Christopher A Harle1, Julie DiIulio2, Sarah M Downs1, Elizabeth C Danielson1, Shilo Anders3, Robert L Cook4, Robert W Hurley5, Burke W Mamlin6, Laura G Militello2. 1. Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, United States. 2. Applied Decision Science, LLC, Dayton, Ohio, United States. 3. Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, United States. 4. Department of Epidemiology, University of Florida, Gainesville, Florida, United States. 5. Department of Anesthesiology, Wake Forest University School of Medicine, Wake Forest University, Winston-Salem, North Carolina, United States. 6. Regenstrief Institute, Indianapolis, Indiana, United States.
Abstract
BACKGROUND: For complex patients with chronic conditions, electronic health records (EHRs) contain large amounts of relevant historical patient data. To use this information effectively, clinicians may benefit from visual information displays that organize and help them make sense of information on past and current treatments, outcomes, and new treatment options. Unfortunately, few clinical decision support tools are designed to support clinical sensemaking. OBJECTIVE: The objective of this study was to describe a decision-centered design process, and resultant interactive patient information displays, to support key clinical decision requirements in chronic noncancer pain care. METHODS: To identify key clinical decision requirements, we conducted critical decision method interviews with 10 adult primary care clinicians. Next, to identify key information needs and decision support design seeds, we conducted a half-day multidisciplinary design workshop. Finally, we designed an interactive prototype to support the key clinical decision requirements and information needs uncovered during the previous research activities. RESULTS: The resulting Chronic Pain Treatment Tracker prototype summarizes the current treatment plan, past treatment history, potential future treatments, and treatment options to be cautious about. Clinicians can access additional details about each treatment, current or past, through modal views. Additional decision support for potential future treatments and treatments to be cautious about is also provided through modal views. CONCLUSION: This study designed the Chronic Pain Treatment Tracker, a novel approach to decision support that presents clinicians with the information they need in a structure that promotes quick uptake, understanding, and action. Georg Thieme Verlag KG Stuttgart · New York.
BACKGROUND: For complex patients with chronic conditions, electronic health records (EHRs) contain large amounts of relevant historical patient data. To use this information effectively, clinicians may benefit from visual information displays that organize and help them make sense of information on past and current treatments, outcomes, and new treatment options. Unfortunately, few clinical decision support tools are designed to support clinical sensemaking. OBJECTIVE: The objective of this study was to describe a decision-centered design process, and resultant interactive patient information displays, to support key clinical decision requirements in chronic noncancer pain care. METHODS: To identify key clinical decision requirements, we conducted critical decision method interviews with 10 adult primary care clinicians. Next, to identify key information needs and decision support design seeds, we conducted a half-day multidisciplinary design workshop. Finally, we designed an interactive prototype to support the key clinical decision requirements and information needs uncovered during the previous research activities. RESULTS: The resulting Chronic Pain Treatment Tracker prototype summarizes the current treatment plan, past treatment history, potential future treatments, and treatment options to be cautious about. Clinicians can access additional details about each treatment, current or past, through modal views. Additional decision support for potential future treatments and treatments to be cautious about is also provided through modal views. CONCLUSION: This study designed the Chronic Pain Treatment Tracker, a novel approach to decision support that presents clinicians with the information they need in a structure that promotes quick uptake, understanding, and action. Georg Thieme Verlag KG Stuttgart · New York.
Authors: Laura G Militello; Shilo Anders; Sarah M Downs; Julie Diiulio; Elizabeth C Danielson; Robert W Hurley; Christopher A Harle Journal: Cogn Technol Work Date: 2018-05-30 Impact factor: 2.372
Authors: Kirk D Wyatt; Louise M Stuart; Juan P Brito; Barbara Carranza Leon; Juan Pablo Domecq; Gabriela J Prutsky; Jason S Egginton; Andrew D Calvin; Nilay D Shah; Mohammad Hassan Murad; Victor M Montori Journal: Med Care Date: 2014-03 Impact factor: 2.983
Authors: Katie S Allen; Elizabeth C Danielson; Sarah M Downs; Olena Mazurenko; Julie Diiulio; Ramzi G Salloum; Burke W Mamlin; Christopher A Harle Journal: Appl Clin Inform Date: 2022-06-01 Impact factor: 2.762
Authors: Nate C Apathy; Lindsey Sanner; Meredith C B Adams; Burke W Mamlin; Randall W Grout; Saura Fortin; Jennifer Hillstrom; Amit Saha; Evgenia Teal; Joshua R Vest; Nir Menachemi; Robert W Hurley; Christopher A Harle; Olena Mazurenko Journal: JAMIA Open Date: 2022-09-15
Authors: Ramzi G Salloum; Lori Bilello; Jiang Bian; Julie Diiulio; Laura Gonzalez Paz; Matthew J Gurka; Maria Gutierrez; Robert W Hurley; Ross E Jones; Francisco Martinez-Wittinghan; Laura Marcial; Ghania Masri; Cara McDonnell; Laura G Militello; François Modave; Khoa Nguyen; Bryn Rhodes; Kendra Siler; David Willis; Christopher A Harle Journal: Implement Sci Date: 2022-07-15 Impact factor: 7.960