Aimée E M J H Linkens, Dennis Kurstjens, N Anne Zwietering, Vanja Milosevic, Kim P G M Hurkens, Noémi van Nie, Bob P A van de Loo, P Hugo M van der Kuy, Bart Spaetgens
{"title":"住院老年患者的临床决策支持系统:在现实临床环境中的探索性分析。","authors":"Aimée E M J H Linkens, Dennis Kurstjens, N Anne Zwietering, Vanja Milosevic, Kim P G M Hurkens, Noémi van Nie, Bob P A van de Loo, P Hugo M van der Kuy, Bart Spaetgens","doi":"10.1007/s40801-023-00365-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Inappropriate prescribing is associated with negative patient outcomes. In hospitalized patients, the use of Clinical Decision Support Systems (CDSSs) may reduce inappropriate prescribing and thereby improve patient-related outcomes. However, recently published large clinical trials (OPERAM and SENATOR) have shown negative results on the use of CDSSs and patient outcomes and strikingly low acceptance of recommendations.</p><p><strong>Objective: </strong>The purpose of the present study was to investigate the use of a CDSS in a real-life clinical setting of hospitalized older patients. As such, we report on the real-life pattern of this in-hospital implemented CDSS, including (i) whether generated alerts were resolved; (ii) whether a recorded action by the pharmacist led to an improved number of resolved alerts; and (iii) the natural course of generated alerts, in particular of those in the non-intervention group; as these data are largely lacking in current studies.</p><p><strong>Methods: </strong>Hospitalized patients, aged 60 years and older, admitted to Zuyderland Medical Centre, the Netherlands, in 2018 were included. The evaluation of the CDSS was investigated using a database used for standard care. Alongside demographic and clinical data, we also collected the total numbers of CDSS alerts, the number of alerts 'handled' by the pharmacist, those that resulted in an action by the pharmacist, and finally the outcome of the alerts at day 1 and day 3 after the alert was generated.</p><p><strong>Results: </strong>A total of 3574 unique hospitalized patients, mean age 76.7 (SD 8.3) years and 53% female, were included. From these patients, 8073 alerts were generated, of which 7907 (97.9% of total) were handled by the pharmacist (day 1). In 51.6% of the alerts handled by the pharmacist, an action was initiated, resulting in 36.1% of the alerts resolved after day 1, compared with 27.3% if the pharmacist did not perform an action (p < 0.001). On day 3, in 52.6% of the alerts an action by the pharmacist was initiated, resulting in 62.4% resolved alerts, compared with 48.0% when no action was performed (p < 0.001). In the category renal function, the percentages differed significantly between an action versus no action of the pharmacist at day 1 and at day 3 (16.6% vs 10.6%, p < 0.001 [day 1]; 29.8% vs 19.4%, p < 0.001 [day 3]).</p><p><strong>Conclusion: </strong>This study demonstrates the pattern and natural course of clinical alerts of an in-hospital implemented CDSS in a real-life clinical setting of hospitalized older patients. Besides the already known beneficial effect of actions by pharmacists, we have also shown that many alerts become resolved without any specific intervention. As such, our study provides an important insight into the spontaneous course of resolved alerts, since these data are currently lacking in the literature.</p>","PeriodicalId":11282,"journal":{"name":"Drugs - Real World Outcomes","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/33/3e/40801_2023_Article_365.PMC10491559.pdf","citationCount":"1","resultStr":"{\"title\":\"Clinical Decision Support Systems in Hospitalized Older Patients: An Exploratory Analysis in a Real-Life Clinical Setting.\",\"authors\":\"Aimée E M J H Linkens, Dennis Kurstjens, N Anne Zwietering, Vanja Milosevic, Kim P G M Hurkens, Noémi van Nie, Bob P A van de Loo, P Hugo M van der Kuy, Bart Spaetgens\",\"doi\":\"10.1007/s40801-023-00365-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Inappropriate prescribing is associated with negative patient outcomes. In hospitalized patients, the use of Clinical Decision Support Systems (CDSSs) may reduce inappropriate prescribing and thereby improve patient-related outcomes. However, recently published large clinical trials (OPERAM and SENATOR) have shown negative results on the use of CDSSs and patient outcomes and strikingly low acceptance of recommendations.</p><p><strong>Objective: </strong>The purpose of the present study was to investigate the use of a CDSS in a real-life clinical setting of hospitalized older patients. As such, we report on the real-life pattern of this in-hospital implemented CDSS, including (i) whether generated alerts were resolved; (ii) whether a recorded action by the pharmacist led to an improved number of resolved alerts; and (iii) the natural course of generated alerts, in particular of those in the non-intervention group; as these data are largely lacking in current studies.</p><p><strong>Methods: </strong>Hospitalized patients, aged 60 years and older, admitted to Zuyderland Medical Centre, the Netherlands, in 2018 were included. The evaluation of the CDSS was investigated using a database used for standard care. Alongside demographic and clinical data, we also collected the total numbers of CDSS alerts, the number of alerts 'handled' by the pharmacist, those that resulted in an action by the pharmacist, and finally the outcome of the alerts at day 1 and day 3 after the alert was generated.</p><p><strong>Results: </strong>A total of 3574 unique hospitalized patients, mean age 76.7 (SD 8.3) years and 53% female, were included. From these patients, 8073 alerts were generated, of which 7907 (97.9% of total) were handled by the pharmacist (day 1). In 51.6% of the alerts handled by the pharmacist, an action was initiated, resulting in 36.1% of the alerts resolved after day 1, compared with 27.3% if the pharmacist did not perform an action (p < 0.001). On day 3, in 52.6% of the alerts an action by the pharmacist was initiated, resulting in 62.4% resolved alerts, compared with 48.0% when no action was performed (p < 0.001). In the category renal function, the percentages differed significantly between an action versus no action of the pharmacist at day 1 and at day 3 (16.6% vs 10.6%, p < 0.001 [day 1]; 29.8% vs 19.4%, p < 0.001 [day 3]).</p><p><strong>Conclusion: </strong>This study demonstrates the pattern and natural course of clinical alerts of an in-hospital implemented CDSS in a real-life clinical setting of hospitalized older patients. Besides the already known beneficial effect of actions by pharmacists, we have also shown that many alerts become resolved without any specific intervention. 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Clinical Decision Support Systems in Hospitalized Older Patients: An Exploratory Analysis in a Real-Life Clinical Setting.
Background: Inappropriate prescribing is associated with negative patient outcomes. In hospitalized patients, the use of Clinical Decision Support Systems (CDSSs) may reduce inappropriate prescribing and thereby improve patient-related outcomes. However, recently published large clinical trials (OPERAM and SENATOR) have shown negative results on the use of CDSSs and patient outcomes and strikingly low acceptance of recommendations.
Objective: The purpose of the present study was to investigate the use of a CDSS in a real-life clinical setting of hospitalized older patients. As such, we report on the real-life pattern of this in-hospital implemented CDSS, including (i) whether generated alerts were resolved; (ii) whether a recorded action by the pharmacist led to an improved number of resolved alerts; and (iii) the natural course of generated alerts, in particular of those in the non-intervention group; as these data are largely lacking in current studies.
Methods: Hospitalized patients, aged 60 years and older, admitted to Zuyderland Medical Centre, the Netherlands, in 2018 were included. The evaluation of the CDSS was investigated using a database used for standard care. Alongside demographic and clinical data, we also collected the total numbers of CDSS alerts, the number of alerts 'handled' by the pharmacist, those that resulted in an action by the pharmacist, and finally the outcome of the alerts at day 1 and day 3 after the alert was generated.
Results: A total of 3574 unique hospitalized patients, mean age 76.7 (SD 8.3) years and 53% female, were included. From these patients, 8073 alerts were generated, of which 7907 (97.9% of total) were handled by the pharmacist (day 1). In 51.6% of the alerts handled by the pharmacist, an action was initiated, resulting in 36.1% of the alerts resolved after day 1, compared with 27.3% if the pharmacist did not perform an action (p < 0.001). On day 3, in 52.6% of the alerts an action by the pharmacist was initiated, resulting in 62.4% resolved alerts, compared with 48.0% when no action was performed (p < 0.001). In the category renal function, the percentages differed significantly between an action versus no action of the pharmacist at day 1 and at day 3 (16.6% vs 10.6%, p < 0.001 [day 1]; 29.8% vs 19.4%, p < 0.001 [day 3]).
Conclusion: This study demonstrates the pattern and natural course of clinical alerts of an in-hospital implemented CDSS in a real-life clinical setting of hospitalized older patients. Besides the already known beneficial effect of actions by pharmacists, we have also shown that many alerts become resolved without any specific intervention. As such, our study provides an important insight into the spontaneous course of resolved alerts, since these data are currently lacking in the literature.
期刊介绍:
Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.