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The Effect of an Expanded Target on Medication Order Entry Tasks

The Effect of an Expanded Target on Medication Order Entry Tasks. Seminarian: Marc Young, PharmD Advisors: Bill Felkey, MS William Villaume, PhD. Fall Semester October 17,2005 Research Proposal. Thesis Proposal Agenda. The Problem Literature Review Problem Statement

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The Effect of an Expanded Target on Medication Order Entry Tasks

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  1. The Effect of an Expanded Target on Medication Order Entry Tasks Seminarian: Marc Young, PharmD Advisors: Bill Felkey, MS William Villaume, PhD Fall Semester October 17,2005 Research Proposal

  2. Thesis Proposal Agenda • The Problem • Literature Review • Problem Statement • Hypotheses • Methods • Significance of the study • Discussion

  3. The Problem • The Institute of Medicine (IOM) report in 1999 an estimated 44,000-98,000 deaths a year from medical errors (Kohn 1999) • Healthcare sector slower than others to adopt technology; silos of information (Roberts 1997) • IOM reported human factors and underutilization of technology such as Computerized Provider Order Entry (CPOE) contributing to errors (Kohn 1999) Kohn LT et al To Err Is Human: Building a Safer Health SystemNational Academy Press 1999 Roberts, J., & Peel, V. (1997). Studies in health technology and informatics (Vol. 43 Pt B). Amsterdam: IOS Press.

  4. The Problem (cont.) • Literature on healthcare technology appraisal • Often subjective data • Objective performance comparison with legacy paper system • Many organizations are in the process of implementing or planning CPOE

  5. The Problem (cont.) • Studies show improvements with CPOE (Bates 1998 & 1999; King 2003) • Recent concern over technology induced errors (Ash 2004, Koppel 2005) • Vendors now offer handheld versions of CPOE for electronic prescribing • Handheld version is more than shrinking of larger version • Limited data entry and viewing space combined with mobile work environment with mobile devices can frustrate users Ash, J. S., et al (2004). Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. J Am Med Inform Assoc, 11(2), 104-112. Koppel, R., et al. (2005). Role of computerized physician order entry systems in facilitating medication errors. Jama, 293(10), 1197-1203. Bates, D. W., et al. (1998). Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. Jama, 280(15), 1311-1316. Bates, D. W., et al. (1999). The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc, 6(4), 313-321. King, W. J., et al (2003). The effect of computerized physician order entry on medication errors and adverse drug events in pediatric inpatients. Pediatrics, 112(3 Pt 1), 506-509.

  6. The Problem (cont.) • Healthcare environment • Complex social and technical interactions • Professionals rely on both technology and individuals • Many distractions and interruptions • Consequences of a poorly designed technology in this environment Bell, D. S., et al (2004). A conceptual framework for evaluating outpatient electronic prescribing systems based on their functional capabilities. J Am Med Inform Assoc, 11(1), 60-70. Ash, J. S., Berg, M., & Coiera, E. (2004). Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. J Am Med Inform Assoc, 11(2), 104-112.

  7. The Problem (cont.) • With a high-risk industry such as healthcare, the need to evaluate order entry technologies such as CPOE is important • This is even more important if the CPOE is further constrained by its use in a handheld device

  8. Literature Review

  9. Healthcare Technology • Technology can improve workflow efficiency and remove repetitive work • Healthcare technology must adapt to a highly technical environment and users • Changing data entry with technology such as CPOE involves changing workflow and culture Ash, J. S., et al (2004). Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. J Am Med Inform Assoc, 11(2), 104-112. Bell, D. S., et al (2004). A conceptual framework for evaluating outpatient electronic prescribing systems based on their functional capabilities. J Am Med Inform Assoc, 11(1), 60-70. Tang, P. C., & Patel, V. L. (1994). Major issues in user-interface design for health professional workstations - summary and recommendations. International Journal Of Bio-Medical Computing, 34(1-4), 139-148.

  10. Healthcare Technology- CPOE (cont) • Medication-Use-Process • CPOE has the largest impact on the prescribing and transcribing phases Bates DW, Leape LL et al, Effect of Computerized Physician Order Entry and a Team Intervention on Prevention of Serious Medication Errors JAMA October 21, 1998; 280(15) :1311-1316 Bobb A et al, The Epidemiology of Prescribing Errors Arch of Int Med. APR 12 2004;164: 784-792 Bates DW et al The Impact of Computerized Physician Order Entry on Medication Error Prevention JAMIA 1999 6(4): 313-321

  11. Healthcare Technology-CPOE (cont.) • CPOE could reduce errors and provide more efficient prescribing and transcribing of orders • Studies have shown that organizations that utilize CPOE can have a significant reduction in the number of adverse drug events and/or medication errors Bell, D. S., et al (2004). A conceptual framework for evaluating outpatient electronic prescribing systems based on their functional capabilities. J Am Med Inform Assoc, 11(1), 60-70. Bates, D. W., Leape, L. L., Cullen, D. J., Laird, N., Petersen, L. A., Teich, J. M., et al. (1998). Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. Jama, 280(15), 1311-1316. Bates, D. W., Teich, J. M., Lee, J., Seger, D., Kuperman, G. J., Ma'Luf, N., et al. (1999). The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc, 6(4), 313-321. King, W. J., Paice, N., Rangrej, J., Forestell, G. J., & Swartz, R. (2003). The effect of computerized physician order entry on medication errors and adverse drug events in pediatric inpatients. Pediatrics, 112(3 Pt 1), 506-509.

  12. Healthcare Technology- CPOE (cont.) • New data entry methods could also introduce new and unexpected errors as a result of a poorly designed system (Johnson 2005) • Error potential from CPOE: • User interface confusion (Koppel 2005) • Selection error and non-integration (Jones 2004) • Inadequate user interface for some users Jones, J. L. (2004). Implementing computerized prescriber order entry in a children's hospital. Am J Health Syst Pharm, 61(22), 2425-2429. 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. Johnson, C. M., Johnson, T. R., & Zhang, J. (2005). A user-centered framework for redesigning health care interfaces. Journal of Biomedical Informatics, 38(1), 75.

  13. Healthcare Technology-CPOE (cont.) • Introducing a new order entry interface such as with CPOE can affect a users’ performance and perception of the technology as well • The amount of time it takes a user to complete a task with a new interface or how they subjectively rate an interface are important considerations beyond reducing errors and adverse events (Staggers 2000) Chan, W. (2002). Increasing the success of physician order entry through human factors engineering. J Healthc Inf Manag, 16(1), 71-79. Cimino, J. J., et al. (2001). Studying the human-computer-terminology interface. J Am Med Inform Assoc, 8(2), 163-173. Staggers, N., & Kobus, D. (2000). Comparing response time, errors, and satisfaction between text-based and graphical user interfaces during nursing order tasks. J Am Med Inform Assoc, 7(2), 164-176.

  14. Healthcare Technology- CPOE (cont.) • CPOE vendors making mobile versions (i.e. tablet PC, handheld) • Literature reports on handheld applications limited • Currently no standards for electronic prescribing for which handheld is a predominant mobile platform

  15. Human Factors • The interface is the primary way a user communicates with an interactive system (Schneiderman 1986, 2005) • The user interface is a key determinant for the users’ + or – experience • The manipulation and management of an interface is one of the major focuses of Human Computer Interaction (HCI) (Scheiderman 1986, 2005) Shneiderman, B. Designing the User Interface: Strategies for Effective Human-Computer Interaction. Addison-Wesley Publishing Company, Reading, Massachusetts 1986 Schneiderman, B., & Plaisant, C. (2005). Designing the user interface. Boston: Pearson. Tang PC et al, Major Issues in User Interface Design for Health Professional Workstations: Summary and Recommendations Int Jour of Biomed Comp ; 1994 34:139-48

  16. Human Factors (cont.) • The field of HCI incorporates computer science with disciplines such as applied psychology, sociology, and anthropology Shneiderman, B. Designing the User Interface: Strategies for Effective Human-Computer Interaction. Addison-Wesley Publishing Company, Reading, Massachusetts 1986

  17. Human Factors (cont.) • Errors that occur from humans interacting with computers could stem from a man-machine mismatch, often at the user interface ( Bagnara 1989) • With interactive systems, fault tolerance is found within a user interface • Fault tolerant interfaces provide a user with good feedback and opportunities to recover from an error Bagnara, S., & Rizzo, A. (1989). Work with computers: Organizational, management, stress and health aspects (Vol. 12A). Amsterdam: Elsevier Science. Bates, D. W., Cohen, M., Leape, L. L., Overhage, J. M., Shabot, M. M., & Sheridan, T. (2001). Reducing the frequency of errors in medicine using information technology. J Am Med Inform Assoc, 8(4), 299-308.

  18. Human Factors (cont.) • In the field of HCI, human errors can be divided into two categories: • slips • mistakes • Slips result from variability in performance • Internal causes • External causes Park KS, Handbook of Human Factors and Ergonomics , Wiley-Interscience New York, 1997

  19. Human Factors (cont.) • Slips from human interactions with computers generally occur from a misfit between users perception in the interface and what is actually there, an accident or lack of attention during familiar actions (Park 1997, Arnold 1987) • Visual Search Theory (Nagy 2003, Palmer 2000) • Signal Detection Theory (Macmillan 1991) Park KS, Handbook of Human Factors and Ergonomics , Wiley-Interscience New York, 1997 Arnold B et al , Psychological Issues of Human-Computer Interaction in The Workplace , Elesvier Publishers North-Holland, 1987 Macmillan NA et al, Detection Theory: A Users Guide , Cambridge University Press Cambridge, 1991 Palmer J et al, The Psychophysics of Visual SearchVision Research; 2000, 40: 1227-1268 Nagy AL et al, Distrcator Heterogeneity, Attention and Color in Visual SearchVision Research; 2003, 43: 1541-1552

  20. Human Factors (cont.) • For visual search tasks, the presence of distracter stimuli often has a detrimental effect on the accuracy of search performance. Palmer J et al, The Psychophysics of Visual SearchVision Research; 2000, 40: 1227-1268 Nagy AL et al, Distrcator Heterogeneity, Attention and Color in Visual SearchVision Research; 2003, 43: 1541-1552 Lichstein KL, et al (2000). The mackworth clock test: A computerized version. Journal of Psychology, 134(2), 153-161. Mackworth, N. (1961). Researches on the measurement of human performance. New York: Dover Publications.

  21. Human Factors (cont.) • Limits of human attention: • Field dependent vs. independent Dembo, M. (1977). Teaching for learning: Applying educational psychology in the classroom. Santa Monica, Ca: Goodyear. Flynn, E. A., Barker, K. N., Gibson, J. T., Smith, L. A., & Berger, B. A. (1996). Relationships between ambient sounds and the accuracy of pharmacists' prescription-filling performance. AJHP, 38(4), 614-619. Grasha, A. F., & K, S. (2001). Psychosocial factors, workload, and human error in a simulated pharmacy dispensing task. Perceptual and Motor Skills, 92, 53-71.

  22. Human Factors (cont.) • Limits of human attention: • Vigilance Floru, R., Cail, et al (1985). Psychophysiological changes during a vdu repetitive task. Ergonomics, 28(10), 1455-1468. Lichstein KL, et al (2000). The mackworth clock test: A computerized version. Journal of Psychology, 134(2), 153-161. Mackworth, N. (1961). Researches on the measurement of human performance. New York: Dover Publications.

  23. Human Factors (cont.) • Limits of human attention: • Distracter stimuli: “Noise” Palmer J et al, The Psychophysics of Visual SearchVision Research; 2000, 40: 1227-1268 Nagy AL et al, Distrcator Heterogeneity, Attention and Color in Visual SearchVision Research; 2003, 43: 1541-1552

  24. Human Factors (cont.) • Limits of human attention: • Low-threshold/ JND ( just noticeable difference) The gale encyclopedia of psychology. (1996).). Detroit: Gale. Macmillan, N., & Douglas, C. (1991). Detection theory: A users guide. Cambridge: Cambridge University Press.

  25. Human Factors (cont.) • The presence of sound-alike and look-alike names further complicate search tasks Grasha, A. F. (2000). Into the abyss: Seven principles for identifying the causes of and preventing human error in complex systems. Am J Health Syst Pharm, 57(6), 554-564. Grasha, A. F., & K, S. (2001). Psychosocial factors, workload, and human error in a simulated pharmacy dispensing task. Perceptual and Motor Skills, 92, 53-71. Lambert, B. L. (1997). Predicting look-alike and sound-alike medication errors. Am J Health Syst Pharm, 54(10), 1161-1171.

  26. Human Factors (cont.) • To combat the limits of human attention, a user interface should make errors both less likely and make those that occur easier to detect (Bates 2001) • An interface could improve information processing with the use of differentiation of items through factors such as color, size or shape (Nolan 2000) Bates, D. W., Cohen, M., Leape, L. L., Overhage, J. M., Shabot, M. M., & Sheridan, T. (2001). Reducing the frequency of errors in medicine using information technology. J Am Med Inform Assoc, 8(4), 299-308. Nolan, T. W. (2000). System changes to improve patient safety. Bmj, 320(7237), 771-773.

  27. User Interface: Fitts Law • Fitts Law describes the time taken to acquire a visual target using some kind of manual input device • Graphical User Interfaces (GUI) elements such as buttons, menus, icons are fundamental in HCI and require virtual pointing • i.e. mouse, stylus, Balakrishnan R et al, “Beating” Fitts’ Law: Virtual Enhancements for Pointing FacilitationInternational Journal of Human-Computer Studies; 2004, 61: 857-874

  28. User Interface: Fitts Law (cont.) • Spatial accuracy in virtual pointing often poor with the last 10% of the distance and the finite movements critical (Balakrishnan 2004) • Modifications to screen elements based on Fitts Law can improve virtual pointing accuracy Balakrishnan R et al, “Beating” Fitts’ Law: Virtual Enhancements for Pointing FacilitationInternational Journal of Human-Computer Studies; 2004, 61: 857-874 Cockburn, A., et al (2003). Improving the acqusition of small targets. Paper presented at the Proceedings in HCI 2003. Gutwin, C. (2002). Improving focus targeting in interactive fisheye menus. Paper presented at the Proceedings of the SIGCHI conference on Human factors in computing systems: Changing our world, Minneapolis, Minnesota, USA. McGuffin, M., et al (2002, April 20-25). Acquisition of expanding targets. Paper presented at the Proceedings of the SIGCHI conference on Human factors in computing systems: Changing our world, Minneapolis, Minnesota, USA.

  29. User Interface: Fitts Law (cont.) • Improved accuracy and time for pointing tasks with: • Expanded targets, Fisheye views and Bubble cursors • Jones, Jo • Jones, Joe • Jones, Joey • Jones, Joel • Jones, Joseph • Jones, Joshua

  30. Significance • Handheld computer research in its infancy • Mobile devices need to attract a user’s attention while operating in limited space and noisy environment

  31. Significance (cont.) • To make signal detection easier for handheld devices: • a technique such as expanded targets could improve performance and provide a fault tolerant user interface • There exists a need to examine the relationship between the type of user interface and performance measures to prevent errors with the use of handheld order entry

  32. Problem Statement What is the effect of an expanded target interface for field independent and field dependent subjects on the number order entry errors, time to complete order entry and subjective workload for a handheld medication order entry task?

  33. Hypotheses • H1: The expanded target interface will have a lower order entry error rate than the non-expanded target interface • H2: Field dependent subjects will have a higher order entry error rate than field independent subjects • H3: The expanded target interface will reduce the order entry error rate for field dependent subjects more than it will for field independent subjects

  34. Hypotheses (cont.) • H4: The time for order entry will differ for the expanded target and non-expanded target interface • H5: Field independent subjects will be quicker at order entry than field dependent subjects • H6: The time for order entry for expanded target and non-expanded target interface will differ for the field independent and field dependent subjects

  35. Hypotheses (cont.) • H7: The subjective ratings on the NASA-TLX will differ for the expanded target and non-expanded target interface • H8: Field independent subjects will be have different subjective ratings on the NASA-TLX than field dependent subjects • H9: The subjective ratings on the NASA-TLX for expanded target and non-expanded target interface will differ for the field independent and field dependent subjects

  36. Methodology • Study Design: Prospective, randomized, crossover design • IV: Grouping of subjects with types of interfaces, classification of subjects as field dependent/independent • DV: # errors, time to enter order, NASA-TLX ratings • Subjects: Sample drawn from first year student pharmacists at AU Harrison school of pharmacy

  37. Methodology (cont.) • Subjects: • Researcher explains research with a prepared speech • Volunteers enrolled via online form and given unique user ID • Size of sample determined from pilot study of students not part of study population • Subjects with corrected to normal vision asked to bring correction to experiment

  38. Methodology (cont.) • Study Scenario: • Subjects will use pre-printed orders for entry on two versions of a order entry program • Experimental program has expanded target at top of presented lists

  39. Methodology (cont.) • Study Scenario: • A distraction program asking basic pharmacy questions randomly appears on laptops for subjects to answer • After order entry, subjects take subjective measure rating the version they used • After both versions used, take paper/pencil test to establish field independence or dependence along with demographic information collected

  40. Methodology (cont.) • Data Collection: • Objective data ( # errors, time) collected via event logging on handheld devices and laptop computers and Group Embedded Figures Test (GEFT) administered after both devices used

  41. Methodology (cont.) • Subjective measures collected after each handheld version used on computerized NASA-Task Load Index (NASA-TLX)

  42. Methodology (cont.)

  43. Methodology (cont.)

  44. Methodology (cont.) • Statistical Analyses Plan: • Descriptive statistics, regression and ANOVA using SPSS version 12

  45. Questions? “Learning to shrug is the beginning of wisdom” My Last Fortune cookie

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