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© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 1 March 14, 2018 Predictive Analytics- Leveraging Healthcare Through Data Taylor Pressler, d-Wise

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© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 1 March 14, 2018

Predictive Analytics- Leveraging Healthcare Through Data

Taylor Pressler, d-Wise

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 2

Solutions and Consultancy in Healthcare

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 3

Core Challenge Within Healthcare

Organisation for Economic Co-Operation and Development

• Increasing costs for services

• Aging population is

proportionally large

• Smaller proportion of younger

population contributing to

system finances.

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 4

Exponential Growth of Data in Healthcare

• Electronic medical records

• Digitization of results

• Pathology reports

• Laboratory results

• Radiological results

• New technologies create their own data

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 5

Diffusion of Analytics

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 6

Healthcare Analytics – What can you expect?

Ultimately, predictive models become the

foundation for clinical care pathways.

Better, more efficient, more efficacious care

can be delivered with the support of the

predictive models

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 7

Clinical Implementation & Integration

Load models into data system

Retrospective reporting & model tuning

Analytic dashboards and patient forecasts

Clinical coordination based on risk

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 8

Case Study - OhioHealth

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 9

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 10

Readmission Rates

20,0%

22,0%

24,0%

26,0%

28,0%

30,0%

32,0%

34,0%

36,0%

38,0%

40,0%

0,5

0,6

0,7

0,8

0,9

1

1,1

1,2

1,3

1,4

1,5

Readmission Rates After Predictive Model Implementation

Risk Ratio Readmissions

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 11

Solving the challenges

Operational patient list

Reviewed by clinicians

Preventative measures

implemented

Patients monitored

Patient data loaded

Implementation of Predictive

Models

Clinical Workflow

Review

Each Day

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 12

Daily Patient Discharge Lists-Prioritized

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 13

Understand Provider Performance

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 14

Continuous Monitoring Post-Implementation

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 15

Predictive Models: Production

Readmission Models

– All-Cause Readmission

– Condition Specific Readmissions (Heart Failure, Stroke, Pneumonia,

Myocardial Infarction, etc).

Safety of Care Models

– Central Line Infections

– MRSA Infections

– Sepsis

– Surgical Site Infections (Hysterectomy, Colon Surgery)

– Deep Vein Thrombosis

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 16

Predictive Models: Development

Mortality Models

– Condition Specific Mortality (Heart Failure, Stroke, Pneumonia,

Myocardial Infarction, CABG).

Safety of Care Models

– Pulmonary Embolism

– Stage 4 Pressure Ulcer

– C-Diff Infection

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 17

Drive Your Measures….Don’t Just Report Them

© d-Wise Technologies, Inc. 2016 March 14, 2018 Page 18

Contact Details

[email protected]

+1 (413)230-6900

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