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Failure to Rescue

September 7, 2019


The concept of failure to rescue captures the idea that, although not every complication of medical care is preventable, health care systems should be able to rapidly identify and treat complications when they occur. As with many key concepts in patient safety, this one began with work outside of health care, including Reason's organizational accident theory, Perrow's normal accident theory, and other foundational work in human factors and complex systems. Around the same time, Karl Weick and others articulated the notion of high reliability organizations (HROs) and identified resilience as a defining characteristic of HROs.

In the late 1980s, early leaders in patient safety in anesthesiology, including Gaba and Runciman, adopted these concepts to their field. For example, Gaba described most anesthetic accidents as evolving from small errors and system failures that interact to produce more serious consequences, and he noted that there are often multiple opportunities to interrupt the "evolutionary cascade" that results in an adverse event. Throughout the 1990s, anesthesiologists led efforts to promote the capacity to detect and respond to evolving and unpredictable situations through improved crisis management strategies and sophisticated simulation training, and this work continues today.

From an HRO perspective, the capacity for organizational resilience is based on understanding that the unexpected is inevitable, and therefore no amount of planning and anticipation will prevent all complications. Indeed, Weick argues that overreliance on planning and anticipation can actually interfere with adaptive responses to unexpected events, and that resilient teams manage the inherent uncertainty in dynamic situations by maintaining awareness of the potential for things to go wrong. Resilient teams consistently update their understanding of a situation using interpersonal trust and respectful interaction to inquire about the characteristics of the situation and consider new data to inform their situation awareness, processes known as sensemaking. Thus, resilient teams manage what they consider to be inherently uncertain conditions by continually scanning for potential problems and working quickly to mitigate those problems as they arise, often beginning this mitigation work before they fully understand the complete nature of the problem.

When applied to health care, the concept of resilience refers to the ability of the team to identify changes in a patient's condition quickly and to act on those changes in a manner that benefits the patient's health. When viewed conceptually through the lens of resilience, failure to rescue would occur in any situation where the clinical team was unable to mitigate preventable harm to patients. As a safety and quality measure, failure to rescue has been defined as the inability to prevent death after the development of a complication. For example, a woman with no known comorbid conditions who undergoes an abdominal hysterectomy and develops difficulty breathing and tachycardia on the second postoperative day. The failure to identify these symptoms and signs as being consistent with pulmonary embolism, leading to a failure to perform appropriate testing and institute treatment for an ultimately fatal complication, would be consistent with the concept of failure to rescue.

Developing Failure-to-rescue Measures

Approaches to measuring failure to rescue as a quality indicator in health care were first developed in 1992 by Silber and colleagues. These investigators hypothesized that death following complications in common surgeries would be more strongly associated with hospital characteristics than the surgical complication rate, and they confirmed this in their study. They also showed that comparing mortality in patients with complications after surgery had some advantages over comparing overall mortality rates. The idea behind this type of measurement is that high quality hospitals would be more likely to be able to prevent patient death in the face of complications, even when they serve populations of patients with significant surgical risk factors. This is because variation in complication rates can be driven by patient characteristics that are present on admission, whereas the ability to rescue patients (prevent death from complications) was thought to reflect the resources and preparedness of the hospital system, in essence, its resiliency. Multiple studies in the intervening decades have shown that hospitals can have low complication rates but high failure-to-rescue rates, and vice versa. One explanation for this phenomenon may be that hospitals with higher complication rates have more experience recognizing and responding to complications when they develop, whereas hospitals with low complication rates have fewer opportunities to hone their rescue skills.

Needleman and Buerhaus subsequently developed a measure of failure to rescue that could be derived from readily available administrative data, included both medical and surgical populations, used outcomes thought to be sensitive to nursing care, and integrated exclusion rules aimed to eliminate cases in which the complication was present on admission or preoperatively. This measure, sometimes referred to as "failure to rescue—nursing," was later shown to be associated with nurse staffing and was ultimately adopted and modified by the National Quality Forum as a nurse-sensitive quality measure. AHRQ adopted a similar approach to measuring failure to rescue in Patient Safety Indicators (PSIs) in 2003.

The development of these failure-to-rescue measures was considered an important advance in quality and safety measurement. However, controversies about measure details continued for some time. Stakeholders had concerns about which kinds of deaths should be counted in the measure, questioning whether the approach could apply to all hospitalized patients or only specific types of surgical patients. The reliability of various approaches to identifying cases of failure to rescue was also questioned. Finally, there was strong interest in being able to better differentiate whether serious complications were hospital-acquired as opposed to being due to preexisting conditions. In 2007, the Centers for Medicare and Medicaid Services (CMS) addressed this concern by mandating that hospitals report whether or not all diagnoses were present on admission, and CMS began monitoring failure-to-rescue rates in 2010 (measure PSI 04). Since that time, the PSI 04 algorithm has been continuously updated to take advantage of changes in coding practices, including the present on admission indicator and the ICD-10 coding system.

Current Context

Software for calculating the Failure to Rescue PSI version 5 for ICD-9 and version 6 for ICD-10 is publicly available (PSI 04: Death rate among surgical inpatients with serious treatable conditions). This PSI is also publicly reported by CMS and an average national rate of 13.9% is presently reported on Hospital Compare. Many specialty surgical services, including pediatric and adult cardiac surgery, trauma care, gynecologic surgery, and gastrointestinal surgery, have developed context-specific approaches to measuring failure to rescue. Hospital volume, communication failures, and lower nurse staffing have all been associated with higher failure-to-rescue rates, as have staffing models involving fewer physician resources. Detailed analyses of failure-to-rescue rates for specific types of surgery are yielding more granular information about the types of cases with the highest risk for death from complications, which may provide new opportunities for quality improvement. Such measures also may shed light on specific opportunities for changing clinical microsystem processes, such as lowering the number of patients per nurse; increasing nursing surveillance; and improving safety culture, communication, and teamwork to promote early identification of clinical deterioration and timely rescue.

This project was funded under contract number 75Q80119C00004 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services. The authors are solely responsible for this report’s contents, findings, and conclusions, which do not necessarily represent the views of AHRQ. Readers should not interpret any statement in this report as an official position of AHRQ or of the U.S. Department of Health and Human Services. None of the authors has any affiliation or financial involvement that conflicts with the material presented in this report. View AHRQ Disclaimers
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