Evidence map›Paper›PMID 38486200›Full record

ArticleBMC geriatrics2024

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

N A Zwietering, Aemjh Linkens, D Kurstjens, Phm van der Kuy, N van Nie-Visser, Bpa van de Loo, Kpgm Hurkens, B Spaetgens

Abstract read
In one paragraph

Article in BMC geriatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

N A ZwieteringDepartment of Geriatric Medicine, Laurentius Hospital, 6040 AX, Roermond, PO box 920, The Netherlands. anne.zwietering@lzr.nl.ORCID 0000-0003-0573-8621
Aemjh LinkensDepartment of Hospital Pharmacy, Erasmus Medical Centre, Rotterdam, The Netherlands.
D KurstjensDepartment of Internal Medicine, Geriatric Medicine, Zuyderland Medical Centre, Heerlen/Sittard-Geleen, The Netherlands.
Phm van der KuyDepartment of Hospital Pharmacy, Erasmus Medical Centre, Rotterdam, The Netherlands.
N van Nie-VisserSenior Project Manager, Innovation and Funding (Scientific Research), Zuyderland Medical Centre, Heerlen, The Netherlands.
Bpa van de LooDigitalis Rx BV, Amsterdam, The Netherlands.
Kpgm HurkensDepartment of Internal Medicine, Geriatric Medicine, Zuyderland Medical Centre, Heerlen/Sittard-Geleen, The Netherlands.
B SpaetgensDepartment of Internal Medicine, Division of General Internal Medicine, Section Geriatric Medicine, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDrug-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.

objectiveTo 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).

methodsAll 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.

resultsIn 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%.

conclusionThis 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.

Indexed as

Decision Support Systems, ClinicalIchthyosiform Erythroderma, CongenitalLipid Metabolism, Inborn ErrorsMuscular DiseasesAgedDatabases, FactualFemaleHospitalizationHospitalsHumansMaleClinical Decision Support System (CDSS)Drug-related problemsMedication reviewOlder patientsPotentially inappropriate prescribing (PIP)

Identifiers

PMID38486200
PMCPMC10941377

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.