Making electronic prescribing alerts more effective: scenario-based experimental study in junior doctors

OBJECTIVE Expert authorities recommend clinical decision support systems to reduce prescribing error rates, yet large numbers of insignificant on-screen alerts presented in modal dialog boxes persistently interrupt clinicians, limiting the effectiveness of these systems. This study compared the impact of modal and non-modal electronic (e-) prescribing alerts on prescribing error rates, to help inform the design of clinical decision support systems. DESIGN A randomized study of 24 junior doctors each performing 30 simulated prescribing tasks in random order with a prototype e-prescribing system. Using a within-participant design, doctors were randomized to be shown one of three types of e-prescribing alert (modal, non-modal, no alert) during each prescribing task. MEASUREMENTS The main outcome measure was prescribing error rate. Structured interviews were performed to elicit participants' preferences for the prescribing alerts and their views on clinical decision support systems. RESULTS Participants exposed to modal alerts were 11.6 times less likely to make a prescribing error than those not shown an alert (OR 11.56, 95% CI 6.00 to 22.26). Those shown a non-modal alert were 3.2 times less likely to make a prescribing error (OR 3.18, 95% CI 1.91 to 5.30) than those not shown an alert. The error rate with non-modal alerts was 3.6 times higher than with modal alerts (95% CI 1.88 to 7.04). CONCLUSIONS Both kinds of e-prescribing alerts significantly reduced prescribing error rates, but modal alerts were over three times more effective than non-modal alerts. This study provides new evidence about the relative effects of modal and non-modal alerts on prescribing outcomes.

[1]  Sarah Ross,et al.  Foundation year 1 doctors and clinical pharmacology and therapeutics teaching. A retrospective view in light of experience. , 2007, British journal of clinical pharmacology.

[2]  S. Iliffe,et al.  Bmc Medical Research Methodology Open Access the Hawthorne Effect: a Randomised, Controlled Trial , 2022 .

[3]  Jonathan M. Teich,et al.  AMIA Position Paper: Clinical Decision Support in Electronic Prescribing: Recommendations and an Action Plan: Report of the Joint Clinical Decision Support Workgroup , 2005, J. Am. Medical Informatics Assoc..

[4]  Jonathan M. Teich,et al.  The impact of computerized physician order entry on medication error prevention. , 1999, Journal of the American Medical Informatics Association : JAMIA.

[5]  Ann Blandford,et al.  The effect of interruptions on postcompletion and other procedural errors: an account based on the activation-based goal memory model. , 2008, Journal of experimental psychology. Applied.

[6]  J. Sandars,et al.  The frequency and nature of medical error in primary care: understanding the diversity across studies. , 2003, Family practice.

[7]  N. Barber,et al.  Reducing prescribing error: competence, control, and culture , 2003, Quality & safety in health care.

[8]  L. Trinquart,et al.  Computerized advice on drug dosage to improve prescribing practice. , 2013, The Cochrane database of systematic reviews.

[9]  A. B. Prasad,et al.  British National Formulary , 1994 .

[10]  J. Edworthy,et al.  Considering the impact of medicine label design characteristics on patient safety , 2006, Ergonomics.

[11]  W. Denneboom,et al.  Improving medication safety in the elderly. , 2008 .

[12]  Thomas H. Payne,et al.  Review Paper: Medication-related Clinical Decision Support in Computerized Provider Order Entry Systems: A Review , 2007, J. Am. Medical Informatics Assoc..

[13]  Dan Olsen.  Building Interactive Systems: Principles for Human-Computer Interaction , 2009 .

[14]  Diane L. Seger,et al.  Application of Information Technology: Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care , 2006, J. Am. Medical Informatics Assoc..

[15]  Aziz Sheikh,et al.  Enhancing public safety in primary care , 2002, BMJ : British Medical Journal.

[16]  T. Walley,et al.  Teaching safe and effective prescribing in UK medical schools: a core curriculum for tomorrow's doctors. , 2003, British journal of clinical pharmacology.

[17]  David W. Bates,et al.  Review Paper: What Evidence Supports the Use of Computerized Alerts and Prompts to Improve Clinicians' Prescribing Behavior? , 2009, J. Am. Medical Informatics Assoc..

[18]  Ervina Resetar,et al.  Implementing a Commercial Rule Base as a Medication Order Safety Net , 2003, AMIA.

[19]  Margaret Williamson,et al.  Review paper: Computerized clinical decision support for prescribing: provision does not guarantee uptake , 2010, J. Am. Medical Informatics Assoc..

[20]  K. Shojania,et al.  The effects of on-screen, point of care computer reminders on processes and outcomes of care. , 2009, The Cochrane database of systematic reviews.

[21]  Robyn Tamblyn,et al.  A randomized trial of the effectiveness of on-demand versus computer-triggered drug decision support in primary care. , 2008, Journal of the American Medical Informatics Association : JAMIA.

[22]  Diane L. Seger,et al.  Research Paper: Impact of Non-interruptive Medication Laboratory Monitoring Alerts in Ambulatory Care , 2009, J. Am. Medical Informatics Assoc..

[23]  Marc Berg,et al.  Overriding of drug safety alerts in computerized physician order entry. , 2006, Journal of the American Medical Informatics Association : JAMIA.

[24]  Ann A. O'Connell,et al.  Longitudinal Data Analysis , 2013 .

[25]  C. Vincent,et al.  Causes of prescribing errors in hospital inpatients: a prospective study , 2002, The Lancet.

[26]  ELSKE AMMENWERTH,et al.  Review Paper: The Effect of Electronic Prescribing on Medication Errors and Adverse Drug Events: A Systematic Review , 2008, J. Am. Medical Informatics Assoc..

[27]  L. Moore,et al.  Attitudes and behaviour of general practitioners and their prescribing costs: a national cross sectional survey , 2003, Quality & safety in health care.

[28]  Roger B. Davis,et al.  Physicians' decisions to override computerized drug alerts in primary care. , 2003, Archives of internal medicine.

[29]  J. Gregory Trafton,et al.  Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal , 2003, Int. J. Hum. Comput. Stud..

[30]  Diane L. Seger,et al.  A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems , 2010, J. Am. Medical Informatics Assoc..

[31]  J. Grimshaw,et al.  Practice based, longitudinal, qualitative interview study of computerised evidence based guidelines in primary care , 2003, BMJ : British Medical Journal.

[32]  H. Mcdonald,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. , 2005, JAMA.

[33]  Diane L. Seger,et al.  Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support , 2011, J. Am. Medical Informatics Assoc..

[34]  C. Vincent,et al.  Prescribing errors in hospital inpatients: their incidence and clinical significance , 2002, Quality & safety in health care.

[35]  George Hripcsak,et al.  Viewpoint Paper: Clinical Decision Support and Electronic Prescribing Systems: A Time for Responsible Thought and Action , 2005, J. Am. Medical Informatics Assoc..

[36]  R. Cucco,et al.  Prescription Errors and the Impact of Computerized Prescription Order Entry System in a Community-based Hospital , 2007, American journal of therapeutics.

[37]  David Taylor.  An in depth investigation into causes of prescribing errors by foundation trainees in relation to their medical education , 2007 .

[38]  Michael D Ahearn,et al.  General practitioners' perceptions of the pharmaceutical decision‐support tools in their prescribing software , 2003, The Medical journal of Australia.