Applying digital to enhance ECG interpretation and response in mental health settings

Lead Supervisor
Dr Fiona Gaughran
Reader in Psychopharmacology and Physical Health
Dept of Psychosis Studies, Institute of Psychology, Psychiatry and Neuroscience (IoPPN), King’s College London

Professor Ajay Shah (King’s College London), Dr Nicholas Gall (King’s College Hospital)

Project Details


This project will use the digital linkages between mental and physical health services to explore the effects of psychotropic medications on cardiac conduction patterns. 


Patients with severe mental illnesses (SMI), such as schizophrenia or bipolar disorder have twice the risk of cardiac death than the general population (Saha et al, 2007). The prevalence of cardiometabolic risk factors is very high in this group (ref), with risks increased by high rates of smoking, sedentary behaviour and poor diet (Firth et al, 2019)  In addition, anti-psychotics, especially at higher doses, are associated with prolonged QTc intervals and arrhythmias (Straus et al, 2004; Ray et al, 2009). As a result, NICE (2014) recommend that people presenting or admitted with psychotic illnesses have an ECG. 

However, systems for ECG interpretation in mental health settings are underdeveloped and clinical uncertainty can delay psychiatric treatment and so prolong the sequalae of active psychosis, such as admission or social disadvantage. Further evidence is needed to inform protocolised responses to ECG-related queries in mental health settings. 


  1. To use observational data to determine the frequency, context and outcomes of described ECG anomalies in patients with psychosis currently recorded in mental health Electronic Health Records, 
  2. To conduct a literature review of guidelines for the use of ECG in mental health settings, including their propensity for and consequences of false positive findings
  3. To determine the feasibility and acceptability of introducing a remote ECG interpretation and advice service to mental health settings.  
  4. To identify the prevalence of ECG abnormalities in anti-psychotic naïve people presenting with their first episode of psychosis
  5. To conduct in-detail analyses of ECG tracings in relation to anti-psychotic, type, dose and level (where available) and to clinical associations (eg, demographic factors, diagnosis, other cardiometabolic factors) and outcomes (eg, changes in management; further investigations; length of stay; mortality). 
  6. Development of enhanced clinical guidelines for ECG interpretation in people with SMI. 

This project will build on established digital and clinical connections between King’s College Hospital Cardiology Department and the South London and Maudsley NHS Foundation Trust. Digital links to the MUSE ECG system in KCH have been confirmed and firewall rules implemented. Four compatible machines are in place in SLaM. We have also linked with the SLaM ECG machine procurement process, and the machines chosen possess the technical capacity to link with the KCH MUSE system. This will allow easier expansion of the pathway. At present ECGs are recorded in PDF format in the SLaM electronic health records. The ECGs in KCH are stored in XML format or similar, allowing for more detailed analysis of cardiac conduction and its associations. 

We have created a clinical proforma for referral requests and agreed systems for transfer of clinical data between systems, confirming that the pathway is Information Governance compliant.

The PhD student will be a medical doctor with MRCP or equivalent required as the project will involve ECG interpretation. Training will be available in data science and statistical methodologies. 

The first stage of the project (project A) will use the Clinical Record Interactive Search (CRIS). Applications to access CRIS and the analyses carried out using CRIS are closely reviewed, monitored and audited by a CRIS Oversight Committee, which carries representation from the Maudsley Caldicott Guardian and is chaired by a service user. The CRIS Oversight Committee is responsible for ensuring all research applications comply with ethical and legal guidelines.

Project C will utilise a data linkage between CRIS and the KERRI informatics platform in KCH to allow determination of the associations and outcome of ECG changes. 

At King’s College Hospital, routinely collected information is anonymised and added to a secure research database called KERRI. KCH internal research teams use this anonymised data for research projects aimed at patient and societal benefit. The Health Research Authority Research Ethics Committee supports this approach.

KERRI and CRIS linked data require local IG permission and Caldicott Guardian approval. However, HRA research ethics not required for the use of fully anonymised patient-level data (confirmed, Oct 2017, Health Research Authority). 


Informatics, ECG, Psychopharmacology, Severe Mental Illness