harnessing the power of predictive modeling future trends
TRANSCRIPT
Harnessing the Power of Predictive Modeling
Future Trends
Harnessing the Power of Predictive ModelingFuture Trends
• Traditional Applications
• Recent Applications
• Future Trends– Motivation Index– Forecasting Disease Specific Risk– Provider Market
• Forecasting Preventable Events
Predictive ModelsTraditional Applications
• Risk Stratify the Population for care management– Manage complexly ill members (Inpatient avoidance)– Refine disease management strategies– Manage pharmacy services
• Underwrite more accurately• Reimburse based on illness burden• Evaluate physician management strategies
Predictive ModelsChanging Focus
• Traditional Application has been to Identify: – High Risk / High Cost members– Inpatient Risk
• Recent Applications – Forecasting additional Cost Components
• ER Visit Risk• Pharmacy Cost forecasting
– Identify Intervenable or Actionable members
• Future Trends – Member Motivation– Disease Specific Complications– Preventable Events for the Provider Market
Recent ApplicationIdentifying Actionable Members
• Method A– Query population by multiple filters:
• Disease• Cost Risk• Inpatient Risk• Pharmacy Risk• Mover Risk
• Method B – Impact Index : Model that identifies members who
have the greatest potential for outcome improvement based on guideline compliance
Recent ApplicationMultiple filters to identify actionable members
Total Population: 216,842 members
High Risk Index Top 2%
High Risk + Mover
4,362 MembersForecasted Cost: $25,741Prior Year Cost: $45,006
Savings Potential:
-$84,033,930
498 MembersForecasted Cost: $20,084
Prior Year Cost: $8,832
Savings Potential:
$5,603,496
Total Population: 925,407
Diabetes: 50,847
High-Risk Index Risk Level 4&5
High Impact IndexTop 15%
14,250 MembersForecasted Cost: $14,634Prior Year Cost: $14,527
Savings Potential:
$1,524,750
13,872 MembersForecasted Cost: $8,698Prior Year Cost: $5,089
Savings Potential:
$50,064,048
Recent ApplicationImpact Index to identify actionable members
Recent Application Impact Index
• These Drivers– Disease– Age/Sex– Comorbidities– Guideline Compliance
Patterns
• Determine future impactability
• Determine potential cost savings
Guideline Potential Savings
DM ACERx $455
DM HBA1C $449
DM Eye Exam $447
DM LDL $442
Depress RX $360
CHF Rx $302
CVA Coum $290
CHF HTN Ace $280
Asthma Rx $206
Asthma Steroid $186
COPD TheoLvl $167
MI BBlocker $102
CHF InPt-Echo -$14
Future TrendsMotivation Index
• Identify members – more motivated to ‘self-manage’– comply with instructions from providers– pursue ways to improve health status
• To create index use data sources– Lifestyle Data– Health Risk Assessment – Demographics– Claims Data
Future TrendsMotivation Index Drivers
• Lifestyle Data– Net Worth– Credit History– Magazine Subscriptions– Hobbies– Clubs
• Claims Data– Compliance Patterns– Preventive Care– Physician Visit Patterns
Claritas
US Census Bureau
Media Mark
Future TrendsMotivation Index Variables
• Claims Data– Compliance Patterns
• To Guidelines• To Psych-Related Drugs• To Maintenance Drugs
– Preventive Care• Use of preventive health services• Compliance to Preventive Lab Test• Compliance to standard preventive guidelines
– Physician Visit Patterns• Gap/Frequency between Acute Care & Physician visits• Gap/Frequency between Physician visits per disease
– Cost Ratios for Inpt / Rehab / Rx / Physician
• Demographic / Misc– Age/Sex– Obesity / Smoking– Drug/Alcohol Dependency– Mental Health
Future TrendsMotivation Index Drivers
• Patients with higher motivation scores have
– Better guideline compliance – Older age– Higher preventive care use – Lower acute-care use– Shorter (Time-frames from Inpt discharge to phys-visit)– Females 40 t0 65
• Higher mammogram compliance
– Asthmatics • Lower ER visits
– Hypertension• Higher hypertensive drug use
– Depression• Higher depression related drug use
Future TrendsMotivation Index Drivers
30.0
33.0
36.0
39.0
42.0
45.0
48.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
# Antidepress Rx Filled
Av
g M
oti
vat
ion
In
de
x
Higher Antidepressant Use within Depressed Population correlates with higher motivation
Future TrendsMotivation Index Drivers
30
35
40
45
50
55
60
45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
Female, Age
Avg
Mo
tiva
tio
n I
nd
ex
With Mammogram Without Mammogram
Mammogram Use in Female Population correlates with higher motivation
Future TrendsMotivation Index Drivers
20.0
22.0
24.0
26.0
28.0
30.0
32.0
34.0
36.0
38.0
40.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
# ER Visits Per Year
Av
era
ge
Mo
tiv
ati
on
In
de
x
ER Visits within Asthma Population correlates with lower motivation
Future TrendsForecasting Disease Specific Outcomes
Disease Complications DriversComplication Rate
Positive Predictive Value
Diabetes
Macrovascular Microvascular Events Metabolic Complications Infectious Complications
Retinopathy Age Comorbid Condition Cnt 23% 60%
CancerInpatient Admit after Cancer
Mammography RxFills Cancer Severity Resp Severity 11% 59%
Asthma
Respiratory Failure Pulmonary Edema Ventilator Pneumonia Pleural Effusion Pneumothorax Inpt Admit
COPD Rx Cnt Musculoskeletal $ 7% 37%
Future TrendsProvider Market
• Future Trend– Forecasting Preventable Events Pre-discharge
• Preventable readmissions• Catheter-Associated UTI • Pressure Ulcers• Vascular Catheter-Associated Infection • Mediastinitis after CABG-Surgical Site Infection • Hospital-Acquired Injuries
Future TrendsWhy Identify Potentially Preventable Readmissions?
• Comparing provider performance to enhance quality• Developing pay for performance systems• Readmission rates provide quality benchmark• Costs associated with readmissions are substantial
– 30 billion in play for Medicare• Defining Preventable Readmissions
– some initial discharges for which subsequent readmits excluded (e.g. LAMA, cystic fibrosis)
– Readmissions are for same diagnosis– Readmissions are for related diagnosis
Future TrendsData for Forecasting Preventable Events
• Electronic Medical Record Data– HL7 Format – Near-real time data outflow
• Forecasting Model– Near-real time forecast of Readmission / Decubitus
• Probability• Risk Index• Drivers
• Data Needed– Vital Signs– Lab Results– Drug Dosage and Timing– Admission Discharge Transfer Data– Chief Complaint – Prior Discharge Diagnoses– Supplies
Future TrendsForecasting Decubitus
• Drivers to forecasting Risk of Decubitus when a patient is admitted– Vital Signs
• Fever• Pulse / BP / Respirations
– Lab Results• White Blood Cell Counts• Blood Culture Results
– Drug Dosage and Timing• Antibiotic at admission
– Chief Complaint – Diarrhea– Admission Source
• From SNF– Prior Discharge Diagnoses
• Diabetes / CHF / Senility– Demographics– Supplies
• Depends
Future TrendsHospital Revenue Loss with Preventable Events
Condition
Discharges with
Condition Present
Discharges with Change in DRG
Assignment (Worst Case)
Percent of Discharges
Total Revenues at
Risk (Worst Case)
Revenue Loss per HA event
Decubitus Ulcer 259,356 117,852 45% -283,432,250 -2,405Falls and Trauma 201,007 42,943 21% -128,547,128 -2,993Urinary Track Infection 8,832 1,063 12% -1,469,338 -1,382Object Left in Surgery 805 174 22% -454,693 -2,613Mediastinitis 111 31 28% -252,677 -8,151Air Embolism 46 25 54% -109,681 -4,387Blood Incompatibility 35 5 14% -5,180 -1,036
Adjustment for Multiple Conditions Present -$5,867 5,959 -20,030,826Total of Approved Conditions 464,325 168,052 36% -434,301,773 -22,968
Worst Case Revenue at Risk by Condition
Source:CMS;Advisory Board Analysis
Future TrendsROI from Forecasting Preventable Events
Preventable EventPer Hospital Acquired Event
For Avg HospSystem Annually
Per Hospital Acquired Event
For Avg HospSystem Annually
Decubitus Ulcer -$2,405 -$3,366,978 $17,000 $23,800,000Urinary Track Infection -$1,382 -$276,451 $37,000 $7,400,000Methicillin Resistant Staff Aureas NA NA $30,000 $6,000,000
DRG Revenue Loss
Cost Saved by Preventing Hospital Acquired Event
Predictive Modeling Applications for Care Management – Paradigm ChangesHistorical Historical
Current TransformedCurrent Transformed
Predictive Modeling Applications for Care Management – Paradigm Changes
Future ModelFuture Model
Predictive Modeling: Used to identify
early members who are trending
toward high-risk events
MbrEduc UR/UM Demand
MgmtConcurrentCase Mgmt
DiseaseMgmt
PersonalHealth Mgmt
PopulationRisk Mgmt
UR/UM
Proactive Case ManagementDisease Management
DecisionSupport
PersonalHealth Mgmt
PopulationRisk Mgmt
UR/UM
Proactive Case ManagementDisease Management
DecisionSupport
Actionability
Motivation Index
Disease Specific
About the Future
“Never let yesterday use up too much of today.”
- Will Rogers
The best way to predict the future is to create it.
“Never let yesterday use up too much of today.”
- Will Rogers
The best way to predict the future is to create it.