Decision Support for Quality Improvement Welcome to Quality Improvement: Decision Support and Quality Improvement. This is Lecture b. This unit is designed to provide you with a deeper understanding of the role of alerts and clinical reminders, their benefits, and potential hazards. 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.

Decision Support for Quality Improvement Learning Objective─Lecture b Analyze the benefits and shortfalls of alerts and clinical reminders. The Objective for Decision Support and Quality Improvement is to: Analyze the benefits and shortfalls of alerts and clinical reminders. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

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) While decision support in the form of alerts and reminders has the potential to improve patient safety, reality is that, according to the current literature, these alerts are overridden by clinicians 49% to 96% of the time. This is a real concern for HIT professionals. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Quality Improvement Decision Support for Quality Improvement Lecture b 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) One of the main reasons given for overriding alerts is perceived lack of relevance. The so-called nuisance alerts provide little perceived benefit to the prescriber at the time of the alert. This causes great frustration on the part of clinicians and leads to a phenomenon called alert fatigue. Alert fatigue can result in the clinician systematically bypassing the alert, either consciously or subconsciously. The potential for missing a clinically important alert is high. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Responses to Clinical Reminders There are four types of responses identified with respect to clinicians and clinical reminders. Compliance is the tendency to perform an action when a warning system instructs the user to perform a corrective or preventive action, such as checking a hospitalized patient’s status when a clinical monitor issues an alert in the EHR. Reliance, on the other hand, is the tendency to refrain from performing an action when the warning system does not indicate that it is necessary. For example, assuming that the patient’s status is normal and thus not checking the patient when the monitor does not generate an alert in the electronic health record. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Responses to Clinical Reminders Two additional types of responses include spillover and reactance. Spillover can occur when there is a spread of responses merely due to increased awareness of the need for an action, even when the clinician is not prompted by the reminder system. For example, Zanetti’s research team found that reminders for intra-operative drugs increased re-dosing even in a control group that did not receive these reminders and concluded that this spillover occurred because of increased awareness to the importance of re-dosing. Reactance is the tendency for clinicians to experience a threat to their autonomy or freedom of choice in the presence of these systems. The clinician may consciously or unconsciously react by either ignoring the reminders or choosing a different course of action, trying to regain their sense of freedom and autonomy. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Four Types of Alerts/Reminders For the purpose of this discussion, we will look at experience with four types of alerts or reminders: drug alerts, lab test alerts, practice reminders, and administrative reminders. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Quality Improvement Decision Support for Quality Improvement Lecture b Basic Drug Alerts Kuperman and colleagues identify four types of basic drug alerts. Drug allergy warnings are generated on ordering a drug to which the patient has a documented drug allergy. Drug-drug interactions are generated when the mode of action of one drug is known to be affected by simultaneously prescribing a second drug. Duplicate medication or therapeutic duplication alerts are generated when the patient is already receiving the medication just ordered or a different drug in the same therapeutic category. Basic medication order guidance is an alert that provides dosing information with default dosing being the most appropriate initial dosing. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Quality Improvement Decision Support for Quality Improvement Lecture b Advanced Drug Alerts There are eight identified types of advanced drug alerts. Drug-lab alerts are generated when drug administration requires close monitoring of laboratory results before and/or after administration of the drug. Drug-condition interactions are generated to raise awareness of specific prescribing for particular clinical conditions. Drug-disease contraindication alerts are generated to warn against prescribing a certain drug in a specific disease or condition. Drug-condition alerts aimed at appropriate prescribing are generated to encourage prescribing a certain drug in a specific clinical condition. Drug-age alerts are generated to discourage prescribing of a certain drug in the elderly or in children. Drug-formulary alerts are generated to notify the prescriber that a particular brand or drug is neither included nor recommended in the formulary at the prescribing location. Dosing guidelines are alerts that take into account complex patient characteristics such as age, renal/liver function, pregnancy of female of childbearing potential, pediatric weight-based dosing, drug utilization restrictions, or clinical indication. Finally, complex prescribing alerts are generated with combined features of basic and advanced alerts. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

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 Angela Schledlbauer and her colleagues performed a systematic review of the evidence that supports use of computerized alerts and prompts to improve drug prescribing behaviors. Most of the studies reviewed showed positive benefits. In fact, 23 of 27 drug alert types that were identified demonstrated benefits in terms of improving prescriber behavior and reducing error rates. These researchers found that the greatest potential for benefit was with drug-drug interaction and drug-disease contraindication alerts, and age-related dosing guidelines. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

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 Shah and colleagues wanted to find a way to improve clinician acceptance of drug alerts in ambulatory care settings. They designed a selective set of drug alerts using a criticality leveling systems. Level 1 alerts were the most critical and a clinician could not proceed with the prescription without eliminating the contraindication. Level 2 alerts were very important, but the clinician could proceed if he or she provided a reason for overriding the alert. Level 3 alerts were designed to not disrupt workflow, but to provide important information for the clinician to take into consideration. Through this research, Shah’s team was able to demonstrate that by designating only critical to high-severity alerts to be interruptive of clinician workflow, they were able to minimize workflow disruptions and improve clinician acceptance of drug alerts. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Basic Laboratory Alerts Basic laboratory alerts include: drug-lab alerts, which as stated previously, are generated when drug administration requires close monitoring of laboratory results before and/or after administration. Duplicate laboratory testing alerts are generated when the patient has already had the lab test ordered. Basic laboratory test order guidance is an alert that provides ordering information with respect to the particular lab test being ordered. Finally, alerts can be generated to notify clinicians of public health alerts so that they can initiate appropriate laboratory testing during local disease outbreaks. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

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) French researchers recently examined the impact of a serology-clinical decision support system providing point-of-care reminders of previous existing blood test results. These reminders were embedded in a CPOE at a university teaching hospital. The CDSS was implemented in the cardiovascular surgery department of that hospital in order to decrease inappropriate repetitions of viral serology tests (specifically, Hepatitis B Virus). The researchers found that the proportion of unnecessarily repeated tests immediately dropped after implementation of the alert and remained stable over time. In a recent article, Neis and colleagues discussed features of the alert that enhanced its success, “the alert was automatically prompted and was part of clinician workflow, the user could not deactivate the alert output, the most recent laboratory result for viral serology tests and its date was automatically retrieved from the patient's electronic health record, and the alert was displayed at the time and location of decision making that is, before the user ordered an unnecessarily repeated test.” Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Quality Improvement Decision Support for Quality Improvement Lecture b Practice Reminders There are three ways in which practice reminders can be used as clinical decision support. They can guide the clinician to provide the recommended treatment. They can critique the clinician’s course of action by comparing it against guideline recommendations, and they can help with patient follow-up. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Practice Reminders Challenges There are a number of known challenges to incorporating clinical practice guidelines into clinical decision support systems. These challenges were identified more than 10 years ago, and are still applicable today. First, reminders can be false alarms, given as a result of an incorrectly implemented guideline. Next, reminders can be false due to the fact that the guideline is not specific enough to address the particular patient’s condition. For example, a guideline may not specify exceptions, so a patient who falls within the exception may have the guideline inappropriately applied. Third, the patient data in the EHR can be incomplete or inconsistent, resulting in the reminder either firing inappropriately or not firing at all. Fourth, the actions that the reminder generates may not be clinically appropriate for the particular patient, and finally, the reminder itself may generate risk to the patient. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Administrative Reminders Decision support can also be used to provide administrative reminders such as use of suggested coded problems for particular patient populations to support billing and specific guidance on documentation to support collection of quality improvement indicator data. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Decision Support for Quality Improvement 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. This concludes Lecture b of Decision Support for Quality Improvement. In summary, alerts and reminders have the potential to improve patient safety. Types include drug and lab test alerts, practice reminders, and administrative reminders. Alerts and reminders also have the potential to compromise patient safety. Nuisance alerts provide little perceived benefit to the prescriber at the time of the alert and can cause frustration and alert fatigue, which in turn, can result in medical error. Successful alerts are specific, sensitive, clear, concise, and support clinical workflow and efficiency. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Decision Support for Quality Improvement References—Lecture b 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. No audio. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Decision Support for Quality Improvement References─Lecture b 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 No audio. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b

Decision Support for Quality Improvement 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 No audio. Health IT Workforce Curriculum Version 3.0/Spring 2012 Quality Improvement Decision Support for Quality Improvement Lecture b