People: Principal Investigator

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

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

  • Leveraging Digital Data to Inform and Improve Quality Cancer Care. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology Hernandez-Boussard, T., Blayney, D. W., Brooks, J. D. 2020

    Abstract

    BACKGROUND: Efficient capture of routine clinical care and patient outcomes are needed at a population-level, as is evidence on important treatment-related side effects and their effect on well-being and clinical outcomes. The increasing availability of electronic health records (EHRs) offers new opportunities to generate population-level patient-centered evidence on oncological care that can better guide treatment decisions and patient-valued care.METHODS: This study includes patients seeking care at an academic medical center, 2008-2018. Digital data sources are combined to address missingness, inaccuracy, and noise common to EHR data. Clinical concepts were identified and extracted from EHR unstructured data using natural language processing (NLP) and machine/deep learning techniques. All models are trained, tested, and validated on independent data samples using standard metrics.RESULTS: We provide use cases for using EHR data to assess guideline adherence and quality measurements among cancer patients. Pretreatment assessment was evaluated by guideline adherence and quality metrics for cancer staging metrics. Patient outcomes included treatment-related side-effects and patient-reported outcomes.CONCLUSIONS: Advanced technologies applied to EHRs present opportunities to advance population-level quality assessment, to learn from routinely collected clinical data for personalized treatment, and to augment epidemiological and population health studies. The effective use of digital data can inform patient-valued care, quality initiatives and policy guidelines.IMPACT: A comprehensive set of health data analyzed with advanced technologies results in a unique resource that facilitates wide-ranging, innovative, and impactful research on prostate cancer. This work demonstrates novel use of EHRs and technology to advance epidemiological studies and benefit oncological care.

    View details for DOI 10.1158/1055-9965.EPI-19-0873

    View details for PubMedID 32066619

  • Trajectory analysis for postoperative pain using electronic health records: A nonparametric method with robust linear regression and K-medians cluster analysis. Health informatics journal Weng, Y., Tian, L., Tedesco, D., Desai, K., Asch, S. M., Carroll, I., Curtin, C., McDonald, K. M., Hernandez-Boussard, T. 2019: 1460458219881339

    Abstract

    Postoperative pain scores are widely monitored and collected in the electronic health record, yet current methods fail to fully leverage the data with fast implementation. A robust linear regression was fitted to describe the association between the log-scaled pain score and time from discharge after total knee replacement. The estimated trajectories were used for a subsequent K-medians cluster analysis to categorize the longitudinal pain score patterns into distinct clusters. For each cluster, a mixture regression model estimated the association between pain score and time to discharge adjusting for confounding. The fitted regression model generated the pain trajectory pattern for given cluster. Finally, regression analyses examined the association between pain trajectories and patient outcomes. A total of 3442 surgeries were identified with a median of 22 pain scores at an academic hospital during 2009-2016. Four pain trajectory patterns were identified and one was associated with higher rates of outcomes. In conclusion, we described a novel approach with fast implementation to model patients' pain experience using electronic health records. In the era of big data science, clinical research should be learning from all available data regarding a patient's episode of care instead of focusing on the "average" patient outcomes.

    View details for DOI 10.1177/1460458219881339

    View details for PubMedID 31621460

  • Real world evidence in cardiovascular medicine: assuring data validity in electronic health record-based studies. Journal of the American Medical Informatics Association : JAMIA Hernandez-Boussard, T., Monda, K. L., Crespo, B. C., Riskin, D. 2019

    Abstract

    OBJECTIVE: With growing availability of digital health data and technology, health-related studies are increasingly augmented or implemented using real world data (RWD). Recent federal initiatives promote the use of RWD to make clinical assertions that influence regulatory decision-making. Our objective was to determine whether traditional real world evidence (RWE) techniques in cardiovascular medicine achieve accuracy sufficient for credible clinical assertions, also known as "regulatory-grade" RWE.DESIGN: Retrospective observational study using electronic health records (EHR), 2010-2016.METHODS: A predefined set of clinical concepts was extracted from EHR structured (EHR-S) and unstructured (EHR-U) data using traditional query techniques and artificial intelligence (AI) technologies, respectively. Performance was evaluated against manually annotated cohorts using standard metrics. Accuracy was compared to pre-defined criteria for regulatory-grade. Differences in accuracy were compared using Chi-square test.RESULTS: The dataset included 10840 clinical notes. Individual concept occurrence ranged from 194 for coronary artery bypass graft to 4502 for diabetes mellitus. In EHR-S, average recall and precision were 51.7% and 98.3%, respectively and 95.5% and 95.3% in EHR-U, respectively. For each clinical concept, EHR-S accuracy was below regulatory-grade, while EHR-U met or exceeded criteria, with the exception of medications.CONCLUSIONS: Identifying an appropriate RWE approach is dependent on cohorts studied and accuracy required. In this study, recall varied greatly between EHR-S and EHR-U. Overall, EHR-S did not meet regulatory grade criteria, while EHR-U did. These results suggest that recall should be routinely measured in EHR-based studes intended for regulatory use. Furthermore, advanced data and technologies may be required to achieve regulatory grade results.

    View details for DOI 10.1093/jamia/ocz119

    View details for PubMedID 31414700

  • The rise of non-traumatic extremity compartment syndrome in light of the opioid epidemic. The American journal of emergency medicine Sheckter, C. C., Cebron, U., Suarez, P., Rochlin, D., Tedesco, D., Hernandez-Boussard, T., Curtin, C. 2020

    View details for DOI 10.1016/j.ajem.2020.01.020

    View details for PubMedID 32005410

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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)