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Submission Type

Empirical Research

Keywords

Electronic health records; quality improvement; patient-centered care; data mining; health services research

Streaming Media

Abstract

Introduction: National initiatives to develop quality metrics emphasize the need to include patient-centered outcomes. Patient-centered outcomes are complex, require documentation of patient communications, and have not been routinely collected by healthcare providers. The widespread implementation of electronic medical records (EHR) offers opportunities to assess patient-centered outcomes within the routine healthcare delivery system. The objective of this study was to test the feasibility and accuracy of identifying patient centered outcomes within the EHR.

Methods: Data from patients with localized prostate cancer undergoing prostatectomy were used to develop and test algorithms to accurately identify patient-centered outcomes in post-operative EHRs – we used urinary incontinence as the use case. Standard data mining techniques were used to extract and annotate free text and structured data to assess urinary incontinence recorded within the EHRs.

Results. A total 5,349 prostate cancer patients were identified in our EHR-system between 1998-2013. Among these EHRs, 30.3% had a text mention of urinary incontinence within 90 days post-operative compared to less than 1.0% with a structured data field for urinary incontinence (i.e. ICD-9 code). Our workflow had good precision and recall for urinary incontinence (positive predictive value: 0.73 and sensitivity: 0.84).

Discussion. Our data indicate that important patient-centered outcomes, such as urinary incontinence, are being captured in EHRs as free text and highlight the long-standing importance of accurate clinician documentation. Standard data mining algorithms can accurately and efficiently identify these outcomes in existing EHRs; the complete assessment of these outcomes is essential to move practice into the patient-centered realm of healthcare.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

DOI

10.13063/2327-9214.1231

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