Templates as a method for implementing data provenance in decision support systems

AbstractDecision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates – abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project

Public Opinions on Using Social Media Content to Identify Users With Depression and Target Mental Health Care Advertising: Mixed Methods Survey

Background: Depression is a common disorder that still remains underdiagnosed and undertreated in the UK National Health Service. Charities and voluntary organizations offer mental health services, but they are still struggling to promote these services to the individuals who need them. By analyzing social media (SM) content using machine learning techniques, it may be possible to identify which SM users are currently experiencing low mood, thus enabling the targeted advertising of mental health services to the individuals who would benefit from them. Objective: This study aimed to understand SM users’ opinions of analysis of SM content for depression and targeted advertising on SM for mental health services. Methods: A Web-based, mixed methods, cross-sectional survey was administered to SM users aged 16 years or older within the United Kingdom. It asked participants about their demographics, their usage of SM, and their history of depression and presented structured and open-ended questions on views of SM content being analyzed for depression and views on receiving targeted advertising for mental health services. Results: A total of 183 participants completed the survey, and 114 (62.3%) of them had previously experienced depression. Participants indicated that they posted less during low moods, and they believed that their SM content would not reflect their depression. They could see the possible benefits of identifying depression from SM content but did not believe that the risks to privacy outweighed these benefits. A majority of the participants would not provide consent for such analysis to be conducted on their data and considered it to be intrusive and exposing. Conclusions: In a climate of distrust of SM platforms’ usage of personal data, participants in this survey did not perceive that the benefits of targeting advertisements for mental health services to individuals analyzed as having depression would outweigh the risks to privacy. Future work in this area should proceed with caution and should engage stakeholders at all stages to maximize the transparency and trustworthiness of such research endeavors

Public Opinions about Palliative and End-of-life Care during the COVID-19 Pandemic: A Twitter-based Study

BackgroundPalliative and end-of-life care (PEoLC) played a critical role in relieving distress and providing grief support in response to the heavy toll caused by the COVID-19 pandemic. However, little is known about public opinions concerning PEoLC during the pandemic. Given that social media have the potential to collect real-time public opinions, an analysis of this evidence is vital to guide future policy-making. ObjectiveThis study aimed to use social media data to investigate real-time public opinions regarding PEoLC during the COVID-19 crisis and explore the impact of vaccination programs on public opinions about PEoLC. MethodsThis Twitter-based study explored tweets across 3 English-speaking countries: the United States, the United Kingdom, and Canada. From October 2020 to March 2021, a total of 7951 PEoLC-related tweets with geographic tags were retrieved and identified from a large-scale COVID-19 Twitter data set through the Twitter application programming interface. Topic modeling realized through a pointwise mutual information–based co-occurrence network and Louvain modularity was used to examine latent topics across the 3 countries and across 2 time periods (pre- and postvaccination program periods). ResultsCommonalities and regional differences among PEoLC topics in the United States, the United Kingdom, and Canada were identified specifically: cancer care and care facilities were of common interest to the public across the 3 countries during the pandemic; the public expressed positive attitudes toward the COVID-19 vaccine and highlighted the protection it affords to PEoLC professionals; and although Twitter users shared their personal experiences about PEoLC in the web-based community during the pandemic, this was more prominent in the United States and Canada. The implementation of the vaccination programs raised the profile of the vaccine discussion; however, this did not influence public opinions about PEoLC. ConclusionsPublic opinions on Twitter reflected a need for enhanced PEoLC services during the COVID-19 pandemic. The insignificant impact of the vaccination program on public discussion on social media indicated that public concerns regarding PEoLC continued to persist even after the vaccination efforts. Insights gleaned from public opinions regarding PEoLC could provide some clues for policy makers on how to ensure high-quality PEoLC during public health emergencies. In this post–COVID-19 era, PEoLC professionals may wish to continue to examine social media and learn from web-based public discussion how to ease the long-lasting trauma caused by this crisis and prepare for public health emergencies in the future. Besides, our results showed social media’s potential in acting as an effective tool to reflect public opinions in the context of PEoLC

Effectiveness and safety of non-vitamin K oral anticoagulants versus warfarin in patients with atrial fibrillation and previous stroke: A systematic review and meta-analysis

Introduction: Current evidence regarding the clinical outcomes of non-vitamin K oral anticoagulants (NOACs) versus warfarin in patients with atrial fibrillation (AF) and previous stroke is inconclusive, especially in patients with previous intracranial haemorrhage (ICrH). We aim to undertake a systematic review and meta-analysis assessing the effectiveness and safety of NOACs versus warfarin in AF patients with a history of stroke. Methods: We searched studies published up to December 10, 2022, on PubMed, Medline, Embase, and Cochrane Central Register of Controlled Trials. Studies on adults with AF and previous ischaemic stroke (IS) or IrCH receiving either NOACs or warfarin and capturing outcome events (thromboembolic events, ICrH, and all-cause mortality) were eligible for inclusion. Results: Six randomized controlled trials (RCTs) (including 19,489 patients with previous IS) and fifteen observational studies (including 132,575 patients with previous IS and 13,068 patients with previous ICrH) were included. RCT data showed that compared with warfarin, NOACs were associated with a significant reduction in thromboembolic events (odds ratio [OR]: 0.85, 95% confidence interval [CI]: 0.75-0.96), ICrH (OR: 0.57, 95% CI: 0.36-0.90), and all-cause mortality (OR: 0.88, 95% CI: 0.80-0.98). In analysing observational studies, similar results were retrieved. Moreover, patients with previous ICrH had a lower OR on thromboembolic events than those with IS (OR: 0.66, 95% CI: 0.46-0.95 vs. OR: 0.80, 95% CI: 0.70-0.93) in the comparison between NOACs and warfarin. Conclusions: Observational data showed that in AF patients with previous stroke, NOACs showed better clinical performance compared to warfarin and the benefits of NOACs were more pronounced in patients with previous IrCH versus those with IS. RCT data also showed NOACs are superior to warfarin. However, current RCTs only included AF patients who survived an IS, and further large RCTs focused on patients with previous ICrH are warranted.</p

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

Background - Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use this data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. Objective - In this paper, we discuss whether data that is recorded routinely as part of the health care process in primary care is actually fit to use for these other purposes, how the original purpose may affect the extent to which the data is fit for another purpose and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the (re-)use of this type of data. Methods –This discussion paper is based on the authors’ experience as users of electronic health records data, as a general practitioner, health informatics experts, and health services researchers. It is a product of the discussions they had during the TRANSFoRm project, which was funded by the EU and sought to develop, pilot and evaluate a core information architecture for the Learning Health System (LHS) in Europe, based on primary care electronic health records. Results – We first describe the different stages in the processing of EHR data, as well as the different purposes for which this data is used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step, that can generate biased outcomes. We identified thirteen possible sources of bias. Four of them are related to the organization of a health care system, some are of a more technical nature. Conclusions - There are a substantial number of possible sources of bias, and very little is known about the size and direction of their impact. However, any (re-)user of data that was recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor and purpose oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven healthcare will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda

Using Microservices to Design Patient-facing Research Software

With a significant amount of software now being developed for use in patient-facing studies, there is a pressing need to consider how to design this software effectively in order to support the needs of both researchers and patients. We posit that a microservice architecture—which offers a large amount of flexibility for development and deployment, while at the same time ensuring certain quality attributes, such as scalability, are present—provides an effective mechanism for designing such software. To explore this proposition, in this work we show how the paradigm has been applied to the design of CONSULT, a decision support system that provides autonomous support to stroke patients and is characterised by its use of a data-backed AI reasoner. We discuss the impact that the use of this software architecture has had on the teams developing CONSULT and measure the performance of the system produced. We show that the use of microservices can deliver software that is able to facilitate both research and effective patient interactions. However, we also conclude that the impact of the approach only goes so far, with additional techniques needed to address its limitations.10.13039/501100000266-UK Engineering & Physical Sciences Research Council (EPSRC) (Grant Number: EP/P010105/1