Janina A Bittmann, Camilo Scherkl, Andreas D Meid, Walter E Haefeli, Hanna M Seidling
{"title":"通过事件分析自动估计缺席和持续用药警报:新方法","authors":"Janina A Bittmann, Camilo Scherkl, Andreas D Meid, Walter E Haefeli, Hanna M Seidling","doi":"10.2196/54428","DOIUrl":null,"url":null,"abstract":"Background: Event analysis is a promising option to estimate the acceptance of medication alerts issued by computerized physician order entry systems with integrated clinical decision support systems (CPOE-CDSS), particularly when alerts cannot be interactively confirmed in the CPOE-CDSS due to its system architecture. Medication documentation is then reviewed for documented evidence of alert acceptance, a time-consuming process, especially when performed manually. Objective: We present a new approach of an automated event analysis and apply it to a large dataset generated in a CPOE-CDSS with passive, non-interruptive alerts. Methods: Medication and alert data generated over 3.5 months within the CPOE-CDSS at Heidelberg University Hospital were divided into 24-hour time intervals in which alert display was correlated with associated prescription changes. Alerts were considered as “persistent” if they were displayed in every consecutive 24-hour time interval due to a respective active prescription until patient discharge and as “absent” if they were no longer displayed during continuous prescriptions in the subsequent interval. Results: Overall, 1,670 patient cases with 11,428 alerts were analyzed. Alerts were displayed for a median of three consecutive 24-hour time intervals with alerts for drug-allergy interactions displayed the shortest, and the longest for potentially inappropriate medication for the elderly (PIM). A total of 56.1 % of all alerts (n = 6,413) became absent, and among them, alerts for drug-drug interactions were the most common (80.9 %, n = 1,915) and PIM alerts the least common (39.9 %, n = 199). Conclusions: This new approach to estimate alert acceptance based on event analysis can be flexibly adapted to the automated evaluation of passive, non-interruptive alerts. This enables large datasets of longitudinal patient cases to be processed, and to derive the ratios of persistent and absent alerts, compare and prospectively monitor them.","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Event Analysis for Automated Estimation of Absent and Persistent Medication Alerts: Novel Methodology\",\"authors\":\"Janina A Bittmann, Camilo Scherkl, Andreas D Meid, Walter E Haefeli, Hanna M Seidling\",\"doi\":\"10.2196/54428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Event analysis is a promising option to estimate the acceptance of medication alerts issued by computerized physician order entry systems with integrated clinical decision support systems (CPOE-CDSS), particularly when alerts cannot be interactively confirmed in the CPOE-CDSS due to its system architecture. Medication documentation is then reviewed for documented evidence of alert acceptance, a time-consuming process, especially when performed manually. Objective: We present a new approach of an automated event analysis and apply it to a large dataset generated in a CPOE-CDSS with passive, non-interruptive alerts. Methods: Medication and alert data generated over 3.5 months within the CPOE-CDSS at Heidelberg University Hospital were divided into 24-hour time intervals in which alert display was correlated with associated prescription changes. Alerts were considered as “persistent” if they were displayed in every consecutive 24-hour time interval due to a respective active prescription until patient discharge and as “absent” if they were no longer displayed during continuous prescriptions in the subsequent interval. Results: Overall, 1,670 patient cases with 11,428 alerts were analyzed. Alerts were displayed for a median of three consecutive 24-hour time intervals with alerts for drug-allergy interactions displayed the shortest, and the longest for potentially inappropriate medication for the elderly (PIM). A total of 56.1 % of all alerts (n = 6,413) became absent, and among them, alerts for drug-drug interactions were the most common (80.9 %, n = 1,915) and PIM alerts the least common (39.9 %, n = 199). Conclusions: This new approach to estimate alert acceptance based on event analysis can be flexibly adapted to the automated evaluation of passive, non-interruptive alerts. This enables large datasets of longitudinal patient cases to be processed, and to derive the ratios of persistent and absent alerts, compare and prospectively monitor them.\",\"PeriodicalId\":56334,\"journal\":{\"name\":\"JMIR Medical Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/54428\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/54428","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Event Analysis for Automated Estimation of Absent and Persistent Medication Alerts: Novel Methodology
Background: Event analysis is a promising option to estimate the acceptance of medication alerts issued by computerized physician order entry systems with integrated clinical decision support systems (CPOE-CDSS), particularly when alerts cannot be interactively confirmed in the CPOE-CDSS due to its system architecture. Medication documentation is then reviewed for documented evidence of alert acceptance, a time-consuming process, especially when performed manually. Objective: We present a new approach of an automated event analysis and apply it to a large dataset generated in a CPOE-CDSS with passive, non-interruptive alerts. Methods: Medication and alert data generated over 3.5 months within the CPOE-CDSS at Heidelberg University Hospital were divided into 24-hour time intervals in which alert display was correlated with associated prescription changes. Alerts were considered as “persistent” if they were displayed in every consecutive 24-hour time interval due to a respective active prescription until patient discharge and as “absent” if they were no longer displayed during continuous prescriptions in the subsequent interval. Results: Overall, 1,670 patient cases with 11,428 alerts were analyzed. Alerts were displayed for a median of three consecutive 24-hour time intervals with alerts for drug-allergy interactions displayed the shortest, and the longest for potentially inappropriate medication for the elderly (PIM). A total of 56.1 % of all alerts (n = 6,413) became absent, and among them, alerts for drug-drug interactions were the most common (80.9 %, n = 1,915) and PIM alerts the least common (39.9 %, n = 199). Conclusions: This new approach to estimate alert acceptance based on event analysis can be flexibly adapted to the automated evaluation of passive, non-interruptive alerts. This enables large datasets of longitudinal patient cases to be processed, and to derive the ratios of persistent and absent alerts, compare and prospectively monitor them.
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.