Evidence map›Paper›PMID 31324681›Full record

ArticleBMJ open2019

Is it possible to automatically assess pretreatment digital rectal examination documentation using natural language processing? A single-centre retrospective study.

Selen Bozkurt, Kathleen M Kan, Michelle K Ferrari, Daniel L Rubin, Douglas W Blayney, Tina Hernandez-Boussard, James D Brooks

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

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

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

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Leveraging Digital Data to Inform and Improve Quality Cancer Care.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
    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

7 authors.

Selen BozkurtBiomedical Data Science, Stanford University, Stanford, CA, USA.
Kathleen M KanUrology, Stanford Lucile Salter Packard Children's Hospital, Stanford, CA, USA.
Michelle K FerrariUrology, Stanford University, Stanford, CA, USA.
Daniel L RubinBiomedical Data Science, Stanford University, Stanford, CA, USA.
Douglas W BlayneyMedicine (Oncology), Stanford University, Stanford, CA, USA.
Tina Hernandez-BoussardBiomedical Data Science, Stanford University, Stanford, CA, USA.
James D BrooksUrology, Stanford University, Stanford, CA, USA.ORCID 0000-0003-4521-7505

Funding

Utilizing Electronic Health Records to Measure and Improve Prostate Cancer CareR01CA183962 · NCI · STANFORD UNIVERSITY · PI HERNANDEZ-BOUSSARD, TINA · 2015 to 2019
$3.1M
NCI NIH HHS R01 CA183962
6 · The paper itself

Abstract

objectivesTo develop and test a method for automatic assessment of a quality metric, provider-documented pretreatment digital rectal examination (DRE), using the outputs of a natural language processing (NLP) framework.

settingAn electronic health records (EHR)-based prostate cancer data warehouse was used to identify patients and associated clinical notes from 1 January 2005 to 31 December 2017. Using a previously developed natural language processing pipeline, we classified DRE assessment as documented (currently or historically performed), deferred (or suggested as a future examination) and refused. PRIMARY AND SECONDARY OUTCOME MEASURES: We investigated the quality metric performance, documentation 6 months before treatment and identified patient and clinical factors associated with metric performance.

resultsThe cohort included 7215 patients with prostate cancer and 426 227 unique clinical notes associated with pretreatment encounters. DREs of 5958 (82.6%) patients were documented and 1257 (17.4%) of patients did not have a DRE documented in the EHR. A total of 3742 (51.9%) patient DREs were documented within 6 months prior to treatment, meeting the quality metric. Patients with private insurance had a higher rate of DRE 6 months prior to starting treatment as compared with Medicaid-based or Medicare-based payors (77.3%vs69.5%, p=0.001). Patients undergoing chemotherapy, radiation therapy or surgery as the first line of treatment were more likely to have a documented DRE 6 months prior to treatment.

conclusionEHRs contain valuable unstructured information and with NLP, it is feasible to accurately and efficiently identify quality metrics with current documentation clinician workflow.

Indexed as

AlgorithmsNatural Language ProcessingAdultDigital Rectal ExaminationElectronic Health RecordsHumansMaleProstatic NeoplasmsRetrospective Studiesdigital rectal examinationelectronic health recordsnatural language processingprostate cancerquality metrics

Identifiers

PMID31324681
PMCPMC6661600

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