Evidence map›Paper›PMID 37181490›Full record

ArticleInternational journal of population data science2022

How to analyze and link patient experience surveys with administrative data to drive health service improvement - examples from Alberta, Canada.

Kyle A Kemp, Paul Fairie, Brian J Steele, Maria J Santana

Abstract read
In one paragraph

Article in International journal of population data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Kyle A KempDepartment of Community Health Sciences, University of Calgary.
Paul FairiePatient Engagement Platform, Alberta Strategy for Patient Oriented Research (SPOR).
Brian J SteelePatient Engagement Platform, Alberta Strategy for Patient Oriented Research (SPOR).
Maria J SantanaDepartment of Community Health Sciences, University of Calgary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability of hospitals and health systems to learn from those who use its services (i.e., patients and families) is crucial for quality improvement and the delivery of high-quality patient-centered care. To this end, many hospitals and health systems regularly collect survey data from patients and their families, and are engaged in activities to publicly report the results. Despite this, there has been limited research into the experiences of patients and families, and how to improve them. Since 2015, our research team has conducted a variety of studies which have explored patient experience survey data, in isolation, and in linkages with routinely-captured administrative data sets across Alberta; a Canadian province of 4.4 million residents. Via secondary analyses, these studies have shed light upon the drivers of inpatient experience, the specific aspects of care which are most correlated with one's overall experiences, and the association of elements of the patient experience with other measures, such as patient safety indicators and unplanned hospital readmissions. The aim of this paper is to provide an overview of the methods we have used, including further details about the data sets and linkage protocol. The main findings from these papers have been presented for readers and those who wish to conduct their own work in this area.

Indexed as

Health ServicesHospitalsAlbertaHumansPatient Outcome AssessmentSurveys and Questionnairesadministrative datadata linkagepatient experience surveyquality improvement

Identifiers

PMID37181490
PMCPMC10170474

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.