Evidence map›Paper›PMID 33644408›Full record

ArticleInternational journal of population data science2020

Development of comparable algorithms to measure primary care indicators using administrative health data across three Canadian provinces.

M W Alsabbagh, J K Kueper, S T Wong, F Burge, S Johnston, S Peterson, B Lawson, H Chung, M Bennett, S Blackman and 4 more

Abstract read
In one paragraph

Article in International journal of population data science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Primary care usage at the end of life: a retrospective cohort study of cancer patients using linked primary and hospital care data.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024
    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

14 authors.

M W AlsabbaghUniversity of Waterloo.
J K KueperThe University of Western Ontario.
S T WongUniversity of British Columbia.
F BurgeDalhousie University.
S JohnstonBruyère Research Institute, University of Ottawa.
S PetersonUniversity of British Columbia.
B LawsonDalhousie University.
H ChungICES.
M BennettDalhousie University.
S BlackmanDalhousie University.
K McGrailUniversity of British Columbia.
J CampbellUniversity of Exeter.
W HoggUniversity of Ottawa, Montfort Hospital Research Institute.
R GlazierICES.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPerformance measurement has been recognized as key to transforming primary care (PC). Yet, performance reporting in PC lags behind even though high-performing PC is foundational to an effective and efficient health care system.

objectivesWe used administrative data from three Canadian provinces, British Columbia, Ontario and Nova Scotia, to: 1) identify and develop a core set of PC performance indicators using administrative data and 2) examine their ability to capture PC performance.

methodsAdministrative data used included Physician Billings, Discharge Abstract Database, the National Ambulatory Care and Reporting System database, Census and Vital Statistics. Indicators were compiled based on a literature review of PC indicators previously developed with administrative data available in Canada (n=158). We engaged in iterative discussions to assess data conformity, completeness, and plausibility of results in all jurisdictions. Challenges to creating comparable algorithms were examined through content analysis and research team discussions, which included clinicians, analysts, and health services researchers familiar with PC.

resultsOur final list included 21 PC performance indicators pertaining to 1) technical care (n=4), 2) continuity of care (n=6), and 3) health services utilization (n=11). Establishing comparable algorithms across provinces was possible though time intensive. A major challenge was inconsistent data elements. Ease of data access, and a deep understanding of the data and practice context, was essential for selecting the most appropriate data elements.

conclusionsThis project is unique in creating algorithms to measure PC performance across provinces. It was essential to balance internal validity of the indicators within a province and external validity across provinces. The intuitive desire of having the exact same coding across provinces was infeasible due to lack of standardized PC data. Rather, a context-tailored definition was developed for each jurisdiction. This work serves as an example for developing comparable PC performance indicators across different provincial/territorial jurisdictions.

Identifiers

PMID33644408
PMCPMC7893851

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.