1. R ISK F ACTORS O F I NCORRECT S URGICAL C OUNTS F OLLOWING S URGERY Aletha Rowlands PhD, RN, CNOR Assistant Professor West Virginia University School of Nursing Morgantown, WV

2. I NTRODUCTION 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 AORN 3 standards and recommended practices, this type of error continues to occur.

3. B ACKGROUND 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.

4. B ACKGROUND

6. 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.

7. P ROBLEM S TATEMENT 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.

8. R ESEARCH D ESIGN 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.

9. T HEORETICAL FRAMEWORK Quality Health Outcomes Model 4 was used to develop a conceptual framework for patient safety in perioperative nursing practice and for variable selection for the study. System Individual, Organization, Group Outcomes Client Individual, Family, Community Interventions

10. V ARIABLE S ELECTION o Model One: Nurse Characteristics o Education, experience, certification, employer status o Model Two: Patient Characteristics o Age, body-mass-index, surgical risk o Model Three: Surgical Case Characteristics o Duration of the case, difficulty, type of case (elective/non-elective) o Model Four: Staff Involvement o Number of perioperative staff, surgeons, specialty teams

11. D ATA A NALYSIS o Logistic Regression o Univariate Analysis o 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 Counts o Controlled for the Number of Surgical Cases

12. F INDINGS Patient Characteristics (Univariate Analysis) VariablesOdds RatioConfidence IntervalP-Value Age in Years1.0101.000-1.020.047 Surgical Risk2.8812.215-3.747.000 Body-Mass-Index.970.948-.994.010

13. F INDINGS Surgical Case Characteristics (Univariate Analysis) VariablesOdds RatioConfidence IntervalP-Value Type of Procedure4.9563.241-7.579.000 Case Difficulty2.3752.047-2.755.000 Case Duration1.0061.005-1.008.000

14. F INDINGS Staff Involvement (Univariate Analysis) VariablesOdds RatioConfidence IntervalP-Value Perioperative Staff1.7321.541-1.947.000 Surgeons1.4821.181-1.858.001 Specialty Teams4.3072.062-8.995.000

15. F INDINGS Patient Characteristics (Multivariate Analysis) VariablesOdds RatioConfidence IntervalP-Value Age in Years1.005.995-1.015.349 Surgical Risk2.8182.135-3.721.000 Body-Mass-Index.963.939-.987.003

16. F INDINGS Surgical Case Characteristics (Multivariate Analysis) VariablesOdds RatioConfidence IntervalP-Value Type of Procedure6.4863.896-10.798.000 Case Difficulty2.0931.714-2.557.000 Case Duration1.0041.002-1.006.000

17. F INDINGS Staff Involvement (Multivariate Analysis) VariablesOdds RatioConfidence IntervalP-Value Perioperative Staff1.7751.556-2.025.000 Surgeons.669.439-1.018.061 Specialty Teams6.0592.363-15.536.000

18. F INDINGS Patient Characteristics (Final Model) VariablesOdds RatioConfidence IntervalP-Value Age in Years1.003.991-1.015.614 Surgical Risk1.6551.189-2.303.003 Body-Mass-Index.957.928-.986.004

19. 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).

20. F INDINGS Surgical Case Characteristics (Final Model) VariablesOdds RatioConfidence IntervalP-Value Type of Procedure5.6423.279-9.705.000 Case Difficulty1.8591.506-2.294.000 Case Duration1.0021.000-1.004.080

21. F INDINGS Staff Involvement (Final Model) VariablesOdds RatioConfidence IntervalP-Value Perioperative Staff1.3071.094-1.560.003 Surgeons.755.496-1.148.189 Specialty Teams2.4541.042-5.780.040

22. F INDINGS Perioperative Staff (Final Model) VariablesOdds RatioConfidence IntervalP-Value Education.969.682-1.376.859 Certification1.055.714-1.560.788 Employer Status1.253.815-1.924.304 Experience1.005.991-1.019.483

23. L IMITATIONS 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.

24. I MPLICATIONS F OR P RACTICE 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.

25. F UTURE S TUDIES 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.

26. R EFERENCES 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.