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1.  Manual and automated methods for identifying potentially preventable readmissions: a comparison in a large healthcare system 
Background
Identification of potentially preventable readmissions is typically accomplished through manual review or automated classification. Little is known about the concordance of these methods.
Methods
We manually reviewed 459 30-day, all-cause readmissions at 18 Kaiser Permanente Northern California hospitals, determining potential preventability through a four-step manual review process that included a chart review tool, interviews with patients, their families, and treating providers, and nurse reviewer and physician evaluation of findings and determination of preventability on a five-point scale. We reassessed the same readmissions with 3 M’s Potentially Preventable Readmission (PPR) software. We examined between-method agreement and the specificity and sensitivity of the PPR software using manual review as the reference.
Results
Automated classification and manual review respectively identified 78% (358) and 47% (227) of readmissions as potentially preventable. Overall, the methods agreed about the preventability of 56% (258) of readmissions. Using manual review as the reference, the sensitivity of PPR was 85% and specificity was 28%.
Conclusions
Concordance between methods was not high enough to replace manual review with automated classification as the primary method of identifying preventable 30-day, all-cause readmission for quality improvement purposes.
doi:10.1186/1472-6947-14-28
PMCID: PMC3984394  PMID: 24708889
Qualitative research; Quality assurance; Health care/methods; Patient readmission/statistics & numerical data
2.  Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer 
Background
Ensuring that all cancer patients have access to the appropriate treatment within an appropriate time is a strategic priority in many countries. There is in particular a need to describe and analyse cancer care trajectories and to produce waiting time indicators. We developed an algorithm for extracting temporally represented care trajectories from coded information collected routinely by the general cancer Registry in Poitou-Charentes region, France. The present work aimed to assess the performance of this algorithm on real-life patient data in the setting of non-metastatic breast cancer, using measures of similarity.
Methods
Care trajectories were modeled as ordered dated events aggregated into states, the granularity of which was defined from standard care guidelines. The algorithm generates each state from the aggregation over a period of tracer events characterised on the basis of diagnoses and medical procedures. The sequences are presented in simple form showing presence and order of the states, and in an extended form that integrates the duration of the states. The similarity of the sequences, which are represented in the form of chains of characters, was calculated using a generalised Levenshtein distance.
Results
The evaluation was performed on a sample of 159 female patients whose itineraries were also calculated manually from medical records using the same aggregation rules and dating system as the algorithm. Ninety-eight per cent of the trajectories were correctly reconstructed with respect to the ordering of states. When the duration of states was taken into account, 94% of the trajectories matched reality within three days. Dissimilarities between sequences were mainly due to the absence of certain pathology reports and to coding anomalies in hospitalisation data.
Conclusions
These results show the ability of an integrated regional information system to formalise care trajectories and automatically produce indicators for time-lapse to care instatement, of interest in the planning of care in cancer. The next step will consist in evaluating this approach and extending it to more complex trajectories (metastasis, relapse) and to other cancer localisations.
doi:10.1186/1472-6947-14-24
PMCID: PMC3983896  PMID: 24690482
Epidemiology; Evaluation; Care trajectory; Temporal reasoning; Data integration; Cancer
3.  Meeting user needs in national healthcare systems: lessons from early adopter community pharmacists using the electronic prescriptions service 
Background
The Electronic Prescription Service release Two (EPS2) is a new national healthcare information and communication technology in England that aims to deliver effective prescription writing, dispensing and reimbursement service to benefit patients. The aim of the study was to explore initial user experiences of Community Pharmacists (CPs) using EPS2.
Methods
We conducted nonparticipant observations and interviews in eight EPS2 early adopter community pharmacies classified as ‘first-of-type’ in midlands and northern regions in England. We interviewed eight pharmacists and two dispensers in addition to 56 hours recorded nonparticipant observations as field notes. Line-by-line coding and thematic analysis was conducted on the interview transcripts and field notes.
Results
CPs faced two types of challenge. The first was to do with missing electronic prescriptions. This was sometimes very disrupting to work practice, but pharmacists considered it a temporary issue resolvable with minor modifications to the system and user familiarity. The second was to do with long term design-specific issues. Pharmacists could only overcome these by using the system in ways not intended by the developers. Some felt that these issues would not exist had ‘real’ users been involved in the initial development. The issues were: 1) printing out electronic prescriptions (tokens) to dispense from for safe dispensing practices and to free up monitors for other uses, 2) logging all dispensing activities with one user’s Smartcard for convenience and use all human resources in the pharmacy, and, 3) problematic interface causing issues with endorsing prescriptions and claiming reimbursements.
Conclusions
We question if these unintended uses and barriers would have occurred had a more rigorous user-centric principles been applied at the earlier stages of design and implementation of EPS. We conclude that, since modification can occur at the evaluation stage, there is still scope for some of these barriers to be corrected to address the needs, and enhance the experiences, of CPs using the service, and make recommendations on how current challenges could be resolved.
doi:10.1186/1472-6947-14-16
PMCID: PMC3984715  PMID: 24612966
User-centric approaches; Healthcare ICT; Usability; User experience; Social informatics in healthcare; Electronic prescription service release two
4.  The effect of a decision aid intervention on decision making about coronary heart disease risk reduction: secondary analyses of a randomized trial 
Background
Decision aids offer promise as a practical solution to improve patient decision making about coronary heart disease (CHD) prevention medications and help patients choose medications to which they are likely to adhere. However, little data is available on decision aids designed to promote adherence.
Methods
In this paper, we report on secondary analyses of a randomized trial of a CHD adherence intervention (second generation decision aid plus tailored messages) versus usual care in an effort to understand how the decision aid facilitates adherence. We focus on data collected from the primary study visit, when intervention participants presented 45 minutes early to a previously scheduled provider visit; viewed the decision aid, indicating their intent for CHD risk reduction after each decision aid component (individualized risk assessment and education, values clarification, and coaching); and filled out a post-decision aid survey assessing their knowledge, perceived risk, decisional conflict, and intent for CHD risk reduction. Control participants did not present early and received usual care from their provider. Following the provider visit, participants in both groups completed post-visit surveys assessing the number and quality of CHD discussions with their provider, their intent for CHD risk reduction, and their feelings about the decision aid.
Results
We enrolled 160 patients into our study (81 intervention, 79 control). Within the decision aid group, the decision aid significantly increased knowledge of effective CHD prevention strategies (+21 percentage points; adjusted p<.0001) and the accuracy of perceived CHD risk (+33 percentage points; adjusted p<.0001), and significantly decreased decisional conflict (-0.63; adjusted p<.0001). Comparing between study groups, the decision aid also significantly increased CHD prevention discussions with providers (+31 percentage points; adjusted p<.0001) and improved perceptions of some features of patient-provider interactions. Further, it increased participants’ intentions for any effective CHD risk reducing strategies (+21 percentage points; 95% CI 5 to 37 percentage points), with a majority of the effect from the educational component of the decision aid. Ninety-nine percent of participants found the decision aid easy to understand and 93% felt it easy to use.
Conclusions
Decision aids can play an important role in improving decisions about CHD prevention and increasing patient-provider discussions and intent to reduce CHD risk.
doi:10.1186/1472-6947-14-14
PMCID: PMC3943405  PMID: 24575882
Decision support techniques; Medication adherence; Heart disease; Primary prevention
5.  A pipeline to extract drug-adverse event pairs from multiple data sources 
Background
Pharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones.
Method
We present a semi-automated pipeline to extract associations between drugs and side effects from traditional structured adverse event databases, enhanced by potential drug-adverse event pairs mined from user-comments from health-related websites and MEDLINE abstracts. The pipeline was tested using a set of 12 drugs representative of two previous studies of adverse event extraction from health-related websites and MEDLINE abstracts.
Results
Testing the pipeline shows that mining non-traditional sources helps substantiate the adverse event databases. The non-traditional sources not only contain the known AEs, but also suggest some unreported AEs for drugs which can then be analyzed further.
Conclusion
A semi-automated pipeline to extract the AE pairs from adverse event databases as well as potential AE pairs from non-traditional sources such as text from MEDLINE abstracts and user-comments from health-related websites is presented.
doi:10.1186/1472-6947-14-13
PMCID: PMC3936866  PMID: 24559132
Pharmacovigilance; NLP; Text mining; Social media; Adverse event; Biomedical literature; Unstructured text; BCPNN
6.  Design of a randomized, non-inferiority trial to evaluate the reliability of videoconferencing for remote consultation of diabetes 
Background
An estimated 366 million people are living with diabetes worldwide and it is predicted that its prevalence will increase to 552 million by 2030. Management of this disease and its complications is a challenge for many countries. Optimal glycaemic control is necessary to minimize complications, but less than 70% of diabetic patients achieve target levels of blood glucose, partly due to poor access to qualified health care providers. Telemedicine has the potential to improve access to health care, especially for rural and remote residents. Video teleconsultation, a real-time (or synchronous) mode of telemedicine, is gaining more popularity around the world through recent improvements in digital telecommunications. If video consultation is to be offered as an alternative to face-to-face consultation in diabetes assessment and management, then it is important to demonstrate that this can be achieved without loss of clinical fidelity. This paper describes the protocol of a randomised controlled trail for assessing the reliability of remote video consultation for people with diabetes.
Methods/Design
A total of 160 people with diabetes will be randomised into either a Telemedicine or a Reference group. Participants in the Reference group will receive two sequential face-to-face consultations whereas in the Telemedicine group one consultation will be conducted face-to-face and the other via videoconference. The primary outcome measure will be a change in the patient’s medication. Secondary outcome measures will be findings in physical examination, detecting complications, and patient satisfaction. A difference of less than 20% in the aggregated level of agreement between the two study groups will be used to identify if videoconference is non-inferior to traditional mode of clinical care (face-to-face).
Discussion
Despite rapid growth in application of telemedicine in a variety of medical specialities, little is known about the reliability of videoconferencing for remote consultation of people with diabetes. Results of this proposed study will provide evidence of the reliability of specialist consultation offered by videoconference for people with diabetes.
Trial registration number
Australian New Zealand Clinical Trials Registry ACTRN12612000315819.
doi:10.1186/1472-6947-14-11
PMCID: PMC3925960  PMID: 24528569
Diabetes; Telemedicine; Remote consultation; Videoconferencing; Video teleconsultation; Video consultation; Video visit
7.  Refining a brief decision aid in stable CAD: cognitive interviews 
Background
We describe the results of cognitive interviews to refine the “Making Choices©” Decision Aid (DA) for shared decision-making (SDM) about stress testing in patients with stable coronary artery disease (CAD).
Methods
We conducted a systematic development process to design a DA consistent with International Patient Decision Aid Standards (IPDAS) focused on Alpha testing criteria. Cognitive interviews were conducted with ten stable CAD patients using the “think aloud” interview technique to assess the clarity, usefulness, and design of each page of the DA.
Results
Participants identified three main messages: 1) patients have multiple options based on stress tests and they should be discussed with a physician, 2) take care of yourself, 3) the stress test is the gold standard for determining the severity of your heart disease. Revisions corrected the inaccurate assumption of item number three.
Conclusions
Cognitive interviews proved critical for engaging patients in the development process and highlighted the necessity of clear message development and use of design principles that make decision materials easy to read and easy to use. Cognitive interviews appear to contribute critical information from the patient perspective to the overall systematic development process for designing decision aids.
doi:10.1186/1472-6947-14-10
PMCID: PMC3927873  PMID: 24521210
Decision aids; Cognitive interviews; Shared decision-making; Stable CAD; Stress testing
8.  BMC Medical Informatics and Decision Making 
Contributing reviewers
The editors of BMC Medical Informatics and Decision Making would like to thank all our reviewers who have contributed their time to the journal in Volume 13 (2013).
doi:10.1186/1472-6947-14-7
PMCID: PMC3905665
9.  Real-time automatic hospital-wide surveillance of nosocomial infections and outbreaks in a large Chinese tertiary hospital 
Background
We aimed to develop a real-time nosocomial infection surveillance system (RT-NISS) to monitor all nosocomial infections (NIs) and outbreaks in a Chinese comprehensive hospital to better prevent and control NIs.
Methods
The screening algorithm used in RT-NISS included microbiological reports, antibiotic usage, serological and molecular testing, imaging reports, and fever history. The system could, in real-time, identify new NIs, record data, and produce time-series reports to align NI cases.
Results
Compared with a manual survey of NIs (the gold standard), the sensitivity and specificity of RT-NISS was 98.8% (84/85) and 93.0% (827/889), with time-saving efficiencies of about 200 times. RT-NISS obtained the highest hospital-wide monthly NI rate of 2.62%, while physician and medical record reviews reported rates of 1.52% and 2.35% respectively. It took about two hours for one infection control practitioner (ICP) to deal with 70 new suspicious NI cases; there were 3,500 inpatients each day in the study hospital. The system could also provide various updated data (i.e. the daily NI rate, surgical site infection (SSI) rate) for each ward, or the entire hospital. Within 3 years of implementing RT-NISS, the ICPs monitored and successfully controlled about 30 NI clusters and 4 outbreaks at the study hospital.
Conclusions
Just like the “ICPs’ eyes”, RT-NISS was an essential and efficient tool for the day-to-day monitoring of all NIs and outbreak within the hospital; a task that would not have been accomplished through manual process.
doi:10.1186/1472-6947-14-9
PMCID: PMC3922693  PMID: 24475790
10.  A systematic review of interactive multimedia interventions to promote children’s communication with health professionals: implications for communicating with overweight children 
Background
Interactive multimedia is an emerging technology that is being used to facilitate interactions between patients and health professionals. The purpose of this review was to identify and evaluate the impact of multimedia interventions (MIs), delivered in the context of paediatric healthcare, in order to inform the development of a MI to promote the communication of dietetic messages with overweight preadolescent children. Of particular interest were the effects of these MIs on child engagement and participation in treatment, and the subsequent effect on health-related treatment outcomes.
Methods
An extensive search of 12 bibliographic databases was conducted in April 2012. Studies were included if: one or more child-participant was 7 to 11-years-of-age; a MI was used to improve health-related behaviour; child-participants were diagnosed with a health condition and were receiving treatment for that condition at the time of the study. Data describing study characteristics and intervention effects on communication, satisfaction, knowledge acquisition, changes in self-efficacy, healthcare utilisation, and health outcomes were extracted and summarised using qualitative and quantitative methods.
Results
A total of 14 controlled trials, published between 1997 and 2006 met the selection criteria. Several MIs had the capacity to facilitate engagement between the child and a clinician, but only one sought to utilise the MI to improve communication between the child and health professional. In spite of concerns over the quality of some studies and small study populations, MIs were found useful in educating children about their health, and they demonstrated potential to improve children’s health-related self-efficacy, which could make them more able partners in face-to-face communications with health professionals.
Conclusions
The findings of this review suggest that MIs have the capacity to support preadolescent child-clinician communication, but further research in this field is needed. Particular attention should be given to designing appropriate MIs that are clinically relevant.
doi:10.1186/1472-6947-14-8
PMCID: PMC3926331  PMID: 24447844
Children; Preadolescent; Multimedia intervention; Clinicians; Health professionals; Communication; Face-to-face; Treatment; Diet; Overweight
11.  Dynamical density delay maps: simple, new method for visualising the behaviour of complex systems 
Background
Physiologic signals, such as cardiac interbeat intervals, exhibit complex fluctuations. However, capturing important dynamical properties, including nonstationarities may not be feasible from conventional time series graphical representations.
Methods
We introduce a simple-to-implement visualisation method, termed dynamical density delay mapping (“D3-Map” technique) that provides an animated representation of a system’s dynamics. The method is based on a generalization of conventional two-dimensional (2D) Poincaré plots, which are scatter plots where each data point, x(n), in a time series is plotted against the adjacent one, x(n + 1). First, we divide the original time series, x(n) (n = 1,…, N), into a sequence of segments (windows). Next, for each segment, a three-dimensional (3D) Poincaré surface plot of x(n), x(n + 1), h[x(n),x(n + 1)] is generated, in which the third dimension, h, represents the relative frequency of occurrence of each (x(n),x(n + 1)) point. This 3D Poincaré surface is then chromatised by mapping the relative frequency h values onto a colour scheme. We also generate a colourised 2D contour plot from each time series segment using the same colourmap scheme as for the 3D Poincaré surface. Finally, the original time series graph, the colourised 3D Poincaré surface plot, and its projection as a colourised 2D contour map for each segment, are animated to create the full “D3-Map.”
Results
We first exemplify the D3-Map method using the cardiac interbeat interval time series from a healthy subject during sleeping hours. The animations uncover complex dynamical changes, such as transitions between states, and the relative amount of time the system spends in each state. We also illustrate the utility of the method in detecting hidden temporal patterns in the heart rate dynamics of a patient with atrial fibrillation. The videos, as well as the source code, are made publicly available.
Conclusions
Animations based on density delay maps provide a new way of visualising dynamical properties of complex systems not apparent in time series graphs or standard Poincaré plot representations. Trainees in a variety of fields may find the animations useful as illustrations of fundamental but challenging concepts, such as nonstationarity and multistability. For investigators, the method may facilitate data exploration.
doi:10.1186/1472-6947-14-6
PMCID: PMC3899032  PMID: 24438439
Atrial fibrillation; Delay map; Heart rate variability; Nonlinear dynamics; Poincaré plot; Sleep; Time series; Visualisation
12.  Electronic immunization data collection systems: application of an evaluation framework 
Background
Evaluating the features and performance of health information systems can serve to strengthen the systems themselves as well as to guide other organizations in the process of designing and implementing surveillance tools. We adapted an evaluation framework in order to assess electronic immunization data collection systems, and applied it in two Ontario public health units.
Methods
The Centers for Disease Control and Prevention’s Guidelines for Evaluating Public Health Surveillance Systems are broad in nature and serve as an organizational tool to guide the development of comprehensive evaluation materials. Based on these Guidelines, and informed by other evaluation resources and input from stakeholders in the public health community, we applied an evaluation framework to two examples of immunization data collection and examined several system attributes: simplicity, flexibility, data quality, timeliness, and acceptability. Data collection approaches included key informant interviews, logic and completeness assessments, client surveys, and on-site observations.
Results
Both evaluated systems allow high-quality immunization data to be collected, analyzed, and applied in a rapid fashion. However, neither system is currently able to link to other providers’ immunization data or provincial data sources, limiting the comprehensiveness of coverage assessments. We recommended that both organizations explore possibilities for external data linkage and collaborate with other jurisdictions to promote a provincial immunization repository or data sharing platform.
Conclusions
Electronic systems such as the ones described in this paper allow immunization data to be collected, analyzed, and applied in a rapid fashion, and represent the infostructure required to establish a population-based immunization registry, critical for comprehensively assessing vaccine coverage.
doi:10.1186/1472-6947-14-5
PMCID: PMC3898919  PMID: 24423014
Immunization; Information systems; Data collection; Program evaluation
13.  Development of a personalized decision aid for breast cancer risk reduction and management 
Background
Breast cancer risk reduction has the potential to decrease the incidence of the disease, yet remains underused. We report on the development a web-based tool that provides automated risk assessment and personalized decision support designed for collaborative use between patients and clinicians.
Methods
Under Institutional Review Board approval, we evaluated the decision tool through a patient focus group, usability testing, and provider interviews (including breast specialists, primary care physicians, genetic counselors). This included demonstrations and data collection at two scientific conferences (2009 International Shared Decision Making Conference, 2009 San Antonio Breast Cancer Symposium).
Results
Overall, the evaluations were favorable. The patient focus group evaluations and usability testing (N = 34) provided qualitative feedback about format and design; 88% of these participants found the tool useful and 94% found it easy to use. 91% of the providers (N = 23) indicated that they would use the tool in their clinical setting.
Conclusion
BreastHealthDecisions.org represents a new approach to breast cancer prevention care and a framework for high quality preventive healthcare. The ability to integrate risk assessment and decision support in real time will allow for informed, value-driven, and patient-centered breast cancer prevention decisions. The tool is being further evaluated in the clinical setting.
doi:10.1186/1472-6947-14-4
PMCID: PMC3899602  PMID: 24422989
Breast cancer; Decision aid; Risk assessment; Risk reduction; Decision making; Primary care
14.  The Québec BCG Vaccination Registry (1956–1992): assessing data quality and linkage with administrative health databases 
Background
Vaccination registries have undoubtedly proven useful for estimating vaccination coverage as well as examining vaccine safety and effectiveness. However, their use for population health research is often limited. The Bacillus Calmette-Guérin (BCG) Vaccination Registry for the Canadian province of Québec comprises some 4 million vaccination records (1926-1992). This registry represents a unique opportunity to study potential associations between BCG vaccination and various health outcomes. So far, such studies have been hampered by the absence of a computerized version of the registry. We determined the completeness and accuracy of the recently computerized BCG Vaccination Registry, as well as examined its linkability with demographic and administrative medical databases.
Methods
Two systematically selected verification samples, each representing ~0.1% of the registry, were used to ascertain accuracy and completeness of the electronic BCG Vaccination Registry. Agreement between the paper [listings (n = 4,987 records) and vaccination certificates (n = 4,709 records)] and electronic formats was determined along several nominal and BCG-related variables. Linkage feasibility with the Birth Registry (probabilistic approach) and provincial Healthcare Registration File (deterministic approach) was examined using nominal identifiers for a random sample of 3,500 individuals born from 1961 to 1974 and BCG vaccinated between 1970 and 1974.
Results
Exact agreement was observed for 99.6% and 81.5% of records upon comparing, respectively, the paper listings and vaccination certificates to their corresponding computerized records. The proportion of successful linkage was 77% with the Birth Registry, 70% with the Healthcare Registration File, 57% with both, and varied by birth year.
Conclusions
Computerization of this Registry yielded excellent results. The registry was complete and accurate, and linkage with administrative databases was highly feasible. This study represents the first step towards assembling large scale population-based epidemiological studies which will enable filling important knowledge gaps on the potential health effects of early life non-specific stimulation of the immune function, as resulting from BCG vaccination.
doi:10.1186/1472-6947-14-2
PMCID: PMC3893599  PMID: 24400924
Bacillus Calmette-Guérin; Administrative databases; Registry; Validity; Linkage; Epidemiology
15.  In eHealth in India today, the nature of work, the challenges and the finances: an interview-based study 
Background
India is a country with vast unmet medical needs. eHealth has the potential to improve the quality of health care and reach the unreached. We have sought to understand the kinds of eHealth programmes being offered in India today, the challenges they face and the nature of their financing.
Methods
We have adopted an interview-based methodology. The 30 interviews represent 28 organizations, and include designers, implementers, evaluators and technology providers for eHealth programmes.
Results
A range of programmes is being run, including point-of-care in rural and urban areas, treatment compliance, data collection and disease surveillance, and distant medical education. Most programmes provide point-of-care to patients or other beneficiaries in rural areas. Technology is not a limiting factor but the unavailability of suitable health personnel is a major challenge, especially in rural areas. We have identified a few factors that help this situation. Financial sustainability is also a concern for most programmes, which have rarely been scaled up. There are recent for-profit efforts in urban areas, but no reliable business model has been identified yet. Government facilities have not been very effective in eHealth on their own, but collaborations between the government and non-profit (in particular) and for-profit organisations have led to impactful programmes.
Conclusions
It is unlikely that eHealth will have widespread and sustainable impact without government involvement, especially in rural areas. Nevertheless, programmes run solely by the government are unlikely to be the most effective.
doi:10.1186/1472-6947-14-1
PMCID: PMC3893581  PMID: 24387627
eHealth; Telemedicine; mHealth; Rural healthcare; Diagnostics; Distant medical education
16.  Determination of French influenza outbreaks periods between 1985 and 2011 through a web-based Delphi method 
Background
Assessing the accuracy of influenza epidemic periods determined by statistical models is important to improve the performance of algorithms used in real-time syndromic surveillance systems. This is a difficult problem to address in the absence of a reliable gold standard. The objective of this study is to establish an expert-based determination of the start and the end of influenza epidemics in France.
Methods
A three-round international web-based Delphi survey was proposed to 288 eligible influenza experts. Fifty-seven (20%) experts completed the three-rounds of the study. The experts were invited to indicate the starting and the ending week of influenza epidemics, on 32 time-series graphs of influenza seasons drawn using data from the French Sentinelles Network (Influenza-like illness incidence rates) and virological data from the WHO-FluNet. Twenty-six of 32 time-series graphs proposed corresponded to each of the French influenza seasons observed between 1985 and 2011. Six influenza seasons were proposed twice at each round to measure variation among expert responses.
Results
We obtained consensual results for 88% (23/26) of the epidemic periods. In two or three rounds (depending on the season) answers gathered around modes, and the internal control demonstrated a good reproducibility of the answers. Virological data did not appear to have a significant impact on the answers or the level of consensus, except for a season with a major mismatch between virological and incidence data timings.
Conclusions
Thanks to this international web-based Delphi survey, we obtained reproducible, stable and consensual results for the majority of the French influenza epidemic curves analysed. The detailed curves together with the estimates from the Delphi study could be a helpful tool for assessing the performance of statistical outbreak detection methods, in order to optimize them.
doi:10.1186/1472-6947-13-138
PMCID: PMC3898022  PMID: 24364926
Delphi technique; Information science; Consensus; Influenza; Epidemics; Surveillance
17.  Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression 
Background
Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification.
Methods
Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation.
Results
The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression.
Conclusion
A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.
doi:10.1186/1472-6947-13-137
PMCID: PMC3878032  PMID: 24359562
18.  Prevention praised, cure preferred: results of between-subjects experimental studies comparing (monetary) appreciation for preventive and curative interventions 
Background
'An ounce of prevention is worth a pound of cure’ is a common saying, and indeed, most health economic studies conclude that people are more willing to pay for preventive measures than for treatment activities. This may be because most health economic studies ask respondents to compare preventive measures with treatment, and thus prompt respondents to consider other uses of resources. However, psychological theorizing suggests that, when methods do not challenge subjects to consider other uses of resources, curative treatment is favored over prevention. Could it be that while prevention is praised, cure is preferred?
Methods
In two experimental studies, we investigated, from a psychological perspective and using a between-subjects design, whether prevention or treatment is preferred and why. In both studies, participants first read a lung cancer prevention or treatment intervention scenario that varied on the prevention-treatment dimension, but that were the same on factors like 'costs per saved life’ and kind of disease. Then participants completed a survey measuring appreciation (general and monetary) as well as a number of potential mediating variables.
Results
Both studies clearly demonstrated that, when the design was between-subjects, participants had greater (general and monetary) appreciation for treatment interventions than for preventive interventions with perceived urgency of the intervention quite consistently mediating this effect. Differences in appreciation of treatment over preventive treatment were shown to be .59 (Study 1) and .45 (Study 2) on a 5-point scale. Furthermore, participants thought that health insurance should compensate more for the treatment than for preventive measures, differences of 16% (Study 1), and 22% (Study 2). When participants were asked to directly compare both interventions on the basis of a short description, they preferred the preventive intervention.
Conclusion
It appears that people claim to prefer prevention when they are asked to consider other use of resources, but otherwise they prefer treatment. This preference is related to perceived urgency. The preference for treatment may be related to the prevention-treatment dimension itself, but also to variations on other dimensions that are inherently linked to prevention and treatment (like different efficacy rates and costs per treatment).
doi:10.1186/1472-6947-13-136
PMCID: PMC3878324  PMID: 24344779
Prevention; Cure; Treatment; Preference; Preference reversal; Appreciation; Urgency; Between-subjects design
19.  Systematic review of clinical decision support interventions with potential for inpatient cost reduction 
Background
Healthcare costs are increasing rapidly and at an unsustainable rate in many countries, and inpatient hospitalizations are a significant driver of these costs. Clinical decision support (CDS) represents a promising approach to not only improve care but to reduce costs in the inpatient setting. The purpose of this study was to systematically review trials of CDS interventions with the potential to reduce inpatient costs, so as to identify promising interventions for more widespread implementation and to inform future research in this area.
Methods
To identify relevant studies, MEDLINE was searched up to July 2013. CDS intervention studies with the potential to reduce inpatient healthcare costs were identified through titles and abstracts, and full text articles were reviewed to make a final determination on inclusion. Relevant characteristics of the studies were extracted and summarized.
Results
Following a screening of 7,663 articles, 78 manuscripts were included. 78.2% of studies were controlled before-after studies, and 15.4% were randomized controlled trials. 53.8% of the studies were focused on pharmacotherapy. The majority of manuscripts were published during or after 2008. 70.5% of the studies resulted in statistically and clinically significant improvements in an explicit financial measure or a proxy financial measure. Only 12.8% of the studies directly measured the financial impact of an intervention, whereas the financial impact was inferred in the remainder of studies. Data on cost effectiveness was available for only one study.
Conclusions
Significantly more research is required on the impact of clinical decision support on inpatient costs. In particular, there is a remarkable gap in the availability of cost effectiveness studies required by policy makers and decision makers in healthcare systems.
doi:10.1186/1472-6947-13-135
PMCID: PMC3878492  PMID: 24344752
Clinical decision support; Clinical costs; Cost effectiveness; Hospital care; Emergency medical care; Health information technology
20.  Detecting and diagnosing hotspots for the enhanced management of hospital emergency departments in Queensland, Australia 
Background
Predictive tools are already being implemented to assist in Emergency Department bed management by forecasting the expected total volume of patients. Yet these tools are unable to detect and diagnose when estimates fall short. Early detection of hotspots, that is subpopulations of patients presenting in unusually high numbers, would help authorities to manage limited health resources and communicate effectively about emerging risks. We evaluate an anomaly detection tool that signals when, and in what way Emergency Departments in 18 hospitals across the state of Queensland, Australia, are significantly exceeding their forecasted patient volumes.
Methods
The tool in question is an adaptation of the Surveillance Tree methodology initially proposed in Sparks and Okugami (IntStatl 1:2–24, 2010). for the monitoring of vehicle crashes. The methodology was trained on presentations to 18 Emergency Departments across Queensland over the period 2006 to 2008. Artificial increases were added to simulated, in-control counts for these data to evaluate the tool’s sensitivity, timeliness and diagnostic capability. The results were compared with those from a univariate control chart. The tool was then applied to data from 2009, the year of the H1N1 (or ‘Swine Flu’) pandemic.
Results
The Surveillance Tree method was found to be at least as effective as a univariate, exponentially weighted moving average (EWMA) control chart when increases occurred in a subgroup of the monitored population. The method has advantages over the univariate control chart in that it allows for the monitoring of multiple disease groups while still allowing control of the overall false alarm rate. It is also able to detect changes in the makeup of the Emergency Department presentations, even when the total count remains unchanged. Furthermore, the Surveillance Tree method provides diagnostic information useful for service improvements or disease management.
Conclusions
Multivariate surveillance provides a useful tool in the management of hospital Emergency Departments by not only efficiently detecting unusually high numbers of presentations, but by providing information about which groups of patients are causing the increase.
doi:10.1186/1472-6947-13-132
PMCID: PMC3867222  PMID: 24313914
Outbreak detection; Disease surveillance; Multivariate control charts; Emergency departments; EWMA control chart
21.  Risks to patient safety associated with implementation of electronic applications for medication management in ambulatory care - a systematic review 
Background
The objective was to find evidence to substantiate assertions that electronic applications for medication management in ambulatory care (electronic prescribing, clinical decision support (CDSS), electronic health record, and computer generated paper prescriptions), while intended to reduce prescribing errors, can themselves result in errors that might harm patients or increase risks to patient safety.
Methods
Because a scoping search for adverse events in randomized controlled trials (RCTs) yielded few relevant results, we systematically searched nine databases, including MEDLINE, EMBASE, and The Cochrane Database of Systematic Reviews for systematic reviews and studies of a wide variety of designs that reported on implementation of the interventions. Studies that had safety and adverse events as outcomes, monitored for them, reported anecdotally adverse events or other events that might indicate a threat to patient safety were included.
Results
We found no systematic reviews that examined adverse events or patient harm caused by organizational interventions. Of the 4056 titles and abstracts screened, 176 full-text articles were assessed for inclusion. Sixty-one studies with appropriate interventions, settings and participants but without patient safety, adverse event outcomes or monitoring for risks were excluded, along with 77 other non-eligible studies. Eighteen randomized controlled trials (RCTs), 5 non-randomized controlled trials (non-R,CTs) and 15 observational studies were included. The most common electronic intervention studied was CDSS and the most frequent clinical area was cardio-vascular, including anti-coagulants. No RCTS or non-R,CTS reported adverse event. Adverse events reported in observational studies occurred less frequently after implementation of CDSS. One RCT and one observational study reported an increase in problematic prescriptions with electronic prescribing
Conclusions
The safety implications of electronic medication management in ambulatory care have not been established with results from studies included in this systematic review. Only a minority of studies that investigated these interventions included threats to patients’ safety as outcomes or monitored for adverse events. It is therefore not surprising that we found little evidence to substantiate fears of new risks to patient safety with their implementation. More research is needed to focus on the draw-backs and negative outcomes that implementation of these interventions might introduce.
doi:10.1186/1472-6947-13-133
PMCID: PMC3913838  PMID: 24308799
Electronic interventions; Medication management; Computer prescribing; Prescribing errors; Patient safety
22.  The predictability of claim-data-based comorbidity-adjusted models could be improved by using medication data 
Background
Recently, claim-data-based comorbidity-adjusted methods such as the Charlson index and the Elixhauser comorbidity measures have been widely used among researchers. At the same time, there have been an increasing number of attempts to improve the predictability of comorbidity-adjusted models. We tried to improve the predictability of models using the Charlson and Elixhauser indices by using medication data; specifically, we used medication data to estimate omitted comorbidities in the claim data.
Methods
We selected twelve major diseases (other than malignancies) that caused large numbers of in-hospital mortalities during 2008 in hospitals with 700 or more beds in South Korea. Then, we constructed prediction models for in-hospital mortality using the Charlson index and Elixhauser comorbidity measures, respectively. Inferring missed comorbidities using medication data, we built enhanced Charlson and Elixhauser comorbidity-measures-based prediction models, which included comorbidities inferred from medication data. We then compared the c-statistics of each model.
Results
247,712 admission cases were enrolled. 55 generic drugs were used to infer 8 out of 17 Charlson comorbidities, and 106 generic drugs were used to infer 14 out of 31 Elixhauser comorbidities. Before the inclusion of comorbidities inferred from medication data, the c-statistics of models using the Charlson index were 0.633-0.882 and those of the Elixhauser index were 0.699-0.917. After the inclusion of comorbidities inferred from medication data, 9 of 12 models using the Charlson index and all of the models using the Elixhauser comorbidity measures were improved in predictability but, the differences were relatively small.
Conclusion
Prediction models using Charlson index or Elixhauser comorbidity measures might be improved by including comorbidities inferred from medication data.
doi:10.1186/1472-6947-13-128
PMCID: PMC3842675  PMID: 24257030
Severity-of-illness index; Comorbidity; Prescriptions; Drug; Risk-adjustment; Outcome assessment
23.  Evaluation of prediction models for the staging of prostate cancer 
Background
There are dilemmas associated with the diagnosis and prognosis of prostate cancer which has lead to over diagnosis and over treatment. Prediction tools have been developed to assist the treatment of the disease.
Methods
A retrospective review was performed of the Irish Prostate Cancer Research Consortium database and 603 patients were used in the study. Statistical models based on routinely used clinical variables were built using logistic regression, random forests and k nearest neighbours to predict prostate cancer stage. The predictive ability of the models was examined using discrimination metrics, calibration curves and clinical relevance, explored using decision curve analysis. The N = 603 patients were then applied to the 2007 Partin table to compare the predictions from the current gold standard in staging prediction to the models developed in this study.
Results
30% of the study cohort had non organ-confined disease. The model built using logistic regression illustrated the highest discrimination metrics (AUC = 0.622, Sens = 0.647, Spec = 0.601), best calibration and the most clinical relevance based on decision curve analysis. This model also achieved higher discrimination than the 2007 Partin table (ECE AUC = 0.572 & 0.509 for T1c and T2a respectively). However, even the best statistical model does not accurately predict prostate cancer stage.
Conclusions
This study has illustrated the inability of the current clinical variables and the 2007 Partin table to accurately predict prostate cancer stage. New biomarker features are urgently required to address the problem clinician’s face in identifying the most appropriate treatment for their patients. This paper also demonstrated a concise methodological approach to evaluate novel features or prediction models.
doi:10.1186/1472-6947-13-126
PMCID: PMC3834875  PMID: 24238348
Prediction models; Model evaluation; Discrimination; Calibration; Prostate cancer
24.  The challenges of implementing a telestroke network: a systematic review and case study 
Background
The use of telemedicine in acute stroke care can facilitate rapid access to treatment, but the work required to embed any new technology into routine practice is often hidden, and can be challenging. We aimed to collate recommendations and resources to support telestroke implementation.
Methods
Systematic search of healthcare databases and the Internet to identify descriptions of the implementation of telestroke projects; interviews with key stakeholders during the development of one UK telestroke network. Supporting documentation from existing projects was analysed to construct a framework of implementation stages and tasks, and a toolkit of documents. Interviews and literature were analysed with other data sources using Normalisation Process Theory as described in the e-Health Implementation Toolkit.
Results
61 telestroke projects were identified and contacted. Twenty projects provided documents, 13 with published research detailing four stages of telestroke system development, implementation, use, and evaluation. Interviewees identified four main challenges: engaging and maintaining the commitment of a wide range of stakeholders across multiple organisations; addressing clinicians perceptions of evidence, workload, and payback; managing clinical and technical workability across diverse settings; and monitoring how the system is used and reconfigured by users.
Conclusions
Information to guide telestroke implementation is sparse, but available. By using multiple sources of data, sufficient information was collated to construct a web-based toolkit detailing implementation tasks, resources and challenges in the development of a telestroke system for assessment and thrombolysis delivery in acute care. The toolkit is freely available online.
doi:10.1186/1472-6947-13-125
PMCID: PMC3833973  PMID: 24229343
25.  An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data 
Background
Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study.
Methods
Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE) ,cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results.
Results
Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied.
Conclusions
LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR.
doi:10.1186/1472-6947-13-124
PMCID: PMC3829096  PMID: 24207108
Breast cancer; Decision tree; Logistic regression; Imbalanced data; Synthetic minority over-sampling; Cost-sensitive classifier technique

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