HoNOS and HoNOS65+: Is it a Useful Tool in Predicting Length of Stay?

1. HoNOS and HoNOS65+:Is it a Useful Tool in Predicting Length of Stay? Peter Thomas, Glen Bowcock Andrea Taylor & John McMurray Mental Health Drug and Alcohol Directorate, Northern Sydney Local Health District June 2013

2. Background • Introduction • Access and Demand Management is gaining increasing importance as LHDs seek greater control over resource management. • In NSW, all jurisdictions are required to use Estimated Date of Discharge (EDD) & Patient Flow Portal to manage bed resources • EDD has no credibility with MH Clinicians as MH lacks an accurate predictive tool • Variability in MH LOS makes Diagnostic Related Groups a poor predictor of MH LOS

3. Background Previous studies have examined the relationship between HoNOS and LOS with varying and inconclusive results • Tom Trauer et al. 2008 : Psychotic symptoms and disability are associated with longer length of stay • Harnett et al. 2005 : No association between HoNOSCA and Length of Stay • Page et al. 2001: HoNOS Scores could predict length of stay • Goldney et al. 1998: No correlation between HoNOS Scores and Length of Stay • Boot et al. 1997: HoNOS Scores moderately predict length of stay

4. Methodology • Source of Data • Local Health District’s (LHD) Health Information Exchange (HIE) Database • NSLHD MH-OAT and Activity Collection Database (FISCH) • Definitions • Length of Stay includes only Days in Pysch taken from HIE episode table • Inclusions • 1st Admission during the study period for each consumer • HoNOS/HoNOS65+ collection occurred in the period 2003 - 2012 • HoNOS/HoNOS65+ Collection occurred on or after the admission date • All 12 individual items had a valid rating (0 – 4) • MH-OAT Collection linked with Inpatient data using State Unique Patient Identifier (SUPI)

5. Methodology • Exclusions • Non-Acute units • One mixed use Facility (ie. Acute and Non-Acute units) where frequent transfers between Acute and Non-Acute Units distorted the results • All subsequent admissions during the study period for each consumer • Sample Size • 22,364 Acute Mental Health Admissions were recorded in NS LHD during the study period. • 11,311 Consumers admitted during the study period • 3,231 Admissions met the criteria for inclusion • 2,530 consumers had valid HoNOS collections • 701 consumers had valid HoNOS65+ collections

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28. Summary • A moderately high correlation between HoNOS rating on Admission and ALOS has been found in NSLHD MH Acute Units • Removing the Behavioural Sub Scale when reviewing the ratings increases the correlation • Possible implications for developing tools to assist in standardising EDD practices and resource management planning • Generalising these results for potential ABF classification weighting would require care to avoid potential rater bias, as the need to maintain or increase funding may lead to inflated ratings

29. Thank You