BACKGROUND: Residents in anesthesia training programs throughout the world are required to document their clinical cases to help ensure that they receive adequate training. Current systems involve self-reporting, are subject to delayed updates and misreported data, and do not provide a practicable method of validation. Anesthesia information management systems (AIMS) are being used increasingly in training programs and are a logical source for verifiable documentation. We hypothesized that case logs generated automatically from an AIMS would be sufficiently accurate to replace the current manual process. We based our analysis on the data reporting requirements of the American College of Graduate Medical Education (ACGME). METHODS: We conducted a systematic review of ACGME requirements and our AIMS record, and made modifications after identifying data element and attribution issues. We studied 2 methods (parsing of free text procedure descriptions and CPT4 procedure code mapping) to automatically determine ACGME case categories and generated AIMS-based case logs and compared these to assignments made by manual inspection of the anesthesia records. We also assessed under- and overreporting of cases entered manually by our residents into the ACGME website. RESULTS: The parsing and mapping methods assigned cases to a majority of the ACGME categories with accuracies of 95% and 97%, respectively, as compared with determinations made by 2 residents and 1 attending who manually reviewed all procedure descriptions. Comparison of AIMS-based case logs with reports from the ACGME Resident Case Log System website showed that >50% of residents either underreported or overreported their total case counts by at least 5%. CONCLUSION: The AIMS database is a source of contemporaneous documentation of resident experience that can be queried to generate valid, verifiable case logs. The extent of AIMS adoption by academic anesthesia departments should encourage accreditation organizations to support uploading of AIMS-based case log files to improve accuracy and to decrease the clerical burden on anesthesia residents.
BACKGROUND: Residents in anesthesia training programs throughout the world are required to document their clinical cases to help ensure that they receive adequate training. Current systems involve self-reporting, are subject to delayed updates and misreported data, and do not provide a practicable method of validation. Anesthesia information management systems (AIMS) are being used increasingly in training programs and are a logical source for verifiable documentation. We hypothesized that case logs generated automatically from an AIMS would be sufficiently accurate to replace the current manual process. We based our analysis on the data reporting requirements of the American College of Graduate Medical Education (ACGME). METHODS: We conducted a systematic review of ACGME requirements and our AIMS record, and made modifications after identifying data element and attribution issues. We studied 2 methods (parsing of free text procedure descriptions and CPT4 procedure code mapping) to automatically determine ACGME case categories and generated AIMS-based case logs and compared these to assignments made by manual inspection of the anesthesia records. We also assessed under- and overreporting of cases entered manually by our residents into the ACGME website. RESULTS: The parsing and mapping methods assigned cases to a majority of the ACGME categories with accuracies of 95% and 97%, respectively, as compared with determinations made by 2 residents and 1 attending who manually reviewed all procedure descriptions. Comparison of AIMS-based case logs with reports from the ACGME Resident Case Log System website showed that >50% of residents either underreported or overreported their total case counts by at least 5%. CONCLUSION: The AIMS database is a source of contemporaneous documentation of resident experience that can be queried to generate valid, verifiable case logs. The extent of AIMS adoption by academic anesthesia departments should encourage accreditation organizations to support uploading of AIMS-based case log files to improve accuracy and to decrease the clerical burden on anesthesia residents.
Authors: Joseph R Starnes; Matthew D McEvoy; Jesse M Ehrenfeld; Warren S Sandberg; Jonathan P Wanderer Journal: J Med Syst Date: 2015-08-30 Impact factor: 4.460
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