Leveraging Existing (Electronic) Systems for Dissemination & Implementation Research
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1 Leveraging Existing (Electronic) Systems for Dissemination & Implementation Research Elsie M. Taveras, M.D., M.P.H Division Chief, General Academic Pediatrics; Director of Pediatric Population Health Management, MassGeneral Hospital for Children; Harvard Medical School Training Institute for Dissemination & Implementation Research July 24, 2014
2 Goals of this Presentation Describe the uses of electronic health records to support dissemination research; Provide examples of the use of electronic health records and decisions support tools to accelerate the adoption of CER evidence; Lessons learned: Pros and cons Risks and tradeoffs Stakeholder engagement Emerging uses of EHRs to integrate clinical and community resources
3 Research Partnership Harvard Vanguard Medical Associates Multi-site, multi-specialty group practice 14 locations across eastern MA Cares for members of all insurance plans Electronic medical record system (EPIC ) Actual medical record since 1969; includes height, weight, demographics, lab values, utilization data, appointments
4 Overview of Uses of EHRs for Research (1 of 3) Surveillance Obesity trends in defined populations Etiology Determinants of and potential targets for obesity
5 Prevalence (%) Among children 0-6 years in MA, the prevalence of obesity substantially decreased during Decrease was smaller among children insured by Medicaid than children insured by non-medicaid health plans 5 Year
6 Rapid Infant Growth Later Obesity
7 Overview of Uses of EHRs for Research (2 of 3) Interventions Identification of eligible participants, delivery of intervention (e.g. electronic decision support tools), and assessment of outcomes Dissemination Use of EHR to accelerate the adoption, dissemination, and spread of best practices to improve population health Health disparities Assessing differential effects of interventions
8 Overview of Uses of EHRs for Research (3 of 3) Resource mapping Linking patients and primary care to community and public health resources Electronic referrals to community resources for support of chronic disease conditions MA Department of Public Health CMS Innovation funding for e-referrals
9 Goal: To accelerate adoption of childhood obesity comparative effectiveness research evidence among clinicians and parents Characteristics STAR Study Design Cluster-RCT Age Group 6-12 year olds + obese Setting 14 pediatrics offices Sample size 550 Duration 1 year Ix Components Health system Δ Individual Δ Outcomes Health system Δ Individual Δ Follow up Point-of-care alerts; decision support tools; text messaging, telephone coaching Point-of-care HEDIS Change in BMI;behavior 95% follow up at 1 year
10 Participant Contact Schedule Recruitment 2 weeks Baseline Well Child Care Visit 2 weeks Clinician Endorsement Mailing HC 1 month Semi-weekly Weekly Text Text Messages Messages Self-monitoring w/ w/ immediate feedback Skills training HC 3 months Semi-weekly Text Messages Self-monitoring w/ immediate feedback Skills training HC 6 months Semi-weekly Weekly Text Text Messages Messages Self-monitoring w/ w/ immediate feedback Skills training HC 9 months Weekly Text Messages Skills training 1 Yr Follow up Well Child Care Visit
11 Use of EHRs for recruitment in Large Simple Trials HIPAA Waiver of Authorization for recruitment Access minimum necessary personal health information Identification of pre-eligible participants, e.g. all children 2-12 years with a body mass index 95 th %ile Ability to exclude specific ICD-9 codes (chronic medical conditions) Exclude patients on research Do Not Contact list Clinicians sent a list of their pre-eligible patients and asked to exclude any families they do not want us to contact; or Clinicians are prompted at the point-of-care to refer 11 pre-eligible patients to an intervention.
12 Use of EHR for recruitment Using weekly EHR data pulls of upcoming appointments, we can identify visits for patients eligible for an intervention Upcoming scheduled visit triggers research staff to begin recruitment protocol Letter describing study Opt-out toll free number 12
13 Use of EHR for intervention delivery in Large Simple Trials Electronic decision support can be an effective and highly scalable tool for improving uptake of comparative effectiveness research: Point-of-care alerts (Best Practice Alerts) Decision support tools (Smartsets) Electronic patient portals for communication with families/patients Tracking of quality of care 13
14 Electronic Point-of-Care Alert Pediatric Obesity Best Practice Alert The alert contains links to the Centers for Disease Control and Prevention growth charts, links to existing, evidence-based childhood obesity screening and management guidelines, and a link to a pre-populated, standardized note specific for obesity.
15 Obesity Structured Note SmartSet The alert leads to a structured note for obesity management based on Expert guidelines. Diagnoses Lab Orders Referrals Patient Instructions After Visit Summary
16 Clinician Reports: Tracking Quality of Care Reports to each practice on the percent of children who left their well child care visits with: HEDIS code for BMI HEDIS code for Nutrition and Physical Activity Counseling Referral or follow up appointment documented
17 Evaluation of a Point-of-Care Alert for Obesity Diagnosis Ayash, Kasper, Hacker, Chomitz, Marshall, Taveras; Obesity; 2012
18 Quality of Care Results: BMI Documentation using HEDIS codes * Significant improvement in use of BMI HEDIS codes among both intervention grps 18
19 Use of EHR for outcome assessment Tie intervention outcomes to scheduled, annual well child care visits HEDIS, point-of-care outcomes are available immediately following the visit ICD-9 diagnosis codes; V codes; Billing; Orders for referral or follow up; Laboratory results Clinical measures of body mass index from annual visits (>95% follow up rate) 19
20 Cons, Risks, and Tradeoffs 1. Privacy requires a HIPAA Waiver of Authorization to access minimal amount of information from records. 2. Consent - Written consent required to access more detailed EHR outcomes. 3. Programming code for decision support tools are specific to the EHR vendor making dissemination to other vendors difficult. 4. Alert fatigue competing demands of clinicians to respond to alerts 20
21 Cons, Risks, and Tradeoffs 5. Substantial effort required to develop tools, train and support clinicians 6. Accuracy of height and weight measurements In-service training to standardize measurements 7. Careful to not interfere with clinical work flow Point-of-care surveys are not feasible or well-liked 8. Difficulty using EHR to collect outcomes from nondiscrete fields, free text, e.g. smoking, breastfeeding, and other behaviors. Require natural language processing Need for patient surveys 21
22 Physicians Perspectives on Electronic Decision Support Alerts for Obesity Management In-depth interviews with 32 pediatric providers Examined barriers and facilitators to use of an existing electronic alert for obesity diagnosis and management Using a test patient in the electronic health record system, pediatricians gave real-time responses about facilitators and barriers to the use of alerts and SmartSets experience with obesity alerts and the Obesity SmartSet perceptions of effective methods for improving obese patient outcomes. Dryden, Taveras, Hacker; Clin Peds; 2012.
23 Clinician Recommendations Design of point-of-care decision alerts should incorporate tools to assist physicians in providing behavioral interventions Alert and Smart Set should be streamlined E.g. Alert should be very brief, not require clicks to other screens, and should include a specific clinical action to do during the visit. Automate tasks and integrate alerts and Smart Sets into physicians natural work flow E.g. if BMI triggers obesity alerts, should be automatically added to patient problem list; integrated obesity well-child Smart Set should open if vitals suggest child is obese; add hotlinks to educational materials Provide greater visibility, education around SmartSets Multiple methods should be implemented to introduce the staff to a SmartSet and its contents.
24 Overview of Uses of EHRs for Research (3 of 3) Resource mapping Linking patients and primary care to community and public health resources Electronic referrals to community resources for support of chronic disease conditions MA Department of Public Health CMS Innovation funding for e-referrals
25 Goal: To develop and test a system-level intervention that leverages clinical and community resources and addresses socio-contextual factors to improve obesity and family-centered outcomes. Connect 4 Health Individual RCT 2-12 year olds + overweight or obese 6 pediatrics offices that care for children from obesity hot spots year duration Electronic decision support tools; teleconferencing, GIS mapping of community resources Change in BMI; QoL, Behavior
26 Mapping Obesity Hot Spots and Identifying Positive Deviants >60,000, 4-18 years olds in all HVMA practices Residential addresses to identify obesity hot spots neighborhoods with highest prevalence of obesity Identify children who have decreased or maintained their BMI
27 Connect 4 Health Intervention built on a foundation of decision support tools in the electronic health record to improve screening and referral, Employ use of GIS tools to help provide targeted information on neighborhood resources available to support behavior change. Health coaches will link families to community-level resources provided by the Massachusetts Department of Public Health and use multiple support modalities including text messaging, teleconferencing, and social networking platforms. 27
28 Innovative uses of EHRs to support Large Simple Trials: Linking Clinic to Community Resources Community Rx creates electronic prescriptions for community services (HealtheRx), generated through an interface between a patient s EHR and a comprehensive community resource database. 28
29 Massachusetts e-referral Program: Example of bidirectional referral from clinic to community resource Outbound Transaction Clinical Setting Transmission from EMR Community Resource CHC Health care provider diagnoses Jane Smith with diabetes. Jane gives consent for referral to Tobacco Quitline and local CDSMP program. e-referrals from Provider to (1) Quitline & (2) Council on Aging Contact Information: Address, Phone Other Health Data: Current smoker and Type 2 Diabetes Tobacco Quitline & Local Council on Aging Jane is contacted by Quitline and starts counseling program to quit smoking. Jane is also contacted by Senior Center and begins 6 class CDSMP program. CHC Automatic updates of smoking and exercise program added to EMR. At next appointment, health care provider is able to see the update of Jane s progress in Jane s own electronic health record. Inbound Transaction Clinical Setting Transmission to EMR Community Resource Progress report from community resources to provider (Standardized HL7 Formatted Transaction) Jane Smith Smoking status at 6 months post referral, CDSMP sessions attended, and improvement in FV intake and exercise Tobacco Quitline & Local Council on Aging Quitline calls back 6-months post referral for update. Senior Center prepares final CDSMP report on Jane s progress. Updates transmitted to provider as requested.
30 Contact information:
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