David B Wax1, Patrick J McCormick2, Thomas T Joseph3, Matthew A Levin1. 1. From the Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. 2. Department of Anesthesiology and Critical Care, Memorial Sloan Kettering Cancer Center, New York, New York. 3. Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Abstract
BACKGROUND: Anesthesia information management systems make prior anesthesia records readily available for review when patients return for a subsequent procedure but may create a problem of too much documentation to review in a limited amount of time. We implemented a screening tool to facilitate the identification of critical documentation for review. METHODS: An algorithm was developed to electronically search prior anesthesia records for predefined critical events and flag records containing these events. Our web-based daily case schedule was modified to contain a warning message for any patient on the schedule who has a prior record flagged by the system, in addition to a preexisting hyperlink to view the relevant record. A retrospective analysis was performed to determine the impact of the warning messages on the frequency with which the care team reviewed these records before providing anesthesia care. RESULTS: The screening algorithm flagged 13% of archived cases as critical. There were 3329 and 3369 cases in the 6 months before and after system implementation, respectively, that had prior critical records available for review at that time. One or more of these critical records were viewed before the subsequent case start in 39% vs 59% (P < .01) of cases in the pre- versus postimplementation periods. Subgroup analysis revealed that the increase was greatest for attending anesthesiologists working alone. CONCLUSIONS: We created a system to automatically detect critical events in prior anesthesia records for the purpose of forewarning the anesthesia care team when the same patient returns for another procedure. Inclusion of these warnings on the daily case schedule was associated with an increased frequency of preanesthesia review of old records.
BACKGROUND: Anesthesia information management systems make prior anesthesia records readily available for review when patients return for a subsequent procedure but may create a problem of too much documentation to review in a limited amount of time. We implemented a screening tool to facilitate the identification of critical documentation for review. METHODS: An algorithm was developed to electronically search prior anesthesia records for predefined critical events and flag records containing these events. Our web-based daily case schedule was modified to contain a warning message for any patient on the schedule who has a prior record flagged by the system, in addition to a preexisting hyperlink to view the relevant record. A retrospective analysis was performed to determine the impact of the warning messages on the frequency with which the care team reviewed these records before providing anesthesia care. RESULTS: The screening algorithm flagged 13% of archived cases as critical. There were 3329 and 3369 cases in the 6 months before and after system implementation, respectively, that had prior critical records available for review at that time. One or more of these critical records were viewed before the subsequent case start in 39% vs 59% (P < .01) of cases in the pre- versus postimplementation periods. Subgroup analysis revealed that the increase was greatest for attending anesthesiologists working alone. CONCLUSIONS: We created a system to automatically detect critical events in prior anesthesia records for the purpose of forewarning the anesthesia care team when the same patient returns for another procedure. Inclusion of these warnings on the daily case schedule was associated with an increased frequency of preanesthesia review of old records.
Authors: Thomas T Joseph; David B Wax; Raymond Goldstein; Jia Huang; Patrick J McCormick; Matthew A Levin Journal: Anesth Analg Date: 2020-11 Impact factor: 5.108
Authors: Luis E Tollinche; Richard Shi; Margaret Hannum; Patrick McCormick; Alisa Thorne; Kay See Tan; Gloria Yang; Meghana Mehta; Cindy Yeoh Journal: Comput Methods Programs Biomed Date: 2020-02-28 Impact factor: 5.428