episodes of illness

Episodes of Illness

Farrokh Alemi, PhDfalemi@gmu.edu

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

Pia= function {Tia, Sia}

Pro

bab

ility of d

iagn

osis

i and

a bein

g p

art of

same ep

isod

e

Theory

Pia= function {Tia, Sia}

Tim

e betw

een

diag

no

sis i and

a

Similarity of

diagnosis i and a

Theory

Pia=Sia/(1+βTia) Probability of

diagnosis i and a being in same

episode

Pia= function {Tia, Sia}

Theory

Pia=Sia/(1+βTia) S

imil

arit

y o

f D

iag

no

sis

i an

d a

Pia= function {Tia, Sia}

Theory

Pia=Sia/(1+βTia)

A c

on

stan

t

Time

betwee

n

diagnosi

s i a

nd a

Pia= function {Tia, Sia}

Theory

Pia=Sia/(1+βTia)

Pia= function {Tia, Sia}

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-пi (1-Sevi)

Sev

erit

y o

f d

iag

no

sis

i

Why Multiply Severity Scores?

Overall severity of episode=1-пi (1-Sevi)

Sym

bo

l fo

r m

ult

iplic

atio

n

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

    Coefficients P-value

 Intercept   -7297  0.003

 Average severity of episodes   -33.58  0.000

 Number  of episodes   444971 0

Interaction between number of episodes & severity of 

episodes

     756 0

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