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1 RE-ADMITTING IN HOSPITALS: MODELS AND CHALLENGES Murali Parthasarathy Dr. Paul Damien April 11, 2014
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RE-ADMITTING IN HOSPITALS:
MODELS AND CHALLENGES
Murali Parthasarathy
Dr. Paul Damien
April 11, 2014
Hospitals scored on five major pain points M
ajo
r p
ain
po
ints
1. Death rates among heart and surgery patients
2. Readmission (an event for which hospitals are now
subject to penalties by the CMS)
3. Overuse of CT Scans
4. Incidence of Hospital acquired infections
5. Effective communication to patients about medications
and discharge plans
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Four states were selected to look at healthcare quality and readmission rates
Massachusetts became the first state in the country to start healthcare reform for state residents
Illinois healthcare quality is expected to lie between TX and MA
Texas’ health care quality was rated the worst in the nation in the federal government’s annual (2011) nationwide health-care report card
California is one among the states with innovative ideas that improve quality, increase efficiency, and lower the costs of care
QOC analysis arrived at significant measures to improve process of care, ED, surgical care, outcome of care, 30 day hospital mortality rate, healthcare associated infections and measures to identify areas of minimum and maximum care
We analyzed four states and hospitals across them to : Identify major cost utilization for IP and OP hospitalization services Identify initiatives to improve QOC in hospitals
TX CA
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Avoidable hospital readmissions was significant with room for improvement and saving healthcare dollars
THE CHALLENGE One in five patients is readmitted to the hospital within 30 days of discharge
$740 B Waste
97%
Avoidable readmissi
on 3%
$ 2.5 Trillion spent in US on healthcare in 2009
30% ($ 765B)of total spending went on waste
Avoidable readmissions cost Medicare $25 billion per year
Total hospital Readmissions could be Reduced by up to 12% by Improving procedures and utilizing health Information technology
SOLUTION
• In a growing number of best practice studies on avoiding preventable readmissions, one of the principal calls-to-action is the utilization of technology to risk screen patients
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Understanding the problem - Help hospital administrators identify patients who might benefit from specific interventions
DEMOGRAPHIC DATA
DISEASE CONDITION
PRESCRIPTION DATA
IN-PATIENT/OUT- PATIENT COST
LAB RESULTS
ROBUST DATABASE WITH DIFFERENT VARIABLES
HOSPITAL DATA
STEP 1 : DATA BASE
PATIENT X VISITED ED X
MONTHS AGO – AGE –
GENDER – CONDITION –
CLOUD BASED MOBILE APP
STEP 3 : MOBILE APP
APP HELPS IDENTIFY PATIENTS FOR FOLLOW-UPS AND RISK FACTORS
STEP 2 : MODELING & VALIDATION
CREATE MODELS THAT
IDENTIFY SIGNIFICANT
VARIABLES
TRIAGE PATIENTS BASED ON ED
DIAGNOSIS AND SEVERITY
IDENTIFY DISEASES FOR WHICH
PATIENT IS AT RISK
MEASURE QUALITY OF HOSPITAL
CARE RELATIVE TO TREATMENT
OUTCOME AND FOLLOW UP VISITS
IDENTIFY AND VALIDATE SIGNIFICANT VARIABLES, MEASURE QUALITY OF
HOSPITAL CARE RELATIVE TO ED VISIT AND TREATMENT OUTCOME
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Potential relationship between process of care and excess readmissions was analyzed
Tim
ely
an
d E
ffe
ctiv
e C
are
• AMI, HF, PN
• Preventive Care
• Emergency Department Care
• Surgical Care O
utc
om
e M
eas
ure
s
• Hospital 30-days Mortality Rate
• Hospital Associated Infections
• Readmission Rate
As seen by analyzing the CMS data from 2008 to 2010, one out of five patients admitted for treatment for HF, AMI and Pneumonia are likely to be readmitted within 30 days of initial treatment The potential impact of quality of POC in hospitals across four states on readmission was explored
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On comparing process of care in Texas with national average, care was seen to be better for AMI but lower for HF and PN
55005
45195
96%
90%
Process of Care – AMI, HF and PN
Among POC measures for AMI, HF and PN, it was seen that poorest
quality of care was given to Heart Failure patients
TX Average National Average
AMI 86% 84%
HF 94% 95%
PN 95% 96% 7
Analysis of readmission rates in Texas
Total Hospitals
257
Excess Readmission Ratio <= 1
158 hospitals
Excess Readmission Ratio > 1
99 hospitals
39% of hospitals (99) are subject to penalty 24% of patients admitted for heart failure, 20% for heart attack and 17% for Pneumonia readmitted within 30 days
Diseases AMI HF PN
Sum of Discharges 32076 84409 72029
Avg % Readmission 20% 24% 17%
Avg Discharges / Readmission
6 4 6
Excess Readmission Ratio 0.9933 0.9897 0.974
Rate of readmission is higher for HF compared to AMI and PN
Process of care is lowest among HF patients, subsequently increasing rate of readmissions
Datasource : Hospital Compare 2008-2010 8
Among high penalty hospitals, Dimmit Regional hospital provides the least care for HF
Dimmit Regional hospital provides 39% of effective care for process of care – ‘Heart Failure patients given discharge instructions-’
Comparison of care among top 10 penalty hospitals : HF- Discharge instruction
TX average : 94%
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Among high penalty hospitals, Dimmit Regional hospital provides the least care for HF
Number of discharges Vs Percentage of Readmission among top 10 penalty hospitals Heart Failure
TX average : 24%
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TEXAS Hospitals Presence – Demographic and Economic impact
DESCRIPTION TEXAS CEDAR PARK REGIONAL MEDICAL CENTER
CLEVELAND REGIONAL MEDICAL CENTER
DALLAS REGIONAL MEDICAL CENTER
DIMMIT REGIONAL HOSPITAL
GOOD SHEPHERD MEDICAL CENTER
ETMC HENDERSON
TYPE OF HOSPITAL For Profit Hospital For Profit Hospital For Profit Hospital Public Hospital Non Profit Hospital Non Profit Hospital
COUNTY 254 WILLIAMSON LIBERTY DALLAS DIMMIT GREGG RUSK
POPULATION,2012 26,448,193 456,359 76,349 2,453,907 10,481 122,741 54,013
% RURAL 12% 12% 63% 1% 39% 13% 27%
PERSONS >=65 YEARS 2012 10.9% 9.8% 12.1% 9.2% 14.6% 13.7% 14.6%
BELOW POVERTY LEVEL, %, 2008-12 17.4% 6.8% 17.0% 18.8% 27.0% 16.9% 15.3%
PER CAPITA MONEY INCOME IN PAST 12
MONTHS (2012 DOLLARS), 2008-2012
$25,809 $30,540 $20,114 $26,576 $15,995 $23,968 $21,696
# HOSPITAL 420 6 2 26+ 1 5 2
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TX is seen to have lower readmission rates despite lower POC; POC is a low impacting measure on readmissions
Quality Measure – AMI, HF & PN TX IL MA CA National Avg
Process of Care
Average AMI 86% 97% 94% 91% 84%
Average Heart Failure 94% 95% 96% 95% 95%
Average Pneumonia 95% 95% 97% 96% 96%
Average – AMI, HF and PN 92% 96% 96% 94% 92%
Readmission
% HF readmitted 24% 26% 25% 24% 24%
% AMI readmitted 20% 22% 23% 20% 20%
% Pneumonia readmitted 17% 18% 19% 18% 18%
Average Readmission 20% 22% 22% 21% 21%
# Hospitals 257 126 59 720 3113
# Affected (High Discharges/Readmission) 16 51 18 25 15
Subject to penalty 99 (39%) 87 (48%) 17 (25%) 325 (35%) 1519(49%)
State Hospital Name
TX Dimmit Regional hospital
IL Franciscan St James Health
MA Metrowest Medical Center
CA Community Regional Medical Center
Other steps that could positively impact readmissions Identify patients at high risk for
readmissions and connect them to additional discharge support
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Develop an early warning - Help hospital administrators identify patients who might benefit from specific interventions
DEMOGRAPHIC DATA
DISEASE CONDITION
PRESCRIPTION DATA
IN-PATIENT/OUT- PATIENT COST
LAB RESULTS
ROBUST DATABASE WITH DIFFERENT VARIABLES
HOSPITAL DATA
STEP 1 : DATA BASE
PATIENT X VISITED ED X
MONTHS AGO – AGE –
GENDER – CONDITION –
CLOUD BASED MOBILE APP
STEP 3 : MOBILE APP
APP HELPS IDENTIFY PATIENTS FOR FOLLOW-UPS AND RISK FACTORS
STEP 2 : MODELING & VALIDATION
CREATE MODELS THAT
IDENTIFY SIGNIFICANT
VARIABLES
TRIAGE PATIENTS BASED ON ED
DIAGNOSIS AND SEVERITY
IDENTIFY DISEASES FOR WHICH
PATIENT IS AT RISK
MEASURE QUALITY OF HOSPITAL
CARE RELATIVE TO TREATMENT
OUTCOME AND FOLLOW UP VISITS
IDENTIFY AND VALIDATE SIGNIFICANT VARIABLES, MEASURE QUALITY OF
HOSPITAL CARE RELATIVE TO ED VISIT AND TREATMENT OUTCOME
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Estimating the risk factors for hospital readmissions within 30-days of discharge
A high percentage of Heart failure, Heart disease, Stroke , COPD and Kidney disease patients are at a higher risk for readmissions in the US
Summary of Modeling analysis
State Significant co-morbid conditions Other significant risk factors
CA Heart Failure, Cancer, COPD, Ischemic Heart Disease, Kidney disease, Stroke
Number of hospital discharges in the previous year and # of Out patient visits
TX Kidney disease, Heart failure, Alzheimer's, COPD, Stroke Number of hospital discharges in the
previous year and # of Out patient visits
MA Heart failure, Osteoporosis # of Out patient visits
IL Kidney disease, Cancer, COPD, depression, stroke
Number of hospital discharges in the previous year and # of Out patient visits
A Bayes Logistic Regression model is constructed to estimate the most significant factors responsible for hospital readmissions within 30-days of discharge in four selected states: CA, TX, MA & IL
Co-Morbid Conditions Other significant factors
Alzheimer’s Kidney Disease Average # discharges in 2008
Heart Failure Arthritis Age group
Heart Disease Osteoporosis Length of hospital stay
Stroke Depression # of OP visits
COPD Cancer
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Testing Robustness of the model by Simulation
Risk of Readmissions within 30 days of
discharge
DATA SET Readmission Risk Frequency Percent
POPULATION (903)RECORDS)
NO 859 95
YES 44 5
SAMPLE1 (100 RECORDS) NO 89
YES 11 11
SAMPLE2 (100 RECORDS) NO 93
YES 7 7
SAMPLE3 (100 RECORDS) NO 92
YES 8 8
SAMPLE4 (100 RECORDS) NO 92
YES 8 8
SAMPLE5 (100 RECORDS) NO 85
YES 15 15
SAMPLE6 (100 RECORDS) NO 91
YES 9 9
SAMPLE7 (100 RECORDS) NO 92
YES 8 8
SAMPLE8(100 RECORDS) NO 88
YES 12 12
SAMPLE9(100 RECORDS) NO 93
YES 7 7
SAMPLE10(100 RECORDS) NO 90
YES 10 10
Percentage of risk for hospital readmissions is found to be 5% in the population of 903 patients from Texas in 2009.
Robustness of the model is tested by selecting ten samples* of size 100 records each from the population and the percentage of risk for readmissions was estimated for each sample (Table)
Based on the sample simulated, Risk of readmission rates is found to be between 7% and 15%
* Samples are selected from a uniform distribution x following U (0,1) 15
Sample report output can be generated for one patient or groups of patients
PT_ID DIAGNOSIS PROCEDURES IN 2009 YEAR STATE GENDER RACE AGE_GR
PROB READMISSION WITHIN 30_DAYS
PROB NOT WITHIN 30 DAYS
RE-ADMISSION RISK FOR 2010
010678C6770E9E5A Cardiac dysrhythmias Atrial cardioversion 2010 TX FEMALE BLACK
64 & Below 8% 92% LOW
02D7D53671CB0D6B Cardiac dysrhythmias 2010 TX FEMALE WHITE 65-69 4% 96% VERY LOW
055A23F1BBC9B8E1 Cardiac dysrhythmias Int insert lead in vent 2010 TX MALE WHITE 80-84 7% 93% LOW
1099169AEDA62C89 Acute pulmonary heart disease 2010 TX FEMALE BLACK 70-74 4% 96% VERY LOW
23F6F5EDB28F44E9 Cardiac dysrhythmias Dx ultrasound-heart 2010 TX MALE WHITE 75-79 8% 92% LOW
245002B2801903BB Cardiac dysrhythmias Atrial cardioversion 2010 TX FEMALE WHITE
85 & Older 10% 90% LOW
24BDB08EFFA0B90E Cardiac dysrhythmias 2010 TX MALE WHITE
85 & Older 15% 85% HIGH
2F9148D1EB76A2F0 Acute pulmonary heart disease 2010 TX MALE WHITE 75-79 5% 95% VERY LOW
3738355344A9EFD1 Cardiac dysrhythmias 2010 TX MALE WHITE 70-74 4% 96% VERY LOW
4361549110750A3F Cardiac dysrhythmias Dx ultrasound-heart 2010 TX MALE WHITE 65-69 6% 94% VERY LOW
From the risk scored data, the percentage of high risk readmissions within 30 days is found to be 2% 16
Across the four states, the percentage of high risk for readmissions within 30 days of discharge is lowest for the state of Texas
Estimated the risk factors for all hospital readmissions and scored patients from 2010 for the risk of readmissions
State Min Score Max Score # Patients % Very Low % Low % High
Texas 7 15 903 48.1% 50.1% 1.9%
Massachusetts 5 12 328 7.6% 83.0% 9.5%
Illinois 5 14 602 12.0% 81.1% 7.0%
California 5 15 1119 3.8% 90.1% 6.1%
From the risk scored data, the percentage of high risk for readmissions within 30 days of discharge is found to be 1.9% for Texas, 9.5% for Massachusetts, 7.0% for Illinois and 6.1% for California
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Estimating the risk factors for Heart Failure readmissions within 30-days of discharge
A Bayes Logistic Regression model is constructed to estimate the most significant factors responsible for HF hospital readmissions within 30-days of discharge in four selected states: CA, IL, MA & TX
Summary of Modeling analysis
State Significant risk factors
CA Number of hospital discharges in the previous year and # of Out patient visits
IL Number of hospital discharges in the previous year and average length of Stay
MA Average number of Out patient visits
TX Number of hospital discharges in the previous year and average length of Stay
A high percentage of COPD, Depression Stroke and Chronic Kidney disease patients are at a higher risk for HF beneficiaries readmissions in the US
Other significant factors
Average # discharges in 2008
Age group
Length of hospital stay
# of OP visits
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Testing Robustness of the model by Simulation for HF readmission risk in Texas
Risk of Readmissions within 30 days of discharge
DATA SET Readmission Risk Frequency Percent
POPULATION (2285 RECORDS) YES 179 8
NO 2106 92
SAMPLE1 (100 RECORDS) YES 10 10
NO 90
SAMPLE2 (100 RECORDS) YES 9 9
NO 91
SAMPLE3 (100 RECORDS) YES 8 8
NO 92
SAMPLE4 (100 RECORDS) YES 6 6
NO 94
SAMPLE5 (100 RECORDS) YES 9 9
NO 91
SAMPLE6 (100 RECORDS) YES 5 5
NO 95
SAMPLE7 (100 RECORDS) YES 9 9
NO 91
SAMPLE8(100 RECORDS) YES 11 11
NO 89
SAMPLE9(100 RECORDS) YES 6 6
NO 94
SAMPLE10(100 RECORDS) YES 8 8
NO 92 92
Percentage of risk for Heart Failure readmissions is found to be 8% in the population of 2285 patients from Texas in 2009.
Robustness of the model is tested by selecting ten samples* of size 100 records each from the population and the percentage of risk for Heart Failure readmissions was estimated for each sample (Table)
Based on the sample simulated, Risk of Heart Failure readmission rates is found to be between 5% and 11%
* Samples are selected from a uniform distribution x following U (0,1) 19
The percentage of high risk for readmissions for HF within 30 days of discharge is found to be lowest for the state of Illinois
Risk of Readmissions within 30 days of discharge
STATE TOTAL POPULATION RISK INTERVAL HIGH RISK
PERCENTAGE
TEXAS 506 (5,11) 0.6%
MASSACHUSETTS 205 (5,15) 0.7%
CALIFORNIA 645 (5,10) 0.7%
ILLINOIS 322 (5,11) 0.5%
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From the risk scored data, the percentage of high risk for readmissions within 30 days of discharge is found to be 0.59 % for Texas, 0.68% for Massachusetts, 0.72% for California and 0.49 % for Illinois
Summary from Modeling
• 19% of all hospital admissions of beneficiaries with chronic conditions in the US resulted in readmissions within 30 days of discharge, 13% within 15 days and 9% within 7days, in 2008
• A high percentage of Heart failure, Heart disease, Stroke, COPD and Kidney disease patients are at a higher risk for readmissions in the US
• A Bayes Logistic Regression model is constructed to estimate the most significant factors responsible for hospital readmissions within 30-days of discharge in four selected states: CA, TX, MA & IL. Number of out patient visits and hospital discharges in 2008 were found to be significant factors for hospital readmissions within 30 days of discharge in 2009
• Robustness of the model is tested by computer simulation by selecting ten samples of size 100 records each from the population and the percentage of risk for readmissions was estimated for each sample. Based on the sample simulated, Risk of readmission rates is found to be between 7% and 15% for Texas, 5% to 12% for Massachusetts, 5% to 15% for California and 5% to 14% for Illinois.
• The percentage of high risk for readmissions within 30 days of discharge is found to be lowest for the state of Texas and highest for Massachusetts, in 2010
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Develop app - Helps hospital administrators identify patients who might benefit from specific interventions
DEMOGRAPHIC DATA
DISEASE CONDITION
PRESCRIPTION DATA
IN-PATIENT/OUT- PATIENT COST
LAB RESULTS
ROBUST DATABASE WITH DIFFERENT VARIABLES
HOSPITAL DATA
STEP 1 : DATA BASE
PATIENT X VISITED ED X
MONTHS AGO – AGE –
GENDER – CONDITION –
CLOUD BASED MOBILE APP
STEP 3 : MOBILE APP
APP HELPS IDENTIFY PATIENTS FOR FOLLOW-UPS AND RISK FACTORS
STEP 2 : MODELING & VALIDATION
CREATE MODELS THAT
IDENTIFY SIGNIFICANT
VARIABLES
TRIAGE PATIENTS BASED ON ED
DIAGNOSIS AND SEVERITY
IDENTIFY DISEASES FOR WHICH
PATIENT IS AT RISK
MEASURE QUALITY OF HOSPITAL
CARE RELATIVE TO TREATMENT
OUTCOME AND FOLLOW UP VISITS
IDENTIFY AND VALIDATE SIGNIFICANT VARIABLES, MEASURE QUALITY OF
HOSPITAL CARE RELATIVE TO ED VISIT AND TREATMENT OUTCOME
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Analysis of 70 Healthcare companies across Home Healthcare, Insurance, HIE and E H R Analytic Companies underway
List of Healthcare
Companies in US
Home Healthcare Companies
E H R Data/ Analytics
Companies
Comparison of services with Saaraa
Understanding the services for readmission reduction
program
Identify potential opportunity
Insurance Companies
70
HIE Vendors
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39
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Understanding the services for readmission reduction
program
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Companies providing Homecare service to payors have significant opportunities for growth Te
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Sk
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25
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24 Patients Physicians Hospitals Payors
Scoring 10 25 60 5
Capability demonstrated
• Development of Bayes models to identify high risk patients
• Ability to do this by disease conditions, significant co-morbid conditions - by state and hospital
• Enable Hospitals to identify high risk patients early and better provide care during and after they leave the hospital to reduce readmission rates
• Next steps – Quantify benefits in terms of reduced readmission rates and map to services patient need
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