Risk Factors Associated With Incorrect Surgical Counts

Risk Factors Associated With Incorrect Surgical Counts ALETHA ROWLANDS, PhD, RN, CNOR

ABSTRACT Incorrect surgical counts after surgery are a perplexing problem for nurses working in the perioperative environment. To determine factors associated with an incorrect surgical count, this cross-sectional, correlational study examined explanatory variables (eg, patient and nurse characteristics, intraoperative circumstances, staff involvement) by using data abstracted from perioperative medical records and primary data collected from perioperative nurses. In the final multivariate analysis, six variables were significantly associated with an incorrect surgical count: a higher surgical risk, a lower body mass index, a complicated procedure, an unplanned procedure, an increased number of perioperative personnel involved, and an increased number of specialty teams involved. AORN J 96 (September 2012) 272284. Ó AORN, Inc, 2012. http://dx.doi.org/10.1016/j.aorn.2012.06.012 Key words: incorrect surgical counts, retained surgical items, retained foreign bodies, patient safety, perioperative nursing.

I

ncorrect surgical counts are a common occurrence after surgery.1,2 In reviewing incident reports from six hospitals during a three-year period, researchers found that incorrect surgical counts (25%) were the most frequently reported event.1 Despite the availability of AORN standards and recommended practices3 and hospital polices, this type of error continues to occur. The surgical count, which is a patient safety practice, is a manual counting process that is designed to account for items used on the sterile field to prevent their inadvertent retention in the patient. However, even when the final count is recorded as correct, surgical items still can be retained unintentionally. Retained surgical items may manifest acutely or remain dormant for months or even years.4 This can lead to a variety of complications, including unnecessary diagnostic tests, additional surgical

procedures, and even death.5-9 The success of a correct surgical count, as evidenced by patients not retaining items that are used during surgery, is incumbent on many factors and personnel in the OR. BACKGROUND The OR is a highly complex, error-prone environment characterized by nonstop activity, specialization, and intricate interdisciplinary processes. The complexity is manifested not only in the patient and his or her condition but also in the sophistication of instrumentation and technology, which may increase the risk for errors. Although intuitively, increasing technology would decrease the risk of medical errors, patient safety experts caution that technology itself is not a panacea.10 Increasing technology can generate new forms of errors and failures related to workflow and work http://dx.doi.org/10.1016/j.aorn.2012.06.012

272 j AORN Journal



September 2012 Vol 96 No 3

Ó AORN, Inc, 2012

INCORRECT SURGICAL COUNTS processes that are involved in providing direct patient care.10 Surgical procedures also vary in length and complexity, and, during each procedure, physicians, perioperative nurses, and surgical technologists must process large volumes of information. The urgency with which decisions must be made and interventions carried out only increases the complexity of working in this environment.11 This complexity, combined with heavy workloads, fatigue, and production pressure (eg, demand for shorter turnaround times between procedures), makes the OR and perioperative nursing particularly vulnerable to errors, including incorrect surgical counts. The surgical team relies on discrepancies in the surgical count as a screening tool to identify the potential for unintended retained surgical items. Researchers suggest, however, that reliance on the surgical count may not be adequate. Egorova et al12 examined the medical records of 153,263 patients who had undergone surgical procedures. Among the 1,062 count discrepancies, the missing item was found only 51 times. In 34 instances, the missing item was located somewhere in the OR suite and in 17 instances, the missing item was found in the patient. Additionally, there were five instances without a documented count discrepancy (ie, the count was documented as “correct”) in which a retained surgical item was subsequently discovered.12 Gawande et al7 found similar results when examining the medical records of 54 patients with retained surgical items. Of these, 88% of the incidents involved a final count that was documented as “correct.” In another study, Greenberg et al13 reported that incorrect counts were related to misplaced items (59%), an error in documentation (38%), or a miscount (3%). STATEMENT OF THE PROBLEM Given that incorrect surgical counts may result in patient complications (ie, unintended retained surgical items), it is imperative to identify factors associated with an increased risk for an incorrect surgical count after surgery. Studies on the manual

www.aornjournal.org

counting process to date are descriptive in nature, focusing on the frequency and outcomes of the surgical count, including unintended retained surgical items. The purpose of this study was to examine the relationship between the occurrence of an incorrect surgical count and nurse characteristics, patient characteristics, intraoperative circumstances, and staff member involvement in procedures. To my knowledge, this is the first study that has examined the association of explanatory factors with the occurrence of incorrect surgical counts after surgery. THEORETICAL FRAMEWORK To conceptualize patient safety in perioperative nursing and to identify key variables for this study, I used the Quality Health Outcomes Model (QHOM) as a guide (Figure 1). This model helped me as I reviewed three bodies of literature, including the general patient safety literature, health services research, and the nursing literature. The Expert Panel on Quality Health Care of the American Academy of Nursing published the QHOM in 1998 as a conceptual framework for quality and outcomes research.14 The model was built on Donabedian’s classic structure-process-outcome framework15 and Holzemer’s three-dimensional expansion of the Donabedian triad16 to provide multiple levels of relationships among the patient, provider, and setting. The QHOM has reciprocal interactions among four constructs: system, client, interventions, and outcomes. The system construct is further divided into the characteristics of the individual, organization, or group. The client construct comprises the characteristics of the individual (ie, patient), the family, and the community. The dynamic relationship between the four constructs is captured in the QHOM with indicators that not only act upon but also reciprocally affect the various components.14 In following the lead of Wilson and Cleary,17 the developers of the QHOM intergraded clinical and functional outcomes into this model; the path from intervention to outcomes, however, was conceived as being mediated and moderated by the system and client characteristics.18 AORN Journal j 273

September 2012 Vol 96

No 3

ROWLANDS

Figure 1. The Quality Health Outcomes Model proposes two-directional relationships among components, with interventions acting through characteristics of the system and of the client.

The connection between system and client indicates that no single intervention acts directly through either system or client alone.14,18 The QHOM has been used by a variety of researchers. Mayberry and Gennaro,19 for example, applied the model to a review of second-stage labor in obstetric care and suggested that specific interventions, such as cesarean delivery and epidural analgesia, will differ in their outcomes, depending on system (eg, skill mix, policies) and patient characteristics. Radwin and Fawcett20 retrospectively used the model to guide their work in identifying aspects of patient characteristics, interventions, and systems of care for oncology patients. Although I used the QHOM to guide a comprehensive literature review to develop a conceptual framework for a program of patient safety research in the context of perioperative nursing practice, in this article, I discuss only those articles directly relevant to this study. 274 j AORN Journal

LITERATURE REVIEW AND VARIABLE SELECTION The studies presented in this literature review focused on retained surgical items, the surgical count, and nurse characteristics associated with patient outcomes. I reviewed three studies relevant to retained surgical items, each of which identified risk factors associated with the occurrence of a retained surgical item. I reviewed two studies relevant to the surgical count. I also reviewed nine studies that examined nurse characteristicsdeducation, experience, certification, and employer statusdrelated to patient outcomes. Retained Surgical Items I identified three published studies that examined risk factors associated with retained surgical items. In a case-control study design, Gawande et al7 used medical records associated with malpractice claims

INCORRECT SURGICAL COUNTS and incident reports from 22 hospitals. Lincourt et al8 also used a case-control design to review the medical records of patients with retained surgical items from a single institution during a 10-year period. Both of these studies used logistic regression to examine the association of a set of variables to the risk of a retained item. In the third study, Wang et al9 used meta-analysis to examine the risk factors identified by Gawande et al7 and Lincourt et al.8 The purpose of the meta-analysis was to evaluate the cumulative published data for retained surgical items after surgery. Data from these three studies showed eight factors to be significantly associated with an increased risk of a retained item: n n n n n n n n

patients with an increased body mass index (BMI), procedures performed on an emergency basis, procedures with an unexpected change during the surgery, instances in which more than one major procedure is performed during the surgery, procedures with a longer duration, procedures with multiple surgical teams, procedures resulting in an incorrect surgical count, and procedures in which no surgical count is performed.

The Surgical Count I identified two studies that focused on the surgical count. Both used a qualitative approach as a strategy for inquiry to identify the “essence” of human experiences concerning the manual counting process. Understanding the “lived-experiences” involves extensive and prolonged engagement to develop patterns and relationships of meaning.21 In the first study, Riley et al21 explored the power relationships of the surgical team as they relate to the manual counting process (eg, striking a balance between the standards, hospital policies, and professional judgment). The researchers presented their findings in terms of discursive practices that governed and controlled the surgical count (eg, judging, coping with normalization, establishing

www.aornjournal.org

priorities). In the second study, Rowlands and Steeves2 explored the interrelationship among people, situations, and processes to better understand how an incorrect surgical count might occur. From the stories of perioperative personnel involved in incorrect surgical counts, three distinct themes emerged: bad behavior, general chaos, and communication difficulties.2 These themes represent the challenges that perioperative personnel face daily during the manual counting process. Nurse Education Although results of some studies demonstrated that higher educational preparation for RNs results in better patient outcomes, results of other studies did not. For example, Aiken et al22 linked data from 10,184 staff RNs and 232,342 surgical patients in 168 hospitals and demonstrated that hospitals with higher proportions of RNs educated at the baccalaureate level or higher experienced lower mortality and failure-to-rescue rates. Similar findings were reported by Estabrooks et al23 and Tourangeau et al.24 Conversely, Blegen et al25 and Sasichay-Akkadechanunt et al26 found no significant relationship between the educational preparedness of RNs and patient outcomes. Nurse Experience Dissimilar findings have been reported on the association between nurse experience and patient outcomes. Kendall-Gallagher and Blegen27 found that years of RN experience in the intensive care unit were inversely related to the frequency of urinary tract infections in patients. Tourangeau et al24 reported lower 30-day mortality rates on units with nurses who had more years of experience. Using secondary analysis of data collected in two previous studies, Blegen et al25 reported that units with more experienced nurses had fewer medication errors and lower patient fall rates. However, Aiken et al22 did not find nurses’ experience to be a significant predictor of mortality or failure to rescue, nor did SasichayAkkadechanunt et al,26 who examined the relationship of nurse experience and in-hospital mortality. AORN Journal j 275

September 2012 Vol 96

No 3

Nurse Certification To explore the relationship between the proportion of certified staff RNs in a unit and risk of harm to patients, researchers used hierarchical linear modeling in a secondary data analysis of 48 intensive care units from a random sample of 29 hospitals. Kendall-Gallagher and Blegen27 reported that the unit proportion of certified RNs was inversely related to the rate of falls and urinary tract infections in patients. Additionally, the total number of nursing hours per patient day was associated with fewer medication administration errors.27 Zulkowski et al28 used the Pieper Pressure Ulcer Knowledge Tool to assess the knowledge and clinical judgment of 460 RNs. The descriptive analysis of certified nurses’ educational levels included diploma (8%), associate degree (3%), baccalaureate (54%), master’s degree (30%), and doctorate (3%). For noncertified nurses, the educational preparation included diploma (10%), associate degree (49%), baccalaureate (33%), and master’s degree (4%). The knowledge and clinical judgment scores showed a significant difference between nurses certified and those not certified (89% versus 76%). Employer Status The use of supplemental nurses to bolster permanent nursing staff in hospitals is widespread but controversial.29,30 The term supplemental nurses is defined as nurses employed by external agencies or internal hospital per-diem pools, and permanently employed nurses who “float” from their permanent units to fill shortages in other units within the hospital.29 Aiken et al29 used data from the 2000 National Sample Survey of Registered Nurses to determine whether the qualifications of supplemental nurses differed from permanent staff nurses in hospitals. In addition, they used data from a Pennsylvania nurse survey to examine whether nurse outcomes and adverse events differed among hospitals with varying proportions of supplemental nurses. They found that nearly 49,819 hospital staff nurses were employed by supplemental staffing agencies as either their primary or secondary 276 j AORN Journal

ROWLANDS position.29 This represented almost 6% of the hospital staff nurses. Supplemental nurses were similar to permanent nurses with respect to age; however, they were about twice as likely to be men (13% versus 6%), less likely to be married (53% versus 72%), less likely to be white (76% versus 86%), more likely to be Asian (15% versus 5%), and more likely to hold baccalaureate or higher degrees (46% versus 40%). According to Aiken et al,29 supplemental nurses were educated as well as permanent nurses, and the analysis of nursereported outcomes in 198 hospitals did not suggest a negative effect of employing supplemental nurses on patient care quality. DESIGN AND SAMPLE I used a cross-sectional correlational design to determine significant predictors of incorrect surgical counts after surgery. I used the surgical procedure as the level of analysis and abstracted data from electronic perioperative medical records. Through a power analysis, I determined that a minimum of 100 incorrect surgical counts would be required to identify significant risk factors associated with an incorrect count. Because I was interested in nurse characteristics, I collected primary data from perioperative nurses who were willing to participate in the study. I selected two hospitals based on surgical specialties and the type and number of procedures performed annually in the main OR. Before I began the data collection, the institutional review board at each facility approved the study. The first hospital is an academic, medical, level I trauma center with approximately 600 beds. Surgical specialties include bariatric surgery, cardiac surgery, dentistry, general surgery, gynecology, neurosurgery, ophthalmology, orthopedics, otolaryngology, plastic surgery, thoracic surgery, trauma surgery, urology, and vascular surgery. The main OR consists of 27 surgical suites in which 18,631 surgeries were performed in 2008 (Deborah W. Lamb, MBA, clinical application systems analyst, University of Virginia Health

INCORRECT SURGICAL COUNTS

www.aornjournal.org

System, Charlottesville; written communication; November 2008). The second hospital is a nonprofit community hospital with fewer than 150 beds. The hospital provides a variety of surgical services, including bariatric surgery, dentistry, general surgery, gynecology, ophthalmology, orthopedics, otolaryngology, pediatrics (limited), plastic surgery, thoracic surgery, urology, and vascular surgery. The main OR consists of seven surgical suites in which 6,593 surgeries were performed in 2008 (Deborah S. Urciolo, RN, IS technology coordinator SIPC, SurgiNet application specialist/DBA, Martha Jefferson Hospital, Charlottesville, VA; written communication; November 2008). I reviewed 2,540 medical records to identify 1,122 procedures that met the inclusion criteria (Table 1). Inclusion criteria comprised procedures that were performed in the main OR at either

facility. Exclusion criteria were procedures with no primary perioperative nurse (ie, a perioperative nurse who had circulated for at least 80% of the procedure), with critical information missing (eg, American Society of Anesthesiologists physical status classification [ASA score], BMI), or that involved a primary perioperative nurse who was unwilling to participate in the study. Sixty-five percent (n ¼ 729) of the procedures came from the academic medical center, and 35% (n ¼ 393) came from the smaller community hospital. I grouped the explanatory variables into four domains: n

patient characteristicsdage, surgical risk, and BMI; n intraoperative circumstancesddifficulty, duration of the procedure, and type (eg, elective);

TABLE 1. Demographics of the ORs and Perioperative Personnel at the Two Clinical Sites Involved in the Study

Academic medical center

Annual number of procedures Procedures in the study How the procedure was posted Elective procedures Emergency procedures Other Difficulty of the procedure Simple procedures Difficult/very difficult procedures Other Hospital employees RNs Surgical technologists Agency employees RNs Surgical technologists Nurse recruitment for study Eligible RNs Participants Nonparticipants Ratio of RNs to surgical technologists

Community hospital

Total

n

%

n

%

N

%

18,631

74

6,593

26

25,224

100

622 24 83

63 55 92

366 20 7

37 45 8

988 44 90

100 100 100

279 186 264

49 93 75

290 13 90

51 7 25

569 199 354

100 100 100

75 32

79 67

20 16

21 33

95 48

100 100

15 8

100 100

e e

e e

15 8

100 100

90 55 35

82 80 85

20 18 14 20 6 15 1.25 to 1

110 69 41

100 100 100

2.25 to 1

AORN Journal j 277

September 2012 Vol 96

ROWLANDS

No 3

n

OR staff involvementdthe number of perioperative nurses and surgical technologists, attending surgeons and surgical residents, and surgical specialty teams; and n characteristics of the primary perioperative nursededucation level, certification, experience, and employer status. I examined the effect of each variable on the associated risk of an incorrect surgical count. The variable definitions used for the purpose of this study are presented in Table 2, and the nurse characteristics are presented in Table 3. Of the 110 eligible perioperative RNs, 69 were willing

to participate in the study. Of those, 49.3% had a BSN or higher, 21.7% were certified, and 82.6% were employed by the hospital. STATISTICAL ANALYSIS I generated descriptive statistics for all variables used in this study. Next, I used logistic regression analysis to examine the relationship of the explanatory variables to the occurrence of an incorrect surgical count. For this statistical analysis, I grouped the independent categorical variables into subcategories. I grouped the patient’s surgical risk variable into two groups:

TABLE 2. Explanatory Variables and Definitions Selected From a Review of the Literature Explanatory variables Domain 1: patient characteristics 1. Age (years) 2. Patient’s surgical risk* ASA 1 ASA 2 ASA 3 ASA 4 3. Body mass index Domain 2: intraoperative circumstances 4. Difficulty of the procedure Simple Somewhat simple Somewhat difficult Difficult Very difficult 5. Duration of procedure (minutes) 6. Type of procedure Elective Other Domain 3: staff involvement 7. Perioperative personnel 8. Surgeons/surgical residents 9. Multiple surgical teams Outcome variable 10. Surgical count Correct surgical count Incorrect surgical count

Definitions Chronological age of the patient Normal, otherwise healthy patient Patient with mild systemic disease that does not limit activity Patient with severe systemic disease that limits activity but is not incapacitating Patient with severe systemic disease that is a constant threat to life and is incapacitating Patient’s body mass index

Procedure duration < 120 minutes with a documented count of only sharps/sponges Procedure duration  121 minutes with a documented count of only sharps/sponges Procedure duration < 120 with a documented count of all items Procedure duration of 121-240 minutes with a documented count of all items Procedure duration  241 minutes with a documented count of all items Start-time (incision) to end-time (ie, incision closed) as documented in the medical record A scheduled elective procedure All other procedures including emergencies and unplanned changes in the procedure Total number of perioperative nurses and surgical technologists involved in the procedure Total number of attending physicians and surgical residents involved in the procedure Total number of specialty teams involved in the procedure

Final result of the surgical count was documented as “correct” Final result of the surgical count was documented as “incorrect”

* American Society of Anesthesiologists physical status classification (ASA score) represents the surgical risk status of the patient undergoing surgical intervention as determined by anesthesia care providers. Source: Saklad M. Grading of patients for surgical procedures. Anesthesiology. 1941;2(3):281-284.

278 j AORN Journal

INCORRECT SURGICAL COUNTS

www.aornjournal.org

TABLE 3. Effects of Perioperative Nurse Characteristics on the Number of Occurrences of an Incorrect Surgical Count After Surgery

Independent variables Education Low (diploma/associate degree) High (baccalaureate or higher) Total Certification (CNOR) Yes No Total Experience Number of years in the OR Employer status Agency Hospital Total

n

%

P

Odds ratio (confidence interval)

35 34 69

50.7 49.3 100

.859

0.969 (0.682-1.376)

15 54 69

21.7 78.3 100

.788

1.055 (0.714-1.560)

e

e

.483

1.005 (0.991-1.019)

12 57 69

17.4 82.6 100

.304

1.253 (0.815-1.924)

Note: Dependent variable: Number of procedures with an incorrect surgical count for each nurse. Off-set variable: Number of procedures for each perioperative nurse.

n

low riskdcombining ASA scores 1 and 2, and n high riskdcombining ASA scores 3 and 4. I divided the type of procedures into two groups: n n

planneddelective procedures, and unplanneddprocedures that were urgent, emergencies, or had an unexpected change during the procedure.

Using the operational definition of “difficulty,” which ranged from simple procedures to very difficult procedures, I assigned each procedure to one of two groups: n

uncomplicateddsimple to somewhat difficult procedures, and n complicatedddifficult or very difficult procedures. I began the logistic regression analysis with a univariate analysis of the variables from the first three domains (ie, patient characteristics, intraoperative circumstances, staff involvement). I examined the relationship between each explanatory variable and the occurrence of an incorrect

surgical count. Next, I used multivariate logistic regression analysis to examine the relationship of the variables to the occurrence of an incorrect surgical count for domains 1 through 3. In the final multivariate logistic regression analysis, I analyzed all variables from domains 1, 2, and 3 together. For the fourth domain (ie, characteristics of the primary perioperative nurse), I applied Poisson regression to model the link between count data (eg, the rate of procedures, the occurrence of an incorrect count) and the characteristics of the primary perioperative nurse. I performed all statistical analyses by using SPSS 16.031 with a two-side alpha of .05 as the level of significance. RESULTS In the univariate analyses, the odds of an incorrect surgical count were significantly associated with each of the explanatory variables (domains 1, 2, and 3): n

increased age of the patient (odds ratio [OR], 1.010; P ¼ .047), n a higher surgical risk (ASA score 3 or 4) (OR, 2.881; P ¼ .000), AORN Journal j 279

September 2012 Vol 96 n n n n n n n

No 3

a lower BMI (OR, 0.970; P ¼ .010), an unplanned procedure (OR, 4.956; P ¼ .000), a complicated procedure (OR, 2.375; P ¼ .000), a procedure with a longer duration (OR, 1.006; P ¼ .000), an increased number of perioperative staff members involved (OR, 1.732; P ¼ .000), an increased number of surgeons/surgical residents involved (OR, 1.482; P ¼ .001), and an increased number of surgical specialty teams involved (OR, 4.307; P ¼ .000).

In the multivariate logistic regression analyses of each domain, seven variables were significantly associated with an incorrect surgical count: n n n n n n n

a higher surgical risk (ASA score 3 or 4) (OR, 2.818; P ¼ .000), a lower BMI (OR, 0.963; P ¼ .003), an unplanned procedure (OR, 6.486; P ¼ .000), a complicated procedure (OR, 2.093; P ¼ .000), a procedure with a longer duration (OR, 1.004; P ¼ .000), increased number of perioperative staff members involved (OR, 1.775; P ¼ .000), and an increased number of specialty teams involved (OR, 6.059; P ¼ .000).

The patient’s age and the number of surgeons involved were not significantly associated with an occurrence of an incorrect surgical count in the multivariate analysis. In the final multivariate logistic regression analysis, six variables from the first three domains were significantly associated with an occurrence of an incorrect surgical count: n n n n n n

a higher surgical risk (ASA score 3 or 4) (OR, 1.655; P ¼ .003), a lower BMI (OR, 0.957; P ¼ .004), an unplanned procedure (OR, 5.642; P ¼ .000), a complicated procedure (OR, 1.859; P ¼ .000), an increased number of perioperative personnel involved (OR, 1.307; P ¼ .003), and an increased number of specialty teams involved (OR, 2.454; P ¼ .040).

280 j AORN Journal

ROWLANDS By using Poisson regression in the fourth domain (ie, characteristics of the primary perioperative nurse), I found none of the characteristicsdeducation, experience, certification, and employer statusdto be associated with an occurrence of an incorrect surgical count (Table 4).

DISCUSSION I undertook this study with the aim of examining explanatory variables to identify risk factors or predictors associated with an incorrect surgical count after surgery. Because no previously published studies identified predictors of incorrect surgical counts, I compared my findings with studies that identified predictors for retained surgical items. In examining patient characteristics in my study, I noted that the age of the patient was not significantly associated with an incorrect count, which is consistent with the findings of other studies.7-9 I did find an inverse relationship of incorrect surgical count with the patient’s BMI; that is, as the patient’s BMI increased, the risk of an incorrect surgical count decreased slightly (OR, 0.957). I further examined the direction of the variable’s relationship with an incorrect surgical count by dividing the procedures into 10 groups according to ascending BMIs (n ¼ 112 for nine groups; n ¼ 114 for one group) and calculating the rate of incorrect surgical counts for each group. The highest rate of incorrect surgical counts was in the first group (ie, lowest BMI), and the lowest rate was in the last group (ie, highest BMI). For the 80% of the procedures in between, there was no linear correlation (Figure 2). Gawande et al7 found a higher BMI was significantly associated with an occurrence of a retained surgical item. However, neither Lincourt et al8 nor Wang et al9 found a significant association. A plausible explanation for the inverse direction that I found might be the increased diligence of perioperative nurses and other surgical team members with patients who have an increased BMI as a response to the findings of Gawande et al7 and AORN’s proactive approach

Multivariate analysis by domain Univariate analysis Explanatory variables Domain 1 Age in years Surgical risk Body mass index Domain 2 Type of procedure Difficulty of procedure Duration of procedure (minutes) Domain 3 Number of RNs and surgical technologists Number of surgeons Number of specialty teams

OR (CI)

P

Patient characteristics OR (CI)

Intraoperative circumstances P

1.010 (1.000-1.020) .047 1.005 (0.995-1.015) .349 2.881 (2.215-3.747) .000 2.818 (2.135-3.721) .000 0.970 (0.948-0.994) .010 0.963 (0.939-0.987) .003

Multivariate analysis Staff involvement

Final model (all variables)

OR (CI)

P

OR (CI)

P

-

-

-

-

1.003 (0.991-1.015) .614 1.655 (1.189-2.303) .003 0.957 (0.928-0.986) .004

-

-

5.642 (3.279-9.705) .000 1.859 (1.506-2.294) .000 1.002 (1.000-1.004) .080

6.486 (3.896-10.798) .000 2.093 (1.714-2.557) .000 1.004 (1.002-1.006) .000

OR (CI)

P

4.956 (3.241-7.579) .000 2.375 (2.047-2.755) .000 1.006 (1.005-1.008) .000

-

-

1.732 (1.541-1.947) .000

-

-

-

-

1.775 (1.556-2.025)

1.482 (1.181-1.858) .001 4.307 (2.062-8.995) .000

-

-

-

-

0.669 (0.439-1.018) .061 0.755 (0.496-1.148) .189 6.059 (2.363-15.536) .000 2.454 (1.042-5.780) .040

INCORRECT SURGICAL COUNTS

TABLE 4. Relationship of Explanatory Variables and the Occurrence of an Incorrect Surgical Count After Surgery

.000 1.307 (1.094-1.560) .003

CI ¼ confidence interval; OR ¼ odds ratio. Note: This table presents the results of regressions on risk factors identified from the literature. (1) Separate univariate logistic regressions of the probability of an incorrect surgical count associated with each risk factor were performed first. (2) Models were then estimated separately for three groups (domains) of factors: patient characteristics, intraoperative circumstances, and OR staff involvement. (3) The final multivariate model included all of the risk factors. The numeric figures in bold type represent statistical significance.

www.aornjournal.org

AORN Journal j 281

September 2012 Vol 96

No 3

ROWLANDS

Figure 2. The body mass index (BMI) was statistically significant; however, the direction of the significance was that patients with lower BMIs were at a higher risk for an incorrect surgical count. To better understand the direction, the incorrect surgical count error rate was compared by patients (N [ 1,122), who were divided into 10 groups sorted by ascending BMI. Each group has a circle representing the error rate with a line representing the 95% confidence interval for that error rate. The highest rate of incorrect surgical counts was in the first group (patients with the lowest BMI) and the lowest error rate of incorrect surgical counts was in the last group (patients with the highest BMI). The middle groups (80%) showed no linear trend.

to patient safety through the Perioperative Standards and Recommended Practices.3 In this study, the odds of an incorrect surgical count were 1.7 times greater for a patient with a higher surgical risk (ie, ASA score 3 or 4) than for a lower-risk patient (ie, ASA score 1 or 2). None of the other studies that I reviewed7-9 examined patients’ surgical risk. In examining intraoperative circumstances, I found that the odds of an incorrect surgical count were 1.9 times greater for a patient who had undergone a complicated procedure (ie, difficult or very difficult) compared with patients who had an uncomplicated procedure (ie, simple to somewhat difficult). The difficulty of a procedure was not examined in the previously published studies.7-9 The duration of the procedure was not associated with an incorrect count in my study; however, there was a trend toward significance (P ¼ .08). Because the operational definition of “difficulty of the procedure” included length of time, I performed another analysis with that variable 282 j AORN Journal

removed. The result showed a significant association of procedure duration with incorrect surgical count (P ¼ .000), which is consistent with the findings of Wang et al.9 Neither Gawande et al7 nor Lincourt et al8 found a significant association of the odds of an incorrect count with duration of procedure, which could be accounted for by their small sample sizes. I also found that the odds of an incorrect surgical count were 5.6 times greater for unplanned procedures compared with planned procedures. This finding is also consistent with the findings of Gawande et al7 and Wang et al.9 When examining staff involvement, I found that the odds of an incorrect surgical count were 2.5 times greater for procedures requiring more than one surgical team, which is consistent with the findings of Wang et al.9 In my study, I combined perioperative nurses and surgical technologists for one variable describing a total number of “perioperative personnel.” Comparatively, Lincourt et al8 examined perioperative nurses and surgical

INCORRECT SURGICAL COUNTS technologists independently (ie, two variables) and found no significantly increased odds of an incorrect surgical count with either. I found that the odds of an incorrect surgical count were 1.3 times greater during procedures with more than two perioperative personnel. Neither this study nor the other studies7-9 showed significant association of incorrect surgical counts with the number of surgeons involved during the procedure. LIMITATIONS Certain limitations should be considered in interpreting the results from this study and the value of its methodology for research on other patient safety issues in perioperative nursing. Data extraction is a time-consuming manual process, and researchers must consider the probability of human error. Because 52% of the procedures that I excluded were excluded because the primary nurses did not give permission to use their data, it is possible that there are other factors associated with incorrect surgical counts that are not included in my study. Although I conducted a sample estimation to determine the number of procedures needed to find a specific number of errors (ie, incorrect surgical counts), I did not conduct a power analysis to determine a sample size for the number of primary perioperative nurses. The number of primary perioperative nurses in the study (n ¼ 69) may have been too small to detect the influence of nurse characteristics. CONCLUSION Several characteristics of the procedure were significantly associated with the risk of an incorrect surgical count. However, I found no relationship between the characteristics of the primary perioperative nurse (ie, education, experience, certification, employer status) and the occurrence of an incorrect

www.aornjournal.org

surgical count. Because strong evidence suggests that focusing on nursing would improve patient safety,32 research must include a focus on perioperative nursing. Perioperative nurses bring considerable expertise and leadership to the field of patient safety. As point-of-care providers, perioperative nurses are well prepared to design systems and processes to accomplish the goals of patient safety, which include reducing the risk of injury and maximizing the likelihood of identifying errors when they occur and intercepting potential errors when or before they occur.10 Having empirical evidence of factors associated with incorrect surgical counts is an essential step in developing perioperative patient safety practices aimed at preventing this type of error. Because this is the first published study identifying risk factors associated with an occurrence of an incorrect surgical count, future studies should include a replication of the current study by using a large multisite study design. Additional inquiry is needed on the relationship of nurse characteristics to patient outcomes. If the nurse characteristics examined in this study do not explain the occurrence of incorrect surgical counts, then perhaps something else about the individual nurse does. For example, researchers might examine the role of job satisfaction or psychological empowerment in the nurse as it relates to providing safe quality care for patients undergoing surgical intervention. Additionally, when using the results of this study, a risk assessment tool capable of identifying high-risk patients could be developed. Such

AORN Resources n

AORN Video Library: Preventing Retained Surgical Items. http://cine-med.com/index.php?nav¼aorn. n Clinical FAQs: Counts/retained surgical items [membership required]. http://www.aorn.org/clinicalfaqs. n Confidence-based Learning Module: Preventing retained surgical items. http://www.aorn.org/Education/Curriculum/Co nfidence_Based_Learning/Retained_Surgical_Items.aspx. Web site access verified May 24, 2012.

AORN Journal j 283

September 2012 Vol 96

No 3

a tool could be used by perioperative nurses during the preoperative phase. Based on the perioperative nurse’s assessment of his or her patient, additional patient safety practices could be implemented for patients who are in a high-risk category. More studies are needed that focus on the effectiveness of patient safety practices in perioperative nursing. Acknowledgment: The author thanks her dissertation chair, Elizabeth Merwin, PhD, RN, FAAN, and the other dissertation committee members at the University of Virginia, Charlottesville, School of Nursing, Public Health Sciences, and the Department of Surgery, for their assistance and support. References 1. Chappy S. Perioperative patient safety: a multisite qualitative analysis. AORN J. 2006;83(4):871-897. 2. Rowlands A, Steeves R. Insights into incorrect surgical counts: a qualitative analysis. AORN J. 2010;92(4):410-419. 3. Recommended practices for prevention of retained surgical items. In: Perioperative Standards and Recommended Practices. Denver, CO: AORN, Inc; 2012:313-332. 4. Stawicki SP, Seamon MJ, Martin ND, et al. Retained surgical foreign bodies: a synopsis. OPUS 12 Scientist. 2008;2(2):1-6. 5. Bani-Hani KE, Gharaibeh KA, Yaghan RJ. Retained surgical sponges (gossypiboma). Asian J Surg. 2005; 28(2):109-115. 6. Berkowitz S, Marshall H, Charles A. Retained intraabdominal surgical instruments: time to use nascent technology? Am Surg. 2007;73(11):1083-1085. 7. Gawande AA, Studdert DM, Orav EJ, Brennan TA, Zinner MJ. Risk factors for retained instruments and sponges after surgery. New Engl J Med. 2003;348(3):229-235. 8. Lincourt AE, Harrell A, Cristiano J, Sechrist C, Kercher K, Keniford BT. Retained foreign bodies after surgery. J Surg Res. 2007;138(2):170-174. 9. Wang CF, Cook CH, Whitmill ML, et al. Risks factors for retained surgical foreign bodies: a meta-analysis. OPUS 12 Scientist. 2009;3(2):21-27. 10. Page A, ed. Keeping Patients Safe: Transforming the Work Environment of Nurses. Washington, DC: National Academies Press; 2004. 11. Vincent C, Moorthy K, Sarker SK, Chang A, Darzi AW. Systems approaches to surgical quality and safety: from concept to measurement. Ann Surg. 2004;239(4):475-482. 12. Egorova NN, Moskowitz A, Gelijns A, et al. Managing the prevention of retained surgical instruments: what is the value of counting? Ann Surg. 2008;247(1):13-18. 13. Greenberg CC, Regenbogen SE, Lipsitz SR, Diaz-Flores R, Gawande AA. The frequency and significance of discrepancies in the surgical count. Ann Surg. 2008;248(2):337-341. 14. Mitchell PH, Ferketich S, Jennings BM. Quality health outcomes model. J Nurs Scholarsh. 1998;30(1):43-46. 15. Donabedian A. Evaluating the quality of medical care. Milbank Mem Fund Q. 1966;44(3 Suppl):166-206.

284 j AORN Journal

ROWLANDS 16. Holzemer WL. The impact of nursing care in Latin American and in the Caribbean: a focus on outcomes. J Adv Nurs. 1994;20(1):5-12. 17. Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA. 1995;273(1):59-65. 18. Mitchell PH, Lang NM. Framing the problem of measuring and improving healthcare quality: has the Quality Health Outcomes Model been useful? Med Care. 2004;42(2 Suppl):II4-II11. 19. Mayberry LJ, Gennaro S. A quality of health outcomes model for guiding obstetrical practice. J Nurs Scholarsh. 2001;33(2):141-146. 20. Radwin L, Fawcett J. A conceptual model-based programme of nursing research: retrospective and prospective applications. J Adv Nurs. 2002;40(3):355-360. 21. Riley R, Manias E, Polglase A. Governing the surgical count through communication interactions: implications for patient safety. Qual Saf Health Care. 2006;15(5):369-374. 22. Aiken LH, Clarke SP, Cheung RB, Sloane DM, Silber JH. Education levels of hospital nurses and surgical patient mortality. JAMA. 2003;290(12):1617-1623. 23. Estabrooks CA, Midodzi WK, Cummings GG, Ricker KL, Giovannetti P. The impact of hospital nursing characteristics on 30-day mortality. Nurs Res. 2005;54(2):74-84. 24. Tourangeau AE, Giovannetti P, Tu JV, Wood M. Nursingrelated determinants of 30-day mortality for hospitalized patients. Can J Nurs Res. 2002;33(4):71-88. 25. Blegen MA, Vaughn TE, Goode CJ. Nurse experience and education: effect on quality of care. J Nurs Admin. 2001;31(1):33-39. 26. Sasichay-Akkadechanunt T, Scalzi CC, Jawad AF. The relationship of nurse staffing and patient outcomes. J Nurs Adm. 2003;33(9):478-485. 27. Kendall-Gallagher D, Blegen MA. Competence and certification of registered nurses and safety of patients in intensive care units. Am J Crit Care. 2009;18(2):106-113. 28. Zulkowski K, Ayello EA, Wexler S. Certification and education: do they affect pressure ulcer knowledge in nursing? Adv Skin Wound Care. 2007;20(1):34-38. 29. Aiken LH, Xue Y, Clarke SP, Sloane DM. Supplemental nurse staffing in hospitals and quality of care. J Nurs Adm. 2007;37(7-8):335-342. 30. May JH, Bazzoli GJ, Gerland AM. Hospitals’ responses to nurse staffing shortages. Health Aff (Milwood). 2006; 25(4):W316-W323. 31. SPSS 16.0. Chicago, IL: IBM Corp; 2010. 32. Aiken LH. The unfinished patient safety agenda. AHRQ Web M&M [serial online]. July/August 2005. http:// www.webmm.ahrq.gov/perspective.aspx?perspective ID¼7. Accessed May 24, 2012.

Aletha Rowlands, PhD, RN, CNOR, is an assistant professor at the School of Nursing, West Virginia University, Morgantown. Dr Rowlands has no declared affiliation that could be perceived as posing a potential conflict of interest in the publication of this article.