Holmusk Announces E-Poster and Publication Through Royal College of Psychiatrists

June 20, 2022

Representatives from Holmusk recently presented a poster at the Royal College of Psychiatrists International Congress and shared findings from the evaluation of one of the company’s digital health solutions, MaST, a clinical decision support tool that is currently being implemented to improve mental health caseload management within seven NHS Trusts in the United Kingdom. The abstract was simultaneously published in BJPsych Open.

MaST (Management and Supervision Tool) uses analyses of electronic health record (EHR) data to provide data visualizations that help clinicians predict whether their patients are more likely to need mental health crisis services such as an inpatient psychiatric hospital or outpatient community crisis service. Clinicians can then intervene early to prevent or improve the management of people’s mental health crises.

The Risk of Crisis algorithm within MaST was developed using EHR data from patients receiving mental health care at one NHS Trust and validated using EHR data from five more NHS Trusts. Upon validation, the algorithm performed with a high degree of accuracy, with up to 80% of uses of crisis services coming from the group of people that MaST had identified as highest Risk of Crisis.

The MaST team also worked with NHS Trusts to collect qualitative data via a staff survey and found that users of MaST said the tool increased productivity and supported caseload management by reducing the amount of time it previously to manually review EHR data without MaST visualizations. The average length of stay for crisis inpatient admissions was also found to be lower after MaST was implemented, suggesting that MaST helps to improve system efficiencies.

“We are so pleased to see that MaST is being used to improve clinical decision making by identifying high-risk patients within the NHS Trusts,” said Caroline Gadd, Director at Holmusk. “Not only does this help make the jobs of care teams easier, but it could also help improve patient outcomes. MaST is a great example of how real-world data can be analyzed and applied to improve clinical practice.”

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