Introduction: To evaluate the correlation between the presence of an independent EHR (compared to a shared EHR system within an adult hospital system) and an externally-derived third party ranking of children’s hospi...Introduction: To evaluate the correlation between the presence of an independent EHR (compared to a shared EHR system within an adult hospital system) and an externally-derived third party ranking of children’s hospitals. Methods: Children’s hospitals that ranked in the top fifty of the 2019-2020 US News and World Report (USNWR) were included in the analysis. The mean and median ranking of children’s hospitals with independent versus a shared EHR was evaluated. The 2019-2020 USNWR rankings of the top twenty adult hospitals in the United States were then evaluated. For each children’s hospital with an associated adult hospital that was both ranked, it was noted as to whether the EHR for the children’s hospital was independent or shared and statistical differences in rankings compared. Results: Among the top 50 children’s hospitals included, the median USNWR ranking for hospitals was statistically different with an independent EHR than with a shared EHR (13 vs. 30.0) (p = 0.002). The 21 top ranked adult hospitals were associated with 17 children’s hospitals ranked in the top 50. The median ranking for those with an independent EHR was statistically different for those with independent EHR versus shared EHR (7 vs. 28) (p = 0.002). Conclusion: Children’s hospitals with an independent EHR are associated with higher scores on an independent external ranking of hospital quality compared to those which share an EHR with a partner adult hospital.展开更多
BACKGROUND The use of machine learning(ML)to predict colonoscopy procedure duration has not been examined.AIM To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estima...BACKGROUND The use of machine learning(ML)to predict colonoscopy procedure duration has not been examined.AIM To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estimate procedure duration more accurately than the current practice.METHODS Total 40168 colonoscopies from the Clinical Outcomes Research Initiative database were collected.ML models predicting procedure duration were developed using data available at time of scheduling.The top performing model was compared against historical practice.Models were evaluated based on accuracy(prediction–actual time)±5,10,and 15 min.RESULTS ML outperformed historical practice with 77.1%to 68.9%,87.3%to 79.6%,and 92.1%to 86.8%accuracy at 5,10 and 15 min thresholds.CONCLUSION The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.展开更多
文摘Introduction: To evaluate the correlation between the presence of an independent EHR (compared to a shared EHR system within an adult hospital system) and an externally-derived third party ranking of children’s hospitals. Methods: Children’s hospitals that ranked in the top fifty of the 2019-2020 US News and World Report (USNWR) were included in the analysis. The mean and median ranking of children’s hospitals with independent versus a shared EHR was evaluated. The 2019-2020 USNWR rankings of the top twenty adult hospitals in the United States were then evaluated. For each children’s hospital with an associated adult hospital that was both ranked, it was noted as to whether the EHR for the children’s hospital was independent or shared and statistical differences in rankings compared. Results: Among the top 50 children’s hospitals included, the median USNWR ranking for hospitals was statistically different with an independent EHR than with a shared EHR (13 vs. 30.0) (p = 0.002). The 21 top ranked adult hospitals were associated with 17 children’s hospitals ranked in the top 50. The median ranking for those with an independent EHR was statistically different for those with independent EHR versus shared EHR (7 vs. 28) (p = 0.002). Conclusion: Children’s hospitals with an independent EHR are associated with higher scores on an independent external ranking of hospital quality compared to those which share an EHR with a partner adult hospital.
文摘BACKGROUND The use of machine learning(ML)to predict colonoscopy procedure duration has not been examined.AIM To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estimate procedure duration more accurately than the current practice.METHODS Total 40168 colonoscopies from the Clinical Outcomes Research Initiative database were collected.ML models predicting procedure duration were developed using data available at time of scheduling.The top performing model was compared against historical practice.Models were evaluated based on accuracy(prediction–actual time)±5,10,and 15 min.RESULTS ML outperformed historical practice with 77.1%to 68.9%,87.3%to 79.6%,and 92.1%to 86.8%accuracy at 5,10 and 15 min thresholds.CONCLUSION The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.
