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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
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