Financial and Clinical Benefits of IT Implementation. John P Hoyt EVP HIMSS Analytics

Transcription

1 Financial and Clinical Benefits of IT Implementation John P Hoyt EVP HIMSS Analytics

2 So, What Are Stage 7 Organizations? A Little background Required

3 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, the Middle East and AsiaPac From this data, we populate the EMR Adoption Models

4 Complete EMR, CCDA transactions; Data Analytics to Improve Care Physician documentation (structured templates), full CDSS, full R-PACS Closed Loop Medication Administration = Bar Code Enablement CPOE, Clinical Decision Support (clinical protocols) 2011 Q % 2.9% 4.0% 12.5% 6.1% 22.0% 12.3% 15.5% Clinical documentation (flow sheets), CDSS (error checking), CDR, Controlled Medical Vocabulary, CDS, HIE capable Ancillaries - Lab, Rad, Pharmacy - All Installed All Three Ancillaries Not Installed 46.3% 13.7% 6.6% 10.0% 30.3% 7.6% 3.3% 5.8% Data from HIMSS Analytics Database 2012 HIMSS Analytics N = 5439 N = 5458

5 Why Do We Do It? Thought leadership Quality, Safety, Efficiency improvements To inform government policy Numerous countries and regions use HIMSS Analytics to gather data for their policy formulation Clearly the EMRAM model was a contributor to the architecture of the Obama administration s Meaningful Use program To reflect the market Where is the market heading To drive the market

6 Stage 7 Studies on a Macro Scale Correlations With Stage 7 Status

7 All hospitals within each EMRAM Stage Representation of TJC Top Performing Hospitals BY Number of Quality Metrics Excelling In, within each EMRAM Stage 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% 2.3% 0.4% 1.9% 6.5% 1.7% 4.8% 16.3% 18.1% 6.2% 10.0% 10.1% 8.1% 10.6% 6.4% 12.9% 6.4% 20.7% 12.8% 39.8% 30.1% 4.2% 6.5% 7.9% 9.7% EMRAM Stage 3 or less 4 or more Source: HIMSS Analytics

8 Representation of Hospitals with an "A" Leapfrog Hospital Safety Grade by EMRAM Stage All hospitals within each EMRAM Stage 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 62.6% 30.8% 20.1% 21.8% 12.8% 14.3% 5.9% 0.0% Stage 0 Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7

9 AVG Clinical Score Value-based purchasing (vbp) Tipping Point Clinical Scores Tipping Point Stage 0 Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7 EMR Adoption Model Stage

10 What About Cost Efficiency? Some Ground Breaking Research on the Effect of EMR Deployment

11 Used by permission Avi Goldfarb The Trillion Dollar Conundrum: Complementarities and Health Information Technology (NBER Working Paper No )

12 Used by permission Avi Goldfarb

13 Used by permission Avi Goldfarb

14 Are You Still Questioning Bar Code Enablement? Some Are. But Why??

15 4. Knowledge is shared and information flows freely 6. Safety is a system property.

16 A Literature Review for Medications, Blood Products and Human Milk (EBM)

17 Medication Safety Compare nursing units with & without bar coded medications Timing errors without & with bar codes 1 6,723 without bar codes : 11.5% timing errors 3.1% were judged serious ADE 7,318 with bar codes: 6.8% timing errors (-40.9%) 1.6% were judged serious ADE ( -50.8%) Wrong Medications 57.4% Wrong dose 41.9% Improper documentation 80.3% Note: More errors were prevented on surgical and ICU units than medical units Transcription errors: from 6.1 errors/100 orders to 0 errors/100 orders 2.9/100 orders were P-ADE 1 N Engl J Med 2005; 353: July 28, 2005

18 Medication Safety Sentara Health System Stage 7 & Davies Award 96% compliance on scanning 12,459 Medication errors avoided per month SSM Health System Stage 7 4 Serious ADEs per 1,000 errors prevented ~~4,000 per month across their health system Journal of Health Care Quality sites 59% reduction in medication errors 2 BCMA led to decreased time on medication administration and increased time on direct patient care in ICU 3 Medication errors reduced 58%, but timing errors did not change significantly 4 2 JHCQ, Vol 26, #6, pgs Am J Health Syst Pharm Jun 1;68(11): American Journal of Health-System Pharmacy July 1, 2009 vol. 66 no

19 Medication Safety From 1,465 medications administrations observed, errors reduced 56% - mostly timing errors 5 Cardiac surgery Increased the quantity of drugs administered 21.7% Increased drug charges by 18.8% Decreased documentation time by 8 minutes per case 6 Pediatric dosing & medication administration Reduced ADE by 47% 7 5American Journal of Health-System Pharmacy Vol 65, pgs American Journal of Health-System Pharmacy Vol 66, pgs The Journal of Pediatrics Volume 154, Issue 3, Pages e1

20 Blood Products Administration Mis-transfusion errors accounted for nearly 40% of the ABOincompatible transfusions reported by Linden et al (2000) who estimated that 1 in transfusions involved ABO errors 8 A Hong Kong health system reported 100% reduction in administration errors of 27,000 units administered 9 100% accuracy of patient identification obtained for blood samples and blood products administration with bar code enablement 10 8 British Journal of Haematology Volume 136, Issue 2, pages , January Hong Kong Med J Vol 10 No 3 June 2004, pgs TRANSFUSION 2003;43:

21 NICU & Breast Milk Errors Northern Westchester Neonatal Intensive Care Unit was error free after implementing bar code identification of EBM, increased staff satisfaction 11 A Level III NICU reduced feeding errors substantially with bar code labeling of EBM aliquots 12 Main Line Health System reduced to 1 error in 200,000 feedings over 79 months, May 2003 to December Delaware Valley Patient Safety Award Sunnybrook Health Sciences Centre in Toronto had 28,000 units of milk to the correct 175 infants by 159 different personnel with 0% errors Journal of Obstetric, Gynecologic, & Neonatal Nursing Special Issue: 2012 Convention Proceedings Volume 41, Issue s1, page S61, June Neonatal Network VOL. 28, NO. 5, september/oc tober file_main_line_health_dvpsa_2010_08 (1).pdf 14

22 So, What is Expected at Stage 7? Very Good Analytics Use Analytics to Find Care Issues to Address Use Analytics to Prove that IT Tools are Enabling Improvements

23 62% Reduction in Hospital Acquired Infections

24 Improvements in Immunizations: 77% to 97%

25 Obtaining HA1C Testing in Office

26 Mammogram Protocol Adherence From 55% to 71% in a Year

27 Find Patients Who Could Benefit From BRCA1 and BRCA2 DNA Testing Major University devised rules to search for child bearing age patients who have relatives with estrogen driven CA s Rule fired 1,355 times in October Generated 22 referrals Rule fired 1,478 time in November Generated ~140 referrals in November This tells us several things: A profound finding Hard to sell DNA testing

28 Predictive Alerting for Potential Readmissions 40 key variable are tracked to generate predictive score Alerts to physicians with advice on best practice updated hourly!

29 Their Model is at 80% Accuracy

30 Use Predictive Alerting to Drive VTE Alerts

31 Leading Organizations Use Externally Derived Data for Benchmarking

32 Using I.T. Tools to Improve Patient Engagement

33 Target Your Problems and Cohorts Rural north central health system attacked CHF readmission rate 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 Reduced readmissions by 42% over 12 months

34 Target Your Problems and Cohorts Asthma protocol adherence was an issue with children & teenagers Use PHR to engage patients Improved asthma protocol adherence from 6% to 78% in 18 months Teenage obesity & Fit Bits.. Create competitive cohorts with PHR

35 Find That Which is NOT Intuitive Probably all hospitals track surgeons use of O.R. resources People, Time, Equipment, Expensive implements We all search for the most efficient surgeons Except. Do we track their patients over time? A stage 7 site tracked less expensive surgeons and found Higher infection rate Higher surgical revision rate They are not so cheap after all.

36 Do Not Underestimate Value of Data Visualization From This To This

37 The Mature Organizations Have Moved to:

38 Thank You! John P Hoyt Executive Vice President HIMSS Analytics jhoyt@himss.org