Predicting high-cost care in a mental health setting
BackgroundThe density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes.AimsWe investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whethe...
Main Authors: | Craig Colling, Mizanur Khondoker, Rashmi Patel, Marcella Fok, Robert Harland, Matthew Broadbent, Paul McCrone, Robert Stewart |
---|---|
Format: | Article |
Language: | English |
Published: |
Cambridge University Press
2020-01-01
|
Series: | BJPsych Open |
Subjects: | |
Online Access: | https://www.cambridge.org/core/product/identifier/S2056472419000966/type/journal_article |
Similar Items
-
Temporal information extraction from mental health records to identify duration of untreated psychosis
by: Natalia Viani, et al.
Published: (2020-03-01) -
Insights from electronic health record data to improve mental health service delivery during the COVID-19 pandemic
by: R. Patel, et al.
Published: (2021-04-01) -
Impact on patient-provider relationship and documentation practices when mental health patients access their electronic health records online: a qualitative study among health professionals in an outpatient setting
by: Paolo Zanaboni, et al.
Published: (2022-07-01) -
Prevalence and incidence of clinical outcomes in patients presenting to secondary mental health care with mood instability and sleep disturbance
by: Keltie McDonald, et al.
Published: (2020-01-01) -
Incidence of suicidality in people with depression over a 10-year period treated by a large UK mental health service provider
by: Emma R. Francis, et al.
Published: (2021-11-01)