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Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study

BACKGROUND: Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation...

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Autores principales: Solomon, Jeffrey, Dauber-Decker, Katherine, Richardson, Safiya, Levy, Sera, Khan, Sundas, Coleman, Benjamin, Persaud, Rupert, Chelico, John, King, D'Arcy, Spyropoulos, Alex, McGinn, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623239/
https://www.ncbi.nlm.nih.gov/pubmed/37856193
http://dx.doi.org/10.2196/44065
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author Solomon, Jeffrey
Dauber-Decker, Katherine
Richardson, Safiya
Levy, Sera
Khan, Sundas
Coleman, Benjamin
Persaud, Rupert
Chelico, John
King, D'Arcy
Spyropoulos, Alex
McGinn, Thomas
author_facet Solomon, Jeffrey
Dauber-Decker, Katherine
Richardson, Safiya
Levy, Sera
Khan, Sundas
Coleman, Benjamin
Persaud, Rupert
Chelico, John
King, D'Arcy
Spyropoulos, Alex
McGinn, Thomas
author_sort Solomon, Jeffrey
collection PubMed
description BACKGROUND: Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation for tool development; for example, the individual CDS tools need to be built independently for each different EHR. OBJECTIVE: The objective of our research was to build and implement an EHR-agnostic platform for integrating CDS tools, which would remove the technical constraints inherent in relying on the EHR as the foundation and enable a single set of CDS tools that can work with any EHR. METHODS: We developed EvidencePoint, a novel, cloud-based, EHR-agnostic CDS platform, and we will describe the development of EvidencePoint and the deployment of its initial CDS tools, which include EHR-integrated applications for clinical use cases such as prediction of hospitalization survival for patients with COVID-19, venous thromboembolism prophylaxis, and pulmonary embolism diagnosis. RESULTS: The results below highlight the adoption of the CDS tools, the International Medical Prevention Registry on Venous Thromboembolism-D-Dimer, the Wells’ criteria, and the Northwell COVID-19 Survival (NOCOS), following development, usability testing, and implementation. The International Medical Prevention Registry on Venous Thromboembolism-D-Dimer CDS was used in 5249 patients at the 2 clinical intervention sites. The intervention group tool adoption was 77.8% (4083/5249 possible uses). For the NOCOS tool, which was designed to assist with triaging patients with COVID-19 for hospital admission in the event of constrained hospital resources, the worst-case resourcing scenario never materialized and triaging was never required. As a result, the NOCOS tool was not frequently used, though the EvidencePoint platform’s flexibility and customizability enabled the tool to be developed and deployed rapidly under the emergency conditions of the pandemic. Adoption rates for the Wells’ criteria tool will be reported in a future publication. CONCLUSIONS: The EvidencePoint system successfully demonstrated that a flexible, user-friendly platform for hosting CDS tools outside of a specific EHR is feasible. The forthcoming results of our outcomes analyses will demonstrate the adoption rate of EvidencePoint tools as well as the impact of behavioral economics “nudges” on the adoption rate. Due to the EHR-agnostic nature of EvidencePoint, the development process for additional forms of CDS will be simpler than traditional and cumbersome IT integration approaches and will benefit from the capabilities provided by the core system of EvidencePoint.
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spelling pubmed-106232392023-11-04 Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study Solomon, Jeffrey Dauber-Decker, Katherine Richardson, Safiya Levy, Sera Khan, Sundas Coleman, Benjamin Persaud, Rupert Chelico, John King, D'Arcy Spyropoulos, Alex McGinn, Thomas JMIR Form Res Original Paper BACKGROUND: Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation for tool development; for example, the individual CDS tools need to be built independently for each different EHR. OBJECTIVE: The objective of our research was to build and implement an EHR-agnostic platform for integrating CDS tools, which would remove the technical constraints inherent in relying on the EHR as the foundation and enable a single set of CDS tools that can work with any EHR. METHODS: We developed EvidencePoint, a novel, cloud-based, EHR-agnostic CDS platform, and we will describe the development of EvidencePoint and the deployment of its initial CDS tools, which include EHR-integrated applications for clinical use cases such as prediction of hospitalization survival for patients with COVID-19, venous thromboembolism prophylaxis, and pulmonary embolism diagnosis. RESULTS: The results below highlight the adoption of the CDS tools, the International Medical Prevention Registry on Venous Thromboembolism-D-Dimer, the Wells’ criteria, and the Northwell COVID-19 Survival (NOCOS), following development, usability testing, and implementation. The International Medical Prevention Registry on Venous Thromboembolism-D-Dimer CDS was used in 5249 patients at the 2 clinical intervention sites. The intervention group tool adoption was 77.8% (4083/5249 possible uses). For the NOCOS tool, which was designed to assist with triaging patients with COVID-19 for hospital admission in the event of constrained hospital resources, the worst-case resourcing scenario never materialized and triaging was never required. As a result, the NOCOS tool was not frequently used, though the EvidencePoint platform’s flexibility and customizability enabled the tool to be developed and deployed rapidly under the emergency conditions of the pandemic. Adoption rates for the Wells’ criteria tool will be reported in a future publication. CONCLUSIONS: The EvidencePoint system successfully demonstrated that a flexible, user-friendly platform for hosting CDS tools outside of a specific EHR is feasible. The forthcoming results of our outcomes analyses will demonstrate the adoption rate of EvidencePoint tools as well as the impact of behavioral economics “nudges” on the adoption rate. Due to the EHR-agnostic nature of EvidencePoint, the development process for additional forms of CDS will be simpler than traditional and cumbersome IT integration approaches and will benefit from the capabilities provided by the core system of EvidencePoint. JMIR Publications 2023-10-19 /pmc/articles/PMC10623239/ /pubmed/37856193 http://dx.doi.org/10.2196/44065 Text en ©Jeffrey Solomon, Katherine Dauber-Decker, Safiya Richardson, Sera Levy, Sundas Khan, Benjamin Coleman, Rupert Persaud, John Chelico, D'Arcy King, Alex Spyropoulos, Thomas McGinn. Originally published in JMIR Formative Research (https://formative.jmir.org), 19.10.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Solomon, Jeffrey
Dauber-Decker, Katherine
Richardson, Safiya
Levy, Sera
Khan, Sundas
Coleman, Benjamin
Persaud, Rupert
Chelico, John
King, D'Arcy
Spyropoulos, Alex
McGinn, Thomas
Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study
title Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study
title_full Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study
title_fullStr Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study
title_full_unstemmed Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study
title_short Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study
title_sort integrating clinical decision support into electronic health record systems using a novel platform (evidencepoint): developmental study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623239/
https://www.ncbi.nlm.nih.gov/pubmed/37856193
http://dx.doi.org/10.2196/44065
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