Search

This article analyses why people have so many digital identities and offers suggestions to reduce the numbers to more reasonable levels. Digital identity thinking has been dominated by the objective of general-purpose reusable identity as a response to the unwieldy profusion of identifiers that came with expanding ecommerce. The notion of reusable digital identity is somewhat intuitive, energised by the mental model of humans exercising a virtual self in cyberspace. Many user interfaces are constructed to exhibit an intentional stance suggestive of humans having a digital counterpart making our digital actions more lifelike and comprehensible. In some nations, there is a precedent for national identity, which makes general-purpose digital identity culturally more logical, even appealing. In common law countries, however, the market for reusable digital identity is still not mature. To date, there is no solid business case for general purpose reusable identity - largely because it proves costlier than expected to reengineer transactional identifiers to align (or federate) with an intuitive singular digital identity. Thus, individuals must manage many siloed, special purpose identifiers, account names, passwords, and piecemeal authenticators. If transactional identifiers go hand in hand with transaction systems, then there will likely remain a need for about as many identifiers as there are transactional services. Recent technology developments, especially in cryptographic verifiable credentials and mobile digital wallets, may provide ways to automate the management of multiple identifiers and achieve the desired usability anticipated from singular identity without disrupting the forces that have led to transaction specific identification.

BackgroundDiagnosis 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. ObjectiveThe 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. MethodsWe 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.” ResultsOverall, 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%). ConclusionsChatGPT 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.

BackgroundDigital health has been a tool of transformation for the delivery of health care services globally. An electronic health record (EHR) system can solve the bottleneck of paper documentation in health service delivery if it is successfully implemented, but poor implementation can lead to a waste of resources. The study of EHR system implementation in low- and middle-income countries (LMICs) is of particular interest to health stakeholders such as policy makers, funders, and care providers because of the efficiencies and evidence base that could result from the appropriate evaluation of such systems. ObjectiveWe aimed to develop a theory of change (ToC) for the implementation of EHRs for maternal and child health care delivery in LMICs. The ToC is an outcomes-based approach that starts with the long-term goals and works backward to the inputs and mediating components required to achieve these goals for complex programs. MethodsWe used the ToC approach for the whole implementation’s life cycle to guide the pilot study and identify the preconditions needed to realize the study’s long-term goal at Festac Primary Health Centre in Lagos, Nigeria. To evaluate the maturity of the implementation, we adapted previously defined success factors to supplement the ToC approach. ResultsThe initial ToC map showed that the long-term goal was an improved service delivery in primary care with the introduction of EHRs. The revised ToC revealed that the long-term change was the improved maternal and child health care delivery at Festac Primary Health Center using EHRs. We proposed a generic ToC map that implementers in LMICs can use to introduce an optimized EHR system, with assumptions about sustainability and other relevant factors. The outcomes from the critical success factors were sustainability: the sustained improvements included trained health care professionals, a change in mindset from using paper systems toward digital health transformation, and using the project’s laptops to collect aggregate data for the District Health Information System 2–based national health information management system; financial: we secured funding to procure IT equipment, including servers, laptops, and networking, but the initial cost of implementation was high, and funds mainly came from the funding partner; and organizational: the health professionals, especially the head of nursing and health information officers, showed significant commitment to adopting the EHR system, but certain physicians and midwives were unwilling to use the EHR system initially until they were persuaded or incentivized by the management. ConclusionsThis study shows that the ToC is a rewarding approach to framing dialogue with stakeholders and serves as a framework for planning, evaluation, learning, and reflection. We hypothesized that any future health IT implementation in primary care could adapt our ToC approach to their contexts with necessary modifications based on inherent characteristics.

Abstract 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.

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

Abstract 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 .

The developing world faces a series of health crises including HIV/AIDS and tuberculosis that threaten the lives of millions of people. Lack of infrastructure and trained, experienced staff are considered important barriers to scaling up treatment for these diseases. In this paper we explain why information systems are important in many healthcare projects in the developing world. We discuss pilot projects demonstrating that such systems are possible and can expand to manage hundreds of thousands of patients. We also pass on the most important practical lessons in design and implementation from our experience in doing this work. Finally, we discuss the importance of collaboration between projects in the development of electronic medical record systems rather than reinventing systems in isolation, and the use of open standards and open source software.