People: Principal Investigator

Tina Hernandez-Boussard
Associate Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health
(650) 725-5507
boussard@stanford.edu

Bio

Dr. Hernandez-Boussard is an Associate Professor at Stanford University in Medicine (Biomedical Informatics), Biomedical Data Sciences, Surgery and Epidemiology & Population Health (by courtesy). Her background and expertise are in the field of biomedical informatics, health services research, and epidemiology. In her current work, Dr. Hernandez-Boussard develops and evaluates AI technology to accurately and efficiently monitor, measure, and predict healthcare outcomes. She has developed the infrastructure to efficiently capture heterogenous data sources, transform these diverse data to knowledge, and use this knowledge to improve patient outcomes, healthcare delivery, and guide policy.

Publications

  • Gaps in standardized postoperative pain management quality measures: A systematic review. Surgery Joseph, J. M., Gori, D., Curtin, C., Hah, J., Ho, V. T., Asch, S. M., Hernandez-Boussard, T. 2021

    Abstract

    BACKGROUND: The goal of this study was an assessment of availability postoperative pain management quality measures and National Quality Forum-endorsed measures. Postoperative pain is an important clinical timepoint because poor pain control can lead to patient suffering, chronic opiate use, and/or chronic pain. Quality measures can guide best practices, but it is unclear whether there are measures for managing pain after surgery.METHODS: The National Quality Forum Quality Positioning System, Agency for Healthcare Research and Quality Indicators, and Centers for Medicare and Medicaid Services Measures Inventory Tool databases were searched in November 2019. We conducted a systematic literature review to further identify quality measures in research publications, clinical practice guidelines, and gray literature for the period between March 11, 2015 and March 11,2020.RESULTS: Our systematic review yielded 1,328 publications, of which 206 were pertinent. Nineteen pain management quality measures were identified from the quality measure databases, and 5 were endorsed by National Quality Forum. The National Quality Forum measures were not specific to postoperative pain management. Three of the non-endorsed measures were specific to postoperative pain.CONCLUSION: The dearth of published postoperative pain management quality measures, especially National Quality Forum-endorsed measures, highlights the need for more rigorous evidence and widely endorsed postoperative pain quality measures to guide best practices.

    View details for DOI 10.1016/j.surg.2021.08.004

    View details for PubMedID 34538340

  • Health management via telemedicine: Learning from the COVID-19 experience. Journal of the American Medical Informatics Association : JAMIA Sun, R., Blayney, D. W., Hernandez-Boussard, T. 2021

    Abstract

    At the onset of the COVID-19 (coronavirus disease 2019) pandemic, telemedicine was rapidly implemented to protect patients and healthcare providers from infection. It is unlikely that care delivery will fully return to the pre-COVID form. Telemedicine offers many opportunities to improve care efficiency, accessibility, and patient outcomes, but many challenges exist related to technology interoperability, the digital divide, and usability. We propose that telemedicine evolve to support continuity of care throughout the patient journey, including multidisciplinary care teams and the seamless integration of data into the clinical workflow to support a learning healthcare system. Importantly, evidence is needed to support this paradigm shift in care delivery to ensure the quality and efficacy of care delivered via telemedicine. Here, we highlight gaps and opportunities that need to be addressed by the biomedical informatics community to move forward with safe and effective healthcare delivery via telemedicine.

    View details for DOI 10.1093/jamia/ocab145

    View details for PubMedID 34459475

  • Increases in SARS-CoV-2 Test Positivity Rates Among Hispanic People in a Northern California Health System. Public health reports (Washington, D.C. : 1974) Rodriguez, F., Coquet, J., Harrington, R., Hernandez-Boussard, T. 2021: 333549211026778

    Abstract

    Racial/ethnic minority groups are disproportionately affected by the COVID-19 pandemic. We examined ethnic differences in SARS-CoV-2 testing patterns and positivity rates in a large health care system in Northern California. The study population included patients tested for SARS-CoV-2 from March 4, 2020, through January 12, 2021, at Stanford Health Care. We used adjusted hierarchical logistic regression models to identify factors associated with receiving a positive test result. During the study period, 282 916 SARS-CoV-2 tests were administered to 179 032 unique patients, 32 766 (18.3%) of whom were Hispanic. Hispanic patients were 3 times more likely to receive a positive test result than patients in other racial/ethnic groups (odds ratio = 3.16; 95% CI, 3.00-3.32). The rate of receiving a positive test result for SARS-CoV-2 among Hispanic patients increased from 5.4% in mid-March to 15.7% in mid-July, decreased to 3.9% in mid-October, and increased to 21.2% toward the end of December. Hispanic patients were more likely than non-Hispanic patients to receive a positive test result for SARS-CoV-2, with increasing trends during regional surges. The disproportionate and growing overrepresentation of Hispanic people receiving a positive test result for SARS-CoV-2 demonstrates the need to focus public health prevention efforts on these communities.

    View details for DOI 10.1177/00333549211026778

    View details for PubMedID 34161176

  • Assessment of a Clinical Trial-Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer. JAMA network open Coquet, J. n., Bievre, N. n., Billaut, V. n., Seneviratne, M. n., Magnani, C. J., Bozkurt, S. n., Brooks, J. D., Hernandez-Boussard, T. n. 2021; 4 (1): e2031730

    Abstract

    Randomized clinical trials (RCTs) are considered the criterion standard for clinical evidence. Despite their many benefits, RCTs have limitations, such as costliness, that may reduce the generalizability of their findings among diverse populations and routine care settings.To assess the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic castration-resistant prostate cancer (CRPC) when the model is applied to real-world data from electronic health records (EHRs).The RCT-trained model and patient data from the RCTs were obtained from the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge for prostate cancer, which occurred from March 16 to July 27, 2015. This challenge included 4 phase 3 clinical trials of patients with metastatic CRPC. Real-world data were obtained from the EHRs of a tertiary care academic medical center that includes a comprehensive cancer center. In this study, the DREAM challenge RCT-trained model was applied to real-world data from January 1, 2008, to December 31, 2019; the model was then retrained using EHR data with optimized feature selection. Patients with metastatic CRPC were divided into RCT and EHR cohorts based on data source. Data were analyzed from March 23, 2018, to October 22, 2020.Patients who received treatment for metastatic CRPC.The primary outcome was the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic CRPC when the model is applied to real-world data. Model performance was compared using 10-fold cross-validation according to time-dependent integrated area under the curve (iAUC) statistics.Among 2113 participants with metastatic CRPC, 1600 participants were included in the RCT cohort, and 513 participants were included in the EHR cohort. The RCT cohort comprised a larger proportion of White participants (1390 patients [86.9%] vs 337 patients [65.7%]) and a smaller proportion of Hispanic participants (14 patients [0.9%] vs 42 patients [8.2%]), Asian participants (41 patients [2.6%] vs 88 patients [17.2%]), and participants older than 75 years (388 patients [24.3%] vs 191 patients [37.2%]) compared with the EHR cohort. Participants in the RCT cohort also had fewer comorbidities (mean [SD], 1.6 [1.8] comorbidities vs 2.5 [2.6] comorbidities, respectively) compared with those in the EHR cohort. Of the 101 variables used in the RCT-derived model, 10 were not available in the EHR data set, 3 of which were among the top 10 features in the DREAM challenge RCT model. The best-performing EHR-trained model included only 25 of the 101 variables included in the RCT-trained model. The performance of the RCT-trained and EHR-trained models was adequate in the EHR cohort (mean [SD] iAUC, 0.722 [0.118] and 0.762 [0.106], respectively); model optimization was associated with improved performance of the best-performing EHR model (mean [SD] iAUC, 0.792 [0.097]). The EHR-trained model classified 256 patients as having a high risk of mortality and 256 patients as having a low risk of mortality (hazard ratio, 2.7; 95% CI, 2.0-3.7; log-rank P < .001).In this study, although the RCT-trained models did not perform well when applied to real-world EHR data, retraining the models using real-world EHR data and optimizing variable selection was beneficial for model performance. As clinical evidence evolves to include more real-world data, both industry and academia will likely search for ways to balance model optimization with generalizability. This study provides a pragmatic approach to applying RCT-trained models to real-world data.

    View details for DOI 10.1001/jamanetworkopen.2020.31730

    View details for PubMedID 33481032

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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)