RISK FACTORS OF INCORRECT SURGICAL
COUNTS FOLLOWING SURGERY
Aletha Rowlands PhD, RN, CNORAssistant Professor
West Virginia University School of NursingMorgantown, WV
INTRODUCTION
The inadvertent retention of a surgical item after the incision has been closed is a preventable medical error that should never occur.
An unintended retained item is a direct result of an incorrect surgical count.
Incorrect surgical counts following surgery are common.1,2
One study reviewing incident reports from six hospitals over three years found incorrect surgical counts (25%) were the most frequently reported medical error by perioperative nurses.1
Despite the availability of AORN3 standards and recommended practices, this type of error continues to occur.
BACKGROUND
The surgical count, a patient safety practice, is a labor-intensive manual counting process designed to account for items used on the sterile field to prevent an inadvertent retention.
The success of a correct surgical count, as evidenced by the patient remaining free of items used during surgery,3 is incumbent on many factors and people in the operating room.
BACKGROUND
BACKGROUND
BACKGROUND
This x-ray shows a 13-inch long retractor that was retained during a surgical procedure.
The unintended surgical item was removed when the patient complained of pain following the initial surgery.
PROBLEM STATEMENT
An incorrect surgical count is avoidable, could be injurious as a result of a retained surgical item, and if so, the likelihood of ligation is high for both surgeons and perioperative nurses.
Identifying risk factors associated with this type of medical error is imperative.
RESEARCH DESIGN
This study employed a cross-sectional correlational design to identify significant predictors of incorrect surgical counts.
Using the surgical case as the level of analysis, a retrospective review of 2,540 medical records was conducted at two hospitals.
Data were extracted from 1,122 surgical cases that met study criteria.
To link the perioperative nurse to the result of the surgical count, primary data were collected from perioperative nurses who provided direct patient care for patients requiring surgical intervention.
THEORETICAL FRAMEWORK
Quality Health Outcomes Model4 was used to develop a conceptual framework for patientsafety in perioperative nursing practice and for variable selection for the study.
SystemIndividual, Organization, Group
Outcomes
ClientIndividual, Family, Community
Interventions
VARIABLE SELECTION
o Model One: Nurse Characteristicso Education, experience, certification, employer status
o Model Two: Patient Characteristicso Age, body-mass-index, surgical risk
o Model Three: Surgical Case Characteristicso Duration of the case, difficulty, type of case (elective/non-elective)
o Model Four: Staff Involvemento Number of perioperative staff, surgeons, specialty teams
DATA ANALYSISo Logistic Regression
o Univariate Analysiso Each Variable
o Multivariate Analysis o Each Model
o Patient Characteristics (3 Variables)o Surgical Case Characteristics (3 Variables)o Staff Involvement (3 variables)
o Final Multivariate Model (9 Variables) o Poisson Regression
o Nurse Characteristics o Rate of Incorrect Countso Controlled for the Number of Surgical Cases
FINDINGS
Patient Characteristics (Univariate Analysis)Variables Odds Ratio Confidence Interval P-Value
Age in Years 1.010 1.000-1.020 .047
Surgical Risk 2.881 2.215-3.747 .000
Body-Mass-Index .970 .948-.994 .010
FINDINGS
Surgical Case Characteristics (Univariate Analysis)Variables Odds Ratio Confidence Interval P-Value
Type of Procedure 4.956 3.241-7.579 .000
Case Difficulty 2.375 2.047-2.755 .000
Case Duration 1.006 1.005-1.008 .000
FINDINGS
Staff Involvement (Univariate Analysis)Variables Odds Ratio Confidence Interval P-Value
Perioperative Staff 1.732 1.541-1.947 .000
Surgeons 1.482 1.181-1.858 .001
Specialty Teams 4.307 2.062-8.995 .000
FINDINGS
Patient Characteristics (Multivariate Analysis)Variables Odds Ratio Confidence Interval P-Value
Age in Years 1.005 .995-1.015 .349
Surgical Risk 2.818 2.135-3.721 .000
Body-Mass-Index .963 .939-.987 .003
FINDINGS
Surgical Case Characteristics (Multivariate Analysis)Variables Odds Ratio Confidence Interval P-Value
Type of Procedure 6.486 3.896-10.798 .000
Case Difficulty 2.093 1.714-2.557 .000
Case Duration 1.004 1.002-1.006 .000
FINDINGS
Staff Involvement (Multivariate Analysis)Variables Odds Ratio Confidence Interval P-Value
Perioperative Staff 1.775 1.556-2.025 .000
Surgeons .669 .439-1.018 .061
Specialty Teams 6.059 2.363-15.536 .000
FINDINGS
Patient Characteristics (Final Model)Variables Odds Ratio Confidence Interval P-Value
Age in Years 1.003 .991-1.015 .614
Surgical Risk 1.655 1.189-2.303 .003
Body-Mass-Index .957 .928-.986 .004
Confidence
interval (95%) for the error rate of
incorrect surgical counts of each
group of surgical patients.
Study sample (n = 1,122) divided into 10 groups according to ascending body mass index with corresponding error rate of incorrect surgical counts (circle).
The BMI of the patient was statistically significant; however, the direction of the significance was patients with lower BMIs were at a higher risk for an incorrect surgical count. 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).
FINDINGS
Surgical Case Characteristics (Final Model)Variables Odds Ratio Confidence Interval P-Value
Type of Procedure 5.642 3.279-9.705 .000
Case Difficulty 1.859 1.506-2.294 .000
Case Duration 1.002 1.000-1.004 .080
FINDINGS
Staff Involvement (Final Model)Variables Odds Ratio Confidence Interval P-Value
Perioperative Staff 1.307 1.094-1.560 .003
Surgeons .755 .496-1.148 .189
Specialty Teams 2.454 1.042-5.780 .040
FINDINGS
Perioperative Staff (Final Model)Variables Odds Ratio Confidence Interval P-Value
Education .969 .682-1.376 .859
Certification 1.055 .714-1.560 .788
Employer Status 1.253 .815-1.924 .304
Experience 1.005 .991-1.019 .483
LIMITATIONS o The setting was limited to two hospitals.
o Only the characteristics of the primary nurse were linked to the incorrect surgical count. Thus, the data is not reflective of other nurses and surgical technologist involved on the surgical case.
IMPLICATIONS FOR PRACTICE o Dissemination of the findings to increase awareness of risk
factors associated with incorrect surgical counts.
o Develop and implement patient safety practices for high-risk patients (e.g., use of a wand; scanners; use of x-ray).
o Implementation of a “pause” for the surgical count.
FUTURE STUDIES o Multisite study using randomized hospitals (40-45) in several
states.
o Development of a “risk assessment” tool to identify patients at risk for an incorrect surgical count.
o Interdisciplinary qualitative study using focus groups to identify barriers to the manual counting process.
REFERENCES 1. Chappy S. Perioperative patient safety: A multisite qualitative analysis.
AORN Journal. 2006;83(4):871-97.2. Rowlands A & Steeves R. Insights into incorrect surgical counts: A
qualitative analysis from the stories of perioperative personnel. AORN Journal. 2010;92(4):410-419.
3. Association of Perioperative Registered Nurses. Standards, Recommended Practices, & Guidelines. Denver, CO: AORN, INC; 2010.
4. Mitchell P, Ferketich S, & Jennings B. Quality health outcomes model. Image, 1998;30(1):43-46.