enquiries@pcmis.co.uk     01904 321 322

enquiries@pcmis.co.uk
01904 321 322

Latest innovations in mental health research

The latest updates from the University of York's Mental Health and Addiction Research Group (MHARG), which underpins the development of PCMIS software product.

The ethos of PCMIS and the research team is to get everyone involved so many of our customers actively participate in the research which informs our software development.

Embedded research tools pave the way for PCMIS

The Outcome Feedback (OF) tool helps psychological therapists and other mental health professionals to track progress and to identify cases at risk of poor response to treatment in order to intervene with solutions as early as possible.

Studies in USA and Europe indicate that using OF can help improve clinical outcomes for patients at risk of poor progress.

Our research aims to introduce OF into IAPT services.

The goals of the programme include:

  • Introducing state-of-the-art outcome prediction and feedback tools into the PCMIS case management system
  • Working with IAPT services to assess the feasibility and acceptability of using OF tools
  • Trialling OF tools in IAPT services and evaluating whether this has an effect on clinical outcomes
  • Working with IAPT services to disseminate OF tools in routine practice

November 2015 update

An NHS Research Capability Funding (RCF) grant was obtained in May 2015 to conduct a feasibility study to explore factors that prevent and enable the utilisation of OF in routine practice

NHS ethical approval and research governance permissions for the feasibility study were obtained in September 2015

The feasibility study started 1 October 2015, with a training day, including 15 therapists working in Leeds IAPT. The group of participants includes Psychological Wellbeing Practitioners, CBT and IPT therapists

IAPT services in London, North West and East of England have joined the collaboration and agreed in principle to support a multi-site randomised controlled trail. Its aim will be to test the effectiveness of using OF compared to a 'usual IAPT care' control group

 Future developments

  • A study protocol for the multi-site trial was completed in November 2015.
  • The trial work will run for a year. Therapists across the participating sites will be recruited and trained during December 2015 - January 2016.
  • This groundwork will support the development of a future programme of work aiming to disseminate outcome feedback methods across IAPT services in England. 

Tools created through research

In 2014 our research collaborators* developed state-of-the-art outcome prediction tools using a large IAPT dataset. There are two tools integrated within PCMIS that can be used by clinicians to identify cases at high risk of poor outcomes.

Figure 1. ETR model using PHQ9 depression scores. This model shows a case that is 'on track' (progressing as expected)
Expected Treatment Response (ETR) models

ETR models are used to alert clinicians to cases that are not progressing well in therapy. These models are based on two visual markers: an upper ETR boundary and a lower boundary. ETR boundaries, also referred to as 'curves', are superimposed onto routine symptom tracking charts available on PCMIS. These curves are derived from large datasets of psychotherapy cases that had similar baseline scores on symptom measures (eg PHQ9, GAD7). Approximately 80% of patients will show session-to-session symptom scores contained within the upper and lower ETR curves. The remaining cases show atypical scores, with 10% of cases falling above the upper ETR curve (high risk cases) and 10% falling below the lower boundary. In routine practice, if your patient shows a symptom score that is above the upper ETR curve, this indicates that the patient is not progressing as expected and may be at risk of poor outcomes.

(Figure 2. ETR model using GAD7 anxiety scores. This model shows a case that is 'not on track' (at risk of poor outcomes)
Research in action (Leeds Risk Index) 

The Leeds Risk Index (LRI) is a numerical scale ranging between 0 and 21. Each patient can be assigned an LRI score based on their clinical and demographic characteristics. A higher score has been found to predict greater likelihood of dropout, poor response to therapy, and non-recovery after treatment. This tool was developed using patient profiling methods, and can be used as a measure of case complexity and risk.

Together, ETR and LRI tools can be used by clinicians to detect high risk cases before they drop out, and early enough to review and modify treatment to improve clinical outcomes.

(Figure 3. Associations between LRI scores and recovery rates. This graph shows that cases with the highest LRI scores had the lowest % of reliable and clinically significant improvement (RCSI).
*Collaborators:
Dr Jaime Delgadillo (University of York, UK),
Mr Omar Moreea (Leeds Community Healthcare NHS Trust, UK),
Professor Wolfgang Lutz (University of Trier, Germany)


For further information about our research: http://www.pcmis.com/research.html

For further information about our research group: http://www.york.ac.uk/healthsciences/research/mental-health/


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The PCMIS Story

PCMIS was pioneered by the University of York's Mental Health Research Group that models, develops, measures and tests new ways of organising treatment for people with mental health problems.

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Rich Features

PCMIS is designed to suit your specific needs, be they extra datasets, customised reports, configured alerts and more. Its web-based platform allows us to modify and configure your system remotely.

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Excellent Support

Our service desk provides dedicated support by phone: 01904 321 322 and email. We provide flexible system training and documentation to end-users. We frequently upgrade and improve PCMIS in line with your IAPT requirements.

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