To systematically assess enhanced personal protective equipment (PPE) doffing safety risks.

We employed a 3-part approach to this study: (1) hierarchical task analysis (HTA) of the PPE doffing process; (2) human factors-informed failure modes and effects analysis (FMEA); and (3) focus group sessions with a convenience sample of infection prevention (IP) subject matter experts.

A large academic US hospital with a regional Special Pathogens Treatment Center and enhanced PPE doffing protocol experience.

Eight IP experts.

The HTA was conducted jointly by 2 human-factors experts based on the Centers for Disease Control and Prevention PPE guidelines. The findings were used as a guide in 7 focus group sessions with IP experts to assess PPE doffing safety risks. For each HTA task step, IP experts identified failure mode(s), assigned priority risk scores, identified contributing factors and potential consequences, and identified potential risk mitigation strategies. Data were recorded in a tabular format during the sessions.

Of 103 identified failure modes, the highest priority scores were associated with team members moving between clean and contaminated areas, glove removal, apron removal, and self-inspection while preparing to doff. Contributing factors related to the individual (eg, technical/ teamwork competency), task (eg, undetected PPE contamination), tools/technology (eg, PPE design characteristics), environment (eg, inadequate space), and organizational aspects (eg, training) were identified. Participants identified 86 types of risk mitigation strategies targeting the failure modes.

Despite detailed guidelines, our study revealed 103 enhanced PPE doffing failure modes. Analysis of the failure modes suggests potential mitigation strategies to decrease self-contamination risk during enhanced PPE doffing.

Blood and body fluid exposures are frequently evaluated in emergency departments (EDs). However, efficient and effective methods for estimating their incidence are not yet established.

Evaluate the efficiency and accuracy of estimating statewide ED visits for blood or body fluid exposures using International Classification of Diseases, Ninth Revision (ICD-9), code searches.

Secondary analysis of a database of ED visits for blood or body fluid exposure.

EDs of 11 civilian hospitals throughout Rhode Island from January 1, 1995, through June 30, 2001.

Patients presenting to the ED for possible blood or body fluid exposure were included, as determined by prespecified ICD-9 codes.

Positive predictive values (PPVs) were estimated to determine the ability of 10 ICD-9 codes to distinguish ED visits for blood or body fluid exposure from ED visits that were not for blood or body fluid exposure. Recursive partitioning was used to identify an optimal subset of ICD-9 codes for this purpose. Random-effects logistic regression modeling was used to examine variations in ICD-9 coding practices and styles across hospitals. Cluster analysis was used to assess whether the choice of ICD-9 codes was similar across hospitals.

The PPV for the original 10 ICD-9 codes was 74.4% (95% confidence interval [CI], 73.2%–75.7%), whereas the recursive partitioning analysis identified a subset of 5 ICD-9 codes with a PPV of 89.9% (95% CI, 88.9%–90.8%) and a misclassification rate of 10.1%. The ability, efficiency, and use of the ICD-9 codes to distinguish types of ED visits varied across hospitals.

Although an accurate subset of ICD-9 codes could be identified, variations across hospitals related to hospital coding style, efficiency, and accuracy greatly affected estimates of the number of ED visits for blood or body fluid exposure.