Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage.

Background: Explicit documentation of stage is an endorsed quality metric by the National Quality Forum. Clinical and pathological cancer staging is inconsistently recorded within clinical narratives but can be derived from text in the Electronic Health Record (EHR). To address this need, we developed a Natural Language Processing (NLP) solution for extraction of clinical and pathological TNM stages from the clinical notes in prostate cancer patients. Methods: Data for patients diagnosed with prostate cancer between 2010 and 2018 were collected from a tertiary care academic healthcare system's EHR records in the United States. This system is linked to the California Cancer Registry, and contains data on diagnosis, histology, cancer stage, treatment and outcomes. A randomly selected sample of patients were manually annotated for stage to establish the ground truth for training and validating the NLP methods. For each patient, a vector representation of clinical text (written in English) was used to train a machine learning model alongside a rule-based model and compared with the ground truth. Results: A total of 5,461 prostate cancer patients were identified in the clinical data warehouse and over 30% were missing stage information. Thirty-three to thirty-six percent of patients were missing a clinical stage and the models accurately imputed the stage in 21-32% of cases. Twenty-one percent had a missing pathological stage and using NLP 71% of missing T stages and 56% of missing N stages were imputed. For both clinical and pathological T and N stages, the rule-based NLP approach out-performed the ML approach with a minimum F1 score of 0.71 and 0.40, respectively. For clinical M stage the ML approach out-performed the rule-based model with a minimum F1 score of 0.79 and 0.88, respectively. Conclusions: We developed an NLP pipeline to successfully extract clinical and pathological staging information from clinical narratives. Our results can serve as a proof of concept for using NLP to augment clinical and pathological stage reporting in cancer registries and EHRs to enhance the secondary use of these data.

Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.

Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health records (EHRs). Therefore, we evaluated three supervised machine learning methods (Support Vector Machine, Decision Trees, Gradient Boosting) to classify free-text pathology reports for prostate cancer into T, N and M stage groups.

Distribution of global health measures from routinely collected PROMIS surveys in patients with breast cancer or prostate cancer.

BACKGROUND: The collection of patient-reported outcomes (PROs) is an emerging priority internationally, guiding clinical care, quality improvement projects and research studies. After the deployment of Patient-Reported Outcomes Measurement Information System (PROMIS) surveys in routine outpatient workflows at an academic cancer center, electronic health record data were used to evaluate survey completion rates and self-reported global health measures across 2 tumor types: breast and prostate cancer. METHODS: This study retrospectively analyzed 11,657 PROMIS surveys from patients with breast cancer and 4411 surveys from patients with prostate cancer, and it calculated survey completion rates and global physical health (GPH) and global mental health (GMH) scores between 2013 and 2018. RESULTS: A total of 36.6% of eligible patients with breast cancer and 23.7% of patients with prostate cancer completed at least 1 survey, with completion rates lower among black patients for both tumor types (P < .05). The mean T scores (calibrated to a general population mean of 50) for GPH were 48.4 ± 9 for breast cancer and 50.6 ± 9 for prostate cancer, and the GMH scores were 52.7 ± 8 and 52.1 ± 9, respectively. GPH and GMH were frequently lower among ethnic minorities, patients without private health insurance, and those with advanced disease. CONCLUSIONS: This analysis provides important baseline data on patient-reported global health in breast and prostate cancer. Demonstrating that PROs can be integrated into clinical workflows, this study shows that supportive efforts may be needed to improve PRO collection and global health endpoints in vulnerable populations.

Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer.

BACKGROUND: Electronic health record (EHR) based research in oncology can be limited by missing data and a lack of structured data elements. Clinical research data warehouses for specific cancer types can enable the creation of more robust research cohorts. METHODS: We linked data from the Stanford University EHR with the Stanford Cancer Institute Research Database (SCIRDB) and the California Cancer Registry (CCR) to create a research data warehouse for prostate cancer. The database was supplemented with information from clinical trials, natural language processing of clinical notes and surveys on patient-reported outcomes. RESULTS: 11,898 unique prostate cancer patients were identified in the Stanford EHR, of which 3,936 were matched to the Stanford cancer registry and 6153 in the CCR. 7158 patients with EHR data and at least one of SCIRDB and CCR data were initially included in the warehouse. CONCLUSIONS: A disease-specific clinical research data warehouse combining multiple data sources can facilitate secondary data use and enhance observational research in oncology.

HealthNavigator: a mobile application for chronic disease screening and linkage to services at an urban Primary Health Network.

Mobile applications (apps) are promising tools to support chronic disease screening and linkage to health services. They have the potential to increase healthcare access for vulnerable populations. The HealthNavigator app was developed to provide chronic disease risk assessments, linkage to local general practitioners (GPs) and lifestyle programs, and a personalised health report for discussion with a GP. Assessments were either self-administered or facilitated by community health workers through a Primary Health Network (PHN) initiative targeting ethnically diverse communities. In total, 1492 assessments (80.4% self-administered, 19.6% facilitated) were conducted over a 12-month period in Queensland, Australia. Of these, 26% of people screened came from postcodes representing the lowest quartile of socioeconomic disadvantage. When compared against self-administered assessments, subjects screened by the facilitated program were more likely to be born outside Australia (80.5 v. 33.2%, P<0.001), and to fall within a high risk category based on cardiovascular risk scores (19.8 v. 13.7%, P<0.01) and type 2 diabetes mellitus risk scores (58.0 v. 40.1%, P<0.001). Mobile apps embedded into PHN programs may be a useful adjunct for the implementation of community screening programs. Further research is needed to determine their effect on health service access and health outcomes.

Identifying Cases of Metastatic Prostate Cancer Using Machine Learning on Electronic Health Records.

Cancer stage is rarely captured in structured form in the electronic health record (EHR). We evaluate the performance of a classifier, trained on structured EHR data, in identifying prostate cancer patients with metastatic disease. Using EHR data for a cohort of 5,861 prostate cancer patients mapped to the Observational Health Data Sciences and Informatics (OHDSI) data model, we constructed feature vectors containing frequency counts of conditions, procedures, medications, observations and laboratory values. Staging information from the California Cancer Registry was used as the ground-truth. For identifying patients with metastatic disease, a random forest model achieved precision and recall of 0.90, 0.40 using data within 12 months of diagnosis. This compared to precision 0.33, recall 0.54 for an ICD code-based query. High-precision classifiers using hundreds of structured data elements significantly outperform ICD queries, and may assist in identifying cohorts for observational research or clinical trial matching.