1010 — Creation and Validation of a Near Real-Time Bladder Cancer Patient Tracking and Reporting Tool at Veterans Affairs Greater Los Angeles
Shelton JB, VA Greater Los Angeles Healthcare System, VA GLA HSRD, UCLA; Wong G, VA Greater Los Angeles Healthcare System; Bennett C, VA Greater Los Angeles Healthcare System, UCLA; Saigal C, UCLA; Ho S, VA Greater Los Angeles Healthcare System; Goldzweig C, VA Greater Los Angeles Healthcare System;
The care of non-muscle-invasive urothelial bladder cancer is effective at preventing disease recurrence and disease progression, but is intensive and compliance is often poor. We sought to leverage existing information technology resources at the Veterans Health Administration (VHA) Greater Los Angeles Medical Center (GLA VAMC) to develop a tool to identify and track Veterans with bladder cancer in near real-time as an aid to ensuring timely receipt of guideline recommended care through a population management approach.
We developed a patient identification algorithm using combinations of administrative and clinical data elements including, ICD-9 and CPT codes, VHA facility and clinic codes, and VHA death and facility attribution tables. We validated the identification algorithm through comparison to two independent, but not real-time, data sources: Systematized Nomenclature of Medicine (SNOMED) codes for bladder malignancy generated by Pathology, and the VA Comprehensive Cancer Registry (VACCR). We developed clinical management algorithms similarly, but implemented new clinical documentation methods to generate structured data where it was unavailable. We validated clinical management algorithms by chart abstraction of a 10% sample.
Our algorithm identified 471 patients with bladder cancer followed by the Division of Urology at the Greater Los Angeles VA between 1/1/2006 and 4/30/2012. After excluding patients who were deceased or who moved there were 361 active patients. This cohort included all patients identified by SNOMED codes and by the VACCR. The sensitivity of clinical management algorithms was 100% for immunotherapy, surgical resection and surveillance cystoscopy and 80% for peri-operative intravesical chemotherapy.
We created and validated algorithms and clinical care documentation processes to identify patients with bladder cancer in near real-time and detect nearly all critical elements of clinical care.
We developed a user interface dashboard based on these algorithms for population management of bladder cancer patients. This near real-time registry has the potential to serve as a platform for a learning healthcare system in bladder cancer care and to facilitate delivery of evidence based care, thereby reducing the time from "bench to bedside" and giving providers another tool to reduce facility, provider, and patient barriers to delivery of high quality healthcare.