People: Principal Investigator

Tina Hernandez-Boussard
Associate Professor (Research) of Medicine (Biomedical Informatics), of Biomedical Data Science and of Surgery
(650) 725-5507

Bio

Dr Hernandez-Boussard is an Associate Professor in Medicine (Biomedical Informatics), Biomedical Data Science, and Surgery at the Stanford University School of Medicine. Dr. Hernandez-Boussard's background and expertise is in the field of computational biology, with concentration on accountability measures, population health, and health policy. A key focus of her research is the application of novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery.

Publications

  • Predicting inadequate postoperative pain management in depressed patients: A machine learning approach. PloS one Parthipan, A., Banerjee, I., Humphreys, K., Asch, S. M., Curtin, C., Carroll, I., Hernandez-Boussard, T. 2019; 14 (2): e0210575

    Abstract

    Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. Using EHR data from an academic medical center, we identified patients receiving surgery over a 9-year period. We developed and validated natural language processing (NLP) algorithms to extract depression-related information (diagnosis, SSRI use, symptoms) from structured and unstructured data elements. The primary outcome was the difference between preoperative pain score and postoperative pain at discharge, 3-week and 8-week time points. We developed computational models to predict the increase or decrease in the postoperative pain across the 3 time points by using the patient's EHR data (e.g. medications, vitals, demographics) captured before surgery. We evaluate the generalizability of the model using 10-fold cross-validation method where the holdout test method is repeated 10 times and mean area-under-the-curve (AUC) is considered as evaluation metrics for the prediction performance. We identified 4,306 surgical patients with symptoms of depression. A total of 14.1% were prescribed both an SSRI and a prodrug opioid, 29.4% were prescribed an SSRI and a non-prodrug opioid, 18.6% were prescribed a prodrug opioid but were not on SSRIs, and 37.5% were prescribed a non-prodrug opioid but were not on SSRIs. Our NLP algorithm identified depression with a F1 score of 0.95 against manual annotation of 300 randomly sampled clinical notes. On average, patients receiving prodrug opioids had lower average pain scores (p<0.05), with the exception of the SSRI+ group at 3-weeks postoperative follow-up. However, SSRI+/Prodrug+ had significantly worse pain control at discharge, 3 and 8-week follow-up (p < .01) compared to SSRI+/Prodrug- patients, whereas there was no difference in pain control among the SSRI- patients by prodrug opioid (p>0.05). The machine learning algorithm accurately predicted the increase or decrease of the discharge, 3-week and 8-week follow-up pain scores when compared to the pre-operative pain score using 10-fold cross validation (mean area under the receiver operating characteristic curve 0.87, 0.81, and 0.69, respectively). Preoperative pain, surgery type, and opioid tolerance were the strongest predictors of postoperative pain control. We provide the first direct clinical evidence that the known ability of SSRIs to inhibit prodrug opioid effectiveness is associated with worse pain control among depressed patients. Current prescribing patterns indicate that prescribers may not account for this interaction when choosing an opioid. The study results imply that prescribers might instead choose direct acting opioids (e.g. oxycodone or morphine) in depressed patients on SSRIs.

    View details for DOI 10.1371/journal.pone.0210575

    View details for PubMedID 30726237

  • Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer. EGEMS (Washington, DC) Seneviratne, M. G., Seto, T., Blayney, D. W., Brooks, J. D., Hernandez-Boussard, T. 2018; 6 (1): 13

    Abstract

    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.

    View details for DOI 10.5334/egems.234

    View details for PubMedID 30094285

  • Distribution of global health measures from routinely collected PROMIS surveys in patients with breast cancer or prostate cancer. Cancer Seneviratne, M. G., Bozkurt, S., Patel, M. I., Seto, T., Brooks, J. D., Blayney, D. W., Kurian, A. W., Hernandez-Boussard, T. 2018

    Abstract

    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.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.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.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.

    View details for DOI 10.1002/cncr.31895

    View details for PubMedID 30512191

  • Opioid Abuse And Poisoning: Trends In Inpatient And Emergency Department Discharges. Health affairs (Project Hope) Tedesco, D., Asch, S. M., Curtin, C., Hah, J., McDonald, K. M., Fantini, M. P., Hernandez-Boussard, T. 2017; 36 (10): 1748–53

    Abstract

    Addressing the opioid epidemic is a national priority. We analyzed national trends in inpatient and emergency department (ED) discharges for opioid abuse, dependence, and poisoning using Healthcare Cost and Utilization Project data. Inpatient and ED discharge rates increased overall across the study period, but a decline was observed for prescription opioid-related discharges beginning in 2010, while a sharp increase in heroin-related discharges began in 2008.

    View details for DOI 10.1377/hlthaff.2017.0260

    View details for PubMedID 28971919

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Academic Appointments

Associate Professor, Medicine - Biomedical Informatics Research 

Associate Professor, Biomedical Data Science

Associate Professor, Surgery - General Surgery

Member, Stanford Cancer Institute

Professional Education

M.S., Stanford University, Health Services Research (2013)

Ph.D., University Claude Bernard, Lyon 1, Computational Biology (1999)

M.P.H., Yale University, Epidemiology (1993)

B.A., University California, Irvine, Psychology (1991)

B.S., University of California, Irvine, Biology (1991)