RISK FACTORS OF INCORRECT SURGICAL COUNTS FOLLOWING SURGERY

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RISK FACTORS OF INCORRECT SURGICAL COUNTS
FOLLOWING SURGERY

• Aletha Rowlands PhD, RN, CNOR
• Assistant Professor
• West Virginia University School of Nursing
• Morgantown, WV

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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.

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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.

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BACKGROUND
5
BACKGROUND
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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.

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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.

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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.

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THEORETICAL FRAMEWORK
System Individual, Organization, Group
Outcomes
Interventions
Client Individual, Family, Community
Quality Health Outcomes Model4 was used to
develop a conceptual framework for patient safety
in perioperative nursing practice and for
variable selection for the study.
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VARIABLE SELECTION

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

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DATA ANALYSIS

• Logistic Regression
• Univariate Analysis
• Each Variable
• Multivariate Analysis
• Each Model
• Patient Characteristics (3 Variables)
• Surgical Case Characteristics (3 Variables)
• Staff Involvement (3 variables)
• Final Multivariate Model (9 Variables)
• Poisson Regression
• Nurse Characteristics
• Rate of Incorrect Counts
• Controlled for the Number of Surgical Cases

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FINDINGS

Patient Characteristics (Univariate Analysis) Patient Characteristics (Univariate Analysis) Patient Characteristics (Univariate Analysis) 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
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FINDINGS

Surgical Case Characteristics (Univariate Analysis) Surgical Case Characteristics (Univariate Analysis) Surgical Case Characteristics (Univariate Analysis) 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
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FINDINGS

Staff Involvement (Univariate Analysis) Staff Involvement (Univariate Analysis) Staff Involvement (Univariate Analysis) 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
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FINDINGS

Patient Characteristics (Multivariate Analysis) Patient Characteristics (Multivariate Analysis) Patient Characteristics (Multivariate Analysis) 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
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FINDINGS

Surgical Case Characteristics (Multivariate Analysis) Surgical Case Characteristics (Multivariate Analysis) Surgical Case Characteristics (Multivariate Analysis) 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
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FINDINGS

Staff Involvement (Multivariate Analysis) Staff Involvement (Multivariate Analysis) Staff Involvement (Multivariate Analysis) 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
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FINDINGS

Patient Characteristics (Final Model) Patient Characteristics (Final Model) Patient Characteristics (Final Model) 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
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  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).
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FINDINGS

Surgical Case Characteristics (Final Model) Surgical Case Characteristics (Final Model) Surgical Case Characteristics (Final Model) 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
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FINDINGS

Staff Involvement (Final Model) Staff Involvement (Final Model) Staff Involvement (Final Model) 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
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FINDINGS

Perioperative Staff (Final Model) Perioperative Staff (Final Model) Perioperative Staff (Final Model) 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

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LIMITATIONS

• The setting was limited to two hospitals.
• 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.

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IMPLICATIONS FOR PRACTICE

• Dissemination of the findings to increase
  awareness of risk factors associated with
  incorrect surgical counts.
• Develop and implement patient safety practices
  for high-risk patients (e.g., use of a wand
  scanners use of x-ray).
• Implementation of a pause for the surgical
  count.

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FUTURE STUDIES

• Multisite study using randomized hospitals
  (40-45) in several states.
• Development of a risk assessment tool to
  identify patients at risk for an incorrect
  surgical count.
• Interdisciplinary qualitative study using focus
  groups to identify barriers to the manual
  counting process.

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REFERENCES

• Chappy S. Perioperative patient safety A
  multisite qualitative analysis. AORN Journal.
  200683(4)871-97.
• Rowlands A Steeves R. Insights into incorrect
  surgical counts A qualitative analysis from the
  stories of perioperative personnel. AORN Journal.
  201092(4)410-419.
• Association of Perioperative Registered Nurses.
  Standards, Recommended Practices, Guidelines.
  Denver, CO AORN, INC 2010.
• Mitchell P, Ferketich S, Jennings B. Quality
  health outcomes model. Image, 199830(1)43-46.