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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Jun 2, 2023
Date Accepted: Dec 17, 2023

The final, peer-reviewed published version of this preprint can be found here:

Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial

Patel D, Msosa Y, Wang T, Williams J, Mustafa OG, Gee S, Arroyo B, Larkin D, Tiedt T, Roberts A, Dobson RJ, Gaughran F

Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial

JMIR Res Protoc 2024;13:e49548

DOI: 10.2196/49548

PMID: 38578666

PMCID: 11031689

Implementation of an electronic clinical decision support system (eCDSS) for the early recognition and management of dysglycaemia in an inpatient mental health setting using CogStack: Protocol for a pilot hybrid type 3 effectiveness-implementation randomized controlled cluster trial

  • Dipen Patel; 
  • Yamiko Msosa; 
  • Tao Wang; 
  • Julie Williams; 
  • Omar G Mustafa; 
  • Siobhan Gee; 
  • Barbara Arroyo; 
  • Damian Larkin; 
  • Trevor Tiedt; 
  • Angus Roberts; 
  • Richard JB Dobson; 
  • Fiona Gaughran

ABSTRACT

Background:

Severe mental illnesses (SMI), including schizophrenia, bipolar disorder and major depressive disorder are associated with an excess of physical health comorbidities and premature mortality. Digital technologies such as electronic clinical decision support systems (eCDSSs) could play a crucial role in improving clinician-led management of conditions such as dysglycaemia and associated conditions such as diabetes in people with a diagnosis of SMI.

Objective:

We have developed a real-time eCDSS using CogStack, an information retrieval and extraction platform, to automatically alert clinicians with National Health Services (NHS) Trust-approved, guideline-based recommendations for dysglycaemia monitoring and management, in secondary mental healthcare. This protocol describes a feasibility study of its implementation in a mental health inpatient setting.

Methods:

This will be a pilot hybrid type 3 effectiveness-implementation randomized controlled cluster trial in inpatient mental health trust wards. A ward will be the unit of recruitment where it will be randomly allocated to receive either access to the eCDSS plus usual care or usual care alone over a four- month period.

Results:

We will measure implementation outcomes, including feasibility and acceptability of the eCDSS to clinicians as primary outcomes, alongside secondary outcomes relating to process of care measures such as dysglycaemia screening rates. An evaluation of other implementation outcomes relating to the eCDSS will be conducted, identifying facilitators and barriers based on established implementation science frameworks. Enrolment of wards began in April 2022. The trial is currently in progress and data collection is expected to complete in August 2023.

Conclusions:

An eCDSS can have the potential to improve clinician-led management of dysglycaemia in inpatient mental health settings. If found feasible and acceptable, then in combination with results of a planned implementation evaluation, the system can be refined to support future implementation. A larger and more definitive effectiveness trial should then be conducted to assess impact on clinical outcomes and to inform scalability and application to other conditions in wider mental healthcare settings. Clinical Trial: Clinicaltrials.gov NCT04792268


 Citation

Please cite as:

Patel D, Msosa Y, Wang T, Williams J, Mustafa OG, Gee S, Arroyo B, Larkin D, Tiedt T, Roberts A, Dobson RJ, Gaughran F

Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial

JMIR Res Protoc 2024;13:e49548

DOI: 10.2196/49548

PMID: 38578666

PMCID: 11031689

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