Open Source Software in Healthcare: International Case Series from the IMIA Open Source Working Group

In this case series, we demonstrate how open-source software has been widely adopted as the primary health information system in many low- and middle-income countries, and for government-developed applications in high-income settings. We discuss the concept of Digital Global Goods and how the general approach of releasing software developed through public funding under open-source licences could improve the delivery of healthcare in all settings through increased transparency and collaboration as well as financial efficiency.

Comparison of Diagnostic and Triage Accuracy of Ada Health and WebMD Symptom Checkers, ChatGPT, and Physicians for Patients in an Emergency Department: Clinical Data Analysis Study

BACKGROUND

Diagnosis is a core component of effective health care, but misdiagnosis is common and can put patients at risk. Diagnostic decision support systems can play a role in improving diagnosis by physicians and other health care workers. Symptom checkers (SCs) have been designed to improve diagnosis and triage (ie, which level of care to seek) by patients.

OBJECTIVE

The aim of this study was to evaluate the performance of the new large language model ChatGPT (versions 3.5 and 4.0), the widely used WebMD SC, and an SC developed by Ada Health in the diagnosis and triage of patients with urgent or emergent clinical problems compared with the final emergency department (ED) diagnoses and physician reviews.

METHODS

We used previously collected, deidentified, self-report data from 40 patients presenting to an ED for care who used the Ada SC to record their symptoms prior to seeing the ED physician. Deidentified data were entered into ChatGPT versions 3.5 and 4.0 and WebMD by a research assistant blinded to diagnoses and triage. Diagnoses from all 4 systems were compared with the previously abstracted final diagnoses in the ED as well as with diagnoses and triage recommendations from three independent board-certified ED physicians who had blindly reviewed the self-report clinical data from Ada. Diagnostic accuracy was calculated as the proportion of the diagnoses from ChatGPT, Ada SC, WebMD SC, and the independent physicians that matched at least one ED diagnosis (stratified as top 1 or top 3). Triage accuracy was calculated as the number of recommendations from ChatGPT, WebMD, or Ada that agreed with at least 2 of the independent physicians or were rated "unsafe" or "too cautious."

RESULTS

Overall, 30 and 37 cases had sufficient data for diagnostic and triage analysis, respectively. The rate of top-1 diagnosis matches for Ada, ChatGPT 3.5, ChatGPT 4.0, and WebMD was 9 (30%), 12 (40%), 10 (33%), and 12 (40%), respectively, with a mean rate of 47% for the physicians. The rate of top-3 diagnostic matches for Ada, ChatGPT 3.5, ChatGPT 4.0, and WebMD was 19 (63%), 19 (63%), 15 (50%), and 17 (57%), respectively, with a mean rate of 69% for physicians. The distribution of triage results for Ada was 62% (n=23) agree, 14% unsafe (n=5), and 24% (n=9) too cautious; that for ChatGPT 3.5 was 59% (n=22) agree, 41% (n=15) unsafe, and 0% (n=0) too cautious; that for ChatGPT 4.0 was 76% (n=28) agree, 22% (n=8) unsafe, and 3% (n=1) too cautious; and that for WebMD was 70% (n=26) agree, 19% (n=7) unsafe, and 11% (n=4) too cautious. The unsafe triage rate for ChatGPT 3.5 (41%) was significantly higher (P=.009) than that of Ada (14%).

CONCLUSIONS

ChatGPT 3.5 had high diagnostic accuracy but a high unsafe triage rate. ChatGPT 4.0 had the poorest diagnostic accuracy, but a lower unsafe triage rate and the highest triage agreement with the physicians. The Ada and WebMD SCs performed better overall than ChatGPT. Unsupervised patient use of ChatGPT for diagnosis and triage is not recommended without improvements to triage accuracy and extensive clinical evaluation.

Barriers, Priorities and Lessons Learned in Achieving Electronic Health Records Interoperability in Low- and Middle-Income Countries: Workshop Findings

Electronic health records (EHR) interoperability is a complex topic that continues to gain traction in the digital health landscape. We facilitated a qualitative workshop consisting of domain experts in EHR implementation and health IT managers. The workshop aimed to identify critical barriers to achieving interoperability, priorities for new EHR implementations and lessons learned from managing existing implementations. The workshop highlighted that data modelling and interoperability standards are vital priorities for maternal and child health data services in low- and middle-income countries (LMICs).

Abstract TMP53: Prediction Of Acute Cerebrovascular Events Based On Patient Reported Symptoms

Introduction: Many patients fail to recognize the initial symptoms of TIA or stroke and delay seeking urgent medical care. Algorithms based on patient reported symptoms have the potential to improve patient decision making and care seeking behavior. Though machine learning (ML) based on clinical and imaging data has been used to predict stroke, little work has been done to create ML methods based on patient symptom data. We aimed to evaluate the efficacy of ML algorithms for prediction of stroke or TIA from data collected at presentation among patients admitted to an emergency department TIA/Stroke observational unit (ED-OU).

Methods: Patients in a large, urban, academic ED admitted to the ED-OU for suspected TIA from 3/2013 - 2/2020 were included. A full medical workup, including blood work, EKG, vessel imaging, DWI MRI, and neurology consultation was performed. The outcome was a discharge diagnosis of stroke or TIA. Demographic data, symptomatology, past medical and surgical history, and relevant medication data was utilized to predict a stroke or TIA. After applying recursive feature selection (RFE), we utilized logistic regression (LR) to predict stroke/TIA diagnosis. Model performance was evaluated using accuracy, precision, and AUROC.

Results: 1441 patients were included; (52.4% female; 23.4% non-white; 57.7% diagnosed with stroke/TIA). Average age was 62.5 (SD = 30.2). The most common presenting symptoms were headache, dizziness, slurred speech, visual disturbance, and aphasia.Our classifier predicted the diagnosis of a stroke/TIA with AUROC 0.705, Accuracy 0.717, Precision 0.722, Recall 0.802, NPV 0.709.

Conclusions: These results show algorithms utilizing patient-reported symptoms and risk factors can predict TIA/stroke. ML-based algorithms could support improved patient decision making in seeking medical care and reduce delays, and such algorithms could be incorporated in symptom checker apps.

Crystallographic and Compositional Evolution of Ordered B2 and Disordered BCC Phases During Isothermal Annealing of Refractory High-Entropy Alloys

Refractory high-entropy alloys (RHEAs) are promising candidates for next-generation high-temperature materials. RHEAs containing Al, often exhibit a checkered pattern microstructure comprising a combination of disordered BCC and ordered B2 phases. Since the ordered B2 phase is based on the BCC parent matrix, distinguishing these two phases can be rather challenging. Advanced characterization techniques are necessary for a reliable qualitative and quantitative analysis of BCC and B2 phases in RHEAs. Additionally, there is a tendency for transformation of the ordered B2 phase into more complex ordered-omega type phases that are usually deleterious to mechanical properties. The current study focuses on the phase stability of a candidate RHEA, Al0.5Mo0.5NbTa0.5TiZr. Correlative transmission electron microscopy (TEM) and atom probe tomography (APT) have been employed to investigate the phase stability and transformation pathway of this RHEA when isothermally annealed at 800°C. The results show that a metastable two-phase BCC + B2 microstructure formed at the early stages of decomposition, eventually transforming into a three-phase BCC + B2 + hP18 microstructure. The hP18 phase is an ordered omega derivative of the ordered B2 phase. The correlative microscopy techniques (TEM and APT) reveal a very interesting interplay of compositional partitioning between the different phases and their respective stability.

Open Source Digital Health Software for Resilient, Accessible and Equitable Healthcare Systems

OBJECTIVE

To assess the impact of open-source projects on making healthcare systems more resilient, accessible and equitable.

METHODS

In response to the International Medical Informatics Association (IMIA) call for working group contributions for the IMIA Yearbook, the Open Source Working Group (OSWG) conducted a rapid review of current open source digital health projects to illustrate how they can contribute to making healthcare systems more resilient, accessible and equitable. We sought case studies from the OSWG membership to illustrate these three concepts and how open source software (OSS) addresses these concepts in the real world. These case studies are discussed against the background of literature identified through the rapid review.

RESULTS

To illustrate the concept of resilience, we present case studies from the adoption of District Health Information Software version 2 (DHIS2) for managing the Covid pandemic in Rwanda, and the adoption of the OpenEHR open Health IT standard. To illustrate accessibility, we show how open source design systems for user interface design have been used by governments to ensure accessibility of digital health services for patients and healthy individuals, and by the OpenMRS community to standardise their user interface design. Finally, to illustrate the concept of equity, we describe the OpenWHO framework and two open source digital health projects, GNU Health and openIMIS, that both aim to reduce health inequities through the use of open source digital health software.

CONCLUSION

This review has demonstrated that open source software addresses many of the challenges involved in making healthcare more accessible, equitable and resilient in high and low income settings.

Implementing Electronic Health Records in Primary Care Using the Theory of Change: A Nigerian Case Study (Preprint)

Background

Objective

Methods

Results

Conclusions

Mathematically aggregating experts' predictions of possible futures

Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction. When experts' performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts' estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.

Using Open Source, Open Data, and Civic Technology to Address the COVID-19 Pandemic and Infodemic

OBJECTIVES

The emerging COVID-19 pandemic has caused one of the world's worst health disasters compounded by social confusion with misinformation, the so-called "Infodemic". In this paper, we discuss how open technology approaches - including data sharing, visualization, and tooling - can address the COVID-19 pandemic and infodemic.

METHODS

In response to the call for participation in the 2020 International Medical Informatics Association (IMIA) Yearbook theme issue on Medical Informatics and the Pandemic, the IMIA Open Source Working Group surveyed recent works related to the use of Free/Libre/Open Source Software (FLOSS) for this pandemic.

RESULTS

FLOSS health care projects including GNU Health, OpenMRS, DHIS2, and others, have responded from the early phase of this pandemic. Data related to COVID-19 have been published from health organizations all over the world. Civic Technology, and the collaborative work of FLOSS and open data groups were considered to support collective intelligence on approaches to managing the pandemic.

CONCLUSION

FLOSS and open data have been effectively used to contribute to managing the COVID-19 pandemic, and open approaches to collaboration can improve trust in data.

How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs

OBJECTIVES

To compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps.

DESIGN

Vignettes study.

SETTING

200 primary care vignettes.

INTERVENTION/COMPARATOR

For eight apps and seven general practitioners (GPs): breadth of coverage and condition-suggestion and urgency advice accuracy measured against the vignettes' gold-standard.

PRIMARY OUTCOME MEASURES

(1) Proportion of conditions 'covered' by an app, that is, not excluded because the user was too young/old or pregnant, or not modelled; (2) proportion of vignettes with the correct primary diagnosis among the top 3 conditions suggested; (3) proportion of 'safe' urgency advice (ie, at gold standard level, more conservative, or no more than one level less conservative).

RESULTS

Condition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%; WebMD: 93.0%. Top-3 suggestion accuracy was GPs (average): 82.1%±5.2%; Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps excluded certain user demographics or conditions and their performance was generally greater with the exclusion of corresponding vignettes. For safe urgency advice, tested GPs had an average of 97.0%±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 SD of the GPs-Ada: 97.0%; Babylon: 95.1%; Symptomate: 97.8%. One app had a safety performance within 2 SDs of GPs-Your.MD: 92.6%. Three apps had a safety performance outside 2 SDs of GPs-Buoy: 80.0% (p<0.001); K Health: 81.3% (p<0.001); Mediktor: 87.3% (p=1.3×10^-3).

CONCLUSIONS

The utility of digital symptom assessment apps relies on coverage, accuracy and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.

Mining Misdiagnosis Patterns from Biomedical Literature

Diagnostic errors can pose a serious threat to patient safety, leading to serious harm and even death. Efforts are being made to develop interventions that allow physicians to reassess for errors and improve diagnostic accuracy. Our study presents an exploration of misdiagnosis patterns mined from PubMed abstracts. Article titles containing certain phrases indicating misdiagnosis were selected and frequencies of these misdiagnoses calculated. We present the resulting patterns in the form of a directed graph with frequency-weighted misdiagnosis edges connecting diagnosis vertices. We find that the most commonly misdiagnosed diseases were often misdiagnosed as many different diseases, with each misdiagnosis having a relatively low frequency, rather than as a single disease with greater probability. Additionally, while a misdiagnosis relationship may generally exist, the relationship was often found to be one-sided.

Digital health Systems in Kenyan Public Hospitals: a mixed-methods survey

Background

As healthcare facilities in Low- and Middle-Income Countries adopt digital health systems to improve hospital administration and patient care, it is important to understand the adoption process and assess the systems’ capabilities. This survey aimed to provide decision-makers with information on the digital health systems landscape and to support the rapidly developing digital health community in Kenya and the region by sharing knowledge.

Methods

We conducted a survey of County Health Records Information Officers (CHRIOs) to determine the extent to which digital health systems in public hospitals that serve as internship training centres in Kenya are adopted. We conducted site visits and interviewed hospital administrators and end users who were at the facility on the day of the visit. We also interviewed digital health system vendors to understand the adoption process from their perspective. Semi-structured interview guides adapted from the literature were used. We identified emergent themes using a thematic analysis from the data.

Results

We obtained information from 39 CHRIOs, 58 hospital managers and system users, and 9 digital health system vendors through semi-structured interviews and completed questionnaires.

From the survey, all facilities mentioned purchased a digital health system primarily for administrative purposes. Radiology and laboratory management systems were commonly standalone systems and there were varying levels of interoperability within facilities that had multiple systems. We only saw one in-patient clinical module in use. Users reported on issues such as system usability, inadequate training, infrastructure and system support. Vendors reported the availability of a wide range of modules, but implementation was constrained by funding, prioritisation of services, users’ lack of confidence in new technologies and lack of appropriate data sharing policies.

Conclusion

Public hospitals in Kenya are increasingly purchasing systems to support administrative functions and this study highlights challenges faced by hospital users and vendors. Significant work is required to ensure interoperability of systems within hospitals and with other government services. Additional studies on clinical usability and the workflow fit of digital health systems are required to ensure efficient system implementation. However, this requires support from key stakeholders including the government, international donors and regional health informatics organisations.

Modelling the impact of a national scale-up of interventions on hepatitis C virus transmission among people who inject drugs in Scotland

BACKGROUND AND AIMS

To reduce hepatitis C virus (HCV) transmission among people who inject drugs (PWID), Scottish Government-funded national strategies, launched in 2008, promoted scaling-up opioid substitution therapy (OST) and needle and syringe provision (NSP), with some increases in HCV treatment. We test whether observed decreases in HCV incidence post-2008 can be attributed to this intervention scale-up.

DESIGN

A dynamic HCV transmission model among PWID incorporating intervention scale-up and observed decreases in behavioural risk, calibrated to Scottish HCV prevalence and incidence data for 2008/09.

SETTING

Scotland, UK.

PARTICIPANTS

PWID.

MEASUREMENTS

Model projections from 2008 to 2015 were compared with data to test whether they were consistent with observed decreases in HCV incidence among PWID while incorporating the observed intervention scale-up, and to determine the impact of scaling-up interventions on incidence.

FINDINGS

Without fitting to epidemiological data post-2008/09, the model incorporating observed intervention scale-up agreed with observed decreases in HCV incidence among PWID between 2008 and 2015, suggesting that HCV incidence decreased by 61.3% [95% credibility interval (CrI) = 45.1-75.3%] from 14.2/100 person-years (py) (9.0-20.7) to 5.5/100 py (2.9-9.2). On average, each model fit lay within 84% (10.1/12) of the confidence bounds for the 12 incidence data points against which the model was compared. We estimate that scale-up of interventions (OST + NSP + HCV treatment) and decreases in high-risk behaviour from 2008 to 2015 resulted in a 33.9% (23.8-44.6%) decrease in incidence, with the remainder [27.4% (17.6-37.0%)] explained by historical changes in OST + NSP coverage and risk pre-2008. Projections suggest that scaling-up of all interventions post-2008 averted 1492 (657-2646) infections over 7 years, with 1016 (308-1996), 404 (150-836) and 72 (27-137) due to scale-up of OST + NSP, decreases in high-risk behaviour and HCV treatment, respectively.

CONCLUSIONS

Most of the decline in hepatitis C virus (HCV) incidence in Scotland between 2008 and 2015 appears to be attributable to intervention scale-up (opioid substitution therapy and needle and syringe provision) due to government strategies on HCV and drugs.

Implementing an Open Source Electronic Health Record System in Kenyan Health Care Facilities: Case Study

BACKGROUND

The Kenyan government, working with international partners and local organizations, has developed an eHealth strategy, specified standards, and guidelines for electronic health record adoption in public hospitals and implemented two major health information technology projects: District Health Information Software Version 2, for collating national health care indicators and a rollout of the KenyaEMR and International Quality Care Health Management Information Systems, for managing 600 HIV clinics across the country. Following these projects, a modified version of the Open Medical Record System electronic health record was specified and developed to fulfill the clinical and administrative requirements of health care facilities operated by devolved counties in Kenya and to automate the process of collating health care indicators and entering them into the District Health Information Software Version 2 system.

OBJECTIVE

We aimed to present a descriptive case study of the implementation of an open source electronic health record system in public health care facilities in Kenya.

METHODS

We conducted a landscape review of existing literature concerning eHealth policies and electronic health record development in Kenya. Following initial discussions with the Ministry of Health, the World Health Organization, and implementing partners, we conducted a series of visits to implementing sites to conduct semistructured individual interviews and group discussions with stakeholders to produce a historical case study of the implementation.

RESULTS

This case study describes how consultants based in Kenya, working with developers in India and project stakeholders, implemented the new system into several public hospitals in a county in rural Kenya. The implementation process included upgrading the hospital information technology infrastructure, training users, and attempting to garner administrative and clinical buy-in for adoption of the system. The initial deployment was ultimately scaled back due to a complex mix of sociotechnical and administrative issues. Learning from these early challenges, the system is now being redesigned and prepared for deployment in 6 new counties across Kenya.

CONCLUSIONS

Implementing electronic health record systems is a challenging process in high-income settings. In low-income settings, such as Kenya, open source software may offer some respite from the high costs of software licensing, but the familiar challenges of clinical and administration buy-in, the need to adequately train users, and the need for the provision of ongoing technical support are common across the North-South divide. Strategies such as creating local support teams, using local development resources, ensuring end user buy-in, and rolling out in smaller facilities before larger hospitals are being incorporated into the project. These are positive developments to help maintain momentum as the project continues. Further integration with existing open source communities could help ongoing development and implementations of the project. We hope this case study will provide some lessons and guidance for other challenging implementations of electronic health record systems as they continue across Africa.

Identifying effective components for mobile health behaviour change interventions for smoking cessation and service uptake: protocol of a systematic review and planned meta-analysis

BACKGROUND

Mobile health (mHealth) interventions for smoking cessation have been shown to be associated with an increase in effectiveness. However, interventions using mobile phones to change people's behaviour are often perceived as complex interventions, and the interactions between several components within them may affect the outcome. Therefore, it is important to understand how we can improve the design of mHealth interventions using mobile phones as a medium to deliver services.

METHODS

Randomised controlled trials (RCTs) of mHealth interventions to support smoking cessation or uptake of smoking cessation services for smokers will be included in this systematic review. A search will be performed by searching MEDLINE, MEDLINE(R) In-Process & Other Non-Indexed Citations, EMBASE, PsycINFO, Web of Science, and CINAHL. A search for new publications will be conducted 3 months prior to submission for publication as mHealth is an emerging area of research. A random-effects meta-analysis model will be used to summarise the effectiveness of mHealth interventions. The risk ratio will be used for the primary outcome, self-reported or verified smoking abstinence, and any binary outcomes for uptake of smoking cessation services. The standardised mean difference using Hedges' g will be reported for continuous data. Heterogeneity will be assessed using I ² statistics. Where feasible, meta-regression analysis using random-effects multilevel modelling will be conducted to examine the association of pre-specified characteristics (covariates) at the study level with the effectiveness of interventions. Publication bias will be explored using Egger's test for continuous outcomes and Harbord and Peters tests for dichotomous outcomes. The funnel plot will be used to evaluate the presence of publication bias. The Cochrane Risk of Bias Tool will be used to assess differences in risks of bias.

DISCUSSION

The results of this systematic review will provide future research with a foundation for designing and evaluating complex interventions that use mobile phones as a platform to deliver behaviour change techniques.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42016026918 .

Design and development of an EMR for Ebola Treatment Centers in Sierra Leone using OpenMRS

Ebola treatment presents unique challenges for medical records because strict infection control requirements rule out most conventional record-keeping systems. We used the OpenMRS platform to rapidly develop an EMR system for the recently opened Kerry Town, Sierra Leone Ebola Treatment Centre. This system addresses the need for recording patient data and communicating it between the infectious and non-infectious zones, and is specifically designed for maximum usability by staff wearing cumbersome protective equipment. This platform is interoperable with other key eHealth systems in the country, and is extensible to other sites and diseases.

mHealth for HIV Treatment & Prevention: A Systematic Review of the Literature

This systematic review assesses the published literature to describe the landscape of mobile health technology (mHealth) for HIV/AIDS and the evidence supporting the use of these tools to address the HIV prevention, care, and treatment cascade. The speed of innovation, broad range of initiatives and tools, and heterogeneity in reporting have made it difficult to uncover and synthesize knowledge on how mHealth tools might be effective in addressing the HIV pandemic.

To do address this gap, a team of reviewers collected literature on the use of mobile technology for HIV/AIDS among health, engineering, and social science literature databases and analyzed a final set of 62 articles. Articles were systematically coded, assessed for scientific rigor, and sorted for HIV programmatic relevance. The review revealed evidence that mHealth tools support HIV programmatic priorities, including: linkage to care, retention in care, and adherence to antiretroviral treatment. In terms of technical features, mHealth tools facilitate alerts and reminders, data collection, direct voice communication, educational messaging, information on demand, and more. Studies were mostly descriptive with a growing number of quasi-experimental and experimental designs. There was a lack of evidence around the use of mHealth tools to address the needs of key populations, including pregnant mothers, sex workers, users of injection drugs, and men who have sex with men.

The science and practice of mHealth for HIV are evolving rapidly, but still in their early stages. Small-scale efforts, pilot projects, and preliminary descriptive studies are advancing and there is a promising trend toward implementing mHealth innovation that is feasible and acceptable within low-resource settings, positive program outcomes, operational improvements, and rigorous study design

A survey of community exercise programmes for stroke survivors in Scotland

Stroke is the most common cause of complex disability in the community. Physical fitness is often reduced after stroke, but training can improve fitness and function. UK and international stroke clinical guidelines recommend long-term exercise participation for stroke survivors. However, there has been no previous research into what services are available to support this. In 2009, we conducted the first European survey of community Exercise after Stroke services. A link to our web-based survey was emailed to health, leisure service and stroke charity contacts in Scotland with email and telephone follow-up to non-respondents. The overall response rate was 64% (230/361). A total of 14 Exercise after Stroke services were identified, the majority of which were run by charity collaborations (7/14), followed by leisure centre services (4/14) and health services (3/14). We sought information on session content, referral and assessment processes, and the qualifications of exercise instructors. This information was cross-referenced with current clinical and exercise guidelines to determine whether existing resources were sufficient to meet stroke survivors' needs for safe, effective and sustainable access to exercise. The results indicated a shortage of stroke-specific community exercise programmes. Further service development is required to ensure appropriate instructor training and referral pathways are in place to enable stroke survivors to access exercise services in accordance with current guidelines.

Electronic decision protocols for ART patient triaging to expand access to HIV treatment in South Africa: a cross sectional study for development and validation

BACKGROUND

The shortage of doctors and nurses, along with future expansion into rural clinics, will require that the majority of clinic visits by HIV infected patients on antiretroviral therapy (ART) are managed by non-doctors. The goal of this study was to develop and evaluate a screening protocol to determine which patients needed a full clinical assessment and which patients were stable enough to receive their medications without a doctor's consultation. For this study, we developed an electronic, handheld tool to guide non-physician counselors through screening questions.

METHODS

Patients visiting two ART clinics in South Africa for routine follow-up visits between March 2007 and April 2008 were included in our study. Each patient was screened by non-physician counselors using the handheld device and then received a full clinical assessment. Clinicians' report on whether full clinical assessment had been necessary was used as the gold standard for determining "required referral". Observations were randomly divided into two datasets--989 for developing a referral protocol and 200 for validating protocol performance.

RESULTS

A third of patients had at least one physical complaint, and 16% had five or more physical complaints. 38% of patients required referral for full clinical assessment. We identify a subset of questions which are 87% sensitive and 47% specific for recommended patient referral.

CONCLUSIONS

The final screening protocol is highly sensitive and could reduce burden on ART clinicians by 30%. The uptake and acceptance of the handheld tool to support implementation of the protocol was high. Further examination of the data reveals several important questions to include in future referral algorithms to improve sensitivity and specificity. Based on these results, we identify a refined algorithm to explore in future evaluations.

Cost and implementation analysis of a personal digital assistant system for laboratory data collection

SETTING

One hundred and twenty-six public health centers and laboratories in Lima, Peru, without internet.

BACKGROUND

We have previously shown that a personal digital assistant (PDA) based system reduces data collection delays and errors for tuberculosis (TB) laboratory results when compared to a paper system.

OBJECTIVE

To assess the data collection efficiency of each system and the resources required to develop, implement and transfer the PDA-based system to a resource-poor setting.

DESIGN

Time-motion study of data collectors using the PDA-based and paper systems. Cost analysis of developing, implementing and transferring the PDA-based system to a local organization and their redeployment of the system.

RESULTS

Work hours spent collecting and processing results decreased by 60% (P < 0.001). Users perceived this decrease to be 70% and had no technical problems they failed to fix. The total cost and time to develop and implement the intervention was US$26092 and 22 weeks. The cost to extend the system to cover nine more districts was $1125 and to implement collecting patient weights was $4107.

CONCLUSION

A PDA-based system drastically reduced the effort required to collect TB laboratory results from remote locations. With the framework described, open-source software and local development, organizations in resource-poor settings could reap the benefits of this technology.

Expanding an electronic medical record to support community health worker and nutritional support programs in rural Rwanda

Scaling up HIV and TB treatment rapidly in a resource poor setting is greatly facilitated when community health workers can monitor patient well-being and ensure that patients adhere to medication. In addition, it is almost essential that patients receive a food package while being treated for HIV and TB, since medication can be ineffective if patient is undernourished. However,a community health worker program and food program can add significant administrative overhead,particularly if reporting or evaluation is required. By expanding an Electronic Medical Record to cover these programs in addition to treatment programs, it becomes easier to administer them and combine interesting data from different sources.

Assessing effects of the e-Chasqui laboratory information system on accuracy and timeliness of bacteriology results in the Peruvian tuberculosis program

We created a web-based laboratory information system, e-Chasqui to connect public laboratories to health centers to improve communication and analysis. After one year, we performed a pre and post assessment of communication delays and found that e-Chasqui maintained the average delay but eliminated delays of over 60 days. Adding digital verification maintained the average delay, but should increase accuracy. We are currently performing a randomized evaluation of the impacts of e-Chasqui.

Inhibition of the late sodium current as a potential cardioprotective principle: effects of the late sodium current inhibitor ranolazine

Pathological conditions linked to imbalances in oxygen supply and demand (for example, ischaemia, hypoxia and heart failure) are associated with disruptions in intracellular sodium ([Na(+)](i)) and calcium ([Ca(2+)](i)) concentration homeostasis of myocardial cells. A decreased efflux or increased influx of sodium may cause cellular sodium overload. Sodium overload is followed by an increased influx of calcium through sodium-calcium exchange. Failure to maintain the homeostasis of [Na(+)](i) and [Ca(2+)](i) leads to electrical instability (arrhythmias), mechanical dysfunction (reduced contractility and increased diastolic tension) and mitochondrial dysfunction. These events increase ATP hydrolysis and decrease ATP formation and, if left uncorrected, they cause cell injury and death. The relative contributions of various pathways (sodium channels, exchangers and transporters) to the rise in [Na(+)](i) remain a matter of debate. Nevertheless, both the sodium-hydrogen exchanger and abnormal sodium channel conductance (that is, increased late sodium current (I(Na))) are likely to contribute to the rise in [Na(+)](i). The focus of this review is on the role of the late (sustained/persistent) I(Na) in the ionic disturbances associated with ischaemia/hypoxia and heart failure, the consequences of these ionic disturbances, and the cardioprotective effects of the antianginal and anti-ischaemic drug ranolazine. Ranolazine selectively inhibits late I(Na), reduces [Na(+)](i)-dependent calcium overload and attenuates the abnormalities of ventricular repolarisation and contractility that are associated with ischaemia/reperfusion and heart failure. Thus, inhibition of late I(Na) can reduce [Na(+)](i)-dependent calcium overload and its detrimental effects on myocardial function.

Rapid deployment of electronic medical records for ARV rollout in rural Rwanda

While most people with AIDS do not yet have access to anti-retroviral drugs (ARVs), large ARV treatment programs are being rolled out in many areas in Sub-Saharan Africa. ARV programs have substantial data management needs, which electronic medical record systems (EMRs) are helping to address. While most sophisticated EMRs in low-income regions are in large cities, where infrastructure and staffing needs are more easily met, Partners In Health (PIH) has pioneered web-based EMRs for HIV and TB treatment in rural areas. The HIV-EMR, developed in Haiti [1], was de-ployed in two Rwandan health districts starting in Au-gust 2005. The addition of new features and adaptation to local needs is happening concurrently with the rapid scale-up and evolution of the medical program itself.

Cooking up an open source EMR for developing countries: OpenMRS - a recipe for successful collaboration

Millions of people are continue to die each year from HIV/AIDS. The majority of infected persons (>95%) live in the developing world. A worthy response to this pandemic will require coordinated, scalable, and flexible information systems. We describe the OpenMRS system, an open source, collaborative effort that can serve as a foundation for EMR development in developing countries. We report our progress to date, lessons learned, and future directions.

Clinical utility of magnetic resonance imaging and the preoperative identification of low risk endometrial cancer

BACKGROUND

Magnetic resonance imaging (MRI) is reported to offer the best imaging of local disease in endometrial cancer. We audited MRI scans to identify their clinical utility, particularly in the preoperative identification of 'low risk' endometrial cancer (grade one or two endometrioid tumours confined to the inner half of the myometrium).

AIM

To correlate histological and MRI findings and to establish our ability to preoperatively identify women with 'low risk' tumours.

STUDY DESIGN

A retrospective audit of MRI scans in women with a new diagnosis of endometrial cancer from July 1998 to November 2002. Radiology and pathology reports and surgical staging data were extracted. Independently a team of radiologists reviewed MRI films and the findings were compared to pathology.

RESULTS

Thirty-nine patients were included. Only 10% of original reports contained all the clinically relevant information. On review, the sensitivity for the detection of myometrial invasion was 90%, specificity 71%, positive predictive value (PPV) 93% and negative predictive value (NPV) 63%. For the detection of deep invasion, sensitivity was 56%, specificity 77%, PPV 64% and NPV 71%. All women with grade one or two tumours having no invasion or grade one having superficial invasion detected on MRI had pathological 'low risk' disease.

CONCLUSIONS

Magnetic resonance imaging scans as reported offered limited clinical benefit. Attention needs to be given to MRI sequencing and reporting protocols. If the review results can be confirmed by prospective studies, MRI offers significant clinical utility in the identification of low risk patients and their surgical treatment planning.

HIV Lipodystrophy Case Definition using Artificial Neural Network Modelling

Objective

A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy.

Methods

The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers were trained and validated. Results were compared against logistic regression models using the same information.

Results

Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under the ROC curve. Their average sensitivity and specificity were 72.4 and 71.2%, as compared with 73.0 and 62.9% for logistic regression, respectively (area under the ROC curve, 0.784 vs 0.748). The discriminating performance of the neural networks was largely unaffected when built excluding 13 parameters that patients may not have readily available. The average sensitivity and specificity of the neural networks remained the same when metabolic variables were also considered (total 60 items) without a clear advantage against logistic regression (overall accuracy 71.8%). The performance of networks considering also body composition variables was similar to that of logistic regression (overall accuracy 78.5% for both).

Conclusions

Neural networks may offer a means to improve the discriminating performance for HIV lipodystrophy, when only clinical data are available and a rapid approximate diagnostic decision is needed. In this context, information on metabolic parameters is apparently not helpful in improving the diagnosis of HIV lipodystrophy, unless imaging and body composition studies are also obtained.