Predictive Analytics

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Predictive

Analytics

for hospital management

Hans Levenbach, Delphus, Inc. and Paul Savage, HCI

Hans Levenbach, Delphus, Inc. and Paul Savage, HCI-

-LLC

LLC

Email:

Email: hlevenbach

[email protected]

@delphus.com

ISF 2010

–

San Diego, CA

June 21, 2010

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Overview

Introduction

Predictive Analytics –

Predictive Analytics

–

something new?

Approaches and methods –

Approaches and methods

–

model complexity vs

model complexity

vs data volume

data volume

Supporting the Hospital Value Chain

Geography and product lines

-

Patient care activity

Multiple competitor and product

-

mix forecasting

Competing for new hospital locations

–

Simulations

Hospital closings

-

Berger Commission type simulations

–

Data architecture and reporting

Multi

-

State & Current Perspective

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“

_{We have to bring the}

science of management back

into Healthcare

”

Donald Berwick, MD

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Predictive Analytics –

Something New?

Predictive analytics encompasses a variety of

techniques from

statistics

, , , ,

data mining

and

game

theory

that analyze current and historical facts

to make predictions about future events.

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Predictive Analytics - Methods

CLUSTERING CLUSTERINGCLUSTERING CLUSTERING FORECASTING FORECASTING FORECASTING FORECASTING MONITORING MONITORING MONITORING MONITORING &&&&

ADVISING ADVISINGADVISING ADVISING

SIMULATION SIMULATIONSIMULATION SIMULATION &&&& SCENARIO

SCENARIO SCENARIO

SCENARIO PLANNINGPLANNINGPLANNINGPLANNING DECISION

DECISIONDECISION

DECISION TREETREETREETREE

The use of current and past data, in conjunction with statistical, structural or other analytical

models and methods, to determine the likelihood of certain future events

Predictive methods cover a range from relatively simple classification and forecasting to

more advanced techniques such as dynamic modeling and

simulation

As you move down the spectrum, the complexity of the

approaches and their implementation increase, while

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Framework for SaaS Planning

SPARCS

Data

Resource

Mining &

Analytics

PEER

Planner

Dashboard

_Competitive

Simulation

Model

Multi-year

Multi-hospital

Information

Product Line

Trends

Spatial Analysis

Market Shares

Budget

Forecasting &

Collaborative

Planning

_Interactive

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Decision Tree:

All Patients

Primary Tumor Site

Psychiatry

Surgery

Maternity

Surgery _{Colon: Classification by Stage}

C1 Oncology Cardiac New Born Cardiology Pulmonary

> 100+ product line/service combinations

> 220 Hospitals > Payor Class > Region

Descend tree using any available

differentiating attributes; natural, derived or inferred; separately or in combination

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Intelligent Dashboard Environment

Manage strategic opportunities

Monitor competitive environment

Enhance physician relations

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Dashboard

History, Adjustments and

Forecasts

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Report Writing and Additional

Capabilities

Physician Activity Reports

Market Level Assessment

Simulation Modeling

Product Level Demography

Benchmarking: Quality, Safety &

Performance

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Physician Level Reporting

Attending Physician Activity

Surgeons Activity

Measures of Loyalty Ranking

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Physician

&

Group Practice

Analysis

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Total Group Practice Inpatient Activity

( 8 0 % p re d i c t i o n i n t e r v a l s ) 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0 3 0 0 J a n- 0 4 Ma y - 0 5 Oc t - 0 6 Fe b - 0 8 J u l - 0 9 No v - 1 0

Low er P-L (10%)

Upper P-L (90%)

History

History T-C

Forecast T-C

A v g . 2 16 / Mo 19 % S e a s o n a l R a n g e

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Percent of Group Practice by Hospital

8 0 % p re d ic t io n int e rv a l s 0 % 1 0 % 2 0 % 3 0 % 4 0 % 5 0 % 6 0 % 7 0 % 8 0 % 9 0 % 1 0 0 % J a n- 0 4 Ma y - 0 5 Oc t - 0 6 Fe b- 0 8 J u l - 0 9 No v - 1 0

G-S_History

G-S_History T-C

G-S_Forecast T-C

N-I_History

N-I_History T-C

N-I_Forecast T-C

P rim a ry Ho s p ita l S ha re o f P ra c tic e

S e c o n da ry H o s pita l S h a re o f P ra c tic e

17 %

8 3 %

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New Physician Growth

( 8 0 % p re d ic t io n i nt e rv a ls ) 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 J a n- 0 4 Ma y - 0 5 Oc t - 0 6 Fe b - 0 8 J u l - 0 9 No v - 1 0

Lower P-C (10%)

Upper P-C (90%)

History

History L-C

Forecast L-C

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Transition to Mature In-Patient Activity

( 8 0 % pr e di c t i o n i nt e r v a l s ) 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 J a n - 0 4 M a y - 0 5 O c t - 0 6 F e b - 0 8 J u l - 0 9 N o v - 1 0

Forecast T-C

History

History T-C

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Senior Partner In-Patient Activity

( 8 0 % p re d ic t io n int e rv a l s ) 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 J a n- 0 4 Ma y - 0 5 Oc t - 0 6 Fe b - 0 8 J ul - 0 9 No v - 1 0

Low er P-C (10%)

Upper P-C (90%)

History

History T-C

Forecast T-C

Avg 26/ Mo

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Mature Partner (Stable) Activity

(80% prediction intervals)

0 10 2 0 3 0 4 0 5 0 6 0 7 0 8 0 J a n - 0 4 Ma y - 0 5 Oc t - 0 6 F e b - 0 8 J u l - 0 9 N o v - 10

Low er P-L (10%)

Upper P-L (90%)

History

History T-C

Forecast T-C

Avg 50/Mo

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Hospital

&

Product Line

Analysis

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Community Hospital

Forecast Patient Activity

1400

1500

1600

1700

1800

1900

2000

P

a

ti

e

n

ts

/M

o

.

22,000 Pts/Yr

20,000

Pts/Yr

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Simulation

Modeling :

Competing For New

Hospital Locations

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Extending Visibility Into The Enterprise

Executive User Functional User Power User Highly Aggregated More Detail

Complete Raw Data •Graphical Display, Dashboards, Interactive

•Aggregated Data, Model Execution

•Limited Drill Down

•Standard & Ad hoc Reporting

-Parameter-Driven by Users at Run-Time

-Sorting, Selection, Filtering, Drill-Down

•Utilizing Standard Functions and Models

•Direct Access to Detailed Raw Data

-“Just give me the data in Excel”

•Model Development & Deployment

• Traceability • Consistency

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Questions?

Contact: Hans Levenbach

Email: [email protected]

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SaaS

Implementation: System & Data Architecture

Hospital Data Operations Data Financial Data External Data Data Warehouse Multi-Dimensional Data Store Data Files Data Sets

Integration Cleansing Data Quality Formatting Aggregation

Predictive Models Report Management Test Management Model/Version Management DATA PRESENTATION/ CONSUMPITON LAYER APPLICATION/ MODEL MANAGEMENT LAYER DATA STORAGE LAYER DATA INTEGRATION LAYER DATA SOURCE LAYER