Episodes of Illness Farrokh Alemi, PhD

Objectives This presentation trains you in using our procedures for measuring episodes of illness Based on United States patent application 10/054,706 filed on 1/24/2002 by George Mason University. We grant permission to individual scientists within university, Federal and State governments settings to use these procedures free of licensing fees. Permission is also granted to all students using this procedure as part of an educational class.

Existing Approaches  Prospective Risk Adjustment  Ambulatory Visit Groups  Disease Staging  Products of Ambulatory Care  Ambulatory Diagnosis Groups  Ambulatory Care Groups.

New Approach  Easy to implement Built using Standard Query Language operations on existing data within your organization  Tailored to the special populations served by your organization  Dynamically changing Changing as the nature of diseases change

Advantage: Built on Existing Data  Simple database manipulations can produce the desired episodes of illness from Existing Organization’s Data Can be used within electronic health records Works on any administrative database, which has information on date of visit and diagnoses

A Mathematical Theory  Not a black box, shows in detail how episodes are measured  Makes it possible for researchers to build on each other’s work

No Clusters  Existing approaches Schneeweiss and colleagues classified all diagnoses into 92 clusters.  Otitis media infection not same as wound infection  Not limited to the etiology of the disease All operations are defined on individual diagnoses without need for broad clusters

Not a Measure of Treatment Intensity  Not intended to classify patients into homogenous resource use groups All short visits do not belong to same episode  Intensity-based measures can measure if length of visit is appropriate but not if number of visits are appropriate.

Terminology  Episode of care Does not depend on the nature of services Does not assume that temporally contiguous  Anchor diagnosis  Trigger diagnosis  Stopping point  Rate of progression  Peak severity  Outcomes

Theory P ia = function {T ia, S ia } Probability of diagnosis i and a being part of same episode

Theory P ia = function {T ia, S ia } Time between diagnosis i and a Similarity of diagnosis i and a

Theory P ia =S ia /(1+βT ia ) Probability of diagnosis i and a being in same episode P ia = function {T ia, S ia }

Theory P ia =S ia /(1+βT ia ) Similarity of Diagnosis i and a P ia = function {T ia, S ia }

Theory P ia =S ia /(1+βT ia ) A constant Time between diagnosi s i and a P ia = function {T ia, S ia }

Theory P ia =S ia /(1+βT ia ) P ia = function {T ia, S ia }

Theory  When a patient presents with several diagnoses … Probability that any two of the diagnoses may belong to an episode is calculated Pair-wise probabilities are used to classify diagnosis into groups

Severity of an Episode Overall severity of episode=1- п i (1-Sev i ) Severity of diagnosis i

Why Multiply Severity Scores? Overall severity of episode=1- п i (1-Sev i ) Symbol for multiplication

Evaluation of the Theory  565 Developmentally delayed children who were enrolled in the Medicaid program of one Southeastern State Randomly sampled Included both in-patient and outpatient Medicaid payments for the patient State paid $9,296 per patient per year. The standard error of the cost was $2,238

Constructing Episode Measures  Time between two diagnoses  Severity of each diagnosis  Similarity of the two diagnoses The number of times the two diagnoses co- occur within a specific time frame  Mean number of episodes was 147 (standard error = 320).

Results of Test of Theory CoefficientsP-value Intercept Average severity of episodes Number of episodes Interaction between number of episodes & severity of episodes 7560 Regression of "Amount paid by the State" on severity and number of episodes Number of observations = 565, Adjusted R Squared = 53.11%

Conclusions of Pilot Test  Episodes of care can be constructed  Explained a large percentage of variance in cost of care 53% versus typical 10%-20%

Take Home Lesson Simple database queries can create a measure of episodes of illness that could explain a large portion of variation in outcomes