Behind-the-Scenes of Patient-Centered Care

BACKGROUND: Transitions to patient-centered health care, the increasing complexity of care, and growth in self-management have all increased the frequency and intensity of clinical services provided outside office settings and between visits. Understanding how electronic messaging, which is often used to coordinate care, affects care is crucial. A taxonomy for codifying clinical text messages into standardized categories could facilitate content analysis of work performed or enhanced via electronic messaging. OBJECTIVE: To codify electronic messages exchanged among the primary care providers and the staff managing diabetes patients at an academic medical center. RESEARCH DESIGN: Retrospective analysis of 27,061 electronic messages exchanged among 578 providers and staff caring for a cohort of 639 adult primary care patients with diabetes between April 1, 2003 and October 31, 2003. SUBJECTS: Providers and staff using locally developed electronic messaging in an academic medical center's adult primary care clinic. MEASURES: Raw data included clinical text message content, message ID, thread ID, and user ID. Derived measures included user job classification, 35 flags codifying message content, and a taxonomy grouping the flags. RESULTS: Messages contained diverse content: communications with patients, families, and other providers (47.2%), diagnoses (25.4%), documentation (33%), logistics and support functions (29.6%), medications (32.9%), and treatments (28.9%). All messages could be classified; 59.5% of messages addressed 2 or more content areas. CONCLUSIONS: Systematic content analysis of provider and staff electronic messages yields specific insight regarding clinical and administrative work carried out via electronic messaging

Content analysis · Documentation · Family medicine · Health care · Medical emergency · Medical record · Computer Science · Diabetes Management and Education · Electronic Health Records Systems · Healthcare Systems and Technology · Medicine · Nursing