Implementation of the HEART Pathway: Using the Consolidated Framework for Implementation Research

Abstract

Objective The HEART Pathway is an evidence-based decision tool for identifying emergency department (ED) patients with acute chest pain who are candidates for early discharge, to reduce unhelpful and potentially harmful hospitalizations. Guided by the Consolidated Framework for Implementation Research (CFIR), we sought to identify important barriers and facilitators to implementation of the HEART Pathway. Study Setting Data were collected at 4 academic medical centers. Study Design We conducted semi-structured interviews with 25 key stakeholders (e.g., health system leaders, ED physicians). We conducted interviews before implementation of the HEART Pathway tool to identify potential barriers and facilitators to successful adoption at other regional academic medical centers. We also conducted post-implementation interviews at one medical center, to understand factors that contributed to successful adoption. Data Collection Interviews were recorded and transcribed verbatim. We used a CFIR framework-driven deductive approach for coding and analysis. Principal Findings Potential barriers to implementation include time and resource burden, challenges specific to the electronic health record (EHR), sustained communication with and engagement of stakeholders, and patient concerns. Facilitators to implementation include strength of evidence for reduced length of stay and unnecessary testing and iatrogenic complications, ease of use, and supportive provider climate for evidence-based decision tools. Conclusions Successful dissemination of the HEART Pathway will require addressing institution-specific barriers, which includes engaging clinical and financial stakeholders. New SMART-FHIR technologies, compatible with many EHR systems, can overcome barriers to health systems with limited information technology resources.

Implementing an electronic health record dashboard for safe anticoagulant management: learning from qualitative interviews with existing and potential users to develop an implementation process

“…This shows how much the timing of an assessment is an important consideration for comparing experienced to novice users or sites in any implementation project. Concerns about IT programmer workload and prioritization for IT implementation have been identified as important barriers in similar work across other clinical domains [ 25 ]. While decentralized technology-associated solutions (e.g., SMART on FHIR application programming interface) have been proposed to reduce the programmer burden at individual sites, these tools are not yet widely implemented at many health centers.…”

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

“…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]).…”

Reduced admission rates and resource utilization for chest pain patients using an electronic health record‐embedded clinical pathway in the emergency department

“…6 Thus, while the decision logic in these evidence-based guidelines has been validated, 3 the optimal integration of guidelines into the electronic medical record warrants further study, particularly as it relates to guideline implementation and clinical outcomes. 7 Computerized clinical decision support systems have been promoted as an effective mechanism for improving adherence to evidencebased guidelines, closing the evidence-to-practice gap. To that end, the 8 Yet, studies of these systems have yielded mixed results and adherence remains low.…”

“…Similarly, retrospective studies have not used computerized clinical decision support systems 4,5 or integration of evidence‐based decision logic into the electronic health record (EHR) 6 . Thus, while the decision logic in these evidence‐based guidelines has been validated, 3 the optimal integration of guidelines into the electronic medical record warrants further study, particularly as it relates to guideline implementation and clinical outcomes 7 …”