Drug Safety

, Volume 34, Issue 4, pp 289–298 | Cite as

The Role of Computerized Decision Support in Reducing Errors in Selecting Medicines for Prescription

Narrative Review
  • Melissa T. Baysari
  • Johanna Westbrook
  • Jeffrey Braithwaite
  • Richard O. Day
Review Article


This narrative review includes a summary of research examining prescribing errors, prescription decision making and the role computerized decision support plays in this decision-making process. A reduction in medication prescribing errors, specifically a reduction in the selection of inappropriate medications, is expected to result from the implementation of an effective computerized decision support system. Previous research has investigated the impact of the implementation of electronic systems on medication errors more broadly. This review examines the specific characteristics of decision support systems that may contribute to fewer knowledge-based mistakes in prescribing, and critically appraises the large volume of information available on the decision-making process of selecting medicines for prescription. The results highlight a need for work investigating what decision strategies are used by doctors with different levels of expertise in the prescribing of medications. The nature of the relationship between decision support and decision performance is not well understood and future research is needed to determine the mechanisms by which computerized decision support influences medication selection.



This research is supported by NHMRC Program Grant 568612. The funding agreement ensured the authors’ independence in compiling this review. The authors have no conflicts of interest to declare that are directly relevant to the content of this review.


  1. 1.
    Watcher RM. Understanding patient safety. New York (NY): The McGraw-Hill Companies, 2008Google Scholar
  2. 2.
    Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety. Arch Intern Med 2003; 163: 1409–16PubMedCrossRefGoogle Scholar
  3. 3.
    Lewis PJ, Dorman T, Taylor D, et al. Prevalence, incidence and nature of prescribing errors in hospital inpatients: a systematic review. Drug Saf 2009; 32(5): 379–89PubMedCrossRefGoogle Scholar
  4. 4.
    Franklin BD, Vincent C, Schachter M, et al. The incidence of prescribing errors in hospital inpatients: an overview of the research methods. Drug Saf 2005; 28(10): 891–900CrossRefGoogle Scholar
  5. 5.
    Bates DW, Boyle DL, Vander Vliet M, et al. Relationship between medication errors and adverse drug events. J Gen Intern Med 1995; 10: 199–205PubMedCrossRefGoogle Scholar
  6. 6.
    Wilson DG, McArtney RG, Newcombe RG, et al. Medication errors in paediatric practice: insights from a continuous quality improvement approach. Eur J Pediatr 1998; 157: 769–74PubMedCrossRefGoogle Scholar
  7. 7.
    Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events. JAMA 1995; 274(1): 29–34PubMedCrossRefGoogle Scholar
  8. 8.
    van Doormaal JE, van den Bernt PMLA, Zaal RJ, et al. The influence that electronic prescribing has on medication errors and preventable adverse drug events: an interrupted time series study. J Am Med Inform Assoc 2009; 16: 816–25PubMedCrossRefGoogle Scholar
  9. 9.
    van Doormaal JE, van den Bernt PMLA, Mol PGM, et al. Medication errors: the impact of prescribing and transcribing errors on preventable harm in hospital patients. Qual Saf Health Care 2009; 18: 22–7PubMedCrossRefGoogle Scholar
  10. 10.
    Stanhope N, Vincent C, Adams S, et al. Applying human factors methods to clinical risk management in obstetrics. Br J Obstet Gynaecol 1997; 104: 1225–32PubMedCrossRefGoogle Scholar
  11. 11.
    Leape LL, Bates DW, Cullen DJ, et al. Systems analysis of adverse drug events. JAMA 1995; 274(1): 35–43PubMedCrossRefGoogle Scholar
  12. 12.
    Bobb A, Gleason K, Husch M, et al. The epidemiology of prescribing errors. Arch Intern Med 2004; 164: 785–92PubMedCrossRefGoogle Scholar
  13. 13.
    Dean B, Schachter M, Vincent C, et al. Causes of prescribing errors in hospital inpatients: a prospective study. Lancet 2002; 359: 1373–8PubMedCrossRefGoogle Scholar
  14. 14.
    Folli HL, Poole RL, Benitz WE, et al. Medication error prevention by clinical pharmacists in two children’s hospitals. Pediatrics 1987; 79: 718–22PubMedGoogle Scholar
  15. 15.
    Lesar TS, Briceland L, Delcoure K, et al. Medication prescribing errors in a teaching hospital. JAMA 1990; 263(17): 2329–34PubMedCrossRefGoogle Scholar
  16. 16.
    Lesar TS, Briceland L, Stein DS. Factors related to errors in medication prescribing. JAMA 1997; 277(4): 312–7PubMedCrossRefGoogle Scholar
  17. 17.
    Coombes ID, Stowasser DA, Coombes JA, et al. Why do interns make prescribing errors? A qualitative study. Med J Aust 2008; 188: 89–94PubMedGoogle Scholar
  18. 18.
    Dean B, Schachter M, Vincent C, et al. Prescribing errors in hospital inpatients: their incidence and clinical significance. Qual Saf Health Care 2002; 11: 340–4PubMedCrossRefGoogle Scholar
  19. 19.
    Coombes ID, Pillans PI, Storie WJ, et al. Quality of medication ordering at a large teaching hospital. Aust J Hosp Pharm 2001;31: 102–6Google Scholar
  20. 20.
    Nichols P, Copeland T, Craib IA, et al. Learning from error: identifying contributory causes of medication errors in an Australian hospital. Med J Aust 2008; 188: 276–9PubMedGoogle Scholar
  21. 21.
    Tully MP, Ashcroft DM, Dorman T, et al. The causes of and factors associated with prescribing errors: systematic review. Drug Saf 2009; 32: 819–36PubMedCrossRefGoogle Scholar
  22. 22.
    Reason J. Human error. Cambridge: Cambridge University Press, 1990CrossRefGoogle Scholar
  23. 23.
    Lederman RM, Parkes C. Systems failure in hospitals: using Reason’s model to predict problems in a prescribing information system. J Med Syst 2005; 29(1): 33–43PubMedCrossRefGoogle Scholar
  24. 24.
    Horsky J, Kaufman DR, Oppenheim MI, et al. A framework for analyzing the cognitive complexity of computer-assisted clinical ordering. J Biomed Inform 2003; 36(1–2): 4–22PubMedCrossRefGoogle Scholar
  25. 25.
    Zhan C, Hicks RW, Blanchette CM, et al. Potential benefits and problems with computerized prescriber order entry: analysis of a voluntary medication error-reporting database. Am J Health Syst Pharm 2006; 63: 353–8PubMedCrossRefGoogle Scholar
  26. 26.
    Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 2005; 293(10): 1197–203PubMedCrossRefGoogle Scholar
  27. 27.
    Sintchenko V, Magrabi F, Tipper S. Are we measuring the right thing? Variables that affect the impact of computerised decision support on patient outcomes: a systematic review. Med Inform Internet Med 2007; 32(3): 225–40PubMedCrossRefGoogle Scholar
  28. 28.
    Hunt DL, Haynes B, Hanna SE, et al. Effects of computerbased clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998; 280(15): 1339–46PubMedCrossRefGoogle Scholar
  29. 29.
    Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144: 742–52PubMedGoogle Scholar
  30. 30.
    Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 1998; 280(15): 1311–6PubMedCrossRefGoogle Scholar
  31. 31.
    Terrell KM, Perkins AJ, Dexter PR, et al. Computerized decision support to reduce potentially inappropriate prescribing to older emergency department patient: a randomized controlled trial. J Am Geriatr Soc 2009; 57: 1388–94PubMedCrossRefGoogle Scholar
  32. 32.
    Han YY, Carcillo JA, Venkataraman ST, et al. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatr 2005; 116: 1506–12CrossRefGoogle Scholar
  33. 33.
    Nebeker JR, Hoffman JM, Weir CR, et al. High rates of adverse drug events in a highly computerized hospital. Arch Intern Med 2005; 165: 1111–6PubMedCrossRefGoogle Scholar
  34. 34.
    Campbell EM, Sittig DF, Ash JS, et al. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2006; 13(5): 547–56PubMedCrossRefGoogle Scholar
  35. 35.
    Schedlbauer A, Prasad V, Mulvaney C, et al. What evidence supports the use of computerized alerts and prompts to improve clinicians’ prescribing behavior? J Am Med Inform Assoc 2009; 16(4): 531–8PubMedCrossRefGoogle Scholar
  36. 36.
    Reckmann MH, Westbrook J, Koh Y, et al. Does computerized provider order entry reduce prescribing errors for hospital inpatients? A systematic review. J Am Med Inform Assoc 2009; 16(5): 613–23PubMedCrossRefGoogle Scholar
  37. 37.
    Donyai P, O’Grady K, Jacklin A, et al. The effects of electronic prescribing on the quality of prescribing. Br J Clin Pharmacol 2007; 65(2): 230–7PubMedCrossRefGoogle Scholar
  38. 38.
    Judge J, Field TS, DeFlorio M, et al. Prescribers’ responses to alerts during medication ordering in the long term care setting. J Am Med Inform Assoc 2006; 13(4): 385–90PubMedCrossRefGoogle Scholar
  39. 39.
    Paterno MD, Maviglia SM, Gorman PN. Tiering drug-drug interaction alerts by severity increases compliance rates. J Am Med Inform Assoc 2009; 16: 40–6PubMedCrossRefGoogle Scholar
  40. 40.
    Galanter WL, Didomenico RJ, Polikaitis A. A trial of automated decision support alerts for contraindicated medications using computerised physician order entry. J Am Med Inform Assoc 2005; 12(3): 269–74PubMedCrossRefGoogle Scholar
  41. 41.
    Kawamoto K, Houlihan CA, Balas A, et al. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330(7494): 765–8PubMedCrossRefGoogle Scholar
  42. 42.
    Pearson S, Moxey A, Robertson J, et al. Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990–2007). BMC Health Serv Res 2009; 9: 154PubMedCrossRefGoogle Scholar
  43. 43.
    van der Sijs H, Aarts J, Vulto A, et al. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006; 13(2): 138–47PubMedCrossRefGoogle Scholar
  44. 44.
    Kushniruk AW, Patel VL. Cognitive evaluation of decision making processes and assessment of information technology in medicine. Int JMed Inform 1998; 51(2–3): 83–90CrossRefGoogle Scholar
  45. 45.
    Kaplan B. Evaluating informatics applications —some alternative approaches: theory, social interactionism, and call for methodological pluralism. Int J Med Inform 2001; 64(1): 39–56PubMedCrossRefGoogle Scholar
  46. 46.
    Ash JS, Berg M, Coiera EW. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11(2): 104–12PubMedCrossRefGoogle Scholar
  47. 47.
    Beuscart-Zephir MC, Pelayo S, Anceaux F, et al. Impact of CPOE on doctor-nurse cooperation for the medication ordering and administration process. Int J Med Inform 2005; 74: 629–41PubMedCrossRefGoogle Scholar
  48. 48.
    Larum H, Ellingsen G, Faxvaag A. Doctors’ use of electronic medical records systems in hospitals: cross sectional survey. BMJ 2001 Dec 8; 323(7325): 1344–8CrossRefGoogle Scholar
  49. 49.
    Lee F, Teich JM, Spurr CD, et al. Implementation of physician order entry: user satisfaction and self reported usage patterns. J Am Med Inform Assoc 1996; 3: 42–55PubMedCrossRefGoogle Scholar
  50. 50.
    Glassman PA, Simon B, Belperio P, et al. Improving recognition of drug interactions: benefits and barriers to using automated drug alerts. Med Care 2002; 40(12): 1161–71PubMedCrossRefGoogle Scholar
  51. 51.
    Magnus D, Rodgers S, Avery AJ. GP’s views on computerized drug interaction alerts: questionnaire survey. J Clin Pharm Ther 2002; 27: 377–82PubMedCrossRefGoogle Scholar
  52. 52.
    Murff HJ, Kannry J. Physician satisfaction with two order entry systems. J Am Med Informs Assoc 2001; 8(5): 499–511CrossRefGoogle Scholar
  53. 53.
    Feldstein A, Simon S, Schneider J, et al. How to design computerized alerts to ensure safe prescribing practices. Jt Comm J Qual Saf 2004; 30: 602–13PubMedGoogle Scholar
  54. 54.
    van der Sijs H, Aarts J, Gelder TV, et al. Turning off frequently overridden drug alerts: limited opportunities for doing it safely. J Am Med Inform Assoc 2008; 15(4): 439–48PubMedCrossRefGoogle Scholar
  55. 55.
    Weir CR, Nebeker JJR, Hicken BL, et al. A cognitive task analysis of information management strategies in a computerized provider order entry environment. J Am Med Inform Assoc 2007; 14(1): 65–75PubMedCrossRefGoogle Scholar
  56. 56.
    Shachak A, Hadas-Dayagi M, Ziv A, et al. Primary care physicians’ use of an electronic medical record system: a cognitive task analysis. J Gen Intern Med 2009; 24(3): 341–8PubMedCrossRefGoogle Scholar
  57. 57.
    Skitka LJ. Automation: decision aid or decision maker. Washington, DC: National Aeronautics and Space Administration, 1998Google Scholar
  58. 58.
    Mosier KL, Skitka LJ. Human decision makers and automation decision aids: made for each other? In: Parasuraman R, Mouloua M, editors. Automation and human performance: theory and applications. Mahwah (NJ): Lawrence Erlbaum Associates, 1996: 201–20Google Scholar
  59. 59.
    Chinburapa V, Larson LN, Brucks M, et al. Physician prescribing decisions: the effects of situational involvement and task complexity on information acquisition and decision making. Soc Sci Med 1993; 36(11): 1473–82PubMedCrossRefGoogle Scholar
  60. 60.
    Kuipers B, Moskowitz AJ, Kassirer JP. Critical decisions under uncertainty: representation and structure. Cogn Sci 1988; 12(2): 177–210CrossRefGoogle Scholar
  61. 61.
    Schumock GT, Walton SM, Park HY, et al. Factors that influence prescribing decisions. Ann Pharmacother 2004; 38: 557–62PubMedCrossRefGoogle Scholar
  62. 62.
    Pearson S, Rolfe I, Smith T. Factors influencing prescribing: an intern’s perspective. Med Educ 2002; 36: 781–7PubMedCrossRefGoogle Scholar
  63. 63.
    Campo K, Staebel OD, Gijsbrechts E, et al. Physicians’ decision process for drug prescription and the impact of pharmaceutical marketing mix instruments. Health Mark Q 2006; 22(4): 73–107CrossRefGoogle Scholar
  64. 64.
    Segal R, Hepler CD. Drug choice as a problem-solving process. Med Care 1985; 23(8): 967–76PubMedCrossRefGoogle Scholar
  65. 65.
    Becker MH, Stolley PD, Lasagna L, et al. Differential education concerning therapeutics and resultant physician prescribing patterns. J Med Educ 1972; 47(2): 118–27PubMedGoogle Scholar
  66. 66.
    Denig P, Haaijer-Ruskamp FM, Zijsling DH. How physicians choose drugs. Soc Sci Med 1988; 27(12): 1381–6PubMedCrossRefGoogle Scholar
  67. 67.
    Worthen DB. Prescribing influences. Br J Med Educ 1973; 7: 109–17PubMedCrossRefGoogle Scholar
  68. 68.
    Hemminki E. Review of literature on the factors affecting drug prescribing. Soc Sci Med 1975; 9(2): 111–6PubMedCrossRefGoogle Scholar
  69. 69.
    Bradley CP. Decision making and prescribing patterns: a literature review. Fam Pract 1991; 8: 2762–87CrossRefGoogle Scholar
  70. 70.
    Anderson N, Fuller R, Dudley N. ‘Rules of thumb’ or reflective practive? Understanding senior physicians’ decisionmaking about anti-thrombotic usage in atrial fibrillation. QJM 2007; 100(5): 263–9PubMedCrossRefGoogle Scholar
  71. 71.
    Nutescu EA, Park HY, Walton SM, et al. Factors that influence prescribing within a therapeutic drug class. J Eval Clin Pract 2005; 11(4): 357–65PubMedCrossRefGoogle Scholar
  72. 72.
    Reichert S, Simon T, Halm EA. Physicians’ attitudes about prescribing and knowledge of the costs of common medications. Arch Intern Med 2000; 160: 2799–803PubMedCrossRefGoogle Scholar
  73. 73.
    Denig P, Haaijer-Ruskamp FM, Wesseling H, et al. Towards understanding treatment preferences of hospital physicians. Soc Sci Med 1993; 36(7): 915–24PubMedCrossRefGoogle Scholar
  74. 74.
    Lambert BL, Salmon JW, Stubbings J, et al. Factors associated with antibiotic prescribing in a managed care setting: an exploratory investigation. Soc Sci Med 1997; 45(12): 1767–79PubMedCrossRefGoogle Scholar
  75. 75.
    Ford JK, Schmitt N, Schechtman SL, et al. Process tracing methods: contributions, problems, and neglected research questions. Organ Behav Hum Decis Process 1989; 43(1): 75–117CrossRefGoogle Scholar
  76. 76.
    Silver MS. Systems that support decision makers: description and analysis. West Sussex: John Wiley & Sons Ltd, 1991Google Scholar
  77. 77.
    Montgomery H. Decision rules and the search for a dominance structure: towards a process model of decision making. In: Humphreys P, Svenson O, Vari A, editors. Analysing and aiding decision processes. Budapest: North-Holland Publishing Company, 1983Google Scholar
  78. 78.
    Chu PC, Spires EE. The joint effects of effort and quality on decision strategy choice with computerized decision aids. Decis Sci 2000; 31(2): 259–92CrossRefGoogle Scholar
  79. 79.
    Svenson O. Process descriptions of decision making. Organ Behav Hum Perform 1979; 23: 86–112CrossRefGoogle Scholar
  80. 80.
    Payne JW, Bettman JR, Johnson EJ. The adaptive decision maker. Cambridge: Cambridge University Press, 1993CrossRefGoogle Scholar
  81. 81.
    Hogarth R. Judgement and choice: the psychology of decision. 2nd ed. Chichester: John Wiley & Sons, 1980Google Scholar
  82. 82.
    Tamayo-Sarver JH, Dawson NV, Cydulka RK, et al. Variability in emergency physician decision making about prescribing opioid analgesics. Ann Emerg Med 2004; 43(4): 483–93PubMedCrossRefGoogle Scholar
  83. 83.
    Mancuso CA, Rose DN. A model for physicians’ therapeutic decision making. Arch Intern Med 1987; 147: 1281–5PubMedCrossRefGoogle Scholar
  84. 84.
    Moskowitz AJ, Kuipers B, Kassirer JP. Dealing with uncertainty, risks, and tradeoffs in clinical decisions: a cognitive science approach. Ann Intern Med 1988; 108: 435–49PubMedGoogle Scholar
  85. 85.
    Backlund L, Skaner Y, Montgomery H, et al. GPs’ decisions on drug treatment for patients with high cholesterol values: a think-aloud study. BMC Med Inform Decis Mak 2004; 4: 23PubMedCrossRefGoogle Scholar
  86. 86.
    Tversky A, Kahneman D. Judgement under uncertainty: heuristics and biases. Science 1974; 185: 1124–31PubMedCrossRefGoogle Scholar
  87. 87.
    Cook RI, Woods DD. Operating at the sharp end: the complexity of human error. In: Bogner MS, editor. Human error in medicine. Hillsdale (NJ): Lawrence Earlbaum Associates, 1994Google Scholar
  88. 88.
    Bornstein BH, Emler AC. Rationality in medical decision making: a review of the literature on doctors’ decisionmaking biases. J Eval Clin Pract 2001; 7: 97–107PubMedCrossRefGoogle Scholar
  89. 89.
    Christenson C, Heckerling PS, Mackesy-Amiti ME, et al. Pervasiveness of framing effects among physicians and medical students. J Behav Decis Mak 1995; 8: 169–80CrossRefGoogle Scholar
  90. 90.
    Cohen H, Robinson ES, Mandrack M. Getting to the root of medication errors: survey results. Nursing 2003; 33(9): 36–46PubMedGoogle Scholar
  91. 91.
    Aberegg SK, Arkes HR, Terry PB. Failure to adopt beneficial therapies caused by bias in medical evidence evaluation. Med Decis Mak 2006; 26(6): 575–82CrossRefGoogle Scholar
  92. 92.
    Flin R, Salas E, Strub M, et al., editors. Decision making under stress: emerging themes and applications. Aldershot: Ashgate, 1997Google Scholar
  93. 93.
    Zachary WW, Ryder JM. Decision support systems: integrating decision aiding and decision training. In: Helander M, Landauer TK, Prabhu P, editors. Handbook of human-computer interaction. 2nd ed. Elsevier Science, 1997: 1235–58Google Scholar
  94. 94.
    Westbrook J, Woods A, Rob M, et al. Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med 2010; 170(8): 683–90PubMedCrossRefGoogle Scholar
  95. 95.
    Westbrook J, Coiera EW, Dunsmuir W, et al. The impact of interruptions on clinical task completion. Qual Saf Health Care 2010; 19(4): 284–9PubMedCrossRefGoogle Scholar
  96. 96.
    Lipshitz R. Converging themes in the study of decision making in realistic settings. In: Klein GA, Orasanu J, Calderwood R, et al., editors. Decision making in action: models and methods. Norwood (NJ): Ablex Publishing Corporation, 1993: 103–37Google Scholar
  97. 97.
    Spring B. Health decision making: lynchpin of evidencebased practice. Med Decis Mak 2008; 28(6): 866–74CrossRefGoogle Scholar
  98. 98.
    Klein GA, Calderwood R, MacGregor D. Critical decision method for eliciting knowledge. IEEE Trans Syst Man Cybern 1989; 19(3): 462–72CrossRefGoogle Scholar
  99. 99.
    Higgins MP, Tully MP. Hospital doctors and their schemas about appropriate prescribing. Med Educ 2005; 39(2): 184–93PubMedCrossRefGoogle Scholar
  100. 100.
    Harries C, Evans JSBT, Dennis I. Measuring doctors’ selfinsight into their treatment decisions. Appl Cogn Psychol 2000; 14(5): 455–77CrossRefGoogle Scholar
  101. 101.
    Evans J, Harries C, Dennis I, et al. General practitioners’ tacit and stated policies in the prescription of lipid lowering agents. Br J Gen Pract 1995; 45: 15–8PubMedGoogle Scholar
  102. 102.
    Patel VL, Kaufman DR, Arocha JF. Emerging paradigms of cognition in medical decision-making. J Biomed Inform 2002; 35(1): 52–75PubMedCrossRefGoogle Scholar
  103. 103.
    Todd P, Benbasat I. Evaluating the impact of DSS, cognitive effort, and incentives on strategy selection. Inform Syst Res 1999; 10(4): 356–74CrossRefGoogle Scholar
  104. 104.
    Beach LR, Mitchell TR. A contingency model for the selection of decision strategies. Acad Manage Rev 1978; 3(3): 439–49Google Scholar
  105. 105.
    Todd P, Benbasat I. An experimental investigation of the impact of computer based decision aids on decision making strategies. Inform Syst Res 1991; 2(2): 87–115CrossRefGoogle Scholar
  106. 106.
    Sintchenko V, Coiera EW. Which clinical decisions benefit from automation? A task complexity approach. Int J Med Inform 2003; 70: 309–16PubMedCrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2011

Authors and Affiliations

  • Melissa T. Baysari
    • 1
    • 2
  • Johanna Westbrook
    • 3
  • Jeffrey Braithwaite
    • 4
  • Richard O. Day
    • 2
    • 5
  1. 1.Australian Institute of Health Innovation, Faculty of MedicineUniversity of New South WalesSydneyAustralia
  2. 2.Department of Clinical Pharmacology and ToxicologySt Vincent’s HospitalSydneyAustralia
  3. 3.Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of MedicineUniversity of New South WalesSydneyAustralia
  4. 4.Centre for Clinical Governance Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South WalesSydneyAustralia
  5. 5.Faculty of MedicineUniversity of New South WalesSydneyAustralia

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