Predicting high-cost care in a mental health setting.

ABSTRACT:

Background

The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes.

Aims

We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service cost after first presentation.

Method

We used data obtained from a clinical database derived from the EHR of a large mental healthcare provider within the UK. We combined structured data with text-derived data relating to diagnosis statements, medication and psychiatric symptomatology. Predicto

SUBMITTER: Colling C 

PROVIDER: S-EPMC7001466 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature