the analytics opportunity in healthcare

The Analytics Opportunity

in Healthcare

Agenda

• Brief IHBI overview

• Value of Analytics

• Share best/worst project

• Brief MiHIN introduction

• ADT example

• Adding intelligence to the network

2

IHBI@CMU: Snapshot

Founded in 2001 as a center of excellence specializing in data mining and predictive

analytics.

Purpose

• Promote the application of predictive analytics to solve social, economic and

business challenges

• Build bridges between the public and private sector

• Train the next generation of data scientists

2010

• Moved under the auspices of the Herbert H. and Grace A. Dow College of Health

Professions

• Specifically charged to develop a focus on healthcare

Team

• PhD and MS in physics, statistics, economics, computer science, geography, etc.

3

Customers and Partners

Health and Healthcare

• Central Michigan District Health Dept.

• College of Health Professions, CMU

• College of Medicine, CMU

• Eli Lilly

• Henry Ford Health System

• Michigan Health Information Alliance

• Michigan Health Information Network

• Partners Healthcare (Boston)

• Spectrum Health System

• Synergy Medical

Manufacturing

• The Dow Chemical Company

• The Dow Corning Corporation

• Ford Motor Company

• General Motors

• Harley-Davidson

• Monsanto

• Steelcase

• Whirlpool Corporation

Technology

• IBM

• SAS Institute

• Hewlett-Packard (EDS)

• Greenplum Pivotal

Banking, Finance, Insurance

• Auto-Owners Insurance

• Comerica Bank

Other

• Proctor and Gamble

• DTE Energy

• Domino's Pizza

• Gordon Food Service

• State of Michigan

4

Data Science & Big Data

Reporting

Queries/drill down

Alerts

Statistical Analysis

Forecasting

Predictive Modeling

Optimization

What happened?

Where is the problem?

What action are needed?

Why is this happening?

What if these trends continue?

What will happen next?

What is the best that can happen?

An

aly

tica

lly im

pa

ired

Adapted from Competing on Analytics: The New Science of Winning (Davenport, 2007).

5

IHBI Contextual Dataset

One of the unique assets that IHBI has developed over the last few years is a substantial dataset called the Contextual Dataset

• Based on a collection of population, socioeconomic, environmental, geographic, and health care variables

• Designed to be integrated with private data to enhance modeling and knowledge

• Data granularity is at the zip code level for all variables nationwide

• We are gradually shifting the granularity of all variables to census tract by downloading the data at that level where available or otherwise using area and/or population weighted distribution methods to convert from a different granularity

6

Research ICE3Potential Reusable Data Sources

8

9

A Time and Place

• Guess/Intuition– Outcome does not matter-low value or (low

accountability)– No other choice

• Data-Driven Results– Reports Sales Are Down or performance metrics

• Fact-based Assessments– Correlation vs. causation

• Model- and Scenario- Assisted Decision Making– Predicting customer response– Predicting outliers– Simulation

• Experimental Design “Test & Learn”

10

Project Example

Scenario: Hospital had a very low census. Relatively closed system where they owned the providers and only a small percentage of referrals came from outside the system.

Goal: Provide a predictive model that forecasts total weekly admission rates by admitting specialty and weekly census on each nursing unit.

11

Roadmap

Patient

Encounters

PEMSMedical

Records

Classify records:

a=I wi. E or O

b= no I

c=I w/o E or O

a b c

External

referrals

Map weekly

admits to

day-of-week

Seq

Rules

Recent

E/R &

Outpt

Internal

referrals

Admits

by week by

DR_SPEC

Map DR_SPEC

to LOS &

NUR_UNIT

Forecast

integration

DEMAND

beds per

NUR_UNIT

Major

Deliverable

To Capacity &

resource planning

List of

unusual

events

12

Total Forecasted vs. Actual Admits

May04-April05

500

550

600

650

700

750

800

850

5/2

/04

5/1

6/0

4

5/3

0/0

4

6/1

3/0

4

6/2

7/0

4

7/1

1/0

4

7/2

5/0

4

8/8

/04

8/2

2/0

4

9/5

/04

9/1

9/0

4

10/3

/04

10/1

7/0

4

10/3

1/0

4

11/1

4/0

4

11/2

8/0

4

12/1

2/0

4

12/2

6/0

4

1/9

/05

1/2

3/0

5

2/6

/05

2/2

0/0

5

3/6

/05

3/2

0/0

5

4/3

/05

4/1

7/0

5

weekly_fcst actual admits

13

Predicted vs. actual admits in June 05 for major doctor specialty codes

6/5/05 6/12/05 6/19/05 6/26/05 Total Ratio

CAR 89 ( 79 ) 89 ( 75 ) 90( 78 ) 90 ( 83 ) 358 ( 315 ) 1.14

GYN 54 ( 49 ) 54 ( 69 ) 53 ( 63 ) 53 ( 52 ) 214 ( 233 ) 0.92

IMG 192 ( 198 ) 193 ( 168 ) 193 (209 ) 194 (164 ) 772 ( 739 ) 1.04

NES 33 ( 43 ) 33 ( 33 ) 33 ( 30 ) 33 ( 35 ) 132 ( 141 ) 0.94

NEU 34 ( 22 ) 34 ( 35 ) 34 ( 39 ) 34 ( 42 ) 136 ( 138 ) 0.99

ONC 28 ( 30 ) 27 ( 25 ) 27 ( 39 ) 27 ( 33 ) 109 ( 127 ) 0.86

ORT 26 ( 31 ) 26 ( 47 ) 26 ( 39 ) 26 ( 38 ) 104 ( 155 ) 0.67

PUL 61 ( 55 ) 61 ( 71 ) 62 ( 46 ) 62 (50 ) 246 ( 222 ) 1.11

SUR 67 ( 55 ) 67 ( 58 ) 67 ( 52 ) 66 ( 55 ) 267 ( 220 ) 1.21

14

Length of Stay by Doctor Specialty

SPEC LOS Pts % pts %beds

COL 1 96 0.0592 1.0000

COL 2 111 0.0685 0.9408

COL 3 161 0.0993 0.8723

COL 4 301 0.1857 0.7730

COL 5 269 0.1659 0.5873

COL 6 183 0.1129 0.4213

COL 7 145 0.0895 0.3085

COL 8 89 0.0549 0.2190

COL 9 76 0.0469 0.1641

COL 10 39 0.0241 0.1172

COL 11 21 0.0130 0.0932

COL 12 21 0.0130 0.0802

COL 13 19 0.0117 0.0672

COL 14 10 0.0062 0.0555

COL 15 10 0.0062 0.0494

Example:

COL = Colon & rectal surgery; 1621

patients in first 5 months of 2004

COL- LOS Early 2004

0

50

100

150

200

250

300

350

0 2 4 6 8 10 12 14 16

15

Predicted vs. actual of bed occupancy for

the 2nd week of June 05

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

Unit 12-Jun-05 13-Jun-05 14-Jun-05 15-Jun-05 16-Jun-05 17-Jun-05 18-Jun-05

ICU's

C5M 25 (28) 26 (29) 27 (29) 28 (28) 28 (28) 28 (29) 27 (31)

C6N 10 (11) 10 (10) 11 (10) 11 (9) 11 (10) 11 (10) 11 (9)

Cardiology

H5 25 (26) 27 (30) 29 (30) 29 (27) 29 (23) 30 (26) 27 (23)

I5 25 (30) 27 (30) 28 (30) 29 (28) 29 (24) 29 (23) 27 (22)

Medical GPU's

B1 26 (30) 27 (30) 29 (28) 29 (27) 30 (28) 30 (28) 28 (29)

B2 25 (29) 27 (27) 28 (28) 28 (26) 29 (26) 29 (27) 27 (23)

B6 20 (19) 21 (21) 22 (22) 23 (20) 23 (19) 23 (18) 22 (16)

F6 11 (11) 12 (13) 12 (16) 13 (15) 13 (13) 13 (11) 12 (10)

Surgical GPU's

B4 21 (16) 22 (19) 24 (24) 24 (28) 24 (28) 25 (21) 23 (14)

F4 24 (28) 25 (27) 26 (26) 27 (28) 27 (27) 27 (25) 26 (23)

Total predicted 511 542 571 579 587 592 553

Total census 551 586 603 596 589 575 547

16

Patient History

DATE Type SITE DR SPEC CD

03/19/2001 O 20 GAS

03/30/2001 O 20 GAS

04/30/2001 O 20 GAS

06/18/2001 O 20 GAS

06/20/2001 O 20 GAS

06/26/2001 O 20 GAS

07/19/2001 O 20 GAS

08/21/2001 O 20 GAS

09/27/2001 O 20 GAS

11/27/2001 O 20 GAS

01/10/2002 O 20 GAS

01/15/2002 O 20 GAS

05/23/2002 O 20 GAS

08/19/2002 O 20 GAS

09/25/2002 I 20 SUR

MRN=5213569

Visits:

O.GAS.20

Leads to:

I.SUR.20

17

Examples of the rules

• There are 406 rules in which ‘E’ or ‘O’ are followed by ‘I’

COUNT SUPPORT CONF RULE

1755 2.017798013 71.98523 O.NES.20 ==> I.NES.20

1131 1.30035872 32.48133 O.ONC.20 ==> I.ONC.20

930 1.069260486 21.57272 O.NEP.20 ==> I.IMG.20

1014 1.165838852 17.61946 O.GAS.20 ==> I.IMG.20

2478 2.84906181 13.18576 O.CAR.20 ==> I.IMG.20

475 0.546127667 10.86957 O.CAR.36 ==> I.CAR.20

642 0.738134658 9.610778 O.URO.20 ==> I.CAR.20

493 0.56682303 8.56646 O.GAS.20 ==> I.SUR.20

504 0.579470199 4.474432 O.IMG.30 ==> I.GYN.20

448 0.515084621 13.37713 O.IMG.81 ==> I.IMG.20

18

Distribution of Time Lags for one rule

19

Proj of DSC(i) by week

Proj of DSC(i) by week

Projection with Rules

Various DR_SPEC_CD

Proj of DSC(i) by week 1-52

Wk 52

Wk -50Wk -51

Wk 0

Rules

with Weibull

distribution

Rules

Left-Hand-Side Wk 2Wk 1

MiHIN Role

• Manage statewide legal trust fabric for data sharing

• Maintain statewide “master data” in Active Care

Relationship Service, Health Provider Directory, Trusted

Identities, Consumer Preferences

• Connect HIEs, Payers, Pharmacies, DCH, Federal

Government, others

• Align incentives or regulations to fairly share data and

promote data standardization (via Use Cases)

• Convene groups to identify data sharing barriers, reduce

provider burdens, engage consumers, & enable

population health

Copyright 2015 Michigan Health Information Network Shared

Service20

State-wide

Shared Services

MDCH Data Hub

Medicaid

MSSS

State

LABS

Doctors & Community

Providers

HIE QOs(Qualified sub-state HIEs)

Network of Networks:

Data

Warehouse

21

Health Plan QOs

(more coming)

Single point of

entry/exit for state

Virtual QOs

Pharmacies

(more coming)

Immunizations

Mi Syndromic

Surveillance System

Mi Disease

Surveillance System

Consumer QOs

(more coming)

Federal

Copyright 2015 Michigan Health Information Network Shared Service

PIHPs

Transitional care management

Medicare & BCBSM fees Jan 2013

Payer Code Non-Facility Facility Locality

Medicare 99495 $120.39 $99.38 Detroit

Medicare 99496 $169.65 $145.70 Detroit

BCBSM 99496 $329.33 $281.38 All

22

99495 -

• Communication (direct contact,

telephone, electronic) with the patient

and/or caregiver within 2 business days

of discharge

• Medical decision making of at least

moderate complexity during the service

period

• Face-to-face visit, within 14 calendar

days of discharge

99496 -

• Communication (direct contact, telephone,

electronic) with the patient and/or

caregiver within 2 business days of

discharge

• Medical decision making of high

complexity during the service period

• Face-to-face visit, within 7 calendar days

of discharge

Copyright 2015 Michigan Health Information Network Shared Services

Statewide Health Provider

Directory

23

• Contains Electronic Service Information (ESI) used to route information to providers

• Flexibly maintains multiple distribution points for single provider or single distribution for organization

• Manages organizations, providers and the multiple relationships between them


Patient Provider Attribution Service

(in Michigan we call this ACRS™)

• Enables providers to declare active care relationships with patients –this attributes to a patient the active members of their care team

• Accurately routes information (e.g. Admit-Discharge-Transfer messages, medication reconciliations)

• Improves care coordination

• Reduces readmissions

• Allows better outcomes

• Enables alerts to providers in active care relationships with patients

• Coordinates entire care team with changes to patient status in real time

• Allows searches by authorized persons or organizations:

• Health systems and provider/physician organizations

• Care coordinators

• Health plans

• Consumers (who can dispute asserted relationships)

24Copyright 2015 Michigan Health Information Network Shared Services

Active Care Relationship Service

(ACRS™)

25

Patient InformationSource Patient ID

First Name

Middle Initial

Last Name

Suffix

Date of Birth

Gender

SSN – Last 4 digits

Address 1 & Address 2

City, State, Zip

Home & Mobile Phones

Physician InformationNPI

First Name

Last Name

Practice Unit ID

Practice Unit Name

Physician Organization ID

Physician Org Name

Physician DIRECT Address

DIRECT Preferences


Data Sharing

Organization

(DSO)

Data Sharing

Organization

(DSO)

Supports Seamless Exchange

Alerting Disconnected Entities

Patient to Provider

Attribution

Health

Provider

Directory

1) Patient goes to hospital which sends message to DSO then to MiHIN

2) MiHIN checks patient-provider attribution and identifies providers

3) MiHIN retrieves contact and delivery preference for each provider from HPD

4) Notifications routed to providers based on electronic address and preferences

Primary Care

Specialist

Care

Coordinator

26

Patient


Adding Intelligence to ADTs

Copyright 2015 - Michigan Health Information Network Shared Services 27

Scoring activity

Patient Risk Score/s

Hospital

Patient Risk Score/s

Adding Intelligence to ACRS


Next Generation Care Coordination

Predictive Scores can help care coordinator s prioritize effort and eventually automate next steps

ALERT!

Readmission Avoided

Schedulefollow up

visit within7-14 days

Primary Care

Specialist

Care

Coordinator

Existing Statewide Capability (90% of All Admissions Available)

Intelligence Opportunity

Patient to Provider

Attribution (ACRS)

Health

Provider

Directory

Patient


Thank You & Contact Info

Tim Pletcher, DHA

Director

Institute for Health & Business

Insight

989.621.7221

[email protected]

http://www.youtube.com/watch?v=elHjESwQ8_o&feature=youtu.be

30

Tim Pletcher, DHA

Executive Director

Michigan Health Information Network

989.621.7221

[email protected]

mailto:[email protected]




mailto:[email protected]

the analytics opportunity in healthcare

Healthcare

private data

data mining

health care variables

knowledge data granularity

generation of data scientists

analytics opportunity

ihbi contextual dataset

closed system