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Kim JY, Hasan A, Kellogg KC, Ratliff W, Murray SG, Suresh H, Valladares A, Shaw K, Tobey D, Vidal DE, Lifson MA, Patel M, Raji ID, Gao M, Knechtle W, Tang L, Balu S, Sendak MP. Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities. PLOS Digit Health 2024; 3:e0000390. [PMID: 38723025 PMCID: PMC11081364 DOI: 10.1371/journal.pdig.0000390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/15/2024] [Indexed: 05/12/2024]
Abstract
The use of data-driven technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is growing in healthcare. However, the proliferation of healthcare AI tools has outpaced regulatory frameworks, accountability measures, and governance standards to ensure safe, effective, and equitable use. To address these gaps and tackle a common challenge faced by healthcare delivery organizations, a case-based workshop was organized, and a framework was developed to evaluate the potential impact of implementing an AI solution on health equity. The Health Equity Across the AI Lifecycle (HEAAL) is co-designed with extensive engagement of clinical, operational, technical, and regulatory leaders across healthcare delivery organizations and ecosystem partners in the US. It assesses 5 equity assessment domains-accountability, fairness, fitness for purpose, reliability and validity, and transparency-across the span of eight key decision points in the AI adoption lifecycle. It is a process-oriented framework containing 37 step-by-step procedures for evaluating an existing AI solution and 34 procedures for evaluating a new AI solution in total. Within each procedure, it identifies relevant key stakeholders and data sources used to conduct the procedure. HEAAL guides how healthcare delivery organizations may mitigate the potential risk of AI solutions worsening health inequities. It also informs how much resources and support are required to assess the potential impact of AI solutions on health inequities.
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Affiliation(s)
- Jee Young Kim
- Duke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America
| | - Alifia Hasan
- Duke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America
| | - Katherine C. Kellogg
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - William Ratliff
- Duke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America
| | - Sara G. Murray
- Division of Hospital Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Harini Suresh
- Cornell University, New York, New York, United States of America
| | | | - Keo Shaw
- FDA Regulatory Group, DLA Piper, San Francisco, California, United States of America
| | - Danny Tobey
- AI and Data Analytics, DLA Piper, Dallas, Texas, United States of America
| | - David E. Vidal
- Center for Digital Health, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Mark A. Lifson
- Center for Digital Health, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Manesh Patel
- Division of Cardiology, Duke Health, Durham, North Carolina, United States of America
| | - Inioluwa Deborah Raji
- Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California, United States of America
| | - Michael Gao
- Duke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America
| | - William Knechtle
- Duke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America
| | - Linda Tang
- School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Suresh Balu
- Duke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America
| | - Mark P. Sendak
- Duke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America
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Arvisais-Anhalt S, Gonias SL, Murray SG. Establishing priorities for implementation of large language models in pathology and laboratory medicine. Acad Pathol 2024; 11:100101. [PMID: 38292297 PMCID: PMC10825232 DOI: 10.1016/j.acpath.2023.100101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 10/02/2023] [Accepted: 10/29/2023] [Indexed: 02/01/2024] Open
Abstract
Artificial intelligence and machine learning have numerous applications in pathology and laboratory medicine. The release of ChatGPT prompted speculation regarding the potentially transformative role of large-language models (LLMs) in academic pathology, laboratory medicine, and pathology education. Because of the potential to improve LLMs over the upcoming years, pathology and laboratory medicine clinicians are encouraged to embrace this technology, identify pathways by which LLMs may support our missions in education, clinical practice, and research, participate in the refinement of AI modalities, and design user-friendly interfaces that integrate these tools into our most important workflows. Challenges regarding the use of LLMs, which have already received considerable attention in a general sense, are also reviewed herein within the context of the pathology field and are important to consider as LLM applications are identified and operationalized.
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Affiliation(s)
- Simone Arvisais-Anhalt
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Steven L. Gonias
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Sara G. Murray
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
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Ravi A, Neinstein A, Murray SG. Large Language Models and Medical Education: Preparing for a Rapid Transformation in How Trainees Will Learn to Be Doctors. ATS Sch 2023; 4:282-292. [PMID: 37795112 PMCID: PMC10547030 DOI: 10.34197/ats-scholar.2023-0036ps] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 06/01/2023] [Indexed: 10/06/2023] Open
Abstract
Artificial intelligence has the potential to revolutionize health care but has yet to be widely implemented. In part, this may be because, to date, we have focused on easily predicted rather than easily actionable problems. Large language models (LLMs) represent a paradigm shift in our approach to artificial intelligence because they are easily accessible and already being tested by frontline clinicians, who are rapidly identifying possible use cases. LLMs in health care have the potential to reduce clerical work, bridge gaps in patient education, and more. As we enter this era of healthcare delivery, LLMs will present both opportunities and challenges in medical education. Future models should be developed to support trainees to develop skills in clinical reasoning, encourage evidence-based medicine, and offer case-based training opportunities. LLMs may also change what we continue teaching trainees with regard to clinical documentation. Finally, trainees can help us train and develop the LLMs of the future as we consider the best ways to incorporate LLMs into medical education. Ready or not, LLMs will soon be integrated into various aspects of clinical practice, and we must work closely with students and educators to make sure these models are also built with trainees in mind to responsibly chaperone medical education into the next era.
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Affiliation(s)
| | - Aaron Neinstein
- Department of Medicine
- Center for Digital Health Innovation and
| | - Sara G. Murray
- Department of Medicine
- Health Informatics, University of California, San Francisco, San Francisco, California
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Olson EM, Sanborn DM, Dyster TG, Kelm DJ, Murray SG, Santhosh L, DesJardin JT. Gender Disparities in Critical Care Procedure Training of Internal Medicine Residents. ATS Sch 2023; 4:164-176. [PMID: 37538076 PMCID: PMC10394715 DOI: 10.34197/ats-scholar.2022-0025oc] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 12/22/2022] [Indexed: 08/05/2023] Open
Abstract
Background Procedural training is a required competency in internal medicine (IM) residency, yet limited data exist on residents' experience of procedural training. Objectives We sought to understand how gender impacts access to procedural training among IM residents. Methods A mixed-methods, explanatory sequential study was performed. Procedure volume for IM residents between 2016 and 2020 was assessed at two large academic residencies (Program A and Program B: 399 residents and 4,020 procedures). Procedural rates and actual versus expected procedure volume by gender were compared, with separate analyses by clinical environment (intensive care unit [ICU] or structured procedural service). Semistructured gender-congruent focus groups were conducted. Topics included identity formation as a proceduralist and the resident procedural learning experience, including perceived gender bias in procedure allocation. Results Compared with men, women residents performed disproportionately fewer ICU procedures per month at Program A (1.4 vs. 2.7; P < 0.05) but not at Program B (0.36 vs. 0.54; P = 0.23). At Program A, women performed only 47% of ICU procedures, significantly fewer than the 54% they were expected to perform on the basis of their time on ICU rotations (P < 0.001). For equal gender distribution of procedural volume at Program A, 11% of the procedures performed by men would have needed to have been performed by women instead. Gender was not associated with differences in the Program A structured procedural service (53% observed vs. 52% expected; P = 0.935), Program B structured procedural service (40% observed vs. 43% expected; P = 0.174), or in Program B ICUs (33% observed vs. 34% expected; P = 0.656). Focus group analysis identified that women from both residencies perceived that assertiveness was required for procedural training in unstructured learning environments. Residents felt that gender influenced access to procedural opportunities, ability to self-advocate for procedural experience, identity formation as a proceduralist, and confidence in acquiring procedural skills. Conclusion Gender disparities in access to procedural training during ICU rotations were seen at one institution but not another. There were ubiquitous perceptions that assertiveness was important to access procedural opportunities. We hypothesize that structured allocation of procedures would mitigate disparities by allowing all residents to access procedural training regardless of self-advocacy. Residency programs should adopt structured procedural training programs to counteract inequities.
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Affiliation(s)
| | | | | | - Diana J. Kelm
- Department of Medicine and
- Division of Pulmonary and Critical Care,
Mayo Clinic, Rochester, Minnesota
| | - Sara G. Murray
- Department of Medicine
- Division of Hospital Medicine, and
| | | | - Jacqueline T. DesJardin
- Department of Medicine
- Division of Cardiology, University of
California San Francisco, San Francisco, California
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5
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Arvisais-Anhalt S, Ravi A, Weia B, Aarts J, Ahmad HB, Araj E, Bauml JA, Benham-Hutchins M, Boyd AD, Brecht-Doscher A, Butler-Henderson K, Butte AJ, Cardilo AB, Chilukuri N, Cho MK, Cohen JK, Craven CK, Crusco S, Dadabhoy F, Dash D, DeBolt C, Elkin PL, Fayanju OA, Fochtmann LJ, Graham JV, Hanna JJ, Hersh W, Hofford MR, Hron JD, Huang SS, Jackson BR, Kaplan B, Kelly W, Ko K, Koppel R, Kurapati N, Labbad G, Lee JJ, Lehmann CU, Leitner S, Liao ZC, Medford RJ, Melnick ER, Muniyappa AN, Murray SG, Neinstein AB, Nichols-Johnson V, Novak LL, Ogan WS, Ozeran L, Pageler NM, Pandita D, Perumbeti A, Petersen C, Pierce L, Puttagunta R, Ramaswamy P, Rogers KM, Rosenbloom ST, Ryan A, Saleh S, Sarabu C, Schreiber R, Shaw KA, Sim I, Sirintrapun SJ, Solomonides A, Spector JD, Starren JB, Stoffel M, Subbian V, Swanson K, Tomes A, Trang K, Unertl KM, Weon JL, Whooley MA, Wiley K, Williamson DFK, Winkelstein P, Wong J, Xie J, Yarahuan JKW, Yung N, Zera C, Ratanawongsa N, Sadasivaiah S. Paging the Clinical Informatics Community: Respond STAT to Dobbs v. Jackson's Women's Health Organization. Appl Clin Inform 2023; 14:164-171. [PMID: 36535703 PMCID: PMC9977563 DOI: 10.1055/a-2000-7590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Simone Arvisais-Anhalt
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California, United States
| | - Akshay Ravi
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Benjamin Weia
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Jos Aarts
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Hasan B. Ahmad
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States
| | - Ellen Araj
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, United States
| | - Julie A. Bauml
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Marge Benham-Hutchins
- College of Nursing and Health Science, Texas A&M University, Corpus Christi, Corpus Christi, Texas, United States
| | - Andrew D. Boyd
- Department of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, Illinois, United States
| | - Aimee Brecht-Doscher
- Department of Obstetrics and Gynecology, Ventura County Healthcare Agency, Ventura, California, United States
| | | | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States
| | - Anthony B. Cardilo
- Department of Emergency Medicine, NYU Langone Health, New York, New York, United States
| | - Nymisha Chilukuri
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
| | - Mildred K. Cho
- Departments of Medicine and Pediatrics, Stanford University School of Medicine, Stanford, California, United States
- Stanford Center for Biomedical Ethics, Stanford University, Stanford, California, United States
| | - Jenny K. Cohen
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Catherine K. Craven
- Division of Clinical Research Informatics, Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, Texas, United States
| | - Salvatore Crusco
- The Feinstein Institutes for Medical Research, Northwell Health, New Hyde Park, New York, United States
| | - Farah Dadabhoy
- Department of Emergency Medicine, Mass General Brigham, Boston, Massachusetts, United States
| | - Dev Dash
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, United States
| | - Claire DeBolt
- Department of Pulmonary Critical Care, University of Virginia, Charlottesville, Virginia, United States
- Department of Clinical Informatics, University of Virginia, Charlottesville, Virginia, United States
| | - Peter L. Elkin
- Department of Biomedical Informatics, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, New York, United States
| | - Oluseyi A. Fayanju
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States
| | - Laura J. Fochtmann
- Department of Psychiatry, Stony Brook University, Stony Brook, New York, United States
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, United States
| | | | - John J. Hanna
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, United States
| | - William Hersh
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
| | - Mackenzie R. Hofford
- Division of General Medicine, Department of Medicine, Washington University in St. Louis, St Louis, Missouri, United States
| | - Jonathan D. Hron
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Sean S. Huang
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Brian R. Jackson
- Department of Pathology, University of Utah, Salt Lake City, Utah, United States
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Bonnie Kaplan
- Bioethics Center, Information Society Project, Solomon Center for Health Care Policy, Yale University Center for Medical Informatics, New Haven, Connecticut, United States
| | - William Kelly
- Department of Biomedical Informatics, University at Buffalo, Buffalo, New York, United States
| | - Kyungmin Ko
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas, United States
- Department of Pathology, Texas Children's Hospital, Houston, Texas, United States
| | - Ross Koppel
- Department of Medical informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Department of Medical informatics, University at Buffalo, Buffalo, New York, United States
| | - Nikhil Kurapati
- Department of Family Medicine Soin Medical Center, Kettering Health, Dayton, Ohio
| | - Gabriel Labbad
- Enterprise Information Systems, Cedars Sinai, Los Angeles, California, United States
| | - Julie J. Lee
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
| | - Christoph U. Lehmann
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States
| | - Stefano Leitner
- Department of Hospital Medicine, University of California San Francisco, San Francisco, California, United States
| | | | - Richard J. Medford
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States
| | - Edward R. Melnick
- Department of Emergency Medicine and Biostatistics (Health Informatics), Yale School of Medicine, New Haven, Connecticut, United States
| | - Anoop N. Muniyappa
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Sara G. Murray
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Aaron Barak Neinstein
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Victoria Nichols-Johnson
- Department of OB/Gyn (Emerita), Southern Illinois University School of Medicine, Springfield, Illinois, United States
| | - Laurie Lovett Novak
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - William Scott Ogan
- Division of Bioinformatics, Department of Medicine, University of California San Diego Health, La Jolla, California, United States
| | - Larry Ozeran
- Clinical Informatics, Inc., Yuba City, California, United States
| | - Natalie M. Pageler
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
| | - Deepti Pandita
- Department of Medicine, Hennepin HealthCare, Minneapolis, Minnesota, United States
| | - Ajay Perumbeti
- University of Arizona College of Medicine-Phoenix, Phoenix, Arizona, United States
| | - Carolyn Petersen
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, United States
| | - Logan Pierce
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Raghuveer Puttagunta
- Department of Internal Medicine, Geisinger Health, Danville, Pennsylvania, United States
| | - Priya Ramaswamy
- Department of Anesthesiology and Critical Care, University of California San Francisco, San Francisco, California, United States
| | - Kendall M. Rogers
- Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico, United States
| | - S Trent Rosenbloom
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Angela Ryan
- Australasian Institute of Digital Health, Sydney, New South Wales, Australia
| | - Sameh Saleh
- Department of Biomedical and Health Informatics/Department of Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Chethan Sarabu
- Department of Information Services, Penn State Health, Hershey, Pennsylvania, United States
| | - Richard Schreiber
- Department of Information Services, Penn State Health, Hershey, Pennsylvania, United States
- Department of Medicine, Penn State Health, Hershey, Pennsylvania, United States
| | - Kate A. Shaw
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, California, United States
| | - Ida Sim
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
- University of California San Francisco University of California Berkeley Joint Program in Computational Precision Health, University of California San Francisco and University of California Berkeley, San Francisco, California, United States
| | - S Joseph Sirintrapun
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Anthony Solomonides
- Research Institute, NorthShore University HealthSystem, Evanston, Illinois, United States
| | - Jacob D. Spector
- Information Services Department, Boston Children's Hospital, Boston, Massachusetts, United States
| | - Justin B. Starren
- Division of Health and Biomedical Informatics, Department of Preventative Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
| | - Michelle Stoffel
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, Arizona, United States
| | - Karl Swanson
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Adrian Tomes
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Karen Trang
- Department of Surgery, University of California San Francisco, San Francisco, California, United States
| | - Kim M. Unertl
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Jenny L. Weon
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, United States
| | - Mary A. Whooley
- Departments of Medicine, Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States
- San Francisco Veterans Affairs Healthcare System, San Francisco, California, United States
| | - Kevin Wiley
- Department of Healthcare Leadership and Management, Medical University of South Carolina, Columbia, South Carolina, United States
| | - Drew F. K. Williamson
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Peter Winkelstein
- Institute for Healthcare Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States
| | - Jenson Wong
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, California, United States
| | - James Xie
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States
| | - Julia K. W. Yarahuan
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Nathan Yung
- Department of Hospital Medicine, University of California San Diego Health, La Jolla, California, United States
| | - Chloe Zera
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States
| | - Neda Ratanawongsa
- Division of General Internal Medicine, Department of Medicine, University of California San Francisco Center for Vulnerable Populations, San Francisco, California, United States
| | - Shobha Sadasivaiah
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
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Khanna RR, Murray SG, Wen T, Salmeen K, Illangasekare T, Benfield N, Adler-Milstein J, Savage L. Protecting reproductive health information in the post-Roe era: interoperability strategies for healthcare institutions. J Am Med Inform Assoc 2022; 30:161-166. [PMID: 36287823 PMCID: PMC9748529 DOI: 10.1093/jamia/ocac194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/12/2022] [Accepted: 10/07/2022] [Indexed: 12/15/2022] Open
Abstract
On June 24, 2022, the US Supreme Court ended constitutional protections for abortion, resulting in wide variability in access from severe restrictions in many states and fewer restrictions in others. Healthcare institutions capture information about patients' pregnancy and abortion care and, due to interoperability, may share it in ways that expose their providers and patients to social stigma and potential legal jeopardy in states with severe restrictions. In this article, we describe sources of risk to patients and providers that arise from interoperability and specify actions that institutions can take to reduce that risk. Institutions have significant power to define their practices for how and where care is documented, how patients are identified, where data are sent or hosted, and how patients are counseled, and thus should protect patients' privacy and ability to receive medical care that is safe and legal where it is performed.
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Affiliation(s)
- Raman R Khanna
- Department of Medicine, UCSF, San Francisco, California, USA
| | - Sara G Murray
- Department of Medicine, UCSF, San Francisco, California, USA
| | - Timothy Wen
- Department of Obstetrics, Gynecology, and Reproductive Sciences, UCSF, San Francisco, California, USA
| | - Kirsten Salmeen
- Maternal Fetal Medicine, Kaiser Permanente, San Francisco, California, USA
| | - Tushani Illangasekare
- Department of Obstetrics, Gynecology, and Reproductive Sciences, UCSF, San Francisco, California, USA
| | - Nerys Benfield
- Department of Obstetrics, Gynecology, and Reproductive Sciences, UCSF, San Francisco, California, USA
| | - Julia Adler-Milstein
- Department of Medicine, UCSF, San Francisco, California, USA
- Center for Clinical Informatics and Improvement Research, UCSF, San Francisco, California, USA
| | - Lucia Savage
- Omada Health, Inc., San Francisco, California, USA
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7
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Gong JJ, Soleimani H, Murray SG, Adler-Milstein J. Characterizing styles of clinical note production and relationship to clinical work hours among first-year residents. J Am Med Inform Assoc 2021; 29:120-127. [PMID: 34963142 DOI: 10.1093/jamia/ocab253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/09/2021] [Accepted: 11/03/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE To characterize variation in clinical documentation production patterns, how this variation relates to individual resident behavior preferences, and how these choices relate to work hours. MATERIALS AND METHODS We used unsupervised machine learning with clinical note metadata for 1265 progress notes written for 279 patient encounters by 50 first-year residents on the Hospital Medicine service in 2018 to uncover distinct note-level and user-level production patterns. We examined average and 95% confidence intervals of median user daily work hours measured from audit log data for each user-level production pattern. RESULTS Our analysis revealed 10 distinct note-level and 5 distinct user-level production patterns (user styles). Note production patterns varied in when writing occurred and in how dispersed writing was through the day. User styles varied in which note production pattern(s) dominated. We observed suggestive trends in work hours for different user styles: residents who preferred producing notes in dispersed sessions had higher median daily hours worked while residents who preferred producing notes in the morning or in a single uninterrupted session had lower median daily hours worked. DISCUSSION These relationships suggest that note writing behaviors should be further investigated to understand what practices could be targeted to reduce documentation burden and derivative outcomes such as resident work hour violations. CONCLUSION Clinical note documentation is a time-consuming activity for physicians; we identify substantial variation in how first-year residents choose to do this work and suggestive trends between user preferences and work hours.
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Affiliation(s)
- Jen J Gong
- Center for Clinical Informatics and Improvement Research, University of California, San Francisco, San Francisco, California, USA.,Department of Medicine, University of California, San Francisco, San Francisco, California, USA, and
| | | | - Sara G Murray
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA, and.,Health Informatics, UCSF Health, San Francisco, California, USA
| | - Julia Adler-Milstein
- Center for Clinical Informatics and Improvement Research, University of California, San Francisco, San Francisco, California, USA.,Department of Medicine, University of California, San Francisco, San Francisco, California, USA, and
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8
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Lin JA, Pierce L, Murray SG, Soleimani H, Wick EC, Sosa JA, Hirose K. Estimation of Surgical Resident Duty Hours and Workload in Real Time Using Electronic Health Record Data. J Surg Educ 2021; 78:e232-e238. [PMID: 34507910 PMCID: PMC9335013 DOI: 10.1016/j.jsurg.2021.08.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/05/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To explore the use of electronic health record (EHR) data to estimate surgery resident duty hours and monitor real time workload. DESIGN Retrospective analysis of resident duty hours logged using a voluntary global positioning system (GPS)-based smartphone application compared to duty hour estimates by an EHR-based algorithm. The algorithm estimated duty hours using EHR activity data and operating room logs. A dashboard was developed through Plan-Do-Study-Act cycles for real-time monitoring of workload. SETTING Single tertiary/quaternary medical center general surgery residency program with approximately 90 categorical, preliminary, and integrated residents at eight clinical sites. PARTICIPANTS Categorical, preliminary, and integrated surgery residents of all clinical years who volunteered to pilot a GPS application to track duty hours. RESULTS Of 2,623 work periods by 59 residents were logged with both methods. EHR-estimated work periods started later than GPS logs (median 0.3 hours, interquartile range [IQR] -0.1 - 0.3); EHR-estimated work periods ended earlier than GPS logs (median 0.1 hours, IQR -0.7 - 0.3); and EHR-estimated duty hour totals were less than totals logged by GPS (median -0.3 hours, IQR -0.8 - +0.1). Overall correlation between weekly duty hours logged by EHR and GPS was 0.79. Correlations between the 2 systems stratified from PGY-1 through PGY-5 were 0.76, 0.64, 0.82, 0.87, and 0.83, respectively. The algorithm identified six 80-hour workweek violations (averaged over 4 weeks), while GPS logs identified 8. EHR-based duty hours and operational data were integrated into a dashboard to enable real time monitoring of resident workloads. CONCLUSIONS EHR-based estimation of surgical resident duty hours has good correlation with GPS-based assessment of duty hours and identifies most workweek duty hour violations. This approach allows for dynamic workload monitoring and may be combined with operational data to anticipate and prevent duty hour violations, thereby optimizing learning.
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Affiliation(s)
- Joseph A Lin
- Department of Surgery, University of California San Francisco, San Francisco, California.
| | - Logan Pierce
- Department of Medicine, University of California San Francisco, San Francisco, California
| | - Sara G Murray
- Department of Medicine, University of California San Francisco, San Francisco, California; Health Informatics, University of California San Francisco, San Francisco, California
| | - Hossein Soleimani
- Health Informatics, University of California San Francisco, San Francisco, California
| | - Elizabeth C Wick
- Department of Surgery, University of California San Francisco, San Francisco, California
| | - Julie Ann Sosa
- Department of Surgery, University of California San Francisco, San Francisco, California; Department of Medicine, University of California San Francisco, San Francisco, California
| | - Kenzo Hirose
- Department of Surgery, University of California San Francisco, San Francisco, California
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Abstract
BACKGROUND Medical training programs across the country are bound to a set of work hour regulations, generally monitored via self-report. OBJECTIVE We developed a computational method to automate measurement of intern and resident work hours, which we validated against self-report. DESIGN, SETTING, AND PARTICIPANTS We included all electronic health record (EHR) access log data between July 1, 2018, and June 30, 2019, for trainees enrolled in the internal medicine training program. We inferred the duration of continuous in-hospital work hours by linking EHR sessions that occurred within 5 hours as "on-campus" work and further accounted for "out-of-hospital" work which might be taking place at home. MAIN OUTCOMES AND MEASURES We compared daily work hours estimated through the computational method with self-report and calculated the mean absolute error between the two groups. We used the computational method to estimate average weekly work hours across the rotation and the percentage of rotations where average work hours exceed the 80-hour workweek. RESULTS The mean absolute error between self-reported and EHR-derived daily work hours for first- (PGY-1), second- (PGY-2), and third- (PGY-3) year trainees were 1.27, 1.51, and 1.51 hours, respectively. Using this computational method, we estimated average (SD) weekly work hours of 57.0 (21.7), 69.9 (12.2), and 64.1 (16.3) for PGY-1, PGY-2, and PGY-3 residents. CONCLUSION EHR log data can be used to accurately approximate self-report of work hours, accounting for both in-hospital and out-of-hospital work. Automation will reduce trainees' clerical work, improve consistency and comparability of data, and provide more complete and timely data that training programs need.
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Affiliation(s)
- Hossein Soleimani
- Health Informatics, University of California, San Francisco, San Francisco, California
| | - Julia Adler-Milstein
- Center for Clinical Informatics and Improvement Research, University of California, San Francisco, San Francisco, California
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Russell J Cucina
- Health Informatics, University of California, San Francisco, San Francisco, California
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Sara G Murray
- Health Informatics, University of California, San Francisco, San Francisco, California
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Affiliation(s)
- Farah Acher Kaiksow
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
- Corresponding Author: Farah Acher Kaiksow, MD, MPP; Telephone: 608-262-2434 ; Twitter: @kaiksow
| | - Christine D Jones
- Department of Medicine, Rocky Mountain Regional VA Medical Center, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Sara G Murray
- Department of Medicine, University of California, San Francisco, California
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Tran AV, Rushakoff RJ, Prasad P, Murray SG, Monash B, Macmaster H. Decreasing Hypoglycemia following Insulin Administration for Inpatient Hyperkalemia. J Hosp Med 2020; 15:E1. [PMID: 32202491 DOI: 10.12788/jhm.3413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Affiliation(s)
- Allen Vinh Tran
- School of Pharmacy, University of California, San Francisco, California
| | - Robert J Rushakoff
- Division of Endocrinology and Metabolism, University of California, San Francisco, California
| | - Priya Prasad
- Division of Hospital Medicine, University of California, San Francisco, California
| | - Sara G Murray
- Division of Hospital Medicine, University of California, San Francisco, California
| | - Bradley Monash
- Division of Hospital Medicine, University of California, San Francisco, California
| | - Heidemarie Macmaster
- Institute for Nursing Excellence, University of California, San Francisco, California, (currently at Lahey Health System, Burlington, Massachusetts)
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Tran AV, Rushakoff RJ, Prasad P, Murray SG, Monash B, Macmaster H. Decreasing Hypoglycemia following Insulin Administration for Inpatient Hyperkalemia. J Hosp Med 2020; 15:368-370. [PMID: 32039749 DOI: 10.12788/jhm.3357] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Accepted: 11/10/2019] [Indexed: 11/20/2022]
Abstract
BACKGROUND Acute hyperkalemia (serum potassium ≥ 5.1 mEq/L) is often treated with a bolus of IV insulin. This treatment may result in iatrogenic hypoglycemia (glucose < 70 mg/dl). OBJECTIVES The aims of this study were to accurately determine the frequency of iatrogenic hypoglycemia following insulin treatment for hyperkalemia, and to develop an electronic health record (EHR) orderset to decrease the risk for iatrogenic hypoglycemia. DESIGN This study was an observational, prospective study. SETTING The setting for this study was a university hospital. PATIENTS All nonobstetric adult inpatients in all acute and intensive care units were eligible. INTERVENTION Implementation of a hyperkalemia orderset (Orderset 1.1) with glucose checks before and then one, two, four, and six hours after regular intravenous insulin administration. Based on the results from Orderset 1.1, Orderset 1.2 was developed and introduced to include weight-based dosing of insulin options, alerts identifying patients at higher risk of hypoglycemia, and tools to guide decision-making based on the preinsulin blood glucose level. MEASUREMENTS Patient demographics, weight, diabetes history, potassium level, renal function, and glucose levels were recorded before, and then glucose levels were measured again at one, two, four, and six hours after insulin was administered. RESULTS The iatrogenic hypoglycemia rate identified with mandatory glucose checks in Orderset 1.1 was 21%; 92% of these occurred within three hours posttreatment. Risk factors for hypoglycemia included decreased renal function (serum creatinine >2.5 mg/dl), a high dose of insulin (>0.14 units/kg), and re-treatment with blood glucose < 140 mg/dl. After the introduction of Orderset 1.2, the rate of iatrogenic hypoglycemia decreased to 10%. CONCLUSIONS The use of an EHR orderset for treating hyperkalemia may reduce the risk of iatrogenic hypoglycemia in patients receiving insulin while still adequately lowering their potassium.
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Affiliation(s)
- Allen Vinh Tran
- School of Pharmacy, University of California, San Francisco, California
| | - Robert J Rushakoff
- Division of Endocrinology and Metabolism, University of California, San Francisco, California
| | - Priya Prasad
- Division of Hospital Medicine, University of California, San Francisco, California
| | - Sara G Murray
- Division of Hospital Medicine, University of California, San Francisco, California
| | - Bradley Monash
- Division of Hospital Medicine, University of California, San Francisco, California
| | - Heidemarie Macmaster
- Institute for Nursing Excellence, University of California, San Francisco, California, (currently at Lahey Health System, Burlington, Massachusetts
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13
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Murray SG, Avati A, Schmajuk G, Yazdany J. Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling. J Am Med Inform Assoc 2019; 26:61-65. [PMID: 30476175 DOI: 10.1093/jamia/ocy154] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 11/13/2018] [Indexed: 12/21/2022] Open
Abstract
Accurate and efficient identification of complex chronic conditions in the electronic health record (EHR) is an important but challenging task that has historically relied on tedious clinician review and oversimplification of the disease. Here we adapt methods that allow for automated "noisy labeling" of positive and negative controls to create a "silver standard" for machine learning to automate identification of systemic lupus erythematosus (SLE). Our final model, which includes both structured data as well as text processing of clinical notes, outperformed all existing algorithms for SLE (AUC 0.97). In addition, we demonstrate how the probabilistic outputs of this model can be adapted to various clinical needs, selecting high thresholds when specificity is the priority and lower thresholds when a more inclusive patient population is desired. Deploying a similar methodology to other complex diseases has the potential to dramatically simplify the landscape of population identification in the EHR. MeSH terms Electronic Health Records, Machine Learning, Lupus Erythematosus, Phenotype, Algorithms.
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Affiliation(s)
- Sara G Murray
- Department of Medicine, University of California, San Francisco, California, USA
| | - Anand Avati
- Department of Computer Science, Stanford University, Stanford, California, USA
| | - Gabriela Schmajuk
- Department of Medicine, University of California, San Francisco, California, USA.,Department of Medicine, San Francisco VA Medical Center, San Francisco, California, USA
| | - Jinoos Yazdany
- Department of Medicine, University of California, San Francisco, California, USA
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14
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Wachter RM, Murray SG, Adler-Milstein J. Restricting the Number of Open Patient Records in the Electronic Health Record: Is the Record Half Open or Half Closed? JAMA 2019; 321:1771-1773. [PMID: 31087007 DOI: 10.1001/jama.2019.3835] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
| | - Sara G Murray
- Department of Medicine, University of California, San Francisco
- UCSF Health Informatics, University of California, San Francisco
| | - Julia Adler-Milstein
- Department of Medicine, University of California, San Francisco
- Center for Clinical Informatics and Improvement Research, University of California, San Francisco
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15
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Murray SG, Yim JWL, Croci R, Rajkomar A, Schmajuk G, Khanna R, Cucina RJ. Using Spatial and Temporal Mapping to Identify Nosocomial Disease Transmission of Clostridium difficile. JAMA Intern Med 2017; 177:1863-1865. [PMID: 29059280 PMCID: PMC5820725 DOI: 10.1001/jamainternmed.2017.5506] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
This study evaluates the use of spatial and temporal mapping to identify nosocomial disease transmission of Clostridium difficile.
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Affiliation(s)
- Sara G Murray
- Division of Hospital Medicine, Department of Medicine, University of California, San Francisco.,Health Informatics, UCSF Health, University of California, San Francisco
| | - Joanne W L Yim
- Health Informatics, UCSF Health, University of California, San Francisco
| | - Rhiannon Croci
- Health Informatics, UCSF Health, University of California, San Francisco
| | - Alvin Rajkomar
- Division of Hospital Medicine, Department of Medicine, University of California, San Francisco
| | - Gabriela Schmajuk
- Division of Rheumatology, Department of Medicine, University of California, San Francisco.,San Francisco VA Medical Center, San Francisco, California
| | - Raman Khanna
- Division of Hospital Medicine, Department of Medicine, University of California, San Francisco.,Health Informatics, UCSF Health, University of California, San Francisco.,Center for Digital Health Innovation, University of California, San Francisco
| | - Russell J Cucina
- Division of Hospital Medicine, Department of Medicine, University of California, San Francisco.,Health Informatics, UCSF Health, University of California, San Francisco
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Smilek DE, Lim N, Ding L, Murray SG, Diamond B, Wofsy D. Correlation of hypogammaglobulinaemia with proteinuria, and the relationship between hypogammaglobulinaemia and infection in active lupus nephritis. Lupus Sci Med 2017; 4:e000229. [PMID: 29214037 PMCID: PMC5704742 DOI: 10.1136/lupus-2017-000229] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 07/17/2017] [Accepted: 07/19/2017] [Indexed: 11/29/2022]
Abstract
Objective To evaluate hypogammaglobulinaemia and risk of serious infectious adverse events in active lupus nephritis. Methods The Abatacept and Cyclophosphamide Combination Efficacy and Safety Study (ACCESS) compared abatacept with placebo in participants with lupus nephritis undergoing treatment with Euro-Lupus Nephritis low-dose cyclophosphamide. Serum IgG levels were assessed prior to initiation of treatment and throughout the trial. Hypogammaglobulinaemia was defined as IgG <450 mg/dL. Results Hypogammaglobulinaemia was observed in 16/102 (15.7%) participants prior to initiation of induction therapy for active lupus nephritis. Participants with nephrotic range proteinuria were more likely to have hypogammaglobulinaemia, and serum IgG levels were inversely correlated with urine protein to creatinine ratio (r=−0.42, p<0.0001). Following initiation of treatment for active lupus nephritis, additional participants developed hypogammaglobulinaemia by weeks 2–4. Serum IgG levels then increased, and all but one participant had serum IgG ≥450 mg/dL at 24 weeks. Hypogammaglobulinaemia was not associated with an increased risk of serious infectious adverse events. Conclusions In active lupus nephritis in ACCESS, hypogammaglobulinaemia was common and inversely correlated with proteinuria. Serum IgG levels were lowest in the weeks immediately following initiation of induction therapy, and subsequently improved by 24 weeks. Hypogammaglobulinaemia was not associated with serious infectious adverse events. Trial registration
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Affiliation(s)
- Dawn Elaine Smilek
- Immune Tolerance Network, University of California San Franciso, San Francisco, California, USA.,Division of Rheumatology, Department of Medicine, and the Lupus Nephritis Trials Network, University of California San Francisco, San Francisco, California, USA
| | - Noha Lim
- Immune Tolerance Network, Massachusetts General Hospital, Bethesda, Maryland, USA
| | - Linna Ding
- Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, Rockville, Maryland, USA
| | - Sara G Murray
- Division of Hospital Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Betty Diamond
- Center for Autoimmune and Musculoskeletal Diseases, The Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - David Wofsy
- Division of Rheumatology, Department of Medicine, and the Russell/Engleman Research Center, University of California San Francisco, San Francisco, California, USA
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17
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Wysham KD, Murray SG, Hills N, Yelin E, Gensler LS. Cervical Spinal Fracture and Other Diagnoses Associated With Mortality in Hospitalized Ankylosing Spondylitis Patients. Arthritis Care Res (Hoboken) 2017; 69:271-277. [PMID: 27159625 PMCID: PMC5102813 DOI: 10.1002/acr.22934] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 03/15/2016] [Accepted: 04/26/2016] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Little data exist regarding mortality in ankylosing spondylitis (AS). We assessed diagnoses associated with in-hospital mortality in AS using a population-based inpatient data set. METHODS Data were abstracted from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample between 2007 and 2011. We identified AS admissions using International Classification of Diseases, Ninth Revision, Clinical Modification code 720.0. In-hospital mortality was the primary outcome. Logistic regression was used to evaluate the association between top diagnoses and in-hospital mortality. We performed a secondary analysis from the same years in all patients (with and without AS) with cervical spine (C-spine) fracture to determine whether AS was an independent risk factor for mortality. RESULTS Between 2007 and 2011, we identified 12,484 admissions and 267 deaths in AS patients. C-spine fracture with spinal cord injury and sepsis had the highest odds of death, with odds ratios (ORs) of 13.43 (95% confidence interval [95% CI] 8.00-22.55, P < 0.0001) and 7.63 (95% CI 5.62-10.36, P < 0.0001), respectively. In the same time period, there were 53,606 C-spine fracture admissions, of which 408 were coded with AS. Among all C-spine fracture hospitalizations, an AS diagnosis was associated with inpatient death (OR 1.61 [95% CI 1.16-2.22], P = 0.004). CONCLUSION In AS patients admitted to the hospital, C-spine fracture is a leading cause of in-hospital mortality. Other diagnoses associated with mortality include sepsis, pneumonia, cardiovascular disease, and comorbid illnesses. Among all hospitalizations with C-spine fracture, AS was associated with increased odds of death. C-spine fracture-associated mortality warrants further study to elucidate risk factors in order to prevent such devastating fractures in AS patients.
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Affiliation(s)
- Katherine D. Wysham
- Department of Medicine, Division of Rheumatology, University of California, San Francisco, San Francisco, CA, USA
| | - Sara G. Murray
- Department of Medicine, Division of Rheumatology, University of California, San Francisco, San Francisco, CA, USA
| | - Nancy Hills
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine San Francisco, CA, USA
| | - Edward Yelin
- Department of Medicine, Division of Rheumatology, University of California, San Francisco, San Francisco, CA, USA
- Institute for Health Policy Studies, University of California, San Francisco, CA, USA
| | - Lianne S. Gensler
- Department of Medicine, Division of Rheumatology, University of California, San Francisco, San Francisco, CA, USA
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Murray SG, Schmajuk G, Trupin L, Gensler L, Katz PP, Yelin EH, Gansky SA, Yazdany J. National Lupus Hospitalization Trends Reveal Rising Rates of Herpes Zoster and Declines in Pneumocystis Pneumonia. PLoS One 2016; 11:e0144918. [PMID: 26731012 PMCID: PMC4701172 DOI: 10.1371/journal.pone.0144918] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Accepted: 11/26/2015] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVE Infection is a leading cause of morbidity and mortality in systemic lupus erythematosus (SLE). Therapeutic practices have evolved over the past 15 years, but effects on infectious complications of SLE are unknown. We evaluated trends in hospitalizations for severe and opportunistic infections in a population-based SLE study. METHODS Data derive from the 2000 to 2011 United States National Inpatient Sample, including individuals who met a validated administrative definition of SLE. Primary outcomes were diagnoses of bacteremia, pneumonia, opportunistic fungal infection, herpes zoster, cytomegalovirus, or pneumocystis pneumonia (PCP). We used Poisson regression to determine whether infection rates were changing in SLE hospitalizations and used predictive marginals to generate annual adjusted rates of specific infections. RESULTS We identified 361,337 SLE hospitalizations from 2000 to 2011 meeting study inclusion criteria. Compared to non-SLE hospitalizations, SLE patients were younger (51 vs. 62 years), predominantly female (89% vs. 54%), and more likely to be racial/ethnic minorities. SLE diagnosis was significantly associated with all measured severe and opportunistic infections. From 2000 to 2011, adjusted SLE hospitalization rates for herpes zoster increased more than non-SLE rates: 54 to 79 per 10,000 SLE hospitalizations compared with 24 to 29 per 10,000 non-SLE hospitalizations. Conversely, SLE hospitalizations for PCP disproportionately decreased: 5.1 to 2.5 per 10,000 SLE hospitalizations compared with 0.9 to 1.3 per 10,000 non-SLE hospitalizations. CONCLUSIONS Among patients with SLE, herpes zoster hospitalizations are rising while PCP hospitalizations are declining. These trends likely reflect evolving SLE treatment strategies. Further research is needed to identify patients at greatest risk for infectious complications.
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Affiliation(s)
- Sara G. Murray
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Gabriela Schmajuk
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
- Department of Medicine, Veterans Administration, San Francisco, California, United States of America
| | - Laura Trupin
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Lianne Gensler
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Patricia P. Katz
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
- Phillip R. Lee Institute for Health Policy Studies, University of California San Francisco, San Francisco, California, United States of America
| | - Edward H. Yelin
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
- Phillip R. Lee Institute for Health Policy Studies, University of California San Francisco, San Francisco, California, United States of America
| | - Stuart A. Gansky
- Division of Oral Epidemiology and Dental Public Health, Department of Preventive and Restorative Dental Sciences, University of California San Francisco, San Francisco, California, United States of America
| | - Jinoos Yazdany
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
- Phillip R. Lee Institute for Health Policy Studies, University of California San Francisco, San Francisco, California, United States of America
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20
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Murray SG, Schmajuk G, Trupin L, Lawson E, Cascino M, Barton J, Margaretten M, Katz PP, Yelin EH, Yazdany J. A population-based study of infection-related hospital mortality in patients with dermatomyositis/polymyositis. Arthritis Care Res (Hoboken) 2015; 67:673-80. [PMID: 25331828 PMCID: PMC4404175 DOI: 10.1002/acr.22501] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2014] [Revised: 10/02/2014] [Accepted: 10/14/2014] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Dermatomyositis (DM) and polymyositis (PM) are debilitating inflammatory myopathies associated with significant mortality. We evaluated the relative contribution of infection to hospital mortality in a large population-based study of individuals with PM/DM. METHODS Data derive from the 2007 to 2011 Healthcare Cost and Utilization Project National Inpatient Samples and include all hospital discharges that met a validated administrative definition of PM/DM. The primary outcome was hospital mortality. Variables for infections and comorbidities were generated from discharge diagnoses using validated administrative definitions. Logistic regression was used to investigate the relationship between infection and mortality in individuals with PM/DM, adjusting for sociodemographics, utilization variables, and comorbidities. Relative risks (RRs) were calculated to compare the overall prevalence of specific infections and associated mortality in PM/DM hospitalizations with those seen in the general hospitalized population. RESULTS A total of 15,407 hospitalizations with PM/DM met inclusion criteria for this study and inpatient mortality was 4.5% (700 deaths). In adjusted logistic regression analyses, infection (odds ratio [OR] 3.4, 95% confidence interval [95% CI] 2.9-4.0) was the strongest predictor of hospital mortality among individuals with PM/DM. Bacterial infection (OR 3.5, 95% CI 3.0-4.1), comprised primarily of pneumonia and bacteremia, and opportunistic fungal infections (OR 2.5, 95% CI 1.5-4.0) were independently associated with hospital mortality. The overall burden of infection in hospitalizations with PM/DM was significantly increased in comparison with the general hospitalized population (RR 1.5, 95% CI 1.4-1.6). CONCLUSION Among hospitalized individuals with PM/DM, infection is the leading cause of mortality. Strategies to mitigate infection risk in both the clinic and hospital settings should be evaluated to improve disease outcomes.
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Affiliation(s)
- Sara G. Murray
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Gabriela Schmajuk
- Department of Medicine, Veterans Administration, San Francisco, California
| | - Laura Trupin
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Erica Lawson
- Department of Pediatrics, University of California, San Francisco, San Francisco, California
| | - Matthew Cascino
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Jennifer Barton
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Mary Margaretten
- Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Patricia P. Katz
- Department of Medicine, University of California, San Francisco, San Francisco, California
- Phillip R. Lee Institute for Health Policy Studies, San Francisco, California
| | - Edward H. Yelin
- Department of Medicine, University of California, San Francisco, San Francisco, California
- Phillip R. Lee Institute for Health Policy Studies, San Francisco, California
| | - Jinoos Yazdany
- Department of Medicine, University of California, San Francisco, San Francisco, California
- Phillip R. Lee Institute for Health Policy Studies, San Francisco, California
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Ryu JK, Baeten KM, Petersen MA, Murray SG, Meyer-Franke A, Davalos D, Bedard C, Prod’homme T, Charo IF, Lassmann H, Degen JL, Zamvil SS, Akassoglou K. 220. Cytokine 2013. [DOI: 10.1016/j.cyto.2013.06.223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Murray SG, Yazdany J, Kaiser R, Criswell LA, Trupin L, Yelin EH, Katz PP, Julian LJ. Cardiovascular disease and cognitive dysfunction in systemic lupus erythematosus. Arthritis Care Res (Hoboken) 2012; 64:1328-33. [PMID: 22549897 PMCID: PMC3705733 DOI: 10.1002/acr.21691] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Cognitive dysfunction and cardiovascular disease are common and debilitating manifestations of systemic lupus erythematosus (SLE). In this study, we evaluated the relationship between cardiovascular events, traditional cardiovascular risk factors, and SLE-specific risk factors as predictors of cognitive dysfunction in a large cohort of participants with SLE. METHODS Subjects included 694 participants from the Lupus Outcomes Study (LOS), a longitudinal study of SLE outcomes based on an annual telephone survey querying demographic and clinical variables. The Hopkins Verbal Learning Test-Revised and the Controlled Oral Word Association Test were administered to assess cognitive function. Multiple logistic regression was used to identify cardiovascular events (myocardial infarction, stroke), traditional cardiovascular risk factors (hypertension, hyperlipidemia, diabetes mellitus, obesity, smoking), and SLE-specific risk factors (antiphospholipid antibodies [aPL], disease activity, disease duration) associated with cognitive impairment in year 7 of the LOS. RESULTS The prevalence of cognitive impairment as measured by verbal memory and verbal fluency metrics was 15%. In adjusted multiple logistic regression analyses, aPL (odds ratio [OR] 2.10, 95% confidence interval [95% CI] 1.3-3.41), hypertension (OR 2.06, 95% CI 1.19-3.56), and a history of stroke (OR 2.27, 95% CI 1.16-4.43) were significantly associated with cognitive dysfunction. In additional analyses evaluating the association between these predictors and severity of cognitive impairment, stroke was significantly more prevalent in participants with severe impairment when compared to those with mild or moderate impairment (P = 0.036). CONCLUSION These results suggest that the presence of aPL, hypertension, and stroke are key variables associated with cognitive impairment, which may aid in identification of patients at greatest risk.
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Affiliation(s)
- Sara G Murray
- University of California, San Francisco, 94143-0320, USA.
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Jones MH, Levason W, McAuliffe CA, Murray SG, Johns DM. The relationship of some copper (II) complexes of facultative tetrathioethers to the coordination environment in the "blue" copper proteins. Bioinorg Chem 1978; 8:267-78. [PMID: 647058 DOI: 10.1016/s0006-3061(00)80194-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The facultative potentially tetradentate thioether ligands 1,2-bis(methylthioethylthio)ethane (2,2,2), 1,3-bis(2-methylthioethylthio)propane (2,3,2) and 1,2-bis(3-methylthiopropylthio)ethane (3,2,3) react with copper(II) salts to form Cu2(2,2,2)Cl4, Cu3(ligand)X6 (ligand = 2,3,2 and 3,2,3 X = Cl; ligand = 2,2,2 2,3,2 and 3,2,3 X = Br), and Cu(ligand)(ClO4)2. The stoichiometry and structures of these complexes are discussed in terms of the steric demands of the ligand and the nature of the halide. The [Cu(2,3,2)(ClO4)] ClO4 and [Cu(3,2,3)(ClO4) [ClO4 complexes have electronic spectra which exhibit the intense 600 nm band characteristic of the "blue" copper proteins. In fact, the spectrum of [Cu(2,3,2)(ClO4)]ClO4 is very similar to that of pseudomonas aeroginosa azurin.
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