Background and aims
Mersey Care NHS Foundation Trust serves more than 11 million people offering specialist inpatient and community services that support mental health, learning disabilities, addictions, brain injuries and physical health. It is one of only three trusts in the UK that offer high secure mental health facilities. At the heart of everything they do, is their commitment to ‘perfect care’ – safe, effective, positively experienced, timely, equitable and efficient.
Challenges within Mersey Care involved:
- Caseloads within the trust’s community mental health teams were at an all-time high, whilst complexity and risk appeared to be getting increasingly severe.
- Managers were struggling to identify capacity within the teams to meet presenting needs. As a result, staff used workarounds, such as relying on knowledge of colleagues and care records, which was time consuming and inefficient.
- Difficulty identifying the most vulnerable and ensuring that their holistic needs are met.
MaST was initially piloted to see whether it could provide managers and clinicians with a tool that would support them to prioritise their workloads in the interests of patient safety and staff wellbeing. It was hoped that MaST would enable managers and clinicians to prioritise their time as effectively as possible, ensuring that the riskiest and most complex patients were receiving support, whilst less complex patients were enabled to transition to a less resource-intensive pathway.
MaST is a decision support tool developed by Holmusk in partnership with mental health providers enabling mental health staff to adopt a dynamic approach to prioritisation and resourcing. MaST provides insight into people’s risk of crisis and complexity and identifies those who may benefit from a review of their care to improve quality and safety outcomes.
MaST can also be used to identify and protect people with pre-existing mental health conditions who are vulnerable because of Covid-19. It supports the clinical prioritisation of people who are vulnerable because of physical health conditions or social vulnerability.
MaST is powered by the risk of crisis algorithm that uses data from existing mental health records to identify service users who are most likely to require crisis services within 28 days. Mersey Care and Holmusk collaborated to adapt the MaST to Mersey Care data sets, which involved using the algorithm on clinical records to determine a risk propensity score for each service user.
This forecasted which service users were at ‘higher risk of using crisis services’ therefore in need of enhanced levels of care, or at ‘lower risk of using crisis services’ and might be better supported by another service such as a primary care or voluntary sector support.
There are approximately 8700 patients in Mersey Care who are benefitting from the use of MaST. It is being actively used by staff in supervision, for individual caseload management and in multi-disciplinary team meetings. The impact so far has included:
- 70-80% of all uses of crisis services identified by MaST analytics as ‘high risk of crisis’ up to 28 days in advance.
- An overall reduction in caseload sizes.
- Improved patient flow: consistent step down from CMHTs to primary care services with reduced re-referrals within three months.
- Prioritisation of the most vulnerable patients.
- Rapid identification of unment needs for serious mental illness patients
- Improved compliance with 48 hour and seven day follow-up following hospital admission.
- Reduced reliance on workarounds which increased frontline staff confidence in managing caseloads efficiently.
- Improved quality and safety performance indicators including risk assessments, physical health reviews and care planning.
“MaST allows us to see where the risk and complexity is in our caseloads, so that we can prioritise who we need to see right now and who might be ready to move on to a different care pathway. MaST is a solution which helps clinicians, team managers and strategic leaders to understand our community service caseloads.”
Adam Drage Clinical Change Manager, Mersey Care Trust.
Additional outcomes of using MaST:
- The shift in activity from inpatient to community services with MaST in use near crisis time resulted in significant resource use and cost savings.
- Among people under the care of the community crisis services during the six months before and after MaST was introduced, there was a reduction in the duration of mental health crises and length of stay in an inpatient service setting following a mental health crisis.
- The shift in activity from inpatient service to community setting is estimated to have resulted in a cost saving of £1.7million* in the six-month period after MaST was introduced. This assumes that a person has on average one contact per day with a consultant-led community crisis services team while experiencing a mental health crisis in the community.
*Information on PLICS mental health provider spell day was used as a proxy for inpatient service bed days (£524) and PLICS mental health care contacts was used a proxy for the number of days under the care of the community crisis services team. The average cost of a mental health care contact is £210 and it is assumed that a service user had on average one contact per day.
MaST is provided by Holmusk, a company that specialises in improving the way mental health services use data. You can find more about the MaST tool by accessing https://www.holmusk.co.uk
You can request a full copy of the MaST case studies by email to email@example.com
The aim of the ZSA Case Studies is to introduce users to a range of examples of new and innovative practice, with the broad aim of working to support people with their mental health, bring awareness to and help prevent incidence of suicide. Please seek further information by contacting the ZSA and appropriate professional input prior to making a decision over its use.
This content was developed and approved in collaboration with the case study owner. If you would like to update this content, please contact us
For more information on ZSA case studies, please refer to ZSA Policies.
Content last updated: 28/11/2022.