Development and Validation of an Automated Method to Identify Patients Undergoing Radical Cystectomy for Bladder Cancer Using Natural Language Processing.

INTRODUCTION: Measurement for quality improvement relies on accurate case identification and characterization. With electronic health records now widely deployed, natural language processing, the use of software to transform text into structured data, may enrich quality measurement. Accordingly we evaluated the application of natural language processing to radical cystectomy procedures for patients with bladder cancer. METHODS: From a sample of 497 procedures performed from March 2013 to October 2014 we identified radical cystectomy for primary bladder cancer using the approaches of 1) a natural language processing enhanced algorithm, 2) an administrative claims based algorithm and 3) manual chart review. We also characterized treatment with robotic surgery and continent urinary diversion. Using chart review as the reference standard we calculated the observed agreement (kappa statistic), sensitivity, specificity, positive predictive value and negative predictive value for natural language processing and administrative claims. RESULTS: We confirmed 84 radical cystectomies were performed for bladder cancer, with 50.0% robotic and 38.6% continent diversions. The natural language processing enhanced and claims based algorithms demonstrated 99.8% (κ=0.993, 95% CI 0.979-1.000) and 98.6% (κ=0.951, 95% CI 0.915-0.987) agreement with manual review, respectively. Both approaches accurately characterized robotic vs open surgery, with natural language processing enhanced algorithms showing 98.8% (κ=0.976, 95% CI 0.930-1.000) and claims based 90.5% (κ=0.810, 95% CI 0.686-0.933) agreement. For urinary diversion natural language processing enhanced algorithms correctly specified 96.4% of cases (κ=0.924, 95% CI 0.839-1.000) compared with 83.3% (κ=0.655, 95% CI 0.491-0.819). CONCLUSIONS: Natural language processing enhanced and claims based algorithms accurately identified radical cystectomy cases at our institution. However, natural language processing appears to better classify specific aspects of cystectomy surgery, highlighting a potential advantage of this emerging methodology.

Building the Infrastructure for Value at UCLA: Engaging Clinicians and Developing Patient-Centric Measurement.

PROBLEM: Evolving payer and patient expectations have challenged academic health centers (AHCs) to improve the value of clinical care. Traditional quality approaches may be unable to meet this challenge. APPROACH: One AHC, UCLA Health, has implemented a systematic approach to delivery system redesign that emphasizes clinician engagement, a patient-centric scope, and condition-specific, clinician-guided measurement. A physician champion serves as quality officer (QO) for each clinical department/division. Each QO, with support from a central measurement team, has developed customized analytics that use clinical data to define targeted populations and measure care across the full treatment episode. OUTCOMES: From October 2012 through June 2015, the approach developed rapidly. Forty-three QOs are actively redesigning care delivery protocols within their specialties, and 95% of the departments/divisions have received a customized measure report for at least one patient population. As an example of how these analytics promote systematic redesign, the authors discuss how Department of Urology physicians have used these new measures, first, to better understand the relationship between clinical practice and outcomes for patients with benign prostatic hyperplasia and, then, to work toward reducing unwarranted variation. Physicians have received these efforts positively. Early outcome data are encouraging. NEXT STEPS: This infrastructure of engaged physicians and targeted measurement is being used to implement systematic care redesign that reliably achieves outcomes that are meaningful to patients and clinicians-incorporating both clinical and cost considerations. QOs are using an approach, for multiple newly launched projects, to identify, test, and implement value-oriented interventions tailored to specific patient populations.