Configuring Electronic Health Records

1. Configuring Electronic Health Records Implementing Clinical Decision Support This material (Comp 11 Unit 3) was developed by Oregon Health & Science University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 90WT0001. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/.

2. Implementing ClinicalDecision SupportLearning Objectives - 1 • Define and discuss clinical decision support (Lecture) • Describe, view and create alerts/notifications in a VistA simulation EHR environment (Lecture, Lab exercise 1) • Describe, view and create Order Checks in a VistA simulation EHR environment (Lecture, Lab exercise 2)

3. Implementing Clinical Decision SupportLearning Objectives - 2 • Describe, view and resolve Reminders in a VistA simulation EHR environment (Lecture, Lab exercise 3) • Discuss the value of these EHR functions as clinical decision support tools (Lecture)

4. Clinical Decision Support • AMIA Roadmap • “Clinical decision support (CDS) provides clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care” • Some Overviews • Greenes, 2007 • Sittig, 2008 • Osheroff, 2009 • Berner, 2009 • Liang, 2011

5. Why Is CDS Needed? - 1 • McGlynn, 2003 • Nearly 7,000 adults in 12 U.S. metro areas assessed for 30 conditions • Average - 54.9% was consistent with known quality • NCQA, 2009 – annual report shows “gaps” • 49,400-115,300 avoidable deaths • $12 billion in avoidable medical costs • Quality of care for patients with chronic disease no better in U.S. than for other developed countries

6. Why Is CDS Needed? - 2 • IOM “Errors” report: 98,000 Americans die each year due to medical errors • Some say numbers are too high or too low • None argue with the concept • Lost in discussion: • Who is to blame? • Result of faulty system, not people • Build better systems • “Medicine used to be simple, ineffective, and relatively safe. Now it is complex, effective, and potentially dangerous.”

7. CDS: Historical Perspectives • Early focus on application of artificial intelligence and expert • 1970s/1980s - Diagnostic decision support was a major focus of the field • Computer-aided diagnosis proved • Laid the intellectual groundwork for modern techniques • Shifted focus to therapeutic areas • Availability of modern EHR – older approaches may be useful

8. Definitions • Artificial Intelligence (AI) – computer programs that exhibit characteristics associated with human intelligence • Expert System (ES) – program that mimics human expertise • Decision Support System (DSS) – mimics human expertise but acts in more of a supportive than independent role • Diagnostic – focused on aiding in diagnosis • Therapeutic – focused on aiding in treatment

9. Toward The Modern Era • Late 1980s • Diagnostic process too complex for computer programs • Systems took long time to use and did not provide needed information • Recent Developments • Diagnostic decision support systems less effective than therapeutic systems • Acknowledgement of AI and ES failure to live up to hype • Diagnostic errors still does continue

10. Where Is CDS Headed Now? • 1990s - Decision support evolved • Rules and algorithms most useful • Evolution from broad-based diagnostic decision support to narrower therapeutic decision support • AMIA “roadmap” for future provided three “key pillars” • Best knowledge available when needed • High adoption and effective use • Continuous improvement of knowledge and methods

11. Modern Approaches to Clinical Decision Support • Take advantage of the context of the electronic health record (EHR) • Reminders • Remind clinicians to perform various actions • Alerts • Alert clinicians to critical situations

12. Computer-Based Reminders • Clem McDonald • 1976 – “non-perfectibility of man” • Showed reduction in error of delivering care • Octo Barnett • Reminders for strep throat led to increase of treatment • Can progress to Acute Rheumatic Fever • 1984 Study • Paper printout for preventive care increased utilization • When removed, behavior returned to baseline • Effects not educational

13. Alerts • Usually used to detect and report adverse events • Often used in context of CPOE • Successfully used in many clinical situations • Nosocomial infections • Adverse drug events • Injurious falls • Emergent diseases, e.g., bioterrorism

14. Issues Concerning Alerts • How to deliver to clinician? • Volume control, aka “alert fatigue” • Medico-legal issues • How to detect? • How to standardize alerts across different systems

15. CPOE • CPOE.org: “a computer system that allows direct entry of medical orders by the person with the licensure and privileges to do so” • CDS is usually viewed as an essential component of CPOE to obtain its full potential • E-Prescribing is a subset of full CPOE, with order entry limited to prescribing

16. CPOE Exemplifies Everything Discussed About Informatics • About information, not technology • Used where CDS can have the most impact • Rosenthal - “The single most expensive piece of hospital equipment is the doctor’s pen.” • Issues essential in implementation relate to organizational structure, attention to workflow, provider autonomy, etc.

17. Order Sets • Streamline order entry by reducing steps for their input • Consist of instructions based on a diagnosis, treatment, or medical specialty • Ability to provide guideline-based or evidence-based care • Must be modifiable for local practices • Best managed at departmental and not institutional or individual level

18. CPOE Screen from VistA (Payne, 2003)

19. Grand Challenges for CDS - 1 • Improve the effectiveness of CDS interventions • Improve the human-computer interface • Summarize patient-level information • Prioritize and filter recommendations to the user • Combine recommendations for patients with co-morbidities • Use free-text information to drive clinical decision support

20. Grand Challenges for CDS - 2 • Create new CDS interventions • Prioritize content development and implementation • Mine large clinical databases • Disseminate existing knowledge and interventions • Disseminate best practices in design, development, and implementation • Create architecture for sharing executable modules and services • Create Internet-accessible CDS repositories • Rules.gov

21. Alerts/Notifications • Timely feedback on clinical events • May be informational or require action • Some may be mandatory (always on) while others can be enabled/disabled for user customization • Dynamic (Real time)

22. Order Checking • Rules based • Real time • Provides recommendations that the clinician usually has the option of overruling – i.e. does not replace clinician expertise.

23. Clinical Reminders • Help deliver quality preventive care and management of chronic diseases • Prompts regarding recurring events and follow ups • Assist in decision making process • Provide relevant and timely information

24. Labs and Exercises • Hands-on look at alerts/notifications, order checks, and reminders. • Provided by working through 3 corresponding lab exercises. • To begin labs and exercises go to these files: • comp11_unit3_lab_exercise1 • comp11_unit3_lab_exercise2 • comp11_unit3_lab_exercise3

25. Implementing Clinical Decision Support – 1 – References References Amatayakul MK. Electronic health records: A practical guide for professionals and organizations. 4th ed. Chicago IL: AHIMA; 2009. Barnett G., Winickoff R, Dorsey J, Morgan M, Lurie R. (1978). Quality assurance through automated monitoring and concurrent feedback using a computer-based medical information system. Med Care. 1978:16:962-970. Bates D, Evans R, Murfe H, Stetson P, Pizziferri L, Hripcsak G. Detecting adverse events using information technology. J Am Med Inform Assoc. 2003:10:115-128. Berner E. (2009). Clinical decision support systems: State of the art [internet]. Rockville, MD: Agency for Healthcare Research and Quality; 2009 [cited 2017]. Available from: https://healthit.ahrq.gov/sites/default/files/docs/biblio/09-0069-EF_1.pdf Berwick D. Errors today and errors tomorrow. N Engl J Med. 2003:348:2570-2572. Bobb A, Payne T, Gross P. Viewpoint: controversies surrounding use of order sets for clinical decision support in computerized provider order entry. J Am Med Inform Assoc. 2007:14:41-47.

26. Implementing Clinical Decision Support – 2 – References References Cao H, Stetson P, Hripcsak G. Assessing explicit error reporting in the narrative electronic medical record using keyword searching. J Biomed Inform. 2003:36: 99-105. Carter JH. Electronic health records: A guide for clinicians and administrators. 2nd ed. Philadelphia: ACP Press: 2008. Chantler S. The role and education of doctors in the delivery of health care. Lancet. 1999:353:1178-118. Eichenwald Maki S, Petterson B. Using the electronic health record. Canada: Delmar Cengage Learning; 2008. Garg A, Adhikari N, McDonald H, Rosas-Arellano M, Devereaux P., Beyene J, et al. (2005). Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. J Am Med Assoc. 2005:293:1223-1238. Graber M. Diagnostic errors in medicine: what do doctors and umpires have in common? [internet]. AHRQ WebM&M; 2007 [cited 2011]. Available from: https://psnet.ahrq.gov/perspectives/perspective/36

27. Implementing Clinical Decision Support – 3 – References References Greenes R. editor. Clinical decision support - The road ahead. Amsterdam, Holland: Elsevier: 2007 Hebda T, Czar P. Handbook of informatics for nurses & healthcare professionals. 4th ed. New Jersey: Pearson: 2009. Kohn L, Corrigan J, Donaldson M. editors. To Err Is human: Building a safer health system. Washington, DC: National Academies Press; 2000. Lehman HP, Abbot PA, Roderer NK, Rothschild A, Mandell SF, Ferrer JA, et al, editors. Aspects of electronic health record systems. U.SA: Springer; 2006 Liang L. Connected for Health - Using electronic health records to transform care delivery. San Francisco, CA: Jossey-Bass; 2010. McGlynn E, Asch S, Adams J, Keesey J, Hicks J, DeCristofaro A, Kerr E. The quality of health care delivered to adults in the United States. N Engl J Med. 2003:348: 2635-2645. Melton G, Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries. J Am Med Inform Assoc. 2005:12:448-457.

28. Implementing Clinical Decision Support – 4 – References References Miller R, Masarie F. The demise of the "Greek Oracle" model for medical diagnostic systems. Meth Inform Med. 1990:29:1-2. Mullins J. (2005, April 23, 2005). Whatever happened to machines that think? New Scientist [internet]. 2005 Apr: 2496 [cited 2011]. Available from: http://www.newscientist.com/channel/info-tech/mg18624961.700. Garg A, Adhikari N, McDonald H, Rosas-Arellano M, Devereaux P, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. J Am Med Assoc. 2005:293:1223-1238. National Committee for Quality Assurance. The state of health care quality: 2010. Washington, DC. Osheroff J. editor. Improving medication use and outcomes with clinical decision support. Chicago, IL: Healthcare Information Management Systems Society; 2005. Osheroff J, Teich J, Middleton B, Steen E, Wright A, Detmer D. A roadmap for national action on clinical decision support. [internet] Bethesda, MD: American Medical Informatics Association; 2006. [cited 2017]. Available from: https://www.amia.org/sites/default/files/files_2/A-Roadmap-for-National-Action-on-Clinical-Decision-Support-June132006.pdf.

29. Implementing Clinical Decision Support – 5 – References References Osheroff J, Teich J, Middleton B, Steen, E, Wright A, Detmer D. (2007). A roadmap for national action on clinical decision support. J Am Med Inform Assoc. 2007:14:141-145. Payne T, Hoey P, Nichol P, Lovis C. (2003). Preparation and use of pre-constructed orders, order sets, and order menus in a computerized provider order entry system. J Am Med Inform Assoc. 2003:10:322-329. Schoen C, Osborn R, How S, Doty M, Peugh J. (2009). In chronic condition: experiences of patients with complex health care needs, in eight countries. 2008. Health Affairs [internet]. 2008 [cited 2009]; 28:w1-w16. Available from: http://content.healthaffairs.org/cgi/content/full/28/1/w1 Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, et al. Grand challenges in clinical decision support. J Biomed Inform. 2008; 41: 387–392.

30. Configuring Electronic Health RecordsImplementing Clinical Decision Support This material was developed by Oregon Health & Science University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 90WT0001.