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Background

Anticholinergic medicines are prescribed for various conditions such as urinary incontinence and intestinal spasm and can pose harmful unintended side-effects. The cumulative effect of multiple anticholinergic medicines can lead to adverse outcomes such as delirium or falls.

Anticholinergic medicines represent a potentially reversible contributor to delirium and falls that is often overlooked in practice. We developed and validated an Anticholinergic Medication Index (ACMI) to quantify the association between anticholinergic medicines and the adverse outcome of delirium/falls. We also co-designed a training package for use in primary-care. A crucial next step is to further develop ACMI for use in hospital electronic patient record (EPR) systems as an electronic Clinical Decision Support System (CDSS) to automatically identify high-risk patients in hospital and support safer prescribing.

Aims & Objectives

Aim:

To design an Anticholinergic Medication Index Clinical Decision Support System (ACMICS) to optimise prescribing of anticholinergic medicines in secondary-care, and assess feasibility of its use as part of routine care in acute hospital wards.

Methods

The ACMICS will be designed using Contextual Inquiry methods with healthcare professionals from two hospitals. One-to-one observations in the geriatric/general medicine and orthogeriatric wards of each hospital will be conducted. Findings will be analysed with input from a Clinical Reference group (CRG) and PPIE group to create ACMICS visualisations. Integration into hospital EPRs for feasibility testing will occur, with the development of a generalisable technical specification enabling future implementation into other secondary EPR systems. The primary-care ACMI training package will be refined for secondary-care in collaboration with the CRG and PPIE group.

Normalisation Process Theory will be used to assess the feasibility of implementing ACMICS and the training

package. We will conduct semi-structured interviews with healthcare professionals and patients from each site. Findings will inform refinement of the ACMICS and training, with a Good Practice Guide developed for operationalising ACMICS into hospital clinical workflow.

Partners & Collaborators

Investigators
Aseel S Abuzour, Andrew Clegg, Kate Best, David Alldred, Sir Munir Pirmohamed, Lauren Walker, Mohammed Rauf, Hilary Garrett

Collaborators:

Bradford Teaching Hospitals NHS Foundation Trust, University of Leeds, University of Liverpool, Meri Yaadain Community Interest Company

Timescales

01/05/2025-30/04/2027

Funding agency

This study is independent research funded by the National Institute for Health and Care Research (NIHR) Research for Patient Benefit (RfPB) Programme NIHR208083. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health and Care Research or the Department of Health.

Further information

For further information contact Dr Aseel S Abuzour a.s.m.abuzour@leeds.ac.uk