BACKGROUND: This study tested validity and efficiency of Orthopaedic Minimal Data Set (OrthoMiDaS) Episode of Care (OME). METHODS: We analyzed 100 isolated rotator cuff repair cases in the OME database. Surgeons completed a traditional operative note and OME report. A blinded reviewer extracted data from operative notes and implant logs in electronic medical records by manual chart review. OME and electronic medical record data were compared with data counts and agreement between 40 variables of rotator cuff disease and repair procedures. Data counts were assessed using raw percentages and McNemar test (with continuity correction). Agreement of categorical variables was analyzed using Cohen κ (unweighted) and of numerical variables using the concordance correlation coefficient (CCC). Efficiency was assessed by median time to complete. RESULTS: OME database had significantly higher data counts for 25% (10/40) of variables. A high level of proportional and statistical agreement was demonstrated between the data. Among 35 categorical variables, proportional agreement was perfect for 17%, almost perfect (0.81 ≤ κ ≤ 1.00) for 37%, substantial (0.61 ≤ κ ≤ 0.80) for 20%, moderate (0.41 ≤ κ ≤ 0.60) for 14%, fair (0.21 ≤ κ ≤ 0.40) for 6%, and slight (0.0 ≤ κ ≤ 0.20) for 6%. Of 5 numerical variables, agreement was almost perfect (CCC > 0.99) for 20% and poor (CCC < 0.90) for 80%. Median OME completion time was 161.5 seconds (interquartile range, 116-224.5). CONCLUSION: OME is an efficient, valid tool for collecting comprehensive, standardized data on rotator cuff repair.
BACKGROUND: This study tested validity and efficiency of Orthopaedic Minimal Data Set (OrthoMiDaS) Episode of Care (OME). METHODS: We analyzed 100 isolated rotator cuff repair cases in the OME database. Surgeons completed a traditional operative note and OME report. A blinded reviewer extracted data from operative notes and implant logs in electronic medical records by manual chart review. OME and electronic medical record data were compared with data counts and agreement between 40 variables of rotator cuff disease and repair procedures. Data counts were assessed using raw percentages and McNemar test (with continuity correction). Agreement of categorical variables was analyzed using Cohen κ (unweighted) and of numerical variables using the concordance correlation coefficient (CCC). Efficiency was assessed by median time to complete. RESULTS: OME database had significantly higher data counts for 25% (10/40) of variables. A high level of proportional and statistical agreement was demonstrated between the data. Among 35 categorical variables, proportional agreement was perfect for 17%, almost perfect (0.81 ≤ κ ≤ 1.00) for 37%, substantial (0.61 ≤ κ ≤ 0.80) for 20%, moderate (0.41 ≤ κ ≤ 0.60) for 14%, fair (0.21 ≤ κ ≤ 0.40) for 6%, and slight (0.0 ≤ κ ≤ 0.20) for 6%. Of 5 numerical variables, agreement was almost perfect (CCC > 0.99) for 20% and poor (CCC < 0.90) for 80%. Median OME completion time was 161.5 seconds (interquartile range, 116-224.5). CONCLUSION: OME is an efficient, valid tool for collecting comprehensive, standardized data on rotator cuff repair.
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