UTILIZING ELECTRONIC HEALTH RECORD DOCUMENTATION TO MEASURE VALUE FOR PROSTATE CANCER CLINICAL CARE

Purpose: Electronic health records (EHRs) are a widely adopted but underutilized source of data for systematic assessment of quality of care metrics.  Barriers for use of this data include its non-structured, free text nature, non-uniform recording by clinicians and non-standard vocabulary.  The aim of this project is to extraction useful knowledge from EHRs, link elements to a registry and develop standardized quality process and outcome metrics. 

Method(s): We use ICD-9-CM diagnosis codes to identify prostate cancer patients receiving care at an academic medical center. Patients are confirmed in the California Cancer Registry, which returns tumor characteristic and treatment data on all patients with a confirmed cancer diagnosis, including curated pathology and tumor staging information. Using all proposed prostate cancer quality metrics, we define each quality metric using target terms and concepts to extract from the EHRs. These terms may include diagnostic procedures and tests and their results (such as Digital Rectal Exam, DRE), therapeutic procedures, and drugs (both ordered and administered). Terms are mapped to a standardized medical vocabulary, enabling us to represent the elements of a metric by a concept domain and its permissible values. The structured representation of the quality metric data elements are used to create quality phenotypes, which are rules involving the temporal order of components of the quality metrics. 

Result(s): We have developed an EHR database that draws healthcare records from an academic center and link these records to the California Cancer Registry. This allows for clinical care data to be analyzed alongside diagnostic details, which are not usually captured in EHR. This database includes unstructured clinician notes to ensure the broad evaluation of patient-centered data. Furthermore, our system enables real-time extraction of treatment processes and outcome measures, allowing us to use EHR data to track process improvements.

The quality metric phenotypes we create are standardized code that can be used across different EHR systems. For example, the algorithm to detect DRE documentation contains prostate cancer diagnosis code (ICD-9 185), dates (ensure the DRE was performed prior to treatment), and textual concepts (e.g. DRE, digital rectal exam, and rectal exam). 

Conclusion(s):

EHR-systems can be used to assess and report quality metrics systematically, efficiently, and with high accuracy. The development of such systems moves the quality assessment field into large-scale analyses.