Evidence map›Paper›PMID 31497617›Full record

ArticleEGEMS (Washington, DC)2019

Beyond CHNAS: Performance Measurement for Community Health Improvement.

Michael A Stoto, Mary V Davis, Abby Atkins

Abstract read
In one paragraph

Article in EGEMS (Washington, DC), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Value-based focused global population health management.Journal of gastrointestinal oncology · 2021
    Review
  5. OPTIK: a database for understanding catchment areas to guide mobilization of cancer center assets.Database : the journal of biological databases and curation · 2020
    Article
  6. Article
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

3 authors.

Michael A StotoGeorgetown University, US.
Mary V DavisHealth Resources in Action, US.
Abby AtkinsHealth Resources in Action, US.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RESEARCH

objectiveNon-profit hospitals are required to work with community organizations to prepare Community Health Needs Assessment (CHNA) and implementation strategy (IS). In concert with the health care delivery system's transformation from volume to value and efforts to enhance multi-sector collaboration, such community health improvement (CHI) processes have the potential to bridge efforts of the health care delivery sector, public health agencies, and community organizations to improve population health. Having a shared measurement system is critical to achieving collective impact, yet despite the availability of community-level data from a variety of sources, many CHI processes lack clear, measurable objectives and evaluation plans. Through an in-depth analysis of ten exemplary CHI processes, we sought to identify best practices for population health measurement with a focus on monitoring collaborative implementation strategies. STUDY

designBased on a review of the scientific literature, professional publications and presentations, and nominations from a national advisory panel, we identified 10 exemplary CHI processes. Criteria of choice were whether (1) the CHIs articulate a clear definition of intended outcomes; (2) clear, focused, measurable objectives and expected outcomes, including health equity; (3) expected outcomes are realistic and addressed with specific action plans; and (4) whether the plans and their associated performance measures become fully integrated into agencies and become a way of being for the agencies. We then conducted an in-depth analysis of CHNA, IS, and related documents created by health departments and leading hospitals in each process. POPULATION STUDIED: U.S. hospitals. PRINCIPAL

findingsCommunity health improvement processes benefit from a shared measurement system that indicate accountability for specific activities. Despite the importance of measurement and evaluation, existing community health improvement efforts often fall short in these areas. There is more variability in format and content of ISs than CHNAs; the most developed models include population-level goals/objectives and strategies with clear accountability and metrics. Other hospital IS's are less developed.Although all U.S. hospitals are familiar with performance measurement in their management, this familiarity does not seem to carry over to Community Benefit and CHNA efforts. Indeed, 5 of the 10 CHI processes we examined have some Accountable Care Organization (ACO) involvement, where population-health performance measures are commonplace. Yet this involvement is not mentioned in the CHNAs and ISs, nor are ACO data cited.

conclusionsStrengthening the CHNA regulations to require that hospitals report the evaluation measures they intend to monitor based on an established community health improvement model could help communities demonstrate impact. As in other areas of health care, performance measures should be tailored to implementation strategy, with clear indication of accountability, and move from outputs to process and outcome measures with established validity and reliability. IMPLICATIONS FOR POLICY OR PRACTICE: Although performance measurement is now commonplace throughout the health care system, the individuals who manage CHI processes may not be that familiar with this approach. This suggests that it is important to develop practitioners' knowledge and skills needed to use it population health data effectively.

Indexed as

Community health improvement processCommunity Health Needs Assessment (CHNA)Performance measurementPopulation health dataShared measurement systems

Identifiers

PMID31497617
PMCPMC6706995

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.