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26.  Ambient-aware continuous care through semantic context dissemination 
Background
The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e.g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate.
The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data.
Methods
The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability.
Results
A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered.
Conclusions
The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results.
doi:10.1186/1472-6947-14-97
PMCID: PMC4320491  PMID: 25476007
eHealth; Semantic modelling; Continuous care; Context dissemination; Ontology; Ambient intelligence
27.  Development of a culturally appropriate computer-delivered tailored internet-based health literacy intervention for spanish-dominant hispanics living with HIV 
Background
Low health literacy is associated with poor medication adherence in persons with human immunodeficiency virus (HIV), which can lead to poor health outcomes. As linguistic minorities, Spanish-dominant Hispanics (SDH) face challenges such as difficulties in obtaining and understanding accurate information about HIV and its treatment. Traditional health educational methods (e.g., pamphlets, talking) may not be as effective as delivering through alternate venues. Technology-based health information interventions have the potential for being readily available on desktop computers or over the Internet. The purpose of this research was to adapt a theoretically-based computer application (initially developed for English-speaking HIV-positive persons) that will provide linguistically and culturally appropriate tailored health education to Spanish-dominant Hispanics with HIV (HIV + SDH).
Methods
A mixed methods approach using quantitative and qualitative interviews with 25 HIV + SDH and 5 key informants guided by the Information-Motivation-Behavioral (IMB) Skills model was used to investigate cultural factors influencing medication adherence in HIV + SDH. We used a triangulation approach to identify major themes within cultural contexts relevant to understanding factors related to motivation to adhere to treatment. From this data we adapted an automated computer-based health literacy intervention to be delivered in Spanish.
Results
Culture-specific motivational factors for treatment adherence in HIV + SDH persons that emerged from the data were stigma, familismo (family), mood, and social support. Using this data, we developed a culturally and linguistically adapted a tailored intervention that provides information about HIV infection, treatment, and medication related problem solving skills (proven effective in English-speaking populations) that can be delivered using touch-screen computers, tablets, and smartphones to be tested in a future study.
Conclusion
Using a theoretically-grounded Internet-based eHealth education intervention that builds on knowledge and also targets core cultural determinants of adherence may prove a highly effective approach to improve health literacy and medication decision-making in this group.
doi:10.1186/s12911-014-0103-9
PMCID: PMC4260191  PMID: 25433489
eHealth; Consumer health informatics; Health literacy; HIV/AIDS; Internet; Hispanics/Latinos; Tailored intervention; Medication adherence; Health technology; Ethnic minority; Health disparities
28.  RMS: a platform for managing cross-disciplinary and multi-institutional research project collaboration 
Background
Cross-institutional cross-disciplinary collaboration has become a trend as researchers move toward building more productive and innovative teams for scientific research. Research collaboration is significantly changing the organizational structure and strategies used in the clinical and translational science domain. However, due to the obstacles of diverse administrative structures, differences in area of expertise, and communication barriers, establishing and managing a cross-institutional research project is still a challenging task. We address these challenges by creating an integrated informatics platform to reduce the barriers to biomedical research collaboration.
Results
The Request Management System (RMS) is an informatics infrastructure designed to transform a patchwork of expertise and resources into an integrated support network. The RMS facilitates investigators’ initiation of new collaborative projects and supports the management of the collaboration process. In RMS, experts and their knowledge areas are categorized and managed structurally to provide consistent service. A role-based collaborative workflow is tightly integrated with domain experts and services to streamline and monitor the life-cycle of a research project. The RMS has so far tracked over 1,500 investigators with over 4,800 tasks. The research network based on the data collected in RMS illustrated that the investigators’ collaborative projects increased close to 3 times from 2009 to 2012. Our experience with RMS indicates that the platform reduces barriers for cross-institutional collaboration of biomedical research projects.
Conclusion
Building a new generation of infrastructure to enhance cross-disciplinary and multi-institutional collaboration has become an important yet challenging task. In this paper, we share the experience of developing and utilizing a collaborative project management system. The results of this study demonstrate that a web-based integrated informatics platform can facilitate and increase research interactions among investigators.
doi:10.1186/s12911-014-0106-6
PMCID: PMC4264263  PMID: 25433526
Biomedical research; Organization & administration; Research collaboration; System design and development; Collaborative research; Communication networks; Systems integration; Data-driven analysis
29.  Designing effective visualizations of habits data to aid clinical decision making 
Background
Changes in daily habits can provide important information regarding the overall health status of an individual. This research aimed to determine how meaningful information may be extracted from limited sensor data and transformed to provide clear visualization for the clinicians who must use and interact with the data and make judgments on the condition of patients. We ascertained that a number of insightful features related to habits and physical condition could be determined from usage and motion sensor data.
Methods
Our approach to the design of the visualization follows User Centered Design, specifically, defining requirements, designing corresponding visualizations and finally evaluating results. This cycle was iterated three times.
Results
The User Centered Design method was successfully employed to converge to a design that met the main objective of this study. The resulting visualizations of relevant features that were extracted from the sensor data were considered highly effective and intuitive to the clinicians and were considered suitable for monitoring the behavior patterns of patients.
Conclusions
We observed important differences in the approach and attitude of the researchers and clinicians. Whereas the researchers would prefer to have as many features and information as possible in each visualization, the clinicians would prefer clarity and simplicity, often each visualization having only a single feature, with several visualizations per page. In addition, concepts considered intuitive to the researchers were not always to the clinicians.
Electronic supplementary material
The online version of this article (doi:10.1186/s12911-014-0102-x) contains supplementary material, which is available to authorized users.
doi:10.1186/s12911-014-0102-x
PMCID: PMC4265320  PMID: 25433372
Visualization; Decision making; User centered design; Habits data; Feature extraction
30.  The SMART personalised self-management system for congestive heart failure: results of a realist evaluation 
Background
Technology has the potential to provide support for self-management to people with congestive heart failure (CHF). This paper describes the results of a realist evaluation of the SMART Personalised Self-Management System (PSMS) for CHF.
Methods
The PSMS was used, at home, by seven people with CHF. Data describing system usage and usability as well as questionnaire and interview data were evaluated in terms of the context, mechanism and outcome hypotheses (CMOs) integral to realist evaluation.
Results
The CHF PSMS improved heart failure related knowledge in those with low levels of knowledge at baseline, through providing information and quizzes. Furthermore, participants perceived the self-regulatory aspects of the CHF PSMS as being useful in encouraging daily walking. The CMOs were revised to describe the context of use, and how this influences both the mechanisms and the outcomes.
Conclusions
Participants with CHF engaged with the PSMS despite some technological problems. Some positive effects on knowledge were observed as well as the potential to assist with changing physical activity behaviour. Knowledge of CHF and physical activity behaviour change are important self-management targets for CHF, and this study provides evidence to direct the further development of a technology to support these targets.
Electronic supplementary material
The online version of this article (doi:10.1186/s12911-014-0109-3) contains supplementary material, which is available to authorized users.
doi:10.1186/s12911-014-0109-3
PMCID: PMC4246999  PMID: 25421307
Technology; Realist evaluation; User-centred design; Heart failure; Self-management
31.  An innovative approach to near-infrared spectroscopy using a standard mobile device and its clinical application in the real-time visualization of peripheral veins 
Background
Excessive venipunctures are a significant problem both in emergency rooms and during hospital stays. Near-infrared (NIR) illumination devices improve venipuncture success rate but their usage is limited by their availability and economic cost. The objectives of this study were to develop a low-cost NIR spectroscopy prototype from a standard mobile device, to evaluate its efficacy and acceptance as an educational tool, and in a clinical setting.
Methods
Through a user-centric design process a prototype device was developed. Its educational efficacy was evaluated through a non-invasive, observational study (20 student clinicians, 25 subjects) and its acceptance was assessed using quantitative and qualitative analysis. A smaller clinical trial was performed by a group of 4 medical professionals over a period of 6 weeks that involved 64 patients.
Results
The prototype enables real-time visualization of peripheral veins on a variety of Android-based devices. The prototype was 35.2% more successful in visualizing and locating veins (n = 500 attempts) than the nursing students. The acceptance assessment revealed high perception of usefulness, satisfaction, and ease of use. In the clinical trial, 1.6 (SD 1.3) additional veins per patient were identified compared with the traditional visualization methods.
Conclusions
To the best of our knowledge this is the first study that describes the design, feasibility and application of an NIR spectroscopy prototype developed on a standard mobile device.
Electronic supplementary material
The online version of this article (doi:10.1186/s12911-014-0100-z) contains supplementary material, which is available to authorized users.
doi:10.1186/s12911-014-0100-z
PMCID: PMC4251692  PMID: 25421099
Mobile applications; Spectroscopy; Near-infrared; Vascular access devices; Health education; Feasibility studies
32.  Optimum binary cut-off threshold of a diagnostic test: comparison of different methods using Monte Carlo technique 
Background
Using Monte Carlo simulations, we compare different methods (maximizing Youden index, maximizing mutual information, and logistic regression) for their ability to determine optimum binary cut-off thresholds for a ratio-scaled diagnostic test variable. Special attention is given to the stability and precision of the results in dependence on the distributional characteristics as well as the pre-test probabilities of the diagnostic categories in the test population.
Methods
Fictitious data sets of a ratio-scaled diagnostic test with different distributional characteristics are generated for 50, 100 and 200 fictitious “individuals” with systematic variation of pre-test probabilities of two diagnostic categories. For each data set, optimum binary cut-off limits are determined employing different methods. Based on these optimum cut-off thresholds, sensitivities and specificities are calculated for the respective data sets. Mean values and SD of these variables are computed for 1000 repetitions each.
Results
Optimizations of cut-off limits using Youden index and logistic regression-derived likelihood ratio functions with correct adaption for pre-test probabilities both yield reasonably stable results, being nearly independent from pre-test probabilities actually used. Maximizing mutual information yields cut-off levels decreasing with increasing pre-test probability of disease. The most precise results (in terms of the smallest SD) are usually seen for the likelihood ratio method. With this parametric method, however, cut-off values show a significant positive bias and, hence, specificities are usually slightly higher, and sensitivities are consequently slightly lower than with the two non-parametric methods.
Conclusions
In terms of stability and bias, Youden index is best suited for determining optimal cut-off limits of a diagnostic variable. The results of Youden method and likelihood ratio method are surprisingly insensitive against distributional differences as well as pre-test probabilities of the two diagnostic categories. As an additional bonus of the parametric procedure, transfer of the likelihood ratio functions, obtained from logistic regression analysis, to other diagnostic scenarios with different pre-test probabilities is straightforward.
Electronic supplementary material
The online version of this article (doi:10.1186/s12911-014-0099-1) contains supplementary material, which is available to authorized users.
doi:10.1186/s12911-014-0099-1
PMCID: PMC4253606  PMID: 25421000
33.  Improving bone mineral density reporting to patients with an illustration of personal fracture risk 
Background
To determine patients’ preferences for, and understanding of, FRAX® fracture risk conveyed through illustrations.
Methods
Drawing on examples from published studies, four illustrations of fracture risk were designed and tested for patient preference, ease of understanding, and perceived risk. We enrolled a convenience sample of adults aged 50 and older at two medical clinics located in the Midwestern and Southern United States. In-person structured interviews were conducted to elicit patient ranking of preference, ease of understanding, and perceived risk for each illustration.
Results
Most subjects (n = 142) were female (64%), Caucasian (76%) and college educated (78%). Of the four risk depictions, a plurality of participants (37%) listed a bar graph as most preferred. Subjects felt this illustration used the stoplight color system to display risk levels well and was the most “clear,” “clean,” and “easy to read”. The majority of subjects (52%) rated the pictogram as the most difficult to understand as this format does not allow people to quickly ascertain their individual risk category.
Conclusions
Communicating risk to patients with illustrations can be done effectively with clearly designed illustrations responsive to patient preference.
Trial Registration
ClinicalTrials.gov Identifier: NCT01507662
doi:10.1186/s12911-014-0101-y
PMCID: PMC4260260
Osteoporosis; DXA Scan; Risk; Fracture; Bone; Patient education
34.  Development and pilot testing of an online case-based approach to shared decision making skills training for clinicians 
Background
Although research suggests that patients prefer a shared decision making (SDM) experience when making healthcare decisions, clinicians do not routinely implement SDM into their practice and training programs are needed. Using a novel case-based strategy, we developed and pilot tested an online educational program to promote shared decision making (SDM) by primary care clinicians.
Methods
A three-phased approach was used: 1) development of a conceptual model of the SDM process; 2) development of an online teaching case utilizing the Design A Case (DAC) authoring template, a well-tested process used to create peer-reviewed web-based clinical cases across all levels of healthcare training; and 3) pilot testing of the case. Participants were clinician members affiliated with several primary care research networks across the United States who answered an invitation email. The case used prostate cancer screening as the clinical context and was delivered online. Post-intervention ratings of clinicians’ general knowledge of SDM, knowledge of specific SDM steps, confidence in and intention to perform SDM steps were also collected online.
Results
Seventy-nine clinicians initially volunteered to participate in the study, of which 49 completed the case and provided evaluations. Forty-three clinicians (87.8%) reported the case met all the learning objectives, and 47 (95.9%) indicated the case was relevant for other equipoise decisions. Thirty-one clinicians (63.3%) accessed supplementary information via links provided in the case. After viewing the case, knowledge of SDM was high (over 90% correctly identified the steps in a SDM process). Determining a patient’s preferred role in making the decision (62.5% very confident) and exploring a patient’s values (65.3% very confident) about the decisions were areas where clinician confidence was lowest. More than 70% of the clinicians intended to perform SDM in the future.
Conclusions
A comprehensive model of the SDM process was used to design a case-based approach to teaching SDM skills to primary care clinicians. The case was favorably rated in this pilot study. Clinician skills training for helping patients clarify their values and for assessing patients’ desire for involvement in decision making remain significant challenges and should be a focus of future comparative studies.
Electronic supplementary material
The online version of this article (doi:10.1186/1472-6947-14-95) contains supplementary material, which is available to authorized users.
doi:10.1186/1472-6947-14-95
PMCID: PMC4283132  PMID: 25361614
Decision making; Medical education; Primary health care
35.  A systematic review of speech recognition technology in health care 
Background
To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care.
Methods
A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered.
Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained.
Results
The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes.
Conclusions
SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.
doi:10.1186/1472-6947-14-94
PMCID: PMC4283090  PMID: 25351845
Nursing; Systematic review; Speech recognition; Interactive voice response systems; Human transcriptions; Health professionals
36.  Clinical data needs in the neonatal intensive care unit electronic medical record 
Background
The amount of clinical information that providers encounter daily creates an environment for information overload and medical error. To create a more efficient EMR human-computer interface, we aimed to understand clinical information needs among NICU providers.
Methods
A web-based survey to evaluate 98 data items was created and distributed to NICU providers. Participants were asked to rate the importance of each data item in helping them make routine clinical decisions in the NICU.
Results
There were 23 responses (92% – response rate) with participants distributed among four clinical roles. The top 5 items with the highest mean score were daily weight, pH, pCO2, FiO2, and blood culture results. When compared by clinical role groupings, supervisory physicians gave individual data item ratings at the extremes of the scale when compared to providers more responsible for the daily clinical care of NICU patients.
Conclusion
NICU providers demonstrate a need for large amounts of EMR data to help guide clinical decision making with differences found when comparing by clinical role. When creating an EMR interface in the NICU there may be a need to offer options for varying degrees of viewable data densities depending on clinical role.
doi:10.1186/1472-6947-14-92
PMCID: PMC4283115  PMID: 25341847
NICU; Computerized medical record systems; Health information technology; Medical informatics; Electronic health records
37.  Mining Adverse Drug Reactions from online healthcare forums using Hidden Markov Model 
Background
Adverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance.
Methods
We treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.comis used in the training and validation of the HMM based Text Mining system.
Results
A 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.comand http://www.steadyhealth.comwere found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also identified.
Conclusions
The results from the HMM based Text Miner are encouraging to pursue further enhancements to this approach. The mined novel side-effects can act as early indicators for health authorities to help focus their efforts in post-marketing drug surveillance.
doi:10.1186/1472-6947-14-91
PMCID: PMC4283122  PMID: 25341686
Adverse drug reaction; Pharmacovigilance; Text mining; Machine learning; Online healthcare forums; Hidden Markov model
38.  V-Model: a new perspective for EHR-based phenotyping 
Background
Narrative resources in electronic health records make clinical phenotyping study difficult to achieve. If a narrative patient history can be represented in a timeline, this would greatly enhance the efficiency of information-based studies. However, current timeline representations have limitations in visualizing narrative events. In this paper, we propose a temporal model named the ‘V-Model’ which visualizes clinical narratives into a timeline.
Methods
We developed the V-Model which models temporal clinical events in v-like graphical structure. It visualizes patient history on a timeline in an intuitive way. For the design, the representation, reasoning, and visualization (readability) aspects were considered. Furthermore, the unique graphical notation helps to find hidden patterns of a specific patient group. For evaluation, we verified our distinctive solutions, and surveyed usability. The experiments were carried out between the V-Model and a conventional timeline model group. Eighty medical students and physicians participated in this evaluation.
Results
The V-Model was proven to be superior in representing narrative medical events, provide sufficient information for temporal reasoning, and outperform in readability compared to a conventional timeline model. The usability of the V-Model was assessed as positive.
Conclusions
The V-Model successfully resolves visualization issues of clinical documents, and provides better usability compared to a conventional timeline model.
Electronic supplementary material
The online version of this article (doi:10.1186/1472-6947-14-90) contains supplementary material, which is available to authorized users.
doi:10.1186/1472-6947-14-90
PMCID: PMC4283133  PMID: 25341558
39.  A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients 
Background
Length-of-stay prediction for cardiac surgery patients is a key point for medical management issues, such as optimization of resources in intensive care units and operating room scheduling. Scoring systems are a very attractive family of predictive models, but their retraining and updating are generally critical. The present approach to designing a scoring system for predicting length of stay in intensive care aims to overcome these difficulties, so that a model designed in a given scenario can easily be adjusted over time or for internal purposes.
Methods
A naïve Bayes approach was used to develop a simple scoring system. A set of 36 preoperative, intraoperative and postoperative variables collected in a sample of 3256 consecutive adult patients undergoing heart surgery were considered as likely risk predictors. The number of variables was reduced by selecting an optimal subset of features. Scoring system performance was assessed by cross-validation.
Results
After the selection process, seven variables were entered in the prediction model, which showed excellent discrimination, good generalization power and suitable sensitivity and specificity. No significant difference was found between AUC of the training and testing sets. The 95% confidence interval for AUC estimated by the BCa bootstrap method was [0.841, 0.883] and [0.837, 0.880] in the training and testing sets, respectively. Chronic dialysis, low postoperative cardiac output and acute myocardial infarction proved to be the major risk factors.
Conclusions
The proposed approach produced a simple and trustworthy scoring system, which is easy to update regularly and to customize for other centers. This is a crucial point when scoring systems are used as predictive models in clinical practice.
doi:10.1186/1472-6947-14-89
PMCID: PMC4203871  PMID: 25311154
Intensive care unit; Decision-support system; Prediction model; Scoring system
40.  Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment? 
Background
Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information.
Methods
Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index).
Results
548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers.
Conclusions
Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation.
doi:10.1186/1472-6947-14-87
PMCID: PMC4197237  PMID: 25274085
Breast cancer; BRCA1; BRCA2; Single nucleotide polymorphism; Cox regression; Random survival forests; Survival analysis; Prognostic model; Concordance index
41.  Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission 
Background
Childhood cancer relies heavily on inpatient hospital services to deliver tumor-directed therapy and manage toxicities. Hospitalizations have increased over the past decade, though not uniformly across childhood cancer diagnoses. Analysis of the reasons for admission of children with cancer could enhance comparison of resource use between cancers, and allow clinical practice data to be interpreted more readily. Such comparisons using nationwide data sources are difficult because of numerous subdivisions in the International Classification of Diseases Clinical Modification (ICD-9) system and inherent complexities of treatments. This study aimed to develop a systematic approach to classifying cancer-related admissions in administrative data into categories that reflected clinical practice and predicted resource use.
Methods
We developed a multistep algorithm to stratify indications for childhood cancer admissions in the Kids Inpatient Databases from 2003, 2006 and 2009 into clinically meaningful categories. This algorithm assumed that primary discharge diagnoses of cancer or cytopenia were insufficient, and relied on procedure codes and secondary diagnoses in these scenarios. Clinical Classification Software developed by the Healthcare Cost and Utilization Project was first used to sort thousands of ICD-9 codes into 5 mutually exclusive diagnosis categories and 3 mutually exclusive procedure categories, and validation was performed by comparison with the ICD-9 codes in the final admission indication. Mean cost, length of stay, and costs per day were compared between categories of indication for admission.
Results
A cohort of 202,995 cancer-related admissions was grouped into four categories of indication for admission: chemotherapy (N=77,791, 38%), to undergo a procedure (N=30,858, 15%), treatment for infection (N=30,380, 15%), or treatment for other toxicities (N=43,408, 21.4%). The positive predictive value for the algorithm was >95% for each category. Admissions for procedures had higher mean hospital costs, longer hospital stays, and higher costs per day compared with other admission reasons (p<0.001).
Conclusions
This is the first description of a method for grouping indications for childhood cancer admission within an administrative dataset into clinically relevant categories. This algorithm provides a framework for more detailed analyses of pediatric hospitalization data by cancer type.
Electronic supplementary material
The online version of this article (doi:10.1186/1472-6947-14-88) contains supplementary material, which is available to authorized users.
doi:10.1186/1472-6947-14-88
PMCID: PMC4197316  PMID: 25274165
Cancer; Health administrative data; Healthcare utilization; Child
42.  Optimal strategy for linkage of datasets containing a statistical linkage key and datasets with full personal identifiers 
Background
Linkage of aged care and hospitalisation data provides valuable information on patterns of health service utilisation among aged care service recipients. Many aged care datasets in Australia contain a Statistical Linkage Key (SLK-581) instead of full personal identifiers. We linked hospital and death records using a full probabilistic strategy, the SLK-581, and three combined strategies; and compared results for each strategy.
Methods
Linkage of Admitted Patient Data for 2000–01 to 2008–09 and Registry of Births, Deaths and Marriages death registration data for 2008–09 for New South Wales, Australia, was carried out using probabilistic methods and compared to links created using four strategies incorporating a SLK-581. The Basic SLK-581 strategy used the SLK-581 alone. The Most Recent SLK-581, Most Frequent SLK-581, and Any Match SLK-581 strategies leveraged probabilistic links between hospital records drawn from the Centre for Health Record Linkage Master Linkage Key. Rates of hospitalisations among people who died were calculated for each strategy and a range of health conditions.
Results
Compared to full probabilistic linkage, the basic SLK-581 strategy produced substantial rates of missed links that increased over the study period and produced underestimates of hospitalisation rates that varied by health condition. The Most Recent SLK-581, Most Frequent SLK-581, and Any Match SLK-581 strategies resulted in substantially lower rates of underestimation than the Basic SLK-581. The Any Match SLK-581 strategy gave results closest to full probabilistic linkage.
Conclusions
Hospitalisation rates prior to death are substantially underestimated by linkage using a SLK-581 alone. Linkage rates can be increased by combining deterministic methods with probabilistically created links across hospital records.
doi:10.1186/1472-6947-14-85
PMCID: PMC4236530  PMID: 25257549
Data linkage; Record linkage; SLK-581; Linkage methods
43.  Variability of the impact of adverse events on physicians’ decision making 
Background
Physicians frequently differ in their treatment recommendations. However, few studies have examined the reasons underlying these differences. The objective of this study was to examine whether physicians vary in the importance they attach to specific adverse events for two treatment options found in recent randomized controlled trials to have equivalent efficacy and overall toxicity.
Methods
A Max-Diff survey was administered to physicians attending a national scientific conference to quantify the influence of 23 specific adverse events on decision making related to two treatment options for vasculitis. This approach was chosen because it results in greater item discrimination compared to rating scales. We used Hierarchical Bayes modeling to generate the relative importance score for each adverse event and examined the association between physicians’ characteristics and the five most influential factors.
Results
118 physicians completed the survey. The mean age (SD) was 48 years (10); 68% were male and 81% reported spending the majority of time in clinical practice. There was significant variability in the ratings of the relative importance of all adverse events, except those that were mild and easily reversible. We found a positive correlation between increasing physician age with ratings of sepsis (r = 0.29, p = 0.002) and opportunistic infection (r = 0.23, p = 0.016), and an inverse association between age with progressive multifocal leukoencephalopathy (r = - 0.28, p = 0.003). Physician sex, work setting, location, and number of patients with vasculitis seen per year were not associated with the influence of specific adverse events on decision making.
Conclusion
Our findings demonstrate that physicians differ substantially in how they perceive the importance of specific adverse events which may help explain observed unwarranted variability in physicians’ recommendations in clinical practice. Further efforts are needed to ensure that the reasons underlying variability in physicians’ recommendations are transparent.
doi:10.1186/1472-6947-14-86
PMCID: PMC4236563  PMID: 25257678
Vasculitis; Decision-making; Drug toxicity; Cyclophosphamide; Rituximab; Best-worst scaling
44.  Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance 
Background
Syndromic surveillance in emergency departments (EDs) may be used to deliver early warnings of increases in disease activity, to provide situational awareness during events of public health significance, to supplement other information on trends in acute disease and injury, and to support the development and monitoring of prevention or response strategies. Changes in mental health related ED presentations may be relevant to these goals, provided they can be identified accurately and efficiently. This study aimed to measure the accuracy of using diagnostic codes in electronic ED presentation records to identify mental health-related visits.
Methods
We selected a random sample of 500 records from a total of 1,815,588 ED electronic presentation records from 59 NSW public hospitals during 2010. ED diagnoses were recorded using any of ICD-9, ICD-10 or SNOMED CT classifications. Three clinicians, blinded to the automatically generated syndromic grouping and each other’s classification, reviewed the triage notes and classified each of the 500 visits as mental health-related or not. A “mental health problem presentation” for the purposes of this study was defined as any ED presentation where either a mental disorder or a mental health problem was the reason for the ED visit. The combined clinicians’ assessment of the records was used as reference standard to measure the sensitivity, specificity, and positive and negative predictive values of the automatic classification of coded emergency department diagnoses. Agreement between the reference standard and the automated coded classification was estimated using the Kappa statistic.
Results
Agreement between clinician’s classification and automated coded classification was substantial (Kappa = 0.73. 95% CI: 0.58 - 0.87). The automatic syndromic grouping of coded ED diagnoses for mental health-related visits was found to be moderately sensitive (68% 95% CI: 46%-84%) and highly specific at 99% (95% CI: 98%-99.7%) when compared with the reference standard in identifying mental health related ED visits. Positive predictive value was 81% (95% CI: 0.57 – 0.94) and negative predictive value was 98% (95% CI: 0.97-0.99).
Conclusions
Mental health presentations identified using diagnoses coded with various classifications in electronic ED presentation records offers sufficient accuracy for application in near real-time syndromic surveillance.
doi:10.1186/1472-6947-14-84
PMCID: PMC4177714  PMID: 25245567
45.  Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases 
Background
Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays.
Methods
We used a set of complex detection rules to take account of the patient’s clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules’ analytical quality was evaluated for ADEs.
Results
In terms of recall, 89.5% of ADEs with hyperkalaemia “with or without an abnormal symptom” were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs.
Conclusions
The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases.
doi:10.1186/1472-6947-14-83
PMCID: PMC4164763  PMID: 25212108
46.  A systematic review of the implementation and impact of asthma protocols 
Background
Asthma is one of the most common childhood illnesses. Guideline-driven clinical care positively affects patient outcomes for care. There are several asthma guidelines and reminder methods for implementation to help integrate them into clinical workflow. Our goal is to determine the most prevalent method of guideline implementation; establish which methods significantly improved clinical care; and identify the factors most commonly associated with a successful and sustainable implementation.
Methods
PUBMED (MEDLINE), OVID CINAHL, ISI Web of Science, and EMBASE.
Study Selection: Studies were included if they evaluated an asthma protocol or prompt, evaluated an intervention, a clinical trial of a protocol implementation, and qualitative studies as part of a protocol intervention. Studies were excluded if they had non-human subjects, were studies on efficacy and effectiveness of drugs, did not include an evaluation component, studied an educational intervention only, or were a case report, survey, editorial, letter to the editor.
Results
From 14,478 abstracts, we included 101 full-text articles in the analysis. The most frequent study design was pre-post, followed by prospective, population based case series or consecutive case series, and randomized trials. Paper-based reminders were the most frequent with fully computerized, then computer generated, and other modalities. No study reported a decrease in health care practitioner performance or declining patient outcomes. The most common primary outcome measure was compliance with provided or prescribing guidelines, key clinical indicators such as patient outcomes or quality of life, and length of stay.
Conclusions
Paper-based implementations are by far the most popular approach to implement a guideline or protocol. The number of publications on asthma protocol reminder systems is increasing. The number of computerized and computer-generated studies is also increasing. Asthma guidelines generally improved patient care and practitioner performance regardless of the implementation method.
doi:10.1186/1472-6947-14-82
PMCID: PMC4174371  PMID: 25204381
Review; Asthma; Medical informatics; Systematic review
47.  Genders of patients and clinicians and their effect on shared decision making: a participant-level meta-analysis 
Background
Gender differences in communication styles between clinicians and patients have been postulated to impact patient care, but the extent to which the gender dyad structure impacts outcomes in shared decision making remains unclear.
Methods
Participant-level meta-analysis of 775 clinical encounters within 7 randomized trials where decision aids, shared decision making tools, were used at the point of care. Outcomes analysed include decisional conflict scale scores, satisfaction with the clinical encounter, concordance between stated decision and action taken, and degree of patient engagement by the clinician using the OPTION scale. An estimated minimal important difference was used to determine if nonsignificant results could be explained by low power.
Results
We did not find a statistically significant interaction between clinician/patient gender mix and arm for decisional conflict, satisfaction with the clinical encounter or patient engagement. A borderline significant interaction (p = 0.05) was observed for one outcome: concordance between stated decision and action taken, where encounters with female clinician/male patient showed increased concordance in the decision aid arm compared to control (8% more concordant encounters). All other gender dyads showed decreased concordance with decision aid use (6% fewer concordant encounters for same-gender, 16% fewer concordant encounters for male clinician/female patient).
Conclusions
In this participant-level meta-analysis of 7 randomized trials, decision aids used at the point of care demonstrated comparable efficacy across gender dyads. Purported barriers to shared decision making based on gender were not detected when tested for a minimum detected difference.
Trial registrations
ClinicalTrials.gov NCT00888537, NCT01077037, NCT01029288, NCT00388050, NCT00578981, NCT00949611, NCT00217061.
doi:10.1186/1472-6947-14-81
PMCID: PMC4170214  PMID: 25179289
Gender; Shared decision making; Decision aids
48.  A survey on computer aided diagnosis for ocular diseases 
Background
Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients.
Method
We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed.
Result
We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively.
Conclusion
While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.
doi:10.1186/1472-6947-14-80
PMCID: PMC4163681  PMID: 25175552
Computer Aided Diagnosis (CAD); Ocular diseases; Review; Clinical data; Ocular imaging; Genetic information
49.  Effect of electronic prescribing with formulary decision support on medication tier, copayments, and adherence 
Background
Medication non-adherence is prevalent. We assessed the effect of electronic prescribing (e-prescribing) with formulary decision support on preferred formulary tier usage, copayment, and concomitant adherence.
Methods
We retrospectively analyzed 14,682 initial pharmaceutical claims for angiotensin receptor blocker and inhaled steroid medications among 14,410 patients of 2189 primary care physicians (PCPs) who were offered e-prescribing with formulary decision support, including 297 PCPs who adopted it. Formulary decision support was initially non-interruptive, such that formulary tier symbols were displayed adjacent to medication names. Subsequently, interruptive formulary decision support alerts also interrupted e-prescribing when preferred-tier alternatives were available. A difference in differences design was used to compare the pre-post differences in medication tier for each new prescription attributed to non-adopters, low user (<30% usage rate), and high user PCPs (>30% usage rate). Second, we modeled the effect of formulary tier on prescription copayment. Last, we modeled the effect of copayment on adherence (proportion of days covered) to each new medication.
Results
Compared with non-adopters, high users of e-prescribing were more likely to prescribe preferred-tier medications (vs. non-preferred tier) when both non-interruptive and interruptive formulary decision support were in place (OR 1.9 [95% CI 1.0-3.4], p = 0.04), but no more likely to prescribe preferred-tier when only non-interruptive formulary decision support was in place (p = 0.90). Preferred-tier claims had only slightly lower mean monthly copayments than non-preferred tier claims (angiotensin receptor blocker: $10.60 versus $11.81, inhaled steroid: $14.86 versus $16.42, p < 0.0001). Medication possession ratio was 8% lower for each $1.00 increase in monthly copayment to the one quarter power (p < 0.0001). However, we detected no significant direct association between formulary decision support usage and adherence.
Conclusion
Interruptive formulary decision support shifted prescribing toward preferred tiers, but these medications were only minimally less expensive in the studied patient population. In this context, formulary decision support did not significantly increase adherence. To impact cost-related non-adherence, formulary decision support will likely need to be paired with complementary drug benefit design. Formulary decision support should be studied further, with particular attention to its effect on adherence in the setting of different benefit designs.
doi:10.1186/1472-6947-14-79
PMCID: PMC4236533  PMID: 25167807
Clinical decision support; Electronic prescribing; Medication adherence
50.  A cross-sectional pilot study assessing needs and attitudes to implementation of Information and Communication Technology for rational use of medicines among healthcare staff in rural Tanzania 
Background
In resource-poor countries access to essential medicines, suboptimal prescribing and use of medicines are major problems. Health workers lack updated medical information and treatment support. Information and Communication Technology (ICT) could help tackle this. The impact of ICT on health systems in resource-poor countries is likely to be significant and transform the practice of medicine just as in high-income countries. However, research for finding the best way of doing this is needed. We aimed to assess current approaches to and use of ICT among health workers in two rural districts of Tanzania in relation to the current drug distribution practices, drug stock and continuing medical information (CME), as well as assessing the feasibility of using ICT to improve ordering and use of medicines.
Methods
This pilot study was conducted in 2010–2011, mapping the drug distribution chain in Tanzania, including problems and barriers. The study was conducted in Bunda and Serengeti districts, both part of the ICT4RD (ICT for rural development) project. Health workers involved in drug procurement and use at 13 health facilities were interviewed on use and knowledge of ICT, and their attitudes to its use in their daily work. They were also shown and interviewed about their thoughts on an android tablet application prototype for drug stock inventory and drug ordering, based on the Tanzanian Medical Stores Department (MSD) current paper forms.
Results
The main challenge was a stable supply of essential medicines. Drug supplies were often delayed and incomplete, resulting in stock-outs. All 20 interviewed health workers used mobile phones, 8 of them Smartphones with Internet connection. The Health workers were very positive to the tablet application and saw its potential in reducing drug stock-outs. They also expressed a great need and wish for CME by distance.
Conclusion
The tablet application was easily used and appreciated by health workers, and thus has the potential to save time and effort, reduce transportation costs and minimise drug stock-outs. Furthermore, the android tablet could be used to reach out with CME programs to health care workers at remote health facilities, as well as those in towns.
doi:10.1186/1472-6947-14-78
PMCID: PMC4164118  PMID: 25158806
Access to medicines; Africa; Android tablet; Decision support; Health systems; ICT; iPad; Low-income countries; Rational use of medicines

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