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

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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

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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 –

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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).

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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.

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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.

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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

LIST OF REFERENCES

Barlow, J., & Moller, C. (1996). A Complaint is a Gift: Using Customer Feedback as a

Strategic Tool. San Francisco, California: Berrett-Koehler.

Bendall-Lyon, D., & Powers, T. L. (2001). The role of complaint management in the

service recovery process. The Joint Commission Journal on Quality Improvement,

27(5), 278-286.

Bismark, M. M., Brennan, T. A., Paterson, R. J., Davis, P. B., & Studdert, D. M. (2006).

Relationship between complaints and quality of care in New Zealand: a

descriptive analysis of complainants and non-complainants following adverse

events. Quality & Safety in Health Care, 15(1), 17-22. doi:

10.1136/qshc.2005.015743

Bismark, M. M., & Studdert, D. M. (2010). Realising the research power of complaints

data. New Zealand Medical Journal, 123(1314), 12-17.

Brook, R. H. (1991). Quality of care: do we care? Annals of Internal Medicine, 115(6),

486-490.

Brook, R. H., McGlynn, E. A., & Cleary, P. D. (1996). Quality of health care. Part 2:

measuring quality of care. The New England Journal of Medicine, 335(13), 966-

970. doi: 10.1056/NEJM199609263351311

Centers for Disease Control and Preventions (2014). Body Mass Index. Available from

Internet: http://www.cdc.gov/healthyweight/assessing/bmi/Index.html

Centers for Medicare and Medicaid Services (2005). Revisions to Interpretive Guidelines

for Centers for Medicare & Medicaid Services Hospital Conditions of

Participation 42 CFR §§ 482.12, 482.13, 482.27, and 482.28. Available from

Internet: https://www.cms.gov/Medicare/Provider-Enrollment-and-

Certification/SurveyCertificationGenInfo/downloads/SCLetter05-42.pdf

Centers for Medicare & Medicaid Services (2011). Medicare Program: Hospital Inpatient

Value-Based Purchasing Program. Federal Register. 2011;76(9):2454-2491. To be

codified at 42 CFR 422 and 480. Available from Internet:

http://www.gpo.gov/fdsys/pkg/FR-2011-05-06/pdf/2011-10568.pdf

45

Donabedian, A. (1980). Explorations in quality assessment and monitoring. Volume 1.

The definition of quality and approaches to its assessment. Ann Arbor, Michigan:

Health Administration Press.

Donabedian, A. (1982). Explorations in quality assessment and monitoring. Volume 2.

The criteria and standards of quality. Ann Arbor, Michigan: Health

Administration Press.

Donabedian, A. (1985). Explorations in quality assessment and monitoring. Volume 3.

The methods and findings of quality assessment and monitoring: an illustrated

analysis. Ann Arbor, Michigan: Health Administration Press.

Donabedian, A. (1988). The quality of care. How can it be assessed? The Journal of the

American Medical Association, 260(12), 1743-1748.

Englesbe, M. J., Dimick, J. B., Sonnenday, C. J., Share, D. A., & Campbell, D. A., Jr.

(2007). The Michigan surgical quality collaborative: will a statewide quality

improvement initiative pay for itself? Annals of Surgery, 246(6), 1100-1103. doi:

10.1097/SLA.0b013e31815c3fe5

Entman, S. S., Glass, C. A., Hickson, G. B., Githens, P. B., Whetten-Goldstein, K., &

Sloan, F. A. (1994). The relationship between malpractice claims history and

subsequent obstetric care. The Journal of the American Medical Association,

272(20), 1588-1591.

Fink, A. S., Campbell, D. A., Jr., Mentzer, R. M., Jr., Henderson, W. G., Daley, J.,

Bannister, J., . . . Khuri, S. F. (2002). The National Surgical Quality Improvement

Program in non-veterans administration hospitals: initial demonstration of

feasibility. Annals of Surgery, 236(3), 344-353; discussion 353-344. doi:

10.1097/01.SLA.0000027082.79556.55

Friele, R. D., Sluijs, E. M., & Legemaate, J. (2008). Complaints handling in hospitals: an

empirical study of discrepancies between patients' expectations and their

experiences. BMC Health Services Research, 8, 199. doi: 10.1186/1472-6963-8-

199

Hickson, G. B., Clayton, E. W., Githens, P. B., & Sloan, F. A. (1992). Factors that

prompted families to file medical malpractice claims following perinatal injuries.

The Journal of the American Medical Association, 267(10), 1359-1363.

Hickson, G. B., Clayton, E. W., Entman, S. S., Miller, C. S., Githens, P. B., Whetten-

Goldstein, K., & Sloan, F. A. (1994). Obstetricians' prior malpractice experience

and patients' satisfaction with care. The Journal of the American Medical

Association, 272(20), 1583-1587.

46

Hickson, G. B., Pichert, J. W., Federspiel, C. F., Clayton, E. W. (1997). Development of

an early identification and response model of malpractice prevention. Law and

Contemporary Problems, 60, 7-29.

Hickson, G. B., Federspiel, C. F., Pichert, J. W., Miller, C. S., Gauld-Jaeger, J., & Bost,

P. (2002). Patient complaints and malpractice risk. The Journal of the American

Medical Association, 287(22), 2951-2957.

Hickson, G. B., Pichert, J. W., Webb, L. E., & Gabbe, S. G. (2007). A complementary

approach to promoting professionalism: identifying, measuring, and addressing

unprofessional behaviors. Academic Medicine, 82(11), 1040-1048. doi:

10.1097/ACM.0b013e31815761ee

Hickson, G. B., Federspiel, C. F., Blackford, J., Pichert, J. W., Gaska, W., Merrigan, M.

W., & Miller, C. S. (2007). Patient complaints and malpractice risk in a regional

healthcare center. Southern Medical Journal, 100(8), 791-796. doi:

10.1097/SMJ.0b013e318063bd75

Iezzoni, L. I. (1997). The risks of risk adjustment. The Journal of the American Medical

Association, 278(19), 1600-1607.

Institute of Medicine, Committee on Quality of Health Care in America. (2001). Crossing

the Quality Chasm: A New Health System for the 21st Century. Washington, DC:

National Academy Press.

Khuri, S. F., Daley, J., Henderson, W., Barbour, G., Lowry, P., Irvin, G., . . . et al. (1995).

The National Veterans Administration Surgical Risk Study: risk adjustment for

the comparative assessment of the quality of surgical care. Journal of the

American College of Surgeons, 180(5), 519-531.

Khuri, S. F., Daley, J., Henderson, W., Hur, K., Demakis, J., Aust, J. B., . . . Stremple, J.

F. (1998). The Department of Veterans Affairs' NSQIP: the first national,

validated, outcome-based, risk-adjusted, and peer-controlled program for the

measurement and enhancement of the quality of surgical care. National VA

Surgical Quality Improvement Program. Annals of Surgery, 228(4), 491-507.

Khuri, S. F., Daley, J., & Henderson, W. G. (1999). The measurement of quality in

surgery. Advances in Surgery, 33, 113-140.

Khuri, S. F., Daley, J., & Henderson, W. G. (2002). The comparative assessment and

improvement of quality of surgical care in the Department of Veterans Affairs.

Archives of Surgery, 137(1), 20-27.

Kline, T. J., Willness, C., & Ghali, W. A. (2008). Predicting patient complaints in

hospital settings. Quality & Safety in Health Care, 17(5), 346-350. doi:

10.1136/qshc.2007.024281

47

Kravitz, R. L., Callahan, E. J., Paterniti, D., Antonius, D., Dunham, M., & Lewis, C. E.

(1996). Prevalence and sources of patients' unmet expectations for care. Annals of

Internal Medicine, 125(9), 730-737.

Krupka, D. C., Sandberg, W. S., & Weeks, W. B. (2012). The impact on hospitals of

reducing surgical complications suggests many will need shared savings programs

with payers. Health Affairs (Millwood), 31(11), 2571-2578. doi:

10.1377/hlthaff.2011.0605

Mazor, K. M., Roblin, D. W., Greene, S. M., Lemay, C. A., Firneno, C. L., Calvi, J., . . .

Gallagher, T. H. (2012). Toward patient-centered cancer care: patient perceptions

of problematic events, impact, and response. Journal of Clinical Oncology,

30(15), 1784-1790. doi: 10.1200/JCO.2011.38.1384

McCole, P. (2004). Dealing with complaints in services. International Journal of

Contemporary Hospitality Management, 16(6), 345-354

Milton, J. (1644). Areopagitica; A Speech Of Mr. John Milton For the Liberty of

Vnlicenc'd Printing. London: Printed in the Yeare.

Montini, T., Noble, A. A., & Stelfox, H. T. (2008). Content analysis of patient

complaints. International Journal for Quality in Health Care, 20(6), 412-420. doi:

10.1093/intqhc/mzn041

Murff, H. J., France, D. J., Blackford, J., Grogan, E. L., Yu, C., Speroff, T., . . . Hickson,

G. B. (2006). Relationship between patient complaints and surgical

complications. Quality & Safety in Health Care, 15(1), 13-16. doi:

10.1136/qshc.2005.013847

Paterson, R. (2013). Not so random: patient complaints and 'frequent flier' doctors. BMJ

Quality & Safety, 22(7), 525-527. doi: 10.1136/bmjqs-2013-001902

Pichert, J. W., Miller, C. S., Hollo, A. H., Gauld-Jaeger, J., Federspiel, C. F., & Hickson,

G. B. (1998). What health professionals can do to identify and resolve patient

dissatisfaction. The Joint Commission Journal on Quality Improvement, 24(6),

303-312.

Pichert, J. W., Federspiel, C. F., Hickson, G. B., Miller, C. S., Gauld-Jaeger, J., & Gray,

C. L. (1999). Identifying medical center units with disproportionate shares of

patient complaints. The Joint Commission Journal on Quality Improvement,

25(6), 288-299.

Rosenthal, M. B. (2007). Nonpayment for performance? Medicare's new reimbursement

rule. The New England Journal of Medicine, 357(16), 1573-1575.

48

Sage, W. M. (2002). Putting the patient in patient safety: linking patient complaints and

malpractice risk. The Journal of the American Medical Association, 287(22),

3003-3005.

Siyambalapitiya, S., Caunt, J., Harrison, N., White, L., Weremczuk, D., & Fernando, D.

J. (2007). A 22 month study of patient complaints at a National Health Service

hospital. International Journal of Nursing Practice, 13(2), 107-110. doi:

10.1111/j.1440-172X.2007.00613.x

Sloan, F. A., Mergenhagen, P. M., Burfield, W. B., Bovbjerg, R. R., & Hassan, M.

(1989). Medical malpractice experience of physicians. Predictable or haphazard?

The Journal of the American Medical Association, 262(23), 3291-3297.

Stevenson, D. G. (2005). Nursing home consumer complaints and their potential role in

assessing quality of care. Medical Care, 43(2): 102-111.

Tax, S. S., Brown, S. W., & Chandrashekaran, M. (1998). Customer evaluations of

service complaint experiences: Implications for relationship marketing. Journal of

Marketing, 62(2), 60-76. doi: Doi 10.2307/1252161

The Joint Commission (2014). Accreditation Survey Activity Guide for Health Care

Organizations. Available from Internet:

http://www.jointcommission.org/assets/1/18/2014_Organization_SAG.pdf

Wofford, M. M., Wofford, J. L., Bothra, J., Kendrick, S. B., Smith, A., & Lichstein, P. R.

(2004). Patient complaints about physician behaviors: a qualitative study.

Academic Medicine, 79(2), 134-138.

Young, G. J., Charns, M. P., Daley, J., Forbes, M. G., Henderson, W., & Khuri, S. F.

(1997). Best practices for managing surgical services: the role of coordination.

Health Care Management Review, 22(4), 72-81.

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