Clinical decision support models and frameworks: Seeking to address research issues underlying implementation successes and failures

Greenes, Robert A.; Bates, David W.; Kawamoto, Kensaku; Middleton, Blackford; Osheroff, Jerome A.; Shaḥar, Yuval

doi:10.1016/j.jbi.2017.12.005

Cited by 118 publications

(102 citation statements)

References 53 publications

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“…All of the CDS PGx rules developed under eMerge and IGNITE are available, 51 but vary in format. Efforts such as the multilayered framework for disseminating knowledge 52 and other standards-based approaches to shareable CDS 53,54 appear promising.…”

Section: Discussionmentioning

confidence: 99%

A Pharmacogenomics Clinical Decision Support Service Based on FHIR and CDS Hooks

2018

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Objectives Pharmacogenomics (PGx) is often considered a low-hanging fruit for genomics–electronic health record (EHR) integrations, and many have expressed the notion that drug–gene interaction checking might one day become as much a commodity in EHRs as drug–drug and drug–allergy checking. In addition, the U.S. Office of the National Coordinator has recognized the trend toward storing complete sequencing data outside the EHR in a Genomic Archiving and Communication System (GACS) and has emphasized the need for “pilots that test Fast Healthcare Interoperability Resources (FHIR) Genomics for GACS integration with EHRs.” We sought to develop a PGx clinical decision support (CDS) service, leveraging the emerging FHIR and CDS Hooks standards, and based on an assumption that pharmacogene sequencing data would be stored alongside the EHR in a GACS. Methods We developed a PGx CDS service as a functional prototype. The service is triggered by a medication order in the EHR. When evoked, the service looks for relevant genetic data in a GACS and returns corresponding recommendations back to the ordering clinician. Where the patient has no genetic data on file, the service can recommend pretreatment genetic testing where applicable. Results Overall, we were able to meet our objectives and deploy a functional prototype, interfaced with a commercial EHR. We identified several areas where FHIR or CDS Hooks lacked necessary semantics or have implementation ambiguity. Primary FHIR challenges included multiple ways to say the same thing, which exacerbated the complexity of variant to allele conversion and lack of representation of deoxyribonucleic acid region(s) studied. Primary CDS Hooks challenges included the complexity of executing an authenticated query against one system (GACS) upon being triggered by a different system (the EHR), and limitations in the types of actionable recommendations that can be returned to the EHR. Conclusions In conclusion, we have found that PGx CDS based on FHIR and CDS Hooks appears to represent a promising means of genomics–EHR integration. More real-world testing along with a set of use-case driven GACS interface requirements will push us closer to the U.S. National Human Genome Research Institute vision of a plug-in PGx app.

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Section: Discussionmentioning

confidence: 99%

A Pharmacogenomics Clinical Decision Support Service Based on FHIR and CDS Hooks

2018

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“…Historically, the CDSs delivered promising results in diverse systems and services such as the reminder systems, the drug dosing and drug-drug interactions, the diagnoses and treatment, and the pharmarelated fields [89]- [91]. Despite its potentials in improving health and healthcare, CDS has several challenges to accomplish its full promise [92]. Moreover, the fresh developments in the medicine domain and the presence of disruptive technologies pose a new set of challenges to develop models for CDS.…”

Section: A Redesign Of Clinical Design Supportmentioning

confidence: 99%

Precision Medicine Informatics: Principles, Prospects, and Challenges

et al. 2020

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Precision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace of the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological perspective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide-scale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.

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“…Similarly, an organization may develop a new capacity such as usercentered design but not yet apply it systematically or comprehensively across all CDS artifacts or processes. Advancing in tandem along the columns can confer additional advantages; for example, a robust analytics platform can yield greater benefits when coupled with interdisciplinary Chief medical information officer (CMIO) 49 (39) Physician informaticist (not CMIO) 47 (37) Nonphysician informaticist 11 (9) Knowledge manager/engineer 7 (5) Other 13 (10) Abbreviation: CDS, clinical decision support. a Each individual categorized into a single role based on online profiles.…”

Section: Proposed Maturity Modelmentioning

confidence: 99%

Towards a Maturity Model for Clinical Decision Support Operations

et al. 2019

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Clinical decision support (CDS) systems delivered through the electronic health record are an important element of quality and safety initiatives within a health care system. However, managing a large CDS knowledge base can be an overwhelming task for informatics teams. Additionally, it can be difficult for these informatics teams to communicate their goals with external operational stakeholders and define concrete steps for improvement. We aimed to develop a maturity model that describes a roadmap toward organizational functions and processes that help health care systems use CDS more effectively to drive better outcomes. We developed a maturity model for CDS operations through discussions with health care leaders at 80 organizations, iterative model development by four clinical informaticists, and subsequent review with 19 health care organizations. We ceased iterations when feedback from three organizations did not result in any changes to the model. The proposed CDS maturity model includes three main “pillars”: “Content Creation,” “Analytics and Reporting,” and “Governance and Management.” Each pillar contains five levels—advancing along each pillar provides CDS teams a deeper understanding of the processes CDS systems are intended to improve. A “roof” represents the CDS functions that become attainable after advancing along each of the pillars. Organizations are not required to advance in order and can develop in one pillar separately from another. However, we hypothesize that optimal deployment of preceding levels and advancing in tandem along the pillars increase the value of organizational investment in higher levels of CDS maturity. In addition to describing the maturity model and its development, we also provide three case studies of health care organizations using the model for self-assessment and determine next steps in CDS development.

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Clinical decision support models and frameworks: Seeking to address research issues underlying implementation successes and failures

Cited by 118 publications

References 53 publications

A Pharmacogenomics Clinical Decision Support Service Based on FHIR and CDS Hooks

A Pharmacogenomics Clinical Decision Support Service Based on FHIR and CDS Hooks

Precision Medicine Informatics: Principles, Prospects, and Challenges

Towards a Maturity Model for Clinical Decision Support Operations

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