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. Predictors of the three different clinical outcomes were modelled using logistic regression with performance evaluated against a validation set to derive areas under receiver operating characteristic curves. Results In validation samples, the full models (using all available data) achieved areas under receiver operating characteristic curves between 0.59 and 0.85 (in-patient duration 0.63, readmission 0.59, high service use 0.85). Adding natural language processing-derived data to the models increased the variance explained across all clinical scenarios (observed increase in r2 = 12–46%). Conclusions EHR data offer the potential to improve routine clinical predictions by utilising previously inaccessible data. Of our scenarios, prediction of high service use after initial presentation achieved the highest performance.

Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes

“…The analysis of free text in EHR combined with structured data using machine learning approaches is gaining interest as anonymized EHR data become available for research [7,17,18]. However, the analysis of clinical free-text data presents numerous challenges due to (i) highly imbalanced data with respect to the class of interest [19]; (ii) lack of publicly available datasets, limiting research on private institutional data [20] and (iii) relatively small data sizes compared to the amounts of data currently used in text processing research.…”

“…Indeed, machine learning approaches trained on English-language psychiatric notes have shown promising results with values of the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) of 0.85 and higher [6][7][8][9][10].…”

The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature

“…Medication information was often extracted from free text, particularly in studies using the SLaM BRC CRIS case register ( 57 , 60 , 63 , 64 , 66 – 73 ). Also extracted from free text were disease symptoms and drug reactions ( 46 , 47 , 50 , 60 , 67 , 72 , 74 – 76 ); test scores, such as for the MMSE ( 58 , 59 , 61 , 63 , 64 , 77 , 78 ), and angiogram results ( 50 ); treatments such as cognitive behavioral therapy (CBT) ( 60 , 79 ); substance use behaviors such as cannabis ( 49 , 72 , 80 ), alcohol ( 43 , 44 , 49 ) or smoking status ( 81 ); housing status ( 45 ); and information on symptom severity and functional status ( 61 , 73 ).…”

“…Fourteen papers reported healthcare or service quality or safety improvements that were enabled or augmented with the addition of free text information to coded data in health records, five studies focused on physical health ( 46 , 48 , 73 , 81 , 82 ) mainly drawn from general practice records; and nine on mental health ( 42 , 59 , 62 , 66 , 67 , 69 , 79 , 83 , 84 ). Authors suggested that the increase in information accuracy extracted from health records would result in service improvements such as better service planning due to more accurate prevalence or risk factor estimates, as well as better understanding of symptoms of diseases pre-diagnosis, which might speed up recognition of conditions in primary care.…”

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

“…We observed an interest for neurological and psychiatric disorders, which are mainly issued from clinical context: detection of duration of untreated psychosis [38], analysis of language in patients with aphasia [60], Alzheimer's disease [61], and autism spectrum disorder [62], generation of artificial mental health records and their evaluation [63], detection and prediction of suicide in mental illness [64][65][66], automatic detection of agitation and related symptoms among hospitalized patients [67], analysis 6 https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge of COVID impact on people with epilepsy [36], prediction of care cost in mental health setting [68], and, more generally, the use of artificial intelligence in mental health and its biases [69].…”