Towards Computable Guidelines and Beyond with FHIR

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Adapting Clinical Guidelines for the Digital Age

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Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Second Annual MCBK public meeting, which was held at the National Institutes of Health on July 18‐19, 2019 and brought together over 150 participants from various domains to frame and address important dimensions for mobilizing CBK.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/1/lrh2-sup-0001-supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/2/lrh210222.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/3/lrh210222_am.pd

Building and maintaining trust in clinical decision support: Recommendations from the Patient‐Centered CDS Learning Network

Knowledge artifacts in digital repositories for clinical decision support (CDS) can promote the use of CDS in clinical practice. However, stakeholders will benefit from knowing which they can trust before adopting artifacts from knowledge repositories. We discuss our investigation into trust for knowledge artifacts and repositories by the Patient‐Centered CDS Learning Network’s Trust Framework Working Group (TFWG). The TFWG identified 12 actors (eg, vendors, clinicians, and policy makers) within a CDS ecosystem who each may play a meaningful role in prioritizing, authoring, implementing, or evaluating CDS and developed 33 recommendations distributed across nine “trust attributes.” The trust attributes and recommendations represent a range of considerations such as the “Competency” of knowledge artifact engineers and the “Organizational Capacity” of institutions that develop and implement CDS. The TFWG findings highlight an initial effort to make trust explicit and embedded within CDS knowledge artifacts and repositories and thus more broadly accepted and used.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154962/1/lrh210208.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154962/2/lrh210208_am.pd

Lessons Learned from Implementing Service-Oriented Clinical Decision Support at Four Sites: A Qualitative Study

Objective To identify challenges, lessons learned and best practices for service-oriented clinical decision support, based on the results of the Clinical Decision Support Consortium, a multi-site study which developed, implemented and evaluated clinical decision support services in a diverse range of electronic health records. Methods Ethnographic investigation using the rapid assessment process, a procedure for agile qualitative data collection and analysis, including clinical observation, system demonstrations and analysis and 91 interviews. Results We identified challenges and lessons learned in eight dimensions: (1) hardware and software computing infrastructure, (2) clinical content, (3) human-computer interface, (4) people, (5) workflow and communication, (6) internal organizational policies, procedures, environment and culture, (7) external rules, regulations, and pressures and (8) system measurement and monitoring. Key challenges included performance issues (particularly related to data retrieval), differences in terminologies used across sites, workflow variability and the need for a legal framework. Discussion Based on the challenges and lessons learned, we identified eight best practices for developers and implementers of service-oriented clinical decision support: (1) optimize performance, or make asynchronous calls, (2) be liberal in what you accept (particularly for terminology), (3) foster clinical transparency, (4) develop a legal framework, (5) support a flexible front-end, (6) dedicate human resources, (7) support peer-to-peer communication, (8) improve standards. Conclusion The Clinical Decision Support Consortium successfully developed a clinical decision support service and implemented it in four different electronic health records and four diverse clinical sites; however, the process was arduous. The lessons identified by the Consortium may be useful for other developers and implementers of clinical decision support services

Electronic health records (EHRs) in clinical research and platform trials: Application of the innovative EHR-based methods developed by EU-PEARL

Electronic health records; Platform trialsRegistros médicos electrónicos; Pruebas de plataformaRegistres mèdics electrònics; Proves de plataformaObjective Electronic Health Record (EHR) systems are digital platforms in clinical practice used to collect patients’ clinical information related to their health status and represents a useful storage of real-world data. EHRs have a potential role in research studies, in particular, in platform trials. Platform trials are innovative trial designs including multiple trial arms (conducted simultaneously and/or sequentially) on different treatments under a single master protocol. However, the use of EHRs in research comes with important challenges such as incompleteness of records and the need to translate trial eligibility criteria into interoperable queries. In this paper, we aim to review and to describe our proposed innovative methods to tackle some of the most important challenges identified. This work is part of the Innovative Medicines Initiative (IMI) EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project’s work package 3 (WP3), whose objective is to deliver tools and guidance for EHR-based protocol feasibility assessment, clinical site selection, and patient pre-screening in platform trials, investing in the building of a data-driven clinical network framework that can execute these complex innovative designs for which feasibility assessments are critically important. Methods ISO standards and relevant references informed a readiness survey, producing 354 criteria with corresponding questions selected and harmonised through a 7-round scoring process (0–1) in stakeholder meetings, with 85% of consensus being the threshold of acceptance for a criterium/question. ATLAS cohort definition and Cohort Diagnostics were mainly used to create the trial feasibility eligibility (I/E) criteria as executable interoperable queries. Results The WP3/EU-PEARL group developed a readiness survey (eSurvey) for an efficient selection of clinical sites with suitable EHRs, consisting of yes-or-no questions, and a set-up of interoperable proxy queries using physicians’ defined trial criteria. Both actions facilitate recruiting trial participants and alignment between study costs/timelines and data-driven recruitment potential. Conclusion The eSurvey will help create an archive of clinical sites with mature EHR systems suitable to participate in clinical trials/platform trials, and the interoperable proxy queries of trial eligibility criteria will help identify the number of potential participants. Ultimately, these tools will contribute to the production of EHR-based protocol design.“EU-PEARL has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 853966-2. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and CHILDREN'S TUMOR FOUNDATION, GLOBAL ALLIANCE FOR TB DRUG DEVELOPMENT NON PROFIT ORGANISATION, SPRINGWORKS THERAPEUTICS INC.

Developing Metadata Categories as a Strategy to Mobilize Computable Biomedical Knowledge

A work by a group of volunteer members drawn from the Mobilizing Computable Biomedical Knowledge community's Standards Workgroup. See mobilizecbk.org for more information about this community and workgroup.Computable biomedical knowledge artifacts (CBKs) are digital objects or entities representing biomedical knowledge as machine-independent data structures that can be parsed and processed by different information systems. The breadth of content represented in CBKs spans all biomedical knowledge related to human health and so it includes knowledge about molecules, cells, organs, individual people, human populations, and the environment. CBKs vary in their scope, purpose, and audience. Some CBKs support biomedical research. Other CBKs help improve health outcomes by enabling clinical decision support, health education, health promotion, and population health analytics. In some instances, CBKs have multiple uses that span research, education, clinical care, or population health. As the number of CBKs grows large, producers must describe them with structured, searchable metadata so that consumers can find, deploy, and use them properly. This report delineates categories of metadata for describing CBKs sufficiently to enable CBKs to be mobilized for various purposes.https://deepblue.lib.umich.edu/bitstream/2027.42/155655/1/MCBK.Metadata.Paper.June2020.f.pdfDescription of MCBK.Metadata.Paper.June2020.f.pdf : MCBK 2020 Virtual Meeting version of Standards Workgroup's Working Paper on CBK Metadat

Digital clinical support tools to improve child health: development, implementation, and evaluation of ePOCT+ to support healthcare providers in the management of sick children at primary care health facilities in Tanzania

Bacterial antimicrobial resistance due to inappropriate antibiotic use, and poor quality of care are major contributors to the unacceptably high childhood mortality in Tanzania. Electronic Clinical Decision Support Algorithms (CDSAs) are evidence based digital health tools based on clinical guidelines that guide health providers through a consultation to ultimately propose the diagnosis and treatment based on the inputs entered. Such tools have been found to reduce antibiotic prescription and improve quality of care. Nonetheless, there is a lack of pragmatic studies evaluating CDSAs in Tanzania, and there are many remaining challenges with previously developed CDSAs. The aim of this project was to improve quality of care and reduce antibiotic prescription at primary care level health facilities in Tanzania. This was done by first developing the ePOCT+ clinical decision support algorithm, addressing challenges identified in other CDSAs, and secondly evaluating the effect of ePOCT+ on antibiotic prescription in a pragmatic cluster randomized controlled trial (DYNAMIC Tanzania study). To improve uptake, adherence, safety, and potential for antibiotic stewardship, ePOCT+ expanded the clinical scope of the clinical algorithms, expanded the age range of patients it could manage, and assured comprehensive input from clinical and digital experts, as well as health provider end-users. Numerous meetings were conducted, Delphi processes were utilized, and comprehensive piloting was performed to develop ePOCT+. In order to assure safety, a systematic review was conducted to identify the best performing predictors of severe disease to integrate within the algorithm. The DYNAMIC Tanzania study was a pragmatic, open-label, parallel-group, cluster randomized trial in 40 primary health facilities in the Mbeya and Morogoro regions of Tanzania. Randomization of health facilities were stratified by region, council, level of health facility, and attendance rate. The intervention consisted of the ePOCT+ CDSA with supporting IT infrastructure, C-reactive protein rapid test, hemoglobin rapid test, pulse oximeter, training and supportive mentorship. Co-primary outcomes were 1) antibiotic prescription at the time of the initial consultation (superiority analysis), and 2) clinical failure at day 7 defined as “not cured” and “not improved”, or unscheduled hospitalization (non-inferiority analysis). Secondary safety outcomes include unscheduled re-attendance visits, non-referred secondary hospitalization and death by day 7. Analyses were performed using a random effects logistic regression model using the cluster and patient as random effects, with further adjustment using fixed effect terms for randomization stratification factors, and baseline characteristics. The systematic review on predictors of severe disease in febrile children presenting from the community identified 18 studies evaluating 200 prognostic factors and 25 clinical prediction models in 24 530 children. There were few outpatient and primary care studies identified. The most common and best preforming predictors of severe disease were malnutrition, altered consciousness, markers of acidosis, and poor peripheral perfusion. In expanding the age scope of ePOCT+ to manage children 1 day to 15 years, and based on feedback from previous studies and CDSAs, additional illnesses were integrated in the ePOCT+ clinical algorithm. These include trauma, urinary tract infection, and abdominal pain, selected based on 1) incidence, 2) morbidity/mortality, and 3) feasibility at primary care. A Delphi survey among 30 Tanzanian health providers evaluated feasibility, acceptability and reliability of integrating specific predictors within ePOCT+, notably predictors identified within the systematic review. Feasibility tests in over 200 patients in 20 health facilities, and pilots in over 2000 consultations, lead to modifications to ePOCT+ based on user-experience feedback and observations, notably providing option to not measure some clinical signs when not feasible, allow health providers to accept or refuse a proposed diagnosis or treatment, provide alternative medicines in case of stock-outs, and highlighting clinical elements that would result in referral. The DYNAMIC Tanzania cluster randomized trial took place between December 2021 to October 2022. Over 40,000 children under 15 years of age were enrolled in 20 health facilities (clusters) where health providers could use ePOCT+, and 20 health facilities where health providers provided care as usual. The co-primary outcomes found that the use of ePOCT+ resulted in a 3-fold reduction in the likelihood of a sick child receiving an antibiotic prescription compared to children in usual care health facilities. Despite substantially fewer antibiotics prescriptions, the co-primary outcome of clinical failure was non-inferior. There were also no differences in the secondary outcomes of death, secondary hospitalization, and additional medications after the initial consultations between study arms by day 7. In conclusion, the ePOCT+ electronic clinical decision support algorithm if implemented to scale could help address the urgent problem of antimicrobial resistance by safely reducing antibiotic prescribing. Transfer of ownership to the ministry of health of Tanzania and integration within the Tanzanian digital health landscape will be essential in order to achieve wide scale implementation

Automated Injection of Curated Knowledge Into Real-Time Clinical Systems: CDS Architecture for the 21st Century

abstract: Clinical Decision Support (CDS) is primarily associated with alerts, reminders, order entry, rule-based invocation, diagnostic aids, and on-demand information retrieval. While valuable, these foci have been in production use for decades, and do not provide a broader, interoperable means of plugging structured clinical knowledge into live electronic health record (EHR) ecosystems for purposes of orchestrating the user experiences of patients and clinicians. To date, the gap between knowledge representation and user-facing EHR integration has been considered an “implementation concern” requiring unscalable manual human efforts and governance coordination. Drafting a questionnaire engineered to meet the specifications of the HL7 CDS Knowledge Artifact specification, for example, carries no reasonable expectation that it may be imported and deployed into a live system without significant burdens. Dramatic reduction of the time and effort gap in the research and application cycle could be revolutionary. Doing so, however, requires both a floor-to-ceiling precoordination of functional boundaries in the knowledge management lifecycle, as well as formalization of the human processes by which this occurs. This research introduces ARTAKA: Architecture for Real-Time Application of Knowledge Artifacts, as a concrete floor-to-ceiling technological blueprint for both provider heath IT (HIT) and vendor organizations to incrementally introduce value into existing systems dynamically. This is made possible by service-ization of curated knowledge artifacts, then injected into a highly scalable backend infrastructure by automated orchestration through public marketplaces. Supplementary examples of client app integration are also provided. Compilation of knowledge into platform-specific form has been left flexible, in so far as implementations comply with ARTAKA’s Context Event Service (CES) communication and Health Services Platform (HSP) Marketplace service packaging standards. Towards the goal of interoperable human processes, ARTAKA’s treatment of knowledge artifacts as a specialized form of software allows knowledge engineers to operate as a type of software engineering practice. Thus, nearly a century of software development processes, tools, policies, and lessons offer immediate benefit: in some cases, with remarkable parity. Analyses of experimentation is provided with guidelines in how choice aspects of software development life cycles (SDLCs) apply to knowledge artifact development in an ARTAKA environment. Portions of this culminating document have been further initiated with Standards Developing Organizations (SDOs) intended to ultimately produce normative standards, as have active relationships with other bodies.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201