Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification. (1st September 2016)

• Record Type: Journal Article
• Authors: Kricke, Gayle Shier; Carson, Matthew B; Lee, Young Ji; Benacka, Corrine; Mutharasan, R. Kannan; Ahmad, Faraz S; Kansal, Preeti; Yancy, Clyde W; Anderson, Allen S; Soulakis, Nicholas D
• Is Part Of: Journal of the American Medical Informatics Association. Volume 24:Number 2(2017:Mar.)
• Page Start: 288
• Page End: 294
• Publication Date: 2016-09-01
• Subjects: electronic health record -- risk assessment -- workflow -- cardiology hospital service -- discharge planning; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocw083
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract : Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning. Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation. Results: EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation. Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.