Health Informatics studies
SCENT Study: Identifying primary and recurrent cancer diagnosis with SAS computer program
Developing a Risk Prediction Model and Evaluating the Economic Burden of Ankylosing Spondylitis in an Integrated Healthcare System
The primary aim is to develop an ankylosing spondylitis risk prediction model using data from Kaiser’s electronic medical records (EMR). We will also evaluate medical expenditure, health related quality of life and work productivity losses associated with ankylosing spondylitis.
Principal Investigator:Aniket A. Kawatkar, PhD, MS
Funding Source:Novartis Pharmaceuticals Corporation
Funding Years:2020 - 2022
Effects of Medical Products on Suicidal Ideation and Behavior in Serious Mental Illness
Reducing risk of suicidal behavior is an urgent public health priority. Suicide accounted for approximately 45,000 deaths in the United States in 2016. While most major causes of death have steadily declined, suicide mortality in the US has increased by over 25% in the past 15 years, with greater increases in young people. Increasing rates of suicidal behavior are intertwined with prescription opioid use and opioid overdose. Traditional clinical trials will not be able to enroll large enough nor generalizable enough samples of patients to adequately inform regulation regarding these issues. We propose a comprehensive program of infrastructure development and methods development to support future generation of real-world evidence addressing these critical gaps. The project team will include health systems and embedded research organizations with deep expertise in stakeholder engagement, medical informatics, data science, clinical epidemiology, biostatistics, pragmatic clinical trial methods, implementation science, and innovations in care delivery. This program will be embedded in 4 integrated health systems serving a combined population of approximately 10 million members. This work will be conducted in collaboration with health system and patient/family stakeholders, to assure that methods and evidence developed will actually address real-world questions.
Site Principal Investigator:Karen J. Coleman, PhD, MS
Funding Source:Food and Drug Administration (FDA)
Funding Years:09/30/2018 - 09/30/2021
Identifying cases of shoulder injury related to vaccine administration (SIRVA) using natural language processing (NLP) methods.
The objective of this study is to explore the feasibility of using NLP to identify potential SIRVA cases among vaccinated patients at a single VSD site, KPSC. The purpose is to develop an NLP algorithm to narrow down potential SIRVA cases to facilitate manual chart review for future studies.
Principal Investigator:Steven J. Jacobsen, MD, PhD
Funding Source:Centers for Disease Control and Prevention (CDC)
Mental Health Research Network II
We have established a Mental Health Research Network including 13 established public-domain research centers based in integrated not-for-profit health systems. These systems provide care to a diverse population of 10 million people in 11 states, and they share rich and compatible data resources to support a range of effectiveness research. Diversity of member demographics, insurance coverage, and organization of health services make this network an ideal environment for studying variation in care, comparing effectiveness and cost of treatments across practice environments, and studying dissemination and health policies. Participating research centers are experienced in a wide range of clinical areas and research methods. The long-term objectives are to expand the Mental Health Research Network to include additional health systems and external investigators, to conduct multi-site observational and experimental studies of comparative effectiveness, to develop and evaluate methods for dissemination and implementation, and to become a national resource of research methods and effectiveness evidence for researchers, patients, providers and healthcare leaders.