result of US litigation cases) for documents outlining how tobacco companies tried to influence the IOM Committee to Assess the Science Base for Tobacco Harm Reduction and created a timeline of events from the 1,000 or so documents they retrieved. They confirmed and supplemented this timeline using information in 80 files that detailed written interactions between the tobacco companies and the IOM committee, which they obtained through a public records access request. Analysis of these documents indicates that the tobacco companies considered the IOM report to have important regulatory implications, that they developed and implemented strategies with consulting and legal firms to access the IOM proceedings, and that tobacco company lawyers, consultants, and regulatory staff shaped presentations to the IOM committee by company scientists on various aspects of tobacco harm reduction products. The analysis also shows that tobacco companies were pleased with the final report, particularly its recommendation that tobacco products can be marketed with exposure or risk reduction claims provided the products substantially reduce exposure and provided the behavioral and health consequences of these products are determined in post-marketing surveillance and epidemiological studies (“tiered testing”) and its recommendation that, provided no claim of reduced exposure or risk is made, new products comparable to existing conventional cigarettes (“substantial equivalence”) can be marketed without prior regulatory approval.
What Do These Findings Mean?
These findings suggest that tobacco companies used their legal and regulatory staff to access the IOM committee that advised the FDA on modified risk tobacco products and that they used this access to deliver specific, carefully formulated messages designed to serve their business interests. Although these findings provide no evidence that the efforts of tobacco companies influenced the IOM committee in any way, they show that the companies were satisfied with the final IOM report and its recommendations, some of which have policy implications that continue to reverberate today. The researchers therefore call for the FDA and other regulatory bodies to remember that they are dealing with companies with a long history of intentionally misleading the public when assessing the information presented by tobacco companies as part of the regulatory process and to actively protect their public-health policies from the commercial interests of the tobacco industry.
Additional Information
Please access these Web sites via the online version of this summary at
This study is further discussed in a PLOS Medicine Perspective by Thomas Novotny
The World Health Organization provides information about the dangers of tobacco (in several languages); for information about the tobacco industry's influence on policy, see the 2009 World Health Organization report Tobacco interference with tobacco control
A PLOS Medicine Research Article by Heide Weishaar and colleagues describes tobacco company efforts to undermine the Framework Convention on Tobacco Control, an international instrument for tobacco control
Wikipedia has a page on tobacco harm reduction (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
The IOM report Clearing the Smoke: Assessing the Science Base for Tobacco Harm Reduction is available to read online
The Legacy Tobacco Documents Library is a public, searchable database of tobacco company internal documents detailing their advertising, manufacturing, marketing, sales, and scientific activities
The University of California, San Francisco Center for Tobacco Control Research and Education is the focal point for University of California, San Francisco (UCSF) scientists in disciplines ranging from the molecular biology of nicotine addiction through political science who combine their efforts to eradicate the use of tobacco and tobacco-induced cancer and other diseases worldwide
SmokeFree, a website provided by the UK National Health Service, offers advice on quitting smoking and includes personal stories from people who have stopped smoking, from the US National Cancer Institute, offers online tools and resources to help people quit smoking
PMCID: PMC3665841  PMID: 23723740
9.  Epidemiology and Reporting Characteristics of Systematic Reviews 
PLoS Medicine  2007;4(3):e78.
Systematic reviews (SRs) have become increasingly popular to a wide range of stakeholders. We set out to capture a representative cross-sectional sample of published SRs and examine them in terms of a broad range of epidemiological, descriptive, and reporting characteristics, including emerging aspects not previously examined.
Methods and Findings
We searched Medline for SRs indexed during November 2004 and written in English. Citations were screened and those meeting our inclusion criteria were retained. Data were collected using a 51-item data collection form designed to assess the epidemiological and reporting details and the bias-related aspects of the reviews. The data were analyzed descriptively. In total 300 SRs were identified, suggesting a current annual publication rate of about 2,500, involving more than 33,700 separate studies including one-third of a million participants. The majority (272 [90.7%]) of SRs were reported in specialty journals. Most reviews (213 [71.0%]) were categorized as therapeutic, and included a median of 16 studies involving 1,112 participants. Funding sources were not reported in more than one-third (122 [40.7%]) of the reviews. Reviews typically searched a median of three electronic databases and two other sources, although only about two-thirds (208 [69.3%]) of them reported the years searched. Most (197/295 [66.8%]) reviews reported information about quality assessment, while few (68/294 [23.1%]) reported assessing for publication bias. A little over half (161/300 [53.7%]) of the SRs reported combining their results statistically, of which most (147/161 [91.3%]) assessed for consistency across studies. Few (53 [17.7%]) SRs reported being updates of previously completed reviews. No review had a registration number. Only half (150 [50.0%]) of the reviews used the term “systematic review” or “meta-analysis” in the title or abstract. There were large differences between Cochrane reviews and non-Cochrane reviews in the quality of reporting several characteristics.
SRs are now produced in large numbers, and our data suggest that the quality of their reporting is inconsistent. This situation might be improved if more widely agreed upon evidence-based reporting guidelines were endorsed and adhered to by authors and journals. These results substantiate the view that readers should not accept SRs uncritically.
Data were collected on the epidemiological, descriptive, and reporting characteristics of recent systematic reviews. A descriptive analysis found inconsistencies in the quality of reporting.
Editors' Summary
In health care it is important to assess all the evidence available about what causes a disease or the best way to prevent, diagnose, or treat it. Decisions should not be made simply on the basis of—for example—the latest or biggest research study, but after a full consideration of the findings from all the research of good quality that has so far been conducted on the issue in question. This approach is known as “evidence-based medicine” (EBM). A report that is based on a search for studies addressing a clearly defined question, a quality assessment of the studies found, and a synthesis of the research findings, is known as a systematic review (SR). Conducting an SR is itself regarded as a research project and the methods involved can be quite complex. In particular, as with other forms of research, it is important to do everything possible to reduce bias. The leading role in developing the SR concept and the methods that should be used has been played by an international network called the Cochrane Collaboration (see “Additional Information” below), which was launched in 1992. However, SRs are now becoming commonplace. Many articles published in journals and elsewhere are described as being systematic reviews.
Why Was This Study Done?
Since systematic reviews are claimed to be the best source of evidence, it is important that they should be well conducted and that bias should not have influenced the conclusions drawn in the review. Just because the authors of a paper that discusses evidence on a particular topic claim that they have done their review “systematically,” it does not guarantee that their methods have been sound and that their report is of good quality. However, if they have reported details of their methods, then it can help users of the review decide whether they are looking at a review with conclusions they can rely on. The authors of this PLoS Medicine article wanted to find out how many SRs are now being published, where they are being published, and what questions they are addressing. They also wanted to see how well the methods of SRs are being reported.
What Did the Researchers Do and Find?
They picked one month and looked for all the SRs added to the main list of medical literature in that month. They found 300, on a range of topics and in a variety of medical journals. They estimate that about 20% of reviews appearing each year are published by the Cochrane Collaboration. They found many cases in which important aspects of the methods used were not reported. For example, about a third of the SRs did not report how (if at all) the quality of the studies found in the search had been assessed. An important assessment, which analyzes for “publication bias,” was reported as having been done in only about a quarter of the cases. Most of the reporting failures were in the “non-Cochrane” reviews.
What Do These Findings Mean?
The authors concluded that the standards of reporting of SRs vary widely and that readers should, therefore, not accept the conclusions of SRs uncritically. To improve this situation, they urge that guidelines be drawn up regarding how SRs are reported. The writers of SRs and also the journals that publish them should follow these guidelines.
Additional Information.
Please access these Web sites via the online version of this summary at
An editorial discussing this research article and its relevance to medical publishing appears in the same issue of PLoS Medicine
A good source of information on the evidence-based approach to medicine is the James Lind Library
The Web site of the Cochrane Collaboration is a good source of information on systematic reviews. In particular there is a newcomers' guide and information for health care “consumers”. From this Web site, it is also possible to see summaries of the SRs published by the Cochrane Collaboration (readers in some countries can also view the complete SRs free of charge)
Information on the practice of evidence-based medicine is available from the US Agency for Healthcare Research and Quality and the Canadian Agency for Drugs and Technologies in Health
PMCID: PMC1831728  PMID: 17388659
10.  A current perspective on medical informatics and health sciences librarianship 
Objective: The article offers a current perspective on medical informatics and health sciences librarianship.
Narrative: The authors: (1) discuss how definitions of medical informatics have changed in relation to health sciences librarianship and the broader domain of information science; (2) compare the missions of health sciences librarianship and health sciences informatics, reviewing the characteristics of both disciplines; (3) propose a new definition of health sciences informatics; (4) consider the research agendas of both disciplines and the possibility that they have merged; and (5) conclude with some comments about actions and roles for health sciences librarians to flourish in the biomedical information environment of today and tomorrow.
Summary: Boundaries are disappearing between the sources and types of and uses for health information managed by informaticians and librarians. Definitions of the professional domains of each have been impacted by these changes in information. Evolving definitions reflect the increasingly overlapping research agendas of both disciplines. Professionals in these disciplines are increasingly functioning collaboratively as “boundary spanners,” incorporating human factors that unite technology with health care delivery.
PMCID: PMC1082936  PMID: 15858622
11.  Publication of Clinical Trials Supporting Successful New Drug Applications: A Literature Analysis 
PLoS Medicine  2008;5(9):e191.
The United States (US) Food and Drug Administration (FDA) approves new drugs based on sponsor-submitted clinical trials. The publication status of these trials in the medical literature and factors associated with publication have not been evaluated. We sought to determine the proportion of trials submitted to the FDA in support of newly approved drugs that are published in biomedical journals that a typical clinician, consumer, or policy maker living in the US would reasonably search.
Methods and Findings
We conducted a cohort study of trials supporting new drugs approved between 1998 and 2000, as described in FDA medical and statistical review documents and the FDA approved drug label. We determined publication status and time from approval to full publication in the medical literature at 2 and 5 y by searching PubMed and other databases through 01 August 2006. We then evaluated trial characteristics associated with publication. We identified 909 trials supporting 90 approved drugs in the FDA reviews, of which 43% (394/909) were published. Among the subset of trials described in the FDA-approved drug label and classified as “pivotal trials” for our analysis, 76% (257/340) were published. In multivariable logistic regression for all trials 5 y postapproval, likelihood of publication correlated with statistically significant results (odds ratio [OR] 3.03, 95% confidence interval [CI] 1.78–5.17); larger sample sizes (OR 1.33 per 2-fold increase in sample size, 95% CI 1.17–1.52); and pivotal status (OR 5.31, 95% CI 3.30–8.55). In multivariable logistic regression for only the pivotal trials 5 y postapproval, likelihood of publication correlated with statistically significant results (OR 2.96, 95% CI 1.24–7.06) and larger sample sizes (OR 1.47 per 2-fold increase in sample size, 95% CI 1.15–1.88). Statistically significant results and larger sample sizes were also predictive of publication at 2 y postapproval and in multivariable Cox proportional models for all trials and the subset of pivotal trials.
Over half of all supporting trials for FDA-approved drugs remained unpublished ≥ 5 y after approval. Pivotal trials and trials with statistically significant results and larger sample sizes are more likely to be published. Selective reporting of trial results exists for commonly marketed drugs. Our data provide a baseline for evaluating publication bias as the new FDA Amendments Act comes into force mandating basic results reporting of clinical trials.
Ida Sim and colleagues investigate the publication status and publication bias of trials submitted to the US Food and Drug Administration (FDA) for a wide variety of approved drugs.
Editors' Summary
Before a new drug becomes available for the treatment of a specific human disease, its benefits and harms are carefully studied, first in the laboratory and in animals, and then in several types of clinical trials. In the most important of these trials—so-called “pivotal” clinical trials—the efficacy and safety of the new drug and of a standard treatment are compared by giving groups of patients the different treatments and measuring several predefined “outcomes.” These outcomes indicate whether the new drug is more effective than the standard treatment and whether it has any other effects on the patients' health and daily life. All this information is then submitted by the sponsor of the new drug (usually a pharmaceutical company) to the government body responsible for drug approval—in the US, this is the Food and Drug Administration (FDA).
Why Was This Study Done?
After a drug receives FDA approval, information about the clinical trials supporting the FDA's decision are included in the FDA “Summary Basis of Approval” and/or on the drug label. In addition, some clinical trials are described in medical journals. Ideally, all the clinical information that leads to a drug's approval should be publicly available to help clinicians make informed decisions about how to treat their patients. A full-length publication in a medical journal is the primary way that clinical trial results are communicated to the scientific community and the public. Unfortunately, drug sponsors sometimes publish the results only of trials where their drug performed well; as a consequence, trials where the drug did no better than the standard treatment or where it had unwanted side effects remain unpublished. Publication bias like this provides an inaccurate picture of a drug's efficacy and safety relative to other therapies and may lead to excessive prescribing of newer, more expensive (but not necessarily more effective) treatments. In this study, the researchers investigate whether selective trial reporting is common by evaluating the publication status of trials submitted to the FDA for a wide variety of approved drugs. They also ask which factors affect a trial's chances of publication.
What Did the Researchers Do and Find?
The researchers identified 90 drugs approved by the FDA between 1998 and 2000 by searching the FDA's Center for Drug Evaluation and Research Web site. From the Summary Basis of Approval for each drug, they identified 909 clinical trials undertaken to support these approvals. They then searched the published medical literature up to mid-2006 to determine if and when the results of each trial were published. Although 76% of the pivotal trials had appeared in medical journals, usually within 3 years of FDA approval, only 43% of all of the submitted trials had been published. Among all the trials, those with statistically significant results were nearly twice as likely to have been published as those without statistically significant results, and pivotal trials were three times more likely to have been published as nonpivotal trials, 5 years postapproval. In addition, a larger sample size increased the likelihood of publication. Having statistically significant results and larger sample sizes also increased the likelihood of publication of the pivotal trials.
What Do These Findings Mean?
Although the search methods used in this study may have missed some publications, these findings suggest that more than half the clinical trials undertaken to support drug approval remain unpublished 5 years or more after FDA approval. They also reveal selective reporting of results. For example, they show that a pivotal trial in which the new drug does no better than an old drug is less likely to be published than one where the new drug is more effective, a publication bias that could establish an inappropriately favorable record for the new drug in the medical literature. Importantly, these findings provide a baseline for monitoring the effects of the FDA Amendments Act 2007, which was introduced to improve the accuracy and completeness of drug trial reporting. Under this Act, all trials supporting FDA-approved drugs must be registered when they start, and the summary results of all the outcomes declared at trial registration as well as specific details about the trial protocol must be publicly posted within a year of drug approval on the US National Institutes of Health clinical trials site.
Additional Information.
Please access these Web sites via the online version of this summary at
PLoS Medicine recently published an editorial discussing the FDA Amendment Act and what it means for medical journals: The PLoS Medicine Editors (2008) Next Stop, Don't Block the Doors: Opening Up Access to Clinical Trials Results. PLoS Med 5(7): e160
The US Food and Drug Administration provides information about drug approval in the US for consumers and for health care professionals; detailed information about the process by which drugs are approved is on the Web site of the FDA Center for Drug Evaluation and Research (in English and Spanish) provides information about the US National Institutes of Health clinical trial registry, background information about clinical trials, and a fact sheet detailing the requirements of the FDA Amendments Act 2007 for trial registration
The World Health Organization's International Clinical Trials Registry Platform is working toward international norms and standards for reporting the findings of clinical trials
PMCID: PMC2553819  PMID: 18816163
12.  The Effectiveness of Mobile-Health Technologies to Improve Health Care Service Delivery Processes: A Systematic Review and Meta-Analysis 
PLoS Medicine  2013;10(1):e1001363.
Caroline Free and colleagues systematically review controlled trials of mobile technology interventions to improve health care delivery processes and show that current interventions give only modest benefits and that high-quality trials measuring clinical outcomes are needed.
Mobile health interventions could have beneficial effects on health care delivery processes. We aimed to conduct a systematic review of controlled trials of mobile technology interventions to improve health care delivery processes.
Methods and Findings
We searched for all controlled trials of mobile technology based health interventions using MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, Cochrane Library, UK NHS HTA (Jan 1990–Sept 2010). Two authors independently extracted data on allocation concealment, allocation sequence, blinding, completeness of follow-up, and measures of effect. We calculated effect estimates and we used random effects meta-analysis to give pooled estimates.
We identified 42 trials. None of the trials had low risk of bias. Seven trials of health care provider support reported 25 outcomes regarding appropriate disease management, of which 11 showed statistically significant benefits. One trial reported a statistically significant improvement in nurse/surgeon communication using mobile phones. Two trials reported statistically significant reductions in correct diagnoses using mobile technology photos compared to gold standard. The pooled effect on appointment attendance using text message (short message service or SMS) reminders versus no reminder was increased, with a relative risk (RR) of 1.06 (95% CI 1.05–1.07, I2 = 6%). The pooled effects on the number of cancelled appointments was not significantly increased RR 1.08 (95% CI 0.89–1.30). There was no difference in attendance using SMS reminders versus other reminders (RR 0.98, 95% CI 0.94–1.02, respectively). To address the limitation of the older search, we also reviewed more recent literature.
The results for health care provider support interventions on diagnosis and management outcomes are generally consistent with modest benefits. Trials using mobile technology-based photos reported reductions in correct diagnoses when compared to the gold standard. SMS appointment reminders have modest benefits and may be appropriate for implementation. High quality trials measuring clinical outcomes are needed.
Please see later in the article for the Editors' Summary
Editors’ Summary
Over the past few decades, computing and communication technologies have changed dramatically. Bulky, slow computers have been replaced by portable devices that can complete increasingly complex tasks in less and less time. Similarly, landlines have been replaced by mobile phones and other mobile communication technologies that can connect people anytime and anywhere, and that can transmit text messages (short message service; SMS), photographs, and data at the touch of a button. These advances have led to the development of mobile-health (mHealth)—the use of mobile computing and communication technologies in health care and public health. mHealth has many applications. It can be used to facilitate data collection and to encourage health-care consumers to adopt healthy lifestyles or to self-manage chronic conditions. It can also be used to improve health-care service delivery processes by targeting health-care providers or communication between these providers and their patients. So, for example, mobile technologies can be used to provide clinical management support in settings where there are no specialist clinicians, and they can be used to send patients test results and timely reminders of appointments.
Why Was This Study Done?
Many experts believe that mHealth interventions could greatly improve health-care delivery processes, particularly in resource-poor settings. The results of several controlled trials (studies that compare the outcomes of people who do or do not receive an intervention) of mHealth interventions designed to improve health-care delivery processes have been published. However, these data have not been comprehensively reviewed, and the effectiveness of this type of mHealth intervention has not been quantified. Here, the researchers rectify this situation by undertaking a systematic review and meta-analysis of controlled trials of mobile technology-based interventions designed to improve health-care service delivery processes. A systematic review is a study that uses predefined criteria to identify all the research on a given topic; a meta-analysis is a statistical approach that is used to pool the results of several independent studies.
What Did the Researchers Do and Find?
The researchers identified 42 controlled trials that investigated mobile technology-based interventions designed to improve health-care service delivery processes. None of the trials were of high quality—many had methodological problems likely to affect the accuracy of their findings—and nearly all were undertaken in high-income countries. Thirty-two of the trials tested interventions directed at health-care providers. Of these trials, seven investigated interventions providing health-care provider education, 18 investigated interventions supporting clinical diagnosis and treatment, and seven investigated interventions to facilitate communication between health-care providers. Several of the trials reported that the tested intervention led to statistically significant improvements (improvements unlikely to have happened by chance) in outcomes related to disease management. However, two trials that used mobile phones to transmit photos to off-site clinicians for diagnosis reported significant reductions in correct diagnoses compared to diagnosis by an on-site specialist. Ten of the 42 trials investigated interventions targeting communication between health-care providers and patients. Eight of these trials investigated SMS-based appointment reminders. Meta-analyses of the results of these trials indicated that using SMS appointment reminders significantly but modestly increased patient attendance compared to no reminders. However, SMS reminders were no more effective than postal or phone call reminders, and texting reminders to patients who persistently missed appointments did not significantly change the number of cancelled appointments.
What Do These Findings Mean?
These findings indicate that some mHealth interventions designed to improve health-care service delivery processes are modestly effective, but they also highlight the need for more trials of these interventions. Specifically, these findings show that although some interventions designed to provide support for health-care providers modestly improved some aspects of clinical diagnosis and management, other interventions had deleterious effects—most notably, the use of mobile technology–based photos for diagnosis. In terms of mHealth interventions targeting communication between health-care providers and patients, the finding that SMS appointment reminders have modest benefits suggests that implementation of this intervention should be considered, at least in high-income settings. However, the researchers stress that more trials are needed to robustly establish the ability of mobile technology-based interventions to improve health-care delivery processes. These trials need to be of high quality, they should be undertaken in resource-limited settings as well as in high-income countries, and, ideally, they should consider interventions that combine mHealth and conventional approaches.
Additional Information
Please access these Web sites via the online version of this summary at
A related PLOS Medicine Research Article by Free et al. investigates the effectiveness of mHealth technology-based health behavior change and disease management interventions for health-care consumers
Wikipedia has a page on mHealth (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
mHealth: New horizons for health through mobile technologies is a global survey of mHealth prepared by the World Health Organization’s Global Observatory for eHealth (eHealth is health-care practice supported by electronic processes and communication)
The mHealth in Low-Resource Settings website, which is maintained by the Netherlands Royal Tropical Institute, provides information on the current use, potential, and limitations of mHealth in low-resource settings
The US National Institutes of Health Fogarty International Center provides links to resources and information about mHealth
PMCID: PMC3566926  PMID: 23458994
13.  Information from Pharmaceutical Companies and the Quality, Quantity, and Cost of Physicians' Prescribing: A Systematic Review 
PLoS Medicine  2010;7(10):e1000352.
Geoff Spurling and colleagues report findings of a systematic review looking at the relationship between exposure to promotional material from pharmaceutical companies and the quality, quantity, and cost of prescribing. They fail to find evidence of improvements in prescribing after exposure, and find some evidence of an association with higher prescribing frequency, higher costs, or lower prescribing quality.
Pharmaceutical companies spent $57.5 billion on pharmaceutical promotion in the United States in 2004. The industry claims that promotion provides scientific and educational information to physicians. While some evidence indicates that promotion may adversely influence prescribing, physicians hold a wide range of views about pharmaceutical promotion. The objective of this review is to examine the relationship between exposure to information from pharmaceutical companies and the quality, quantity, and cost of physicians' prescribing.
Methods and Findings
We searched for studies of physicians with prescribing rights who were exposed to information from pharmaceutical companies (promotional or otherwise). Exposures included pharmaceutical sales representative visits, journal advertisements, attendance at pharmaceutical sponsored meetings, mailed information, prescribing software, and participation in sponsored clinical trials. The outcomes measured were quality, quantity, and cost of physicians' prescribing. We searched Medline (1966 to February 2008), International Pharmaceutical Abstracts (1970 to February 2008), Embase (1997 to February 2008), Current Contents (2001 to 2008), and Central (The Cochrane Library Issue 3, 2007) using the search terms developed with an expert librarian. Additionally, we reviewed reference lists and contacted experts and pharmaceutical companies for information. Randomized and observational studies evaluating information from pharmaceutical companies and measures of physicians' prescribing were independently appraised for methodological quality by two authors. Studies were excluded where insufficient study information precluded appraisal. The full text of 255 articles was retrieved from electronic databases (7,185 studies) and other sources (138 studies). Articles were then excluded because they did not fulfil inclusion criteria (179) or quality appraisal criteria (18), leaving 58 included studies with 87 distinct analyses. Data were extracted independently by two authors and a narrative synthesis performed following the MOOSE guidelines. Of the set of studies examining prescribing quality outcomes, five found associations between exposure to pharmaceutical company information and lower quality prescribing, four did not detect an association, and one found associations with lower and higher quality prescribing. 38 included studies found associations between exposure and higher frequency of prescribing and 13 did not detect an association. Five included studies found evidence for association with higher costs, four found no association, and one found an association with lower costs. The narrative synthesis finding of variable results was supported by a meta-analysis of studies of prescribing frequency that found significant heterogeneity. The observational nature of most included studies is the main limitation of this review.
With rare exceptions, studies of exposure to information provided directly by pharmaceutical companies have found associations with higher prescribing frequency, higher costs, or lower prescribing quality or have not found significant associations. We did not find evidence of net improvements in prescribing, but the available literature does not exclude the possibility that prescribing may sometimes be improved. Still, we recommend that practitioners follow the precautionary principle and thus avoid exposure to information from pharmaceutical companies.
Please see later in the article for the Editors' Summary
Editors' Summary
A prescription drug is a medication that can be supplied only with a written instruction (“prescription”) from a physician or other licensed healthcare professional. In 2009, 3.9 billion drug prescriptions were dispensed in the US alone and US pharmaceutical companies made US$300 billion in sales revenue. Every year, a large proportion of this revenue is spent on drug promotion. In 2004, for example, a quarter of US drug revenue was spent on pharmaceutical promotion. The pharmaceutical industry claims that drug promotion—visits from pharmaceutical sales representatives, advertisements in journals and prescribing software, sponsorship of meetings, mailed information—helps to inform and educate healthcare professionals about the risks and benefits of their products and thereby ensures that patients receive the best possible care. Physicians, however, hold a wide range of views about pharmaceutical promotion. Some see it as a useful and convenient source of information. Others deny that they are influenced by pharmaceutical company promotion but claim that it influences other physicians. Meanwhile, several professional organizations have called for tighter control of promotional activities because of fears that pharmaceutical promotion might encourage physicians to prescribe inappropriate or needlessly expensive drugs.
Why Was This Study Done?
But is there any evidence that pharmaceutical promotion adversely influences prescribing? Reviews of the research literature undertaken in 2000 and 2005 provide some evidence that drug promotion influences prescribing behavior. However, these reviews only partly assessed the relationship between information from pharmaceutical companies and prescribing costs and quality and are now out of date. In this study, therefore, the researchers undertake a systematic review (a study that uses predefined criteria to identify all the research on a given topic) to reexamine the relationship between exposure to information from pharmaceutical companies and the quality, quantity, and cost of physicians' prescribing.
What Did the Researchers Do and Find?
The researchers searched the literature for studies of licensed physicians who were exposed to promotional and other information from pharmaceutical companies. They identified 58 studies that included a measure of exposure to any type of information directly provided by pharmaceutical companies and a measure of physicians' prescribing behavior. They then undertook a “narrative synthesis,” a descriptive analysis of the data in these studies. Ten of the studies, they report, examined the relationship between exposure to pharmaceutical company information and prescribing quality (as judged, for example, by physician drug choices in response to clinical vignettes). All but one of these studies suggested that exposure to drug company information was associated with lower prescribing quality or no association was detected. In the 51 studies that examined the relationship between exposure to drug company information and prescribing frequency, exposure to information was associated with more frequent prescribing or no association was detected. Thus, for example, 17 out of 29 studies of the effect of pharmaceutical sales representatives' visits found an association between visits and increased prescribing; none found an association with less frequent prescribing. Finally, eight studies examined the relationship between exposure to pharmaceutical company information and prescribing costs. With one exception, these studies indicated that exposure to information was associated with a higher cost of prescribing or no association was detected. So, for example, one study found that physicians with low prescribing costs were more likely to have rarely or never read promotional mail or journal advertisements from pharmaceutical companies than physicians with high prescribing costs.
What Do These Findings Mean?
With rare exceptions, these findings suggest that exposure to pharmaceutical company information is associated with either no effect on physicians' prescribing behavior or with adverse affects (reduced quality, increased frequency, or increased costs). Because most of the studies included in the review were observational studies—the physicians in the studies were not randomly selected to receive or not receive drug company information—it is not possible to conclude that exposure to information actually causes any changes in physician behavior. Furthermore, although these findings provide no evidence for any net improvement in prescribing after exposure to pharmaceutical company information, the researchers note that it would be wrong to conclude that improvements do not sometimes happen. The findings support the case for reforms to reduce negative influence to prescribing from pharmaceutical promotion.
Additional Information
Please access these Web sites via the online version of this summary at
Wikipedia has pages on prescription drugs and on pharmaceutical marketing (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
The UK General Medical Council provides guidelines on good practice in prescribing medicines
The US Food and Drug Administration provides information on prescription drugs and on its Bad Ad Program
Healthy Skepticism is an international nonprofit membership association that aims to improve health by reducing harm from misleading health information
The Drug Promotion Database was developed by the World Health Organization Department of Essential Drugs & Medicines Policy and Health Action International Europe to address unethical and inappropriate drug promotion
PMCID: PMC2957394  PMID: 20976098
14.  Validation of the Symptom Pattern Method for Analyzing Verbal Autopsy Data 
PLoS Medicine  2007;4(11):e327.
Cause of death data are a critical input to formulating good public health policy. In the absence of reliable vital registration data, information collected after death from household members, called verbal autopsy (VA), is commonly used to study causes of death. VA data are usually analyzed by physician-coded verbal autopsy (PCVA). PCVA is expensive and its comparability across regions is questionable. Nearly all validation studies of PCVA have allowed physicians access to information collected from the household members' recall of medical records or contact with health services, thus exaggerating accuracy of PCVA in communities where few deaths had any interaction with the health system. In this study we develop and validate a statistical strategy for analyzing VA data that overcomes the limitations of PCVA.
Methods and Findings
We propose and validate a method that combines the advantages of methods proposed by King and Lu, and Byass, which we term the symptom pattern (SP) method. The SP method uses two sources of VA data. First, it requires a dataset for which we know the true cause of death, but which need not be representative of the population of interest; this dataset might come from deaths that occur in a hospital. The SP method can then be applied to a second VA sample that is representative of the population of interest. From the hospital data we compute the properties of each symptom; that is, the probability of responding yes to each symptom, given the true cause of death. These symptom properties allow us first to estimate the population-level cause-specific mortality fractions (CSMFs), and to then use the CSMFs as an input in assigning a cause of death to each individual VA response. Finally, we use our individual cause-of-death assignments to refine our population-level CSMF estimates. The results from applying our method to data collected in China are promising. At the population level, SP estimates the CSMFs with 16% average relative error and 0.7% average absolute error, while PCVA results in 27% average relative error and 1.1% average absolute error. At the individual level, SP assigns the correct cause of death in 83% of the cases, while PCVA does so for 69% of the cases. We also compare the results of SP and PCVA when both methods have restricted access to the information from the medical record recall section of the VA instrument. At the population level, without medical record recall, the SP method estimates the CSMFs with 14% average relative error and 0.6% average absolute error, while PCVA results in 70% average relative error and 3.2% average absolute error. For individual estimates without medical record recall, SP assigns the correct cause of death in 78% of cases, while PCVA does so for 38% of cases.
Our results from the data collected in China suggest that the SP method outperforms PCVA, both at the population and especially at the individual level. Further study is needed on additional VA datasets in order to continue validation of the method, and to understand how the symptom properties vary as a function of culture, language, and other factors. Our results also suggest that PCVA relies heavily on household recall of medical records and related information, limiting its applicability in low-resource settings. SP does not require that additional information to adequately estimate causes of death.
Chris Murray and colleagues propose and, using data from China, validate a new strategy for analyzing verbal autopsy data that combines the advantages of previous methods.
Editors' Summary
All countries need to know the leading causes of death among their people. Only with accurate cause-of-death data can their public-health officials and medical professionals develop relevant health policies and programs and monitor how they affect the nation's health. In developed countries, vital registration systems record specific causes of death that have been certified by doctors for most deaths. But, in developing countries, vital registration systems are rarely anywhere near complete, a situation that is unlikely to change in the near future. An approach that is being used increasingly to get information on the patterns of death in poor countries is “verbal autopsy” (VA). Trained personnel interview household members about the symptoms the deceased had before his/her death, and the circumstances surrounding the death, using a standard form. These forms are then reviewed by a doctor, who assigns a cause of death from a list of codes called the International Classification of Diseases. This process is called physician-coded verbal autopsy (PCVA).
Why Was This Study Done?
PCVA is a costly, time-consuming way of analyzing VA data and may not be comparable across regions, because it relies on the views of local doctors about the likely causes of death. In addition, although several studies have suggested that PCVA is reasonably accurate, such studies have usually included information collected from household members about medical records or contacts with health services. In regions where there is little contact with health services, PCVA may be much more inaccurate. Ideally what is needed is a method for assigning causes of death from VA data that does not involve physician review. In this study, the researchers have developed a statistical method—the symptom pattern (SP) method—for analyzing VA data and asked whether it can overcome the limitations of PCVA.
What Did the Researchers Do and Find?
The SP method uses VA data collected about a group of patients for whom the true cause of death is known to calculate the probability for each cause of death that a household member will answer yes when asked about various symptoms. These so-called “symptom properties” can be used to calculate population cause-specific mortality fractions (CSMFs—the proportion of the population that dies from each disease) from VA data and, using a type of statistical analysis called Bayesian statistics, can be used to assign causes of deaths to individuals. When used with data from a VA study done in China, the SP method estimated population CSMFs with an average relative error of 16% (this measure indicates how much the estimated and true CSMFs deviate), whereas PCVA estimated them with an average relative error of 27%. At the individual level, the SP method assigned the correct cause of death in 83% of cases; PCVA was right only 69% of the time. Removing the medical record recall section of the VA data had little effect on the accuracy with which the two methods estimated population CSMFs. However, whereas the SP method still assigned the correct cause of death in 78% of individual cases, the PVCA did so in only 38% of cases
What Do These Findings Mean?
These findings suggest that the SP method for analyzing VA data can outperform PCVA at both the population and the individual level. In particular, the SP method may be much better than PCVA at assigning the cause of death for individuals who have had little contact with health services before dying, a common situation in the poorest regions of world. The SP method needs to be validated using data from other parts of the world and also needs to be tested in multi-country validation studies to build up information about how culture and language affect the likelihood of specific symptoms being reported in VAs for each cause of death. Provided the SP method works as well in other countries as it apparently does in China, its adoption, together with improvements in how VA data are collected, has the potential to improve the accuracy of cause-of-death data in developing countries.
Additional Information.
Please access these Web sites via the online version of this summary at
• An accompanying paper by Murray and colleagues describes an alternative approach to collecting accurate cause-of-death data in developing countries
• World Health Organization provides information on health statistics and health information systems, on the International Classification of Diseases, on the Health Metrics Network, a global collaboration focused on improving sources of vital statistics and cause-of-death data, and on verbal autopsy standards
• Grand Challenges in Global Health provides information on research into better ways for developing countries to measure their health status
PMCID: PMC2080648  PMID: 18031196
15.  Reinterpreting Ethnic Patterns among White and African American Men Who Inject Heroin: A Social Science of Medicine Approach 
PLoS Medicine  2006;3(10):e452.
Street-based heroin injectors represent an especially vulnerable population group subject to negative health outcomes and social stigma. Effective clinical treatment and public health intervention for this population requires an understanding of their cultural environment and experiences. Social science theory and methods offer tools to understand the reasons for economic and ethnic disparities that cause individual suffering and stress at the institutional level.
Methods and Findings
We used a cross-methodological approach that incorporated quantitative, clinical, and ethnographic data collected by two contemporaneous long-term San Francisco studies, one epidemiological and one ethnographic, to explore the impact of ethnicity on street-based heroin-injecting men 45 years of age or older who were self-identified as either African American or white. We triangulated our ethnographic findings by statistically examining 14 relevant epidemiological variables stratified by median age and ethnicity. We observed significant differences in social practices between self-identified African Americans and whites in our ethnographic social network sample with respect to patterns of (1) drug consumption; (2) income generation; (3) social and institutional relationships; and (4) personal health and hygiene. African Americans and whites tended to experience different structural relationships to their shared condition of addiction and poverty. Specifically, this generation of San Francisco injectors grew up as the children of poor rural to urban immigrants in an era (the late 1960s through 1970s) when industrial jobs disappeared and heroin became fashionable. This was also when violent segregated inner city youth gangs proliferated and the federal government initiated its “War on Drugs.” African Americans had earlier and more negative contact with law enforcement but maintained long-term ties with their extended families. Most of the whites were expelled from their families when they began engaging in drug-related crime. These historical-structural conditions generated distinct presentations of self. Whites styled themselves as outcasts, defeated by addiction. They professed to be injecting heroin to stave off “dopesickness” rather than to seek pleasure. African Americans, in contrast, cast their physical addiction as an oppositional pursuit of autonomy and pleasure. They considered themselves to be professional outlaws and rejected any appearance of abjection. Many, but not all, of these ethnographic findings were corroborated by our epidemiological data, highlighting the variability of behaviors within ethnic categories.
Bringing quantitative and qualitative methodologies and perspectives into a collaborative dialog among cross-disciplinary researchers highlights the fact that clinical practice must go beyond simple racial or cultural categories. A clinical social science approach provides insights into how sociocultural processes are mediated by historically rooted and institutionally enforced power relations. Recognizing the logical underpinnings of ethnically specific behavioral patterns of street-based injectors is the foundation for cultural competence and for successful clinical relationships. It reduces the risk of suboptimal medical care for an exceptionally vulnerable and challenging patient population. Social science approaches can also help explain larger-scale patterns of health disparities; inform new approaches to structural and institutional-level public health initiatives; and enable clinicians to take more leadership in changing public policies that have negative health consequences.
Bourgois and colleagues found that the African American and white men in their study had a different pattern of drug use and risk behaviors, adopted different strategies for survival, and had different personal histories.
Editors' Summary
There are stark differences in the health of different ethnic groups in America. For example, the life expectancy for white men is 75.4 years, but it is only 69.2 years for African-American men. The reasons behind these disparities are unclear, though there are several possible explanations. Perhaps, for example, different ethnic groups are treated differently by health professionals (with some groups receiving poorer quality health care). Or maybe the health disparities are due to differences across ethnic groups in income level (we know that richer people are healthier). These disparities are likely to persist unless we gain a better understanding of how they arise.
Why Was This Study Done?
The researchers wanted to study the health of a very vulnerable community of people: heroin users living on the streets in the San Francisco Bay Area. The health status of this community is extremely poor, and its members are highly stigmatized—including by health professionals themselves. The researchers wanted to know whether African American men and white men who live on the streets have a different pattern of drug use, whether they adopt varying strategies for survival, and whether they have different personal histories. Knowledge of such differences would help the health community to provide more tailored and culturally appropriate interventions. Physicians, nurses, and social workers often treat street-based drug users, especially in emergency rooms and free clinics. These health professionals regularly report that their interactions with street-based drug users are frustrating and confrontational. The researchers hoped that their study would help these professionals to have a better understanding of the cultural backgrounds and motivations of their drug-using patients.
What Did the Researchers Do and Find?
Over the course of six years, the researchers directly observed about 70 men living on the streets who injected heroin as they went about their usual lives (this type of research is called “participant observation”). The researchers specifically looked to see whether there were differences between the white and African American men. All the men gave their consent to be studied in this way and to be photographed. The researchers also studied a database of interviews with almost 7,000 injection drug users conducted over five years, drawing out the data on differences between white and African men. The researchers found that the white men were more likely to supplement their heroin use with inexpensive fortified wine, while African American men were more likely to supplement heroin with crack. Most of the white men were expelled from their families when they began engaging in drug-related crime, and these men tended to consider themselves as destitute outcasts. African American men had earlier and more negative contact with law enforcement but maintained long-term ties with their extended families, and these men tended to consider themselves as professional outlaws. The white men persevered less in attempting to find a vein in which to inject heroin, and so were more likely to inject the drug directly under the skin—this meant that they were more likely to suffer from skin abscesses. The white men generated most of their income from panhandling (begging for money), while the African American men generated most of their income through petty crime and/or through offering services such as washing car windows at gas stations.
What Do These Findings Mean?
Among street-based heroin users, there are important differences between white men and African American men in the type of drugs used, the method of drug use, their social backgrounds, the way in which they identify themselves, and the health risks that they take. By understanding these differences, health professionals should be better placed to provide tailored and appropriate care when these men present to clinics and emergency rooms. As the researchers say, “understanding of different ethnic populations of drug injectors may reduce difficult clinical interactions and resultant physician frustration while improving patient access and adherence to care.” One limitation of this study is that the researchers studied one specific community in one particular area of the US—so we should not assume that their findings would apply to street-based heroin users elsewhere.
Additional Information.
Please access these Web sites via the online version of this summary at
The US Centers for Disease Control (CDC) has a web page on HIV prevention among injection drug users
The World Health Organization has collected documents on reducing the risk of HIV in injection drug users and on harm reduction approaches
The International Harm Reduction Association has information relevant to a global audience on reducing drug-related harm among individuals and communities
US-focused information on harm reduction is available via the websites of the Harm Reduction Coalition and the Chicago Recovery Alliance
Canada-focused information can be found at the Street Works Web site
The Harm Reduction Journal publishes open-access articles
The CDC has a web page on eliminating racial and ethnic health disparities
The Drug Policy Alliance has a web page on drug policy in the United States
PMCID: PMC1621100  PMID: 17076569
16.  An Epidemiological Network Model for Disease Outbreak Detection 
PLoS Medicine  2007;4(6):e210.
Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most.
Methods and Findings
To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health–surveillance system, developed by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology on behalf of the Massachusetts Department of Public Health. We performed experiments with semi-synthetic outbreaks of different magnitudes and simulated baseline shifts of different types and magnitudes. The results show that the network models provide better detection of localized outbreaks, and greater robustness to unpredictable shifts than a reference time-series modeling approach.
The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events.
Most surveillance systems are not robust to shifts in health care utilization. Ben Reis and colleagues developed network models that detected localized outbreaks better and were more robust to unpredictable shifts.
Editors' Summary
The main task of public-health officials is to promote health in communities around the world. To do this, they need to monitor human health continually, so that any outbreaks (epidemics) of infectious diseases (particularly global epidemics or pandemics) or any bioterrorist attacks can be detected and dealt with quickly. In recent years, advanced disease-surveillance systems have been introduced that analyze data on hospital visits, purchases of drugs, and the use of laboratory tests to look for tell-tale signs of disease outbreaks. These surveillance systems work by comparing current data on the use of health-care resources with historical data or by identifying sudden increases in the use of these resources. So, for example, more doctors asking for tests for salmonella than in the past might presage an outbreak of food poisoning, and a sudden rise in people buying over-the-counter flu remedies might indicate the start of an influenza pandemic.
Why Was This Study Done?
Existing disease-surveillance systems don't always detect disease outbreaks, particularly in situations where there are shifts in the baseline patterns of health-care use. For example, during an epidemic, people might stay away from hospitals because of the fear of becoming infected, whereas after a suspected bioterrorist attack with an infectious agent, hospitals might be flooded with “worried well” (healthy people who think they have been exposed to the agent). Baseline shifts like these might prevent the detection of increased illness caused by the epidemic or the bioterrorist attack. Localized population surges associated with major public events (for example, the Olympics) are also likely to reduce the ability of existing surveillance systems to detect infectious disease outbreaks. In this study, the researchers developed a new class of surveillance systems called “epidemiological network models.” These systems aim to improve the detection of disease outbreaks by monitoring fluctuations in the relationships between information detailing the use of various health-care resources over time (data streams).
What Did the Researchers Do and Find?
The researchers used data collected over a 3-y period from five Boston hospitals on visits for respiratory (breathing) problems and for gastrointestinal (stomach and gut) problems, and on total visits (15 data streams in total), to construct a network model that included all the possible pair-wise comparisons between the data streams. They tested this model by comparing its ability to detect simulated disease outbreaks implanted into data collected over an additional year with that of a reference model based on individual data streams. The network approach, they report, was better at detecting localized outbreaks of respiratory and gastrointestinal disease than the reference approach. To investigate how well the network model dealt with baseline shifts in the use of health-care resources, the researchers then added in a large population surge. The detection performance of the reference model decreased in this test, but the performance of the complete network model and of models that included relationships between only some of the data streams remained stable. Finally, the researchers tested what would happen in a situation where there were large numbers of “worried well.” Again, the network models detected disease outbreaks consistently better than the reference model.
What Do These Findings Mean?
These findings suggest that epidemiological network systems that monitor the relationships between health-care resource-utilization data streams might detect disease outbreaks better than current systems under normal conditions and might be less affected by unpredictable shifts in the baseline data. However, because the tests of the new class of surveillance system reported here used simulated infectious disease outbreaks and baseline shifts, the network models may behave differently in real-life situations or if built using data from other hospitals. Nevertheless, these findings strongly suggest that public-health officials, provided they have sufficient computer power at their disposal, might improve their ability to detect disease outbreaks by using epidemiological network systems alongside their current disease-surveillance systems.
Additional Information.
Please access these Web sites via the online version of this summary at
Wikipedia pages on public health (note that Wikipedia is a free online encyclopedia that anyone can edit, and is available in several languages)
A brief description from the World Health Organization of public-health surveillance (in English, French, Spanish, Russian, Arabic, and Chinese)
A detailed report from the US Centers for Disease Control and Prevention called “Framework for Evaluating Public Health Surveillance Systems for the Early Detection of Outbreaks”
The International Society for Disease Surveillance Web site
PMCID: PMC1896205  PMID: 17593895
17.  Republican Scientific-Medical Library, The Republic of Armenia: progress and programs* 
In 1990, the Republican Scientific-Medical Library (RSML) of the Ministry of Health of Armenia in collaboration with the Fund for Armenian Relief created a vision of a national library network supported by information technology. This vision incorporated four goals: (1) to develop a national resource collection of biomedical literature accessible to all health professionals, (2) to develop a national network for access to bibliographic information, (3) to develop a systematic mechanism for sharing resources, and (4) to develop a national network of health sciences libraries. During the last decade, the RSML has achieved significant progress toward all four goals and has realized its vision of becoming a fully functional national library. The RSML now provides access to the literature of the health sciences including access to the Armenian medical literature, provides education and training to health professionals and health sciences librarians, and manages a national network of libraries of the major health care institutions in Armenia. The RSML is now able to provide rapid access to the biomedical literature and train health professionals and health sciences librarians in Armenia in information system use. This paper describes the evolution of the RSML and how it was accomplished.
PMCID: PMC31703  PMID: 11209800
18.  e-Health, m-Health and healthier social media reform: the big scale view 
In the upcoming decade, digital platforms will be the backbone of a strategic revolution in the way medical services are provided, affecting both healthcare providers and patients. Digital-based patient-centered healthcare services allow patients to actively participate in managing their own care, in times of health as well as illness, using personally tailored interactive tools. Such empowerment is expected to increase patients’ willingness to adopt actions and lifestyles that promote health as well as improve follow-up and compliance with treatment in cases of chronic illness. Clalit Health Services (CHS) is the largest HMO in Israel and second largest world-wide. Through its 14 hospitals, 1300 primary and specialized clinics, and 650 pharmacies, CHS provides comprehensive medical care to the majority of Israel’s population (above 4 million members). CHS e-Health wing focuses on deepening patient involvement in managing health, through personalized digital interactive tools. Currently, CHS e-Health wing provides e-health services for 1.56 million unique patients monthly with 2.4 million interactions every month (August 2011). Successful implementation of e-Health solutions is not a sum of technology, innovation and health; rather it’s the expertise of tailoring knowledge and leadership capabilities in multidisciplinary areas: clinical, ethical, psychological, legal, comprehension of patient and medical team engagement etc. The Google Health case excellently demonstrates this point. On the other hand, our success with CHS is a demonstration that e-Health can be enrolled effectively and fast with huge benefits for both patients and medical teams, and with a robust business model.
CHS e-Health core components
They include:
1. The personal health record layer (what the patient can see) presents patients with their own medical history as well as the medical history of their preadult children, including diagnoses, allergies, vaccinations, laboratory results with interpretations in layman’s terms, medications with clear, straightforward explanations regarding dosing instructions, important side effects, contraindications, such as lactation etc., and other important medical information. All personal e-Health services require identification and authorization.
2. The personal knowledge layer (what the patient should know) presents patients with personally tailored recommendations for preventative medicine and health promotion. For example, diabetic patients are push notified regarding their yearly eye exam. The various health recommendations include: occult blood testing, mammography, lipid profile etc. Each recommendation contains textual, visual and interactive content components in order to promote engagement and motivate the patient to actually change his health behaviour.
3. The personal health services layer (what the patient can do) enables patients to schedule clinic visits, order chronic prescriptions, e-consult their physician via secured e-mail, set SMS medication reminders, e-consult a pharmacist regarding personal medications. Consultants’ answers are sent securely to the patients’ personal mobile device.
On December 2009 CHS launched secured, web based, synchronous medical consultation via video conference. Currently 11,780 e-visits are performed monthly (May 2011). The medical encounter includes e-prescription and referral capabilities which are biometrically signed by the physician. On December 2010 CHS launched a unique mobile health platform, which is one of the most comprehensive personal m-Health applications world-wide. An essential advantage of mobile devices is their potential to bridge the digital divide. Currently, CHS m-Health platform is used by more than 45,000 unique users, with 75,000 laboratory results views/month, 1100 m-consultations/month and 9000 physician visit scheduling/month.
4. The Bio-Sensing layer (what physiological data the patient can populate) includes diagnostic means that allow remote physical examination, bio-sensors that broadcast various physiological measurements, and smart homecare devices, such as e-Pill boxes that gives seniors, patients and their caregivers the ability to stay at home and live life to its fullest. Monitored data is automatically transmitted to the patient’s Personal Health Record and to relevant medical personnel.
The monitoring layer is embedded in the chronic disease management platform, and in the interactive health promotion and wellness platform. It includes tailoring of consumer-oriented medical devices and service provided by various professional personnel—physicians, nurses, pharmacists, dieticians and more.
5. The Social layer (what the patient can share). Social media networks triggered an essential change at the humanity ‘genome’ level, yet to be further defined in the upcoming years. Social media has huge potential in promoting health as it combines fun, simple yet extraordinary user experience, and bio-social-feedback. There are two major challenges in leveraging health care through social networks:
a. Our personal health information is the cornerstone for personalizing healthier lifestyle, disease management and preventative medicine. We naturally see our personal health data as a super-private territory. So, how do we bring the power of our private health information, currently locked within our Personal Health Record, into social media networks without offending basic privacy issues?
b. Disease management and preventive medicine are currently neither considered ‘cool’ nor ‘fun’ or ‘potentially highly viral’ activities; yet, health is a major issue of everybody’s life. It seems like we are missing a crucial element with a huge potential in health behavioural change—the Fun Theory. Social media platforms comprehends user experience tools that potentially could break current misconception, and engage people in the daily task of taking better care of themselves.
CHS e-Health innovation team characterized several break-through applications in this unexplored territory within social media networks, fusing personal health and social media platforms without offending privacy. One of the most crucial issues regarding adoption of e-health and m-health platforms is change management. Being a ‘hot’ innovative ‘gadget’ is far from sufficient for changing health behaviours at the individual and population levels.
CHS health behaviour change management methodology includes 4 core elements:
1. Engaging two completely different populations: patients, and medical teams. e-Health applications must present true added value for both medical teams and patients, engaging them through understanding and assimilating “what’s really in it for me”. Medical teams are further subdivided into physicians, nurses, pharmacists and administrative personnel—each with their own driving incentive. Resistance to change is an obstacle in many fields but it is particularly true in the conservative health industry. To successfully manage a large scale persuasive process, we treat intra-organizational human resources as “Change Agents”. Harnessing the persuasive power of ~40,000 employees requires engaging them as the primary target group. Successful recruitment has the potential of converting each patient-medical team interaction into an exposure opportunity to the new era of participatory medicine via e-health and m-health channels.
2. Implementation waves: every group of digital health products that are released at the same time are seen as one project. Each implementation wave leverages the focus of the organization and target populations to a defined time span. There are three major and three minor implementation waves a year.
3. Change-Support Arrow: a structured infrastructure for every implementation wave. The sub-stages in this strategy include:
Cross organizational mapping and identification of early adopters and stakeholders relevant to the implementation wave
Mapping positive or negative perceptions and designing specific marketing approaches for the distinct target groups
Intra and extra organizational marketing
Conducting intensive training and presentation sessions for groups of implementers
Running conflict-prevention activities, such as advanced tackling of potential union resistance
Training change-agents with resistance-management behavioural techniques, focused intervention for specific incidents and for key opinion leaders
Extensive presence in the clinics during the launch period, etc.
The entire process is monitored and managed continuously by a review team.
4. Closing Phase: each wave is analyzed and a “lessons-learned” session concludes the changes required in the modus operandi of the e-health project team.
PMCID: PMC3571141
e-Health; mobile health; personal health record; online visit; patient empowerment; knowledge prescription
19.  The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic Review 
PLoS Medicine  2013;10(1):e1001362.
Caroline Free and colleagues systematically review a fast-moving field, that of the effectiveness of mobile technology interventions delivered to healthcare consumers, and conclude that high-quality, adequately powered trials of optimized interventions are required to evaluate effects on objective outcomes.
Mobile technologies could be a powerful media for providing individual level support to health care consumers. We conducted a systematic review to assess the effectiveness of mobile technology interventions delivered to health care consumers.
Methods and Findings
We searched for all controlled trials of mobile technology-based health interventions delivered to health care consumers using MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, Cochrane Library, UK NHS HTA (Jan 1990–Sept 2010). Two authors extracted data on allocation concealment, allocation sequence, blinding, completeness of follow-up, and measures of effect. We calculated effect estimates and used random effects meta-analysis. We identified 75 trials. Fifty-nine trials investigated the use of mobile technologies to improve disease management and 26 trials investigated their use to change health behaviours. Nearly all trials were conducted in high-income countries. Four trials had a low risk of bias. Two trials of disease management had low risk of bias; in one, antiretroviral (ART) adherence, use of text messages reduced high viral load (>400 copies), with a relative risk (RR) of 0.85 (95% CI 0.72–0.99), but no statistically significant benefit on mortality (RR 0.79 [95% CI 0.47–1.32]). In a second, a PDA based intervention increased scores for perceived self care agency in lung transplant patients. Two trials of health behaviour management had low risk of bias. The pooled effect of text messaging smoking cessation support on biochemically verified smoking cessation was (RR 2.16 [95% CI 1.77–2.62]). Interventions for other conditions showed suggestive benefits in some cases, but the results were not consistent. No evidence of publication bias was demonstrated on visual or statistical examination of the funnel plots for either disease management or health behaviours. To address the limitation of the older search, we also reviewed more recent literature.
Text messaging interventions increased adherence to ART and smoking cessation and should be considered for inclusion in services. Although there is suggestive evidence of benefit in some other areas, high quality adequately powered trials of optimised interventions are required to evaluate effects on objective outcomes.
Please see later in the article for the Editors' Summary
Editors’ Summary
Every year, millions of people die from cardiovascular diseases (diseases of the heart and circulation), chronic obstructive pulmonary disease (a long-term lung disease), lung cancer, HIV infection, and diabetes. These diseases are increasingly important causes of mortality (death) in low- and middle-income countries and are responsible for nearly 40% of deaths in high-income countries. For all these diseases, individuals can adopt healthy behaviors that help prevent disease onset. For example, people can lower their risk of diabetes and cardiovascular disease by maintaining a healthy body weight, and, if they are smokers, they can reduce their risk of lung cancer and cardiovascular disease by giving up cigarettes. In addition, optimal treatment of existing diseases can reduce mortality and morbidity (illness). Thus, in people who are infected with HIV, antiretroviral therapy delays the progression of HIV infection and the onset of AIDS, and in people who have diabetes, good blood sugar control can prevent retinopathy (a type of blindness) and other serious complications of diabetes.
Why Was This Study Done?
Health-care providers need effective ways to encourage "health-care consumers" to make healthy lifestyle choices and to self-manage chronic diseases. The amount of information, encouragement and support that can be conveyed to individuals during face-to-face consultations or through traditional media such as leaflets is limited, but mobile technologies such as mobile phones and portable computers have the potential to transform the delivery of health messages. These increasingly popular technologies—more than two-thirds of the world's population now owns a mobile phone—can be used to deliver health messages to people anywhere and at the most relevant times. For example, smokers trying to quit smoking can be sent regular text messages to sustain their motivation, but can also use text messaging to request extra support when it is needed. But is "mHealth," the provision of health-related services using mobile communication technology, an effective way to deliver health messages to health-care consumers? In this systematic review (a study that uses predefined criteria to identify all the research on a given topic), the researchers assess the effectiveness of mobile technology-based health behavior change interventions and disease management interventions delivered to health-care consumers.
What Did the Researchers Do and Find?
The researchers identified 75 controlled trials (studies that compare the outcomes of people who do and do not receive an intervention) of mobile technology-based health interventions delivered to health-care consumers that met their predefined criteria. Twenty-six trials investigated the use of mobile technologies to change health behaviors, 59 investigated their use in disease management, most were of low quality, and nearly all were undertaken in high-income countries. In one high-quality trial that used text messages to improve adherence to antiretroviral therapy among HIV-positive patients in Kenya, the intervention significantly reduced the patients’ viral load but did not significantly reduce mortality (the observed reduction in deaths may have happened by chance). In two high-quality UK trials, a smoking intervention based on text messaging (txt2stop) more than doubled biochemically verified smoking cessation. Other lower-quality trials indicated that using text messages to encourage physical activity improved diabetes control but had no effect on body weight. Combined diet and physical activity text messaging interventions also had no effect on weight, whereas interventions for other conditions showed suggestive benefits in some but not all cases.
What Do These Findings Mean?
These findings provide mixed evidence for the effectiveness of health intervention delivery to health-care consumers using mobile technologies. Moreover, they highlight the need for additional high-quality controlled trials of this mHealth application, particularly in low- and middle-income countries. Specifically, the demonstration that text messaging interventions increased adherence to antiretroviral therapy in a low-income setting and increased smoking cessation in a high-income setting provides some support for the inclusion of these two interventions in health-care services in similar settings. However, the effects of these two interventions need to be established in other settings and their cost-effectiveness needs to be measured before they are widely implemented. Finally, for other mobile technology–based interventions designed to change health behaviors or to improve self-management of chronic diseases, the results of this systematic review suggest that the interventions need to be optimized before further trials are undertaken to establish their clinical benefits.
Additional Information
Please access these Web sites via the online version of this summary at
A related PLOS Medicine Research Article by Free et al. investigates the ability of mHealth technologies to improve health-care service delivery processes
Wikipedia has a page on mHealth (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
mHealth: New horizons for health through mobile technologies is a global survey of mHealth prepared by the World Health Organization’s Global Observatory for eHealth (eHealth is health-care practice supported by electronic processes and communication)
The mHealth in Low-Resource Settings website, which is maintained by the Netherlands Royal Tropical Institute, provides information on the current use, potential, and limitations of mHealth in low-resource settings
More information about Txt2stop is available, the UK National Health Service Choices website provides an analysis of the Txt2stop trial and what its results mean, and the UK National Health Service Smokefree website provides a link to a Quit App for the iPhone
The US Centers for Disease Control and Prevention has launched a text messaging service that delivers regular health tips and alerts to mobile phones
PMCID: PMC3548655  PMID: 23349621
20.  Physician Awareness of Drug Cost: A Systematic Review 
PLoS Medicine  2007;4(9):e283.
Pharmaceutical costs are the fastest-growing health-care expense in most developed countries. Higher drug costs have been shown to negatively impact patient outcomes. Studies suggest that doctors have a poor understanding of pharmaceutical costs, but the data are variable and there is no consistent pattern in awareness. We designed this systematic review to investigate doctors' knowledge of the relative and absolute costs of medications and to determine the factors that influence awareness.
Methods and Findings
Our search strategy included The Cochrane Library, EconoLit, EMBASE, and MEDLINE as well as reference lists and contact with authors who had published two or more articles on the topic or who had published within 10 y of the commencement of our review. Studies were included if: either doctors, trainees (interns or residents), or medical students were surveyed; there were more than ten survey respondents; cost of pharmaceuticals was estimated; results were expressed quantitatively; there was a clear description of how authors defined “accurate estimates”; and there was a description of how the true cost was determined. Two authors reviewed each article for eligibility and extracted data independently. Cost accuracy outcomes were summarized, but data were not combined in meta-analysis because of extensive heterogeneity. Qualitative data related to physicians and drug costs were also extracted. The final analysis included 24 articles. Cost accuracy was low; 31% of estimates were within 20% or 25% of the true cost, and fewer than 50% were accurate by any definition of cost accuracy. Methodological weaknesses were common, and studies of low methodological quality showed better cost awareness. The most important factor influencing the pattern and accuracy of estimation was the true cost of therapy. High-cost drugs were estimated more accurately than inexpensive ones (74% versus 31%, Chi-square p < 0.001). Doctors consistently overestimated the cost of inexpensive products and underestimated the cost of expensive ones (binomial test, 89/101, p < 0.001). When asked, doctors indicated that they want cost information and feel it would improve their prescribing but that it is not accessible.
Doctors' ignorance of costs, combined with their tendency to underestimate the price of expensive drugs and overestimate the price of inexpensive ones, demonstrate a lack of appreciation of the large difference in cost between inexpensive and expensive drugs. This discrepancy in turn could have profound implications for overall drug expenditures. Much more focus is required in the education of physicians about costs and the access to cost information. Future research should focus on the accessibility and reliability of medical cost information and whether the provision of this information is used by doctors and makes a difference to physician prescribing. Additionally, future work should strive for higher methodological standards to avoid the biases we found in the current literature, including attention to the method of assessing accuracy that allows larger absolute estimation ranges for expensive drugs.
From a review of data from 24 studies, Michael Allan and colleagues conclude that doctors often underestimate the price of expensive drugs and overestimate the price of those that are inexpensive.
Editors' Summary
Many medicines are extremely expensive, and the cost of buying them is a major (and increasing) proportion of the total cost of health care. Governments and health-care organizations try to find ways of keeping down costs without reducing the effectiveness of the health care they provide, but their efforts to control what is spent on medicines have not been very successful. There are often two or more equally effective drugs available for treating the same condition, and it would obviously help keep costs down if, when a doctor prescribes a medicine, he or she chose the cheapest of the effective drugs available. This choice could result in savings for whoever is paying for the drugs, be it the government, the patient, or a medical insurance organization.
Why Was This Study Done?
Doctors who prescribe drugs cannot be expected to know the exact cost of each drug on the market, but it would he helpful if they had some impression of the cost of a treatment and how the various alternatives compare in price. However, systems deciding how drugs are priced are often very complex. (This is particularly the case in the US.) The researchers wanted to find out how aware doctors are regarding drug costs and the difference between the alternatives. They also wanted to know what factors affected their awareness.
What Did the Researchers Do and Find?
They decided to do a systematic review of all the research already conducted that addressed this issue so that the evidence from all of them could be considered together. In order to do such a review they had to specify precise requirements for the type of study that they would include and then comprehensively search the medical literature for such studies. They found 24 studies that met their requirements. From these studies, they concluded that doctors were usually not accurate when asked to estimate the cost of drugs; doctors came up with estimates that were within 25% of the true cost less than one-third of the time. In particular doctors tended to underestimate the cost of expensive drugs and overestimate the cost of the cheaper alternatives. A further analysis of the studies showed that many doctors said they would appreciate more accurate information on costs to help them choose which drugs to prescribe but that such information was not readily available.
What Do These Findings Mean?
The researchers concluded that their systematic review demonstrates a lack of appreciation by prescribing doctors of the large difference in cost between inexpensive and expensive drugs, and that this finding has serious implications for overall spending on drugs. They call for more education and information to be provided to doctors on the cost of medicines together with better processes to help doctors in making such decisions.
Additional Information.
Please access these Web sites via the online version of this summary at
A brief guide to systematic reviews has been published by the BMJ (British Medical Journal)
The Web site of the Cochrane Collaboration is a more detailed source of information on systematic reviews; in particular there is a newcomers' guide and information for health-care consumers
The Kaiser Family Foundation, a nonprofit, private operating foundation focusing on the major health care issues in the US, has a section on prescription drugs and their costs
PMCID: PMC1989748  PMID: 17896856
21.  Active Assistance Technology for Health-Related Behavior Change: An Interdisciplinary Review 
Information technology can help individuals to change their health behaviors. This is due to its potential for dynamic and unbiased information processing enabling users to monitor their own progress and be informed about risks and opportunities specific to evolving contexts and motivations. However, in many behavior change interventions, information technology is underused by treating it as a passive medium focused on efficient transmission of information and a positive user experience.
To conduct an interdisciplinary literature review to determine the extent to which the active technological capabilities of dynamic and adaptive information processing are being applied in behavior change interventions and to identify their role in these interventions.
We defined key categories of active technology such as semantic information processing, pattern recognition, and adaptation. We conducted the literature search using keywords derived from the categories and included studies that indicated a significant role for an active technology in health-related behavior change. In the data extraction, we looked specifically for the following technology roles: (1) dynamic adaptive tailoring of messages depending on context, (2) interactive education, (3) support for client self-monitoring of behavior change progress, and (4) novel ways in which interventions are grounded in behavior change theories using active technology.
The search returned 228 potentially relevant articles, of which 41 satisfied the inclusion criteria. We found that significant research was focused on dialog systems, embodied conversational agents, and activity recognition. The most covered health topic was physical activity. The majority of the studies were early-stage research. Only 6 were randomized controlled trials, of which 4 were positive for behavior change and 5 were positive for acceptability. Empathy and relational behavior were significant research themes in dialog systems for behavior change, with many pilot studies showing a preference for those features. We found few studies that focused on interactive education (3 studies) and self-monitoring (2 studies). Some recent research is emerging in dynamic tailoring (15 studies) and theoretically grounded ontologies for automated semantic processing (4 studies).
The potential capabilities and risks of active assistance technologies are not being fully explored in most current behavior change research. Designers of health behavior interventions need to consider the relevant informatics methods and algorithms more fully. There is also a need to analyze the possibilities that can result from interaction between different technology components. This requires deep interdisciplinary collaboration, for example, between health psychology, computer science, health informatics, cognitive science, and educational methodology.
PMCID: PMC3415065  PMID: 22698679
Behavior change; consumer health informatics; health communication; health promotion; personalization
22.  Dr Google and the Consumer: A Qualitative Study Exploring the Navigational Needs and Online Health Information-Seeking Behaviors of Consumers With Chronic Health Conditions 
The abundance of health information available online provides consumers with greater access to information pertinent to the management of health conditions. This is particularly important given an increasing drive for consumer-focused health care models globally, especially in the management of chronic health conditions, and in recognition of challenges faced by lay consumers with finding, understanding, and acting on health information sourced online. There is a paucity of literature exploring the navigational needs of consumers with regards to accessing online health information. Further, existing interventions appear to be didactic in nature, and it is unclear whether such interventions appeal to consumers’ needs.
Our goal was to explore the navigational needs of consumers with chronic health conditions in finding online health information within the broader context of consumers’ online health information-seeking behaviors. Potential barriers to online navigation were also identified.
Semistructured interviews were conducted with adult consumers who reported using the Internet for health information and had at least one chronic health condition. Participants were recruited from nine metropolitan community pharmacies within Western Australia, as well as through various media channels. Interviews were audio-recorded, transcribed verbatim, and then imported into QSR NVivo 10. Two established approaches to thematic analysis were adopted. First, a data-driven approach was used to minimize potential bias in analysis and improve construct and criterion validity. A theory-driven approach was subsequently used to confirm themes identified by the former approach and to ensure identified themes were relevant to the objectives. Two levels of analysis were conducted for both data-driven and theory-driven approaches: manifest-level analysis, whereby face-value themes were identified, and latent-level analysis, whereby underlying concepts were identified.
We conducted 17 interviews, with data saturation achieved by the 14th interview. While we identified a broad range of online health information-seeking behaviors, most related to information discussed during consumer-health professional consultations such as looking for information about medication side effects. The barriers we identified included intrinsic barriers, such as limited eHealth literacy, and extrinsic barriers, such as the inconsistency of information between different online sources. The navigational needs of our participants were extrinsic in nature and included health professionals directing consumers to appropriate online resources and better filtering of online health information. Our participants’ online health information-seeking behaviors, reported barriers, and navigational needs were underpinned by the themes of trust, patient activation, and relevance.
This study suggests that existing interventions aimed to assist consumers with navigating online health information may not be what consumers want or perceive they need. eHealth literacy and patient activation appear to be prevalent concepts in the context of consumers’ online health information-seeking behaviors. Furthermore, the role for health professionals in guiding consumers to quality online health information is highlighted.
PMCID: PMC4275480  PMID: 25470306
online health information seeking; health information search; health seeking behavior; consumer health information; information needs; Internet; chronic disease; patients; qualitative research; interview
23.  Consumer language, patient language, and thesauri: a review of the literature 
Online social networking sites are web services in which users create public or semipublic profiles and connect to build online communities, finding likeminded people through self-labeled personal attributes including ethnicity, leisure interests, political beliefs, and, increasingly, health status. Thirty-nine percent of patients in the United States identified themselves as users of social networks in a recent survey. “Tags,” user-generated descriptors functioning as labels for user-generated content, are increasingly important to social networking, and the language used by patients is thus becoming important for knowledge representation in these systems. However, patient language poses considerable challenges for health communication and networking. How have information systems traditionally incorporated these languages in their controlled vocabularies and thesauri? How do system builders know what consumers and patients say?
This comprehensive review of the literature of health care (PubMed MEDLINE, CINAHL), library science, and information science (Library and Information Science and Technology Abstracts, Library and Information Science Abstracts, and Library Literature) examines the research domains in which consumer and patient language has been explored.
Consumer contributions to controlled vocabulary appear to be seriously under-researched inside and outside of health care.
The author reflects on the implications of these findings for online social networks devoted to patients and the patient experience.
PMCID: PMC3066584  PMID: 21464851
24.  Moving from Data on Deaths to Public Health Policy in Agincourt, South Africa: Approaches to Analysing and Understanding Verbal Autopsy Findings 
PLoS Medicine  2010;7(8):e1000325.
Peter Byass and colleagues compared two methods of assessing data from verbal autopsies, review by physicians or probabilistic modeling, and show that probabilistic modeling is the most efficient means of analyzing these data
Cause of death data are an essential source for public health planning, but their availability and quality are lacking in many parts of the world. Interviewing family and friends after a death has occurred (a procedure known as verbal autopsy) provides a source of data where deaths otherwise go unregistered; but sound methods for interpreting and analysing the ensuing data are essential. Two main approaches are commonly used: either physicians review individual interview material to arrive at probable cause of death, or probabilistic models process the data into likely cause(s). Here we compare and contrast these approaches as applied to a series of 6,153 deaths which occurred in a rural South African population from 1992 to 2005. We do not attempt to validate either approach in absolute terms.
Methods and Findings
The InterVA probabilistic model was applied to a series of 6,153 deaths which had previously been reviewed by physicians. Physicians used a total of 250 cause-of-death codes, many of which occurred very rarely, while the model used 33. Cause-specific mortality fractions, overall and for population subgroups, were derived from the model's output, and the physician causes coded into comparable categories. The ten highest-ranking causes accounted for 83% and 88% of all deaths by physician interpretation and probabilistic modelling respectively, and eight of the highest ten causes were common to both approaches. Top-ranking causes of death were classified by population subgroup and period, as done previously for the physician-interpreted material. Uncertainty around the cause(s) of individual deaths was recognised as an important concept that should be reflected in overall analyses. One notably discrepant group involved pulmonary tuberculosis as a cause of death in adults aged over 65, and these cases are discussed in more detail, but the group only accounted for 3.5% of overall deaths.
There were no differences between physician interpretation and probabilistic modelling that might have led to substantially different public health policy conclusions at the population level. Physician interpretation was more nuanced than the model, for example in identifying cancers at particular sites, but did not capture the uncertainty associated with individual cases. Probabilistic modelling was substantially cheaper and faster, and completely internally consistent. Both approaches characterised the rise of HIV-related mortality in this population during the period observed, and reached similar findings on other major causes of mortality. For many purposes probabilistic modelling appears to be the best available means of moving from data on deaths to public health actions.
Please see later in the article for the Editors' Summary
Editors' Summary
Whenever someone dies in a developed country, the cause of death is determined by a doctor and entered into a “vital registration system,” a record of all the births and deaths in that country. Public-health officials and medical professionals use this detailed and complete information about causes of death to develop public-health programs and to monitor how these programs affect the nation's health. Unfortunately, in many developing countries dying people are not attended by doctors and vital registration systems are incomplete. In most African countries, for example, less than one-quarter of deaths are recorded in vital registration systems. One increasingly important way to improve knowledge about the patterns of death in developing countries is “verbal autopsy” (VA). Using a standard form, trained personnel ask relatives and caregivers about the symptoms that the deceased had before his/her death and about the circumstances surrounding the death. Physicians then review these forms and assign a specific cause of death from a shortened version of the International Classification of Diseases, a list of codes for hundreds of diseases.
Why Was This Study Done?
Physician review of VA forms is time-consuming and expensive. Consequently, computer-based, “probabilistic” models have been developed that process the VA data and provide a likely cause of death. These models are faster and cheaper than physician review of VAs and, because they do not rely on the views of local doctors about the likely causes of death, they are more internally consistent. But are physician review and probabilistic models equally sound ways of interpreting VA data? In this study, the researchers compare and contrast the interpretation of VA data by physician review and by a probabilistic model called the InterVA model by applying these two approaches to the deaths that occurred in Agincourt, a rural region of northeast South Africa, between 1992 and 2005. The Agincourt health and sociodemographic surveillance system is a member of the INDEPTH Network, a global network that is evaluating the health and demographic characteristics (for example, age, gender, and education) of populations in low- and middle-income countries over several years.
What Did the Researchers Do and Find?
The researchers applied the InterVA probabilistic model to 6,153 deaths that had been previously reviewed by physicians. They grouped the 250 cause-of-death codes used by the physicians into categories comparable with the 33 cause-of-death codes used by the InterVA model and derived cause-specific mortality fractions (the proportions of the population dying from specific causes) for the whole population and for subgroups (for example, deaths in different age groups and deaths occurring over specific periods of time) from the output of both approaches. The ten highest-ranking causes of death accounted for 83% and 88% of all deaths by physician interpretation and by probabilistic modelling, respectively. Eight of the most frequent causes of death—HIV, tuberculosis, chronic heart conditions, diarrhea, pneumonia/sepsis, transport-related accidents, homicides, and indeterminate—were common to both interpretation methods. Both methods coded about a third of all deaths as indeterminate, often because of incomplete VA data. Generally, there was close agreement between the methods for the five principal causes of death for each age group and for each period of time, although one notable discrepancy was pulmonary (lung) tuberculosis, which accounted for 6.4% and 21.3% of deaths in this age group, respectively, according to the physicians and to the model. However, these deaths accounted for only 3.5% of all the deaths.
What Do These Findings Mean?
These findings reveal no differences between the cause-specific mortality fractions determined from VA data by physician interpretation and by probabilistic modelling that might have led to substantially different public-health policy programmes being initiated in this population. Importantly, both approaches clearly chart the rise of HIV-related mortality in this South African population between 1992 and 2005 and reach similar findings on other major causes of mortality. The researchers note that, although preparing the amount of VA data considered here for entry into the probabilistic model took several days, the model itself runs very quickly and always gives consistent answers. Given these findings, the researchers conclude that in many settings probabilistic modeling represents the best means of moving from VA data to public-health actions.
Additional Information
Please access these Web sites via the online version of this summary at
The importance of accurate data on death is further discussed in a perspective previously published in PLoS Medicine Perspective by Colin Mathers and Ties Boerma
The World Health Organization (WHO) provides information on the vital registration of deaths and on the International Classification of Diseases; the WHO Health Metrics Network is a global collaboration focused on improving sources of vital statistics; and the WHO Global Health Observatory brings together core health statistics for WHO member states
The INDEPTH Network is a global collaboration that is collecting health statistics from developing countries; it provides more information about the Agincourt health and socio-demographic surveillance system and access to standard VA forms
Information on the Agincourt health and sociodemographic surveillance system is available on the University of Witwatersrand Web site
The InterVA Web site provides resources for interpreting verbal autopsy data and the Umeå Centre for Global Health Reseach, where the InterVA model was developed, is found at
A recent PLoS Medicine Essay by Peter Byass, lead author of this study, discusses The Unequal World of Health Data
PMCID: PMC2923087  PMID: 20808956
25.  The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview 
PLoS Medicine  2011;8(1):e1000387.
Aziz Sheikh and colleagues report the findings of their systematic overview that assessed the impact of eHealth solutions on the quality and safety of health care.
There is considerable international interest in exploiting the potential of digital solutions to enhance the quality and safety of health care. Implementations of transformative eHealth technologies are underway globally, often at very considerable cost. In order to assess the impact of eHealth solutions on the quality and safety of health care, and to inform policy decisions on eHealth deployments, we undertook a systematic review of systematic reviews assessing the effectiveness and consequences of various eHealth technologies on the quality and safety of care.
Methods and Findings
We developed novel search strategies, conceptual maps of health care quality, safety, and eHealth interventions, and then systematically identified, scrutinised, and synthesised the systematic review literature. Major biomedical databases were searched to identify systematic reviews published between 1997 and 2010. Related theoretical, methodological, and technical material was also reviewed. We identified 53 systematic reviews that focused on assessing the impact of eHealth interventions on the quality and/or safety of health care and 55 supplementary systematic reviews providing relevant supportive information. This systematic review literature was found to be generally of substandard quality with regards to methodology, reporting, and utility. We thematically categorised eHealth technologies into three main areas: (1) storing, managing, and transmission of data; (2) clinical decision support; and (3) facilitating care from a distance. We found that despite support from policymakers, there was relatively little empirical evidence to substantiate many of the claims made in relation to these technologies. Whether the success of those relatively few solutions identified to improve quality and safety would continue if these were deployed beyond the contexts in which they were originally developed, has yet to be established. Importantly, best practice guidelines in effective development and deployment strategies are lacking.
There is a large gap between the postulated and empirically demonstrated benefits of eHealth technologies. In addition, there is a lack of robust research on the risks of implementing these technologies and their cost-effectiveness has yet to be demonstrated, despite being frequently promoted by policymakers and “techno-enthusiasts” as if this was a given. In the light of the paucity of evidence in relation to improvements in patient outcomes, as well as the lack of evidence on their cost-effectiveness, it is vital that future eHealth technologies are evaluated against a comprehensive set of measures, ideally throughout all stages of the technology's life cycle. Such evaluation should be characterised by careful attention to socio-technical factors to maximise the likelihood of successful implementation and adoption.
Please see later in the article for the Editors' Summary
Editors' Summary
There is considerable international interest in exploiting the potential of digital health care solutions, often referred to as eHealth—the use of information and communication technologies—to enhance the quality and safety of health care. Often accompanied by large costs, any large-scale expenditure on eHealth—such as electronic health records, picture archiving and communication systems, ePrescribing, associated computerized provider order entry systems, and computerized decision support systems—has tended to be justified on the grounds that these are efficient and cost-effective means for improving health care. In 2005, the World Health Assembly passed an eHealth resolution (WHA 58.28) that acknowledged, “eHealth is the cost-effective and secure use of information and communications technologies in support of health and health-related fields, including health-care services, health surveillance, health literature, and health education, knowledge and research,” and urged member states to develop and implement eHealth technologies. Since then, implementing eHealth technologies has become a main priority for many countries. For example, England has invested at least £12.8 billion in a National Programme for Information Technology for the National Health Service, and the Obama administration in the United States has committed to a US$38 billion eHealth investment in health care.
Why Was This Study Done?
Despite the wide endorsement of and support for eHealth, the scientific basis of its benefits—which are repeatedly made and often uncritically accepted—remains to be firmly established. A robust evidence-based perspective on the advantages on eHealth could help to suggest priority areas that have the greatest potential for benefit to patients and also to inform international eHealth deliberations on costs. Therefore, in order to better inform the international community, the authors systematically reviewed the published systematic review literature on eHealth technologies and evaluated the impact of these technologies on the quality and safety of health care delivery.
What Did the Researchers Do and Find?
The researchers divided eHealth technologies into three main categories: (1) storing, managing, and transmission of data; (2) clinical decision support; and (3) facilitating care from a distance. Then, implementing methods based on those developed by the Cochrane Collaboration and the NHS Service Delivery and Organisation Programme, the researchers used detailed search strategies and maps of health care quality, safety, and eHealth interventions to identify relevant systematic reviews (and related theoretical, methodological, and technical material) published between 1997 and 2010. Using these techniques, the researchers retrieved a total of 46,349 references from which they identified 108 reviews. The 53 reviews that the researchers finally selected (and critically reviewed) provided the main evidence base for assessing the impact of eHealth technologies in the three categories selected.
In their systematic review of systematic reviews, the researchers included electronic health records and picture archiving communications systems in their evaluation of category 1, computerized provider (or physician) order entry and e-prescribing in category 2, and all clinical information systems that, when used in the context of eHealth technologies, integrate clinical and demographic patient information to support clinician decision making in category 3.
The researchers found that many of the clinical claims made about the most commonly used eHealth technologies were not substantiated by empirical evidence. The evidence base in support of eHealth technologies was weak and inconsistent and importantly, there was insubstantial evidence to support the cost-effectiveness of these technologies. For example, the researchers only found limited evidence that some of the many presumed benefits could be realized; importantly, they also found some evidence that introducing these new technologies may on occasions also generate new risks such as prescribers becoming over-reliant on clinical decision support for e-prescribing, or overestimate its functionality, resulting in decreased practitioner performance.
What Do These Findings Mean?
The researchers found that despite the wide support for eHealth technologies and the frequently made claims by policy makers when constructing business cases to raise funds for large-scale eHealth projects, there is as yet relatively little empirical evidence to substantiate many of the claims made about eHealth technologies. In addition, even for the eHealth technology tools that have proven to be successful, there is little evidence to show that such tools would continue to be successful beyond the contexts in which they were originally developed. Therefore, in light of the lack of evidence in relation to improvements in patient outcomes, as well as the lack of evidence on their cost-effectiveness, the authors say that future eHealth technologies should be evaluated against a comprehensive set of measures, ideally throughout all stages of the technology's life cycle, and include socio-technical factors to maximize the likelihood of successful implementation and adoption in a given context. Furthermore, it is equally important that eHealth projects that have already been commissioned are subject to rigorous, multidisciplinary, and independent evaluation.
Additional Information
Please access these websites via the online version of this summary at
The authors' broader study is: Car J, Black A, Anandan C, Cresswell K, Pagliari C, McKinstry B, et al. (2008) The Impact of eHealth on the Quality and Safety of Healthcare. Available at:
More information is available on the World Health Assembly eHealth resolution
The World Health Organization provides information at the Global Observatory on eHealth, as well as a global insight into eHealth developments
The European Commission provides Information on eHealth in Europe and some examples of good eHealth practice
More information is provided on NHS Connecting for Health
PMCID: PMC3022523  PMID: 21267058

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