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“Large clinical datasets contain a goldmine of information on healthcare quality and safety. With funding from AHRQ, I’ve analyzed these datasets to provide clinicians with knowledge to improve surgical outcomes and reduce harms.” 

Tina Hernandez-Boussard, Ph.D., M.P.H., associate dean of research at Stanford University School of Medicine, has dedicated her career to analyzing “clinical big data” to identify quality and safety issues that can result in adverse events. With funding from AHRQ, Dr. Hernandez-Boussard has reviewed large datasets, such as ones with administrative claims data and electronic health records, to develop evidence-based standards, guidelines, and decision-making tools for general healthcare practice, including surgical specialties and pain management. In conducting her research, Dr. Hernandez-Boussard also is working to address and unravel bias in healthcare data.

Dr. Hernandez-Boussard, who also is a professor of medicine, biomedical data science, and surgery at Stanford, was awarded her first 5-year ������Ƶ in 2010 to research and design (PSIs) for a wide range of surgical specialties. PSIs are administratively coded identifiers that provide information about potentially avoidable safety events during care delivery and are used by hospitals to evaluate and prioritize patient safety policies. As an expert in biomedical informatics, Dr. Hernandez-Boussard was concerned that PSI benchmarks often were not used by surgical subgroups and that national trends for surgical subgroups often were not available, putting patient safety at risk.

Using large datasets on safety events, including AHRQ’s Healthcare Cost and Utilization Project (HCUP), she profiled adverse events for various surgical events such as heart bypass surgery, breast reconstruction, and bone fracture repair. Dr. Hernandez-Boussard also examined factors that could contribute to adverse events such as , , and patient demographics. The work developed by Dr. Hernandez-Boussard and her team is filling critical gaps in clinical knowledge by providing a foundation on which hospitals can focus their surgical quality improvement efforts.

In 2015, Dr. Hernandez-Boussard received another 5-year ������Ƶ to research quality metrics, which are measures used to assess care quality. While her initial grant identified quality metrics for different types of surgeries, this project focused on metrics for pain management care after surgery. “High-quality pain management care can improve patients' healthcare experience and well-being, and lower costs,” said Dr. Hernandez-Boussard. Using data from HCUP and electronic health records, she examined the quality of pain management care provided to surgical patients, determined how variations in care influenced patient outcomes, and provided evidence to support multi-modal pain management regimes. Her published findings on pain treatments for specific surgeries, such as , , and , have helped to enhance clinical knowledge on postoperative pain management. Dr. Hernandez-Boussard also developed multiple analyses on the use of opioids for pain management, including on national trends in both inpatient and emergency department discharges for opioid abuse, dependence, and poisoning; as well as   of opioid-sparing postoperative pain management therapies.  

Through these initial ������Ƶgrants, Dr. Hernandez-Boussard gained expertise in analyzing data to identify and address healthcare quality and safety issues. These projects also increased her understanding of the racial and ethnic bias built into algorithms that guide clinical decision making. “Race-based medicine, which relies on algorithms that use race as a proxy for biological differences, has led to treatment patterns that are inappropriate, unjust, and harmful to minoritized racial and ethnic groups. These patterns have contributed to persistent disparities in health and healthcare,” she said. Committed to addressing these disparities, Dr. Hernandez-Boussard now reviews algorithms for racial and ethnic bias as part of her research process. Her approach involves engaging diverse teams in the process of algorithm development and ensuring that race is not used as a proxy for actual risk factors in the algorithm.

Building on her pain care management research, Dr. Hernandez-Boussard received a third 5-year ������Ƶ in 2021 that uses data from electronic health records to critically assess biases and evaluate fairness in algorithmic tools. Her research focuses on older patients and aims to mitigate the risk of adverse outcomes related to postoperative pain management in this vulnerable population. She also aims to apply her race-aware approach to this project to identify ways to reduce bias toward older patients. “Currently, prejudice rather than evidence guides the complex problem of elder perioperative pain management,” said Dr. Hernandez-Boussard. She noted that “a lot of the tools and technology that were being developed were not taking into account the specific needs of the elder population, such as frailty and polypharmacy.” For example, “Recent advancements in machine learning are leading to the development of prediction models for perioperative and postoperative pain management. Although these models show promise for personalizing pain management strategies, they are primarily designed for a broad spectrum of adult patients and necessitate additional improvement and validation prior to extensive implementation.” The project will end March 31, 2026.

Dr. Hernandez-Boussard is a member of AHRQ’s National Advisory Council Subcommittee on Healthcare Quality Measurement, AcademyHealth, and the American Medical Informatics Association.

Principal Investigator: Tina Hernandez-Boussard, Ph.D., M.P.H.
Institution: Stanford University School of Medicine
Grantee Since: 2010
Type of Grant: Various

Related ������ƵResources

• ������ƵData Resources
• Disparities
• Patient Safety and Quality Measures