Improving outpatient safety through effective electronic communication: a study protocol

Implement Sci. 2009 Sep 25:4:62. doi: 10.1186/1748-5908-4-62.

Abstract

Background: Health information technology and electronic medical records (EMRs) are potentially powerful systems-based interventions to facilitate diagnosis and treatment because they ensure the delivery of key new findings and other health related information to the practitioner. However, effective communication involves more than just information transfer; despite a state of the art EMR system, communication breakdowns can still occur. [1-3] In this project, we will adapt a model developed by the Systems Engineering Initiative for Patient Safety (SEIPS) to understand and improve the relationship between work systems and processes of care involved with electronic communication in EMRs. We plan to study three communication activities in the Veterans Health Administration's (VA) EMR: electronic communication of abnormal imaging and laboratory test results via automated notifications (i.e., alerts); electronic referral requests; and provider-to-pharmacy communication via computerized provider order entry (CPOE).

Aim: Our specific aim is to propose a protocol to evaluate the systems and processes affecting outcomes of electronic communication in the computerized patient record system (related to diagnostic test results, electronic referral requests, and CPOE prescriptions) using a human factors engineering approach, and hence guide the development of interventions for work system redesign.

Design: This research will consist of multiple qualitative methods of task analysis to identify potential sources of error related to diagnostic test result alerts, electronic referral requests, and CPOE; this will be followed by a series of focus groups to identify barriers, facilitators, and suggestions for improving the electronic communication system. Transcripts from all task analyses and focus groups will be analyzed using methods adapted from grounded theory and content analysis.