Episodes of Illness

1
Episodes of Illness

• Farrokh Alemi, PhD
• falemi_at_gmu.edu

2
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.

3
Existing Approaches

• Prospective Risk Adjustment
• Ambulatory Visit Groups
• Disease  Staging
• Products of Ambulatory Care
• Ambulatory Diagnosis Groups
• Ambulatory Care Groups.

4
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

5
Advantage Built on Existing Data

• Simple database manipulations can produce the
  desired episodes of illness from Existing
  Organizations Data
• Can be used within electronic health records
• Works on any administrative database, which has
  information on date of visit and diagnoses

6
A Mathematical Theory

• Not a black box, shows in detail how episodes are
  measured
• Makes it possible for researchers to build on
  each others work

7
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

8
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.

9
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

10
Theory

• Pia function Tia, Sia

Probability of diagnosis i and a being part
of same episode
11
Theory
Similarity of diagnosis i and a

• Pia function Tia, Sia

Time between diagnosis i and a
12
Theory
Pia function Tia, Sia
Probability of diagnosis i and a being in same
episode

• PiaSia/(1ßTia)

13
Theory
Pia function Tia, Sia

• PiaSia/(1ßTia)

Similarity of Diagnosis i and a
14
Theory
Pia function Tia, Sia
Time between diagnosis i and a

• PiaSia/(1ßTia)

A constant
15
Theory
Pia function Tia, Sia

• PiaSia/(1ßTia)

16
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

17
Severity of an Episode

• Overall severity of episode1-?i (1-Sevi)

Severity of diagnosis i
18
Why Multiply Severity Scores?

• Overall severity of episode1-?i (1-Sevi)

Symbol for multiplication
19
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

20
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).

21
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 Regression of "Amount paid by the State" on  severity and number of episodes Regression of "Amount paid by the State" on  severity and number of episodes
Number  of observations   565,     Adjusted R Squared   53.11  Number  of observations   565,     Adjusted R Squared   53.11  Number  of observations   565,     Adjusted R Squared   53.11 
22
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

23
Take Home Lesson

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