Evidence map›Paper›PMID 29873794›Full record

ArticleInternational journal for quality in health care : journal of the International Society for Quality in Health Care2018

A framework for learning about improvement: embedded implementation and evaluation design to optimize learning.

Danika Barry, Leighann E Kimble, Bejoy Nambiar, Gareth Parry, Ashish Jha, Vijay Kumar Chattu, M Rashad Massoud, Don Goldmann

Abstract read
In one paragraph

Article in International journal for quality in health care : journal of the International Society for Quality in Health Care, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Methodological considerations for evaluating scale-up programmes in healthcare: a methods review.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2026
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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.

Danika BarryHarvard Medical School, 25 Shattuck St, Boston, MA 02115, USA.
Leighann E KimbleUSAID Applying Science to Strengthen and Improve Systems (ASSIST) Project, Quality & Performance Institute, University Research Co., LLC, 5404 Wisconsin Ave, Suite 800, Chevy Chase, MD 20815, USA.
Bejoy NambiarInstitute for Global Health, UCL, 30, Guilford Street, London WC1N 1EH, UK.
Gareth ParryInstitute for Healthcare Improvement, Cambridge, 53 State Street, 19th Floor, Boston, MA 02109, USA.
Ashish JhaHarvard T.H. Chan School of Public Health, 158 Longwood Ave, Boston, MA 02115-5810, USA.
Vijay Kumar ChattuPublic Health and Primary Care Unit, Faculty of Medical Sciences, The University of the West Indies, Mona, Kingston 7, Jamaica.
M Rashad MassoudUSAID Applying Science to Strengthen and Improve Systems (ASSIST) Project, Quality & Performance Institute, University Research Co., LLC, 5404 Wisconsin Ave, Suite 800, Chevy Chase, MD 20815, USA.
Don GoldmannInstitute for Healthcare Improvement, Cambridge, 53 State Street, 19th Floor, Boston, MA 02109, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Improving health care involves many actors, often working in complex adaptive systems. Interventions tend to be multi-factorial, implementation activities diverse, and contexts dynamic and complicated. This makes improvement initiatives challenging to describe and evaluate as matching evaluation and program designs can be difficult, requiring collaboration, trust and transparency. Collaboration is required to address important epidemiological principles of bias and confounding. If this does not take place, results may lack credibility because the association between interventions implemented and outcomes achieved is obscure and attribution uncertain. Moreover, lack of clarity about what was implemented, how it was implemented, and the context in which it was implemented often lead to disappointment or outright failure of spread and scale-up efforts. The input of skilled evaluators into the design and conduct of improvement initiatives can be helpful in mitigating these potential problems. While evaluation must be rigorous, if it is too rigid necessary adaptation and learning may be compromised. This article provides a framework and guidance on how improvers and evaluators can work together to design, implement and learn about improvement interventions more effectively.

Indexed as

HumansLearningModels, OrganizationalProgram DevelopmentProgram EvaluationQuality Assurance, Health CareQuality Improvement

Identifiers

PMID29873794
PMCPMC5909667

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.