Designing and pilot testing an anticholinergic medication index clinical decision support system (ACMICS) for use in hospitals to support safer prescribing for older people at risk of medication-related harm.

Background Anticholinergic medicines are prescribed for various conditions such as urinary incontinence and intestinal spam 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.

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. Research Questions 1.

How should the existing primary-care ACMI be developed into a CDSS suitable for secondary-care, and how should the existing primary-care ACMI training package be refined? 2. How should the ACMICS be integrated into secondary-care EPR systems to optimise anticholinergic medicines? 3.

Is it feasible to integrate use of the ACMICS into the clinical workflow of secondary-care healthcare professionals (HCPs) during the care of older adults acutely admitted to hospital?

Methods The ACMICS will be designed using Contextual Inquiry methods with 8-12 HCPs recruited per site 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 up to 10 HCPs and 5 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.

Timelines for delivery Work-Stream 1 Month1-8: Recruitment, observations, data analysis for contextual observations M5-10: ACMICS development and integration into EPR; technical specification development; refining primary-care ACMI training package Work-Stream 2 M11-19: Recruitment, interviews, analysis, Good Practice Guide development M16-21: Refining ACMICS and training package M19-24: Final report writing Anticipated Impact and Dissemination The ACMICS will automatically identify patients at risk of harm from anticholinergic medicines, reducing the clinical time to complete this task.

Automatic identification of people for targeted medicines review will potentially lead to safer prescribing of medicines in hospital.

We will leverage our strong track-record of translating research into practice and routes to dissemination including Health Data Research UK, Cerner Prescribing Group, and NHS England.