Research on health informatics at Kaiser Permanente Washington focuses on developing and using health information technology (IT) to transform health care delivery. By testing new paradigms of care that provide more opportunities to engage patients, this research is supplying valuable evidence that is helping shape federal policy and guiding innovative redesign of health care.
“We’re working to understand how to make health IT practical so patients and care teams find it useful and engaging,” explained Kaiser Permanente Washington Health Research Institute (KPWHRI) Senior Investigator James Ralston, MD, MPH. “We want to find ways to use information technologies to support patients and providers together, both inside and outside the office.”
Integral to this support is designing technologies that are user-friendly and meet the needs of both patients and providers. By applying human-centered methods that focus on needs, use, and usability, KPWHRI researchers inform the design of health IT with direct participation from users.
Groundbreaking methodological work by KPWHRI health informatics researchers includes developing natural language processing (NLP) to analyze text such as notes and written reports in electronic health records (EHRs). Assistant Investigator David Carrell, PhD, leads in the area of using NLP and machine learning to identify patient phenotypes, or specific health characteristics such as possible heart disease, risk of opioid overdose, or suggestion of colon cancer. This information can assist researchers in studying how genetics and other factors influence disease.
Other examples of KPWHRI health informatics research include projects using EHRs and secure electronic communications such as:
Examples of KPWHRI research in mobile health (mHealth) and user-centered design include:
“Our studies on using health IT to improve care are showing that we can achieve better outcomes when we shift care from the doctor’s office to where people live: in their homes—and online,” said Senior Investigator Beverly B. Green, MD, MPH.
Weberpals J, Shaw PA, Lin KJ, Wyss R, Plasek JM, Zhou L, Ngan K, DeRamus T, Raman SR, Hammill BG, Lee H, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Schneeweiss S, Desai RJ High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates 2025 Jan 22 doi: 10.1093/aje/kwaf017. Epub 2025-01-22. PubMed
Tiro JA, Lykken JM, Chen PM, Clark CR, Kobrin S, Chubak J, Feldman S, Werner C, Atlas SJ, Silver MI, Haas JS Delivering Guideline-Concordant Care for Patients With High-Risk HPV and Normal Cytologic Findings 2025 Jan 2;8(1):e2454969. doi: 10.1001/jamanetworkopen.2024.54969. Epub 2025-01-02. PubMed
Walsh CG, Wilimitis D, Chen Q, Wright A, Kolli J, Robinson K, Ripperger MA, Johnson KB, Carrell D, Desai RJ, Mosholder A, Dharmarajan S, Adimadhyam S, Fabbri D, Stojanovic D, Matheny ME, Bejan CA Scalable incident detection via natural language processing and probabilistic language models 2024 Oct 8;14(1):23429. doi: 10.1038/s41598-024-72756-7. Epub 2024-10-08. PubMed
Hazlehurst B, Carrell DS, Bann MA, Nelson J, Gruber S, Slaughter M, Cronkite DJ, Ball R, Floyd JS Finding uncoded anaphylaxis in electronic health records to estimate the sensitivity of ICD10 codes 2024 Oct 7;193(10):1494-1496. doi: 10.1093/aje/kwae063. Epub 2024-05-16. PubMed
Lu Y, Tong J, Chubak J, Lumley T, Hubbard RA, Xu H, Chen Y Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data 2024 Sep;157:104690. doi: 10.1016/j.jbi.2024.104690. Epub 2024-07-14. PubMed
Claire Allen, MPHManager, Collaborative Science |
Katharine A. Bradley, MD, MPHSenior Investigator |
Yates Coley, PhDAssociate Biostatistics Investigator |
Beverly B. Green, MD, MPHSenior Investigator |
Annie Hoopes, MD, MPHAssistant Investigator |
Paula Lozano, MD, MPHSenior Investigator; Director, ACT Center |
James D. Ralston, MD, MPHSenior Investigator |
Brian D. Williamson, PhDAssociate Biostatistics Investigator |