Evidence map›Paper›PMID 24610308›Full record

ArticleJournal of general internal medicine2014

Exploration of an automated approach for receiving patient feedback after outpatient acute care visits.

Eta S Berner, Midge N Ray, Anantachai Panjamapirom, Richard S Maisiak, James H Willig, Thomas M English, Marc Krawitz, Christa R Nevin, Shannon Houser, Mark P Cohen and 1 more

Abstract readMulticenter Study
In one paragraph

Article in Journal of general internal medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Eta S BernerUniversity of Alabama at Birmingham [UAB], Birmingham, AL, USA, eberner@uab.edu.
Midge N Ray
Anantachai Panjamapirom
Richard S Maisiak
James H Willig
Thomas M English
Marc Krawitz
Christa R Nevin
Shannon Houser
Mark P Cohen
Gordon D Schiff

Funding

Virology CoreP30AI027767 · NIAID · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Paul A. Goepfert · 1988 to 2026
$84.1M
Closing the Feedback Loop to Improve Diagnostic QualityR18HS017060 · AHRQ · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI BERNER, ETA · 2007 to 2008
$967k
AHRQ HHS R18 HS017060NIAID NIH HHS P30 AI027767
6 · The paper itself

Abstract

backgroundTo 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.

objectiveTo provide post-visit feedback to physicians on patient outcomes following acute care visits.

designA 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. PATIENTS: A total of 616 patients were included: 142 from Phase 1, 352 from Phase 2 and 122 from Phase 3. MAIN MEASURES: Primary outcomes included: problem resolution, provider satisfaction with the system, and comparison of IVRS with live calls made by research staff. KEY

resultsDuring 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.

conclusionsMany 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.

Indexed as

Patient PreferenceSpeech Recognition SoftwareTelephoneAdultAgedAged, 80 and overAmbulatory CareCohort StudiesContinuity of Patient CareCross-Sectional StudiesEmergency Medical ServicesFeedback, PsychologicalFemaleFollow-Up StudiesHumansMale

Identifiers

PMID24610308
PMCPMC4099452

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.