TY - JOUR
T1 - Developing EMR-based algorithms to Identify hospital adverse events for health system performance evaluation and improvement: Study protocol
AU - Wu, Guosong
AU - Eastwood, Cathy A
AU - Zeng, Yong
AU - Quan, Hude
AU - Long, Quan
AU - Zhang, Zilong
AU - Ghali, William A.
AU - Bakal, Jeffrey
AU - Boussat, Bastien
AU - Flemons, Ward
AU - Forster, Alan
AU - Southern, Danielle A.
AU - Knudsen, Søren
AU - Popowich, Brittany
AU - Xu, Yuan
PY - 2022/10/1
Y1 - 2022/10/1
N2 - Background Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely implemented and contain detailed and comprehensive information regarding all aspects of patient care, offering a valuable complement to administrative data. Harnessing the rich clinical data in EMRs offers a unique opportunity to improve detection, identify possible risk factors of AE and enhance surveillance. However, the methodological tools for detection of AEs within EMR need to be developed and validated. The objectives of this study are to develop EMR-based AE algorithms from hospital EMR data and assess AE algorithm’s validity in Canadian EMR data. Methods Patient EMR structured and text data from acute care hospitals in Calgary, Alberta, Canada will be linked with discharge abstract data (DAD) between 2010 and 2020 (n~1.5 million). AE algorithms development. First, a comprehensive list of AEs will be generated through a systematic literature review and expert recommendations. Second, these AEs will be mapped to EMR free texts using Natural Language Processing (NLP) technologies. Finally, an expert panel will assess the clinical relevance of the developed NLP algorithms. AE algorithms validation: We will test the newly developed AE algorithms on 10,000 randomly selected EMRs between 2010 to 2020 from Calgary, Alberta. Trained reviewers will review the selected 10,000 EMR charts to identify AEs that had occurred during hospitalization. Performance indicators (e.g., sensitivity, specificity, positive predictive value, negative predictive value, F1 score, etc.) of the developed AE algorithms will be assessed using chart review data as the reference standard. Discussion The results of this project can be widely implemented in EMR based healthcare system to accurately and timely detect in-hospital AEs.
AB - Background Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely implemented and contain detailed and comprehensive information regarding all aspects of patient care, offering a valuable complement to administrative data. Harnessing the rich clinical data in EMRs offers a unique opportunity to improve detection, identify possible risk factors of AE and enhance surveillance. However, the methodological tools for detection of AEs within EMR need to be developed and validated. The objectives of this study are to develop EMR-based AE algorithms from hospital EMR data and assess AE algorithm’s validity in Canadian EMR data. Methods Patient EMR structured and text data from acute care hospitals in Calgary, Alberta, Canada will be linked with discharge abstract data (DAD) between 2010 and 2020 (n~1.5 million). AE algorithms development. First, a comprehensive list of AEs will be generated through a systematic literature review and expert recommendations. Second, these AEs will be mapped to EMR free texts using Natural Language Processing (NLP) technologies. Finally, an expert panel will assess the clinical relevance of the developed NLP algorithms. AE algorithms validation: We will test the newly developed AE algorithms on 10,000 randomly selected EMRs between 2010 to 2020 from Calgary, Alberta. Trained reviewers will review the selected 10,000 EMR charts to identify AEs that had occurred during hospitalization. Performance indicators (e.g., sensitivity, specificity, positive predictive value, negative predictive value, F1 score, etc.) of the developed AE algorithms will be assessed using chart review data as the reference standard. Discussion The results of this project can be widely implemented in EMR based healthcare system to accurately and timely detect in-hospital AEs.
KW - Electronic Medical Records
KW - Adverse Event Detection
KW - Natural Language Processing
KW - Algorithm Development
KW - Healthcare Quality and Safety
KW - Electronic Medical Records
KW - Adverse Event Detection
KW - Natural Language Processing
KW - Algorithm Development
KW - Healthcare Quality and Safety
U2 - 10.1371/journal.pone.0275250
DO - 10.1371/journal.pone.0275250
M3 - Journal article
SN - 1932-6203
VL - 17
SP - 1
EP - 10
JO - PLOS ONE
JF - PLOS ONE
IS - 10
ER -