Literature DB >> 27028469

Convergent Validity of Three Methods for Measuring Postoperative Complications.

Bradley A Fritz1, Krisztina E Escallier, Arbi Ben Abdallah, Jordan Oberhaus, Jennifer Becker, Kristin Geczi, Sherry McKinnon, Dan L Helsten, Anshuman Sharma, Troy S Wildes, Michael S Avidan.   

Abstract

BACKGROUND: Anesthesiologists need tools to accurately track postoperative outcomes. The accuracy of patient report in identifying a wide variety of postoperative complications after diverse surgical procedures has not previously been investigated.
METHODS: In this cohort study, 1,578 adult surgical patients completed a survey at least 30 days after their procedure asking if they had experienced any of 18 complications while in the hospital after surgery. Patient responses were compared to the results of an automated electronic chart review and (for a random subset of 750 patients) to a manual chart review. Results from automated chart review were also compared to those from manual chart review. Forty-two randomly selected patients were contacted by telephone to explore reasons for discrepancies between patient report and manual chart review.
RESULTS: Comparisons between patient report, automated chart review, and manual chart review demonstrated poor-to-moderate positive agreement (range, 0 to 58%) and excellent negative agreement (range, 82 to 100%). Discordance between patient report and manual chart review was frequently explicable by patients reporting events that happened outside the time period of interest.
CONCLUSIONS: Patient report can provide information about subjective experiences or events that happen after hospital discharge, but often yields different results from chart review for specific in-hospital complications. Effective in-hospital communication with patients and thoughtful survey design may increase the quality of patient-reported complication data.

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Year:  2016        PMID: 27028469      PMCID: PMC5083125          DOI: 10.1097/ALN.0000000000001108

Source DB:  PubMed          Journal:  Anesthesiology        ISSN: 0003-3022            Impact factor:   7.892


  36 in total

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3.  Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications.

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4.  Protocol for the Effectiveness of an Anesthesiology Control Tower System in Improving Perioperative Quality Metrics and Clinical Outcomes: the TECTONICS randomized, pragmatic trial.

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  4 in total

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