Evidence map›Paper›PMID 30201667›Full record

ArticleJournal of the American Board of Family Medicine : JABFM

Evaluation of an Electronic Health Record (EHR) Tool for Integrated Behavioral Health in Primary Care.

Katelyn K Jetelina, Tanisha Tate Woodson, Rose Gunn, Brianna Muller, Khaya D Clark, Jennifer E DeVoe, Bijal A Balasubramanian, Deborah J Cohen

Abstract readEvaluation Study
In one paragraph

Article in Journal of the American Board of Family Medicine : JABFM. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Katelyn K JetelinaFrom Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (KKJ, BAB); Department of Family Medicine, Oregon Health & Science University, Portland, OR (TTW, RG, BM, KDC, JED, DJC); OCHIN, Inc., Portland (JED). katelyn.k.jetelina@uth.tmc.edu.
Tanisha Tate WoodsonFrom Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (KKJ, BAB); Department of Family Medicine, Oregon Health & Science University, Portland, OR (TTW, RG, BM, KDC, JED, DJC); OCHIN, Inc., Portland (JED).
Rose GunnFrom Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (KKJ, BAB); Department of Family Medicine, Oregon Health & Science University, Portland, OR (TTW, RG, BM, KDC, JED, DJC); OCHIN, Inc., Portland (JED).
Brianna MullerFrom Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (KKJ, BAB); Department of Family Medicine, Oregon Health & Science University, Portland, OR (TTW, RG, BM, KDC, JED, DJC); OCHIN, Inc., Portland (JED).
Khaya D ClarkFrom Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (KKJ, BAB); Department of Family Medicine, Oregon Health & Science University, Portland, OR (TTW, RG, BM, KDC, JED, DJC); OCHIN, Inc., Portland (JED).
Jennifer E DeVoeFrom Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (KKJ, BAB); Department of Family Medicine, Oregon Health & Science University, Portland, OR (TTW, RG, BM, KDC, JED, DJC); OCHIN, Inc., Portland (JED).
Bijal A BalasubramanianFrom Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (KKJ, BAB); Department of Family Medicine, Oregon Health & Science University, Portland, OR (TTW, RG, BM, KDC, JED, DJC); OCHIN, Inc., Portland (JED).
Deborah J CohenFrom Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health in Dallas, Dallas, TX (KKJ, BAB); Department of Family Medicine, Oregon Health & Science University, Portland, OR (TTW, RG, BM, KDC, JED, DJC); OCHIN, Inc., Portland (JED).

Funding

Turning EHRs into Assets for Mental Health and Uniting Practice (Team Up)R34MH100371 · NIMH · OREGON HEALTH & SCIENCE UNIVERSITY · PI COHEN, DEBORAH JILL · 2013 to 2015
$701k
NIMH NIH HHS R34 MH100371
6 · The paper itself

Abstract

backgroundIntegrating behavioral health into primary care can improve care quality; however, most electronic health records are not designed to meet the needs of integrated teams. We worked with practices and behavioral health (BH) clinicians to design a suite of electronic health record tools to address these needs ("BH e-Suite"). The purpose of this article is to examine whether implementation of the BH e-Suite changes process of care, intermediate clinical outcomes, and patient experiences, and whether its use is acceptable to practice members and BH clinicians.

methodsWe conducted a convergent mixed-methods proof-of-concept study, implementing the BH e-Suite across 6 Oregon federally qualified community health centers ("intervention clinics"). We matched intervention clinics to 6 control clinics, based on location and patient panel characteristics, to assess whether process of care (Patient Health Questionnaire-9 [PHQ-9] and Generalized Anxiety Disorder-7 screening) and intermediate outcomes (PHQ-9, Generalized Anxiety Disorder-7 scores) changed postimplementation. Prepost patient surveys were used to assess changes in patient experience. To elucidate factors influencing implementation, we merged quantitative findings with structured observations, surveys, and interviews with practice members.

resultsImplementation improved process of care (PHQ-9 screening). During the course of the study, change in intermediate outcomes was not observed. Degree of BH e-Suite implementation varied: 2 clinics fully implemented, 2 partially implemented, and 2 practices did not implement at all. Initial practice conditions (eg, low resistance to change, higher capacity), process characteristics (eg, thoughtful planning), and individual characteristics (eg, high self-efficacy) were related to degree of implementation.

conclusionsHealth information technology tools designed for behavioral health integration must fit the needs of clinics for the successful uptake and improvement in patient experiences. Research is needed to further assess the effectiveness of this tool in improving patient outcomes and to optimize broader dissemination of this tool among primary care clinics integrating behavioral health.

Indexed as

Electronic Health RecordsMental Health ServicesPrimary Health CareAdultFemaleHealth Plan ImplementationHumansMaleMiddle AgedPatient SatisfactionProof of Concept StudyCommunity Health CentersElectronic Health RecordsOregonPatient Health QuestionnairePrimary Health CareProof of Concept StudySurveys and Questionnaires

Identifiers

PMID30201667
PMCPMC6261664

What OpenQuestion holds

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Registered trials

None linked

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.