Implementation of a Fast Healthcare Interoperability Resources–Based Clinical Decision Support Tool for Calculating CHA
            <sub>2</sub>
            DS
            <sub>2</sub>
            -VASc Scores

Abedin, Zameer; Hoerner, Robert; Habboushe, Joseph; Lu, Yi; Kawamoto, Kensaku; Warner, Phillip B.; Shields, David; Shah, Rashmee U.

doi:10.1161/circoutcomes.119.006286

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…Outcomes: An initial evaluation of MDCalc Connect for the CHA 2 DS 2 -VASc calculator showed that EHR integration enabled the automatic identification of potential patient risk factors that were not otherwise noted by clinicians in more than half of patients. 47 …”

Section: Resultsmentioning

confidence: 99%

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center

et al. 2021

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Objective To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations. Materials and Methods We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations. Results University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape. Discussion Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation. Conclusion EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.

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Section: Resultsmentioning

confidence: 99%

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center

et al. 2021

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“…A significant proportion of included studies focused on the development of CDS for genomics lab result interpretation (n=7). Similarly, the analysis of risk factors was an important focus of work (n=6), including work on sepsis risk [ 23 24 ], familial cancers [ 25 ], drug-gene interactions [ 14 ], and CHA2DS2-VASc scores [ 26 ]. Tools intended to assist clinicians or patients by monitoring particular health metrics, whether for inpatient or outpatient care, constituted a fifth category (n=5).…”

Section: Resultsmentioning

confidence: 99%

“…Condition Management and Treatment (n=15) [12,27,28,30,31,35,36,42,44,45,50,53,54,56,58] CDS Infrastructure (n=7) [15,33,40,41,52,60,63] Lab Result Interpretation --Genomics (n=7) [29,32,46,48,49,58,59] Risk Factor Analysis (n=6) [14,[23][24][25][26]39] Health Metric Monitoring (n=5) [34,37,43,51,61] Lab Result Interpretation --Other (n=3) [38,57,62] HIV Screening (n=1) [55] FHIR n (%) Standards was observational (n=8; 67%), with three studies (25%) reporting CDS needs evaluations. We identified only two studies with randomized components reporting on the use of any of the standards of interest (Curran et al [27] and Kawamoto et al [28]).…”

Section: Topicmentioning

confidence: 99%

New Standards for Clinical Decision Support: A Survey of The State of Implementation

et al. 2021

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Summary Objectives: To review the current state of research on designing and implementing clinical decision support (CDS) using four current interoperability standards: Fast Healthcare Interoperability Resources (FHIR); Substitutable Medical Applications and Reusable Technologies (SMART); Clinical Quality Language (CQL); and CDS Hooks. Methods: We conducted a review of original studies describing development of specific CDS tools or infrastructures using one of the four targeted standards, regardless of implementation stage. Citations published any time before the literature search was executed on October 21, 2020 were retrieved from PubMed. Two reviewers independently screened articles and abstracted data according to a protocol designed by team consensus. Results: Of 290 articles identified via PubMed search, 44 were included in this study. More than three quarters were published since 2018. Forty-three (98%) used FHIR; 22 (50%) used SMART; two (5%) used CQL; and eight (18%) used CDS Hooks. Twenty-four (55%) were in the design stage, 15 (34%) in the piloting stage, and five (11%) were deployed in a real-world setting. Only 12 (27%) of the articles reported an evaluation of the technology under development. Three of the four articles describing a deployed technology reported an evaluation. Only two evaluations with randomized study components were identified. Conclusion: The diversity of topics and approaches identified in the literature highlights the utility of these standards. The infrequency of reported evaluations, as well as the high number of studies in the design or piloting stage, indicate that these technologies are still early in their life cycles. Informaticists will require a stronger evidence base to understand the implications of using these standards in CDS design and implementation.

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“…Nowadays, prediction models are increasingly complex, sometimes opaque, and often delivered across entire health systems via the electronic medical record. 1 These changes represent a potential shift from experience or intuition-based decision making to quantitative or data-driven decision making, thereby reducing variability, increasing efficiency, and improving health outcomes. Of course, this assumes the models are valid and accurate in the population where they are applied.…”

Section: See Article By Gulati Et Almentioning

confidence: 99%

Do Prediction Models Do More Harm Than Good?

Shah

Bress

2022

Circ: Cardiovascular Quality and Outcomes

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Implementation of a Fast Healthcare Interoperability Resources–Based Clinical Decision Support Tool for Calculating CHA ₂ DS ₂ -VASc Scores

Cited by 9 publications

References 4 publications

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center

New Standards for Clinical Decision Support: A Survey of The State of Implementation

Do Prediction Models Do More Harm Than Good?

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Implementation of a Fast Healthcare Interoperability Resources–Based Clinical Decision Support Tool for Calculating CHA 2 DS 2 -VASc Scores

Cited by 9 publications

References 4 publications

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center

New Standards for Clinical Decision Support: A Survey of The State of Implementation

Do Prediction Models Do More Harm Than Good?

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Product

Resources

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Implementation of a Fast Healthcare Interoperability Resources–Based Clinical Decision Support Tool for Calculating CHA ₂ DS ₂ -VASc Scores