Evidence map›Paper›PMID 25889831›Full record

ArticleImplementation science : IS2015

Learning Evaluation: blending quality improvement and implementation research methods to study healthcare innovations.

Bijal A Balasubramanian, Deborah J Cohen, Melinda M Davis, Rose Gunn, L Miriam Dickinson, William L Miller, Benjamin F Crabtree, Kurt C Stange

Abstract read
In one paragraph

Article in Implementation science : IS, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 3 pooled it
–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

63 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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3 more citing papers are in PubMed but not listed here.

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.

Bijal A BalasubramanianDepartment of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center Houston School of Public Health, Dallas Regional Campus, 5323 Harry Hines Blvd, V8.112, Dallas, TX, 75390, USA. bijal.a.balasubramanian@uth.tmc.edu.
Deborah J CohenDepartment of Family Medicine, Oregon Health & Science University, Portland, OR, USA. cohendj@ohsu.edu.
Melinda M DavisDepartment of Family Medicine, Oregon Health & Science University, Portland, OR, USA. davismel@ohsu.edu.
Rose GunnDepartment of Family Medicine, Oregon Health & Science University, Portland, OR, USA. gunnr@ohsu.edu.
L Miriam DickinsonDepartment of Family Medicine, University of Colorado School of Medicine, Aurora, CO, USA. miriam.dickinson@UCdenver.edu.
William L MillerDepartment of Family Medicine, Lehigh Valley Health Network, Allentown, PA, USA. William.Miller@lvhn.org.
Benjamin F CrabtreeDepartment of Family Medicine and Community Health, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA. crabtrbf@rwjms.rutgers.edu.
Kurt C StangeDepartments of Family Medicine and Community Health, Epidemiology and Biostatistics, and Sociology, Case Western Reserve University, Cleveland, OH, USA. kcs@case.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn healthcare change interventions, on-the-ground learning about the implementation process is often lost because of a primary focus on outcome improvements. This paper describes the Learning Evaluation, a methodological approach that blends quality improvement and implementation research methods to study healthcare innovations.

methodsLearning Evaluation is an approach to multi-organization assessment. Qualitative and quantitative data are collected to conduct real-time assessment of implementation processes while also assessing changes in context, facilitating quality improvement using run charts and audit and feedback, and generating transportable lessons. Five principles are the foundation of this approach: (1) gather data to describe changes made by healthcare organizations and how changes are implemented; (2) collect process and outcome data relevant to healthcare organizations and to the research team; (3) assess multi-level contextual factors that affect implementation, process, outcome, and transportability; (4) assist healthcare organizations in using data for continuous quality improvement; and (5) operationalize common measurement strategies to generate transportable results.

resultsLearning Evaluation principles are applied across organizations by the following: (1) establishing a detailed understanding of the baseline implementation plan; (2) identifying target populations and tracking relevant process measures; (3) collecting and analyzing real-time quantitative and qualitative data on important contextual factors; (4) synthesizing data and emerging findings and sharing with stakeholders on an ongoing basis; and (5) harmonizing and fostering learning from process and outcome data. Application to a multi-site program focused on primary care and behavioral health integration shows the feasibility and utility of Learning Evaluation for generating real-time insights into evolving implementation processes.

conclusionsLearning Evaluation generates systematic and rigorous cross-organizational findings about implementing healthcare innovations while also enhancing organizational capacity and accelerating translation of findings by facilitating continuous learning within individual sites. Researchers evaluating change initiatives and healthcare organizations implementing improvement initiatives may benefit from a Learning Evaluation approach.

Indexed as

Diffusion of InnovationProgram DevelopmentQuality ImprovementHealth ServicesHealth Services ResearchHumansOrganizational InnovationOutcome and Process Assessment, Health Care

Identifiers

PMID25889831
PMCPMC4357215

What OpenQuestion holds

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LicenceCC BY
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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.