Beyond Predictive Modeling: Advanced Analytics Opportunities with Medicaid Data

The Third National Predictive Modeling SummitSeptember 15, 2009

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Outline

1. Examples of predictive modeling• Hospital census prediction• Forecasting with events and scenarios

2. Predictive modeling with Medicaid data• Forecasting (simple)• Inpatient Medicaid data• Use of models for future scenario analysis

3. Applications and Conclusion• Applicability to Medicaid program directors• Applicability to hospital administrators for planning purposes• Summary of opportunities

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About Central Michigan University and CMU Research Corporation

Central Michigan University• 44th largest university in the US; 4th largest in Michigan; 700 PhD-level faculty• CMU has a broad set of disciplines (Health Professions, Science & Technology,

MIS, Business, Education, Humanities, Arts and Music) with strong programs in Data Mining, Predictive Modeling, Geographic Information Systems and SAP

CMU Research Corporation• Established in 2002 as not-for-profit• Purpose: Catalyze and facilitate innovation and research between academia and

industry.• Greater Purpose: Through the Research Corporation, CMU will become the

easiest university for business to work with.• Full-time staff engage faculty and students for mutual learning.

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Advanced Analytics at CMURC

• Lines of Business– Health Information Technology– Business Insight

• University-affiliated consulting group with expertise in

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Health care Manufacturing Other areas

Predictive Modeling X X X

Forecasting X X

Incorporating Geographical information X

Use of external datasets X X X

Strategic Staffing X X

Warranty X

Text Mining X X

PREDICTIVE MODELING ON THE CONTINUUM OF ANALYSIS

Beyond Predictive Modeling: Advanced Analytics Opportunities with Medicaid Data

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Past and Present Aspects

Future Aspects

The Progression of Business Intelligence

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Advanced Analytics

Data Information Knowledge Insight Action

Ret

urn

On

Inve

stm

ent &

Val

ue

StandardReports

Ad hoc Reports& OLAP

Trend Statistics

Predictive Modeling

Future Impact

Analysis

Raw Data

From BI reporting to Advanced Analytics

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Data Infrastructure Standard Reports

Enhanced ReportingPredictive metricsWhat-if analysisSpecialized reports

Dashboard Picture from SAS

EXAMPLES OF PREDICTIVE MODELING

Beyond Predictive Modeling: Advanced Analytics Opportunities with Medicaid Data

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Modeling Examples

• Hospital patient volume forecasting (closed system)• Forecasting with outside influences

Goals• Show modeling techniques• Demonstrate the added predictive power of combining

datasets• Discuss the possibilities of modeling with Medicaid data• Lay the framework for a Medicaid scenario

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Business Challenge – Hospital Volume Introduction

Challenge:– Hospital beds were not fully occupied.– Hospital was losing money.– It was difficult to allocate hospital staff to meet

needs.– Need to predict patient volume at the nursing

unit level.– Need to identify drivers for hospital

occupancy.

Standard time-series forecasting techniques could not provide sufficiently accurate forecasts.

More sophisticated methods were required.10

Business Challenge – Hospital Volume Approach

• Model flow of patients from each source:– Emergency department– Outpatient clinics– Non-system referrals

• Examine length of stay to determine probability distribution

• Map admitting patient to hospital floor by specialty

• Sum existing patient population with expected number of incoming patients to determine hospital census

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Business Challenge – Hospital Volume Complications / Insights

• Emergencies are random events which follow a probability distribution.

• Progression from outpatient clinic to hospital follows predictable paths.

• Hospital floors are specialized to care for certain types of patients (cardiology unit, pediatric unit, maternity, …).

• Doctors tend not to admit on weekends or when away at conventions.

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Business Challenge – Hospital Volume Solution Strategy

Mine data sources for patterns and relationships.– Data from outpatient claims– Historical bed census (Inpatient data)

Utilize modeling techniques for components of the model.

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Modeling challenge Modeling techniqueRandom arrival of emergency room patients Poisson arrivals, regression analysis for rates

Admissions from outpatient clinics Survival and sequence analysis of doctor visits, rule induction, Weibull distributions for time to failure

Determine probable length of stay Data mining of patient stay data

Low admission days Factors for admission by day, special events

Business Challenge – Hospital Volume Modeling Flow

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Outpatient Clinics

Outpatient Clinics

Non-system Referrals

Non-system Referrals

Predict daily census by nursing unit

Emergency Department Emergency Department

Projection with rules

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Outpatient visits by Various DR_SPEC_CD

Proj of DSC(i) by week 1-52

Wk 52

Rules with Weibull distribution

Wk 2Wk 1

Week -51Week -50

…

Every outpatient visit generates a hospital admission at time t with a certain probability (Weibull distribution with scaling).

Length of stay by doctor specialty

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Example: COL = Colon & rectal surgery

COL- LOS Early 2004

0

50

100

150

200

250

300

350

0 2 4 6 8 10 12 14 16

Hospital admissions are expanded to hospital census by taking into account length of stay by doctor specialty.

These curves will change over time due to changes in technology and procedures.

Total Forecasted vs. Actual Admits May 2004 – April 2005

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500

550

600

650

700

750

800

850

5/2/

04

5/16

/04

5/30

/04

6/13

/04

6/27

/04

7/11

/04

7/25

/04

8/8/

04

8/22

/04

9/5/

04

9/19

/04

10/3

/04

10/1

7/04

10/3

1/04

11/1

4/04

11/2

8/04

12/1

2/04

12/2

6/04

1/9/

05

1/23

/05

2/6/

05

2/20

/05

3/6/

05

3/20

/05

4/3/

05

4/17

/05

weekly_fcst actual admits

Total Actual = 37948Total Predicted = 37935

Diff = 13 or .034%

Business Challenge – Hospital Volume Modeling Results and Applications

Results• Aggregated forecasts for admissions were within 1.1% of actual (better than 3%

error of previous forecasts).• More granular forecasts at the nursing unit by day of the week were less

accurate.

Multiple Datasets• The inpatient database could provide summary statistics.• Joining the outpatient clinic datasets to the inpatient datasets enabled greater

predictive power.

Applications• Accurate forecast for budgeting purposes• Determination of bed capacity requirements for better allocation of capital and

human resources• Scenario testing and what-if analysis

New Availability of the Model - www.thepvforecaster.com18

Business challenge: Anticipate changing demand Forecasting with outside influences

Challenge:What factors cause changes in consumer demand for goods and services (shifts in model and features; shift in volume expectation)?

Examples of demand:- Medical services- Health insurance- Social services- Telephone service including additional features- Educational institutions (K-12, community college, university)

We will later extrapolate this to health coverage.19

Business challenge: Anticipate changing demand Forecasting with outside influences

Develop a forecast methodology to explain:• Baseline (annual) demand

– Do characteristics of home zip code correlate with baseline demand (e.g., demographics, economic climate, geography, land use, predominant mode of transportation)?

• Cyclic patterns to demand– Are there regular patterns to demand?

• Event-driven changes in demand• Can non-cyclical changes be attributed to external events?• If so, can events be a leading indicator of change in consumer interest or

demand?

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Business challenge: Anticipate changing demand Variation due to events

Baseline demand+ cyclical variation+ events= Observed demand

7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10

2004 2005 2006

Sum of NamePlateCount

year month21

Business challenge: Anticipate changing demand Example model inputs

Baseline forecasts– Demographics (population, education, income, etc.)– Geography (Commuting patterns, Agricultural base)– Economic Climate (Value of economic base, economic development)

Cyclic / Seasonal Effects– Seasons, holidays, school year, tax year

Events which change demand– Perceived change in price of product– Perceived change in economic conditions

(Housing market, Unemployment rates, Interest rates, Credit availability)– Change in incentives (e.g., home buyer tax credit)

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Business challenge: Anticipate changing demand Baseline annual forecasts

Income

Population

Education

other

Amount of demand by typeAmount of demand by type

model

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Business challenge: Anticipate changing demand Influence of economic variable on demand

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Question: Does a change in the unemployment rate affect the demand for a product or service?

The unemployment rate and demand can be modeled as a time series.

Did the rise in unemployment affect demand? With what delay?

Demand is influenced by the unemployment rate, and the lag terms can be computed. Lag terms represent the delay in the effect of unemployment on demand.

Mathematically, these are cross-correlation functions.

Lag

Business challenge: Anticipate changing demand Rule discovery

A goal of the project was to discover how much external variables affect demand, for example (not real rules):

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If Then

Increased DJIA in a location with a high median income,

increased demand for luxury products

Increased product prices, lower overall demand and to a shift towards more economical products

A blizzard in a region, more demand for durable products

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Business challenge: Anticipate changing demand Resultant Forecast

Component Use

Baseline Baseline demandProduct / feature mix

Seasonal Medium-term scheduling (budgets, production)

Events Fine tuning for scheduling adjustments, short-term changes

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Business challenge: Anticipate changing demand Modeling Results and Applications

Results• Demand is a complex function of customer features plus cyclic

patterns.• External events change consumer behavior (with measurable delay

for different types of events).

Multiple Datasets• Joining datasets of external influencers enables greater predictive

power.

Applications• Ability to monitor for known events and alter demand expectations.• Ability to anticipate scenarios and plan accordingly.

PREDICTIVE MODELING WITH MEDICAID DATA

Beyond Predictive Modeling: Advanced Analytics Opportunities with Medicaid Data

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Summary

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A hospital has access to its internal data.

Patient activity outside of the hospital provides predictive capability for the hospital to forecast census.

Can additional information from the greater market area provide strategic information to the hospital?- Economic trends- Economic events (layoffs, bankruptcy)- Demographic changes- Payer mix (e.g., Medicaid)

Expanded example: Michigan’s economy

Michigan’s economy has been hard hit in the recent decade.– Companies leaving the state

• Pfizer• Electrolux

– Bankruptcies• General Motors• Chrysler

– Decreasing state budget

Is it possible to use historic data to measure future impact on demand for Medicaid services? 30

Greenville struggles to cope without Electrolux plant

Pfizer stuns Mich. with huge job cuts

Steelcase To Close Grand Rapids Area Locations

Hospital admission statistics by payer

Monthly admissions Let’s consider Medicaid-funded hospital admissions in Michigan.

From the forecasting example, this is the baseline.

Source: Michigan Inpatient Database

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Medicaid admissions by originating zip code

Number of admissions

Saginaw

Flint

Detroit

Admissions occur in population centers

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Medicaid admissions (normalized by population)

Number of admissions(normalized by population)

Saginaw

Flint

Detroit

Large number of Medicaid admissions in rural and sparsely populated areas.

From the forecasting example, this is the baseline.

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Medicaid admissions: highest volume major diagnostic codes (MDCs)

Births

Respiratory

Noticeable seasonality

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Medicaid admits in October 2006 (normalized by population)

Even though there is seasonality in admissions, different geographies react differently.

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Medicaid admits in November 2006 (normalized by population)

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Medicaid admits in December 2006 (normalized by population)

What is unique to this region that admits are now lower?

There are opportunities to understand local behavior and anticipate demand and align resources. 37

Events

• Building models using historical data can help to explain variations in demand for Medicaid services.

• The use of external data can help to understand local behavior.

• The use of external data with a model can be used to anticipate future scenarios for both:– Medicaid program directors– Hospital administrators

• Consider the following new scenario.

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Grant allocations (millions of $) by city

Michigan Gets $1 Billion Plus In Battery Grants – August 2009

“Tens of thousands of jobs”

Would it be possible to use historical data to understand when the impact of these new plant openings will occur?- New skilled jobs - Increased economic activity in the area- Job training- Reduced Medicaid demand and with what delay? 39

Conclusions

• Standard and ad hoc reports give us insight into overall historical demand.

• Aggregation to larger time intervals and geographies tends to hide interesting and actionable insights in the data.

• Tested explanatory models using past data can help administrators anticipate future needs by testing alternate scenarios.

• Joining datasets provides expanded predictive power to models.

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Applications

• For Medicaid analysis, join two datasets from MAX (Medicaid Analytic Extract) to increase predictive power:– Inpatient file– Other file (physician services, etc.)

• Hospital administrators can join internal data to Medicaid data to other external data for additional predictive power

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Future areas of discussion

• Text MiningThe use of data mining to find relationships in text.Categorize comments into particular classes.Identify new keywords and concepts that are appearing in

comment fields.Check for consistency between structured data in a record

and the unstructured text data.

• Fraud detection using network analysis

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Contact Information

For more information, please contact:

Joe Czyzyk, Ph.D.Sr. Research Analyst989-774-1622Joe.Czyzyk@thepvforecaster.com

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