Beyond Predictive Modeling: Advanced Analytics ...Advanced Analytics Opportunities with Medicaid...
Transcript of Beyond Predictive Modeling: Advanced Analytics ...Advanced Analytics Opportunities with Medicaid...
Beyond Predictive Modeling: Advanced Analytics Opportunities with Medicaid Data
The Third National Predictive Modeling SummitSeptember 15, 2009
1
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
2
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.
3
Advanced Analytics at CMURC
• Lines of Business– Health Information Technology– Business Insight
• University-affiliated consulting group with expertise in
4
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
5
Past and Present Aspects
Future Aspects
The Progression of Business Intelligence
6
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
7
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
8
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
9
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
11
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.
12
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.
13
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
14
Outpatient Clinics
Outpatient Clinics
Non-system Referrals
Non-system Referrals
Predict daily census by nursing unit
Emergency Department Emergency Department
Projection with rules
15
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
16
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
17
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?
20
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)
22
Business challenge: Anticipate changing demand Baseline annual forecasts
Income
Population
Education
other
Amount of demand by typeAmount of demand by type
model
23
Business challenge: Anticipate changing demand Influence of economic variable on demand
24
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):
25
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
26
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
27
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
28
Summary
29
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
31
Medicaid admissions by originating zip code
Number of admissions
Saginaw
Flint
Detroit
Admissions occur in population centers
32
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.
33
Medicaid admissions: highest volume major diagnostic codes (MDCs)
Births
Respiratory
Noticeable seasonality
34
Medicaid admits in October 2006 (normalized by population)
Even though there is seasonality in admissions, different geographies react differently.
35
Medicaid admits in November 2006 (normalized by population)
36
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.
38
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.
40
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
41
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
42
Contact Information
For more information, please contact:
Joe Czyzyk, Ph.D.Sr. Research [email protected]
43