Evidence map›Paper›PMID 35560153›Full record

ArticlePloS one2022

Using electronic health records to streamline provider recruitment for implementation science studies.

Chiamaka L Okorie, Elise Gatsby, Florian R Schroeck, A Aziz Ould Ismail, Kristine E Lynch

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Robust privacy amidst innovation with large language models through a critical assessment of the risks.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  2. Article
  3. 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

5 authors.

Chiamaka L OkorieFrom Geisel School of Medicine at Dartmouth College, Lebanon, NH, United States of America.ORCID 0000-0003-2233-4557
Elise GatsbyVA Salt Lake City Health Care System and University of Utah, Salt Lake City, UT, United States of America.
Florian R SchroeckFrom Geisel School of Medicine at Dartmouth College, Lebanon, NH, United States of America.
A Aziz Ould IsmailWhite River Junction VA Medical Center, White River Junction, VT, United States of America.ORCID 0000-0001-8775-5913
Kristine E LynchVA Salt Lake City Health Care System and University of Utah, Salt Lake City, UT, United States of America.

Funding

Implementing Risk-aligned Bladder Cancer SurveillanceI01HX002780 · VA · WHITE RIVER JUNCTION VA MEDICAL CENTER · PI SCHROECK, FLORIAN R · 2020 to 2023
–
HSRD VA I01 HX002780
6 · The paper itself

Abstract

backgroundHealthcare providers are often targeted as research participants, especially for implementation science studies evaluating provider- or system-level issues. Frequently, provider eligibility is based on both provider and patient factors. Manual chart review and self-report are common provider screening strategies but require substantial time, effort, and resources. The automated use of electronic health record (EHR) data may streamline provider identification for implementation science research. Here, we describe an approach to provider screening for a Veterans Health Administration (VHA)-funded study focused on implementing risk-aligned surveillance for bladder cancer patients.

methodsOur goal was to identify providers at 6 pre-specified facilities who performed ≥10 surveillance cystoscopy procedures among bladder cancer patients in the 12 months prior to recruitment start on January 16, 2020, and who were currently practicing at 1 of 6 pre-specified facilities. Using VHA EHR data (using CPT, ICD10 procedure, and ICD10 diagnosis codes), we identified cystoscopy procedures performed after an initial bladder cancer diagnosis (i.e., surveillance procedures). Procedures were linked to VHA staff data to determine the provider of record, the number of cystoscopies they performed, and their current location of practice. To validate this approach, we performed a chart review of 105 procedures performed by a random sample of identified providers. The proportion of correctly identified procedures was calculated (Positive Predictive Value (PPV)), along with binomial 95% confidence intervals (CI).

findingsWe identified 1,917,856 cystoscopies performed on 703,324 patients from October 1, 1999-January 16, 2020, across the nationwide VHA. Of those procedures, 40% were done on patients who had a prior record of bladder cancer and were completed by 15,065 distinct providers. Of those, 61 performed ≥ 10 procedures and were currently practicing at 1 of the 6 facilities of interest in the 1 year prior to study recruitment. The random chart review of 7 providers found 101 of 105 procedures (PPV: 96%; 95% CI: 91% to 99%) were surveillance procedures and were performed by the selected provider on the recorded date. IMPLICATIONS: These results show that EHR data can be used for accurate identification of healthcare providers as research participants when inclusion criteria consist of both patient- (temporal relationship between diagnosis and procedure) and provider-level (frequency of procedure and location of current practice) factors. As administrative codes and provider identifiers are collected in most, if not all, EHRs for billing purposes this approach can be translated from provider recruitment in VHA to other healthcare systems. Implementation studies should consider this method of screening providers.

Indexed as

Electronic Health RecordsUrinary Bladder NeoplasmsCystoscopyHealth PersonnelHumansImplementation Science

Identifiers

PMID35560153
PMCPMC9106149

What OpenQuestion holds

Textmetadata
LicenceCC0
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.