Understanding data provenance when using electronic medical records for research: Lessons learned from the Deliver Primary Healthcare Information (DELPHI) database.

Jason Edward Black, Amanda L Terry, Sonny Cejic, Tom Freeman, Dan Lizotte, Scott McKay, Mark Speechley, Bridget Ryan

Abstract read

In one paragraph

Article in International journal of population data science, 2023. 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.

Temporal Variability in Diagnosis Code Distributions Across Extraction Time Points in a Multicenter Integrated EHR Database: A Snapshot Comparison Study.Journal of medical systems · 2026
Observational

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.

Jason Edward BlackDepartment of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.

Amanda L TerryDepartment of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.

Sonny CejicDepartment of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.

Tom FreemanDepartment of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.

Dan LizotteDepartment of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.

Scott McKayDepartment of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.

Mark SpeechleyDepartment of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.

Bridget RyanDepartment of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: We set out to assess the impact of Choosing Wisely Canada recommendations (2014) on reducing unnecessary health investigations and interventions in primary care across Southwestern Ontario. Methods: We used the Deliver Primary Healthcare Information (DELPHI) database, which stores deidentified electronic medical records (EMR) of nearly 65,000 primary care patients across Southwestern Ontario. When conducting research using EMR data, data provenance (i.e., how the data came to be) should first be established. We first considered DELPHI data provenance in relation to longitudinal analyses, flagging a change in EMR software that occurred during 2012 and 2013. We attempted to link records between EMR databases produced by different software using probabilistic linkage and inspected 10 years of data in the DELPHI database (2009 to 2019) for data quality issues, including comparability over time. Results: We encountered several issues resulting from this change in EMR software. These included limited linkage of records between software without a common identifier; data migration issues that distorted procedure dates; and unusual changes in laboratory test and medication prescription volumes. Conclusion: This study reinforces the necessity of assessing data provenance and quality for new research projects. By understanding data provenance, we can anticipate related data quality issues such as changes in EMR data over time-which represent a growing concern as longitudinal data analyses increase in feasibility and popularity.

Indexed as

Electronic Health RecordsPrimary Health CareData AccuracyHumansOntarioSoftwareChoosing Wisely Canadadata linkagedata provenancedata qualityDELPHIelectronic medical recordsinterrupted time serieslongitudinal analysis

Identifiers

PMID38425492

DOI10.23889/ijpds.v8i5.2177

PMCPMC10900298

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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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.