Research
Chronic pain and opioid misuse pose significant challenges to healthcare. Our lab’s research initiatives includes three key areas: evaluating and mitigating risks associated with opioid use, enhancing perioperative pain management to accelerate patient recovery, and leveraging digital health tools to improve healthcare delivery. By integrating data-driven risk assessments, tailored pain management techniques, and innovative digital solutions, we aim to advance therapeutic strategies and improve patient safety and outcomes.
Opioids and Cancer
In collaboration with the Dr. Arash Etemadi at the National Cancer Institute, we are leveraging rich datasets within the Veterans Healthcare Syste encompassing opioid prescription histories and cancer outcomes. Through a sophisticated analysis that contrasts long-term opioid users with non-users, one study aims to uncover patterns and benefits of opioid use for patients with cancer. Another study is evaluating potential carcinogenic risks linked to opioid exposure. Key components include assessing opioid prescription patterns, analyzing demographic variables, and evaluating related comorbidities. This extensive research seeks to refine our understanding of opioid impacts in clinical settings, guiding the development of targeted interventions that enhance patient safety and treatment efficacy. By harmonizing vast data with advanced statistical models, these project provides crucial insights that could reshape pain management protocols for cancer-afflicted veterans, ultimately aiming to balance effective pain relief with minimized cancer risk.
Opioids and Cancer
In collaboration with the Dr. Arash Etemadi at the National Cancer Institute, we are leveraging rich datasets within the Veterans Healthcare Syste encompassing opioid prescription histories and cancer outcomes. Through a sophisticated analysis that contrasts long-term opioid users with non-users, one study aims to uncover patterns and benefits of opioid use for patients with cancer. Another study is evaluating potential carcinogenic risks linked to opioid exposure. Key components include assessing opioid prescription patterns, analyzing demographic variables, and evaluating related comorbidities. This extensive research seeks to refine our understanding of opioid impacts in clinical settings, guiding the development of targeted interventions that enhance patient safety and treatment efficacy. By harmonizing vast data with advanced statistical models, these project provides crucial insights that could reshape pain management protocols for cancer-afflicted veterans, ultimately aiming to balance effective pain relief with minimized cancer risk.
Multiomics and Opioid Use Disorder Risk
In collaboration with Dr. Rodney Gabriel from the University of California San Diego, our labs objectives are to conduct three interconnected tasks, each targeting a different aspect of opioid use disorder (OUD) risk assessment and prediction. The first task will help develop genomic predictors that have been linked to opioid-related outcomes. We builds on the genomic data along with detailed patient histories that include socioeconomic and behavioral data. This effort is to craft advanced deep learning models that predict the onset of OUD,utilizing these insights for more informed healthcare strategies. The third task will involve prediction of OUD and its various outcomes by analyzing microbiome and metabolomic patterns. Together, these tasks form a comprehensive approach to understanding and mitigating the risks associated with opioid use.
Multiomics and Opioid Use Disorder Risk
In collaboration with Dr. Rodney Gabriel from the University of California San Diego, our labs objectives are to conduct three interconnected tasks, each targeting a different aspect of opioid use disorder (OUD) risk assessment and prediction. The first task will help develop genomic predictors that have been linked to opioid-related outcomes. We builds on the genomic data along with detailed patient histories that include socioeconomic and behavioral data. This effort is to craft advanced deep learning models that predict the onset of OUD,utilizing these insights for more informed healthcare strategies. The third task will involve prediction of OUD and its various outcomes by analyzing microbiome and metabolomic patterns. Together, these tasks form a comprehensive approach to understanding and mitigating the risks associated with opioid use.
Perioperative Pain Management and Outcomes
Our work in this area focuses on enhancing veteran patient care by optimizing pain management strategies in perioperative settings. We have a number of collaborators both at the VA Palo Alto and Stanford. In collaboration with Dr Edward Mariano and the Regional Anesthesia and Pain Medicine service at the VA Palo Alto, we have been tracking functional outcomes of regional anesthesia, procedure-specific definitions for chronic postoperative opioid use, measures of multimodal analgesia, and the use of neuraxial anesthesia for specific surgeries, like total hip arthroplasty, to improve outcomes and reduce opioid use. In collaboration with Dr. Sean Mackey our work involves deploying Perioperative CHOIR project, which applies clinical informatics and advanced learning health systems approaches to phenotype surgical candidates using a mix of medical, functional, and psychosocial measures. This system, supported by machine learning, will aid in refining decision-making and deploying targeted treatments.
Perioperative Pain Management and Outcomes
Our work in this area focuses on enhancing veteran patient care by optimizing pain management strategies in perioperative settings. We have a number of collaborators both at the VA Palo Alto and Stanford. In collaboration with Dr Edward Mariano and the Regional Anesthesia and Pain Medicine service at the VA Palo Alto, we have been tracking functional outcomes of regional anesthesia, procedure-specific definitions for chronic postoperative opioid use, measures of multimodal analgesia, and the use of neuraxial anesthesia for specific surgeries, like total hip arthroplasty, to improve outcomes and reduce opioid use. In collaboration with Dr. Sean Mackey our work involves deploying Perioperative CHOIR project, which applies clinical informatics and advanced learning health systems approaches to phenotype surgical candidates using a mix of medical, functional, and psychosocial measures. This system, supported by machine learning, will aid in refining decision-making and deploying targeted treatments.
Digital Health
Our work focuses on leveraging digital tools and technologies to enhance healthcare delivery. Collaborating with Dr. Karthik Raghunathan and Dr. Atilio Barbeito from Duke University, we are evaluating ways to advance patient engagement, monitor health metrics, and deliver personalized health interventions effectively through digital means. A significant component of this initiative is studying perioperative telehealth initiatives within the Veterans Health Administration. The project explores the utilization of telemedicine in pre-anesthesia evaluation clinics, which assess patients' conditions and risks prior to surgical or invasive procedures. Traditionally conducted in-person, these evaluations have increasingly incorporated telehealth, especially accelerated by the COVID-19 pandemic.
Digital Health
Our work focuses on leveraging digital tools and technologies to enhance healthcare delivery. Collaborating with Dr. Karthik Raghunathan and Dr. Atilio Barbeito from Duke University, we are evaluating ways to advance patient engagement, monitor health metrics, and deliver personalized health interventions effectively through digital means. A significant component of this initiative is studying perioperative telehealth initiatives within the Veterans Health Administration. The project explores the utilization of telemedicine in pre-anesthesia evaluation clinics, which assess patients' conditions and risks prior to surgical or invasive procedures. Traditionally conducted in-person, these evaluations have increasingly incorporated telehealth, especially accelerated by the COVID-19 pandemic.