Acute Renal Failure, Patient Acceptance of Health Care
Conditions
Keywords
Acute renal failure, hyperkaliemia, older people, computerized decision support system, stepped-wedge
Brief summary
Current evidence shows that computerized decision support systems (CDSS) have shown to be insufficiently effective to prevent adverse drug reactions (ADRs) at large scale (e.g. whole hospital). Several barriers for successful implementation of CDSS have been identified: over-alerting, lack of specificity of rules, and physician interruption during prescription. The effectiveness of CDSS could be increased in two ways. Firstly, by creating rules that are more specific to a given adverse drug reaction: the current study focuses on acute renal failure and hyperkalemia (two serious and frequent ADR in older hospitalized patients). Secondly, by involving the pharmacist in the review of the alerts so that he/she can transmit, if deemed necessary, a pharmaceutical recommendation to the clinician. This procedure will reduce over-alerting and prevent task interruption. The hypothesis is that the use of specific rules created by a multidisciplinary team and implemented in a CDSS, combined with a strategy for managing and transmitting alerts, can reduce specific ADRs such as hyperkalemia and acute renal failure.
Interventions
OTHERClinical decision support
In the intervention group, the pharmaceutical validation will be based on routine care, often on entry to a ward and by analysis of all the alerts produced by the CDSS. Some alerts will result in a pharmaceutical intervention being provided to the medical team
OTHERWill not receive Clinical Decision Support
In the control group, the pharmaceutical validation will be based on routine care, often on entry to a ward or in a particular situation
Sponsors
OméDIT (Observatory of Medicines, Medical Devices and Therapeutic Innovations
Regional Agency of Sante Nord Pas-de-Calais
University Hospital, Lille
Study design
Allocation
RANDOMIZED
Intervention model
SEQUENTIAL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE (Subject)
Intervention model description
Prospective, multicentre, controlled, single-blind, randomised cluster study with stepped-wedge permutations. The centres (hospitals) will be the clusters.
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Hospitalized for 3 days or more in an MCO (medicine surgery obstetrics) department participating in the study * Patient who gave oral consent to participate in the study * Socially insured patient
Exclusion criteria
* Patient discharged or died before D3 of hospitalization * Patient in palliative care or end of life on entry to the service * Person under legal protection (curatorship) * Lack of coverage by the social security system, Failure to obtain oral consent to participate in the study
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Number of adverse drug events such as acute renal failure and/or hyperkalemia in older hospitalized patients. | through study completion, an average of 20 days |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Presence of an adverse event related to the intervention provided (change of prescription, discontinuation of drug) | through study completion, an average of 20 days | — |
| Therapeutic adaptations implemented in case of acute renal failure (ARF) or hyperkalemia upon hospital admission | through study completion, an average of 15 days | Therapeutic changes within 72 hours of a CDSS alert for acute renal failure or hyperkalemia. Therapeutic changes include discontinuation of drug therapy, introduction of a new drug, dose reduction or change of drug |
| Relevance of CDSS alerts | through study completion, an average of 20 days | Relevance of CDSS alerts is defined in a standard way. Each CDSS alert is evaluated by a clinical pharmacist according to their own expertise and data available in the EHR. If the alert was deemed not relevant, the clinical pharmacist did not perform any pharmaceutical intervention. The CDSS software register the classification of the alert as not relevant. This approach was used the last 4 years in our hospital and as been published in an article published in the International Journal of Medical Informatics: Cuvelier E, Robert L, Musy E, Rousselière C, Marcilly R, Gautier S, Odou P, Beuscart JB, Décaudin B. The clinical pharmacist's role in enhancing the relevance of a clinical decision support system. Int J Med Inform. 2021 Nov;155:104568. doi: 10.1016/j.ijmedinf.2021.104568. Epub 2021 Sep 2. PMID: 34537687 |
| Number of pharmaceutical interventions accepted | through study completion, an average of 20 days | When an alert is received by the pharmacist, it is analyzed and the pharmacist forwards a pharmaceutical intervention to the physician in charge of the patient to propose a modification of the treatment (dosage, dose, stop |
| Changes in ADEs (Adverse Drug Event) prevention/management work process induced by the introduction of alerts | Through study completion, an average of 20 days | Changes in the work system are identified through a comparison of its elements (tools, tasks, organization, interactions, work environment, professionals), before and after the introduction of alerts, using qualitative system engineering methods. |
| Cost-effectiveness of the pharmaceutical intervention | through study completion, an average of 20 days | Use medico-economic data such as time spent treating an alert, cost of treating an adverse drug reaction to estimate the cost-effectiveness of the intervention |
Countries
France
Outcome results
None listed