49th Annual Meeting Disclosures Optimizing Alerts within Electronic Health Records Arti N. Bhavsar, Pharm.D., August 8 th, 2015, FSHP Annual Meeting OWNING CHANGE: Taking Charge of Your Profession Disclosure statement: Arti Bhavsar, Pharm.D. has the following to disclose concerning possible financial or personal relationships with commercial entities (or their competitors) that may be referenced in this presentation: Arti Bhavsar, Pharm.D. is an employee of Truven Health Analytics Objectives Pharmacist & Technician Review current regulatory standards of Healthcare IT for drug allergies, interactions, and clinical decision support Review current trends and methods utilized to diminish alert fatigue for drug allergies, interactions, and clinical decision support Describe methods for reviewing drug interaction alert content, presentation, and effectiveness Discuss progress in developing and maintaining a national standardized set of drug interactions for clinical decision support Information is the lifeblood of medicine, and improving the availability and uses of health information is foundational for enhancing the modern health care system s efficiency and effectiveness. Executive Summary - Opening Statement REPORT TO CONGRESS, OCTOBER 2014 UPDATE ON THE ADOPTION OF HEALTH INFORMATION TECHNOLOGY AND RELATED EFFORTS TO FACILITATE THE ELECTRONIC USE AND EXCHANGE OF HEALTH INFORMATION http://www.healthit.gov/sites/default/files/rtc_adoption_and_exchange9302014.pdf ONC Data Brief No. 23 April 2015 Adoption of Electronic Health Record Systems among U.S. Nonfederal Acute Care Hospitals: 2008-2014; https://healthit.gov/sites/default/files/data-brief/2014hospitaladoptiondatabrief.pdf ONC Data Brief No. 23 April 2015 Adoption of Electronic Health Record Systems among U.S. Nonfederal Acute Care Hospitals: 2008-2014; https://healthit.gov/sites/default/files/data-brief/2014hospitaladoptiondatabrief.pdf 1
Meaningful Use Requirements Stage 1 (2014 Definition) Measure 2 of 13 Implement drug-drug & drug-allergy Interaction checks Stage 2 (October 2012) Measure 6 of 17 Use clinical decision support to improve performance on high-priority health conditions + Implement drug-drug & drug-allergy Interaction checks ONC Data Brief No. 23 April 2015 Adoption of Electronic Health Record Systems among U.S. Nonfederal Acute Care Hospitals: 2008-2014; https://healthit.gov/sites/default/files/data-brief/2014hospitaladoptiondatabrief.pdf Centers for Medicare and Medicaid Services. Eligible Professional Meaningful Use Core Measures: Measure 2 of 13, 2014 and Core Measures 6 of 17, 2012. US Department of Health and Human Services. [cited 06/21/2015]; Available from: http://www.cms.gov/regulations-and-guidance/legislation/ehrincentiveprograms/downloads/2_drug_interaction_checksep.pdf & https://www.cms.gov/regulations-and-guidance/legislation/ehrincentiveprograms/downloads/stage2_epcore_6_clinicaldecisionsupport.pdf Clinical Decision Support (CDS) Use clinical decision support to improve performance on highpriority health conditions Measure 1: Implement five clinical decision support interventions related to four or more clinical quality measures at a relevant point in patient care for the entire EHR reporting period. Measure 2: Implement drug-drug and drug-allergy interaction checks Can be accomplished several different ways Centers for Medicare and Medicaid Services. Eligible Professional Meaningful Use Core Measures 6 of 17, 2012. US Department of Health and Human Services. [cited 06/21/2015]; Available from: https://www.cms.gov/regulations-and- Guidance/Legislation/EHRIncentivePrograms/downloads/Stage2_EPCore_6_ClinicalDecisionSupport.pdf CDS 5 Rights 1. The right information 2. To the right people Alerts & Reminders Guidelines & Order Sets Types of CDS Patient Data & Reports Documentation Templates 3. Through the right channels Context Specific Reference Info Diagnostic Support 4. In the right intervention formats 5. At the right points in workflow http://www.cms.gov/regulations-and- Guidance/Legislation/EHRIncentivePrograms/Downloads/ClinicalDecisionSupport_Tipsheet-.pdf Various Platforms (Mobile, Desktop, Patient Portal, etc) 2
CPOE Related Errors FLAWED SYSTEM. Medication Computerized Provider Order Entry (CPOE) has been shown to decrease errors, however, CPOE also has potential to introduce and contribute to errors Evaluation of CPOE-related medication errors reported in MEDMARX between 2003 to 2010 1.04 million reported errors 63,040 were CPOE related (6.1%) Pharmacist s reviewed and coded 10,060 (15.7% sample) Coded for What Occurred, Why Errors Occurred, and Prevention Strategies Established 21 scenarios to test with a total of 375 erroneous orders during 24 testing sessions on 13 systems at 16 sites Schiff GD, Amato MG, Eguale T, et al. BMJ Qual Saf 2015; 0:1-8. doi:10.1136/bmjqs-2014-003555 Schiff GD, Amato MG, Eguale T, et al. BMJ Qual Saf 2015; 0:1-8. doi:10.1136/bmjqs-2014-003555 Schiff GD, Amato MG, Eguale T, et al. BMJ Qual Saf 2015; 0:1-8. doi:10.1136/bmjqs-2014-003555 More Flaws. Knowledge Base Vendors create, maintain, and sell drug-drug interaction (DDI) content without standardization within the industry Electronic Health Record (EHR) systems accept data from Knowledge Base Vendors and implement their own approach to classifying DDIs without standardization Alert fatigue leads to high override rates ranging in literature from 49% to 96% with a rate of ~ 90% for DDI alerts specifically Organizations without a formal DDI review process may be suffering from alert fatigue leading to failure in efficacy of the CDS system Schiff GD, Amato MG, Eguale T, et al. BMJ Qual Saf 2015; 0:1-8. doi:10.1136/bmjqs-2014-003555 THIS SEEMS AWFULLY COMPLICATED. HOW ARE WE GOING TO FIX THIS?? 3
Drug-Drug Interaction Clinical Decision Support Conference Series Who is Involved Grant Support by Agency for Healthcare Research and Quality (AHRQ) Goal: Develop an ongoing structured process to improve the quality of drug-drug interaction alerting systems used by health care providers, and thereby improve patient safety Project Time Line: 09/30/2012 09/29/2015 (three years) Access Full Content at: https://sites.google.com/site/ddiconferenceseriessite/ AHRQ Academia HITECH - ONC ASHP EHR Vendors: Cerner-Multum Knowledge Base/ Formulary Service Vendors: Elsevier Clinical Solutions Epocrates, athenahealth, Inc. FDB (First Databank, Inc.) Truven Health Analytics Wolters Kluwer Three Workgroups Evidence Work Group White Paper Evidence Workgroup Develop an ongoing process for DDI evidence integration into clinical decision support (CDS) White paper: Develop guidelines for systematic appraisal of DDI evidence Content Workgroup Recommend principles for including DDIs in drug safety alerts White Paper: Recommend standards for DDI classification for CDS CONSENSUS RECOMMENDATIONS FOR SYSTEMATIC EVALUATION OF DRUG-DRUG INTERACTION EVIDENCE FOR CLINICAL DECISION SUPPORT Scheife RT, Hines LE, Boyce RD, et. al Drug Saf (2015) 38:197-206 DOI 10.1007/s40264-014-0262-8 Usability Workgroup Establish basic standards for communicating DDI information within CDS White paper: Establish preferred strategies for presenting DDI alerts Recommendation #1: Apply Consistent Terminology Consistent use of relevant terminology for evaluation of DDI evidence Glossary as established by the workgroup is accessible at: https://docs.google.com/viewer?a=v&pid=sites&srcid=zgvmyxvsdgrvbw FpbnxkZGljb25mZXJlbmNlc2VyaWVzc2l0ZXxneDo3ZTRiYWY1NzdmYWMz NDYz Definitions include (not inclusive): Drug Drug Interaction Potential DDI Clinically Relevant DDI Narrow Therapeutic Index (NTI) 4
Recommendation #2: Apply Drug Interactions Probability Scale (DIPS) for evaluating DDI Case Reports DDI data is often derived from case reports, retrospective reviews, and extrapolation from in vitro studies, with few controlled clinical studies in relevant populations DIPS is a 10 item scale designed to assess an adverse event for causality by a DDI Recommendation #3: Develop a New DDI Evidence Evaluation Instrument DRug Interaction evidence Evaluation (DRIVE) Instrument i. Use simple evidence categories ii. Include causality assessment via DIPS iii. Apply reasonable extrapolation, including from in vitro studies iv. Address evidence/statements provided in product labeling v. Describe study quality criteria and interpretation in the context of DDIs Needs to be formally evaluated and validated Recommendation #3, continued: Develop a New DDI Evidence Evaluation Instrument DRug Interaction evidence Evaluation (DRIVE) Instrument Serve as the industry standard for adoption by: Drug compendia s Knowledge base editors Healthcare professionals Researchers Journal editors Recommendation #4: Evaluate statements/evidence in FDA documents and product labeling by same criteria as published evidence DDI recommendations provided in labeling that are not supported by pharmacokinetic or pharmacodynamic properties of the drugs are insufficient evidence DDI listings/recommendations in CDS systems do not need to align with unsupported statements in product labeling Content Work Group Draft White Paper Recommendation #5: Classify DDIs by therapeutic/pharmacologic class only when the evidence applies, or can be reasonably extrapolated, to the entire class of drugs Class effects distinction commonly apply to pharmacodynamic DDIs, but rarely to pharmacokinetic DDIs RECOMMENDATIONS FOR SELECTING DRUG- DRUG INTERACTIONS FOR CLINICAL DECISION SUPPORT Paper pending publication Drug-Drug Interaction Clinical Decision Support Conference Series https://sites.google.com/site/ddiconferenceseriessite/ 5
Primary Recommendation Usability Work Group White Paper Establish an expert panel with a centralized organizer/convener to develop and maintain a standard set of DDIs for CDS in the US Barriers: Sustainability Funding Public, Private, Collaborative? Bias by Stakeholder/Participants conflicts of interest Support for DDI alerting research RECOMMENDATIONS TO IMPROVE THE USABILITY OF DRUG- DRUG INTERACTION CLINICAL DECISION SUPPORT ALERTS Payne TH, Hines LE, Chan RC, et al J Am Med Inform Assoc 2015;0:1-10. doi:10.1093/jamia/ocv011, Review Drug-Drug Interaction Clinical Decision Support Conference Series https://sites.google.com/site/ddiconferenceseriessite/ Goals of the Usability Work Group Question #1 What, How, Where, & When What Information to Include in DDI Alerts? 1. What, how, where, and when do we display DDI decision support? 2. Should presentation of DDI decision support vary by clinicians? 3. How should effectiveness of DDI decision support be measured? Safety Literature Signal word indicating seriousness Hazard information denoting the DDI combination Instructions/Actions on how to reduce risk of injury Clinical consequences if hazard is not averted Details available in supplemental files at: http://jamia.oxfordjournals.org/conten t/suppl/2015/03/31/ocv011.dc1 DDI Workgroup Consensus Drugs Involved Seriousness Clinical Consequences Mechanism of Action Contextual Information/ Modifying Factors Recommended Actions Evidence Question #1 What, How, Where, & When How to Present DDI Alerts Question #1 What, How, Where, & When Where & When to Display DDI Alerts? Workgroup recommends consistency across different EHR systems Consistent use of color and visual cues, think of road signage Consistent terminology & brevity. Minimal text with larger font Minimize impact on workflow Reserve interruptive alerts for serious DDIs DO NOT recommend eliminating alerts Divert interruptive alerts to noninterruptive with ondemand access to information (infobutton,etc.) Salience Hierarchy Serious events = Top-level screen alert, linked information accessible ondemand (infobutton) At the point of decision making Considered establishing a prototype, but recommend further research with formal testing 6
Question #2- Should Presentation of DDI Decision Support Vary by Clinicians? Question #2- Should Presentation of DDI Decision Support Vary by Clinicians? Consistent general alert for all Differences how the information is presented & enacted upon, examples: Nursing DDI Prompt for Patient Education Prescriber DDI Prompt for Change Order Pharmacist DDI Evidence to Inform Prescriber Consider customization options for specialty clinics, services, specialists Warfarin clinics **Note: No evidence to eliminate DDI alerts for specialists Question #3 -Effectiveness of DDI Decision Support, Measuring Success Question #3 -Effectiveness of DDI Decision Support, Measuring Success Current Standard: Override rates Crude estimate of alert adherence Cannot determine if the alert was disregarded, clinician considered risk vs. benefits, or clinician time constraints Override rates can serve as an initial data set, conduct a detailed evaluation with consideration of: Presence of modifying factors (lab values, co-morbidities) Actions taken as a result of the alert (monitoring ordered) Clinicians consensus Parsimonious Alerting = Reduced Alert Fatigue Establish a professional group/trusted agency to create a DDI repository, with a goal to: Standardize, collect, & analyze de-identified DDI decision support/alert data Establish CDS feedback loops to Knowledge base vendors, EHR vendors, and healthcare organizations Let s address the Elephant in the Room Do the recommendations change current practice? Funding and support to address this complex safety issue? Public, Private Collaborative So where are we NOW? Vendor Inertia? Regulatory Standards? FDA vs. ONC/CMS Legal Concerns? 7
Tangible DDI Lists High-Priority Drug List Work effort was sponsored by Office of the National Coordinators to decrease the burden of alert fatigue In 2012* & 2013**, Phansalkar, et. al published two reports of consensus-based recommendations from expert panels on DDIs 2012 publication established a High-priority DDI list Goal: Minimum standard for use in EHRs 2013 publication established a Low-priority DDI list Goal: Establish a list of DDIs that can be made noninterruptive to reduce the alert burden in EHRs Of 31 DDIs considered to be high risk, the panel approved a final list of 15 interactions. Panelists agreed that this list represented drugs that are contraindicated for concurrent use, though it does not necessarily represent a complete list of all such interacting drug pairs. http://www.ncbi.nlm.nih.gov/pmc/articles/pmc3422823/table/tbl2/ *Phansalkar S, van der Sijs H, Tucker AD, et al. J Am Med Inform Assoc 2013;20:489 493 **Phansalkar S, Desai AA, Bell, D, et. al. J Am Med Inform Assoc 2012;19:735-743 Phansalkar S, van der Sijs H, Tucker AD, et al. J Am Med Inform Assoc 2013;20:489 493 Success Story Low-Priority Drug List Implemented a consensusbased process that resulted in the development of a list of DDI that can be safely generated as noninterruptive alerts, with the aim to decrease alert fatigue http://www.ncbi.nlm.nih.gov/pmc/ articles/pmc3628052/table/ami AJNL2012001089TB1/ Phansalkar S, Desai AA, Bell, D, et. al. J Am Med Inform Assoc 2012;19:735-743 Simpao AF, et. al from Children's Hospital of Philadelphia (CHOP) published their organizational approach to reduce DDI alerts Evaluated over 2 million medication alerts between Jan 2011 Jan 2014 Created a Visual Analytics Dashboard using EPIC Clarity and QlikView visual analytics software, dashboard goals: View medication alert volumes Override rates for each medication alert type DDI, allergy, maximum dose, and duplicate medication alerts Override rates by practitioner pharmacist vs. provider Medication pairs Simpao AF, et al. J Am Med Inform Assoc 2015;22:361 369. doi:10.1136/amiajnl-2013-002538 Success Story Success Story CDS committee created a rigorous and systematic workflow to identify and deactivate clinically irrelevant DDIs: Peer review Literature review Consensus agreement by both Pharmacy & Providers for DDI alerts with controversial or questionable clinical significance Simpao AF, et. al from Children's Hospital of Philadelphia (CHOP) published their organizational approach to reduce DDI alerts using a visual analytics dashboard CDS Committee recommended and approved two sets of removals with final approval by the Institutions Therapeutic Standards Committee The organizations Serious Safety Event rate prior to intervention was 0.18 events per 10,000 adjusted patient days 0.08 events per 10,000 adjusted patient days after the study period Simpao AF, et al. J Am Med Inform Assoc 2015;22:361 369. doi:10.1136/amiajnl-2013-002538 8
Proactively review the DDI workgroup recommendations and apply to EHR builds Review and utilize published DDI lists as a guide for alert management Establish organizational structure to routinely review alert issues Who: Clinical Informatics, Medication Safety What: Review alerts data, clinical evidence, and implement change process Evaluate tools available by Formulary Service Vendors (FDB, Medispan) Arti N. Bhavsar, Pharm.D. Clinical Solutions Executive & Consulting Manager Arti.Bhavsar@Truvenhealth.com 407-488-7567 9