Outpatient EMR-Based Clinical Decision Support: Challenges and Opportunities

Transcription

1 Outpatient EMR-Based Clinical Decision Support: Challenges and Opportunities Patrick J. O Connor MD MA MPH Senior Clinical Investigator HealthPartners Research Foundation 1

2 Disclosures Employed full time HealthPartners Research Foundation and HealthPartners Medical Group NIH Grants: HL102144, DK092924, CA128211, AG023410, HS017622, HL093345, DK079861, HL089451, HHSA No industry funding 2

3 Outpatient EMR-Based Clinical Decision Support Does it Work? Prompts and reminders increase preventive health services such as immunization and mammography screening Less effective for acute care Mixed results for chronic disease care Increase test ordering & screening Few studies show improved clinical outcomes (asthma, A1c, LDL, BP) 3

4 Outpatient EMR-Based Clinical Decision Support Does it Save Money? Most studies: no savings Some studies: increases costs but no benefits Lots of drugs Lots of tests Perhaps more visits But no better LDL, BP, or glucose control 4

5 Opportunities to Increase the Effectiveness of EMR-Based CDS 5

6 Op #1: We need fewer but more nuanced prompts and reminders. Too many prompts: Turn them off & ignore all. Too aggressive or beyond evidence: turn them off and ignore them. Patient has CHF so refer immediately to cardiology. 6

7 Op #2: We need personalized CDS algorithms that integrate multiple data sources and identify patientspecific, data-driven treatment options May save provider up to 4 minutes of looking for data for diabetes patients Run the data through clinical algorithms to turn it into useful clinical information and recommendations 7

8 Op #3 We need prioritized CDS recommendations that enable efficient visit planning. All evidence-based recommendations are not of equal benefit to Mrs. Jones today To prioritize you need a RISK ENGINE, such as Framingham or Reynolds Risk Engine (QALYs) Risk engine should be transparent, evidence-based (trials, not only epidemiology data), leave data in situ, and be low cost in exchange for maintenance A short list of the clinical actions with greatest benefit to Jones today, provides a very powerful visit planning tool 8

9 Op #4: We need CDS output designed to elicit patient preferences and facilitate shared decision making Most output designed for providers need it designed for within-visit use and for non-visit use by patients Need verbal and visual tools, need to tailor them to patients with various levels of health literacy and numeracy Does patient prefer action or inaction? Lifestyle or drug? Which drug? Many delivery vectors: EMR, patient portal, smart phone 9

10 Op #5: We need to design clinic workflows and organizational strategies that support consistent and frequent provider use of CDS If providers don t use it it is not going to work This is MAIN OBSTACLE in most failed studies Get more provider input on design and triggering and content. Limit to one or two clicks, max. Allow clinical judgment, multiple choices. Doc knows some things about the patient that the computer does not know. If it saves time, will use it. If major priority in their clinic, will use it. If earn more money, will use it. Do not force provider to use it will backfire. 10

11 Op #6: We need web-based CDS algorithms that can be accessed via multiple EMR systems Saves money Enables rapid and accurate updates (< 10 years) Articulate regional consensus on clinical goals Can tailor to various formularies Will be aided by HL7 11

12 Op #7: We need guidelines we can trust (IOM, March 2011) Current evidence-based guidelines differ in dramatic ways Writing groups rife with personal and institutional conflicts of interest Specialty organization GL are especially self-serving and egregious in many cases 70% of authors of 2011 ADA (diabetes) GL received soft money from pharma, often > $30,000/year Don t provide P4P on things that with no evidence, or risk of harm 12

13 Enough About Theory (EAT) Let s look at some CDS and learning tools that are EMR based and overcome many of these obstacles, or perhaps, take advantage of available opportunities We are just beginning, those of you in this room need to carry this work on and lead us to new frontiers. It will only happen if we share what we have learned, and share ideas in a cooperative way. Intellectual property can be a barrier; nobody is going to patent these algorithms, so might as well share 13

14 CV Wizard: Customized, Evidence-Based, Point-of-Care Clinical Decision Support to Reduce Heart Attacks and Stroke Patrick J. O Connor MD MPH Karen S. Margolis MD MPH William A. Rush Ph.D. JoAnn M. Sperl-Hillen MD HPMG and HPRF 14

15 DM Wizard 15

16 Prioritized Clinical Decision Support to Reduce Cardiovascular Risk NIH (NHLBI) funded 5-year study (HL102144) Primary goal to reduce cardiovascular risk in patients at moderate to high risk for heart attack or stroke Prioritizes based on potential reversible CV risk Physician and Patient interfaces Elicits Patient Preferences on back end Pilot testing now Group randomized trial starts July,

17 Components of Reversible CV Risk Total CV Risk is 10.5% (mean) Irreversible (age & sex) Risk is 8.06% Reversible Risk is 2.44% Among Reversible CV Risk: 40% due to smoking 22% due to uncontrolled Lipids 12% due to uncontrolled BP <10% due to aspirin (>75% of DM, CHD on ASA) < 6% due to glucose in diabetes patients 17

18 10-Year Reversible CV Risk in HPMG Adults age % Base Risk less Reversible Risk 28.82% 8.03% 3.33% 1.69% 1.65% Delta=0 Delta <5 Delta Delta Delta Delta

19 10-Year Reversible CV Risk in Minnesota Adults w/diabetes Base 10-Year Framingham CVD Risk less Non-Reversible Risk 37.94% 26.51% 15.56% 7.46% 4.60% 7.94% Delta=0 Delta <5 Delta Delta Delta Delta 20+

20 CV Wizard Web-based Clinical Decision Support program Rapid Update: formulary, new evidence, FDA Real Time: Uses Same-Day BP, BMI, ASA data Evidence-Based Clinical Algorithms (invisible) Data stays local and EpiCare archives all data Both Customized and Standardized

21 CV Wizard Deployment: Two Options Wizard can be opened at will via Visit Navigator for any adult patient (like DM Wizard) Wizard automatically deploys at the 10% of adult visits with highest reversible CV risk (threshold set by HPMG leadership) Identifies and prioritizes treatment options to reduce CV risk at each encounter using complex evidencebased algorithm 21

22 PT DATA EMR Web Service Deposit PPT HTML Form to chart CUI s Stored Smartform Display Results BPA TBD *Prioritize* Glucose Blood Pressure Lipids ASA BMI SM Patient Hx PPT

23 Step 1: Prioritizes 6 CV Risk Factors Based on Benefit to Patient Identifies which are already at goal Ranks those not at goal according to potential reduction in reversible CV risk for an individual patient at each clinical encounter Uses up-to-date clinical data (same day) Efficiently identifies and selects only patients with substantive reversible CV risk 23

24 Recommendations personalized based on ICSI guidelines A1c risk calculated using UKPDS equation 24

25 BP risk calculated using Framingham BMI equation Goal set to 140/90 based on JNC-7 & ICSI guidelines 25

26 Lipid algorithm uses Framingham equation to calculate reversible risk Recommendations based off most recent evidence-based data and ICSI guideline Accommodates for missing lipid values 26

27 Takes statin use into consideration Lipid algorithm continued 27

28 Differentiates goal for DM & CVD 28

29 Adjusts lipid advice based on current statin use 29

30 Drug contraindications integrated into algorithm 30

31 Algorithm incorporates recommendations for high triglycerides and familial hyperlipidemia 31

32 Uses Framingham BMI calculation to compute risk 32

33 Simple algorithm Recommendations integrate links to overall healthplan and medical group initiatives 33

34 Complicated algorithm based on US Preventative Services Task Force Recommendation Statement GI bleeding risk addressed through alerts 34

35 Step 2: Identifies Open Treatment Options for Each of 6 CV Risk Factors Uses clinical algorithms shown to perform well in DM Wizard Algorithms re-programmed in Java on Website Easy to change and update as needed Faster than DM Wizard Adds Smoking, BMI, and ASA use, and includes behavior change & lifestyle advice/referrals Follows ICSI and National Clinical Guidelines 35

36 CV Wizard Demo 36

37 CV Wizard: Provider Interface 37

38 CV Wizard: Patient Interface Consulted with Mayo, clinic leaders, patient council and other experts Consensus that less is more and better to use symbols than numbers 38

39 CV Wizard: Patient Interface 39

40 Further Information CV Wizard supports patient centered care, informed decision making and patient preference CV Wizard output is saved as part of the permanent medical record CV Wizard goal: improve quality of care and sustain CV risk reduction trends 40

41 Future Applications: Medical Group Goal is truly PATIENT-CENTERED CARE Too many Wizards worse than too many prompts DM Wizard, CV Wizard Hepatitis B Wizard, Dental Wizard Cancer Wizard, Prevention Wizard, Mega Wizard Most chronic disease and prevention priorities will be integrated in future until one Wizard rules them all Wizard(s) can be used in Care Management to coordinate customized (and standardized) care Use can be monitored at provider and clinic level, with targeted incentives (geography of quality) 41

42 Future Applications: Health Plan Could be used to provide standardized evidence-based care recommendations regionally via Web Service and/or HIE Could be provided via low-cost license as community service by HP or by ICSI Could be licensed to EMR vendors, CMS, or professional societies for dissemination Could develop a patient version aimed at patient education/activation 42

43 Challenges Risk Engine Issues Legal liability Privacy Concerns (Regional Use) Financial model to support dissemination & maintenance 43

44 Thank You For more information: 44