Eric A. Johnson, MS

“We’re on the forefront of research in many areas. It’s our responsibility to make sure the research is done well and is useful to those who practice medicine.”

Eric A. Johnson, MS

Senior Collaborative Biostatistician, Kaiser Permanente Washington Health Research Institute

Eric.Johnson@kp.org
206-287-2015

Curriculum vitae (CV)

Biography

Eric Johnson, MS, completed the University of Washington’s biostatistics master’s program in 2008. His thesis explored common theoretical problems involved with missing data. He quantified them, highlighting conditions leading to suboptimal performance, and provided new guidelines for using various multiple imputation techniques.

During his time at KPWHRI, Mr. Johnson has been involved with multiple projects involving direct intervention with the care-delivery system, observational studies and microsimulation modeling on colorectal and ovarian cancer, massage therapy, and estimating radiation exposure from medical imaging. His current responsibilities lie in research on obesity, opioids, and mental health.

Before earning his master’s degree, Mr. Johnson worked for four years as a research assistant on the Multi-Ethnic Study of Atherosclerosis (MESA), performing statistical analyses, verifying incoming data, and developing a process for providing data sets to all MESA researchers. Later, he was a research statistician with the Department of Veteran Affairs (VA), serving as the primary analyst in an investigation of how well VA hospitals performed non-cardiac surgeries. Mr. Johnson assessed statistical models used by the National Surgical Quality Improvement Program—then devised, tested, and implemented improved models, reporting his findings to VA leadership.

Research interests and experience

Biostatistics

Multiple imputation; prediction models, working with large data
Health Systems

Implementing prediction models, and validating that they work as intended. Research into interventions that are financially feasible for health systems to widely implement
Cancer
Complementary & Integrative Health
Health Services & Economics
Obesity
Mental Health

Recent publications

Penfold RB, Yoo HI, Richards JE, Crossnohere NL, Johnson E, Pabiniak CJ, Renz AD, Campoamor NB, Simon GE, Bridges JFP. Acceptability of linking individual credit, financial, and public records data to healthcare records for suicide risk machine learning models. JAMIA Open. 2024 Oct 21;7(4):ooae113. doi: 10.1093/jamiaopen/ooae113. eCollection 2024. PubMed

Harrington LB, Benz L, Haneuse S, Johnson E, Coleman KJ, Courcoulas AP, Li RA, Theis MK, Cooper J, Chin PL, Grinberg GG, Daigle CR, Chang JH, Um SS, Yenumula PR, Getty JZ, Arterburn DE. Bariatric Surgery and the Long-Term Risk of Venous Thromboembolism: A Population-Based Cohort Study. Obes Surg. 2024;34(6):2017-2025. doi: 10.1007/s11695-024-07236-y. Epub 2024 Apr 30. PubMed

Simon GE, Johnson E, Shortreed SM, Ziebell RA, Rossom RC, Ahmedani BK, Coleman KJ, Beck A, Lynch FL, Daida YG. Predicting suicide death after emergency department visits with mental health or self-harm diagnoses. Gen Hosp Psychiatry. 2024 Jan 22;87:13-19. doi: 10.1016/j.genhosppsych.2024.01.009. [Epub ahead of print]. PubMed

Simon GE, Shortreed SM, Johnson E, Yaseen ZS, Stone M, Mosholder AD, Ahmedani BK, Coleman KJ, Coley RY, Penfold RB, Toh S. Predicting risk of suicidal behavior from insurance claims data vs. linked data from insurance claims and electronic health records. Pharmacoepidemiol Drug Saf. 2023 Dec 19. doi: 10.1002/pds.5734. [Epub ahead of print]. PubMed

Shortreed SM, Walker RL, Johnson E, Wellman R, Cruz M, Ziebell R, Coley RY, Yaseen ZS, Dharmarajan S, Penfold RB, Ahmedani BK, Rossom RC, Beck A, Boggs JM, Simon GE. Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction. NPJ Digit Med. 2023;6(1):47. doi: 10.1038/s41746-023-00772-4. PubMed

Cruz M, Shortreed SM, Richards JE, Coley RY, Yarborough BJ, Walker RL, Johnson E, Ahmedani BK, Rossom R, Coleman KJ, Boggs JM, Beck AL, Simon GE. Machine learning prediction of suicide risk does not identify patients without traditional risk factors. J Clin Psychiatry. 2022 Aug 31;83(5):21m14178. doi: 10.4088/JCP.21m14178. PubMed

Penfold RB, Carrell DS, Cronkite DJ, Pabiniak C, Dodd T, Glass AM, Johnson E, Thompson E, Arrighi HM, Stang PE. Development of a machine learning model to predict mild cognitive impairment using natural language processing in the absence of screening. BMC Med Inform Decis Mak. 2022 May 12;22(1):129. doi: 10.1186/s12911-022-01864-z. PubMed

Walker RL, Shortreed SM, Ziebell RA, Johnson E, Boggs JM, Lynch FL, Daida YG, Ahmedani BK, Rossom R, Coleman KJ, Simon GE. Evaluation of electronic health record-based suicide risk prediction models on contemporary data. Appl Clin Inform. 2021;12(4):778-787. doi: 10.1055/s-0041-1733908. Epub 2021 Aug 18. PubMed

Courcoulas AP, Johnson E, Arterburn DE, Haneuse S, Herrinton LJ, Fisher DP, Li RA, Theis MK, Liu L, Taylor B, Cooper J, Chin PL, Grinberg GG, Gupta A, Saurabh S, Um SS, Yenumula PR, Zelada JL, Coleman KJ. Reduction in long-term mortality after sleeve gastrectomy and gastric bypass compared to non-surgical patients with severe obesity. Ann Surg. 2023 Mar 1;277(3):442-448. doi: 10.1097/SLA.0000000000005155. Epub 2021 Aug 13. PubMed

Penfold RB, Whiteside U, Johnson EE, Stewart CC, Oliver MM, Shortreed SM, Beck A, Coleman KJ, Rossom RC, Lawrence JM, Simon GE. Utility of item 9 of the patient health questionnaire in the prospective identification of adolescents at risk of suicide attempt. Suicide Life Threat Behav. 2021 Jul 31. doi: 10.1111/sltb.12751. [Epub ahead of print]. PubMed