Predicting length of stay in psychiatry.
BACKGROUND
Diagnostic Related Groups (DRGs) and Healthcare Resource Groups (HRGs) do not predict accurately length of stay or resources needed for treatment in psychiatry. This preliminary study assessed the relative contribution of severity of illness, in combination with other variables, in predicting length of stay.


METHODS
Data were analysed on 115 consecutive admissions to a district psychiatric in-patient unit to assess the variables which most accurately predict length of stay. The variables included demographic data, diagnosis, clinical, social and behavioural measures.


RESULTS
For initial admission, diagnosis of neurosis predicted shortest stay, but diagnosis alone accounted for only 14.6% of the variation in length of stay. Addition of Social Behaviour Scale score, living alone and specific psychiatric symptoms significantly increased the predictive value (adjusted R2 = 36.6%). Addition of variables available at discharge (use of ECT, major tranquillizers and antidepressants) significantly increased the adjusted R2 to 49.0%. Prediction of total length of hospitalization over a 12-month period, from the date of initial admission, indicated that mania predicted the longest stay and addition of other variables meant that only 18.9% of length of stay was predicted.


CONCLUSIONS
If these results are borne out in a large study, they indicate that diagnostic or health related groups (DRGs) are only likely to be useful in psychiatry if they include more detailed social, clinical and behavioural variables.
Creed
F
F
Department of Psychiatry, University of Manchester.
Tomenson
B
B
Anthony
P
P
Tramner
M
M
D000704:Analysis of Variance; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D004739:England; D005544:Forecasting / Q000379:methods; D006778:Hospitals, Psychiatric / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D001523:Mental Disorders / Q000175:diagnosis / Q000628:therapy; D011446:Prospective Studies; D012044:Regression Analysis; D012111:Residence Characteristics / Q000706:statistics & numerical data; D012189:Retrospective Studies; D012720:Severity of Illness Index