Redesign of computerized decision support to improve antimicrobial prescribing

Summary

Objective: To determine the impact of the introduction of new pre-written orders for antimicrobials in a computerized provider order entry (CPOE) system on 1) accuracy of documented indications for antimicrobials in the CPOE system, 2) appropriateness of antimicrobial prescribing, and 3) compliance with the hospital’s antimicrobial policy. Prescriber opinions of the new decision support were also explored to determine why the redesign was effective or ineffective in altering prescribing practices.

Methods: The study comprised two parts: a controlled pre-post study and qualitative interviews. The intervention involved the redesign of pre-written orders for half the antimicrobials so that approved indications were incorporated into pre-written orders. 555 antimicrobials prescribed before (September – October, 2013) and 534 antimicrobials prescribed after (March – April, 2015) the intervention on all general wards of a hospital were audited by study pharmacists. Eleven prescribers participated in semi-structured interviews.

Results: Redesign of computerized decision support did not result in more appropriate or compliant antimicrobial prescribing, nor did it improve accuracy of indication documentation in the CPOE system (Intervention antimicrobials: appropriateness 49% vs. 50%; compliance 44% vs. 42%; accuracy 58% vs. 38%; all p>0.05). Via our interviews with prescribers we identified five main reasons for this, primarily that indications entered into the CPOE system were not monitored or followed-up, and that the antimicrobial approval process did not align well with prescriber workflow.

Conclusion: Redesign of pre-written orders to incorporate appropriate indications did not improve antimicrobial prescribing. Workarounds are likely when compliance with hospital policy creates additional work for prescribers or when system usability is poor. Implementation of IT, in the absence of support or follow-up, is unlikely to achieve all anticipated benefits.

Citation: Baysari MT, Del Gigante J, Moran M, Sandaradura I, Li L, Richardson KL, Sandhu A, Lehnbom EC, Westbrook JI, Day RO. Redesign of computerized decision support to improve antimicrobial prescribing. Appl Clin Inform 2017; 8: 949–963 https://doi.org/10.4338/ACI2017042017040069

Keywords

Clinical decision support - Alerting - Order entry - Medication management - Hospital information systems

• References

• 1 Antibiotic Expert Group.. Therapeutic Guidelines: Antibiotic (Version 15). Melbourne, Australia: Therapeutic Guidelines Limited; 2014
• 2 US Department of Health and Human Services.. Antibiotic resistance threats in the United States. 2013 Centers for Disease Control and Prevention. 2013.
• 3 Radford JM, Cardiff LM, Pillans PI, Fielding DIK, Looke DFM. Drug usage evaluation of antimicrobial therapy for community-acquired pneumonia. The Australian Journal of Hospital Pharmacy 1999; 29 (06) 317-20.
• 4 Willemsen I, Groenhuijzen A, Bogaers D, Stuurman A, van Keulen P, Kluytmans J. Appropriateness of antimicrobial therapy measured by repeated prevalence surveys. Antimicrob Agents Chemother 2007; 51 (03) 864-7.
• 5 Davey P, Brown E, Charani E, Fenelon L, Gould IM, Holmes A. et al. Interventions to improve antibiotic prescribing practices for hospital inpatients. Cochrane Database of Systematic Reviews 2013; 4: CD003543.
• 6 Venugopal V, Lehmann CU, Diener-West M, Agwu AL. Longitudinal evaluation of a World Wide Web–based antimicrobial stewardship program: Assessing factors associated with approval patterns and trends over time. Am J Infect Control 2014; 42 (02) 100-5.
• 7 Baysari MT, Lehnbom EC, Li L, Hargreaves A, Day RO, Westbrook JI. The effectiveness of information technology to improve antimicrobial prescribing in hospitals: A systematic review and meta-analysis. Int J Med Inform 2016; 92: 15-34.
• 8 Kazemi A, Ellenius J, Pourasghar F, Tofighi S, Salehi A, Amanati A. et al. The effect of Computerized Physician Order Entry and decision support system on medication errors in the neonatal ward: experiences from an Iranian teaching hospital. J Med Syst 2011; 35 (01) 25-37.
• 9 van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006; 13 (02) 138-47.
• 10 Coleman JJ, van der Sijs H, Haefeli WE, Slight SP, McDowell SE, Seidling HM. et al. On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop. BMC Med Inform Decis Mak 2013; 13: 111.
• 11 van der Sijs H, van Gelder T, Vulto A, Berg M, Aarts J. Understanding handling of drug safety alerts: a simulation study. Int J Med Inform 2010; 79 (05) 361-9.
• 12 Devabhakthuni S, Gonzales J, Tata A, Lee S, Shah P, Offurum AI. et al. Evaluation of Vancomycin Dosing and Monitoring in Adult Medicine Patients. Hosp Pharm 2012; 47 (06) 451-9.
• 13 Hall AB, Montero J, Cobian J, Regan T. The effects of an electronic order set on vancomycin dosing in the ED. Am J Emerg Med 2015; 33 (01) 92-4.
• 14 Thiel SW, Asghar MF, Micek ST, Reichley RM, Doherty JA, Kollef MH. Hospital-wide impact of a standardized order set for the management of bacteremic severe sepsis. Crit Care Med 2009; 37 (03) 819-24.
• 15 Karsies TJ, Sargel CL, Marquardt DJ, Khan N, Hall MW. An empiric antibiotic protocol using risk stratification improves antibiotic selection and timing in critically ill children. Ann Am Thorac Soc 2014; 11 (10) 1569-75.
• 16 Haynes K, Linkin DR, Fishman NO, Bilker WB, Strom BL, Pifer EA. et al. Effectiveness of an information technology intervention to improve prophylactic antibacterial use in the postoperative period. J Am Med Inform Assoc 2011; 18 (02) 164-8.
• 17 Baysari MT, Westbrook JI, Richardson KL, Day RO. The influence of computerized decision support on prescribing during ward-rounds: are the decision-makers targeted?. JAMIA 2011; 18: 754-9.
• 18 Baysari MT, Oliver K, Egan B, Li L, Richardson K, Sandaradura I. et al. Audit and feedback of antibiotic use. Utilising electronic prescription data. Appl Clin Inform 2013; 4 (04) 583-95.
• 19 Falck S, Adimadhyam S, Meltzer DO, Walton SM, Galanter WL. A trial of indication based prescribing of antihypertensive medications during computerized order entry to improve problem list documentation. Int J Med Inform. 2013
• 20 Galanter WL, Hier DB, Jao C, Sarne D. Computerized physician order entry of medications and clinical decision support can improve problem list documentation compliance. Int J Med Inform 2010; 79 (05) 332-8.
• 21 Walton SM, Galanter WL, Rosencranz H, Meltzer D, Stafford RS, Tiryaki F. et al. A trial of inpatient indication based prescribing during computerized order entry with medications commonly used off-label. Appl Clin Inform 2011; 2 (01) 94-103.
• 22 Thomas DR. A general inductive approach for analyzing qualitative evaluation data. Am J Eval 2006; 27 (02) 237-46.
• 23 Patton MQ. Qualitative evaluation and research methods. 3rd ed. NewBury Park, CA: Sage Publications Inc; 2001
• 24 Cresswell KM, Mozaffar H, Lee L, Williams R, Sheikh A. Workarounds to hospital electronic prescribing systems: a qualitative study in English hospitals. BMJ Quality & Safety. 2016
• 25 Koppel R, Wetterneck T, Telles JL, Karsh B-T. Workarounds to Barcode Medication Administration Systems: Their Occurrences, Causes, and Threats to Patient Safety. J Am Med Inform Assoc 2008; 15 (04) 408-23.
• 26 Agwu AL, Lee CK, Jain SK, Murray KL, Topolski J, Miller RE. et al. A World Wide Web-based antimicrobial stewardship program improves efficiency, communication, and user satisfaction and reduces cost in a tertiary care pediatric medical center. Clin Infect Dis 2008; 47 (06) 747-53.
• 27 Chernev A, Böckenholt U, Goodman J. Choice overload: A conceptual review and meta-analysis. J Consum Psychol 2015; 25 (02) 333-58.
• 28 Haynes GA. Testing the boundaries of the choice overload phenomenon: The effect of number of options and time pressure on decision difficulty and satisfaction. Psychology and Marketing 2009; 26 (03) 204-12.