Configuring Electronic Health Records Implementing Clinical Decision Support

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 IU 24 OC 000015

Implementing Clinical Decision Support Learning Objectives • Define and discuss clinical decision support (Lecture) • Describe, view and create Alerts/Notifications in a Vist. A simulation EHR environment (Lecture, Lab exercise 1) • Describe, view and create Order Checks in a Vist. A simulation EHR environment (Lecture, Lab exercise 2) • Describe, view and resolve Reminders in a Vist. A simulation EHR environment (Lecture, Lab exercise 3) • Discuss the value of these EHR functions as clinical decision support tools (Lecture) Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 2

Clinical Decision Support • “Clinical decision support (CDS) provides clinicians, staff, patients, or other individuals with knowledge and personspecific information, intelligently filtered or presented at appropriate times, to enhance health and health care” – AMIA Roadmap (Osheroff, 2007) • Some overviews – Greenes, 2007 – Sittig, 2008 – Osheroff, 2009 – Berner, 2009 – Liang, 2011 Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 3

Why Is CDS Needed? • There are many studies to choose from… • Mc. Glynn, 2003 – Sample of nearly 7, 000 adults in 12 US metro areas assessed for 30 conditions – On average, only 54. 9% of care was consistent with known quality • NCQA, 2009 – annual report on quality shows “gaps” to get all health plans to 90 th percentile of current quality – 49, 400 -115, 300 avoidable deaths – $12 billion in avoidable medical costs • Quality of care for patients with chronic disease no better and in many ways worse in US than for other developed countries (Schoen, 2009) Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 4

Why Is CDS Needed? (continued) • The IOM “Errors” report: As many as 98, 000 Americans die each year due to medical errors, mostly medication errors (Kohn, 2000) – Some have argued that the numbers are too high or too low, but none argue with the concept • Lost in the discussion: Most errors are the result of faulty systems; the solution is not in making people smarter or punishing them, but building better “systems” to identify and prevent errors (Berwick, 2003) • “Medicine used to be simple, ineffective, and relatively safe. Now it is complex, effective, and potentially dangerous. ” (Chantler, 1999) Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 5

CDS: Historical Perspectives • Early CDS focused on application of artificial intelligence and expert systems to improve medical diagnosis • Diagnostic decision support was a major focus of the field in the early days, circa 1970 s and 1980 s – But computer-aided diagnosis proved difficult and it became apparent computers could better be used in more focused capacities to reduce errors and improve quality – Laid the intellectual groundwork for techniques used in modern CDS and shift of focus to therapeutic decision support • With the availability of data in the modern electronic health record (EHR), the older approaches may yet be useful in the future Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 6

Definitions • Artificial intelligence (AI) – the area of computer science concerned with building computer programs that exhibit characteristics associated with human intelligence • Expert system (ES) – a computer program that mimics human expertise • Decision support system (DSS) – also mimics human expertise but acts in more of a supportive than independent role – Diagnostic decision support – focused on aiding in diagnosis of patients – Therapeutic decision support – focused on aiding in treatment of patients Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 7

Toward The Modern Era • By the late 1980 s and early 1990 s, it was apparent that – Diagnostic process was too complex for computer programs – Systems took long time to use and did not provide information that clinicians truly needed – “Greek Oracle” model was inappropriate to medical usefulness (Miller, 1990) • More recently – Diagnostic decision support systems less effective than therapeutic systems (Garg, 2005) – General failure of AI and ESs to live up to the hype of the 1980 s has been acknowledged (Mullins, 2005) – Although diagnostic error still does continue, and harms patients (Garber, 2007) Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 8

Where Is CDS Headed Now? • Decision support evolved in the 1990 s with recognition of their value within EHR – Rules and algorithms most useful in this context – Evolution from broad-based diagnostic decision support to narrower therapeutic decision support • AMIA “roadmap” for future provided three “key pillars” (Osheroff, 2006; Osheroff, 2007) – Best knowledge available when needed – High adoption and effective use – Continuous improvement of knowledge and methods Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 9

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 • Computerized provider order entry (CPOE) Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 10

Reminders • Remind clinicians to perform activities, such as – Preventive care – e. g. , screening – Follow-up of conditions – e. g. , routine check of blood coagulation when patient on anti-coagulant therapy • Have been used for long time (Barnett, 1978) – Small number of cases of untreated Streptococcal pharyngitis progress to acute rheumatic fever – Reminders to follow up led to increased treatment – Behavior returned to baseline when reminders removed, i. e. , effects were not educational Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 11

Alerts • Usually used to detect and report adverse events • Often used in context of CPOE • Successfully used in many clinical situations (Bates, 2003) – – Nosocomial infections Adverse drug events Injurious falls Emergent diseases, e. g. , bioterrorism Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 12

Issues Concerning Alerts • How to deliver to clinician? – Pager? Phone call? Email? • Volume control, aka “alert fatigue” – Want to communicate but not overload • Medico-legal issues – What to do about clinicians who do not respond to alerts or when alerts not appropriately generated • How to detect? – Easier with coded or numeric data; harder for information in textual reports (Cao, 2003; Melton, 2005) • How to standardize alerts across different systems – Arden Syntax Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 13

CPOE • From 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 Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 14

CPOE Exemplifies Everything Discussed About Informatics • It is about information, not technology • It is used at the place where CDS can have the most impact – the writing of medical orders – “The single most expensive piece of hospital equipment is the doctor’s pen. ” (Rosenthal, 1984) • Issues essential in implementation relate to organizational structure, attention to workflow, provider autonomy, etc. • But yes, technology is important! – System usability, response time, etc. Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 15

Order Sets • Streamline order entry by reducing steps for their input • Consist of directions, tests, and treatments for patient care based on diagnosis, treatment, or medical specialty category • Have ability to provide guideline-based (and evidence-based) care • Must be modifiable for local practices • Best managed at departmental and not institutional or individual level – can get clinicians to communicate better about consensus practices (Payne, 2003; Bobb, 2007) Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 16

CPOE Screen from Vist. A (Payne, 2003) Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 17

Grand Challenges for CDS • 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 • Create new CDS interventions – Prioritize CDS content development and implementation – Mine large clinical databases to create new CDS • Disseminate existing CDS knowledge and interventions – Disseminate best practices in CDS design, development, and implementation – Create an architecture for sharing executable CDS modules and services – Create Internet-accessible CDS repositories (rules. gov? ) (Sittig, 2008) Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 18

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) Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 19

Order Checking • Rules based • Real time • Provides recommendations that the clinician usually has the option of overruling –i. e. does not replace clinician expertise. Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 20

Clinical Reminders • Especially useful for preventive health care and managing chronic diseases. • Provides timely reminders to aid in the tracking and documentation of care. Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 21

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: – comp 11_unit 3_lab exercise 1 – comp 11_unit 3_lab exercise 2 – comp 11_unit 3_lab exercise 3 Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 22

Implementing Clinical Decision Support Summary • • • Clinical decision support Alerts/Notifications Order Checks Clinical Reminders Value of EHR functions in CDS Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 23

Implementing Clinical Decision Support References • • • Amatayakul MK. Electronic health records: A practical guide for professionals and organizations. 4 th 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 2011]. Available from: http: //healthit. ahrq. gov/portal/server. pt/gateway/PTARGS_0_1248_874024_0_0_18/09 -0069 -EF. 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. 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. 2 nd 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. Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 24

Implementing Clinical Decision Support References • • • Garg A, Adhikari N, Mc. Donald 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 Web. M&M; 2007 [cited 2011]. Available from: http: //webmm. silverchair. com/perspective. aspx? perspective. ID=36 Greenes R. editor. Clinical decision support - The road ahead. Amsterdam, Holland: Elsevier: 2007 Hebda T, Czar P. Handbook of informatics for nurses & healthcare professionals. 4 th 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. Mc. Glynn E, Asch S, Adams J, Keesey J, Hicks J, De. Cristofaro 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. 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/mg 18624961. 700. Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 25

Implementing Clinical Decision Support References • • Garg A, Adhikari N, Mc. Donald 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. Available from: http: //www. ncqa. org/tabid/836/Default. aspx 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 2011]. Available from: http: //www. amia. org/inside/initiatives/cdsroadmap. pdf 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: 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: w 1 -w 16. Available from: http: //content. healthaffairs. org/cgi/content/full/28/1/w 1 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. (Available from: http: //www. ptsafetyresearch. org/journal%20 articles/Original%20273. pdf ). . Health IT Workforce Curriculum Version 3. 0/Spring 2012 Configuring Electronic Health Records Implementing Clinical Decision Support 26