








































Anesthesia eJournal
Volume 4 - Issue 1 2016

AEJ

Educated Hand Publishing LLC 
“The Science Behind the Art”  

 Anesthesia eJournal - Online
ISSN 2333-2611

Page 6

The Effectiveness of an Anesthesia Handoff Tool: An Electronic Health Record 
Application to Enhance Patient Safety
Karen Gillikin, MSN, MSNA DNP, CRNA
Nathaniel Apatov, MSN, MHS, PhD, CRNA

Affiliation: 
Dr Gillikin is Associate Director of Old Dominion University School of Nursing, Nurse Anesthesia Program, Norfolk, VA
Dr Apatov is is the Director at Old Dominion University School of Nursing, Nurse Anesthesia Program, Norfolk, VA

Funding/Conflict of Interest Disclosure:
None

INTRODUCTION

Patient care is transferred from one anesthesia provider to another frequently throughout the day. Ideally, one anesthesia 
provider would be responsible for the entire perioperative phase for a surgical patient; however, discontinuity of care is 
inevitable owing to shift changes, meal breaks, and staffing shortages. It is paramount that handoff processes be accurate, 
thorough, and concise to reduce errors, promote patient safety, and support a busy surgical schedule. Many barriers exist in 
the surgical environment that threaten the integrity of the handoff process. Such barriers include high background noise, high 
activity level, provider fatigue, operating room production pressure, interruptions during handoff, and lack of standardization 
of the handoff process. The critical importance of an accurate handoff and the significance of barriers to effective 
communication demand that nurse anesthetists develop strategies that contribute to patient safety and limit communication 
failures.

Failures in communication among health care providers account for 60% of the root causes associated with sentinel 
events reported annually to the Joint Commission.1 Transfer of patient care, or “handoffs,” in the operating room occur for 
meal breaks, shift changes, and transfer of patient care to the post-anesthesia care unit or intensive care unit. Jayaswal et al2 
report that transfer of patient care between anesthesia providers occurs at least 5 times per operating room each day between 
7:00 AM and 3:00 PM. Since many operating rooms conduct business well past 3:00 PM, the potential for errors and 
omissions of essential patient information during handoffs is considerable. An added impact to potential errors is the lack 
of standardization of the transfer of essential patient information during perioperative handoffs. Some anesthesia providers 
will offer a thorough report that includes the patient’s name, allergies, past medical history, surgical procedure, perioperative 
medications given, fluid status, and anticipatory guidance, whereas others may point to the record and mention 1 or 2 items 
only. Lastly, anesthetists must recognize the complexity of perioperative handoffs. Petrovic et al3 emphasized that perioperative 
handoffs are multidimensional, involving the exchange of information and the transfer of technology, such as monitors, 
ventilators, transducers, and invasive lines for patients who are at higher risk for instability during this phase of care.

Abstract
Perioperative patient care handoffs are complex and multidimensional and require accurate attention to detail. 

Communication failures among health care providers increase the risk of morbidity and mortality. Use of a standardized 
handoff tool located within the electronic anesthesia record formalizes the handoff process and improves patient safety. 
I conducted 82 patient care transfer observations before the introduction of an electronic anesthesia handoff tool and 
75 patient care transfer observations subsequent to the launch of the tool and made before and after comparisons. 
Significantly (P<0.05) fewer errors were made in all categories of patient information after the introduction of the 
electronic anesthesia handoff tool. There were trends toward more handoff omissions after 3:00 PM, but the difference 
in most patient information categories was not significant (P>0.05). In addition, there were no significant differences in 
omissions related to the severity of patient comorbidities according to American Society of Anesthesiologists physical 
status classification. These findings provide information regarding the incidence of patient information inaccuracies and 
omissions during patient care transfer before and after implementation of an electronic patient care transfer tool.

Keywords: anesthesia handoff, electronic health record, patient care transfer



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Volume 4 - Issue 1 2016 Page 7

As stated previously, the Joint Commission reports that communication failures account for the majority of sentinel 
events.1 In fact, in an effort to close this gap in communication errors, the Joint Commission now requires hospitals 
to standardize handoff communications.1 In a study conducted by Jayaswal et al,2 84% of anesthesia providers 
reported receiving a poor or incomplete handoff in the previous year; 57% reported giving an inadequate report in 
the previous year; and 25% of anesthesia providers attributed an adverse outcome to a poor handoff. Hudson et al4 
revealed that “handover of anesthetic care during cardiac surgery is associated with a 43% greater risk of in-hospital 
mortality and 27% greater risk of major morbidity.” Mandating the use of a handoff tool that standardizes the 
patient information exchanged during transfers may be the key to preventing transfer-of-care events. For example, 
after standardizing patient handoff processes from surgery to intensive care, Catchpole et al5 reduced the number of 
technical errors, the number of information omissions, and the duration of the handoff.

In a retrospective study by Wright et al,6 anesthetic adverse events occurred 3 times more frequently after 3:00 
PM. These adverse events included improper dosing of anesthetic agents, difficulty intubating, prolonged sedation, 
wound infection, postoperative nausea and vomiting, pain management issues, and blood pressure changes. Echoing 
this “afternoon effect,” Scott et al7 discovered that the risk of error doubled when nurses worked greater than 12.5 
consecutive hours. In an interview, Dr. M. C. Wright of the Department of Anesthesia at Duke University Medical 
Center maintained that performance suffers after hours of working and stated, “handoffs and transition care might be 
improved by using shared displays, similar to computerized white boards, that provide data from different sources and 
are available for exiting and incoming staff to view at all times.”8

Standardizing the information exchanged during the handoff process is the key to preventing adverse patient 
outcomes. The Joint Commission guidelines for the handoff process recommend incorporating “interactive 
communications, up-to-date and accurate information, limited interruptions, a process for verification, and an 
opportunity to review any relevant historical data.”9 Some authors have standardized handoffs by using acronym 
tools on the premise that a checklist is easy to remember if it has an associated catch phrase, such as Situation-
Background-Assessment-Recommendation, or SBAR, a communication tool widely used in nursing and hospital 
systems.9 Wright10 developed an anesthesia communication tool that uses the acronym PATIENT. Each letter in 
PATIENT represents 1 to 4 components of a typical anesthesia report; for example, the P represents procedure, 
patient, and position. To date, this is the only anesthesia-specific handoff tool noted in the literature.

Electronic health care records have gained popularity in the last decade, and anesthesia departments are utilizing 
electronic anesthesia records with increasing frequency. Bosman11 concluded that incorporating protocols, hospital 
policies, and industry or department guidelines in the computerized information system will optimize workflow. 
Computerized information systems improve patient safety by reducing errors in knowledge and ensuring that patient 
information and online databases are available at the provider’s fingertips.11 When one web-based computerized sign 
out system was trialed by residents, the tool reduced the number of patients missed on rounds, improved the quality 
of sign outs, and reduced the workload by 3 hours per week.12

Few studies have been conducted to evaluate electronic anesthesia handoff tools. Jayaswal et al2 conducted a pilot 
study of a mandatory electronic handoff tool contained in the electronic anesthesia record. The focus of the study by 
Jayaswal et al2 was consumer satisfaction with current patient handoff practice and the development of an electronic 
handoff tool; a follow-up survey regarding satisfaction with the tool is pending. Despite overwhelming evidence of 
inadequate patient transfers between anesthesia providers causing patient harm, no published studies are available 
regarding the effect of a standardized electronic patient care transfer tool on patient safety.

Despite advances in technology, human errors in medicine continue to occur with impressive frequency. 
Furthermore, adverse anesthesia events occur more often after 3:00 PM. Inadequate exchange of patient information 
during transfer of care significantly increases the risk for patient harm. Perioperative handoffs require transfer of 
patient information, surgical information, medication information including response to medications, technology 
information, and anticipatory guidance.3 The development of a provider-friendly electronic handoff tool contained 
within the electronic anesthesia record has the potential to decrease errors and omissions during the exchange of 
information, thereby enhancing patient safety. The emphasis of this study was to compare the incidence of patient 
information inaccuracies and omissions during patient care transfer before and after implementation of an electronic 
patient care transfer tool. Omissions during patient care transfer were also assessed in relation to the time of day and 
to American Society of Anesthesiologists (ASA) physical status classification.



MATERIALS AND METHODS
This study was conducted by use of a preintervention/

postintervention observational design. The researcher observed 
the transfer of patient care by one group of certified registered 
nurse anesthetists (CRNAs) before and after implementation 
of the intervention. Before the intervention, the researcher 
collected data during intraoperative patient care transfers, 
recording any omissions and inaccuracies in the report on a 13-
item checklist. The researcher also recorded time of day, phase 
of case, number of providers, patient’s ASA status, and duration 
of report. All observations of patient care transfer during an 
anesthetic were included. Exclusionary criteria were patient care 
transfers completed by student nurse anesthetists and newly 
hired nurse anesthetists undergoing orientation. Additional 
exclusions were made if patient care transfer communication was 
interrupted by patient care needs. Each observation of patient 
care transfer information was recorded in written format to 
avoid any omissions by the investigator. The information was 
then transferred to the data collection tool. Per usual practice, 
the patient’s electronic heath record was accessed to obtain the 
information required for patient care during the break or relief of 

the intraoperative case.
After 82 patient care transfers were observed, the intervention 

was implemented by educating the staff CRNAs on the 
importance of appropriate patient handoff and the use of the 
tool. All CRNAs in the anesthesia practice were educated on 
the use of the handoff tool by a third party. The majority of the 
CRNAs were introduced to the tool at a staff meeting, where 
the chief CRNA provided education on the use and benefit of 
the electronic patient care transfer tool. Any CRNAs not present 
at the staff meeting were provided individual education. After a 
2-week period during which the staff CRNAs were allowed to 
acclimate to using the electronic handoff tool, 75 postintervention 
intraoperative patient care transfers were observed and recorded. 
The before and after observations were then compared to measure 
reductions in omissions and inaccuracies after implementation of 
the tool.

Demographic data on the CRNAs were collected via 
questionnaire after all observations were completed and included 
the participants’ gender, age, education, and years of practice 
(Table 1). The data were entered into a password-protected 
database, and the questionnaires were stored in a locked cabinet.

Anesthesia eJournal                         www.anesthesiaejournal.com
Volume 4 - Issue 1 2016  Page 8

Table 1. Demographic Data of the Participants
Demographics Staff CRNAs (n=10)

No. %

Gender
 Male
 Female

Age
 25-34 years
 35-44 years
 45-54 years
 55-64 years

Education
 Diploma degree
 Bachelor’s degree
 Master’s degree

CRNA Experience
 1-4 years
 5-9 years
 10-14 years
 15-19 years
 20-24 years
 25-29 years
 30-34 years
 35-39 years

3
7

2
3
3
3

0
3
7

0
3
4
0
0
0
2
2

30%
70%

10%
30%
30%
30%

30%
70%

30%
40%

10%
20%



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Volume 4 - Issue 1 2016 Page 9

The study site was Sentara Careplex Hospital in Hampton, Virginia. A total of 16 full- and part-
time CRNAs practice at Sentara Careplex Hospital. The practice employs male and female CRNAs 
with a variety of educational backgrounds, years of experience, and a wide age range. Targeted 
participants for the study were male and female CRNAs aged 25 to 70 years. The CRNAs practicing 
at Sentara Careplex Hospital were automatically enrolled in the study. The principal investigator was 
studying routine practice habits; therefore, informed consent was waived. This study used a within-
subjects design; there was no randomization or control group.

The electronic patient care transfer tool provided a formal structure for intraoperative patient 
handoff. The information recorded included details of patient name, allergies, health history, surgical 
procedure, airway/intubation, intraoperative events, hemodynamic status, medications, state of 
neuromuscular blockade, fluid status, pertinent laboratory values, and anticipatory guidance (Figure 1).

Statistical Analysis
Data were analyzed by using SPSS 20 software (IBM Corp, 

Armonk, NY) . Demographic data related to sex were analyzed 
by using a chi-square test. Interval demographic data related 
to age, years of practice, and educational level were analyzed by 
using descriptive statistics.

A power analysis was performed for two-tailed analysis with 
alpha at P < 0.05, estimating an effect size at 0.7. Independent 
two-tailed t-tests were used to evaluate the differences in 
omissions and inaccuracies of patient data during perioperative 
handoffs before and after implementation of the electronic 
patient care transfer tool.

Twelve patient care information items were identified as 
essential components of patient care handoff. Data were then 

assigned to the following ordinal categories: 0=no omissions, 
1=partial omission, and 2=full omission. Omissions in all 12 
categories were compared by using independent two-tailed t-tests 
with Levene’s test for equality of variances (equal variances were 
not assumed).

Independent two-tailed t-tests with Levene’s test for equality 
of variances (equal variances were not assumed) were used to 
analyze if there was a significant difference in perioperative 
handoffs provided before and after 3 PM related to omissions and 
inaccuracies of patient data. The Spearman rank-order correlation 
coefficient was used to analyze the difference in the number of 
inaccuracies and omissions of patient data during patient care 
transfer based on ASA physical status classification.

Figure 1. Electronic Patient Care Transfer Tool



Anesthesia eJournal                         www.anesthesiaejournal.com
Volume 4 - Issue 1 2016 Page 10

Figure 2. Omissions During Patient Care Transfer Before and After Implementation of an Electronic Patient Care Transfer Tool.
Abbreviations: I/O, fluid status; PMH, past medical history; NMB, state of neuromuscular blockade.

RESULTS AND DISCUSSION
The demographic data of the CRNA participants are shown in 

Table 1. Three CRNA participants were male and 7 were female. 
The average age of the sample group was 48.3 years (SD: 10.8), 
with a range from 30 to 63 years. The average length of CRNA 
experience was 17.3 years (SD: 12.48). Thirty percent of the 
CRNAs described their highest level of education as a bachelor’s 
degree (n=3), and 70% (n=7) reported holding a master’s degree.

Observations of 157 handoffs were conducted: 52% (n=82) 

before the introduction of the electronic patient care transfer 
tool, and 48% (n=75) after the introduction of the electronic 
patient care transfer tool. Omissions were significantly reduced 
in all of the following patient information categories after the 
introduction of the electronic anesthesia patient care transfer tool: 
patient name, allergies, past medical history, surgical procedure, 
airway/intubation, intraoperative events, hemodynamic status, 
medications given, state of neuromuscular blockade, and fluid 
status (P = 0.000); pertinent laboratory values (P = 0.001); and 
anticipatory guidance (P = 0.005) (Figure 2 and Table 2). The 



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Volume 4 - Issue 1 2016  Page 11

mean total number of omissions before the intervention was 
12.78 (SD: 5.10), and the mean total number of omissions after 
the intervention was 4.05 (SD: 3.87). The difference in patient 
information omissions between the preintervention group 
and the postintervention group was significant (t = 12.14, P = 
0.000). Only 5 inaccuracies were noted during the observations: 
wrong allergy, wrong procedure, wrong medication dose, wrong 
laboratory value, and wrong ventilator mode setting. All 5 
inaccuracies were observed in the preintervention group.

Of the 157 patient care transfers observed, 73% (n=115) were 
conducted before 3:00 PM and 27% (n=42) were conducted after 
3:00 PM. Of the 82 preintervention observations, 74% (n=61) 
were made before 3:00 PM and 26% (n=21) were made after 3:00 
PM. Of the 75 postintervention observations, 72% (n=54) were 
made before 3:00 PM and 28% (n=21) were made after 3:00 PM. 
There were trends toward more omissions after 3:00 PM in 11 of 
the 12 handoff categories in the preintervention phase; however, 
only one category (anticipatory guidance) was statistically 

significant (P = 0.05; Table 3). In the postintervention phase, only 
5 of the 12 handoff item categories had more omissions after 3:00 
PM, with anticipatory guidance remaining the only statistically 
significant category (P = 0.04).

Thus, although the researcher predicted that patient care 
transfers would be more abbreviated during late afternoon 
hours as clinicians grew more fatigued or were anxious to 
leave, the difference in most patient information categories 
was not statistically significant. The sample size for the 
preintervention and postintervention groups was small (n=21). 
The preintervention group also had considerable numbers of 
omissions, with 25 omissions out of 25 possible omissions in 
one perioperative handoff. With the poor quality of patient care 
handoff observed in the preintervention group overall, there was 
not much prospect for omissions to increase after 3:00 PM. A 
larger sample size may have detected a difference.

Of the 157 patient care transfers observed, 4.5% (n=7) of the 
patients were classified as ASA I; 34.4% (n=54) were classified 

Table 2. Omissions During Patient Care Transfer Before and After Implementation of an Electronic Patient Care Transfer Tool
Variable Preintervention

Mean (SD)
Postintervention
Mean (SD)

t P

Patient name 1.73 (0.69) 0.88 (0.99) 6.17 0.000
Allergies 1.70 (0.71) 0.58 (0.90) 8.48 0.000
Past medical history 1.41 (0.68) 0.44 (0.68) 8.93 0.000
Surgical procedure 1.17 (0.99) 0.13 (0.50) 8.37 0.000
Airway/intubation 1.13 (0.99) 0.43 (0.82) 4.88 0.000
Intraoperative events 0.43 (0.82) 0.00 (0.00) 4.73 0.000
Hemodynamic status 1.15 (0.98) 0.19 (0.59) 7.50 0.000
Medications given 1.01 (0.71) 0.44 (0.60) 5.50 0.000
State of NMB 0.68 (0.95) 0.16 (0.55) 4.30 0.000
Fluid status (I/O) 1.27 (0.93) 0.41 (0.79) 6.22 0.000
Pertinent laboratory values 0.71 (0.95) 0.28 (0.69) 3.25 0.001
Anticipatory guidance 0.39 (0.77) 0.11 (0.45) 2.85 0.005
Total handoff items 12.78 (5.10) 4.05 (3.87) 12.14 0.000

Note. Abbreviation: NMB, neuromuscular blockade.



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Volume 4 - Issue 1 2016 Page 12

Table 3. Omissions During Patient Care Transfer Related to Time of Day

Variable Preintervention Postintervention t P
Mean SD Mean SD

Patient name
 Before 1500
 After 1500

1.77
1.61

0.64
080

0.89
0.88

1.00
1.01

Pre 0.78
Post 0.12

0.44
0.90

Allergies
 Before 1500
 After 1500

1.66
1.81

0.75
0.60

0.52
0.76

0.86
0.99

Pre -0.95
Post -0.99

0.35
0.33

Past medical history
 Before 1500
 After 1500

1.36
1.57

0.71
0.60

0.48
0.33

0.72
0.58

Pre -1.33
Post 0.93

0.19
0.35

Surgical procedure
 Before 1500
 After 1500

1.08
1.42

1.00
0.93

0.11
0.19

0.46
0.60

Pre -1.45
Post -0.55

0.16
0.59

Airway/intubation
 Before 1500
 After 1500

1.09
1.23

1.00
1.00

0.44
0.38

0.84
0.80

Pre -0.56
Post 0.30

0.58
0.76

Intraoperative events
 Before 1500
 After 1500

0.33
0.71

0.75
1.00

0.00
0.00

0.00
0.00

Pre -1.68
Post - 0.80

0.10
0.43

Hemodynamic status
 Before 1500
 After 1500

0.98
1.29

1.00
0.96

0.15
0.29

0.53
0.72

Pre -0.77
Post -080

0.45
0.43

Medications given
 Before 1500
 After 1500

0.97
1.14

0.68
0.79

0.43
0.48

0.60
0.60

Pre -0.91
Post -0.33

0.37
0.75

State of NMB
 Before 1500
 After 1500

0.66
0.76

0.95
0.90

0.15
0.29

0.53
0.60

Pre -0.44
Post -0.28

0.67
078

Fluid status (I/O)
 Before 1500
 After 1500

1.26
1.29

0.95
0.96

0.46
0.29

0.82
0.72

Pre -0.10
Post 0.92

0.92
0.36

Pertinent laboratory values
 Before 1500
 After 1500

0.70
0.71

0.95
0.96

0.31
0.19

0.72
0.60

Pre -0.04
Post 0.76

0.97
0.45

Anticipatory guidance
 Before 1500
 After 1500

0.28
0.71

0.69
0.90

0.15
0.00

0.53
0.00

Pre -2.02
Post 2.06

0.05
0.04

Total handoff items
 Before 1500
 After 1500

12.26
14.29

5.10
5.05

4.09
3.95

3.43
4.90

Pre -1.58
Post 0.12

0.12
0.91

Note. Abbreviation: NMB, neuromuscular blockade.



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Volume 4 - Issue 1 2016 Page 13

as ASA II; 51% (n=80) were classified as ASA III; and 10.2% 
(n=16) were classified as ASA IV. In the preintervention phase, 5 
patient care transfer items were negatively associated with ASA 
status: patient name, past medical history, surgical procedure, 
hemodynamic status, and fluid status (Table 4). Two patient care 
transfer items were significantly correlated with ASA status: 
pertinent laboratory values and anticipatory guidance (P < 0.05). 
In the postintervention phase, 8 patient care transfer items were 
negatively associated with ASA status (patient name, allergies, 
surgical procedure, hemodynamic status, medications given, state 
of neuromuscular blockade, pertinent laboratory values, and 
anticipatory guidance), with no items correlating to ASA status.

Thus, there were no differences in inaccuracies and omissions 
related to the severity of patient comorbidities on the basis of 
the patients’ ASA physical status classification. However, the 
distribution among the ASA physical status categories was not 

proportional; there were many more ASA II and III patients 
than I and IV. This distribution may have accounted for the 
nonsignificant findings. The negative Spearman correlation 
indicated that there were fewer handoff omissions in the higher 
ASA classes, which may suggest that practitioners caring for 
more critical patients provided a more thorough handoff.

In the busy operating room environment, anesthesia providers 
care for patients undergoing intricate surgeries. With advances 
in medicine, critically ill patients are living longer and frequent 
surgical arenas worldwide. Christian et al13 note that “complexity 
is manifest in the patient and treatment protocol, as well as 
the high level of technology and coordination required to 
effectively manage rapidly changing conditions.” With the added 
production pressure and time constraints of this setting, handoffs 
are often brief, rushed, or sometimes omitted altogether. These 
types of handoffs lead to confusion, reduce the opportunity for 

Table 4. Omissions Related to American Society of Anesthesiologists Classification Status

Variable Preintervention (n = 82) Postintervention (n= 75) Postintervention (n= 75)
Correlation Coeff. p Correlation Coeff. P

Patient name -0.018 0.875 -0164 0.159
Allergies 0.173 0.120 -2.17 0.062
Past medical history -0.085 0.448 0.056 0.635
Surgical procedure -0031 0.779 -0.208 0.073
Airway/intubation 0.129 0.247 0.034 0.774
Intraoperative events 0.058 0607 0 0
Hemodynamic status -0220 0.047 -0.038 0.745
Medications given 0.035 0.752 -0.233 0.044
State of NMB 0.054 0.631 -0.076 0.518
Fluid status (I/O) -0.024 0.832 0.128 0.273
Pertinent laboratory values 0.356 0.001 -0.020 0.863
Anticipatory guidance 0.253 0.022 -0.027 0.818
Total handoff items 0.095 0.396 -0.090 0.440

Note. Abbreviation: NMB, neuromuscular blockade.



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Volume 4 - Issue 1 2016 Page 14

clarification, and compromise quality patient care.14 This study 
confirms previous findings that communication breakdown 
and loss of information occur during peri-anesthesia handoffs, 
threatening patient safety. Utilizing an electronic anesthesia 
handoff tool within the electronic health record provides much 
needed structure to the complex communication and information 
flow. The results of this study were positive; however, further 
research is required to validate the effectiveness of the electronic 
patient care transfer tool in additional anesthesia settings and 
with a larger number of participants. This was the first study to 
observe perioperative handoff practices of CRNAs; additional 
studies investigating practice habits are needed. Research in 
this area would provide guidance regarding interventions that 
enrich best practice. Additional research regarding how electronic 
patient care transfer tools affect patient morbidity and mortality 
is required to further the CRNA impact on patient safety.

Inadequate handoffs result in a distressing number of patient 
injuries each year.15,16 This is due, in part, to a need for more 
scrutiny of communication patterns in health care and the fact 
that the majority of health care professionals do not receive 
formal education regarding transfer of patient care.14 The results 
of the present study show that standardizing transfer by use 

of a handoff tool contained in the electronic anesthesia record 
significantly reduces the number of omissions and inaccuracies 
during perioperative patient care transfer. In an effort to bolster 
patient safety, the Joint Commission required hospitals to 
employ standardized handoff communications over 8 years ago, 
yet many anesthesia departments have failed to implement any 
formal patient care transfer process.1 The patient care transfer 
tool created and tested in the present study satisfies the Joint 
Commission’s directive and has the potential to improve patient 
safety. This tool may close the gap in communication errors and 
prevent errors. If this tool is adopted system-wide, over 500 
perioperative patient care transfers could be positively impacted 
each day. Furthermore, this tool would enhance patient care 
transfers to the post-anesthesia care unit or to the intensive care 
units.

Communication patterns in health care require scrutiny. 
The majority of health care professionals do not receive formal 
education regarding transfer of patient care.14 Teamwork training 
is integral in many high-risk professions like aviation and the 
military. Further research into teamwork training with respect 
to the effectiveness of patient care transfer would expand the 
scholarship of this sizable problem.



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Volume 4 - Issue 1 2016 Page 15

REFERENCES
1. Goldsmith D, Boomhower M, Lancaster DR, et al. Development of a nursing handoff tool: A web-based application to enhance 

patient safety. AMIA Annu Symp Proc. 2010;2010:256-260. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041387/.
2. Jayaswal S, Berry L, Leopold R, et al. Evaluating safety of handoffs between anesthesia care providers. Ochsner J. 2011;11(2):99-

101.
3. Petrovic MA, Aboumatar H, Baumgartner WA, et al. Pilot implementation of a perioperative protocol to guide operating 

room-to-intensive care unit patient handoffs. J Cardiothorac Vasc Anesth. 2012;26(1):11-16. http://dx.doi.org/10.1053/j.
jvca.2011.07.009.

4. Hudson CCC, McDonald B, Hudson JKC, Tran D, Boodhwani M. Impact of anesthetic handover on mortality and morbidity in 
cardiac surgery: A cohort study. J Cardiothorac Vasc Anesth. 2015;29(1):11-16. http://dx.doi.org/10.1053/j.jvca.2014.05.018.

5. Catchpole KR, De Leval MR, McEwan A, et al. Patient handover from surgery to intensive care unit: using Formula 1 pit-stop 
and aviation models to improve safety and quality. Paediatr Anaesth. 2007;17(5):470-478. http://dx.doi.org/10.1111/j.1460-
9592.2006.02239.x.

6. Wright MC, Phillips-Bute B, Mark JB, et al. Time of day effects on the incidence of anesthetic adverse events. Qual Safe Health 
Care. 2006;15(4):258-263. http://dx.doi.org/10.1136/qshc.2005.017566.

7. Scott LD, Rogers AE, Hwang W, Zhang Y. Effects of critical care nurses’ work on vigilance and patients’ safety. Am J Crit Care. 
2006;15(1):30-37. http://ajcc.aacnjournals.org/content/15/1/30.long. Accessed January 12, 2013.

8. Arevalo JD. Anesthetic adverse events vary based on time of day. Anesthesia Zone website. http://www.anesthesiazone.com/
featured-news-article.aspx?id=2366. Updated 2007. Accessed January 8, 2013.

9. Patient Safety Primer: Handoffs and Signouts. AHRQ Patient Safety Network website. http://www.psnet.ahrq.gov/primer.
aspx?primerID=9. Updated October 2012. Accessed January 8, 2013.

10. Wright S. Examining transfer of care processes in nurse anesthesia practice: introducing the PATIENT protocol. J Am Assoc 
Nurse Anesth. 2013;81(3):225-232.

11. Bosman RJ. Impact of computerized information systems on workload in operating room and intensive care unit. Best Pract Res 
Clin Anaesthesiol. 2009;23(1):15-26. http://dx.doi.org/10.1016/j.bpa.2008.10.001.

12. Van Eaton EG, Horvath KD, Lober WB, Rossini AJ, Pellegrini CA. A randomized, controlled trial evaluating the impact of a 
computerized rounding and sign-out system on continuity of care and resident work hours. J Am Coll Surg. 2005;200(4):538-
545. http://dx.doi.org/10.1016/j.jamcollsurg.2004.11.009.

13. Christian C, Gustafson M, Roth E, et al. A prospective study of patient safety in the operating room. Surgery. 2006;139(2):159-
173. http://dx.doi.org/10.1016/j.surg.2005.07.037.

14. Cohen MD, Hilligoss PB. Handoffs in hospitals: a review of the literature on information exchange while transferring patient 
responsibility or control. 2009. Deep Blue website. http://deepblue.lib.umich.edu/handle/2027.42/61498?show=full. Accessed 
January 12, 2013

15. Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National 
Academy Press; 2001.

16. Wakefield MK. The quality chasm series: implications for nursing. In: Hughes RG, ed. Patient Safety and Quality: An Evidence-
Based Handbook for Nurses. Rockville, MD: Agency for Healthcare Quality and Research; 2008, http://www.ncbi.nlm.nih.gov/
books/NBK2651/. Accessed January 12, 2013.


