HEART FAILUREPrediction and Prevention

December 12, 2015

Agenda

• Project Objective• Why Heart Failure?• Patient Profile with a High After-Event Cost• 5 Procedures that Lead to a Patient…• Our Process

• Analysis 1: Using Check in Data to Determine Patient Risk and Cost• Analysis 2: Using Pre-event Procedures to predict Risk and Cost• Run it by the Experts…

• Summary of Findings• Recommendations for the Client• Conclusion

Objective

Our objective is to offer Allina Health insight into the use of a predictive analytic model that could: 1. Identify patients with heart failure who are readmitted following a

heart issue.2. Identify patients as High-Cost Potential, when they return after

suffering a heart attack

“With its combined analysis and trending techniques, predictive, analytic, intelligent software is useful in healthcare technology for discovering patterns in large amounts of data.”

-Eckerson, W.W. 2006. Predictive analytics: Extending the value of your data warehousing investment.

Why Heart Failure?

• Heart failure is the leading cause of hospitalization among adults over 65 in the US and heart disease claims a life every 60 seconds.

• Over 1 Million are hospitalized in the US annually due to heart failure, costing $17 Billion.

• 50% of patients are readmitted within the first 6 months of discharge and heart failure is the leading cause of readmission in the US. As such, an opportunity exists since the advent of data-driven health care decision making.

• We aim to reduce costs and improve quality of care by building predictive models which indicate if a patient is at risk of exceeding typical patient costs and which procedures lead to less return visits for heart failure.

Why Heart Failure @ Allina?

• Of the 3,242 patients 2,297 were readmitted to the hospital for a second heart related Procedure.

• Of those patients readmitted 2,220 were within 90 days of the first heart failure costing $20M.

• Our client, Allina Health, provided raw data on patients who have experienced a heart attack.

• Our process in exploring a predictive model for return patient behavior follows…

Known Causes for Heart Failure

• High Blood Pressure

• Diabetes

• Obesity

• High Cholesterol

Source: Mayo Clinic. (2015). Heart failure: Causes. Retrieved from http://mayoclinic.com/health/heart-failure

The Truth (If you can handle it)about High Cost After-Event Patients:• White (88%)• Non-Hispanic (98%)• Male (68%) • 60 to 70 yrs old (36%)• Married (60%)• Catholic (28%)

High After-Cost is defined as top third of patients ranked by Cost, or roughly > $50K. Current High After-Cost profile (above) is very similar to the total patient pool, making it difficult to predict based on Demographic or Admit data alone.

Source: Confidential Allina Healthcare System Data

But Who Are We Talking About, Exactly?

Analysis 1: Using Check in data to determine patient risk and Cost

Using patient Demographic and health status data to predict a patient's potential to be in any of the three target groups:

• Return Patient – for Heart Attack related problems• HIGH AFTER Cost (top tercile) – Patient cost after heart event

Proposed Analysis:• Probit Model

• Fine tune Independent Variables using AIC • Build predictive Model / Convert the log• Build a Hit Rate Table to show results of Probit

1A – Predicting Return Patients with Heart Related Treatment

Probit: Initial AIC (with all variables) = 1775Through selective elimination, was able to reduce AIC to 1667 (still relatively high)

d1[2] = Clinic.or.physiciam.officed1[4] = Non.Helath.Care.Facility1.Point.of.origind1[25]= American.Indian.or.Alaskan.Natived1[33]= F (Female)d1[58]= Not.Employedd1[62]= MSdRGd1[65]= BloodPressurediastolicFirstVald1[69]= MortalityRiskScoreNbrd1[71]= BloodPressurediastolicMostReentValue

Predicted >Actual v

0 1

0 1447 214

1 234 71

Conclusion: Admit data not sufficient in predicting risk of Return Heart Procedure, Probit model only successful in predicting 71 out of 285 (25%) returning heart patients.

25%

86%

For Reference (Variable with lowest Pr Score

Source: Confidential Allina Healthcare System Data

Appendix

For Reference (Variable with lowest Pr Score1B – Predicting Above Average After Cost

Probit: Initial AIC (with all variables) = 2770Through selective elimination, was able to reduce AIC to 2625 (still relatively high)

d1[5] = Transfer.from.a.Hospital..different.Facilit1d1[24] = APRSeverit1ofIllnessCdd1[28]= Asiand1[52]= United.Methodistd1[54]= Presbyteriand1[57]= Retiredd1[59]= Full.Timed1[60]= Self.Employedd1[62]= MSdRGd1[64]= CreatinineLastVALd1[66]= BloodPressureSystolicFirstVALd1[69]= MortalityRiskScoreNBRd1[72]= VascularHistoryFLGd1[73]= HypertensionHistoryFLG

Predicted >Actual v

0 1

0 1002 333

1 342 289

Conclusion: Admit data not sufficient in predicting risk of Above Average After Cost. Probit model only successful in predicting 289 out of 622 (46%) of Above Average After Cost patients.

46%

75%

Source: Confidential Allina Healthcare System Data

Appendix

For Reference (Variable with lowest Pr Score)1C – Predicting Above Average After Cost

Probit: Initial AIC (with all variables) = 797Through selective elimination, was able to reduce AIC to 852

d1[5] = Transfer.from.a.Hospital..different.Facilit1d1[18] = Managed.Cared1[24]= APRSeverit1ofIllnessCdd1[29]= Black.or.African.Americand1[35]= Presbyteriand1[38]= divorcedd1[41]= Significant.otherd1[42]= Singled1[53]= Jewishd1[54]= Presbyteriand1[58]= Not.Employedd1[63]= CreatinineFirstVALd1[65]= BloodPressurediastolicFirstVALd1[66]= BloodPressureSystolicFirstVALd1[68]= PulseLastVALd1[69]= MortalityRiskScoreNBRd1[72]= VascularHistoryFLG

Predicted >Actual v

0 1

0 1782 68

1 98 18

Conclusion: Admit data not sufficient in predicting risk of Above Average After Cost. Probit model only successful in predicting 18 out of 86 (21%) of Above Average After Cost patients.

21%

95%

Source: Confidential Allina Healthcare System Data

Appendix

Using patient Pre-event Procedures to predict a patients potential to be in any of the twotarget groups:

• Return Patient – for Heart Attack related problems

• HIGH AFTER Cost (Above Average) – Patient cost after heart event• Average Cost prepresents 33% of the patients in sample

Proposed Analysis:• Probit Model

• Fine tune Independent Variables using AIC and McFadden’s R2

• Build predictive Model / Convert the log

• Build a Hit Rate Table to show results of Probit

Analysis 2: Using Pre-event Procedures to predict Risk and Cost

2A – Predicting Return Patients based on PRE-Event Procedures

Probit: Initial AIC (with all variables) = 25,086Through selective elimination, was able to reduce AIC to 4345 (still relatively high)

Predicted >Actual v

0 1

0 1239 1510

1 356 670

Conclusion: Pre-Event Procedures are not effective in predicting risk of High Cost After Event. Probit model only successful in predicting 65% High cost, but only 45% of Non-High Cost (Binary).

65%

45%

Source: Confidential Allina Healthcare System Data

Appendix

Processing / Methodology [Predicting High Cost]

Method of predicting HIGH COST for patients following a Heart

Attack (based on return visits to Hospital)

● Use sample of patients that were treated both BEFORE and

AFTER having Heart Attack

● Patients were sorted by total cost AFTER heart attack, with the

top third representing the HIGH COST group.

● First, regression model (Gaussian) was used to predict patient

cost, using both PROCEDURES and PATIENT DATA, however,

model was not effective in predicting cost

● Second, PROBIT model was used to predict HIGH COST

(binary) using pre-heart attack procedures, and was successful.

Can we predict which patients will be HIGH COST patients when they return, after experiencing a

heart attack?

Probit ModelStepwise

OptimizationPedict

Compare

Results

Probit Model

2A – Predicting HIGH COST Patients based on PRE-Event Procedures

Probit: Initial AIC (with all variables) = 35,489Through selective elimination, was able to reduce AIC to 654 (very good AIC )

Predicted >Actual v

0 1

0 563 44

1 69 271

Conclusion: Pre-Event procedures are effective in predicting High Cost for returning Patients. Probit model successful in predicting 80% of High Cost and 93% of Non-High Cost patients.

80%

93%

Source: Confidential Allina Healthcare System Data

Clean Up Table for final TT

Probit Summary

Implications of Findings: Predicting HIGH COST PATIENTS

● Of the 1,972 medical procedures, the PROBIT model was effective in

predicting HIGH COST for after event through the use of only 12 unique

procedures

● 4 procedures are related to Heart care

● 3 procedures are linked to CATARACT or Eye care

● 2 procedures involve mammography

● 1 Procedure is use of an opioid painkiller - Hydromorphone

● 1 Procedure is use of an anti nausea medicine - Phacoemulsification

● 1 Procedure, we don’t know! Diffusing Capacit

“Why are CATARACTS correlated with HIGHER COST after a heart attack?”

Cataracts are linked to many

other health conditions such as

diabetes,heart disease,

hypertension, low body mass

index, glaucoma and

renalfailure. While not

appearing to be linked, these

diseases may have a significant

impact on your sight. One of the

many risk factors for developing

cataracts is diabetes

Processing / Methodology [Predicting Return Patients for Heart Related Procedures]

Method of predicting HIGH COST for patients following a Heart

Attack (based on return visits to Hospital)

● Use sample of patients that were treated both BEFORE and

AFTER having Heart Attack

● Patients were sorted by total cost AFTER heart attack, with the

top third representing the HIGH COST group.

● First, regression model (Gaussian) was used to predict patient

cost, using both PROCEDURES and PATIENT DATA, however,

model was not effective in predicting cost

● Second, PROBIT model was used to predict HIGH COST

(binary) using pre-heart attack procedures, and was successful.

Can we predict which patients will be HIGH COST patients when they return, after experiencing a

heart attack?

Probit ModelStepwise

OptimizationPedict

Compare

Results

Probit Model

Update with Probit on full data set: still running through stepwise - will have for

Thurs/Friday TT

Run it by Experts…

We reviewed our process with a real-life cardiologist.

HEART

Summary of Findings

• We understand that there are gaps in the data. Ex: Have patients been treated outside the Allina network?

• In terms of next steps, we’d like to run additional data through models (i.e., not just Allina patients in the Twin Cities).

Recommendations for the Client

• Specific to Allina• Enact rigorous follow-up protocol for technicians serving Allina patients on

pre-heart attack lifestyle and recommendations for post-heart attack lifestyle, as well as ongoing check-ins to track those lifestyle changes

• At a Higher Industry-Level• Create a confidential sharing forum to support patients and medical

technicians regardless of provider

• Consider taking the lead on consolidation of patient data across providers, possibly through a third party

Recommendations for the Client

>Connect patient records with clinics and hospitals within the area that are not currently captured in the data set, to have a better understanding of patient history and risk factors.

>Capture a richer data set on patients by noting life style changes, diagnostics from remote monitoring.

What could a new care model look like?

Conclusion

• We strongly believe that predictive analytics can play a vital role in both the prediction and prevention of heart failure, and that the potential of using patient data in order to do so has yet to be fully leveraged.