A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data


Autoři: Meagan A. Bechel aff001;  Adam R. Pah aff003;  Hanyu Shi aff005;  Sanjay Mehrotra aff006;  Stephen D. Persell aff007;  Shayna Weiner aff009;  Richard G. Wunderink aff010;  Luís A. Nunes Amaral aff003;  Curtis H. Weiss aff012
Působiště autorů: Medical Scientist Training Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America aff001;  Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America aff002;  Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, United States of America aff003;  Kellogg School of Management, Northwestern University, Evanston, IL, United States of America aff004;  Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States of America aff005;  Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, United States of America aff006;  Division of General Internal Medicine and Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America aff007;  Center for Primary Care Innovation, Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America aff008;  Michigan Oncology Quality Consortium, Ann Arbor, MI, United States of America aff009;  Division of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America aff010;  Department of Physics and Astronomy, Northwestern University, Evanston, IL, United States of America aff011;  Division of Pulmonary, Critical Care, Allergy, and Immunology, NorthShore University HealthSystem, Evanston, IL, United States of America aff012
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222826

Souhrn

Importance

Despite its efficacy, low tidal volume ventilation (LTVV) remains severely underutilized for patients with acute respiratory distress syndrome (ARDS). Physician under-recognition of ARDS is a significant barrier to LTVV use. We propose a computational method that addresses some of the limitations of the current approaches to automated measurement of whether ARDS is recognized by physicians.

Objective

To quantify patient and physician factors affecting physicians’ tidal volume selection and to build a computational model of physician recognition of ARDS that accounts for these factors.

Design, setting, and participants

In this cross-sectional study, electronic health record data were collected for 361 ARDS patients and 388 non-ARDS hypoxemic (control) patients in nine adult intensive care units at four hospitals between June 24 and December 31, 2013.

Methods

Standardized tidal volumes (mL/kg predicted body weight) were chosen as a proxy for physician decision-making behavior. Using data-science approaches, we quantified the effect of eight factors (six severity of illness, two physician behaviors) on selected standardized tidal volumes in ARDS and control patients. Significant factors were incorporated in computational behavioral models of physician recognition of ARDS.

Results

Hypoxemia severity and ARDS documentation in physicians’ notes were associated with lower standardized tidal volumes in the ARDS cohort. Greater patient height was associated with lower standardized tidal volumes (which is already normalized for height) in both ARDS and control patients. The recognition model yielded a mean (99% confidence interval) physician recognition of ARDS of 22% (9%-42%) for mild, 34% (19%-49%) for moderate, and 67% (41%-100%) for severe ARDS.

Conclusions and relevance

In this study, patient characteristics and physician behaviors were demonstrated to be associated with differences in ventilator management in both ARDS and control patients. Our model of physician ARDS recognition measurement accounts for these clinical variables, providing an electronic approach that moves beyond relying on chart documentation or resource intensive approaches.

Klíčová slova:

People and places – Population groupings – Professions – Medical personnel – Medicine and health sciences – Health care – Health care providers – Medical doctors – Physicians – Pulmonology – Pulmonary function – Tidal volume – Acute respiratory distress syndrome – Respiratory failure – Critical care and emergency medicine – Body weight – Surgical and invasive medical procedures – Intubation – Biology and life sciences – Physiology – Physiological parameters – Neuroscience – Cognitive science – Cognitive psychology – Decision making – Cognition – Psychology – Research and analysis methods – Database and informatics methods – Health informatics – Electronic medical records – Social sciences – Computer and information sciences – Data visualization – Infographics – Charts


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Článek vyšel v časopise

PLOS One


2019 Číslo 9
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