enquiries@pcmis.com     01904 321 322

01904 321 322

More efficient mental health care using Outcome Feedback technology

With costs and patient numbers increasing, how can technology help improve efficiency and cost effectiveness of treatments for depression and anxiety? Jaime Delgadillo and Kim de Jong explain how one innovation, Outcome Feedback technology within PCMIS, is already having an impact.

The costs of common mental health problems

Mental health problems account for a quarter of all ill health in the UK, with an estimated cost of £105 billion a year. A large proportion of the patients treated in mental health services suffer from common mental health problems such as depression or anxiety disorders.

It is estimated that approximately one in four people meet diagnostic criteria for one of these disorders at some point in their life. Fortunately, depression and anxiety disorders can be treated successfully and there are a number of effective treatment options including psychological support and pharmacotherapy. Moreover, research suggests that money spent on psychological treatment for depression and anxiety disorders has a good return on investment. However, studies also suggest that about a third of the patients with a depression or an anxiety disorder do not benefit from mental health treatment.

The rising costs of mental health care, as well as the substantial group of patients that do not respond well to treatment indicate a strong need for mental health care to become more effective and efficient. In addition to financial costs, enduring depression and anxiety symptoms carry enormous personal costs for sufferers and their families.

Outcome feedback technology

Outcome feedback is a computer-based technology within the PCMIS case management system that assists therapists in monitoring how their patients are responding to treatment. The system tracks changes in each patient’s depression and anxiety symptoms over time through the use of standardised questionnaires. Next, the patient’s symptom trajectory is compared to an expected trajectory, which is based on the observed course of treatment for hundreds of similar cases.

The system alerts the therapist with a “red signal” if a patient’s symptoms are progressing significantly worse than the expected trajectory (symptoms are more severe than those observed in typical cases). The system automatically alerts therapists when a red signal occurs with their patients, which prompts therapists to discuss possible obstacles for improvement with their patients and clinical supervisors.

More efficient treatment

In a recent study, we trained a team of therapists working in a stepped-care psychological service in the UK to use the outcome feedback technology described above. The service was part of NHS England's IAPT programme (Improving Access to Psychological Therapies) and using the PCMIS system to manage their service administration and case loads.

What is interesting about this setting is that it already applies highly standardised treatments, and outcomes are regularly monitored and managed by psychological therapists. As such, we did not know whether our relatively simple intervention would have any effect on treatment outcomes or costs. We gathered clinical data for all of the patients treated by this team of therapists, six months before (controls = 349 patients) and six months after the training (feedback cases = 245 patients). We compared average depression and anxiety symptom reductions and duration of treatment between control and feedback cases.

Results indicated that feedback cases completed treatment with similar levels of symptom improvement compared to the control cases. However, cases treated using the outcome feedback technology accessed a significantly lower number of treatment sessions, and therefore attained similar clinical outcomes at a reduced cost. This resulted in a total saving of almost £24,000 in the costs of care during a study period of six months. Interestingly, these savings exceeded the costs to run the study, which meant that the study in principle financed itself.

Dissemination and impact

Outcome feedback technology is fully integrated into PCMIS, a leading clinical data management system used by thousands of psychological therapists across England and Australia. This integration of technology ensures the large-scale dissemination and sustainability of this work.

Additionally, our research team has recently completed a large-scale randomised controlled trial successfully applying the same technology across multiple psychological services covering London, Cambridge, Cheshire, Humber, West Yorkshire and other regions, the results of which will be published in 2018.



*About the authors

Jaime Delgadillo is a Lecturer in Clinical Psychology at the University of Sheffield (UK) and a Psychotherapist in the English National Health Service (NHS). His research focuses on outcome measurement, prediction and feedback. He has led the development and implementation of feedback and personalized care technologies in NHS psychological services.

Kim de Jong is an Assistant Professor of Clinical Psychology at Leiden University (the Netherlands), a freelance psychologist, and a leading expert in outcome feedback research. Her research focuses on ways to improve clinical outcomes in routine practice, and on methods for therapists to improve themselves.

<< Back to Latest News

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.

Find Out More...

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.

Find Out More...

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 and frequently upgrade and improve PCMIS.

Find Out More...