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(1)

doing the impossible with Diver

M

ANAGING

C

LINICAL

B

IG

D

ATA

4 TIPS TO DEPLOY ACTIONABLE CONTENT TO YOUR USERS

Aaron McMaster | Sr. Data Science Professional Munson Healthcare

(2)

ABOUT

M

UNSON

H

EALTHCARE

8 Hospitals located in

Northern Michigan

871 Licensed beds

7,000 employees

160 Employed

Physicians

Cerner EMR

McKesson Star

billing/rev cycle

(3)

A

BOUT

M

E

 Been with Munson Healthcare for 12

years

 Functions as a conduit between IT and

clinicians

 Drives the adaption of analytics and

support of process improvement efforts

 Responsible for guiding multidisciplinary

teams on the development of clinical intelligence applications to support evidence based decisions

 As a non-clinician having the ability to

increase the quality of care to our patients incredibly engaging and rewarding

(4)

Our goals & challenges

Tip 1. Structure for Success

Tip 2. Efficient Extraction

Tip 3. Big Data Processing

Tip 4. Speed to Users

What we learned

(5)

O

UR

CBI G

OAL

Unlock data within the EMR to facilitate getting the right information for decision making, to the right people, at the right time.

Workflow decision:

At the

bedside Not at the

bedside

DI

(6)

THE CHALLENGE

Lots of data (TB’s)

 Highly normalized  Complex schema

 Multiple & inconsistent levels of parent/child

relationships

 Inconsistent usage, integration to workflows  Data integrity challenges

Many ambiguous customer requests

Challenges in request prioritization

(7)

TIP 1: STRUCTURE FOR SUCCESS

(8)

F

OUNDATION FOR

R

APID

D

EVELOPMENT

 Focus on the

customers request

 Get the right people

involved, cannot be driven by IT priorities

 Balance between

getting value now, and making data available for the future

 Drive towards rapid

development lifecycles of CBI projects projects

(9)

W

AREHOUSING

T

HEORY

Hybrid Model:

• Focus on specific customer requirement

• Acquire most accessible data when accessing a

source system

• Cleanse/validate on a project-by-project basis • Focus on reusable content

Speed-to-Value Approach Traditional Approach

(10)

TIP 2: EFFICIENT EXTRACTION

(11)

G

ET THE

D

ATA

!

No. 1 priority: do not disrupt the production

system

Stability is the key to success

Create monitoring tools

(12)

O

RACLE

: M

ATERIALIZED

V

IEWS

 “An object that contains the result

of a query…”

 Looks and functions like a table

 Updated at some regular frequency  Low/negligible impact on the

source system

 Optimize by balancing the update

frequency (inverse relationship

between network traffic generated & quantity of data transferred)

(13)

O

RACLE

: M

ATERIALIZED

V

IEWS

EMR

Oracle Database: Materialized Views

DI Servers

(stores historic data)

(14)

TIP 3: BIG DATA PROCESSING

(15)

B

IG

D

ATA

R

ESOURCE

M

ANAGEMENT

 Process only the data that is

required to meet the customers needs

 Leverage DI tools/objects to

facilitate this

 As a developer, you need

access to tools to monitor system performance during testing:

 CPU utilization

 Memory utilization

(16)

E

XAMPLE

: B

ASIC

S

CRIPT

(17)

S

ELECTIVE

P

ROCESSING OF

D

ATA

1. Splitting the repository by date

2. Transform directory file list to include only specific files, create File List Input

3. Can be used to process different amounts of data depending on day-of-the-week

Transform

(18)

C

REATE

S

MALL

, E

FFICIENT

M

ODELS

1. Split adhoc into multiple files

2. Transform file list to create parameters for model building (i.e. filename, model number, etc…)

3. Pass parameters to loop & build models

Transform Data

File List Parameter File

(19)

TIP 4: SPEED TO USERS

(20)

T

WO

T

YPES OF

S

PEED

(21)

D

ESIGN FOR

S

PEED OF

U

SE

 Think process improvement – what is the best use of

data/technology to meet the customers requirements

 How does the solution fit into the customers most

efficient workflow?

 The solutions architecture depends on customer

needs

 Near real-time (NRT)  Hourly

 Daily

 Manage increase in granularity when creating

repositories and models

(22)

I

NCREASE

P

ROJECT

I

TERATION

S

PEED

Think process improvement not report

remediation when defining customer

requirements

Invest in developing regular customers

By making data accessible from the source

system even though its not validated

Perform data validation on a project by project

basis

(23)

G

OAL

: B

ALANCE

O

BJECTIVES

Maximize

Speed

Maximize

Automation

Minimize

Complexity

(24)

RESULTS

(25)

MHC C

LINICAL

D

ATA

W

AREHOUSE

Pharm Encounter Person

Tasks

Events

Order Catalog

Orders

Production

Rad MBO Doc

(26)

OUR RESULTS

 Data and reusable content is available for use in

many upcoming projects

 Ease in ongoing prioritization  Faster project iterations

 Clinician workflow changes from “static” reports to

(27)

LESSONS LEARNED

Communicate to customers that CBI projects are

not just report remediation

The timing of initiating customer engagement is

extremely important

Make developing regular customers must be a

priority

Focus on delivering the customers content, try

not to get too distracted

However; if a task will most likely add value at

(28)

WHERE WE GO FROM HERE

Develop and publish more content faster

leveraging existing data & programs

More clinical content encompassing the

continuity of care

 Regular linking of orders to results

 More near-real-time content to clinicians  More predictive content to clinicians

Preparation for integrating semi-structured data

(29)

T

HANK

Y

OU

& C

ONTACT

I

NFO

Aaron McMaster

Sr. Data Science Professional

Munson Healthcare

Phone: 231.935.7815

Email:

amcmaster@mhc.net

LinkedIn:

https://www.linkedin.com/in/amcmaster

References

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