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Part II
https://t.me/med1917
System Design and Improvement
Detecting andReporting Errors,
https://t.me/med1917
Complications, andAdverse Events
MajedEl Hechi andHaythamM.A.Kaafarani
Introduction
Much of modern thinking about safety systems evolved out of military aviation [1].
Until World War II, aviation accidents were blamed primarily on individuals, and
“safety culture” simplistically translated to motivating people to “be safe.”
Throughout the war, United States generals lost aircraft and pilots in domestic operations because of system errors and came to realize that planning for safety was as
important as planning for combat. For this reason, several military aviation safety
centers were created in the early 1950s. Gradually, the eld evolved and eventually
disseminated into the commercial sector. Around the 1970s, the risk of dying on a
commercial ight was 1in two million. By the 1990s, the risk had decreased to 1in
eight million [2].
In healthcare, the importance of patient safety and quality of care came
decades later. The two landmark reports, “To Err Is Human” and “Crossing the
Quality Chasm,” released by the Institute of Medicine in the early 2000s, estimated that between 44,000 and 98,000 patients died every year in the United
States because of medical errors. The same reports highlighted that efforts aimed
at measuring the quality of care were insufcient, and that medical errors were
often caused by awed systems from a patient safety perspective [2, 3]. Applying
12
M. El Hechi
Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
H. M. A. Kaafarani (*)
Division of Trauma, Emergency Surgery and Surgical Critical Care, Department of Surgery,
Massachusetts General Hospital, Boston, MA, USA
e-mail: hkaafarani@mgh.harvard.edu
© Springer Nature Switzerland AG 2024
J. J. Hoballah et al. (eds.), Principles of Perioperative Safety and Efciency,
https://doi.org/10.1007/978-3-031-41089-5_12
201
202
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these healthcare statistics to modern professional aviation equates to nearly 200
jumbo jets crashing every year, or one Boeing 747 airplane crashing every other
day [4]. This staggering insight led to the rapid rise in patient safety as a eld in
healthcare and the launch of many initiatives worldwide to improve patient
safety, such as the “100,000 lives campaign” by the Institute for Healthcare
Improvement (IHI) in 2004 [5].
Nearly all the success stories of the patient safety movement in the last two
decades included robust methodologies to detect and report adverse events and
errors, as one cannot improve what one cannot or does not measure. This chapter will describe the evolution of adverse event and error detection-and-reporting systems in surgery, as well as classification tools that allow for risk
adjustment.
M. El Hechi and H. M. A. Kaafarani
Detection andReporting Systems
Institutional Detection andReporting: Mortality
andMorbidity Conferences
In the early 1900s, after discovering the poor quality of documentation in hospital
records and the corresponding problems with patient safety and care, Ernest
A.Codman, a surgeon at the Massachusetts General Hospital, developed a system
where patient outcomes were documented, and adverse events were systematically reviewed and their causative errors categorized [6]. This initiative led to the
creation of the “Minimum Standard” system, which was adopted by the American
College of Surgeons in 1919 [7]. The aforementioned standard required that hospital staff meet, review, and analyze their clinical experience in various departments at regular intervals, with the intent to inquire whether the “end result”
achieved was optimal. The “minimum standard system” still occurs in all surgical
departments across the United States in the form of a morbidity and mortality
(M&M) conference. The M&M conferences are the primary mechanism by which
surgical departments monitor, report, analyze, and learn from surgical complications [8]. In 1983, it was mandated that all departments with surgical training
programs hold “a weekly review of all current complications and deaths, including radiologic and pathologic correlation of surgical specimens and autopsies.”
[9] However, despite its educational value, it has been repeatedly suggested that
these conferences alone are an insufcient quality-assurance mechanism for the
modern and continuously evolving surgical practice [2, 3, 10]. Multiple studies
have specically shown that the error detection sensitivity of M&M conferences
is 25% at best [8]. Consequently, it became evident that additional systems for
quality measurement, error reporting, and provider feedback are needed to
improve the safety and quality of surgical care [11, 12]. Specically, prospective
data collection that incorporates risk-adjusted performance feedback were deemed
essential for reducing errors, changing physician behavior, and improving surgical outcomes [13–15].
12 Detecting andReporting Errors, Complications, andAdverse Events
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203
Administratively Derived National Detection
andReporting Systems
Patient Safety Indicators
In the 1990s, to improve detection and reporting of medical errors, the Agency for
Healthcare Research and Quality (AHRQ) developed quality indicators based from
the Healthcare Cost and Utilization Project (HCUP) database of inpatient data.
Thirty-three quality indicators were developed from administrative data that measured inpatient mortality as well as the rates of specic procedures being performed
in the healthcare system. As information on the performance of quality indicators
was gathered, they were rened and developed further.
The joint effort between Stanford University and University of California, San
Francisco, yielded a set of 20 provider-level Patient Safety Indicators (PSIs) that use
International Classication of Diseases, Ninth Revision, Clinical Modication
(ICD-9-CM) diagnoses, procedures, and diagnosis-related groups (DRGs). After
extensive evaluation and revision by clinical panels and expert reviewers, the PSIs
were made readily available to providers in 2003 at no cost. Subsequently, seven
area-level quality indicators were added to the list (Table 12.1) [16]. Select PSIs
were later endorsed for hospital proling and pay-for-performance purposes [17].
Notably, The National Quality Forum adopted 10 PSIs, [18] and the Centers for
Table 12.1 Provider and area-level patient safety indicators
Source: AHRQ Quality Indicators. https://www.qualityindicators.ahrq.gov/Downloads/Modules/
PSI/V50/PSI_Brochure.pdf
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M. El Hechi and H. M. A. Kaafarani
Medicare and Medicaid Services (CMS) publicly reported on six individual PSIs as
well as a PSI composite measure [19].
With the use of PSIs in public reporting, concerns emerged about the ability of
these indicators to identify true events and reect hospital performance.
Administrative data is known for its coding variability and inconsistency regarding
diagnoses and procedures, and as a result of PSIs being derived from it, they were
intended to serve as “indicators” for potentially preventable inpatient complications
and for use in quality improvement initiatives and monitoring trends, rather than
denitive measures [16]. Nonetheless, given these concerns, the AHRQ conducted
the PSI Validation Pilot Project to assess the criterion validity of several of the PSIs
in a volunteer sample of nonfederal hospitals, [20–24] with validation of additional
PSIs undertaken by an alliance of 113 academic medical centers and their afliated
hospitals, the United Health Care (UHC). Positive predictive value, the proportion
of agged cases conrmed by chart review to have the PSI event, ranged from 32%
for decubitus ulcer to 91% for accidental puncture or laceration [20–24]. These
studies led to recommendations for coding changes to enhance the specicity and
PPV of certain indicators and provided guidelines for the use of individual PSIs.
Moreover, the individual PSIs were validated in Veterans Affairs hospitals, which
demonstrated that some PSIs perform better than others and that overall, the PSIs
have moderate to high sensitivities and specicities [22–24].
Despite being cost efcient, the PSIs present several disadvantages and challenges. Being administratively derived rendered PSIs unable to determine the root
causes of events. Namely, they do not cover important areas of patient safety events
because of the absence of relevant information from administrative data, such as
adverse drug events. Moreover, they rely heavily on ICD-9-CM codes. This is an
issue, since a wide range of clinical diagnoses can be represented by a single
ICD-9-CM code. Lastly, it has been repeatedly questioned whether the clinical
accuracy of administrative coding can be completely trusted.
Global Trigger Tools
Another response to the need for a practical, less labor-intensive approach to assessing patient safety came in the form of the Global Trigger Tool (GTT), developed by
the Institute for Healthcare Improvement (IHI) in 2003 [25]. The GTT utilizes two
to three nurses or pharmacists to review and analyze patient charts for a “trigger”
[26]. Triggers may include a medication stop order, abnormal lab result, or the use
of an antidote medication. For instance, the administration of naloxone to a patient
already hospitalized would act as a trigger that a patient received a harmful dose of
opioid medication. Fifty-three different triggers were created, some of which apply
to all patients and others that apply only to specic populations or specic settings
of care. The trigger tools are signicant due to their efciency. Whereas a complete
chart review of all medical records would be expensive and labor-intensive, triggers
alert the safety personnel to possible adverse events that warrant a medical chart
review [27]. The detection of a trigger initiates an investigation into whether an
adverse event actually occurred or not, and additionally evaluates the severity of the
event [28]. This feature has the possibility of being integrated into the electronic
medical record to allow for automation and increased efciency of this process.
Risk-adjusted outcome rate, %
Hospital rank
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205
Nonetheless, several drawbacks with trigger tools immediately surfaced. The IHI
emphasizes that the GTT will not identify all sources of patient harm, notably,
events with low severity [26]. Moreover, the reliability of trigger tools is signicantly inuenced by the level of training and the experience of reviewers, as well as
their familiarity with the clinical setting being evaluated. A study found that the
Global Trigger Tool had poor inter-rater reliability for the detection and grading of
adverse events between reviewers experienced in the use of the tool [29]. Finally,
most existing trigger tools have been used to identify adverse events in the inpatient
setting. Although some studies have thought to develop trigger tools for ambulatory
care, there is relatively little data on the accuracy and reliability of these tools [30].
Clinically Derived National Detection andReporting Systems
In 1994, the National Surgical Quality Improvement Program (NSQIP) was conceived in the Veterans Affairs (VA) healthcare system. [31–36] This outcomebased program records the performance of all VA hospitals performing major
surgery and, adjusting for patient preoperative risk, compares these hospitals by
the ratio of observed to expected (O/E) adverse events. The results are then provided to each hospital and used to identify areas of substandard performance and
potential excess adverse events (Fig.12.1) [15]. The NSQIP was widely accepted
20
18
16
14
12
10
8
6
4
2
0
0
10
20
30
40
50
60 70 80
Fig. 12.1 Example performance report (any complication, hospitals with at least 125 cases/year,
laparoscopic gastric bypass procedures). Diamonds: hospital risk-adjusted outcomes rates with
95% CIs. Green: low outliers, have 95% CIs less than the average outcomes rate. Red: high outliers, have 95% CIs greater than the average outcomes rate. Solid horizontal line, overall mean
outcomes rate. (Source: https://www.researchgate.net/gure/Example- performance- report- any-
complication- hospitals- with- at- least- 125- cases- year_g1_263283398. May be subject to
copyright)
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by VA surgeons and managers and has provided annual outcome reports that have
resulted in signicant improvements to the standard of surgical care. Between
1991 and 2002, unadjusted 30-day morbidity and mortality rates for major noncardiac surgery in the VA system decreased from 17.4% to 9.9% and 3.2% to
2.3%, respectively [14].
With the solid success in the VA system, the NSQIP was subsequently adapted
for use in the private sector [14, 15, 37] with trained, audited nurse reviewers collecting 30-day complication and mortality rates based on standardized denitions.
Eventually, the American College of Surgeons (ACS) made this program available
to private sector hospitals with the ACS-NSQIP and is using it as its platform for
promoting quality and safety [8]. ACS-NSQIP derives its data from the patient’s
medical charts, as opposed to other quality programs that rely on claims or administrative data that are easy to obtain [38]. While this process is labor- and timeintensive, the accuracy and clinical utility of the data gathered is of superior quality.
Similarly, clinically derived, eld-specic quality improvement programs have
since been created, such as the Trauma Quality Improvement Program (TQIP) and
the Metabolic and Bariatric Surgery Accreditation and Quality Improvement
Program (MBSAQIP).
M. El Hechi and H. M. A. Kaafarani
Vizient
Vizient, formerly known as University HealthSystem Consortium (UHC), is the
largest member-driven, healthcare performance improvement company in the
United States. It empowers healthcare professionals and scientists to deliver exceptional, cost-effective care to every patient, and helps develop new approaches to
patient care. It contains data representing over 97% of United States academic medical centers and 160 community hospitals. The database contains demographic,
nancial, and clinical information including hospital length of stay, complication
rate, readmission rate, mortality rate, medication use, hospital-acquired conditions,
and direct cost [39]. Additionally, the Vizient database allows for some degree of
risk adjustment based on well-established risk models for certain outcomes [40].
However, the Vizient database lacks specic patient characteristics, and users are
unable to determine the timing of complications with regard to the date of surgery [39].
Surgical Care Improvement Project (SCIP)
In 2002, the Surgical Care Improvement Project (SCIP) was introduced by the
Centers for Medicare and Medicaid Services (CMS) in partnership with national
organizations, such as the American Hospital Association, Centers for Disease
Control and Prevention (CDC), the Institute for Healthcare Improvement, and The
Joint Commission [41]. It targets complications that account for a signicant portion of preventable morbidity and aims to improve cost effectiveness [42]. The specic SCIP initiatives are published in the Specications Manual for National
Inpatient Quality Measures (Specications Manual), which is a constantly evolving
manual [42]. For instance, a goal of this program is to reduce the rates of
12 Detecting andReporting Errors, Complications, andAdverse Events
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postoperative surgical infections. The three core preventative measures that specically relate to healthcare-associated infections (HAIs) include: [41]
1. The initiation of prophylactic antibiotics within 1h before surgical incisions (or
within 2h if the patient is receiving vancomycin or uoroquinolones)
2. The use of prophylactic antibiotics appropriate for the specic procedures of
the patient
3. The discontinuation of prophylactic antibiotics within 24h of surgery comple-
tion, but within 48h for cardiothoracic surgery.
While SCIP reporting is mandatory, reporting measures are not universally
enforced, and it is postulated that tighter enforcement of SCIP measures in hospitals
that are not complying consistently can lead to a substantial improvement in surgical site infection (SSI) rates. This can signicantly reduce the current societal burden of the estimated 750,000–1,000,000 annual SSIs, which includes an economic
annual excess cost of $1.6 billion [41]. Other SCIP projects include the prevention
of venous thromboembolism and adverse cardiac events.
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Anesthesia-Related Intraoperative Adverse Events: Anesthesia
Information Management Systems (AIMS)
The adoption of anesthesia information management systems (AIMS), the primary function of which is to collect intraoperative data that enables anesthesia
care providers to create a more reliable and accurate anesthesia record, has been
shown to improve patient care and quality assurance and has enabled the development of mandatory quality assurance (QA) reporting systems for adverse events
(AEs) [43].
AIMS includes an “outcomes” section, in which the presence or absence of all
intraoperative anesthesia AEs can be entered. Examples of anesthesia AE include,
but are not limited to, airway, pulmonary, cardiovascular, neurologic, and physical
injury events, as well as adverse drug reactions, transfusion reactions, emergence,
and case cancelations. However, the completion of this documentation was not
required for the nalization of documentation. Consequently, entries were sparse
and the capture rate of AEs was inconsistent [44].
When AIMS is implemented, a decrease in the rate of anesthesia-related AEs can
be observed. In one academic center that required in a non-punitive manner entry of
AEs in the AIMS, compliance with the reporting process increased from <10% in
the pre-intervention period to 97% post-intervention and was sustained over the rst
2 years of the program. Over that period, the documented AE rate signicantly
decreased from 1.23 to 0.64%. The decrease occurred mainly in the group of preventable AEs, whereas the rate of unpreventable AEs was unchanged. At another
institution, once AIMS was implemented, the AE rate decreased from 4.20 to 1.36%
over a 7-year period. It is reasonable to assume that, because providers were
expected to report QA data for every surgical case, a focus on reporting complications improved the culture of safety over time [44].
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M. El Hechi and H. M. A. Kaafarani
Classification Systems
Access to reliable and consistent risk-adjusted outcome data is necessary to improve
and benchmark surgical performance. In the absence of reliable markers of outcome, reporting raw, unadjusted performance data to patients to identify “top” physicians or hospitals can be misleading. The challenge is always to create and use
scale systems, which, while maintaining simplicity, are still accurate and generalizable [45]. Severity classication systems of AEs can help.
Postoperative Adverse Events
Clavien-Dindo Classification
In 1992, Clavien etal. published a classication system intended to standardize the
recording of postoperative complications [46]. In this classication, the extent of
treatment required to prevent or treat the adverse effects of a certain complication
dened its severity on a four-level grading system (Table 12.2) [45]. The
Table 12.2 Clavien-Dindo classication of postoperative complications
Grade Denition
Grade I Any deviation from the normal postoperative course without the need for
pharmacological treatment or surgical, endoscopic, and radiological
interventions
Allowed therapeutic regimens are: drugs as antiemetics, antipyretics, analgetics,
diuretics, electrolytes, and physiotherapy. This grade also includes wound
infections opened at the bedside
Grade II Requiring pharmacological treatment with drugs other than such allowed for
grade I complications
Blood transfusions and total parenteral nutrition are also included
Grade III Requiring surgical, endoscopic or radiological intervention
Grade IIIa Intervention not under general anesthesia
Grade IIIb Intervention under general anesthesia
Grade IV Life-threatening complication (including CNS complications)a requiring IC/
ICU management
Grade IVa Single organ dysfunction (including dialysis)
Grade
IVb
Grade V Death of a patient
Sufx “d” If the patient suffers from a complication at the time of discharge (see examples
CNS central nervous system, IC intermediate care, ICU intensive care unit
Source: Dindo D, Demartines N, Clavien P-A.Classication of surgical complications: a new
proposal with evaluation in a cohort of 6336 patients and results of a survey. Annals of surgery.
2004;240(2):205
May be subject to copyright
a
Brain hemorrhage, ischemic stroke, subarachnoidal bleeding, but excluding transient isch-
emic attacks
Multiorgan dysfunction
in Table12.2). The sufx “d” (for “disability”) is added to the respective grade
of complication. This label indicates the need for a follow-up to fully evaluate
the complication
12 Detecting andReporting Errors, Complications, andAdverse Events
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classication has since been used in many studies and large-scale database research
[46–48]. The system was revised internationally, incorporating more objective criteria and some exibility by allowing a contraction of grades to adjust to the patient
population studied. This revised system [49] rapidly gained acceptance and became
increasingly used in large-scale trials.
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Accordion Classification
A newer modied classication (the Accordion Classication) for surgical complications was introduced by Strasberg and colleagues in 2009 [50]. The Accordion
Severity Grading System is named after its ability to expand to accommodate the
range of complications found in large complex studies, while contracting for smaller
studies. The expansion or contraction exclusively happens within the “severe” complications section. In the contracted state, the classication has only four levels,
headed by self-evident terms rather than grades. For large and complex studies, such
as those involving complex procedures such as esophageal or pancreatic resection,
where many severe complications may be expected, there is good reason to subdivide and expand the severe category to fully describe the range of complications.
The Clavien-Dindo and Accordion classications have played a crucial research
and quality-improvement role as a common language to report and compare postoperative complications within and across surgical departments, institutions, and
researchers [45, 50–53].
Intraoperative Adverse Events
Up until nearly a decade ago, most surgical patient safety efforts had exclusively
focused on postoperative adverse events, and successfully established a common
language to nationally and internationally discuss postoperative complications [46,
49, 54–57]. Intraoperatively, however, surgeons relied principally on their surgical
instinct and clinical assessment of the operative course to determine postoperative
course and guide clinical care [58]. While most physicians believed that intraoperative management contributes importantly to overall outcomes, quantitative metrics
of the quality of intraoperative care were not available [59]. Thus, attention was
shifted toward what happens in the “black box” of the operating room. Subsequently,
it was shown that patients who suffer intraoperative adverse events (iAEs) have a
41% increase in hospital costs on average, directly attributable to the occurrence of
the iAE.The increase in cost is not conned to the immediate cost of addressing the
iAE, but also extends to all categories of postoperative care, such as laboratory,
radiology, medication, and nutritional charges. Beyond their nancial implications
for all stakeholders, the true signicance of iAEs lies in the quality and safety of
care delivered to patients. Thus, by tracking, measuring the effects, and developing
systems to reduce the occurrence and/or severity of iAEs, we can achieve higher
value healthcare delivery by simultaneously controlling costs and improving patient
outcomes [60]. In 2006, a review of malpractice claims at four liability insurers
revealed that more than 75% of closed claims related to surgical care resulted from
iAEs [61].
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