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Review of the Trigger Literature: Adverse Events Targeted and Gaps in Detection

Hillary J. Mull, M.P.P.^a
Stephanie Shimada, Ph.D.^a
Jonathan Nebeker, M.D., M.S.^b
Amy Rosen, Ph.D.^a

Background

An important development in adverse event (AE) detection is the use of triggers, algorithms that use patient data to look for patterns consistent with a possible AE.^A1–A4 In a trigger system, when a trigger flags a record, there is a method to further determine whether an AE occurred. In the case of action-oriented trigger systems, triggers are designed to support clinical interventions that prevent or mitigate iatrogenic harm. Trigger systems have been used in inpatient settings for rate detection and to signal providers to investigate a possible AE in real time.^A5–A7 Recently, trigger systems have been used to detect AEs that occur in particular settings, such as emergency departments^A8 or neonatal intensive care units,^A7 or among specific patient groups, such as pediatric populations.^A7,A9,A10 This paper reviews the literature on triggers developed as part of an outpatient trigger development project funded by the Agency for Healthcare Research and Quality (AHRQ).^A11

Methods

This review summarizes the trigger literature published prior to January 1, 2008. In addition to literature from the project team, we conducted searches of information databases using standardized keywords. Forty-five references contained information on triggers or trigger systems. We also reviewed articles for background information on the leading causes and types of AEs.

Summary of Literature on Accounting Trigger Systems

Some triggers are designed to be used together as a trigger system, typically for the purpose of AE rate estimation or accounting.^{A3,A6,A12–A17} Most accounting trigger systems were developed by the Institute for Healthcare Improvement (IHI) and include information on implementation as well as guidelines on classifying the harm and/or preventability of AEs detected.^A16 The objective of accounting trigger systems is not to test and improve the positive predictive value (PPV) of any individual trigger, but to estimate rates of AEs within the system.

Summary of Triggers Linked to Specific AEs

For this paper, our primary focus is on triggers and trigger systems that were linked to specific AEs or specific AE causes. (Therefore, we do not include triggers that were part of accounting trigger systems in this section.) The majority of the triggers linked to AEs were drug related (n = 364). Figure 1 shows the most frequent adverse drug events (ADEs) targeted by triggers or trigger systems in the literature. (Only ADEs with ≥5 triggers are shown.) In addition to the 23 ADEs shown in Figure 1, there were 88 other ADEs targeted by specific triggers. Triggers varied in the amount of detail or in the type of data used to detect a specific AE. For example, one of the triggers that targeted bleeding was "Vitamin K given," while another trigger that also targeted bleeding included information on the type of bleeding by specifying "International Normalized Ratio (INR) elevated or increasing."^A18

Figure 2 presents the frequency of triggers designed to detect specific AEs that occurred because of medical mismanagement and progression of underlying disease. (Only AEs with ≥2 triggers are shown.) In addition to the 18 " medical management failure" events shown, there were 34 other AEs targeted by one trigger. AEs classified as medical mismanagement tend to be rare but harmful, and trigger development in this area is focused primarily on expanding the number of AEs detected, rather than refining the detection process.

Figure 3 shows the distribution of surgical AEs targeted by triggers in the literature. AEs resulting from inpatient and same-day surgeries are prevalent and costly;^A19–A21 however, we found only 31 surgical triggers. Several of these triggers were not part of trigger systems and therefore did not have any mechanism for confirming that an AE occurred.

We found 27 triggers that could not be easily categorized. These triggers concerned global AEs (e.g., a natural language processing discharge summary review that used trigger words like "error"^A22); crimes (e.g., infant abduction^A23); or death/serious injury with an unspecified cause.^A23

Summary of Literature on Triggers Linked to AE Causes

We also reviewed triggers and trigger systems linked to the cause of an event. In some cases, particularly with respect to medical mismanagement and surgical triggers, the event was specified and is therefore included in the previous section. There were 314 drug-related triggers that specified the drug that caused the ADE; types of causal drugs are shown in Figure 4. (Only causal agents with ≥2 triggers are shown.) One hundred drug-related triggers specify the targeted ADE but do not include the drug or drug class that may have been the causal agent.

Gaps and Future Directions for Trigger Development

Our review of the literature found that the majority of triggers and trigger systems were drug related. Based on the ADE prevalence literature, the most frequent drug-related triggers detect the most common ADEs in the population.^A24 However, several drugs that cause high rates of ADEs in the outpatient setting are not in the trigger literature: contraceptives, and drugs used for skin, eye, and dental problems.^A24 Future drug-related trigger system development should consider ADE detection in ambulatory settings, including primary and specialty care.

We found a wide variety of triggers designed to detect specific medical mismanagement AEs. Most of these triggers were designed as accounting triggers; however, there is also an opportunity to use the trigger language to develop action-oriented trigger systems. Only two articles specified a cause of medical mismanagement AEs.^A25–A26 Diagnostic errors and failure to follow up are common causes of AEs, and more work needs to be done in developing action-oriented trigger systems that detect these types of events.

With the exception of the IHI,^A14 surgical trigger systems have not yet been developed. While we found two articles with triggers that could be used in an inpatient action-oriented trigger system,^A22–A23 there were no surgical triggers designed for outpatient surgery. Given the severity and nature of surgical AEs, future research should target the development of action-oriented surgical trigger systems for inpatient and outpatient care.

References

A1. Rozich JD, Haraden CR, Resar RK. Adverse drug event trigger tool: a practical methodology for measuring medication related harm. Qual Saf Health Care 2003 Jun;12(3):194-200.

A2. Resar RK, Rozich JD, Classen D. Methodology and rationale for the measurement of harm with trigger tools. Qual Saf Health Care 2003 Dec;12 Suppl 2:ii39-45.

A3. Resar R. Outpatient Adverse Event Trigger Tool v4. Institute for Healthcare Improvement (IHI) www.IHI.org. 2006.

A4. Classen DC, Pestotnik SL, Evans RS, Burke JP. Computerized surveillance of adverse drug events in hospital patients. JAMA 1991 Nov 27;266(20):2847-2851.

A5. Penz JF, Wilcox AB, Hurdle JF. Automated identification of adverse events related to central venous catheters. J Biomed Inform 2007 Jun 9; 40(2): 174-182.

A6. Resar RK, Rozich JD, Simmonds T, Haraden CR. A trigger tool to identify adverse events in the intensive care unit. Jt Comm J Qual Patient Saf 2006 Oct;32(10):585-590.

A7. Sharek PJ, Horbar JD, Mason W, et al. Adverse events in the neonatal intensive care unit: development, testing, and findings of an NICU-focused trigger tool to identify harm in North American NICUs. Pediatrics 2006 Oct;118(4):1332-1340.

A8. Hefflin BJ, Gross TP, Schroeder TJ. Estimates of medical device-associated adverse events from emergency departments. Am J Prev Med 2004 Oct;27(3):246-253.

A9. Ferranti J, Horvath MM, Cozart H, et al. Reevaluating the safety profile of pediatrics: a comparison of computerized adverse drug event surveillance and voluntary reporting in the pediatric environment. Pediatrics 2008 May;121(5):e1201-1207.

A10. Takata GS, Mason W, Taketomo C, et al. Development, testing, and findings of a pediatric-focused trigger tool to identify medication-related harm in US children's hospitals. Pediatrics 2008 Apr;121(4):e927-935.

A11. Rosen AK, Nebeker JR, Shimada S, et al. Development and Use of Ambulatory Adverse Event Trigger Tools. Rockville (MD): Agency for Healthcare Research and Quality; 2007. AHRQ Contract No. HHSA2902006000012, TO #3.

A12. IHI ICU Adverse Event Trigger Tool version 1. Institute for Healthcare Improvement (IHI) www.IHI.org. 2002.

A13. IHI Trigger Tool for Measuring Adverse Drug Events. Institute for Healthcare Improvement (IHI) www.IHI.org. 2004.

A14. IHI Surgical Trigger Tool Kit Version 2. Institute for Healthcare Improvement (IHI) www.IHI.org. 2006.

A15. Child Health Corporation of America. Trigger Tool for Measuring Adverse Events in the Neonatal Intensive Care Unit. Institute for Healthcare Improvement (IHI) www.IHI.org. 2006.

A16. Griffin FA, Resar R. IHI Global Trigger Tool for Measuring Adverse Events. Institute for Healthcare Improvement (IHI) www.IHI.org. 2007.

A17. Matlow A, Flintoft V, Orrbine E, et al. The development of the Canadian paediatric trigger tool for identifying potential adverse events. Healthc Q 2005;8 Spec No:90-93.

A18. Handler SM, Altman RL, Perera S, et al. A systematic review of the performance characteristics of clinical event monitor signals used to detect adverse drug events in the hospital setting. J Am Med Inform Assoc 2007 Jul-Aug;14(4):451-458.

A19. Bruce J, Russell EM, Mollison J, Krukowski ZH. The measurement and monitoring of surgical adverse events. Health Technol Assess 2001;5(22):1-194.

A20. Johnson RG, Arozullah AM, Neumayer L, et al. Multivariable predictors of postoperative respiratory failure after general and vascular surgery: results from the patient safety in surgery study. J Am Coll Surg 2007 Jun;204(6):1188-1198.

A21. Neumayer L, Hosokawa P, Itani K, et al. Multivariable predictors of postoperative surgical site infection after general and vascular surgery: results from the patient safety in surgery study. J Am Coll Surg 2007 Jun;204(6):1178-1187.

A22. Murff HJ, Forster AJ, Peterson JF, et al. Electronically screening discharge summaries for adverse medical events. J Am Med Inform Assoc 2003 Jul-Aug;10(4):339-350.

A23. Melton GB, Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries. J Am Med Inform Assoc 2005 Jul-Aug;12(4):448-457.

A24. Thomsen LA, Winterstein AG, Sondergaard B, et al. Systematic review of the incidence and characteristics of preventable adverse drug events in ambulatory care. Ann Pharmacother 2007 Sep;41(9):1411-1426.

A25. Mackinnon NJ, Hepler CD. Preventable drug-related morbidity in older adults, 1. Indicator development. J Manag Care Pharm 2002 Sep-Oct;8(5):365-371.

A26. Singh H, Thomas EJ, Khan MM, Petersen LA. Identifying diagnostic errors in primary care using an electronic screening algorithm. Arch Intern Med 2007 Feb 12;167(3):302-308.

^a VA Center for Health Quality Outcomes and Economic Research (CHQOER) and Boston University School of Public Health, Health Policy and Management Department.
^b VA Salt Lake City Geriatrics Research, Education, and Clinical Center (GRECC); Department of Medicine, University of Utah; and Intermountain Institute for Healthcare Delivery.

Note: The views in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.

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