ArticleJMIR formative research2023
Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study.
Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Development and Usability Testing of a Mobile App-Based Clinical Decision Support System for Delirium: Randomized Crossover Trial.JMIR aging · 2024Trial
- Exploring primary care providers' perspectives on clinical decision support for venous thromboembolism diagnosis: a qualitative study.BMJ open · 2026Article
- Nudging provider adoption of clinical decision support: study protocol for a stepped-wedge cluster randomized, hybrid type III trial of an electronic health record-agnostic pulmonary embolism risk tool.Implementation science : IS · 2026Article
- A care quality dashboard for general practitioners managing patients with diabetes mellitus type 2: user-centered design and prototype evaluation.BMC medical informatics and decision making · 2026Article
- Scalable and Privacy-Conscious End-to-End Processing of Large-Scale Clinical Data for Precision Medicine: Empirical Evaluation Study.JMIR medical informatics · 2026Article
- Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study.PloS one · 2026Article
- Deconstructing the Interoperability Stack: A Comparative Technical Analysis of HL7 v2, CDA, and the FHIR Resource Framework.Computational and structural biotechnology journal · 2026Review
- Extraction and processing of intensive care chart data from a patient data management system.Frontiers in digital health · 2026Article
- Optimizing Interdisciplinary Referral Pathways for Chronic Obstructive Pulmonary Disease Management Across Cardiology and Pulmonology Specialties in the Kingdom of Saudi Arabia.Journal of clinical medicine · 2025Article
- Perceived risk of gastric cancer associated with long-term use of proton pump inhibitors: Bridging the gap.World journal of clinical oncology · 2025Article
- Internal Validation of a Machine Learning-Based CDSS for Antimicrobial Stewardship.Life (Basel, Switzerland) · 2025Article
- From Ulcer to Amputation: A Systematic Review of Prognostic Models for Diabetic Foot Ulcer Amputation.Risk management and healthcare policy · 2025Review
- Methods of a cluster-randomized, type II hybrid implementation effectiveness trial to prospectively assess extended-duration thromboprophylaxis for at-risk medical patients being discharged to prevent hospital-associated venous thromboembolism.Research and practice in thrombosis and haemostasis · 2024Article
- System-Wide Thromboprophylaxis Interventions for Hospitalized Patients at Risk of Venous Thromboembolism: Focus on Cross-Platform Clinical Decision Support.Journal of clinical medicine · 2024Review
- Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design.JMIR formative research · 2024Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundThrough 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.
objectiveThe 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.
methodsWe 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.
resultsThe 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.
conclusionsThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.