HIMSS Analytics Data Analytics Examples From Stage 7 Hospitals OECD 20 May 2015 Paris, France

Agenda

• Who is HIMSS & HIMSS Analytics? • What is the EMRAM = EMR Adoption Model?

• Examples of Data Analytics from Stage 7 Hospitals

Who Is HIMSS?

• A not-for-profit advocacy organization • We advocate for the adoption and effective use of

information technology to improve the quality, safety and efficiency of health care

• We are a convener – we bring together buyers, sellers, government officials for education, advocacy and product exhibition

• Offices in: » Europe- London, Berlin, Leipzig » Asia - Singapore » US - Chicago, Washington, Ann Arbor, Burlington

Who Is HIMSS Analytics ?

• A subsidiary of HIMSS • We collect data on what information systems are deployed

in healthcare systems in the U.S. & Canada on a census basis » On a sample basis in Europe, Middle East, AsiaPac, Latin America

• From this data, we populate the EMR Adoption Models (EMRAM)

• EMRAM = the acute care maturity model that reflects increased sophistication in deployment and use of e-health

Why Do We Collect This Data?

Thought leadership

Inform government policy

Reflect the market Push the market

HIMSS Analytics

Data from HIMSS Analytics® Database © 2013 HIMSS Analytics

1.1%

4.0%

6.1%

12.3%

46.3%

13.7%

6.6%

10.0%

2011 Q2

2015 Q1

N = 5439 N = 5462

Complete EMR, Data Analytics to Improve Care

Physician documentation, CDSS, Closed loop medication administration

Full R-PACS CPOE, Clinical Decision Support (clinical protocols)

Clinical documentation, CDSS (error checking)

CDR, Controlled Medical Vocabulary, CDS, HIE capable Ancillaries - Lab, Rad, Pharmacy - All Installed

All Three Ancillaries Not Installed

+236%

+455%

+405%

-69%

-67%

-65%

3.7%

22.2%

30.8%

13.6%

19.7%

4.3%

2.2%

3.5%

The Acute Care EMRAM

Stage 7 Hospitals Must Excel at Data Analytics

• Stage 7 Hospitals have a complete EMR ….

• With all that data they must show three case studies with improvements in: » Quality » Safety » Efficiency

Advanced Skills Derive Value & Enable Differentiation

Predictive Analytics: Likelihood of Readmission

• 40 variables are tracked to generate predictive score • Alerts to physicians with advice on best practice –

updated hourly !

Their Model is at 80% Accuracy

Same Tool – Different Health System

23% Reduction In Readmissions

Use Predictive Alerting to Drive VTE Alerts

Find “Frequent Fliers” For CHF ;Reduced Readmissions in Target Population by 42%

• Rural north central health system attacked CHF readmissions » Weight gain due to medication insufficiency or behavior factors, is a

strong predictor of readmission

• Gave away blue-tooth enabled weight scales to targeted CHF patients

Analytics Found Weakness in Vaccine Compliance

Where Should Vaccines Be Sent in a Few Hour’s Notice?

From This To This

Do Not Underestimate Value of Data Visualization

From This To This

Thank You Very Much

• John P Hoyt • Executive Vice President, HIMSS Analytics

• jhoyt@himss.org