Usability and Human Factors

1. Usability and Human Factors Unit 7: Decision Support Systems: a Human Factors Approach Lecture b This material (Comp15 Unit7) was developed by Columbia University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 1U24OC000003. This material was updated by The University of Texas Health Science Center at Houston under Award Number 90WT0006. 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. Decision Support Systems: a Human Factors ApproachLecture b – Learning Objectives • Describe the cognitive basis for decision making and its effect on clinical errors (Lecture a) • Describe the role and advantages of Clinical Decision Support Systems (CDSS) (Lecture b) • Discuss the role of usability testing, training and implementation of clinical decision support (Lecture c) • Describe and define usability as it pertains to clinical decision support (Lecture c) • Identify examples of usability barriers to adoption of clinical decision support (Lecture d)

3. Decision Support Systems (DSS) • Interactive computer-based systems help individuals use communications, data, documents, knowledge and models to solve problems and make decisions • DSS are auxiliary systems • Intended to assist human decision makers rather than replace them • Not a fully automated system • Designed for specific types of organizations including banks, insurance companies and hospitals

4. Clinical Decision Support Systems • Provides clinicians, staff, patients, with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care • Uses patient data to generate case-specific advice • Primary purpose assist clinicians at point of care • Designed to aid decision making for prevention, screening, diagnosis, treatment, drug dosing, test ordering, and/or chronic disease management

5. Star Trek Tricorder: The Ultimate Clinical Decision Support Tool Johnson, B., 2009 CC BY NC-SA 4.0

6. Forms of CDSS Advice • Alerts • Reminders • Structured order forms • Pick lists • Patient-specific dose checking • Guideline support • Medication reference information

7. The Case for Clinical Decision Support Knowledge base regarding effective medical therapies continues to improve • Practice of medicine continues to lag behind

8. The Case for Clinical Decision Support (Cont’d – 1) CDS achieve the following objectives: • Reduced medication errors and adverse medical events • Improved management of specific acute and chronic conditions • Improved personalization of care for patients • Best clinical practices consistent with medical evidence • Cost-effective and appropriate prescription medication use • Effective communication and collaboration across clinical/prescribing/dispensing/administering settings • Better reporting and follow-up of adverse events

9. Degrees of CDSS Computerization • This computer: • Offers no assistance • Offers a complete set of action alternatives • Narrows the selection • Suggests one action alternative • Executes that selection if the human approves

10. Degrees of CDSS Computerization (Cont’d – 1) • This computer: • Allows human a restricted time to veto before automatic execution • Executes automatically, then informs human • Informs after execution only if asked • Informs him if computer decides to • The computer decides everything and acts autonomously, ignoring the human

11. Computerized Provider Order Entry Systems (CPOE) • Supports electronic entry of clinical orders for the treatment of patients • Medication • Investigative tests • Automate the medication ordering process • Orders communicated over a network to the medical staff or to the departments • Pharmacy • Laboratory • Radiology- responsible for fulfilling the order • Typically includes decision support tools

12. Promise of Order-Entry Systems • Ordering of drugs with computer support is a promising application for reducing medication errors • Most potential adverse events in patients occur at the stage of drug ordering • CPOE offers real-time decision support, alerts and reminders • Improvements in response time, efficiency of dispensing and delivery of medication

13. Some Advantages of CPOE Systems • Faster to reach pharmacy • Less subject to error associated with similar drug names • Easily integrated into medical records and decision-support systems • Easily linked to drug-drug interaction warnings • Able to link to ADE reporting systems • Well suited for training and education

14. Some Advantages of CPOE Systems (Cont’d – 1) • Claimed to generate significant economic savings • With online prompts, CPOE systems can • Link to algorithms to emphasize cost-effective medications • Reduce under prescribing and over prescribing • Reduce incorrect drug choices

15. Unit 7: Decision Support Systems: a Human Factors ApproachSummary – Lecture b • Clinical Decision Support Systems (CDSS) have been designed to be used for a wide range of medical decisions • CDSS advice comes in several different forms • CDSS, despite being controversial technology, has much to offer

16. Unit 7: Decision Support Systems: a Human Factors ApproachReferences – Lecture b References: Ash, J. S., Sittig, D. F., Campbell, E. M., Guappone, K. P., & Dykstra, R. H. (2007, October). Some unintended consequences of clinical decision support systems. In AMIA 2007 Symposium Proceedings, pp. 26-30 Kuperman, G. J., Bobb, A., Payne, T. H., Avery, A. J., Gandhi, T. K., Burns, G., et al. (2007). Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc, 14(1), 29-40. Coiera E, Westbrook J, Wyatt J. The safety and quality of decision support systems. Methods Inf Med. 2006;45 Suppl 1(suppl 1):20–5.me06010020 Eddy, D. M. (1982). Probabilistic reasoning in clinical medicine: Problems and opportunities. In D. Kahneman, P. Slovic & A. Tversky (Eds.), Judgment under uncertainty: Heuristics and biases (pp. 249-267). Cambridge, England: Cambridge University Press. HealthIT.gov. (n.d.). Retrieved April 26, 2016, from https://www.healthit.gov/policy-researchers-implementers/clinical-decision-support-cds Horsky, J., Kaufman, D. R., & Patel, V. L. (2005). When you come to a fork in the road, take it: strategy selection in order entry. AMIA AnnuSymp Proc, 350-354. Horsky, J., Schiff, G. D., Johnston, D., Mercincavage, L., Bell, D., & Middleton, B. (2012). Interface design principles for usable decision support: a targeted review of best practices for clinical prescribing interventions.Journal of biomedical informatics, 45(6), 1202-1216. Institute of Medicine Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press, 2001. J.A. Osheroff, J.M. Teich, B. Middleton, E.B. Steen, A. Wright and D.E. Detmer, A roadmap for national action on clinical decision support, J Am Med Inform Assoc 14 (2) (2007), pp. 141–145. Karsh B-T. Clinical practice improvement and redesign: how change in workflow can be supported by clinical decision support. AHRQ Publication No. 09-0054EF; Rockville (MD): Agency for Healthcare Research and Quality; June 2009.

17. Unit 7: Decision Support Systems: a Human Factors ApproachReferences – Lecture b (Cont’d – 1) References Khajouei R, Jaspers MW: CPOE system design aspects and their qualitative effect on usability. Stud Health Technol Inform 2008 , 136:309-14 Koppel, R., Metlay, J. P., Cohen, A., Abaluck, B., Localio, A. R., Kimmel, S. E., et al. (2005). Role of computerized physician order entry systems in facilitating medication errors. JAMA, 293(10), 1197-1203. Kuperman, G. J., Bobb, A., Payne, T. H., Avery, A. J., Gandhi, T. K., Burns, G., et al. (2007). Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc, 14(1), 29-40. McNeil, B. J., Pauker, S. G., Sox, H. C., Jr., & Tversky, A. (1982). On the elicitation of preferences for alternative therapies. N Engl J Med, 306(21), 1259-1262. Reckmann, M. H., Westbrook, J. I., Koh, Y., Lo, C., & Day, R. O. (2009). Does computerized provider order entry reduce prescribing errors for hospital inpatients? A systematic review. J Am Med Inform Assoc, 16(5), 613-623. Russ, A. L., Zillich, A. J., McManus, M. S., Doebbeling, B. N., & Saleem, J. J. (2009). A human factors investigation of medication alerts: barriers to prescriber decision-making and clinical workflow. AMIA AnnuSymp Proc, 2009, 548-552. Shortliffe, E. H., & Cimino, J. J. (2013). Biomedical informatics: computer applications in health care and biomedicine. Springer Science & Business Media. 4th edition. Teich JM, Osheroff JA, Pifer EA, Sittig DF, Jenders RA, Panel CDSER. Clinical decision support in electronic prescribing: Recommendations and an action plan. J Am Med Inform Assoc 2005;12(4):365-76. Images Slide 5: Johnson, B. (2009). TricoderRunbox [Online image]. Retreived on October 4th, 2010 from http://www.flickr.com/photos/bojo/4078685614/lightbox/#/

18. Unit 7: Decision Support Systems: a Human Factors ApproachLecture b This material was developed by Columbia University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number 1U24OC000003. This material was updated by The University of Texas Health Science Center at Houston under Award Number 90WT0006.