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THE RELATIONSHIP BETWEEN PATIENT COMPLAINTS AND
SURGICAL COMPLICATIONS
by
JOSEPH F. JOHN
LARRY R. HEARLD
GREG L. CARLSON
THOMAS F. CATRON
AMY YARBROUGH LANDRY
JOHN F. SWEENEY
A DISSERTATION
Submitted to the graduate faculty of The University of Alabama at Birmingham,
in partial fulfillment of the requirements for the degree of Doctor of Science in
Administration – Health Services
BIRMINGHAM, ALABAMA
2014
Copyright by
Joseph F. John
2014
iii
THE RELATIONSHIP BETWEEN PATIENT COMPLAINTS AND
SURGICAL COMPLICATIONS
JOSEPH F. JOHN
ADMINISTRATION – HEALTH SERVICES
ABSTRACT
Background: Patient complaints are viewed as indications of dissatisfaction with the
service received or experience at a health care institution. The prediction of patient
complaints is not clearly understood, and to date, very little quantitative research has
evaluated the relationship between patient complaints and health care quality.
Objective: The objective of this study was to examine whether (1) surgeons that operate
on patients with higher perioperative surgical risk are associated with higher levels of
patient complaints and (2) surgeons with higher levels of patient complaints are
associated with higher levels of post-operative occurrences.
Methods: Patient complaint data from Emory Healthcare was aggregated by individual
physician at the Center for Patient and Professional Advocacy at Vanderbilt University
Medical Center. Patient surgical data was aggregated by individual physician at Emory
Healthcare using the American College of Surgeons National Surgical Quality
Improvement Program (NSQIP). Fixed effects panel regression models were used to
analyze the relationship between patient complaints and surgical data from October 1,
2009 through December 31, 2013.
Results: Sixty-one surgeons generated a mean number of cases per quarter of 8.38 and
performed a total of 9,351 NSQIP-abstracted procedures with 4,064 reported post-
operative occurrences. There was not a significant relationship between the perioperative
surgical risk profile and the number of patient complaints (β = .012, p = 0.920). The
iv
number of patient complaints was marginally significant with post-operative occurrences
(β = .062, p < .10). Patient complaints were significantly associated with central nervous
system (β = .181, p < .05), cardiac (β = .118, p < .05), and other (β = .079, p < .05) post-
operative occurrences. The only specific types of patient complaints that were
significantly associated with post-operative occurrences were accessibility related (β =
.265, p < .05).
Conclusion: This study found surgeons that operate on patients with higher
perioperative surgical risk were not associated with patient complaints and that surgeons
that had higher levels of patient complaints were associated with higher levels of post-
operative occurrences. These findings suggest that patient complaints can be viewed as
indicators of quality.
Keywords: Patient Complaints, Surgical Complications, NSQIP, Post-Operative
Occurrence, Perioperative Risk
v
DEDICATION
To my parents – Drs. Lawrence and Martha John. Thank you.
vi
ACKNOWLEDGMENTS
Completing this program would not have been possible without the huge amount
of support from a large cast that I am lucky enough to consider a part of my personal and
professional worlds. Thank you to the chair of my dissertation committee, Dr. Larry
Hearld, for his expertise, insight and patience throughout this process. Additionally,
thank you to each of my committee members – Dr. Carlson, Dr. Catron, Dr. Landry and
Dr. Sweeney – for their time, support and guidance. To my classmates – Kerry Gillihan,
Tom Hunt, Ray Snead, Joe Webb, Mary Temm, Barb Guerard, Holly Jarek and Sylvia
Pan. I am very appreciative for your friendships and how much you each enriched this
entire experience. Thank you to my entire team and family at Emory. Sincere thanks to
Don Brunn for his unwavering encouragement and for being such an incredibly positive
influence on my personal and professional life. Thank you to the faculty and staff of the
School of Health Professions, particularly, Leandra Celaya for everything she does to
make the program so successful. Thank you to the teams at the Department of Surgery at
Emory University and the Center for Patient and Professional Advocacy at Vanderbilt
University, particularly Sebastian Perez and Nik Zakrzewski for their expert data
assistance. And to my family, friends and loved ones, who have supported and
encouraged me in many ways throughout this entire journey – particularly my parents,
Jeff, Katie, Jeremy, Uncle Joe & Keeli.
vii
TABLE OF CONTENTS
Page
ABSTRACT ....................................................................................................................... iii
DEDICATION .....................................................................................................................v
ACKNOWLEDGMENTS ................................................................................................. vi
LIST OF TABLES ............................................................................................................. ix
LIST OF FIGURES .............................................................................................................x
LIST OF ABBREVIATIONS ............................................................................................ xi
CHAPTER
1 INTRODUCTION
Background ..............................................................................................................1
Purpose of the Study ................................................................................................3
Significance of the Study .........................................................................................4
2 LITERATURE REVIEW
Patient Complaints ...................................................................................................6
Background, Antecedents, and Consequences ...................................................6
Improvement Strategies .....................................................................................9
Post-Operative Occurrences...................................................................................10
Background, Antecedents, and Consequences .................................................10
Improvement Strategies ...................................................................................12
Theoretical Framework ..........................................................................................13
Hypotheses .............................................................................................................16
3 METHODOLOGY
Study Objective ......................................................................................................17
Study Setting ..........................................................................................................17
Data Sources ..........................................................................................................18
Study Sample .........................................................................................................20
Measures and Variables .........................................................................................21
Perioperative Surgical Risk Profiles ................................................................21
viii
Post-Operative Occurrences.............................................................................23
Patient Complaints ...........................................................................................23
Control Variables .............................................................................................24
Methods of Analysis ..............................................................................................26
4 RESULTS AND FINDINGS
Descriptive Statistics ..............................................................................................28
Bivariate Analysis ..................................................................................................31
Multivariate Analysis .............................................................................................33
Supplementary Analysis ........................................................................................35
5 SUMMARY AND CONCLUSIONS
Review of Findings ................................................................................................38
Implications of Findings ........................................................................................40
Study Limitations and Opportunities for Future Research ....................................42
Conclusion .............................................................................................................43
LIST OF REFERENCES ...................................................................................................44
APPENDICES
A PROCEDURES BY SURGICAL SPECIALTY.........................................................49
B NATIONAL SURGICAL QUALITY IMPROVEMENT PROGRAM:
ESSENTIALS WORKSHEET ...................................................................................51
C INSTITUTIONAL REVIEW BOARD APPROVAL .................................................57
ix
LIST OF TABLES
Table Page
1 Data Source Date Crosswalk .................................................................................25
2 Variable Definitions / Operationalization ..............................................................26
3 Study Hypotheses, Independent Variables and Dependent Variables ...................27
4 Descriptive Statistics for Post-Operative Occurrences ..........................................29
5 Descriptive Statistics for Patient Complaints ........................................................30
6 Descriptive Statistics for Risk Profile ....................................................................31
7 Pearson’s Correlation Coefficients of Variables....................................................32
8 Risk Profile Association with Patient Complaints –
Fixed Effects Panel Regression .............................................................................33
9 Patient Complaints Association with Aggregated Post-Operative Occurrences –
Fixed Effects Panel Regression .............................................................................34
10 Patient Complaints Association with Specific Post-Operative Occurrences –
Fixed Effects Panel Regression .............................................................................35
11 Patient Complaints Categories Association with Post-Operative Occurrences –
Fixed Effects Panel Regression .............................................................................37
x
LIST OF FIGURES
Figure Page
1 Study Relationships of Interest ................................................................................4
2 Donabedian Quality Framework ............................................................................14
3 Donabedian Quality Framework with Study Relationships of Interest .................16
xi
LIST OF ABBREVIATIONS
ACS American College of Surgeons
ASA American Society of Anesthesiology
BMI Body Mass Index
CMS Centers for Medicare and Medicaid Services
CNS Central Nervous System
CPPA Center for Patient and Professional Advocacy at Vanderbilt University
CPT Current Procedural Terminology
EHC Emory Healthcare
NSQIP National Surgical Quality Improvement Program
NVASRS National Veterans Affairs Surgical Risk Study
PARS® Patient Advocacy Reporting System
PCA Principal Components Analysis
UTI Urinary Tract Infection
VA Veterans Affairs
VUMC Vanderbilt University Medical Center
1
CHAPTER 1
INTRODUCTION
Background
Patient complaints and grievances are viewed as indications of dissatisfaction
with the service received or experience at a health care institution. Complaints are
defined as patient issues that can be resolved promptly or within twenty-four hours and
involve staff members who are present at the time of the complaint. Grievances are
patient issues that may be submitted verbally or in writing, may be submitted after the
patient is discharged (excluding billing issues), may concern unresolved issues or those
that cannot be addressed immediately, may concern an alleged violation of patient rights,
or may involve a patient’s request for response (Centers for Medicare and Medicaid
Services, 2005).
To comply with federal regulations and Joint Commission standards, health care
organizations must develop processes for addressing patient complaints and grievances.
In addition to these regulatory and accreditation requirements, understanding and
responding to patient complaints and grievances is of increasing importance throughout
the industry. The emphasis on patient and family centered care, the patient experience
and patient satisfaction continues to grow, highlighted by components of these concepts
becoming factors in measures of health care quality as well as reimbursement. In 2011,
the Centers for Medicare and Medicaid Services finalized details for a new
reimbursement method that would adjust payments based on patient satisfaction scores –
2
a trend that is also being adopted by private insurers (Centers for Medicare and Medicaid
Services, 2011). This policy reflects the perception that patient satisfaction is an
indicator of health care quality.
As part of a health care institution’s patient satisfaction management strategy,
qualitative data regarding patients’ satisfaction and expectations can also be obtained by
reviewing patients’ complaints (Friele, 2008). Many business and health care
organizations use systematic complaint management to learn how to better serve their
customers and patients. Although accrediting organizations mandate that health care
organizations systematically attend to patient complaints, complaint data have only been
recently recognized as a tool for management and quality improvement in health care
(Bendall-Lyon, 2001). For example, The Center for Patient and Professional Advocacy
(CPPA) at Vanderbilt University Medical Center (VUMC) has demonstrated that
physicians with patterns of unprofessional behavior can be reliably identified through
collecting, coding and analyzing patient complaints. Likewise, one specific study found
that patient complaints about physicians were associated with lawsuits or events
identified as potentially leading to lawsuits against those physicians; in other words, a
physician’s risk of being sued was higher when patients complained about the treatment
received while under his or her care (Hickson, 2002). Other research has found that risk
of malpractice claims is not predicted by patient characteristics, illness complexity or
even physicians’ technical skills (Entman, 1994). Instead, this risk appears related to
patients’ dissatisfaction with their physicians’ ability to establish rapport, provide access,
administer care and treatment consistently with expectations, and communicate
effectively (Hickson, 2002).
3
Despite growing recognition of the importance of understanding patient
complaints, the factors that contribute to or mitigate their occurrence is not clearly
understood (Kline, 2008) and to date, very little quantitative research has evaluated the
relationship between patient complaints and health care quality. There is a growing
interest in harnessing patient dissatisfaction and complaints to address problems with
quality in health care (Hickson, 2002). One study found a positive association between
consumer complaints in nursing homes and deficiencies in subsequent inspections,
suggesting that complaints may signal quality concerns (Stevenson, 2005). Additionally,
a study examining the relationship between patient complaints and post-operative
complications found that patient complaints were significantly more common in cases
where the patient is admitted with a surgical complication as compared to cases where the
patient was not admitted with a complication (Murff, 2006), noting that further research
is necessary to determine if patient complaints might serve as markers for poor clinical
outcomes.
Purpose of the Study
The purpose of this study is to examine the relationship between perioperative
risk profiles and patient complaints as well as analyze the relationship between patient
complaints and post-operative surgical occurrences. Thus, the study is guided by two
primary research questions: (1) are surgeons that operate on patients with higher
perioperative risk associated with higher levels of patient complaints, and (2) are
surgeons with higher levels of patient complaints associated with higher levels of post-
4
operative surgical occurrences? Figure 1 visually displays the relationships of interest for
this study.
Figure 1: Study Relationships of Interest
Significance of the Study
In analyzing the relationship between patient complaints and post-operative
surgical occurrences, the study will help individual surgeons, surgical departments and
health care institutions promote targeted improvement efforts for higher quality patient
care and reduced medical malpractice claims. Previous studies have shown that patient
complaints can reliably predict medical malpractice claims, and specifically, when
collected, coded and aggregated, patient and family complaints have been shown to
identify the subset of physicians with high medical malpractice claims and payouts, when
compared with the experiences of other members of their medical groups or specialties
(Hickson, 2002). When patients complain about their negative health care experience,
some of these experiences describe the behavioral attributes of physicians or medical
team members. Published studies have suggested that provider-patient communication
has a strong influence on patient complaints, and providers with better communication
5
skills generate fewer complaints (Hickson, 1994; Pichert, 1998). Additional research
conducted at the CPPA at VUMC has demonstrated that physicians with patterns of
unprofessional behavior can be reliably identified. A tiered-intervention strategy can
then be targeted at these individuals to reduce patient dissatisfaction and risk for
malpractice litigation (Hickson, 2007). However, if left unaddressed, disruptive
physician behavior may adversely affect the medical team from successfully
accomplishing its patient care goals.
The study of patient complaints could help in modifying unprofessional behavior
of practicing physicians that lead to patient complaints, reducing malpractice risk and
thereby improving provider and institutional quality. Surgeons with high levels of patient
complaints can be viewed as having poor communication skills and creating an
environment that could be perceived as hostile by members of the care team. This leads
to a fragmented unit that is more likely to have lapses in patient care that result in post-
operative occurrences. Additionally, educational approaches based on the analysis of
patient complaints may be useful tools for developing the communication skills and
attitudes of physicians-in-training and for modifying objectionable behavior in practicing
physicians (Wofford, 2004).
The study will also provide insights into whether perioperative surgical risk is
associated with patient complaints. In doing so, this study may help physicians and
health care institutions identify specific types of patients that are more likely to complain,
enabling early efforts to mitigate dissatisfaction with their care or even prevent patient
complaints before they arise.
6
CHAPTER 2
LITERATURE REVIEW
The purpose of this review is to examine the background, antecedents, and
consequences as well as the strategies employed to address and improve patient
complaints and post-operative occurrences.
Patient Complaints
Background, Antecedents, and Consequences
Complaints matter: to the people who make them, usually as a last resort after the
frustration of trying other avenues without success; to the person complained about, in
who the complaint may provoke a fierce reaction, ranging from shame to indignation; and
to the agency required to handle the complaint, charged with resolving a problem when
the parties’ recollections and objectives may sharply diverge (Paterson, 2013).
Complaints also matter to society. As long ago as 1644, John Milton said that ‘When
complaints are freely heard, deeply considered, and speedily reformed, then this is the
utmost bound of civil liberty attained that wise men look for’ (Milton, 1644).
Today, patient complaints are increasingly recognized as a potentially valuable
source of information (Montini, 2008). The Joint Commission and the federal Centers
for Medicare and Medicaid Services mandate the collection and retention of patient
complaints (The Joint Commission, 2014; CMS, 2011), offering a treasury of valuable
information. More than one third of patients in the United States experience some degree
7
of dissatisfaction with hospital care (Steiber, 1990), yet only a minority of these patients
actually complain following a dissatisfying experience, and even fewer lodge a formal
complaint (Tax, 1998). In one study, among patients who believed something “went
wrong” and they experienced a preventable, harmful event during their care, few (13%)
formally reported their problematic events by writing a letter, speaking with someone in
administration, or completing satisfaction surveys (Mazor, 2012). Given that a high
percentage of patients are dissatisfied with their care and hesitate to complain, the current
data on the prevalence of patient complaints represents the tip of the iceberg (Mazor,
2012).
Researchers have found that a small number of physicians generate a
disproportionate share of complaints (Bismark, 2010; Hickson, 1994; Hickson, 1992;
Hickson, 1997; Hickson, 2002; Hickson, 2007; Pichert, 1998; Pichert, 1999). For
example, one study found that the distribution of complaints among Australian doctors
was highly skewed: 3% of all doctors accounted for 49% of all complaints; and of the
doctors who were subject to a complaint, 15% of them accounted for 49% of the
complaints, with 1% accounting for 25% of patient complaints (Bismark, 2010).
Additionally, previous studies have shown that patient complaints can reliably predict
medical malpractice (Hickson, 2002). When collected, coded and aggregated, patient and
family complaints have been shown to identify the subset of physicians with high
medical malpractice claims and payouts, when compared with the experiences of other
members of their medical groups or specialties (Hickson, 2002). Likewise, a study in
Florida found that most payments by malpractice insurers were made on behalf of a
comparatively small number of physicians (Sloan, 1989). More than 85% of all
8
malpractice payments for physicians in medical specialties were made on behalf of only
3% of that physician population and for obstetricians and anesthesiologists, more than
85% of payments were incurred by 6% of these physicians (Sloan, 1989).
The popular business adage, “a complaint is a gift,” suggests that the message
within a customer’s complaint offers valuable feedback (Barlow, 1996). It has been
suggested that health care organizations elicit patients’ stories, capture information
relevant to safety and feed that information back to the professionals who organize and
deliver care (Sage, 2002). In reviewing the specific types of patient complaints received
by health care organizations, one study found that 20% of patient complaints derive from
problems in communication between patients and health care providers, and 10% arise
from some form of perceived disrespect (Pichert, 1998). In another study, unprofessional
conduct (19%), poor provider-patient communication (17%), treatment and care of
patient (16%) and having to wait for care (11%) were identified as the top four reasons
for patient complaints (Montini, 2008). Likewise, other studies have implicated the
behavioral attributes of physicians or medical team members as an important factor
contributing to patient complaints. These studies suggest that provider-patient
communication has a strong influence on patient complaints, and providers with better
communication skills generate fewer complaints (Hickson, 1994; Pichert, 1998). As a
reflection of patient satisfaction, complaints provide important insights into the quality of
care provided to patients (Kravitz, 1996), and thus warrant additional research into their
antecedents and consequences.
9
Improvement Strategies
Since the publication of the Institute of Medicine report Crossing the Quality
Chasm: A New Health System for the 21st Century in 2001, recommendations have been
made to specifically focus on delivering patient-centered care. The Institute of Medicine
defined patient-centered care as care that is responsive and respectful of individual
patients’ preferences, needs, and values, as well as care that ensures that individuals
patients’ values guide all clinical decisions (Institute of Medicine, 2001). Others have
argued that a systems approach (examining all the factors that contribute to the
shortcoming in care and communication) offers the greatest potential for improving the
quality of health care (Paterson, 2013). The existence of a small group of complaint-
prone doctors who loom so large in the corpus of complaints made to external agencies is
sobering. However unfashionable it may be to focus on individuals (“bad apples”), there
is a clearly a need to do so in this context (Paterson, 2013).
There is now a significant body of research on the motivation of patients in
making a complaint (Bismark, 2006), which suggests that patients’ involvement in their
care can improve their medical outcomes (Pichert, 2008). Most patients want to prevent
the same thing from happening to someone else (Paterson, 2010).
Thus, complaints by aggrieved patients have the potential to be an important
window on health care quality (Bismark, 2010). Emergent in studies of patient
complaints is a growing awareness of their potential value in understanding and
improving systems of care (Montini, 2008). Complaints might be taken advantage of by
a learning organization and act as one of the benchmarks used to assess the quality of
10
care delivered, develop interventions and thereby minimize the number of complaints
(Siyambalapitiya, 2007).
Complaint systems are now an integral part of clinical governance focused on
improving patient care, along with other important indicators such as critical incident
reporting, effective communication, clinical audit and risk management (Kline, 2008).
This mirrors the established importance of customer complaints in other industries as
indicators of service quality. A clear relationship has been drawn between customer
satisfaction and firm profitability; satisfied customers are more likely to be retained and
give repeat business to the company that provides them with a satisfying experience
(McCole, 2004). Many experts conclude that complaints should be treated as research
data and analyzed regularly, such that they highlight opportunities for improvement
through what constitutes an inexpensive means of research (Kline, 2008).
In summary, there is a shift occurring in health care that entails greater
involvement of consumers. Today’s well-informed consumers demand a health-care
system that accommodates their busy schedules, provides them with useful information,
and involves them in decision making (Siyambalapitiya, 2007).
Post-Operative Occurrences
Background, Antecedents, and Consequences
The collection of preoperative risk factors and perioperative surgical data to
correlate risk with outcomes in general surgery, vascular and other surgical sub-
specialties began with the National Veterans Affairs Surgical Risk Study (NVASRS)
published in 1995 (Khuri, 1995). In 1986, a congressional mandate led to the validated
11
risk-adjustment model to predict surgical outcomes and to critically compare data across
forty-four medical centers (Khuri, 2002). The rationale underlying the NVASRS was
based on Iezzoni’s “algebra of effectiveness,” a conceptual framework in which
outcomes of health care are determined by the sum of three major factors: patients’ risk
factors before surgery, the effectiveness (quality) of the patients’ care, and random
variation (Iezzoni, 1997). If one accounts for the severity of the patients’ illnesses by
proper statistical methods, one can then equate outcomes with effectiveness of care
(Khuri, 2002). Hence, to enable the use of outcomes as measures of quality of surgical
care, the NVASRS had to (1) develop a reliable clinical database of patients’ relevant
preoperative risk factors and post-operative outcomes and (2) develop analytic tools for
proper risk adjustment and to account for random events (Khuri, 2002). The NVASRS
recognized that surgical care was ideally suited for the use of outcomes rather than
process measures in the comparative assessment of quality of care, because surgical care
revolved primarily around a single event (the operation), which in most cases had an
expected measurable outcome (Khuri, 1999). The benefit of implementing this program
within the Veteran Affairs (VA) institutions was a significant reduction in morbidity
(30%) and mortality (9%) in surgical patients (Khuri, 1998). This led to the adoption of
the program by the American College of Surgeons (ACS) and the formation of the
National Surgical Quality Improvement Program (NSQIP; Fink, 2002). Using risk-
adjusted outcomes, comparison of surgeons across specialty and geography can inform
and instruct improvement efforts at the individual, divisional or institutional level
(Young, 1997).
12
Many payers are no longer reimbursing for certain surgical complications (Center
for Medicare and Medicaid Services, 2007; Rosenthal, 2007). Surgeons have an
important opportunity to partner with regional and national payers in efforts to improve
quality in surgery, with the most important results being better outcomes for a great many
of our patients and decreased health care expenditures (Englesbe, 2007). With changing
reimbursement policies, hospitals may be financially motivated to support programs
aimed at reducing surgical complications (Krupka, 2012).
Improvement Strategies
The NSQIP is first and foremost a quality improvement program. The validity of
its outcome-based methods in assessing the quality of surgical care has been established.
Its primary focus is to provide surgeons and managers in the field with reliable
information, benchmarks, and consultative advice that will guide them in assessing and
continually improving their local processes and structures of care (Khuri, 2002). Since
the inception of the NSQIP data collection in 1991, the 30-day mortality of major surgery
in the VA has decreased by 27% and the 30-day morbidity has decreased by 45% (Khuri,
2002). This led to the adoption of the program by the ACS and the formation of the
NSQIP (Fink, 2002). Using risk-adjusted outcomes, comparison of surgeons across
specialty and geography can inform and instruct improvement efforts at the individual,
divisional or institutional level (Young, 1997). The NSQIP has developed instruments
that it has made available to providers and managers to help them assess the strengths and
weaknesses of their respective programs, particularly when the NSQIP reports show thee
13
programs to be high outliers in their respective risk-adjusted 30-day mortality or
morbidity rates (Khuri, 2002).
Theoretical Framework
The Donabedian Quality Framework provides the framework to guide this
research. Donabedian’s framework illustrates the relationship between three related
concepts: (1) structures of health care, (2) processes of patient care, and (3) health
outcomes (Donabedian, 1980; 1982; 1985; 1988). The Donabedian Quality Framework
is visually represented in Figure 2 and contains the following components:
(1) Structures of Health Care: refers to stable, enduring characteristics the setting
where care is delivered (e.g., size or ownership of a hospital) (Brennan, 1991); the
physical and organizational aspects of care settings (e.g., facilities, equipment,
personnel, operational and financial processes supporting medical care, etc.).
(2) Processes of Patient Care: refers to the activities that occur during an encounter
between a physician or another health care professional and a patient (e.g., tests
ordered); the processes performed to improve patient health in terms of promoting
recovery, functional restoration, survival and even patient satisfaction.
(3) Health Outcomes: refer to the patient’s subsequent health status (e.g., an
improvement in symptoms or mobility) (Brook, 1991; Brook, 1996); changes in
health status or health condition.
14
According to Donabedian, structures of care provide the resources and capabilities
necessary to engage in patient care delivery processes. In turn, engaging in these
processes is believed to improve the likelihood of some preferred change in health
outcomes. Additionally, structures and processes are necessary, but not necessarily
sufficient, components in the care delivery process.
Figure 2: Donabedian Quality Framework
In this study, perioperative surgical risk profiles may be considered structural
aspects of care because they are relatively stable and enduring patient characteristics that
are not directly under the control of the surgeon. As structural aspects of care,
perioperative surgical risk profiles are assumed to impact processes of care, in this case
surgical processes and procedure.
Processes of care are defined as the resources and mechanisms for participants to
carry out patient care activities. In this study, processes of care are the surgical
procedures, the activity occurring during between a physician and a patient to improve
patient health.
15
Health Outcomes are defined as the outcomes of medical care. In this study,
health outcomes are represented in two forms: (1) from a subjective perspective as patient
complaints and (2) from an objective perspective as post-operative occurrences.
Figure 3 (Donabedian Quality Framework with Study Relationships of Interest)
visually displays the fundamentals of the Donabedian Quality Framework in combination
with the study variables and relationships of interest in this study. Highlighted by the
black solid line are the established relationships of the Donabedian Quality Framework.
Highlighted by the green dotted line are the primary relationships of interest of the study.
In the Donabedian Quality Framework, structures of care have a relationship with
processes of care and processes of care have a relationship with health outcomes. In this
study, the relationship of perioperative surgical risk profiles (structures of care) and
patient complaints (health outcomes) will be analyzed. Additionally, the relationship of
patient complaints (health outcomes) and post-operative occurrences (health outcomes)
will be studied. The surgical procedure (process of care) is not directly assessed in this
study as it applies to all patients and is used as one of the inclusion criteria.
16
Figure 3: Donabedian Quality Framework with Study Relationships of Interest
Hypotheses
Based on the aforementioned research and the Donabedian Quality Framework,
the following hypothesis will be formally tested in this study:
Hypothesis 1: General Surgeons and Vascular Surgeons that operate on patients
with higher perioperative surgical risk will experience higher volumes of patient
complaints than General Surgeons and Vascular Surgeons that operate on patients with
lower perioperative surgical risk.
Hypothesis 2: General Surgeons and Vascular Surgeons with higher volumes of
patient complaints will experience higher levels of post-operative occurrences than
General Surgeons and Vascular Surgeons with lower patient complaints volumes.
17
CHAPTER 3
METHODOLOGY
Study Objective
The objective of this study was to examine whether (1) surgeons that operate on
patients with higher perioperative surgical risk are associated with higher levels of patient
complaints and (2) surgeons with higher levels of patient complaints are associated with
higher post-operative occurrences.
Study Setting
Emory Healthcare (EHC), the largest health care system in Georgia, includes
Emory University Hospital, Emory University Hospital Midtown, Emory University
Orthopaedics and Spine Hospital, Emory Johns Creek Hospital, Emory Saint Joseph’s
Hospital, The Emory Clinic, Emory-Children's Center, Wesley Woods Center and Emory
Specialty Associates. EHC is the only academic medical center in metropolitan Atlanta.
In fiscal year 2013, EHC had $2.6 billion in annual net revenue, over 15,000 employees,
1,700 clinical providers and 1,830 hospital beds. EHC treated over 66,000 inpatients and
4.2 million outpatients in fiscal year 2013. The Department of Surgery is comprised of
over 100 faculty members who specialize in burn care, cardiothoracic surgery, general
and gastrointestinal surgery, minimally invasive surgery, oral and maxillofacial surgery,
pediatric surgery, plastic and reconstructive surgery, surgical oncology, transplantation,
trauma surgery, surgical critical care, and vascular surgery.
18
Data Sources
The Department of Surgery at EHC has been collecting operative care data for the
American College of Surgeons National Surgical Quality Improvement Program since the
fourth quarter of 2009 for both General Surgery and Vascular Surgery. EHC collects
NSQIP data retrospectively through nurse abstractors, under the supervision of the Chair
of the Department of Surgery at Emory University’s School of Medicine. The collection
includes data in the pre-operative risk assessment, operative information and post-
operative care of General Surgery and Vascular Surgery cases completed at EHC.
The CPPA at VUMC aims to promote patient and professional satisfaction with
health care experiences and restrain escalating costs associated with patient
dissatisfaction. The CPPA utilizes the Patient Advocacy Reporting System (PARS®), a
system used to aggregate, code and analyze patient complaint data. The CPPA receives
patient complaint reports on over 45,000 physicians from over 70 health care institutions
each year, calculates malpractice risk scores by physician and maintains a database of
those risk scores to facilitate comparisons, by physician and specialty, of patient
complaint levels, against local and national norms and by specialty, to identify physicians
at risk for malpractice. EHC entered in to an agreement on July 1, 2000, with the CPPA
to employ PARS® and currently has an active contract.
The study included data for General and Vascular Surgeons employed by EHC during
the period of October 1, 2009 through December 31, 2013. The patient complaints
included in the study are unsolicited patient and family member complaints received,
investigated, resolved, and recorded by trained Patient Advocates employed by EHC.
The unique patient complaints reports are sent to the CPPA at VUMC to employ
19
PARS®. A team of trained coders employed by CPPA aggregate, code and analyze the
patient complaints using a standard protocol to characterize the nature of the problem and
to uniquely identify the person(s) and unit(s) complained about. The results of the coding
process contain no protected health information.
In this study, the data collection and preparation proceeded in the following steps:
(1) Consistent with the terms of the fully executed business agreement, EHC
sends patient complaint data and a list of physicians who have privileges at
their institutions to the CPPA. The PARS® team assesses and codes the
patient complaint data. The CPPA provides PARS® coded patient complaint
data to EHC, aggregated by individual physician. This information identifies
physicians by name, an institution specific identification number, type of
practice, area of specialty and a PARS® score. The data have already been
collected by EHC and processed by the CPPA. For this study, these data are
not publically available. No personal health information (PHI), patient/family
identities, or any other patient-related information is contained in the PARS®
database.
(2) The NSQIP data, managed by the Department of Surgery at EHC, contains
information about patients undergoing major operations in General Surgery
and Vascular Surgery. The data are inclusive of twenty procedures in the
specialties of General Surgery, Vascular Surgery and Plastic Surgery. The
associated 171 current procedural terminology (CPT) codes for each
20
procedure are identified in Appendix A: Procedures by Surgical Specialty.
The NSQIP data includes nine categories of information; (1) Demographics,
(2) Surgical Profile, (3) Pre-Operative Risk Assessment, (4) Laboratory Data,
(5) Operative Information, (6) Additional Operative Procedures, (7) Post-
Operative Occurrences, (8) Hospital Discharge Information and (9) Follow
Up. The data elements included in each of the categories are identified in the
Appendix B (National Surgical Quality Improvement Program: Essentials
Worksheet). A patient identifier (EHC medical record number) and a
physician identification number are also included. These data have already
been collected by EHC and are not publically available.
(3) A new de-identified database was created for the purposes of this study. The
patient data contained in both the PARS® and NSQIP databases were
aggregated to and then merged at the physician level.
Study Sample
The primary observational unit for this study was the individual physician. From
October 1, 2009 through December 31, 2013 the PARS® database contains 163 providers
in General Surgery and Vascular Surgery. During the same time period, the EHC NSQIP
database contains 64 surgeons in the General Surgery and Vascular Surgery. A
crosswalk of the physicians included in each database identified 61 unique providers that
are active in both the PARS® and NSQIP databases. To maximize statistical power and
better account for temporal fluctuations and seasonal trends, the unit of analysis was the
21
physician-quarter. Thus, variables will be time varying and constructed on a quarterly
basis. Specifically of note, individual surgeon patient complaints were not directly linked
to the corresponding patient post-operative occurrences. Each variable was aggregated
by individual surgeon for each given quarter.
Measures and Variables
Perioperative Surgical Risk Profiles
A single risk profile variable was created to measure perioperative surgical risk.
Eight variables from the NSQIP dataset were used to create the risk profile variable: (1)
American Society of Anesthesiology (ASA) Classification, (2) Wound Classification, (3)
Emergency Case Status, (4) Functional Health Status, (5) Transfer Case Status, (6)
Inpatient Case Status, (7) Body Mass Index (BMI) and (8) Comorbidities. The ASA
classification is measured on a scale of one (lowest risk) to five (highest risk). The
wound classification is measured on a scale of one (lowest risk) to four (highest risk).
Emergency case status is indicated by either no (lowest risk) or yes (highest risk).
Functional health status is measured on a scale of one (lowest risk) to three (highest risk).
Transfer case status is the number of surgical cases that have been accepted from an
external facility. With EHC being a quaternary health care facility and accepting patients
with a higher case mix, the higher the number the higher risk. Inpatient case status is the
number of surgical cases from patients who are considered inpatient, versus outpatient.
BMI is a number calculated from a person’s weight and height to provide a reliable
indicator of body fatness for most people and is used to screen for weight categories that
may lead to health problems (Centers for Disease Control and Prevention, 2014). The
22
number of comorbidities is a measure of the number of additional disorders or diseases
co-occurring with a primary disorder or disease. To reduce the data set to a more
manageable size while retaining as much of the original information as possible, a single
risk profile variable was created through a principal components analysis (PCA). The
PCA provided multiplicative weights to be applied to each variable before adding them
together to create a single composite. The risk profile variable was then rescaled to a
mean of zero by subtracting the average risk profile score for all physicians in a quarter
from each physician’s risk profile score. Thus, a positive risk profile score indicated an
above average perioperative risk while a negative risk profile score indicated a below
average risk. The formula below summarizes the inputs and steps to create the risk
profile.
Risk Profile = ((0.33135 * Average ASA Classification) +
(0.06267 * Average Wound Classification) +
(0.29666 * Average Functional Health Status) +
(0.15751 * Average Emergency Case Status) +
(0.23006 * Average Transfer Status) +
(0.08585 * Average Inpatient Case Status) +
(-0.06216 * Average Body Mass Index) +
(0.36059 * Average Comorbidities)) –
(Mean Score across Physicians in Quarter)
23
Post-Operative Occurrences
Six categories of post-operative surgical occurrences are included in the NSQIP
database: (1) wound occurrences, (2) respiratory occurrences, (3) urinary tract (UTI)
occurrences, (4) central nervous system (CNS) occurrences, (5) cardiac occurrences, and
(6) other occurrences. Higher totals by surgeon by quarter indicate more post-operative
occurrences (i.e., worse outcomes).
Patient Complaints
Patient complaints are coded and grouped by the PARS® team into thirty-four
specific codes that are grouped into six distinct categories: (1) Care and Treatment, (2)
Communication, (3) Concern for the Patient/Family, (4) Accessibility, (5) Environment
and (6) Billing. Only clearly identified complaints about the 61 physicians were used in
the study. Each patient complaint sent to the CPPA from EHC may contain multiple
complaint categories, and therefore, multiple patient complaint codes. The CPPA utilizes
inter-rater reliability on patient complaint coding to ensure coding accuracy. The coded
patient complaint data for each of the 61 physicians will include data from 2009, 2010,
2011, 2012 and 2013. For this study, a global patient complaint measure was created as
the sum of the coded patient complaints, across all six categories, for each physician. Six
variables reflecting the six distinct patient complaint categories were also constructed by
summing the complaints separately within these categories for each physician. All
variables were once again constructed on a quarterly basis.
24
Control Variables
Two control variables are included in the study: (1) time, (2) number of surgeries.
Time was coded as a continuous variable such that each one-unit increment reflected a
change in calendar-quarter, which provided a parsimonious way to account for and
observe time trends. Number of surgeries was also coded as a continuous variable.
Table 1 provides a date crosswalk of the NSQIP and PARS ® data that was used
in constructing the database for this study and Table 2 summarizes the variables and
associated definitions that are utilized for purposes of this study.
25
Table 1: Data Source Date Crosswalk
Annual Quarter NSQIP Date Range PARS Patient Complaint
Date Range
2009 Quarter 4
(Q4 2009)
October 1, 2009 –
December 31, 2009
October 1, 2009 –
December 31, 2009
2010 Quarter 1
(Q1 2010)
January 1, 2010 –
March 31, 2010
January 1, 2010 –
March 31, 2010
2010 Quarter 2
(Q2 2010)
April 1, 2010 –
June 30, 2010
April 1, 2010 –
June 30, 2010
2010 Quarter 3
(Q3 2010)
July 1, 2010 –
September 30, 2010
July 1, 2010 –
September 30, 2010
2010 Quarter 4
(Q4 2010)
October 1, 2010 –
December 31, 2010
October 1, 2010 –
December 31, 2010
2011 Quarter 1
(Q1 2011)
January 1, 2011 –
March 31, 2011
January 1, 2011 –
March 31, 2011
2011 Quarter 2
(Q2 2011)
April 1, 2011 –
June 30, 2011
April 1, 2011 –
June 30, 2011
2011 Quarter 3
(Q3 2011)
July 1, 2011 –
September 30, 2011
July 1, 2011 –
September 30, 2011
2011 Quarter 4
(Q4 2011)
October 1, 2011 –
December 31, 2011
October 1, 2011 –
December 31, 2011
2012 Quarter 1
(Q1 2012)
January 1, 2012 –
March 31, 2012
January 1, 2012 –
March 31, 2012
2012 Quarter 2
(Q2 2012)
April 1, 2012 –
June 30, 202
April 1, 2012 –
June 30, 202
2012 Quarter 3
(Q3 2012)
July 1, 2012 –
September 30, 2012
July 1, 2012 –
September 30, 2012
2012 Quarter 4
(Q4 2012)
October 1, 2012 –
December 31, 2012
October 1, 2012 –
December 31, 2012
2013 Quarter 1
(Q1 2013)
January 1, 2013 –
March 31, 2013
January 1, 2013 –
March 31, 2013
2013 Quarter 2
(Q2 2013)
April 1, 2013 –
June 30, 2013
April 1, 2013 –
June 30, 2013
2013 Quarter 3
(Q3 2013)
July 1, 2013 –
September 30, 2013
July 1, 2013 –
September 30, 2013
2013 Quarter 4
(Q4 2013)
October 1, 2013 –
December 31, 2013
October 1, 2013 –
December 31, 2013
26
Table 2: Variable Definitions / Operationalization
Variable Definition / Operationalization
Patient Complaints
Sum of quarterly patient complaints by physician across six
categories:
(1) Care and Treatment, (2) Communication, (3) Concern for the
Patient/Family, (4) Accessibility, (5) Environment and (6) Billing.
Perioperative Surgical
Risk Profile
A single variable of perioperative risk created through a principal
component analysis using eight variables from the ACS NSQIP
dataset by physician by annual quarter: (1) ASA Classification, (2)
Wound Classification, (3) Emergency Case Status, (4) Functional
Health Status, (5) Transfer Case Status, (6) Inpatient Case Status,
(7) Average BMI and (8) Average Comorbidities.
Post-Operative
Occurrences
Sum of quarterly surgical occurrences by physician across six
categories: (1) Wound Occurrences, (2) Respiratory Occurrences,
(3) Urinary Tract Occurrences, (4) Central Nervous System
Occurrences, (5) Cardiac Occurrences and (6) Other Occurrences.
Methods of Analysis
The unit of analysis used in the study is the physician-quarter. The first step in
the data analysis is the examination and presentation of descriptive statistics. Descriptive
statistics reveal the general characteristics of each variable and allow for the
identification of missing values and extreme values. The hypotheses were tested by
utilizing negative binomial fixed effects panel regression models with the following
independent and dependent variables using SPSS version software 22 (Table 3: Study
Hypotheses, Independent Variables and Dependent Variables). All testing was
completed at the 0.05 level of significance.
27
Table 3: Study Hypotheses, Independent Variables and Dependent Variables
Hypothesis Independent Variable Dependent Variable
Hypothesis 1 Perioperative
Surgical Risk Profiles Patient Complaints
Hypothesis 2 Patient Complaints Post-Operative Occurrences
Negative binomial models were preferred in this case due to the count nature of
the dependent variables and preliminary analyses that identified overdispersion with these
variables. Similarly, fixed effects panel regression models provided a number of
advantages over other alternatives for examining these relationships. Specifically, the
fixed effects regression models allowed for an examination of these relationships over
time while accounting for potential selection biases. That is, by including physician-level
fixed effects, the study accounted for unobserved, time-invariant (or slowly changing)
factors that may have acted as confounders by influencing both the independent and
dependent variables.
The two key data requirements for the application of a fixed effects model are: (1)
each physician in the study must have two or more measurements on the same dependent
variable; and (2) for at least some of the physicians in the study, the values of the
independent variables of interest must be different on at least two of the measurement
occasions. Both of these requirements were met since (1) the study reviewed multiple
measurement periods; and (2) physicians in the study possessed different levels of each
of the independent and dependent variables at the different measurement occasions.
28
CHAPTER 4
RESULTS AND FINDINGS
Descriptive Statistics
Sixty-one surgeons in General and Vascular Surgery met the inclusion criteria for
this study. These surgeons performed a total of 9,351 NSQIP-abstracted procedures
during the study period, October 1, 2009 through December 31, 2013. The mean number
of cases per surgeon per quarter was 8.38 with a standard deviation of 12.91. A total of
4,064 post-operative surgical occurrences were reported during the study period. Of the
occurrences, 1,949 were classified as other occurrences (47.9%), 800 were classified as
wound occurrences (19.7%), 752 were classified as respiratory occurrences (18.5%), 376
were classified as urinary tract occurrences (9.3%), 136 were classified as cardiac
occurrences (3.3%), and 51 were classified as central nervous system occurrences (1.3%).
More complete post-operative occurrence descriptive statistics are provided in Table 4.
29
Table 4: Descriptive Statistics for Post-Operative Occurrences1,2
Variable Mean Standard
Deviation Median Min Max Sum
% of
Sum
Number of Cases 8.38 12.905 1.00 0 62 9,351 .
Total Occurrences 4.10 7.7695 0.00 0 54 4,064 100%
- Other 1.97 3.729 0.00 0 24 1,949 47.9%
- Wound 0.81 1.815 0.00 0 15 800 19.7%
- Respiratory 0.76 1.907 0.00 0 18 752 18.5%
- Urinary Tract 0.38 0.952 0.00 0 9 376 9.3%
- Cardiac 0.14 0.488 0.00 0 6 136 3.3%
- Central Nervous System 0.05 0.267 0.00 0 4 51 1.3%
1 Number of cases and total occurrences do not match because each case may not have an occurrence
and/or cases may have multiple occurrences.
2 Includes all time periods.
A total of 266 Patient Complaint Reports were coded, resulting in 499 total
patient complaints during the study period. Of the 499 total complaints, 236 were care
and treatment related complaints (47.3%), 117 were related to communication (23.4%),
78 were related to accessibility (15.6%), 47 were related to concern for patient / family
(9.4%) and 21 were related to billing (4.2%). More complete patient complaint
descriptive statistics are provided in Table 5.
30
Table 5: Descriptive Statistics for Patient Complaints1
Variable Mean Standard
Deviation Median Min Max Sum
% of
Sum
Total Complaints 0.47 1.179 0.00 0 8 499 100%
- Care and Treatment 0.22 0.655 0.00 0 6 236 47.3%
- Communication 0.11 0.392 0.00 0 4 117 23.4%
- Accessibility 0.07 0.286 0.00 0 3 78 15.6%
- Concern for Patient /
Family 0.04 0.256 0.00 0 4 47 9.4%
- Billing 0.02 0.146 0.00 0 2 21 4.2%
1 Includes all time periods.
The average risk profile by surgeon by quarter was 0.00 (standard deviation =
0.65), as the operationalization of the variable entailed mean centering. The minimum
risk profile score was -1.45 and the maximum risk profile score by surgeon by quarter
was 3.33. For the individual components that made up the risk profile, the average ASA
classification was 2.77 (standard deviation = 0.47), average wound classification was
1.74 (standard deviation = 0.72), average functional health status was 1.12 (standard
deviation = 0.24), average emergency case status was 0.12 (standard deviation = 0.24),
average inpatient case status was 0.71 (standard deviation = 0.32), and average transfer
status was 0.08 (standard deviation = 0.15). The average BMI of the patients from the
9,351 procedures is 27.99 (standard deviation = 3.19) and the average number of patient
comorbidities is 1.76 (standard deviation = 1.10). Table 6 provides additional descriptive
statistics for the risk profile.
31
Table 6: Descriptive Statistics for Risk Profile
Variable Mean Standard
Deviation Median Min Max
Risk Profile 0.00 0.65379 -0.1307 -1.45 3.33
- ASA 2.7684 0.47055 2.7413 1.00 5.00
- Wound Classification 1.7351 0.72371 1.6569 1.00 4.00
- Functional Health Status 1.1174 0.24412 1.0231 0.96 3.00
- Emergency Case Status 0.1245 0.24146 0.0000 0.00 1.00
- Inpatient Case Status 0.7138 0.31819 0.8394 0.00 1.00
- Transfer Case Status 0.0806 0.15391 0.0000 0.00 1.00
- BMI 27.9863 3.19314 27.8928 15.79 47.07
- Comorbidities 1.7582 1.10227 1.4523 0.00 8.00
Bivariate Analysis
An evaluation of the linear relationship between the variables was conducted
using Pearson’s correlation coefficients, a parametric measure of association for two
continuous random variables. The number of patient complaints for an individual
surgeon by quarter were significantly correlated with the total number of post-operative
occurrences (r = 0.306, p < .01). Additionally, the number of complaints was
significantly correlated with all post-operative occurrence categories. The strongest
correlation was between complaints and other occurrences (r = 0.30, p < .01), followed
by wound occurrences (r = 0.284, p < .01), respiratory occurrences (r = 0.259, p < .01),
UTI occurrences (r = 0.251, p < .01), cardiac occurrences (r = 0.192, p < .01) and central
nervous system occurrences (r = 0.184, p < .01). The number of complaints was also
32
correlated with the number of cases performed (r = 0.341, p < .01). The risk profile was
significantly correlated with the number of cases (r = -0.229, p < .01), other occurrences
(r = 0.213, p < .01), respiratory occurrences (r = 0.133, p < .01), UTI occurrences (r =
0.106, p <.05) and total occurrences (r = 0.105, p < .05). Table 7 includes more complete
information on the correlations between study variables.
Table 7: Pearson’s Correlation Coefficients of Variables
Variables 1 2 3 4 5 6 7 8 9
1 Number of
Cases 1.000
2 Cardiac
Occurrences 0.357
**
3 CNS
Occurrences 0.270
** 0.186
**
4 Other
Occurrences 0.654
** 0.448
** 0.387
**
5 Respiratory
Occurrences 0.525
** 0.444
** 0.388
** 0.743
**
6 UTI
Occurrences 0.529
** 0.314
** 0.261
** 0.690
** 0.709
**
7 Wound
Occurrences 0.617
** 0.297
** 0.167
** 0.690
** 0.580
** 0.614
**
8 Total
Occurrences 0.629
** 0.498
** 0.388
** 0.822
** 0.951
** 0.811
** 0.779
**
9 Risk Profile -
0.229**
0.149
** 0.145
** 0.213
** 0.133
** 0.106
* -0.053 0.105
*
10 Complaints 0.341**
0.192**
0.184**
0.300**
0.259**
0.251**
0.284**
0.306**
-0.25
* Correlation is significant at the 0.05 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).
33
Multivariate Analysis
In the first model, the number of patient complaints was included as the
dependent variable, with number of cases, time period (annual quarter) and risk profile
included as the covariates. The model also accounted for time-invariant physician level
attributes over time with physician fixed effects. The results are presented in Table 8.
Table 8: Risk Profile Association with Patient Complaints – Fixed Effects Panel
Regression (N = 547)
β Standard Error p-value
Risk Profile 0.012 0.1198 0.920
Number of Cases 0.038***
0.0050 0.000
Time – Annual Quarter -0.044**
0.0145 0.002
Ϯ p < .10; * p < .05; ** p < 0.01; *** p < .001
There was not a significant relationship between the risk profile and the number
of patient complaints. The hypothesis that General Surgeons and Vascular Surgeons that
operate on patients wither higher perioperative surgical risk will experience higher
volumes of patient complaints than General Surgeons and Vascular Surgeons that operate
on patients with lower perioperative surgical risk was not supported. The number of
cases was significantly related to patient complaints (β = .038, p < .001). Specifically, an
increase of one case per surgeon per quarter, on average, was associated with a 3.87%
increase in patient complaints (IRR = 1.04, p < .001). Additionally, the rate of patient
complaints declined by 4.3%, on average, each quarter (IRR = 0.96, p < 0.01).
34
In the second model, the number of post-operative occurrences was included as
the dependent variable and the number of cases, time period (annual quarter), risk profile,
and patient complaints identified as the covariates. Physician fixed effects were once
again included to account for unobserved, time-invariant physician-level factors. The
results are presented in Table 9.
Table 9: Patient Complaints Association with Aggregated Post-Operative
Occurrences – Fixed Effects Panel Regression (N = 530)
β Standard Error p-value
Patient Complaints 0.062Ϯ 0.0362 0.085
Risk Profile 0.774***
0.1043 0.000
Number of Cases 0.071***
0.0046 0.000
Time – Annual Quarter -0.006 0.0108 0.553
Ϯ p < .10; * p < .05; ** p < 0.01; *** p < .001
The number of patient complaints was marginally significant with post-operative
occurrences (β = .062, p < .10). On average, surgeons who received one additional
patient complaint per quarter experienced a 6.4% increase in post-operative occurrences
(IRR = 1.06, p < .10). These results provide some support for the hypothesis that General
Surgeons and Vascular Surgeons with higher volumes of patient complaints will
experience higher levels of post-operative occurrences than General Surgeons and
Vascular Surgeons with lower patient complaints volumes. For the control variables, the
number of cases had a significant relationship with post-operative occurrences (β = .071,
p < .001). On average, surgeons with an increase of one case per quarter were associated
35
with a 7.36% increase in post-operative occurrences (IRR = 1.07, p < .001). The risk
profile had a significant relationship with post-operative occurrences (β = 0.774, p <
.001). On average, surgeons that had a one unit increase in risk profile score had a 116%
increase in the number of post-operative occurrences (IRR = 2.17, p < .001).
Supplementary Analysis
In an effort to further understand how patient complaints are associated with post-
operative occurrences, additional fixed effects panel regression analyses were conducted
with the specific types of post-operative occurrences as the dependent variables. The
results are presented in table 10.
Table 10: Patient Complaints Association with Specific Post-Operative Occurrences
– Fixed Effects Panel Regression (N = 530)
Wound
Occurrences
UTI
Occurrences
CNS
Occurrences
Respiratory
Occurrences
Cardiac
Occurrences
Other
Occurrences
β
(SE)
β
(SE)
β
(SE)
β
(SE)
β
(SE)
β
(SE)
Patient
Complaints
0.065
(0.0408)
0.078
(0.0483)
0.181*
(0.0885)
0.069
(0.0433)
0.118*
(0.0601)
0.079*
(0.0369)
Risk
Profile
0.171
(0.1199)
0.715***
(0.1412)
1.213***
(0.2552)
0.877***
(0.1234)
1.223***
(0.1897)
0.961***
(0.1082)
Number
of Cases
0.058***
(0.0049)
0.059***
(0.0059)
0.066***
(0.0124)
0.068***
(0.0055)
0.060***
(0.0083)
0.063***
(0.0046)
Time –
Annual
Quarter
0.000
(0.0131)
-0.018
(0.0161)
-0.067Ϯ
(0.0355)
-0.018
(0.0134)
0.068**
(0.0231)
0.011
(0.0115)
Ϯ p < .10; * p < .05; ** p < 0.01; *** p < .001
36
The supplementary analyses found that patient complaints were significantly
associated with central nervous system (β = .181, p < .05), cardiac (β = .118, p < .05), and
other (β = .079, p < .05) post-operative occurrences, but were not associated with wound,
UTI and respiratory post-operative occurrences. Controlling for all other characteristics,
surgeons who received one additional patient complaint per quarter experienced a
19.84% increase, on average, in central nervous system post-operative occurrences (IRR
= 1.19, p < .05), a 12.52% increase in cardiac post-operative occurrences (IRR = 1.13, p
< .05) and an 8.22% increase in other post-operative occurrences (IRR = 1.08, p < .05).
Additionally, the supplementary analyses found that risk profile was significantly
associated with UTI (β = .715, p < .001), central nervous system (β = 1.213, p < .001),
respiratory (β = .877, p < .001), cardiac (β = 1.223, p < .001) and other (β = .961, p <
.001) post-operative occurrences. Controlling for all other characteristics, surgeons who
had a one unit increase in risk profile score experienced a 104% increase, on average, in
UTI post-operative occurrences (IRR = 2.04, p < .001), a 236% increase in central
nervous system post-operative occurrences (IRR = 3.36, p < .001), a 140% increase in
respiratory post-operative occurrences (IRR = 2.40, p < .001), a 240% increase in cardiac
post-operative occurrences (IRR = 3.40, p < .001) and a 161% increase in other post-
operative occurrences (IRR = 2.61, p < .001).
Further, the supplementary analyses found that number of cases was significantly
associated with all six post-operative occurrence categories; wound (β = .058, p < .001),
UTI (β = .059, p < .001), central nervous system (β = .066, p < .001), respiratory (β =
.068, p < .001), cardiac (β = .060, p < .001), and other (β = .063, p < .001). Controlling
for all other characteristics, surgeons who had an increase in one case per quarter
37
experienced a 5.9% increase in wound post-operative occurrences (IRR = 1.06, p < .001),
a 6.1 % increase in UTI post-operative occurrences (IRR = 1.06, p < .001), a 6.8%
increase in central nervous system post-operative occurrences (IRR = 1.07, p < .001), a
7.0% increase in respiratory post-operative occurrences (IRR = 1.07, p < .001), a 6.2%
increase in cardiac post-operative occurrences (IRR = 1.06, p < .001), and a 6.5%
increase in other post-operative occurrences (IRR = 1.07, p < .001).
Similarly, a supplementary analysis explored how specific types of patient
complaints were associated with post-operative occurrences, once again using fixed
effects panel regression models. The results are presented in Table 11.
Table 11: Patient Complaints Categories Association with Post-Operative
Occurrences – Fixed Effects Panel Regression (N = 530)
β Standard Error p-value
Care & Treatment 0.068 0.0640 0.291
Communication 0.153 0.1064 0.150
Concern for Patient / Family -0.049 0.1374 0.719
Accessibility 0.265* 0.1272 0.037
Environment . . .
Billing 0.154 0.2517 0.541
Ϯ p < .10; * p < .05; ** p < 0.01; *** p < .0001
The only specific type of patient complaint that was statistically significant with
post-operative occurrences was accessibility (β = .265, p < .05), with on average,
surgeons receiving one additional accessibility patient complaint per quarter experienced
a 30.3% increase in post-operative occurrences (IRR = 1.30, p < .05).
38
CHAPTER 5
DISCUSSION
Review of Findings
The purpose of this study was to examine the relationship between perioperative
surgical risk profiles and patient complaints as well as between patient complaints and
post-operative surgical occurrences. This study reviewed 4,064 post-operative surgical
occurrences from 9,351 surgical cases and 499 patient complaints, both from 61 General
and Vascular Surgeons at EHC from October 1, 2009 through December 31, 2013.
The first hypothesis – that General Surgeons and Vascular Surgeons that operate
on patients with higher perioperative surgical risk will experience higher volumes of
patient complaints – was not supported. There could be several explanations for these
findings. First, the study may not be sufficiently powered to detect this particular
relationship due to the relatively small sample size. Along similar lines, another
possibility is that the aggregation of the perioperative information into a single composite
may mask factors that act as a predictor of complaints individually. Alternatively, it is
possible that perioperative risk associated with a patient may not be a critical factor in
determining patient complaints. Patients who are more acutely ill and are at a higher
operative risk could have lower, or different, satisfaction expectations for their care and
experience. The satisfaction these patients receive from incremental improvement in
their clinical condition could take precedent over any dissatisfaction they may have had
with their overall experience, making them less likely to submit a complaint. Another
39
potential explanation is that these patients, due to their acute health condition and
increased perceived dependence on their physician over time for additional or follow up
care, may be less likely to submit a complaint, concerned about potential consequences of
their physician-patient relationship. A further explanation is that surgeons may spend
more time with high-risk patients counseling them about potential risks and
complications of a given intervention, therefore shifting their expectations relative to the
surgical outcome. In addition, because surgeons know that higher risk patients are at
higher risk for complications, the surgeon may place more focus on accessibility and
communication for these patients. This would allow the surgeons to ensure any
complications are identified and addressed in a timely matter and be certain that all of the
care expectations are clear. Future research can build on this work and help identify the
most plausible explanation by using a larger sample, for example, by aggregating data
across multiple institutions.
The second hypothesis – that General Surgeons and Vascular Surgeons with
higher volumes of patient complaints will experience higher levels of post-operative
occurrences – was supported. The results suggested that, on average, physicians who
receive one additional patient complaint per quarter experience a 6.4% increase in post-
operative occurrences. Additionally, the supplemental analysis revealed that physicians
who receive one additional patient complaint per quarter experienced a 19.84% increase
in central nervous system post-operative occurrences, a 12.52% increase in cardiac post-
operative occurrences, and an 8.22% increase in other post-operative occurrences. The
results also suggested that an increase in the number of cases and an increase in the risk
profile were associated with an increase in post-operative occurrences. In summary,
40
these results submit that surgeons who have a higher volume of cases, higher
perioperative surgical risk patients and receive higher volumes of patient complaints are
associated with higher volumes of post-operative occurrences. This data supports the
reasoning behind the hypothesis, supporting the concepts in the literature that patient
complaints should be recognized as a valuable source of information and provide
important insights into the quality of care provided to patients.
Implications of Findings
These findings suggest that surgeons who have interactions with their patients
who determine it necessary to submit an unsolicited complaint may also exhibit the same
types of individual characteristics that negatively impact team dynamics and performance
and increase post-operative occurrences. The findings have relevance for health care
organizations and surgeons focused on improving surgical quality, decreasing
malpractice risk and decreasing patient dissatisfaction.
Patient complaints, if captured, aggregated and analyzed, may facilitate
identification of surgeons that model interpersonal behaviors that lead to a poorly
functioning operating room team. This can lead to lapses in care and subsequently to
higher post-operative occurrences for their patients. By sharing specific patient
complaint data with surgeons, opportunities exist to engage in activities to improve
surgeon behavior, their relationships with their patients and team members, ultimately
improving surgical quality. Likewise, by understanding the importance that reducing
unprofessional behavior has on quality of patient care, an opportunity exists to increase
41
the educational content around this topic for medical school students and surgical
residents.
From a risk management perspective, understanding physician specific patient
complaint patterns and their relationship with post-operative occurrences can assist health
care organizations to modify medical malpractice premium internal allocation
methodologies to target physicians with higher post-operative occurrences and increased
risk of a malpractice claim. Specfically, realizing that patient complaints are associated
with certain types of post-operative occurrences, hospitals would be able to monitor and
address specific physicians and procedures for increased likehood of surgical
occurrences.
Noting that accessibility-specific patient complaints are associated with post-
operative occurrences, it highlights the importance for health care institutions to continue
to increase physician appointment availability, improve processes to respond timely to
patients clinical questions and concerns, and proactively monitor and coordinate care.
With patient concerns in these aspects of patient accessiblity suggesting negative impacts
on quality of care, health care institutions efforts need to be aligned with the evolving
patient expectations around increased access to their health care providers.
Although a patients perioperative risk is associated with an increase in post-
operative occurrences, it is not associated with increased patient complaints. Therefore,
from a hospital and health system perspective, this emphasizes the importance of timely
service recovery. Because we may not be reliably able to identify patients more likely to
complain based on their risk profile for surgery, the emphasis of efforts should be
42
focused on improving access for patients and also on developing processes to respond to
the patient complaints more quickly and comprehensively once they have occurred.
With the results submitting that surgeons who have a higher volume of cases,
higher perioperative surgical risk patients and receive higher volumes of patient
complaints are associated with higher volumes of post-operative occurrences, a practical
opportunity for health systems and hospital aiming to improve quality would be to focus
on targeted intervention efforts with high volume surgeons who operate on high risk
patients.
Study Limitations and Opportunities for Future Research
The study has several notable limitations. Three aspects of the study may limit
the ability to generalize the findings across specialties and institutions: (1) the small
sample size, (2) all physician participants are either General Surgeons or Vascular
Surgeons and (3) all physician participants are members of one health care system.
Future studies from additional institutions will be important to validate the findings.
Noting the importance of local data being actionable at the local level, continued
examination of these relationships over time at local institutions will also be important to
validate and further understand the findings. Additionally, aggregating data across
organizations would increase the statistical power of the analysis. In further exploring
the predictors of patient complaints, future research opportunities also exist in
disaggregating the perioperative risk profile composite to assess whether specific factors
are important correlates with complaints. Likewise, with the results suggesting that
perioperative risk is not a predictor of complaints, additional patient complaint potential
43
predictive factors should be explored in future research (i.e., patient-level attributes,
discharge / readmission information, and follow up information). In continuing to assess
surgeon behavior, inclusion of patient satisfaction, physician engagement and employee
engagement data could be explored to expand the patient complaints variable.
Additionally, future research opportunities exist with examining specific types of
procedures of varying complexity with patient complaints.
Conclusion
In conclusion, this study found surgeons that operate on patients with higher
perioperative surgical risk were not associated with patient complaints and that surgeons
that had higher levels of patient complaints were associated with higher levels of post-
operative occurrences. Specifically, surgeons with increases in patient complaints
volumes were associated with higher volumes of central nervous system, cardiac and
other post-operative occurrences. Further, surgeons with increases in accessibility related
patient complaints were associated with increases in post-operative occurrences. These
findings suggest that patient complaints can be viewed as indicators of quality. Future
studies with a larger sample size and from additional institutions will be important to
validate these findings.
44
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49
APPENDIX A
PROCEDURES BY SURGICAL SPECIALITY
50
Specialty Procedure CPT Codes
General Surgery
Pancreatectomy 48105, 48120, 48140, 48145, 48146, 48148,
48150, 48152, 48153, 48154, 48155
Colectomy
44140, 44141, 44143, 44144, 44145, 44146,
44147, 44150, 44151, 44160, 44204, 44205,
44206, 44207, 44208, 44210
Proctectomy
44155, 44156, 44157, 44158, 44211, 44212,
45110, 45111, 45112, 45113, 45114, 45116,
45119, 45120, 45121, 45123, 45126, 45130,
45135, 45160, 45395, 45397, 45402, 45550
Ventral Hernia Repair (VHR)
15734, 49560, 49561, 49565, 49566, 49570,
49572, 49585, 49587, 49590, 49652, 49653,
49654, 49655, 49656, 49657
Bariatric 43644, 43645, 43770, 43773, 43775, 43842,
43843, 43845, 43846, 43847, 43848
Hepatectomy 47120, 47122, 47125, 47130
Thyroidectomy 60200, 60210, 60212, 60220, 60225, 60240,
60252, 60254, 60260, 60270, 60271
Appendectomy 44950, 44955, 44960, 44970
Vascular Surgery
Carotid Endarterectomy (CEA) 35301
Carotid Artery Stenting (CAS) 37215, 37216
Abdominal Aortic Aneurysm
(AAA)
35081, 35082, 35091, 35092, 35102, 35103,
34830, 34831, 34832
Endovascular Aneurysm Repair
(EVAR) 34800, 34802, 34803, 34804, 34805, 34825
Aortoiliac (open) "AI Open" 35558, 35565, 35651, 35654, 35661, 35665
Aortoiliac (endo) "AI Endo" 35472, 37220, 37221, 37222, 37223, 0236T,
0238T
Lower Extremity (open) 35556, 35566, 35571, 35583, 35585, 35587,
35656, 35666, 35671
Lower Extremity (endo) 37224, 37225, 37226, 37227, 37228, 37229,
37230, 37231, 37232, 37233, 37234, 37235
Plastic Surgery
Flap 15731, 15732, 15734, 15736, 15738, 15740,
15756, 15757, 15758
Breast Reduction 19318
Breast Reconstruction 19324, 19325, 19340, 19342, 19357, 19361,
19364, 19366, 19367, 19368, 19369
Abdominoplasy 15830
51
APPENDIX B
NATIONAL SURGICAL QUALITY IMPROVEMENT PROGRAM:
ESSENTIALS WORKSHEET
52
53
54
55
56
57
APPENDIX C
INSTITUTIONAL REVIEW BOARD APPROVAL
58
59
60