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

Publication Type:

Journal Article

Source:

BMJ Open, Volume 9, Number 7, p.e027182 (2019)

ISBN:

2044-6055 (Electronic)<br/>2044-6055 (Linking)

Accession Number:

31324681

Keywords:

digital rectal examination, electronic health records, Natural language processing, prostate cancer, quality metrics

Abstract:

OBJECTIVES: To 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. SETTING: An 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. RESULTS: The 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. CONCLUSION: EHRs contain valuable unstructured information and with NLP, it is feasible to accurately and efficiently identify quality metrics with current documentation clinician workflow.