r isk f actors o f i ncorrect s urgical c ounts f ollowing s urgery aletha rowlands phd, rn, cnor...

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.