{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T11:56:49Z","timestamp":1785153409242,"version":"3.55.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":64,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Many routine patient care items should be reviewed at least daily for intensive care unit (ICU) patients. These items are often incompletely performed, and dynamic clinical decision support tools (CDSTs) may improve attention to these daily items. We sought to evaluate the accuracy of institutionalized electronic health record (EHR) based custom dynamic CDST to support 22 ICU rounding quality metrics across 7 categories (hypoglycemia, venothromboembolism prophylaxis, stress ulcer prophylaxis, mechanical ventilation, sedation, nutrition, and catheter removal).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design<\/jats:title>\n                  <jats:p>The dynamic CDST evaluates patient characteristics and patient orders, then identifies gaps between active interventions and conditions with recommendations of evidence based clinical practice guidelines across 22 areas of care for each patient. The results of the tool prompt clinicians to address any identified care gaps. We completed a confusion matrix to assess the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of the dynamic CDST and the individual metrics.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Setting<\/jats:title>\n                  <jats:p>Tertiary academic medical center and community hospital ICUs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Subject<\/jats:title>\n                  <jats:p>Customized Clinical Decision Support Tool.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Measurements and Main Results<\/jats:title>\n                  <jats:p>The metrics were evaluated 1421 times over 484 patients. The overall accuracy of the entire dynamic CDST is 0.979 with a sensitivity of 0.979, specificity of 0.978, PPV 0.969, and NPV 0.986.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>A customized, EHR based dynamic CDST can be highly accurate. Integrating a comprehensive dynamic CDST into existing workflows could improve attention and actions related to routine ICU quality metrics.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooae143","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T22:26:19Z","timestamp":1733955979000},"source":"Crossref","is-referenced-by-count":5,"title":["A dynamic customized electronic health record rule based clinical decision support tool for standardized adult intensive care metrics"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9497-9528","authenticated-orcid":false,"given":"Eric W","family":"Cucchi","sequence":"first","affiliation":[{"name":"University of Massachusetts Chan Medical School, Departments of Medicine , Worcester, MA 01655,","place":["United States"]},{"name":"UMass Memorial Health, UMass Memorial Medical Center , Worcester, MA 01655,","place":["United States"]},{"name":"University of 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