mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review.

AIMS: Gestational diabetes (GDM) is the most common metabolic disorder of pregnancy, requiring complex management and empowerment of those affected. Mobile health (mHealth) applications (apps) are proposed for streamlining healthcare service delivery, extending care relationships into the community, and empowering those affected by prolonged medical disorders to be equal collaborators in their healthcare. This review investigates mHealth apps intended for use with GDM; specifically those powered by artificial intelligence (AI) or providing decision support. METHODS: A scoping review using the novel Survey Tool approach for collaborative literature Reviews (STaR) process was performed. RESULTS: From 18 papers, 11 discrete GDM-based mHealth apps were identified, but only 3 were reasonably mature with only one currently in use in a clinical setting. Two-thirds of the apps provided condition-relevant contextual user feedback that could aid in patient self care. However, although each app targeted one or more components of the GDM clinical pathway, no app addressed the entirety from diagnosis to postpartum. CONCLUSIONS: There are limited mHealth apps for GDM that incorporate AI or AI-based decision support. Many exist only to record patient information like blood glucose readings or diet, provide generic patient education or advice, or to reduce adverse events by providing medication or appointment alerts. Significant barriers remain that continue to limit the adoption of mHealth apps in clinical care settings. Further research and development are needed to deliver intelligent holistic mHealth apps using AI that can truly reduce healthcare resource use and improve outcomes by enabling patient self care in the community.

Medical idioms for clinical Bayesian network development.

Bayesian Networks (BNs) are graphical probabilistic models that have proven popular in medical applications. While numerous medical BNs have been published, most are presented fait accompli without explanation of how the network structure was developed or justification of why it represents the correct structure for the given medical application. This means that the process of building medical BNs from experts is typically ad hoc and offers little opportunity for methodological improvement. This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs. The proposed method complements and extends the idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We propose instances of their generic idioms that are specific to medical BNs. We refer to the proposed medical reasoning patterns as medical idioms. In addition, we extend the use of idioms to represent interventional and counterfactual reasoning. We believe that the proposed medical idioms are logical reasoning patterns that can be combined, reused and applied generically to help develop medical BNs. All proposed medical idioms have been illustrated using medical examples on coronary artery disease. The method has also been applied to other ongoing BNs being developed with medical experts. Finally, we show that applying the proposed medical idioms to published BN models results in models with a clearer structure.

The delivery of preventive care to clients of community health services.

BACKGROUND: Smoking, poor nutrition, risky alcohol use, and physical inactivity are the primary behavioral risks for common causes of mortality and morbidity. Evidence and guidelines support routine clinician delivery of preventive care. Limited evidence describes the level delivered in community health settings. The objective was to determine the: prevalence of preventive care provided by community health clinicians; association between client and service characteristics and receipt of care; and acceptability of care. This will assist in informing interventions that facilitate adoption of opportunistic preventive care delivery to all clients. METHODS: In 2009 and 2010 a telephone survey was undertaken of 1284 clients across a network of 56 public community health facilities in one health district in New South Wales, Australia. The survey assessed receipt of preventive care (assessment, brief advice, and referral/follow-up) regarding smoking, inadequate fruit and vegetable consumption, alcohol overconsumption, and physical inactivity; and acceptability of care. RESULTS: Care was most frequently reported for smoking (assessment: 59.9%, brief advice: 61.7%, and offer of referral to a telephone service: 4.5%) and least frequently for inadequate fruit or vegetable consumption (27.0%, 20.0% and 0.9% respectively). Sixteen percent reported assessment for all risks, 16.2% received brief advice for all risks, and 0.6% were offered a specific referral for all risks. The following were associated with increased care: diabetes services, number of appointments, being male, Aboriginal, unemployed, and socio-economically disadvantaged. Acceptability of preventive care was high (76.0%-95.3%). CONCLUSIONS: Despite strong client support, preventive care was not provided opportunistically to all, and was preferentially provided to select groups. This suggests a need for practice change strategies to enhance preventive care provision to achieve adherence to clinical guidelines.

Bayesian networks in healthcare: What is preventing their adoption?

There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare. Based on the literature collection that has been systematically narrowed down from 3810 to 116 most relevant articles, this paper analyses the benefits, barriers and facilitating factors (BBF) for implementing BN-based systems in healthcare using the ITPOSMO-BBF framework. A key finding is that works in the literature rarely consider barriers and even when these were identified they were not connected to facilitating factors. The main finding is that the barriers can be grouped into: (1) data inadequacies; (2) clinicians' resistance to new technologies; (3) lack of clinical credibility; (4) failure to demonstrate clinical impact; (5) absence of an acceptable predictive performance; and (6) absence of evidence for model's generalisability. The facilitating factors can be grouped into: (1) data collection improvements; (2) software and technological improvements; (3) having interpretable and easy to use BN-based systems; (4) clinical involvement in the development or review of the model; (5) investigation of model's clinical impact; (6) internal validation of the model's performance; and (7) external validation of the model. These groupings form a strong basis for a generic framework that could be used for formulating strategies for ensuring BN-based clinical decision-support system adoption in frontline care settings. The output of this review is expected to enhance the dialogue among researchers by providing a deeper understanding for the neglected issue of BN adoption in practice and promoting efforts for implementing BN-based systems.

A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future.

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.

Bayesian networks in healthcare: Distribution by medical condition.

Bayesian networks (BNs) have received increasing research attention that is not matched by adoption in practice and yet have potential to significantly benefit healthcare. Hitherto, research works have not investigated the types of medical conditions being modelled with BNs, nor whether there are any differences in how and why they are applied to different conditions. This research seeks to identify and quantify the range of medical conditions for which healthcare-related BN models have been proposed, and the differences in approach between the most common medical conditions to which they have been applied. We found that almost two-thirds of all healthcare BNs are focused on four conditions: cardiac, cancer, psychological and lung disorders. We believe there is a lack of understanding regarding how BNs work and what they are capable of, and that it is only with greater understanding and promotion that we may ever realise the full potential of BNs to effect positive change in daily healthcare practice.

Learning from Behavioural Changes That Fail.

Behavioural change techniques are currently used by many global organisations and public institutions. The amassing evidence base is used to answer practical and scientific questions regarding what cognitive, affective, and environment factors lead to successful behavioural change in the laboratory and in the field. In this piece we show that there is also value to examining interventions that inadvertently fail in achieving their desired behavioural change (e.g., backfiring effects). We identify the underlying causal pathways that characterise different types of failure, and show how a taxonomy of causal interactions that result in failure exposes new insights that can advance theory and practice.

Towards standardisation of evidence-based clinical care process specifications.

There is a strong push towards standardisation of treatment approaches, care processes and documentation of clinical practice. However, confusion persists regarding terminology and description of many clinical care process specifications which this research seeks to resolve by developing a taxonomic characterisation of clinical care process specifications. Literature on clinical care process specifications was analysed, creating the starting point for identifying common characteristics and how each is constructed and used in the clinical setting. A taxonomy for clinical care process specifications is presented. The De Bleser approach to limited clinical care process specifications characterisation was extended and each clinical care process specification is successfully characterised in terms of purpose, core elements and relationship to the other clinical care process specification types. A case study on the diagnosis and treatment of Type 2 Diabetes in the United Kingdom was used to evaluate the taxonomy and demonstrate how the characterisation framework applies. Standardising clinical care process specifications ensures that the format and content are consistent with expectations, can be read more quickly and high-quality information can be recorded about the patient. Standardisation also enables computer interpretability, which is important in integrating Learning Health Systems into the modern clinical environment. The approach presented allows terminologies for clinical care process specifications that were widely used interchangeably to be easily distinguished, thus, eliminating the existing confusion.

Data Visualisation in Midwifery: The Challenge of Seeing what Datasets Hide.

Information visualisation is transforming data into visual representations to convey information hidden within large datasets. Information visualisation in medicine is underdeveloped. In midwifery, the impact of different graphs on clinicians' and patients' understanding is not well understood. We investigate this gap and its potential consequences.

LAGOS: learning health systems and how they can integrate with patient care.

PROBLEM: Learning health systems (LHS) are an underexplored concept. How LHS will operate in clinical practice is not well understood. This paper investigates the relationships between LHS, clinical care process specifications (CCPS) and the established levels of medical practice to enable LHS integration into daily healthcare practice. METHODS: Concept analysis and thematic analysis were used to develop an LHS characterisation. Pathway theory was used to create a framework by relating LHS, CCPS, health information systems and the levels of medical practice. A case study approach evaluates the framework in an established health informatics project. RESULTS: Five concepts were identified and used to define the LHS learning cycle. A framework was developed with five pathways, each having three levels of practice specificity spanning population to precision medicine. The framework was evaluated through application to case studies not previously understood to be LHS. DISCUSSION: Clinicians show limited understanding of LHS, increasing resistance and limiting adoption and integration into care routine. Evaluation of the presented framework demonstrates that its use enables: (1) correct analysis and characterisation of LHS; (2) alignment and integration into the healthcare conceptual setting; (3) identification of the degree and level of patient application; and (4) impact on the overall healthcare system. CONCLUSION: This paper contributes a theoretical framework for analysis, characterisation and use of LHS. The framework allows clinicians and informaticians to correctly identify, characterise and integrate LHS within their daily routine. The overall contribution improves understanding, practice and evaluation of the LHS application in healthcare.

Assessment of the methodological quality of local clinical practice guidelines on the identification and management of gestational diabetes.

OBJECTIVE: Gestational diabetes is the most common metabolic disorder of pregnancy, and it is important that well-written clinical practice guidelines (CPGs) are used to optimise healthcare delivery and improve patient outcomes. The aim of the study was to assess the methodological quality of hospital-based CPGs on the identification and management of gestational diabetes. DESIGN: We conducted an assessment of local clinical guidelines in English for gestational diabetes using the Appraisal of Guidelines for Research and Evaluation (AGREE II) to assess and validate methodological quality. DATA SOURCES AND ELIGIBILITY CRITERIA: We sought a representative selection of local CPGs accessible by the internet. Criteria for inclusion were (1) identified as a guideline, (2) written in English, (3) produced by or for the hospital in a Western country, (4) included diagnostic criteria and recommendations concerning gestational diabetes, (5) grounded on evidence-based medicine and (6) accessible over the internet. No more than two CPGs were selected from any single country. RESULTS: Of the 56 CPGs identified, 7 were evaluated in detail by five reviewers using the standard AGREE II instrument. Interrater variance was calculated, with strong agreement observed for those protocols considered by reviewers as the highest and lowest scoring based on the instrument. CPG results for each of the six AGREE II domains are presented categorically using a 5-point Likert scale. Only one CPG scored above average in five or more of the domains. Overall scores ranged from 91.6 (the strongest) to 50 (the weakest). Significant variation existed in the methodological quality of CPGs, even though they followed the guideline of an advising body. Specifically, appropriate identification of the evidence relied on to inform clinical decision making in CPGs was poor, as was evidence of user involvement in the development of the guideline, resource implications, documentation of competing interests of the guideline development group and evidence of external review. CONCLUSIONS: The limitations described are important considerations for updating current and new CPGs.

A framework for analysing learning health systems: Are we removing the most impactful barriers?

INTRODUCTION: Learning health systems (LHS) are one of the major computing advances in health care. However, no prior research has systematically analysed barriers and facilitators for LHS. This paper presents an investigation into the barriers, benefits, and facilitating factors for LHS in order to create a basis for their successful implementation and adoption. METHODS: First, the ITPOSMO-BBF framework was developed based on the established ITPOSMO (information, technology, processes, objectives, staffing, management, and other factors) framework, extending it for analysing barriers, benefits, and facilitators. Second, the new framework was applied to LHS. RESULTS: We found that LHS shares similar barriers and facilitators with electronic health records (EHR); in particular, most facilitator effort in implementing EHR and LHS goes towards barriers categorised as human factors, even though they were seen to carry fewer benefits. Barriers whose resolution would bring significant benefits in safety, quality, and health outcomes remain.LHS envisage constant generation of new clinical knowledge and practice based on the central role of collections of EHR. Once LHS are constructed and operational, they trigger new data streams into the EHR. So LHS and EHR have a symbiotic relationship. The implementation and adoption of EHRs have proved and continues to prove challenging, and there are many lessons for LHS arising from these challenges. CONCLUSIONS: Successful adoption of LHS should take account of the framework proposed in this paper, especially with respect to its focus on removing barriers that have the most impact.

Systematic reviews and tech mining: A methodological comparison with case study.

When the Medical Library Association identified questions critical for the future of the profession, it assigned groups to use systematic reviews to find the answers to these questions. Group 6, whose question was on emerging technologies, recognized early on that the systematic review process would not work well for this question, which looks forward to predict future trends, whereas the systematic review process looks back in time. We searched for new methodologies that were more appropriate to our question, developing a process that combined systematic review, text mining, and visualization techniques. We then discovered tech mining, which is very similar to the process we had created. In this paper, we describe our research design and compare tech mining and systematic review methodologies. There are similarities and differences in each process: Both use a defined research question, deliberate database selection, careful and iterative search strategy development, broad data collection, and thoughtful data analysis. However, the focus of the research differs significantly, with systematic reviews looking to the past and tech mining mainly to the future. Our comparison demonstrates that each process can be enhanced from a purposeful consideration of the procedures of the other. Tech mining would benefit from the inclusion of a librarian on their research team and a greater attention to standards and collaboration in the research project. Systematic reviews would gain from the use of tech mining tools to enrich their data analysis and corporate management communication techniques to promote the adoption of their findings.

The Heimdall Framework for Supporting Characterisation of Learning Health Systems.

BACKGROUND: Learning Health Systems (LHS) can focus population medicine and Evidence Based Practice; smart technology delivering the next generation of improved healthcare described as Precision Medicine, and yet researchers in the LHS domain presently lack the ability to recognise their relevant works as falling within this domain. OBJECTIVE: To review LHS literature and develop a framework describing the domain that can be used as a tool to analyse the literature and support researchers to identify health informatics investigations as falling with the domain of LHS. METHOD: A scoping review is used to identify literature on which analysis was performed. This resolved the ontology and framework. The ontology was applied to quantify the distribution of classifications of LHS solutions. The framework was used to analyse and characterise the various works within the body of LHS literature. RESULTS: The ontology and framework developed was shown to be easily applicable to the literature, consistently describing and representing the goals, intentions and solutions of each LHS investigation in the literature. More proposed or potential solutions are described in the literature than implemented LHS. This suggests immaturity in the domain and points to the existence of barriers preventing LHS realisation. CONCLUSION: The lack of an ontology and framework may have been one of the causes for the failure to describe research works as falling within the LHS domain. Using our ontology and framework, LHS research works could be easily classified, demonstrating the comprehensiveness of our approach in contrast to earlier efforts.

Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record.

OBJECTIVE: Our objective is to create a source of synthetic electronic health records that is readily available; suited to industrial, innovation, research, and educational uses; and free of legal, privacy, security, and intellectual property restrictions. MATERIALS AND METHODS: We developed Synthea, an open-source software package that simulates the lifespans of synthetic patients, modeling the 10 most frequent reasons for primary care encounters and the 10 chronic conditions with the highest morbidity in the United States. RESULTS: Synthea adheres to a previously developed conceptual framework, scales via open-source deployment on the Internet, and may be extended with additional disease and treatment modules developed by its user community. One million synthetic patient records are now freely available online, encoded in standard formats (eg, Health Level-7 [HL7] Fast Healthcare Interoperability Resources [FHIR] and Consolidated-Clinical Document Architecture), and accessible through an HL7 FHIR application program interface. DISCUSSION: Health care lags other industries in information technology, data exchange, and interoperability. The lack of freely distributable health records has long hindered innovation in health care. Approaches and tools are available to inexpensively generate synthetic health records at scale without accidental disclosure risk, lowering current barriers to entry for promising early-stage developments. By engaging a growing community of users, the synthetic data generated will become increasingly comprehensive, detailed, and realistic over time. CONCLUSION: Synthetic patients can be simulated with models of disease progression and corresponding standards of care to produce risk-free realistic synthetic health care records at scale.