Automatic population of eMeasurements from EHR systems for inpatient falls. (6th April 2018)

• Record Type: Journal Article
• Authors: Cho, Insook; Boo, Eun-Hee; Lee, Soo-Youn; Dykes, Patricia C
• Is Part Of: Journal of the American Medical Informatics Association. Volume 25:Number 6(2018)
• Page Start: 730
• Page End: 738
• Publication Date: 2018-04-06
• Subjects: 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/ocy018
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Representing nursing data sets in a standard way will help to facilitate sharing relevant information across settings. We aimed to populate nursing process and outcome metrics with electronic health record (EHR) data and then compare the results with event reporting systems. Methods: We used the "eMeasure" development process of the National Quality Forum adopted by the American Nurses Association. We used operational definitions of quality measures from the American Nurses Association and the US Institute for Healthcare Improvement and employed concept mapping of local data elements to 2 controlled vocabularies to define a standard data dictionary: (1) Logical Observation Identifiers Names and Codes and (2) International Classification for Nursing Practice. We assessed feasibility using the nursing data set of 7829 and 8199 patients from 2 general hospitals with different EHR systems. Using inpatient falls as a use case, we compared the populated measures with results from the event reporting systems. Results: We identified 17 care components and 118 unique concepts and matched them with data elements in the EHRs. Including suboptimal mapping, 98% of the assessment concepts mapped to Logical Observation Identifiers Names and Codes and 52.9% of intervention concepts mapped to International Classification for Nursing Practice. While not all process indicators were available from event reporting systems, we successfully populated 9 fall prevention process indicators and the fall rate outcome indicator from the 2 EHRs. We were unable to populate the falls with an injury rate indicator. Conclusions: EHR data can populate fall prevention process measure metrics and at least one inpatient fall prevention outcome metric.