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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.
Millions of Americans undergo surgery every year and postoperative pain is common and often poorly managed. Poorly managed postoperative pain may cause severe functional impairment, impaired care of the underlying diseases, transition to chronic pain, and decreased quality of life. Many controlled studies have demonstrated a variety of interventions that benefit postoperative pain, yet their application in a large and more diverse population is unknown and a nationally endorsed, concise quality process metric for postoperative pain management does not exist. One roadblock is that postoperative pain and its related outcomes are complex. The gathering of evidence from electronic health data, which draw from and inform real-world practice, could bypass this roadblock and inform decisions that lead to effective and efficient postoperative pain management. This project seeks to measure quality of care for postoperative pain, assess proposed evidence-based interventions from randomized controlled trails, lay the ground work for systematic pain-related research using EMRs, and produce population-based evidence for a nationally-endorsed postoperative pain management quality metric. To achieve these objectives, this project has three specific aims: (1) to develop standardized electronic definitions of pain-related care processes and outcomes (e.g. prolonged opioid use, readmission for pain, etc.); (2) to extract clinically meaningful data from both structured data and free text in electronic medical records (EMR) and examine the relationship between recommended care processes and outcomes for postoperative pain using EMRs; (3) to validate pain-related process-outcome relationships at a national level and to develop a National Quality Forum submission and evaluation form for a postoperative pain quality metric(s). This project will achieve these aims by developing data capture algorithms on Palo Alto Veterans Administration (VA) Healthcare data, refining algorithms at a tertiary academic hospital, and validating algorithms on the National VA healthcare system. Data will be identified and extracted from the EMR using an extended version of our validated data-mining workflow. Established experience with quality metric development and NQF endorsement will facilitate the dissemination of this work. These approaches are the basis of a learning healthcare system and the proposed research directly aligns with AHRQ’s mission and goals to improve healthcare quality through health information technology and data resources.
Prostate cancer is the most common malignancy in men. Newly diagnosed men face complex treatment choices, each with different risks of acquired morbidities, including patient-centered outcomes (PCOs). The widespread implementation of electronic health records (EHRs) provides opportunities to incorporate PCOs into healthcare quality metric evaluations. However, efforts to assess quality metrics in EHRs have been limited because most relevant data are not reliably captured in structured formats. Our proposal innovates in three ways. First, we will develop an EHR prostate cancer database that will allow for clinical care data to be analyzed alongside diagnostic details. Second, we will create novel ontological representations of quality metrics that will be public and reliably calculable across EHR-systems. Third, we will assemble a robust data-mining workflow that expands on existing quality assessment methods by focusing on ontology-based dictionaries to annotate free text. Combining this set of innovative components will uniquely allow us to use existing EHRs to efficiently study the association between treatment processes and outcomes. Our methods are applicable not only to prostate cancer, but any disease with associated quality metrics. Our primary hypothesis is important prostate cancer PCOs will differ significantly across treatments (i.e. robotic surgery, open prostatectomy, and radiation therapy). To gather data to test our hypothesis, we assemble a data-mining workflow to extract quality metrics from both structured and free-text components of EHRs. In Aim 1 we will create the building blocks needed to identify quality metric data in the EHR. We will develop an EHR-database, map quality metrics to medical vocabularies and ontologies, and create quality metric phenotypes. This will be the first endeavor to generate structured representations of quality metrics. In Aim 2 we will expand our workflow to gather data relevant to quality metrics from EHRs. This will allow us to identify and validate a comprehensive set of quality metrics from the EHR. We will validate our technologies in 3 different EHR systems to ensure transportability. In Aim 3 we will develop a web-based risk assessment tool to compare PCOs across prostate cancer treatments. Our proposal will be the largest assessment of patient-centered quality metrics. It will produce a validated list of structured quality metrics, data-mining workflow, clinician documentation feedback reports, and risk assessment tool. Given the current state of prostate cancer treatment and research, these results will significantly impact clinical care, providing clinicians and patients with evidence needed to balance the risks and benefits of different treatment options. Our work is consistent with our nation’s focus on EHR meaningful use and the comprehensive and systematic assessment of healthcare delivery, and with NCI’s focus on improving the quality of cancer care delivery.
Arastradero, Stanford University
My background and expertise is in the field of computational biology, with concentration in health services research. A key focus of my research is to apply novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery. My research involves managing and manipulating big data, which range from administrative claims data to electronic health records, and applying novel biostatistical techniques to innovatively assess clinical and policy related research questions at the population level. This research enables us to create formal, statistically rigid, evaluations of healthcare data using unique combinations of large datasets.