Brian Patterson
· Physician Administrative Director for Clinical AI, UW HealthUniversity of Wisconsin-Madison · Emergency Medicine
Active 2005–2024
About
Brian Patterson, MD, MPH, is an Associate Professor with tenure in the Department of Emergency Medicine at UW–Madison. He serves as the Administrative Director for Clinical Artificial Intelligence (AI) at UW Health and as the Medical Director for Predictive Analytics within the UW Health Informatics teams. In these roles, Dr. Patterson collaborates with hospital leadership to define AI strategy, governance, and development, leading efforts to develop predictive analytics, machine learning algorithms, and large language model tools aimed at improving patient care and provider well-being. His research program, directed through the Emergency Care Systems Lab, focuses on clinical informatics and geriatric emergency medicine, with a particular emphasis on generating actionable insights from routinely collected clinical data to enhance the quality and safety of emergency care for older adults. His collaborative work spans multiple departments and colleges within UW–Madison, including biostatistics, engineering, and business, and his current research involves automated risk scoring and referral systems to prevent falls among older adults after emergency department visits. Dr. Patterson holds a background in bioengineering from Pennsylvania State University and completed his MD/MPH at Northwestern University Feinberg School of Medicine, with residency training at Northwestern Memorial Hospital where he served as Chief Resident. His contributions to the field have been recognized through numerous awards and honors, including induction as a Fellow of the American Medical Informatics Association and the American College of Emergency Physicians, as well as leadership roles and funded research projects aimed at improving emergency care through informatics and predictive analytics.
Research topics
- Medicine
- Environmental health
- Nursing
- Computer Science
- Family medicine
- Medical emergency
- Internal medicine
- Political Science
- Demography
- Virology
- Pathology
- Internet privacy
- Biology
- Immunology
- Telecommunications
- Psychiatry
- Emergency medicine
- Business
Selected publications
JAMIA Open · 2023 · 13 citations
- Computer Science
- Political Science
- Medicine
Objectives: Substance misuse is a complex and heterogeneous set of conditions associated with high mortality and regional/demographic variations. Existing data systems are siloed and have been ineffective in curtailing the substance misuse epidemic. Therefore, we aimed to build a novel informatics platform, the Substance Misuse Data Commons (SMDC), by integrating multiple data modalities to provide a unified record of information crucial to improving outcomes in substance misuse patients. Materials and Methods: The SMDC was created by linking electronic health record (EHR) data from adult cases of substance (alcohol, opioid, nonopioid drug) misuse at the University of Wisconsin hospitals to socioeconomic and state agency data. To ensure private and secure data exchange, Privacy-Preserving Record Linkage (PPRL) and Honest Broker services were utilized. The overlap in mortality reporting among the EHR, state Vital Statistics, and a commercial national data source was assessed. Results: The SMDC included data from 36 522 patients experiencing 62 594 healthcare encounters. Over half of patients were linked to the statewide ambulance database and prescription drug monitoring program. Chronic diseases accounted for most underlying causes of death, while drug-related overdoses constituted 8%. Our analysis of mortality revealed a 49.1% overlap across the 3 data sources. Nonoverlapping deaths were associated with poor socioeconomic indicators. Discussion: Through PPRL, the SMDC enabled the longitudinal integration of multimodal data. Combining death data from local, state, and national sources enhanced mortality tracking and exposed disparities. Conclusion: The SMDC provides a comprehensive resource for clinical providers and policymakers to inform interventions targeting substance misuse-related hospitalizations, overdoses, and death.
PLoS ONE · 2021 · 94 citations
- Medicine
- Environmental health
- Demography
INTRODUCTION: Vaccination programs aim to control the COVID-19 pandemic. However, the relative impacts of vaccine coverage, effectiveness, and capacity in the context of nonpharmaceutical interventions such as mask use and physical distancing on the spread of SARS-CoV-2 are unclear. Our objective was to examine the impact of vaccination on the control of SARS-CoV-2 using our previously developed agent-based simulation model. METHODS: We applied our agent-based model to replicate COVID-19-related events in 1) Dane County, Wisconsin; 2) Milwaukee metropolitan area, Wisconsin; 3) New York City (NYC). We evaluated the impact of vaccination considering the proportion of the population vaccinated, probability that a vaccinated individual gains immunity, vaccination capacity, and adherence to nonpharmaceutical interventions. We estimated the timing of pandemic control, defined as the date after which only a small number of new cases occur. RESULTS: The timing of pandemic control depends highly on vaccination coverage, effectiveness, and adherence to nonpharmaceutical interventions. In Dane County and Milwaukee, if 50% of the population is vaccinated with a daily vaccination capacity of 0.25% of the population, vaccine effectiveness of 90%, and the adherence to nonpharmaceutical interventions is 60%, controlled spread could be achieved by June 2021 versus October 2021 in Dane County and November 2021 in Milwaukee without vaccine. DISCUSSION: In controlling the spread of SARS-CoV-2, the impact of vaccination varies widely depending not only on effectiveness and coverage, but also concurrent adherence to nonpharmaceutical interventions.
Annals of Internal Medicine · 2020 · 79 citations
- Medicine
- Environmental health
- Virology
BACKGROUND: Across the United States, various social distancing measures were implemented to control the spread of coronavirus disease 2019 (COVID-19). However, the effectiveness of such measures for specific regions with varying population demographic characteristics and different levels of adherence to social distancing is uncertain. OBJECTIVE: To determine the effect of social distancing measures in unique regions. DESIGN: An agent-based simulation model. SETTING: Agent-based model applied to Dane County, Wisconsin; the Milwaukee metropolitan (metro) area; and New York City (NYC). PATIENTS: Synthetic population at different ages. INTERVENTION: Different times for implementing and easing social distancing measures at different levels of adherence. MEASUREMENTS: The model represented the social network and interactions among persons in a region, considering population demographic characteristics, limited testing availability, "imported" infections, asymptomatic disease transmission, and age-specific adherence to social distancing measures. The primary outcome was the total number of confirmed COVID-19 cases. RESULTS: The timing of and adherence to social distancing had a major effect on COVID-19 occurrence. In NYC, implementing social distancing measures 1 week earlier would have reduced the total number of confirmed cases from 203 261 to 41 366 as of 31 May 2020, whereas a 1-week delay could have increased the number of confirmed cases to 1 407 600. A delay in implementation had a differential effect on the number of cases in the Milwaukee metro area versus Dane County, indicating that the effect of social distancing measures varies even within the same state. LIMITATION: The effect of weather conditions on transmission dynamics was not considered. CONCLUSION: The timing of implementing and easing social distancing measures has major effects on the number of COVID-19 cases. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases.
Western Journal of Emergency Medicine · 2020 · 8 citations
Senior authorCorresponding- Medicine
- Medical emergency
- Emergency medicine
INTRODUCTION: SARS-CoV-2, a novel coronavirus, manifests as a respiratory syndrome (COVID-19) and is the cause of an ongoing pandemic. The response to COVID-19 in the United States has been hampered by an overall lack of diagnostic testing capacity. To address uncertainty about ongoing levels of SARS-CoV-2 community transmission early in the pandemic, we aimed to develop a surveillance tool using readily available emergency department (ED) operations data extracted from the electronic health record (EHR). This involved optimizing the identification of acute respiratory infection (ARI)-related encounters and then comparing metrics for these encounters before and after the confirmation of SARS-CoV-2 community transmission. METHODS: We performed an observational study using operational EHR data from two Midwest EDs with a combined annual census of over 80,000. Data were collected three weeks before and after the first confirmed case of local SARS-CoV-2 community transmission. To optimize capture of ARI cases, we compared various metrics including chief complaint, discharge diagnoses, and ARI-related orders. Operational metrics for ARI cases, including volume, pathogen identification, and illness severity, were compared between the preand post-community transmission timeframes using chi-square tests of independence. RESULTS: Compared to our combined definition of ARI, chief complaint, discharge diagnoses, and isolation orders individually identified less than half of the cases. Respiratory pathogen testing was the top performing individual ARI definition but still only identified 72.2% of cases. From the pre to post periods, we observed significant increases in ED volumes due to ARI and ARI cases without identified pathogen. CONCLUSION: Certain methods for identifying ARI cases in the ED may be inadequate and multiple criteria should be used to optimize capture. In the absence of widely available SARS-CoV-2 testing, operational metrics for ARI-related encounters, especially the proportion of cases involving negative pathogen testing, are useful indicators for active surveillance of potential COVID-19 related ED visits.
Recent grants
Preventing Future Falls among Older Adults Presenting to the Emergency Department
NIH · $792k · 2016–2022
Frequent coauthors
- 57 shared
Frank Liao
University of Wisconsin Health
- 55 shared
Manish N. Shah
University of Wisconsin–Madison
- 49 shared
Pascale Carayon
University of Wisconsin–Madison
- 46 shared
Peter Hoonakker
University of Wisconsin Health
- 43 shared
Michael S. Pulia
- 35 shared
Douglas A. Wiegmann
University of Wisconsin–Madison
- 30 shared
Megan E. Salwei
Vanderbilt University Medical Center
- 28 shared
Majid Afshar
Education
M.D., Emergency Medicine
University of Wisconsin–Madison
Other
University of Wisconsin–Madison
Awards & honors
- Fellow induction, American Medical Informatics Association (…
- Leadership in Clinical Practice Physician Excellence Award,…
- Fellow induction, American College of Emergency Physicians (…
- Faculty Award for Excellence in Scholarship, BerbeeWalsh Dep…
- Author Spotlight, April 2019, Academy of Geriatric Emergency…
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