584158
research-article2015
AJMXXX10.1177/1062860615584158American Journal of Medical QualityCatron et al
Article
Patient Complaints and Adverse Surgical
Outcomes
American Journal of Medical Quality
1­–8
© The Author(s) 2015
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DOI: 10.1177/1062860615584158
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Thomas F. Catron, PhD1, Oscar D. Guillamondegui, MD, MPH1,
Jan Karrass, MBA, PhD1, William O. Cooper, MD, MPH1,
Barbara J. Martin, RN, MBA, CCRN1, Roger R. Dmochowski, MD1,
James W. Pichert, PhD1, and Gerald B. Hickson, MD1
Abstract
One factor that affects surgical team performance is unprofessional behavior exhibited by the surgeon, which may be
observed by patients and families and reported to health care organizations in the form of spontaneous complaints.
The objective of this study was to assess the relationship between patient complaints and adverse surgical outcomes.
A retrospective cohort study used American College of Surgeons National Surgical Quality Improvement Program
data from an academic medical center and included 10 536 patients with surgical procedures performed by 66 general
and vascular surgeons. The number of complaints for a surgeon was correlated with surgical occurrences (P <
.01). Surgeons with more patient complaints had a greater rate of surgical occurrences if the surgeon’s aggregate
preoperative risk was higher (β = .25, P < .05), whereas there was no statistically significant relationship between
patient complaints and surgical occurrences for surgeons with lower aggregate perioperative risk (β = −.20, P = .77).
Keywords
patient complaints, quality, safety, professionalism
In an era of increased focus on safety and quality in health
care, health systems are placing greater emphasis on
understanding, identifying, and addressing threats to reliable care delivery. In many health care settings, particularly in surgery, reliability is dependent on well-functioning
teams, wherein professionals are required to monitor
multiple events and tasks at critical points in the patient’s
treatment and must interact in a way that optimizes each
member’s contributions.1-4 One factor that may affect surgical team behavior is the level of professionalism displayed by the surgeon. In addition to cognitive and
technical competence, surgeons are expected to model
behaviors that enhance team performance, including
clear and effective communication, being available, modeling respect, and having awareness of how their performance influences team performance.2,5-8 Unprofessional
behaviors, on the other hand, undermine a culture of
safety, threaten teamwork, and have been linked to medical errors and adverse surgical outcomes.1,6-13
Patients and families are well positioned to observe
surgeons’ behaviors, and in some cases report their concerns to health system representatives.1,11-23 For example,
a patient reporting to a hospital’s patient relations office
that her surgeon “told me, ‘you don’t need to know that.
Just lay down,’” may be recognizing a behavior that fails
to model respect. Another patient who reports, “Dr __
was so mean and rude to my nurse in front of us all. It
makes me very nervous to have him come into my room
now,” may be perceiving a threat to trust and teamwork.
The authors have previously demonstrated that patient
complaints are nonrandomly distributed and high complaint numbers are associated with high malpractice
claims experience.12-20
The objective of this study was to assess the relationship between patient complaints and adverse surgical outcomes. It was hypothesized that surgeons who generated
a greater number of patient complaints about lack of
respect, availability, and ineffective communication also
might model these same behaviors with members of the
surgical team and therefore would have higher occurrences of adverse surgical outcomes. Thus, a study was
performed using data from the American College of
Surgeons (ACS) National Surgical Quality Improvement
1
Vanderbilt University Medical Center, Nashville, TN
Corresponding Author:
William O. Cooper, MD, MPH, Center for Patient and Professional
Advocacy, Vanderbilt University Medical Center, 2135 Blakemore
Ave, Nashville, TN 37212-3505.
Email: william.cooper@vanderbilt.edu
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American Journal of Medical Quality
Program (NSQIP)21,24 linked with an established patient
complaint reporting system used previously to study professionalism concerns and malpractice risk.15,22,23 This
study sought to further understand whether surgeons with
higher aggregate perioperative risk in the cases they performed had a stronger relationship between complaints
and surgical outcomes than surgeons with lower aggregate perioperative risk.
Methods
A retrospective cohort study was performed using spontaneous patient complaint data and ACS NSQIP data from
an academic medical center (AMC)21,25 during the period
from July 1, 2005, to December 31, 2011. The study site
has more than 900 inpatient beds, serves a catchment area
of approximately 4.5 million persons, and performs more
than 50 000 surgical procedures annually.26,27 The data
sets were merged using a unique physician identifier created specifically for this study and known only by 2 data
managers, one responsible for the patient complaint data
and the other responsible for the NSQIP data. The resulting de-identified database was used for all analyses.
The study included all credentialed surgeons with
active surgical practices any time during the study period
and for whom NSQIP data were available. NSQIP data at
the AMC were recorded for members of the Divisions of
General Surgery (includes surgeons who treat disorders
involving emergency general surgery, advanced laparoscopy, bariatric surgery, oncological surgery, colorectal
surgery, and esophageal and pancreatic procedures) and
Vascular Surgery (includes surgeons who care for patients
with peripheral arterial disease, carotid disease, renovascular disorders, and aneurysm disease of the thorax,
abdomen, and extremities).
On an 8-day cycle, NSQIP data were abstracted from
the first 40 general and vascular surgery cases performed at the AMC that met NSQIP inclusion criteria.28
Three variables were chosen to create a single measure
of relative perioperative risk (hereafter “risk”) based on
their ability to predict adverse surgical outcomes in previous studies29: American Society of Anesthesiology
(ASA) classification (1 to 5 range), wound classification (4 levels of contamination), and emergency case
status (yes/no).28
Five categories of surgical occurrences were extracted
from the NSQIP data set28: intraoperative or perioperative, wound, respiratory, urinary/renal, and other occurrences (see online Appendix 1, available at http://ajmq.
sagepub.com/supplemental). Central nervous system
occurrences also were assessed, but were rare (<1% of
all occurrences) and contributed negligible variance, so
were not included in analyses. In addition, beginning
January 1, 2011, NSQIP discontinued required counting
of coma >24 hours, peripheral nerve injury, graft prosthesis flap failure, and other postoperative occurrences,
so these were not included for that year.28 A count of the
number of cases sampled by NSQIP auditors for each
surgeon was included in analyses to control for disproportionate impact of surgeons with higher or lower
volumes.
Reports of unsolicited patient and family member complaints were recorded by the medical center’s patient relations staff for all patients who received care from one of the
study surgeons during the study period. A validated research
database derived from these reports and used in several
prior research studies was used to characterize complaints
received for each study surgeon.14,15,17,18,20,30 Complaints
were attributed to a surgeon only when the surgeon was
specifically identified by the patient and the behavior
explicitly linked to the surgeon.14,15,17,18,20,30 Independent
coders reviewed patient relations reports and reliably (interrater reliabilities of 73% to 100% across categories)
assigned unique complaints to general categories22,31
involving issues of care and treatment, communication,
respect for patient and/or family, accessibility, and billing.
Billing concerns were only included when they described a
patient’s concern related to a physician’s interactions. For
example, a patient who reports that “I asked legitimate
questions and he only gave me smart aleck answers. I
should not have to pay to be treated that way” is expressing
her or his dissatisfaction with the care rather than disputing
a specific issue related to billing. Reports recorded during
the study period that included complaints about uniquely
identified physicians were used. To focus on behaviors that
would be most likely to affect teamwork and influence
quality outcomes, complaint statements about care and
treatment were excluded from the analysis, as these complaints have previously been demonstrated to be associated
with poor surgical outcomes and are intrinsically related to
the outcomes of interest.32
Analyses were performed using surgeons as the unit of
analysis. The study size was fixed based on the available
data from NSQIP and the patient complaints database. In
order to minimize the number of variables, the mean
across each physician’s sampled cases was entered into a
principal components analysis to assign risk for an individual physician across his or her proportion of the sample cases.33 The resultant score defined that physician’s
average patient-related perioperative risk. The analysis
set the group means at zero; higher scores indicated
greater perioperative risk than lower scores.
Multilevel models using full information maximum
likelihood were employed to predict the slope of surgical
occurrences. This 2-step technique has the advantage of
using continuous variables, rather than discrete variables,
as part of an interaction. In the first step, the cumulative
measures of perioperative risk factors and patient
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Catron et al
complaints were centered on their respective sample
means to reduce nonessential multicollinearity among
interaction terms. For example, the mean number of
patient complaints across physicians was subtracted from
each value creating a new, centered variable with a mean
of zero. Centering the variables assured that the interaction term was not highly correlated with the main effect
variables.33 For the purposes of this study, uncorrelated
measures of risk and patient complaints were selected to
permit the best examination of the study hypotheses.33
To test the hypothesis that patient complaints differentially predict surgical occurrences for surgeons across 2
continua of risk, one high and one low, an interaction
term was created by multiplying the centered patient
complaints variable with the perioperative risk variable.
As recommended by Aiken and West,33 for examining
high risk, each surgeon’s perioperative risk score was
transformed by increasing it by one standard deviation;
for examining low risk, one standard deviation was subtracted from each surgeon’s risk score. These algebraic
transformations were used to assess patterns at the upper
and lower levels of performance, and significant interaction terms would indicate that perioperative risk moderated the relationship between patient complaints and each
type of surgical occurrence.
SPSS version 22 (IBM, Armonk, New York) was used
for all analyses. Missing data were rare. NSQIP performs
regular quality audits and provides opportunities for data
correction. In addition, the variables used for this study
have forced data entry to reduce missing data. For a small
proportion (<1.5%) of surgical cases, follow-up for 30-day
occurrences was not available (eg, an acute appendectomy
with a 2-week follow-up visit and no subsequent encounters). For the purposes of this study, occurrences were
included for cases with <30-day follow-up using the latest
data available. The Vanderbilt University Medical Center
Institutional Review Board (#111064) approved the study
and determined that it satisfied the criteria for exemption
from full review and approved the waiver of informed
consent.
Results
Sixty-six surgeons (59 in General Surgery and 7 in
Vascular Surgery) met inclusion criteria and performed a
total of 10 536 NSQIP-abstracted procedures (median =
92). The mean age of the study surgeons was 46.2 years
(Table 1); 82% were males, and nearly all completed
medical school and postgraduate training in the United
States. On average, the study surgeons had been in practice 11.7 years. Study surgeons had between 0.5 and 6.5
years of surgical data and complaint reports in the
research database; 36 (55%) had 4 or more complete
years of data.
Table 1. Characteristics of the Surgeons (n = 66).
Mean age (years)
Mean years of post-residency
experience
Female gender
US medical school graduate
Postgraduate training in United States
Board certified in specialty
46.2 years
11.7 years
18%
88%
100%
92%
Study surgeons generated a mean 7.98 patient complaints during the study period (Table 2). The most frequent patient complaints related to communication and
accessibility. Forty-three percent of surgeon-associated
patient/family complaints resulted from concerns about
outpatient encounters, 41% from inpatient experiences,
and 3% from observations made during emergency
department experiences; 13% of complaint-associated
locations were not reported. On average, study surgeons
had 159.6 cases sampled during follow-up. The most frequent surgical occurrences were respiratory complications and wound infections. Intraoperative occurrences
and urinary occurrences occurred less frequently.
Demographic data about the patients whose surgical
experiences were captured for this study are presented in
online Appendix 2 (available at http://ajmq.sagepub.com/
supplemental). Compared with all patients in the national
NSQIP database, those in the present study were younger,
more likely to be female, and less likely to be members of
racial minority groups.
The number of patient complaints for an individual
surgeon was significantly correlated with all surgical
occurrence categories (range of Pearson’s correlations: rs
= .55-.61, P < .001; Table 3). No correlations between
patient complaints and a surgeon’s perioperative risk,
ASA class, priority status, or wound classification were
statistically significant.
In hierarchical regression analyses (Table 4), the
number of cases sampled, a proxy for volume of service,
was a significant predictor of all 5 categories of occurrences when controlling for perioperative risk and
patient complaints (Table 4, Step 1: βs = .57 to .77, all P
< .0.01). Perioperative risk predicted respiratory and
wound occurrences, over and above the effects of the
number of cases, such that surgeons whose patients presented with higher risk were associated with more
occurrences in these categories (Table 4, Step 1: βs = .27
and .22, respectively, P < .05). Addition of the interaction between perioperative risk and patient complaints
to the model significantly increased the fit of the 4
occurrence-specific models (Table 4, Step 2: βs = .20 to
.29, P < .05).
The interaction between perioperative risk and
patient complaints was significant for predicting wound
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American Journal of Medical Quality
Table 2. Means and Standard Deviations for Surgeons’ Patient Complaints, Perioperative Risk Factors, and Surgical
Occurrences.
Standard
Deviation
Mean
Median
Sum
3.00
1.00
1.00
1.00
6.00
281
96
153
62
527
2.65
0.09
1.00
−0.14
91.50
3.50
6.00
6.50
3.00
8.50
10 536
627
1107
1327
498
1598
Complaints received for a physician during study follow-up (number of complaints)
Communication
4.26
4.20
Concern
1.45
1.96
Accessibility
2.32
2.85
Billing
0.94
1.01
Total
7.98
9.41
Surgeon’s aggregate perioperative risk across sampled patients
ASA class (1-5)
2.63
0.35
Priority (emergent/nonemergent)
0.18
0.21
Wound class (1-4)
1.05
0.69
Risk score
0.00
1.00
Surgical occurrences (number of occurrences)
Cases sampled
159.64
181.87
Intraoperative
9.50
13.34
Wound
16.77
29.52
Respiratory
20.11
30.69
Urinary
7.55
12.02
Other
24.08
36.32
Abbreviation: ASA, American Society of Anesthesiology.
Table 3. Intercorrelations Among Number of Cases, Surgical Occurrences, Perioperative Risk Factors, and Patient Complaints.
Variables
1. Cases sampled
2. Intraoperative
3. Wound
4. Urinary
5. Respiratory
6. Other
7. Perioperative risk score
8. ASA class
9. Priority status
10. Wound class
11. Total complaints
1
.60c
.70c
.73c
.63c
.63c
−.32b
−.01
−.41b
−.24d
.72c
2
.63c
.72c
.95c
.93c
.05
.29a
−.07
.02
.58c
3
.92c
.69c
.78c
−.01
.08
−.21d
.17
.60c
4
5
.75c
.86c
−.05
.13
−.24a
.07
.61c
.90c
.07
.24a
−.07
.08
.59c
6
−.01
.28a
−.17
.02
.55c
7
.48c
.92c
.87c
.12
8
.31a
.13
.19
9
10
.71c
.00
.16
Abbreviation: ASA, American Society of Anesthesiology.
a
P < .05.
b
P < .01.
c
P < .001.
d
P < .10.
occurrences (Figure 1). When a surgeon’s average perioperative risk was increased by one standard deviation,
patient complaints were significantly related to wound
occurrences, such that surgeons with more patient complaints also experienced more patient wound occurrences (β = .42, P < .05). Conversely, when a surgeon’s
average perioperative risk was reduced one standard
deviation (ie, representing relatively low risk), patient
complaints were not significantly related to wound
occurrences; the slope of this line did not differ from
zero (β = −.04, P = .77). The slope of the relationship
between patient complaints and wound occurrences at
average patient perioperative risk was similarly not statistically different from zero. Similar findings were seen
for intraoperative and respiratory occurrences, but not
for urinary occurrences.
Discussion
In this study of surgeons from a large, tertiary care AMC,
the authors reviewed surgical occurrences for 10 536 surgical cases from 66 surgeons and found that spontaneous
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Catron et al
Table 4. Hierarchical Regression Model Predicting Surgical Occurrences From Perioperative Risk and Patient Complaints
(Excluding Care and Treatment Complaints).
Type of Surgical Occurrence
Step and Predictors
Urinary
Intraoperative
Respiratory
Wound
Other
16.08c
.41
.57b
.21d
.15
19.53c
.46
.66c
.27a
.08
24.49c
.52
.74c
.22a
.05
16.70c
.42
.67c
.21d
.04
4.25a
.04
.55b
.25a
.27
.23a
7.91b
.06
.63c
.31b
.24
.29b
6.72a
.05
.71c
.26a
.19
.25a
3.13d
.03
.65c
.24a
.15
.20d
1: Cases + Risk + Complaints
Overall F
28.06c
2
Adjusted R
.56
Cases sampled β
.77c
Perioperative risk score β
.19d
Patient complaints β
.03
2: Cases + Risk + Complaints + Risk × Complaints
F (change)
4.34a
2
R (change)
.03
.76c
Cases sampled β
Perioperative risk score β
.22a
Patient complaints β
.14
.20a
Perioperative risk × Complaints β
a
P < .05.
P < .01.
c
P < .001.
d
P < .10.
b
Figure 1. Simple slopes of patient complaints as a moderator of wound occurrences at 2 levels of perioperative risk (values
adjusted for number of cases sampled).
The solid line represents the slope of the relationship between patient complaints and wound occurrences when surgeons’ perioperative risk is
increased by one standard deviation (ie, representing patients with high risk). Surgeons with higher risk cases and more complaints experienced
significantly more wound occurrences (solid line), but surgeons with lower risk cases and more complaints had no more wound occurrences than
those with fewer patient complaints (dotted line).
patient complaints about communication, respect, and
accessibility were correlated with surgical occurrences
and that this relationship was moderated by a surgeon’s
aggregate perioperative risk. When surgeons with low
aggregate perioperative risk operated, they had the same
low rate of NSQIP-defined occurrences regardless of their
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American Journal of Medical Quality
case-adjusted numbers of complaints. However, surgeons
with the greatest case-adjusted numbers of patient complaints who performed procedures associated with higher
mean risk had a greater likelihood of surgical
occurrences.
These findings suggest that when patients’ experiences
with their surgeons were sufficiently concerning to prompt
a formal complaint to the organization, the patients may
have observed behaviors that also negatively affected a
surgery service team’s ability to achieve desired outcomes, especially as complexity, risk, and stress increased.
For example, aggregated patient complaints about perceived failures to demonstrate respect toward the patient
(eg, “Doctor ____ left our room, walked down the hall and
said to the nurse, ‘This patient has completely [fouled] up
my day . . . go give him some information and get him out
of here’, I heard every word he said about me”) may mirror a physician’s similar behavior toward other medical
team members. Although patients and their families rarely
observe (and evaluate) the surgeon’s teamwork skills in
the operating room, they form impressions during initial
and follow-up consultations, interactions on the day of
surgery, and during ongoing care the surgeon may provide. Patients’ observations, if documented, aggregated,
and analyzed, may facilitate identification of surgeons
who model behaviors that not only create patient dissatisfaction but also increase malpractice risk and may have a
negative impact on teamwork. Engaging patients who
complain about their physicians in these settings as a part
of service recovery also can facilitate a 2-way engagement
that might identify ways to improve physician behavior
and ultimately reduce adverse outcomes.
The finding that patient complaints predicted risk for
adverse surgical outcomes for surgeons with higher
aggregate operative risk and not for surgeons with lower
aggregate operative risk is interesting and warrants further consideration. Previous research on teamwork in a
variety of settings suggests that the behaviors observed
by patients and families and reported to the organization
in the form of a complaint may have a stronger impact on
teamwork and outcomes when the team’s task requires a
greater degree of interdependence. The team member
with behaviors that undermine a culture of safety may
have an even greater impact on interdependent teams
when that individual provides critical skills or expertise
that cannot be provided by others.34 Interdependence and
team members with critical expertise (ie, surgeons) exist
in surgical teams operating in higher risk settings and
typically to a greater degree than surgical teams involving
lower risk settings. The hierarchy of surgical teams also
interferes with a team’s natural moderating of negative
behaviors because of a real or perceived power differential among the surgeon and other team members.34
Negative interactions by the surgeon in these higher risk
operative settings might therefore result in worsened
team performance and higher rates of adverse occurrences because of their impact on individual team members, including team members’ distraction from critical
tasks.34
The findings of this study mirror previous studies
demonstrating a relationship between spontaneous
patient complaints and risk for malpractice claims,
patients’ practice dropout, and nonadherence to medical
recommendations.12-16,19,24,29,35 In the malpractice claims
studies, obstetricians with high claims experience generated 3 times the number of complaints of lower risk
obstetricians. Importantly, when physicians with high
numbers of patient complaints receive feedback from
peers as a part of a structured intervention, their patient
complaints typically are reduced.31 The findings also
underscore previous observations that technical skills
alone are not sufficient to ensure optimal surgical outcomes2,7,9,10—behaviors that promote teamwork are critical to success.
There are some study limitations. The data were
drawn from a single site, which may limit generalizability. Future studies from multiple institutions will be
important to replicate these findings. Complaints from
all patients who received care from the surgeon were
included, not just the sampled cases. Although it may be
true that patients with adverse outcomes may be more
likely to complain in general, this study focused on
complaints unrelated to care and treatment and focused
instead on complaints that might reflect communication, respect, and teamwork. Complaints describing
concerns related to care and treatment were intentionally excluded because of the previously reported intrinsic relationship between these types of complaints and
outcomes.32 Although the number of sampled cases was
controlled for in these analyses, it is possible that the
significant correlations between surgical occurrences
and aggregated spontaneous complaints may be an artifact of surgeons’ varied volumes of cases, but including
number of cases in the analysis should mitigate this
effect somewhat. Finally, patients’ complaints were
based on experiences before and following surgery;
only very rarely did complaints involve observations
made during operating room experiences when all or
virtually all patients are anesthetized.
In conclusion, this study found a significant correlation between patient complaints and surgical occurrences.
For surgeons with low-risk surgical cases, high complaint generation did not predict adverse occurrences.
However, as surgical complexity rose, adverse occurrences were correlated with patient complaints. These
findings have relevance for health care organizations
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Catron et al
seeking to promote team performance and outcomes34;
these organizations might share complaint data with surgeons to help improve teamwork-related behaviors and
outcomes.22,31,36-38
Authors’ Note
Thomas F. Catron and Oscar D. Guillamondegui are first
coauthors.
Declaration of Conflicting Interests
The authors declared the following potential conflicts of interest
with respect to the research, authorship, and/or publication of
this article: Dr Dmochowski worked as a consultant for both
Allergan and Medtronic, but the research described in this article did not relate to these relationships. The other authors
declared no conflicts of interest.
Funding
The authors received no financial support for the research,
authorship, and/or publication of this article.
References
1. Mitchell P, Wynia M, Golden R, et al. Core Principles &
Values of Effective Team-based Health Care. Washington,
DC: Institute of Medicine; 2012.
2. Mazzocco K, Petitti DB, Fong KT, et al. Surgical team
behaviors and patient outcomes. Am J Surg. 2009;197:
678-685.
3. Sexton JB, Paine LA, Manfuso J, et al. A check-up for
safety culture in “my patient care area.” Jt Comm J Qual
Patient Saf. 2007;33:699-703.
4. Makary MA, Sexton JB, Freischlag JA, et al. Operating
room teamwork among physicians and nurses: teamwork
in the eye of the beholder. J Am Coll Surg. 2006;202:
746-752.
5.Hickson GB, Moore IN, Pichert JW, Benegas M Jr.
Balancing systems and individual accountability in a safety
culture. In: Berman S, ed. From the Front Office to the
Front Line: Essential Issues for Healthcare Leaders. 2nd
ed. Oakbrook Terrace, IL: Joint Commission Resources,
Inc.; 2012:1-36.
6. Moran SK, Sicher CM. Interprofessional jousting and medical tragedies: strategies for enhancing professional relations. AANA J. 1996;64:521-524.
7. Catchpole K, Mishra A, Handa A, McCulloch P. Teamwork
and error in the operating room: analysis of skills and roles.
Ann Surg. 2008;247:699-706.
8.Joint Commission on Accreditation of Healthcare
Organizations. Sentinel Events: Behaviors That Undermine
a Culture of Safety. Oakbrook Terrace, IL: Joint
Commission on Accreditation of Healthcare Organizations;
2008.
9. Mishra A, Catchpole K, Dale T, McCulloch P. The influence
of non-technical performance on technical outcome in laparoscopic cholecystectomy. Surg Endosc. 2008;22:68-73.
10.Shouhed D, Gewertz B, Wiegmann D, Catchpole K.
Integrating human factors research and surgery: a review.
Arch Surg. 2012;147:1141-1146.
11. Allsop J. Two sides to every story: complainants’ and doctors’ perspectives in disputes about medical care in a general practice setting. Law Policy. 1994;16:149-183.
12.Cydulka RK, Tamayo-Sarver J, Gage A, Bagnoli D.
Association of patient satisfaction with complaints and risk
management among emergency physicians. J Emerg Med.
2011;41:405-411.
13. Fullam F, Garman AN, Johnson TJ, Hedberg EC. The use of
patient satisfaction surveys and alternative coding procedures
to predict malpractice risk. Med Care. 2009;47:553-559.
14.Hickson GB, Federspiel CF, Blackford J, et al. Patient
complaints and malpractice risk in a regional healthcare
center. South Med J. 2007;100:791-796.
15. Hickson GB, Federspiel CF, Pichert JW, Miller CS, GauldJaeger J, Bost P. Patient complaints and malpractice risk.
JAMA. 2002;287:2951-2957.
16. Levtzion-Korach O, Frankel A, Alcalai H, et al. Integrating
incident data from five reporting systems to assess patient
safety: making sense of the elephant. Jt Comm J Qual
Patient Saf. 2010;36:402-410.
17.Moore IN, Pichert JW, Hickson GB, Federspiel C,
Blackford JU. Rethinking peer review: detecting and
addressing medical malpractice claims risk. Vanderbilt
Law Rev. 2006;59:1175-1206.
18. Mukherjee K, Pichert JW, Cornett MB, Yan G, Hickson
GW, Diaz JJ Jr. All trauma surgeons are not created equal:
asymmetric distribution of malpractice claims risk. J
Trauma. 2010;69:549-554.
19.Stelfox HT, Gandhi TK, Orav EJ, Gustafson ML.
The relation of patient satisfaction with complaints
against physicians and malpractice lawsuits. Am J Med.
2005;118:1126-1133.
20. Stimson CJ, Pichert JW, Moore IN, et al. Medical malpractice claims risk in urology: an empirical analysis of patient
complaint data. J Urol. 2010;183:1971-1976.
21. Guillamondegui OD, Gunter OL, Hines L, et al. Using the
National Surgical Quality Improvement Program and the
Tennessee Surgical Quality Collaborative to improve surgical outcomes. J Am Coll Surg. 2012;214:709-714.
22.Hickson GB, Pichert JW, Federspiel CF, Clayton EW.
Development of an early identification and response model
of malpractice prevention. Law Contemp Probl. 1997;60(12):7-29.
23. Sloan FA, Mergenhagen PM, Burfield WB, Bovbjerg RR,
Hassan M. Medical malpractice experience of physicians.
Predictable or haphazard? JAMA. 1989;262:3291-3297.
24. Vincent C, Davis R. Patients and families as safety experts.
CMAJ. 2012;184:15-16.
25. Fink AS, Campbell DA, Mentzer RM, et al. The National
Surgical Quality Improvement Program in non-veterans
administration hospitals: initial demonstration of feasibility. Ann Surg. 2002;236:344-354.
26.Vanderbilt University Medical Center. Factbook 20132014. http://www.mc.vanderbilt.edu/documents/main/files/
Factbook_2014_web_version.pdf. Accessed April 1, 2015.
Downloaded from ajm.sagepub.com at VANDERBILT UNIV on May 24, 2015
8
American Journal of Medical Quality
27.US News and World Report. , Vanderbilt University
Medical Center. http://health.usnews.com/best-hospitals/
area/tn/vanderbilt-university-medical-center-6521060.
Accessed April 1, 2015.
28. American College of Surgeons National Surgical Quality
Improvement Program (ACS-NSQIP). User Guide for the
2011 Participant Use Data File. Chicago, IL: American
College of Surgeons; 2012:3-6, 25-26, 33.
29. Hickson GB, Clayton EW, Entman SS, et al. Obstetricians’
prior malpractice experience and patients’ satisfaction with
care. JAMA. 1994;272:1583-1587.
30.Pichert JW, Hickson G, Moore I. Using patient com
plaints to promote patient safety. In: Henriksen K, Battles
JB, Keyes MA, Grady ML, eds. Advances in Patient
Safety: New Directions and Alternative Approaches (Vol.
2: Culture and Redesign). Rockville, MD: Agency for
Healthcare Research and Quality; 2008.
31. Pichert JW, Moore IN, Karrass J, et al. An intervention
model that promotes accountability: peer messengers and
patient/family complaints. Jt Comm J Qual Patient Saf.
2013;39:435-446.
32.Murff HJ, France DJ, Blackford J, et al. Relationship
between patient complaints and surgical complications.
Qual Saf Health Care. 2006;15:13-16.
33.Aiken LS, West SG. Multiple Regression: Testing and
Interpreting Interactions. Newbury Park, CA: Sage;
1991.
34. Felps W, Mitchell TR, Byington E. How, when, and why
bad apples spoil the barrel: Negative group members
and dysfunctional groups. In: Staw BM, ed. Research in
Organizational Behavior: An Annual Series of Analytical
Essays and Critical Reviews. Amsterdam, Netherlands:
Elsevier; 2006:175-222.
35. Greene J, Hibbard JH. Why does patient activation matter? An examination of the relationships between patient
activation and health-related outcomes. J Gen Intern Med.
2012;27:520-526.
36. Reiter CE, Pichert JW, Hickson GB. Addressing behavior
and performance issues that threaten quality and patient
safety: what your attorneys want you to know. Prog Pediatr
Cardiol. 2012;33:37-45.
37.Pichert J, Johns JA, Hickson GB. Professionalism in
support of pediatric cardio-thoracic surgery: a case of a
bright young surgeon. Prog Pediatr Cardiol. 2011;32:
89-96.
38. Hickson GB, Clayton EW, Miller CS, Pichert JW, Entman
SS. Satisfaction with obstetric care: relation to neonatal
intensive care. Obstet Gynecol. 1998;91:288-292.
Downloaded from ajm.sagepub.com at VANDERBILT UNIV on May 24, 2015