Clinical decision support system supported interventions in hospitalized older patients: a matter of natural course and adequate timing.


Journal

BMC geriatrics
ISSN: 1471-2318
Titre abrégé: BMC Geriatr
Pays: England
ID NLM: 100968548

Informations de publication

Date de publication:
14 Mar 2024
Historique:
received: 26 05 2023
accepted: 18 02 2024
medline: 18 3 2024
pubmed: 15 3 2024
entrez: 15 3 2024
Statut: epublish

Résumé

Drug-related problems (DRPs) and potentially inappropriate prescribing (PIP) are associated with adverse patient and health care outcomes. In the setting of hospitalized older patients, Clinical Decision Support Systems (CDSSs) could reduce PIP and therefore improve clinical outcomes. However, prior research showed a low proportion of adherence to CDSS recommendations by clinicians with possible explanatory factors such as little clinical relevance and alert fatigue. To investigate the use of a CDSS in a real-life setting of hospitalized older patients. We aim to (I) report the natural course and interventions based on the top 20 rule alerts (the 20 most frequently generated alerts per clinical rule) of generated red CDSS alerts (those requiring action) over time from day 1 to 7 of hospitalization; and (II) to explore whether an optimal timing can be defined (in terms of day per rule). All hospitalized patients aged ≥ 60 years, admitted to Zuyderland Medical Centre (the Netherlands) were included. The evaluation of the CDSS was investigated using a database used for standard care. Our CDSS was run daily and was evaluated on day 1 to 7 of hospitalization. We collected demographic and clinical data, and moreover the total number of CDSS alerts; the total number of top 20 rule alerts; those that resulted in an action by the pharmacist and the course of outcome of the alerts on days 1 to 7 of hospitalization. In total 3574 unique hospitalized patients, mean age 76.7 (SD 8.3) years and 53% female, were included. From these patients, in total 8073 alerts were generated; with the top 20 of rule alerts we covered roughly 90% of the total. For most rules in the top 20 the highest percentage of resolved alerts lies somewhere between day 4 and 5 of hospitalization, after which there is equalization or a decrease. Although for some rules, there is a gradual increase in resolved alerts until day 7. The level of resolved rule alerts varied between the different clinical rules; varying from > 50-70% (potassium levels, anticoagulation, renal function) to less than 25%. This study reports the course of the 20 most frequently generated alerts of a CDSS in a setting of hospitalized older patients. We have shown that for most rules, irrespective of an intervention by the pharmacist, the highest percentage of resolved rules is between day 4 and 5 of hospitalization. The difference in level of resolved alerts between the different rules, could point to more or less clinical relevance and advocates further research to explore ways of optimizing CDSSs by adjustment in timing and number of alerts to prevent alert fatigue.

Sections du résumé

BACKGROUND BACKGROUND
Drug-related problems (DRPs) and potentially inappropriate prescribing (PIP) are associated with adverse patient and health care outcomes. In the setting of hospitalized older patients, Clinical Decision Support Systems (CDSSs) could reduce PIP and therefore improve clinical outcomes. However, prior research showed a low proportion of adherence to CDSS recommendations by clinicians with possible explanatory factors such as little clinical relevance and alert fatigue.
OBJECTIVE OBJECTIVE
To investigate the use of a CDSS in a real-life setting of hospitalized older patients. We aim to (I) report the natural course and interventions based on the top 20 rule alerts (the 20 most frequently generated alerts per clinical rule) of generated red CDSS alerts (those requiring action) over time from day 1 to 7 of hospitalization; and (II) to explore whether an optimal timing can be defined (in terms of day per rule).
METHODS METHODS
All hospitalized patients aged ≥ 60 years, admitted to Zuyderland Medical Centre (the Netherlands) were included. The evaluation of the CDSS was investigated using a database used for standard care. Our CDSS was run daily and was evaluated on day 1 to 7 of hospitalization. We collected demographic and clinical data, and moreover the total number of CDSS alerts; the total number of top 20 rule alerts; those that resulted in an action by the pharmacist and the course of outcome of the alerts on days 1 to 7 of hospitalization.
RESULTS RESULTS
In total 3574 unique hospitalized patients, mean age 76.7 (SD 8.3) years and 53% female, were included. From these patients, in total 8073 alerts were generated; with the top 20 of rule alerts we covered roughly 90% of the total. For most rules in the top 20 the highest percentage of resolved alerts lies somewhere between day 4 and 5 of hospitalization, after which there is equalization or a decrease. Although for some rules, there is a gradual increase in resolved alerts until day 7. The level of resolved rule alerts varied between the different clinical rules; varying from > 50-70% (potassium levels, anticoagulation, renal function) to less than 25%.
CONCLUSION CONCLUSIONS
This study reports the course of the 20 most frequently generated alerts of a CDSS in a setting of hospitalized older patients. We have shown that for most rules, irrespective of an intervention by the pharmacist, the highest percentage of resolved rules is between day 4 and 5 of hospitalization. The difference in level of resolved alerts between the different rules, could point to more or less clinical relevance and advocates further research to explore ways of optimizing CDSSs by adjustment in timing and number of alerts to prevent alert fatigue.

Identifiants

pubmed: 38486200
doi: 10.1186/s12877-024-04823-7
pii: 10.1186/s12877-024-04823-7
pmc: PMC10941377
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

256

Informations de copyright

© 2024. The Author(s).

Références

Eur Geriatr Med. 2022 Apr;13(2):329-337
pubmed: 34755308
JAMA Netw Open. 2019 Aug 2;2(8):e198398
pubmed: 31373653
JAMA Intern Med. 2022 Mar 01;182(3):265-273
pubmed: 35040926
BMJ Open. 2019 Sep 27;9(9):e029477
pubmed: 31562149
Int J Clin Pharmacol Ther. 2008 Feb;46(2):72-83
pubmed: 18218287
Age Ageing. 2018 Sep 1;47(5):670-678
pubmed: 29893779
Springerplus. 2016 Jun 24;5(1):871
pubmed: 27386320
Age Ageing. 2020 Jul 1;49(4):605-614
pubmed: 32484850
Cochrane Database Syst Rev. 2018 Sep 03;9:CD008165
pubmed: 30175841
Clin Interv Aging. 2010 Apr 07;5:75-87
pubmed: 20396637
Age Ageing. 2020 Jul 1;49(4):615-621
pubmed: 32484853
BMJ. 2021 Jul 13;374:n1585
pubmed: 34257088
Clin Pharmacol Ther. 2011 Jun;89(6):845-54
pubmed: 21508941
Drugs Aging. 2020 Feb;37(2):115-123
pubmed: 31782128
Drugs Aging. 2020 Sep;37(9):703-713
pubmed: 32681402
Am J Health Syst Pharm. 2009 Dec 1;66(23):2098-101
pubmed: 19923309
Drugs Aging. 2016 Jan;33(1):63-73
pubmed: 26597401
Int J Med Inform. 2015 Jun;84(6):396-405
pubmed: 25746461
Health Informatics J. 2021 Apr-Jun;27(2):14604582211007536
pubmed: 33853395
BMC Health Serv Res. 2020 Mar 17;20(1):220
pubmed: 32183810
Fundam Clin Pharmacol. 2015 Feb;29(1):106-11
pubmed: 24990220
Drugs Aging. 2022 Jan;39(1):59-73
pubmed: 34877629
Drugs Real World Outcomes. 2023 Sep;10(3):363-370
pubmed: 36964279

Auteurs

N A Zwietering (NA)

Department of Geriatric Medicine, Laurentius Hospital, 6040 AX, Roermond, PO box 920, The Netherlands. anne.zwietering@lzr.nl.
Department of Hospital Pharmacy, Erasmus Medical Centre, Rotterdam, The Netherlands. anne.zwietering@lzr.nl.

Aemjh Linkens (A)

Department of Hospital Pharmacy, Erasmus Medical Centre, Rotterdam, The Netherlands.
Department of Internal Medicine, Division of General Internal Medicine, Section Geriatric Medicine, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht, the Netherlands.

D Kurstjens (D)

Department of Internal Medicine, Geriatric Medicine, Zuyderland Medical Centre, Heerlen/Sittard-Geleen, The Netherlands.

Phm van der Kuy (P)

Department of Hospital Pharmacy, Erasmus Medical Centre, Rotterdam, The Netherlands.

N van Nie-Visser (N)

Senior Project Manager, Innovation and Funding (Scientific Research), Zuyderland Medical Centre, Heerlen, The Netherlands.

Bpa van de Loo (B)

Digitalis Rx BV, Amsterdam, The Netherlands.

Kpgm Hurkens (K)

Department of Internal Medicine, Geriatric Medicine, Zuyderland Medical Centre, Heerlen/Sittard-Geleen, The Netherlands.

B Spaetgens (B)

Department of Internal Medicine, Division of General Internal Medicine, Section Geriatric Medicine, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht, the Netherlands.

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