New Paradigms for Patient-Centered Outcomes Research in Electronic Medical Records: An example of detecting urinary incontinence following prostatectomy

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.ResultsA 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.

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

“…Automated, semiautomated, and accurate identification of cancer cases will be particularly helpful in studying underrepresented patient populations and rare cancers. In addition, cNLP can facilitate analysis of unstructured data that are poorly documented in databases but widely accepted to be critical for prognostication and management decision-making, most notably patient-reported outcomes (91). Our hope is that larger, more accurate, and granular clinical databases can be integrated with -omics databases to enable translational research to better understand oncologic phenotype relationships.…”

Leveraging the electronic health record to improve quality and safety in rheumatology

“…[27,28,29,30] Outside of rheumatology, researchers have developed text data extractors for dementia diagnoses and cancer staging, urinary incontinence-related PROs, identification of out-of-network emergent care encounters, disease phenotyping, and extraction of documentation of advanced directives. [31,32,33,34] This work has shown that important healthcare outcomes are being captured in EHRs as free text and that although challenges exist, NLP and machine learning methods may be increasing feasible options for accurately and efficiently identifying health outcomes.…”

Phenotyping severity of patient‐centered outcomes using clinical notes: A prostate cancer use case

“…In addition, CNN model was less successful at identifying the "severe" category compared to the mild and moderate Both rule-based and machine learning NLP approaches to leverage granular data in EHRs are common and their accuracy has been demonstrated in many recent studies. 5,6,22 In our study, the rulebased approach depends on human expertise outperformed the machine learning approach on UI severity extraction task. As reported in other studies, 9,22,23,[25][26][27] the underlying reason for this may be that the hand-designed rules that precisely capture specific patterns overfit with the data.…”

“…[2][3][4] PCOs are difficult to capture because they can only be described by the patient, are subjective, and often documented only as unstructured text in the electronic health record (EHR). 5 Given these issues and the complexity of PCOs, computerized methods, including machine learning and natural language processing (NLP), are necessary to unlock the wealth of information buried in unstructured textual notes that often document PCOs. 6,7 In fact, using computational methods for clinical phenotyping has created opportunities to expand population-wide assessments of both PCOs and therefore patient-valued care.…”