title: Exploration of an automated approach for receiving patient feedback after outpatient acute care visits.
abstract: BACKGROUND
  To improve and learn from patient outcomes, particularly under new care models such as Accountable Care Organizations and Patient-Centered Medical Homes, requires establishing systems for follow-up and feedback.
  
  
  OBJECTIVE
  To provide post-visit feedback to physicians on patient outcomes following acute care visits.
  
  
  METHODS
  A three-phase cross-sectional study [live follow-up call three weeks after acute care visits (baseline), one week post-visit live call, and one week post-visit interactive voice response system (IVRS) call] with three patient cohorts was conducted. A family medicine clinic and an HIV clinic participated in all three phases, and a cerebral palsy clinic participated in the first two phases. Patients answered questions about symptom improvement, medication problems, and interactions with the healthcare system.
  
  
  METHODS
  A total of 616 patients were included: 142 from Phase 1, 352 from Phase 2 and 122 from Phase 3.
  
  
  METHODS
  Primary outcomes included: problem resolution, provider satisfaction with the system, and comparison of IVRS with live calls made by research staff.
  
  
  RESULTS
  During both live follow-up phases, at least 96% of patients who were reached completed the call compared to only 48% for the IVRS phase. At baseline, 98 of 113 (88%) patients reported improvement, as well as 167 of 196 (85%) in the live one-week follow-up. In the one-week IVRS phase, 25 of 39 (64%) reported improvement. In all phases, the majority of patients in both the improved and unimproved groups had not contacted their provider or another provider. While 63% of providers stated they wanted to receive patient feedback, they varied in the extent to which they used the feedback reports.
  
  
  CONCLUSIONS
  Many patients who do not improve as expected do not take action to further address unresolved problems. Systematic follow-up/feedback mechanisms can potentially identify and connect such patients to needed care.
authors.affiliation: University of Alabama at Birmingham [UAB], Birmingham, AL, USA, eberner@uab.edu.
authors.forename: Eta S
authors.initials: ES
authors.lastname: Berner
authors.forename: Midge N
authors.initials: MN
authors.lastname: Ray
authors.forename: Anantachai
authors.initials: A
authors.lastname: Panjamapirom
authors.forename: Richard S
authors.initials: RS
authors.lastname: Maisiak
authors.forename: James H
authors.initials: JH
authors.lastname: Willig
authors.forename: Thomas M
authors.initials: TM
authors.lastname: English
authors.forename: Marc
authors.initials: M
authors.lastname: Krawitz
authors.forename: Christa R
authors.initials: CR
authors.lastname: Nevin
authors.forename: Shannon
authors.initials: S
authors.lastname: Houser
authors.forename: Mark P
authors.initials: MP
authors.lastname: Cohen
authors.forename: Gordon D
authors.initials: GD
authors.lastname: Schiff
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000553:Ambulatory Care / Q000379:methods / Q000639:trends*; D015331:Cohort Studies; D003266:Continuity of Patient Care / Q000639:trends*; D003430:Cross-Sectional Studies; D004632:Emergency Medical Services / Q000379:methods / Q000639:trends*; D030141:Feedback, Psychological; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D057240:Patient Preference; D057566:Self Report / Q000592:standards; D049250:Speech Recognition Software / Q000639:trends; D013689:Telephone / Q000639:trends