Patient casemix classification for medicare psychiatric prospective payment.
OBJECTIVE
For a proposed Medicare prospective payment system for inpatient psychiatric facility treatment, the authors developed a casemix classification to capture differences in patients' real daily resource use.


METHODS
Primary data on patient characteristics and daily time spent in various activities were collected in a survey of 696 patients from 40 inpatient psychiatric facilities. Survey data were combined with Medicare claims data to estimate intensity-adjusted daily cost. Classification and Regression Trees (CART) analysis of average daily routine and ancillary costs yielded several hierarchical classification groupings. Regression analysis was used to control for facility and day-of-stay effects in order to compare hierarchical models with models based on the recently proposed payment system of the Centers for Medicare & Medicaid Services.


RESULTS
CART analysis identified a small set of patient characteristics strongly associated with higher daily costs, including age, psychiatric diagnosis, deficits in daily living activities, and detox or ECT use. A parsimonious, 16-group, fully interactive model that used five major DSM-IV categories and stratified by age, illness severity, deficits in daily living activities, dangerousness, and use of ECT explained 40% (out of a possible 76%) of daily cost variation not attributable to idiosyncratic daily changes within patients. A noninteractive model based on diagnosis-related groups, age, and medical comorbidity had explanatory power of only 32%.


CONCLUSIONS
A regression model with 16 casemix groups restricted to using "appropriate" payment variables (i.e., those with clinical face validity and low administrative burden that are easily validated and provide proper care incentives) produced more efficient and equitable payments than did a noninteractive system based on diagnosis-related groups.
RTI International, 411 Waverley Oaks Rd., Suite 330, Waltham, MA 02452-8414, USA. edrozd@rti.org
Edward M
EM
Drozd
Jerry
J
Cromwell
Barbara
B
Gage
Jan
J
Maier
Leslie M
LM
Greenwald
Howard H
HH
Goldman
D000203:Activities of Daily Living / Q000145:classification; D000367:Age Factors; D000368:Aged; D014487:Centers for Medicare and Medicaid Services, U.S.; D003617:Dangerous Behavior; D003953:Diagnosis-Related Groups / Q000191:economics / Q000706:statistics & numerical data*; D004565:Electroconvulsive Therapy / Q000191:economics; D017048:Health Care Costs / Q000145:classification / Q000706:statistics & numerical data; D017721:Hospital Costs / Q000706:statistics & numerical data; D006760:Hospitalization / Q000191:economics*; D006778:Hospitals, Psychiatric / Q000191:economics* / Q000706:statistics & numerical data; D006801:Humans; D006278:Medicare / Q000191:economics*; D016344:Medicare Part A / Q000191:economics / Q000706:statistics & numerical data; D001523:Mental Disorders / Q000145:classification* / Q000175:diagnosis / Q000191:economics*; D008875:Middle Aged; D018803:Models, Economic; D011445:Prospective Payment System / Q000191:economics / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012720:Severity of Illness Index; D014481:United States