Developing an algorithm to identify individuals with psychosis in secondary care in England:application using the Mental Health Services Data Set

Background There is currently no definitive method for identifying individuals with psychosis in secondary care on a population-level using administrative healthcare data from England. Aims To develop various algorithms to identify individuals with psychosis in the Mental Health Services Data Set (MHSDS), guided by national estimates of the prevalence of psychosis. Method Using a combination of data elements in the MHSDS for financial years 2017–2018 and 2018–2019 (mental health cluster (a way to describe and classify a group of individuals with similar characteristics), Health of the Nation Outcome Scale (HoNOS) scores, reason for referral, primary diagnosis, first-episode psychosis flag, early intervention in psychosis team flag), we developed 12 unique algorithms to detect individuals with psychosis seen in secondary care. The resulting numbers were then compared with national estimates of the prevalence of psychosis to ascertain whether they were reasonable or not. Results The 12 algorithms produced 99 204–138 516 and 107 545–134 954 cases of psychosis for financial years 2017–2018 and 2018–2019, respectively, in line with national prevalence estimates. The numbers of cases of psychosis identified by the different algorithms differed according to the type and number (3–6) of data elements used. Most algorithms identified the same core of patients. Conclusions The MHSDS can be used to identify individuals with psychosis in secondary care in England. Users can employ several algorithms to do so, depending on the objective of their analysis and their preference regarding the data elements employed. These algorithms could be used for surveillance, research and/or policy purposes.

Publication Details

Journal
White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Published
2025-02-27
Primary Topic
Cancer Genomics and Diagnostics
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article
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article

Developing an algorithm to identify individuals with psychosis in secondary care in England:application using the Mental Health Services Data Set

White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Cancer Genomics and Diagnostics
article

Developing an algorithm to identify individuals with psychosis in secondary care in England:application using the Mental Health Services Data Set

article en

Abstract

Background There is currently no definitive method for identifying individuals with psychosis in secondary care on a population-level using administrative healthcare data from England. Aims To develop various algorithms to identify individuals with psychosis in the Mental Health Services Data Set (MHSDS), guided by national estimates of the prevalence of psychosis. Method Using a combination of data elements in the MHSDS for financial years 2017–2018 and 2018–2019 (mental health cluster (a way to describe and classify a group of individuals with similar characteristics), Health of the Nation Outcome Scale (HoNOS) scores, reason for referral, primary diagnosis, first-episode psychosis flag, early intervention in psychosis team flag), we developed 12 unique algorithms to detect individuals with psychosis seen in secondary care. The resulting numbers were then compared with national estimates of the prevalence of psychosis to ascertain whether they were reasonable or not. Results The 12 algorithms produced 99 204–138 516 and 107 545–134 954 cases of psychosis for financial years 2017–2018 and 2018–2019, respectively, in line with national prevalence estimates. The numbers of cases of psychosis identified by the different algorithms differed according to the type and number (3–6) of data elements used. Most algorithms identified the same core of patients. Conclusions The MHSDS can be used to identify individuals with psychosis in secondary care in England. Users can employ several algorithms to do so, depending on the objective of their analysis and their preference regarding the data elements employed. These algorithms could be used for surveillance, research and/or policy purposes.

White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Openalex Percentile: Top 42%
Cancer Genomics and Diagnostics
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