Quality Improvement Decision Support for Quality Improvement Lecture b This material (Comp12_Unit5b) was developed by Johns Hopkins University, funded

Quality Improvement

Decision Support for Quality Improvement

Lecture b

This material (Comp12_Unit5b) was developed by Johns Hopkins University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000013.


Learning Objective─Lecture b

• Analyze the benefits and shortfalls of alerts and clinical reminders.

2Health IT Workforce Curriculum Version 3.0/Spring 2012

Quality Improvement Decision Support for Quality

Improvement Lecture b

Reminders and Alerts

“…the burden of reminders and alerts must not be too high…or alert fatigue may cause clinicians to override both important and unimportant alerts, in a manner that compromises the desired safety effect of integrating decision support into CPOE.” (Van der Sijs, et al., 2006)




Alerts and Reminders

Nuisance Alert• “…provides little

perceived benefit to the prescriber at the time of the alert”

Alert Fatigue• “…arise when clinicians,

either consciously or unconsciously, begin to systematically bypass CDS alerts without regard to their importance, enabling the possibility that a clinically important alert is missed” (Chaffee, B.W., 2010)




Responses to Clinical Reminders




Responses to Clinical Reminders




Four Types of Alerts/Reminders




Basic Drug Alerts




Advanced Drug Alerts




Evidence to Support Drug Alerts

• Systematic review examined 20 studies that evaluated the impact of efficacy of computerized drug alerts and prompts– 23 of 27 alert types identified demonstrated

benefit • Improving prescribing behavior• Reducing error rates

– Greatest potential for affecting prescribing• Drug-drug interaction alerts• Drug-disease contraindication alerts• Dosing guidelines based on age




Improving Adoption of Drug Alerts

• Shah & colleagues studied improving clinician acceptance of drug alerts in ambulatory care– Designed a selective set of drug alerts for the ambulatory

care setting using a criticality leveling system– Minimized workflow disruptions by designating only critical

to high-severity alerts to be interruptive to clinician workflow

• Alert levels: – 1: clinician could not proceed with the prescription without

eliminating the contraindication– 2: clinician could proceed if provided an override reason – 3: alert displayed at top of screen in red; did not hinder

workflow




Basic Laboratory Alerts




Evidence to Support Lab Alerts

• Research examined the impact of a CDDS that generated reminders of previous lab test results

• Found that the proportion of unnecessarily repeated tests dropped significantly

• Features of the Alert– Alert was automatically prompted and was part of the

clinician workflow– User could not deactivate the alert output– Most recent laboratory result for viral serology test and its

date was automatically retrieved from the patient’s EHR– Alert was displayed at the time and location of decision

making (before the user ordered an unnecessarily repeated test)




Practice Reminders




Practice RemindersChallenges




Administrative Reminders





Summary—Lecture b• Alerts/reminders have the potential to improve

patient safety.

• Types include drug and lab test alerts, practice reminders, and administrative reminders.

• Nuisance alerts provide little perceived benefit to the prescriber at the time of the alert, causing clinician frustration and alert fatigue.

• Successful alerts are specific, sensitive, clear, concise and support clinical workflow, allowing for safe, efficient responses.





References—Lecture bReferences• Chaffee, B.W. Future of clinical decision support in computerized prescriber order entry. American Journal of

Health System Pharmacists. 67: 932-935. 2010. • De Clercq, P.A., Blom, J.A., Hasman, A., Korsten, H.H.M. A strategy for developing practice guidelines for the ICU

using automated knowledge acquisition techniques. Journal of Clinical Monitoring. 15:109-117. 1999. • Kuperman, G,J,, Bobb, A., Payne, T.H., et al. Medication-related clinical decision-support in computerized

provider order entry systems: a review. Journal of the American Medical Informatics Association. 14(1), 29-40. 2007.

• Lami, J.B., Ebrahiminia ,V., Riou C., et al. (2010). How to translate therapeutic recommendations in clinical practice guidelines into rules for critiquing physician prescriptions. Methods and application to five guidelines. BMC Medical Informatics and Decision Making. 2010 May 28;10:31.

• Metzger, J., Macdonald, K. Clinical decision support for the independent physician practice. Health Reports, California Health Care Foundation. 2002.

• Nies, J., Colombet, I., Zapleta,l E., et al. Effects of automated alerts on unnecessarily repeated serology tests in cardiovascular surgery department: a time series analysis. BMC Health Services Research. 10:70. 2010

• Porter, S. Primary care associations release joint principles for accountable care organizations. Available from:

http://www.aafp.org/online/en/home/publications/news/news-now/professional-issues/20101118acojointprinciples.html

• Schedlbauer, A., Prasad, V, Mulvaney, C, et al. What evidence supports the use of computerized alerts and prompts to improve clinicians' prescribing behavior? Journal of the American Medical Informatics Association. 16(4):531-8. 2009.

• Shah, N.R., Seger, A.C., Seger, D.L., et al. Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care. Journal of the American Medical Informatics Association 2006; 13(1): 5–11. 2006. 18Health IT Workforce Curriculum

Version 3.0/Spring 2012






References─Lecture bReferences• Shah, N.R., Seger, A.C., Seger, D.L., et al. Improving Acceptance of Computerized Prescribing Alerts in

Ambulatory Care. Journal of the American Medical Informatics Association 2006; 13(1): 5–11. 2006.• Teich, J.M., Merchia, P.R., Schmiz, J.L., et al. Effects of computerized physician order entry on prescribing

practices. Arch Intern Med. 2000 Oct 9; 160 (18):2471-7)• Van Der Sijs, H., Aarts, J., Vulto, A., Berg, M. Overriding of drug safety alerts in computerized physician order

entry. Journal of the American Medical Informatics Association. 2006; 13(2), 138-147. • Vashitz, G., Meyer, J., Parmet, Y., Peleq, R., et al. Defining and measuring physicians' responses to clinical

reminders. Journal of Biomedical Informatics. 2009; 42(2):317-26. • Zanetti, G., Flanagan, H.L. Cohn, L.H. et al. Improvement of intraoperative antibiotic prophylaxis in prolonged

cardiac surgery by automated alerts in the operating room, Infect Control Hosp Epidemiol 24 (1) (2003), pp. 13–16.

Images

Slide 5: Responses to Clinical Reminders. Adapted by Dr. Anna Maria Izquierdo-Porrera from Vashitz, G., Meyer J, Parmet, Y, et al. (2009). Defining and measuring physicians' responses to clinical reminders. Journal of Biomedical Informatics 42(2):317-26. Epub 2008 Oct 26

Slide 6: Responses to Clinical Reminders. Adapted by Dr. Anna Maria Izquierdo-Porrera from Vashitz, G., Meyer J, Parmet, Y, et al. (2009). Defining and measuring physicians' responses to clinical reminders. Journal of Biomedical Informatics 42(2):317-26. Epub 2008 Oct 26





References─Lecture b

Images

Slide 7: Four Types of Alerts/Reminders. Dr. Anna Maria Izquierdo-Porrera

Slide 8: Basic Drug Alerts. Adapted by Dr. Anna Maria Izquierdo-Porrera from Kuperman, G.J., Bobb, A., Payne, T.H., et al. Medication-related clinical decision-support in computerized provider order entry systems: a review. Journal of the American Medical Informatics Association 14(1), 29-40. 2007.

Slide 9: Advanced Drug Alerts. Dr. Anna Maria Izquierdo-Porrera

Slide 12: Basic laboratory Alerts. Dr. Anna Maria Izquierdo-Porrera

Slide 14: Practice Reminder Challenges. Adapted by Dr. Anna Maria Izquierdo-Porrera from Lami, J.B., Ebrahiminia, V., Riou, C., et al. (2010). How to translate therapeutic recommendations in clinical practice guidelines into rules for critiquing physician prescriptions. Methods and application to five guidelines. BMC Medical Informatics and Decision Making. 2010 May 28;10:31.

Slide 15: Practice Reminders. Dr. Anna Maria Izquierdo-Porrera

Slide 16: Administrative Reminders. Dr. Anna Maria Izquierdo-Porrera




Documents

Quality Improvement Decision Support for Quality Improvement Lecture b This material (Comp12_Unit5b) was developed by Johns Hopkins University, funded