Terrell Preventable Readmission Project

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Terrell Preventable Readmission Project

Jeylan Buyukdura & Natalie Davies

+Presentation Overview

Introduction Analysis of the Situation Technical Description Analysis Q & A

+IntroductionBackground, Objective and Development

+Problem Background

Patients treated at Terrell State Hospital & Green Oaks ER

Patients readmitted into ER within 30 days of TSH discharge.

Extra expenses for the State could be preventable. Overall health of patients could be stabilized. Find a way to help reduce these readmission

numbers.

+Development Approach

Build a model to help predict the type of patient that may be readmitted within 30 days.

Focus on the patient’s characteristics. Find commonalities among this specific group of

patients. Use SAS to formulate new data sets and build a

model.

+Analysis of the Situation

+Analysis of the Situation

Met with supervisor Margaret Balfour to discuss the goals of this project, and how the Behavior Health system works in TX.

Data Sets Given (de-identified): Claims at Terrell State Hospital Claims at Green Oaks ER

(For FY 2008-2010 & in SAS format)

What is the best way to evaluate what these patients have in common?

+Ideal

+Readmission in 30 days** We visited TSH and these were common perceived reasons for readmission

+Technical DescriptionUsing SAS

+Technical Milestones

+Merge Data Sets

****Learn some SAS code!**** Merged based on patient’s case number. Merging TSH and Green Oaks Claims

+Only FY 2010

Model would be easier to develop while focusing on a smaller group.

Struggled with SAS dates to identify difference between patients’ discharge and readmission dates.

+Identify Cohort Patients

Any patient who was discharged from TSH and readmitted into Green Oaks within 30 days was considered a “cohort” patient.

Every patient was assigned either a “1” or “0” as their cohort variable.

The Decision Dependent Variable

+Independent Variables

Identified as: Patient’s primary, secondary, and tertiary diagnosis (DIAGNone

DIAGNtwo DIAGNthree) Sex (Male= 0; Female =1) Race (Black =0; Other =1; White =2; Hispanic =3; Asian =4 ) Age If they were an adult (adult) If they were homeless (homeless2010) The evaluation of the level of care needed after discharge (trag2010) If they were one of the system’s annual most expensive patients

(top200) The cost of their stay at TSH (cost) Length of stay at TSH (LOS).

Note: All Binary Variables were given 1 if True, and a 0 if False.

+Grouping Variables

Some variables had many possible values, so we grouped into smaller segment for an easier output. Age was divided into 10’s (0-10, 20-30, etc.) Length of stay at TSH was segmented into 0-3, 3-6, 6-12,

12-24, and 24+ weeks Cost of stay at TSH was segmented into

$0 – $5K; $5K – $10K; $10K – $20K; $20K – $50K and $50K+

+Grouping Variables Cont.

Individual Diagnoses Schizophrenia and Schizo disorders Mood disorders (depression and bipolar disorder) Physical mental disorders (autism and brain injuries) Other psychiatric disorders not otherwise categorized

(anxiety and OCD) Personality disorders Substance abuse

+Logistical Regression & Output

GOAL: Something that doctors can enter patient data into and it will predict if they are likely to be readmitted within 30 days or not.

Ran in both SAS 9.2 and SAS Enterprise Versions. SAS 9.2:

Intercept Coefficients for the independent variables Level of accuracy for both the model and the coefficient values

for each variable

SAS Enterprise: Visual Representations of the same information

+Logistical Regression & Output

+Logistical Regression & Output

+Model

y + (a X x1) + (b X x2) = 1/0 y as an intercept a and b as independent variable values x1 and x2 as coefficients

(y, x1, and x2 will be given from the logistical analysis, a and b will be new data)

+AnalysisEstimates and Observations

+Logistical Analysis

Unable to construct our model due to time constraint, but able to use regression output and analyze the results.

Overall Chi Squared p value for the logistical model was <0.001 = Model was statistically significant (<0.05) in describing the data.

Other Analysis: Analysis of Maximum Likelihood Estimates Odds Ratio Estimate

+Analysis of Maximum Likelihood Estimates Coefficients for the Independent Variables We had 5 statistically significant coefficients

Primary Diagnosis (DIAGNone) was a significant predictor if the patient had a diagnosis in the categories of schizophrenia (estimate=0.9557, p=0.0010)

Mood Disorders (estimate=0.7920, p=0.0072) Other Psychiatric Disorders (estimate=1.2895,

p=<0.001) Secondary Diagnosis (DIAGNtwo) was a significant

predictor if the patient had a personality disorder (estimate=0.-0.8932, p=0.0226)

Patient was in the Top200 (estimate=1.1284, p<0.001)

+Odds Ratio Estimates Observations Describes how much more likely a patient is to be in

the cohort (readmitted within 30 days) if they have one value in a variable over another.

i.e. Patients who’s stay cost at TSH was $5,000-10,000 were 17.281 times more likely to be readmitted than patients who’s stay cost was $50,000+. And after a patients cost of stay exceeds $20,000 they are only slightly less likely to be readmitted if their Showed that a patient was much more likely to be

readmitted the shorter their stay was at TSH. (But 95% confidence levels were large)

Similar to a patient’s LOS

+Observations Cont. A patient was slightly less likely to be readmitted if

they were male (estimate = 0.859, 95% confidence intervals 0.642-1.149)

The race most likely to be readmitted was Asian. A patient was surprisingly about as likely to be

readmitted if they were homeless as if they were not (estimate = 0.988, 95% confidence intervals 0.636-1.535).

The TRAG variable showed that if a patient was given a lower level of care after being discharged they were more likely to be readmitted (estimate=1.513, 95% confidence intervals 0.910-2.515).

+Odds Ratio

+DFBetas

+Q & A