Triggers and Targeted Injury Detection Systems (TIDS) Expert Panel Meeting: Conference Summary Report

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Challenges in Implementation of Trigger and TIDS Tools for Detection of Adverse Events in Health Care Settings

David Classen, M.D., M.S.^a

Introduction

This conference on targeted injury detection system (TIDS) and trigger tools sponsored by the Agency for Healthcare Research and Quality (AHRQ) outlines the critical need to develop practical and effective systems to measure the safety of care in the health care system. If the goal of patient safety efforts is to reduce the harm to patients while providing them with the care they need, then recognizing the true nature and sources of harm is critical to this endeavor.^F1 This goal requires some form of surveillance for detection of harm to patients and is indispensable to modern patient safety practices: it allows us to overcome the serious defects associated with dependence on spontaneous reporting as a method for detecting adverse events. While such reporting can play an important role in supporting a culture of safety—for example, encouraging the candid discussion of errors—it is by its nature anecdotal and superficial.^F2 In addition to the obvious barriers to reporting (time constraints, fear of retribution, liability concerns), we know that most events causing harm to patients are not even recognized as such by clinicians at the time they occur. Thus, voluntary reporting describes a small—and by no means representative—minority of the universe of harm to our patients. It is useless for the quantitative study of adverse events, and it is not reliable either as an indicator of the principal sources of harm or as a measure by which to assess improvement.

Background

Initially developed trigger tools for adverse event detection were computerized, such as the automated surveillance for adverse drug events, which was first demonstrated on a large scale in the early 1990s by Classen et al. at LDS Hospital.^F3 This methodology was refined and extended by investigators at Harvard^F4 and Duke.^F5 These groups used rules-based computer systems to identify combinations of clinical data (antidotes, toxic drug levels, drug-laboratory combinations, etc.) that suggest that a patient has suffered or is suffering an adverse drug event. In recent years, others have applied the principles of automated surveillance to events beyond adverse drug events—for example, using various technologies to search text documents such as discharge summaries for key words suggestive of adverse events.^F6,F7

However, automated surveillance systems have significant difficulties that have limited their usefulness and broad adoption. Many hospitals lack the technical knowledge and resources to build the sophisticated, rules-based computer systems needed to operate comprehensive surveillance; as yet, these capabilities are not available in most commercial systems. Automated surveillance depends upon the availability in electronic form of data suggestive of an adverse event. The general availability of inpatient pharmacy and laboratory data in electronic form made possible the early work in surveillance of adverse drug events in hospitalized patients. While these systems detect certain types of adverse events very effectively, other event types for which electronic trigger data do not exist are not detected. Finally, perhaps the greatest limitation of comprehensive surveillance is the significant investment in resources required to evaluate the computer alerts.

Recognizing these limitations, a number of investigators have in recent years developed modified manual "trigger" methodologies based on the data types and methods used in automated surveillance.^F8-F10 These tools permit any hospital to conduct a focused explicit chart-review-based evaluation of safety in a small sample of their patient population. Investigators with the Institute for Healthcare Improvement (IHI) have built a series of chart-review-based trigger tools for detection of adverse events in various care settings, including the intensive care unit, labor and delivery, emergency room, and surgical environments.^F8-F10 This work has culminated in the development of a more comprehensive method for detecting adverse events called the global trigger tool.^F11

AHRQ Panel Lessons Learned

Whether it is a manual trigger or an automated trigger system, we have learned a lot about the challenges of implementing and sustaining trigger-based surveillance systems. Many organizations have begun trigger work as a research or pilot project and then struggled to disseminate this approach throughout the organization. Kaiser has learned valuable lessons with the global trigger tool, which include the importance of creating credible and actionable information.^F12 With small sample sizes and few adverse events detected, the credibility of the information gathered from the Global Trigger Tool can be variable and not revealing of any new findings, which can prevent spread throughout the organization. As well, if the information is not felt to be actionable or timely, it is also less likely to be helpful. Baylor may have demonstrated the best initial approach with the Global Trigger Tool: rather than use a small monthly sample, as has been the usual case, Baylor has used a much larger sample to understand the epidemiology of the adverse events in the organization and to help develop organizational awareness, attention, and leadership support to address the problems identified.^F13 Indeed, Baylor has built the use of trigger tools into its ongoing management processes and even into its management incentives. The RTI experience with TIDS reveals how important organizational leadership support for trigger tools is; RTI has experienced challenges getting adoption of the TIDS tools in several health care systems around the country. This only underscores the importance of the organizational self-discovery journey outlined by Baylor and also noted by many other organizations that have successfully implemented trigger tools.

Kaiser has successfully used focused trigger tool modules in problem areas identified, as demonstrated by the intravenous heparin focal study or the oncology trigger tool projects at Kaiser. Indeed, focal trigger tools can support specific quality improvement initiatives, as IHI has demonstrated in numerous collaboratives. This may be a major success factor. It outlines a major issue for all trigger work: its overlap with existing quality monitoring programs, which both makes the trigger work duplicative and requires more resources without clear justification. This requires organizations to decide if trigger-based adverse event detection programs can replace existing programs; indeed, this happened with a surgical trigger tool program that was adopted by one organization to replace its surgical peer review program. A related problem is workflow. If the same person is doing triggers in addition to usual quality monitoring, it requires adjudication. If it creates more work and resource requirements, it is not likely to be sustainable. Direct linkage of trigger tools to quality improvement initiatives may be a critical success factor based on the work at several organizations.

Conclusions

As hospitals learn more about the costs and risks associated with adverse events, and as regulators and other groups demand greater accountability for patient safety, we may see an increased willingness on the part of hospitals to invest in the resources needed to take full advantage of our increasingly sophisticated clinical information systems. Indeed, in the end, implementing and maintaining adverse event surveillance systems are useful only if there exists an interested and motivated executive audience for the data. Many in health care delivery organizations are not interested in knowing their rates of adverse events, at least unless they are immediately able to offer a definitive strategy for adverse event reduction. While this may be understandable, it is only by studying the nature and frequency of these events that effective improvement strategies can be formulated, implemented, and evaluated. Otherwise, hospitals will continue to be limited to the implementation of various generic improvement strategies to focus on what we can only guess are the most pressing problems, and with no hope of ever really knowing whether the time and resources committed have made a difference to patient safety.

References

F1. Classen DC. Medication safety: moving from illusion to reality. JAMA 2003;289:1154-1156.

F2. Vincent C. Incident reporting and patient safety. BMJ 2007;334:51.

F3. Classen DC, Pestotnik SL, Evans RS, et al. Computerized surveillance of adverse drug events in hospital patients. JAMA 1991;266:2847-51.

F4. Jha AK, Kuperman GJ, Teich JM, et al. Identifying adverse drug events: development of a computer based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc 1998;5:305-14.

F5. Kilbridge PM, Alexander L, Ahmad A. Implementation of a system for computerized adverse drug event surveillance and intervention at an academic medical center. J Clin Outcomes Manage 2006;13:94-100.

F6. Forster AJ, Andrade J, van Walraven C. Validation of a discharge summary term search method to detect adverse events. J Am Med Inform Assoc 2005;12:200-6.

F7. Melton GB, Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries. J Am Med Inform Assoc 2005;12:448-57.

F8. Resar RK, Rozich JD, Classen D. Methodology and rationale for the measurement of harm with trigger tools. Qual Saf Health Care 2003;12(Suppl II):ii39-45.

F9. Rozich JD, Haraden CR, Resar RK. Adverse drug event trigger tool: a practical methodology for measuring medication related harm. Qual Saf Health Care 2003;12:194-200.

F10. Resar RK, Simmonds T, Haraden CR. A trigger tool to identify adverse events in the intensive care unit. Jt Comm J Qual Patient Saf 2006;32(10):585-590.

F11. "Introduction to Trigger Tool". [IHI Web site]. Available at: http://www.ihi.org/IHI/Topics/PatientSafety/SafetyGeneral/Tools/IntrotoTriggerToolsforIdentifyingAEs.html. Accessed February 10, 2007.

F12. Snow D. Trigger Tool Implementation Experiences in Kaiser Permanente. In: Triggers and Targeted Injury Detection Systems (TIDS) Expert Panel Meeting: Conference Summary. Agency for Healthcare Research and Quality. Rockville, MD. AHRQ Pub. No. 09-0003. February 2009.

F13. Kennerly D. Presentation at Triggers and Targeted Injury Detection Systems (TIDS) Expert Panel Meeting. Held in Rockville, MD, June 30-July 1, 2008, by Agency for Healthcare Research and Quality.

F14. Bernard SL. Challenges and Incremental Benefits in Implementing Targeted Injury Detection Systems for Adverse Drug Events and Pressure Ulcers in Inpatient Settings. In: Triggers and Targeted Injury Detection Systems (TIDS) Expert Panel Meeting: Conference Summary. Agency for Healthcare Research and Quality. Rockville, MD. AHRQ Pub. No. 09-0003. February 2009.

^aUniversity of Utah School of Medicine and Computer Sciences Corporation.

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