Predictors of certification in infection prevention and control among infection preventionists: APIC MegaSurvey findings.

BACKGROUND: The 2015 APIC MegaSurvey was completed by 4,078 members to assess infection prevention practices. This study's purpose was to examine MegaSurvey results to relate infection preventionist (IP) certification status with demographic characteristics, organizational structure, compensation benefits, and practice and competency factors. METHODS: Descriptive statistics were used to examine population characteristics and certification status. Bivariate logistic regression was performed to evaluate relationships between independent variables and certification status. Variables demonstrating statistical significance (P <.05) were included in multivariable logistic regression analyses. RESULTS: Forty-seven percent of survey respondents had their CIC. IPs were less likely certified if their educational attainment was less than a bachelor's degree, they were aged 18-45 years, they worked in rural facilities, they had <16 years' experience in health care before becoming an IP, and the percentage of job dedicated to infection prevention was <75%. However, certification was associated with CIC benefit paid fully by employer, self-rating as proficient and expert-advanced, and surveillance and epidemiologic investigation competency obtained via professional development and training. CONCLUSIONS: CIC attainment was associated with IP characteristics. Additional research should focus on identifying strategies to increase certification among noncertified IPs because CIC is a measure of proficiency that should be a goal for all IPs.

Understanding the current state of infection preventionists through competency, role, and activity self-assessment.

BACKGROUND: The Association for Professionals in Infection Control and Epidemiology (APIC) MegaSurvey, administered in 2015, was completed by approximately 4,079 APIC members. The survey sought to gain a better understanding the current state of 4 components of infection prevention practice: demographic characteristics, compensation, organizational structure, and practice and competency. METHODS: The data for this analysis come from the APIC MegaSurvey Practice and Competency domain. Descriptive statistics and χ2 analyses were conducted to examine differences in infection preventionist (IP) competency, roles, and activity self-assessments. RESULTS: The majority of IPs self-assessed their competency as Proficient compared with Novice or Expert for each of the 8 IP core competency activities. Forty percent of IPs self-rated their competency as Expert in the Preventing/Controlling the Transmission of Infectious Agents/HAIs component. IPs reported Novice competency in Employee/Occupational Health (29%); Cleaning, Sterilization, Disinfection, and Asepsis (23%); and Education and Research categories (22%). Differences in self-rated competency among IPs by discipline type (public health, nurse, and laboratory) were identified. CONCLUSIONS: Differences in self-rated competency were identified for each of the 8 IP core competency activities. IPs report using various resource types to gain competency. Future research is needed to identify opportunities to increase competency levels in the weakest-rated competency activities.

Infection prevention outside of the acute care setting: Results from the MegaSurvey of infection preventionists.

BACKGROUND: In recent years, there has been a significant shift of health care delivery to nonacute care settings. However, research on staffing and resources dedicated to infection prevention and control (IPC) in these settings is lacking. METHODS: The data for this analysis come from the 2015 APIC MegaSurvey. Descriptive statistics were computed to describe infection preventionists (IPs) employed in nonacute care settings. Bivariate analyses were conducted to examine differences in facility and demographic characteristics by type of nonacute care setting. RESULTS: In total, 861 IPs represented ambulatory surgical centers (33%), long-term care facilities (23%), long-term acute care facilities (20%), inpatient behavioral or mental health care (12%), clinic or outpatient services (10%), and home health care (3%). Few (15%) were Certified in Infection Control. Most (58%) reported that less than half of their job was officially dedicated to IPC. On average, respondents reported spending the largest proportion of their time on surveillance and epidemiologic investigation (19%). IPs lacked support for secretarial functions (23%), data management (14%), and electronic medical records (32%). IPC activities, staffing, and resources differed significantly by type of nonacute care facility. CONCLUSIONS: This study indicates that resources directed to IPC in nonacute care settings may be lacking and identifies important areas for IPC education and program improvement. Research is needed to further examine staffing and IPC resources in these settings, which represent unique challenges to infection prevention and control.

Combining free text and structured electronic medical record entries to detect acute respiratory infections.

BACKGROUND: The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI). METHODOLOGY: A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis. PRINCIPAL FINDINGS: An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%. CONCLUSION: Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties.

Optimizing A syndromic surveillance text classifier for influenza-like illness: Does document source matter?

Syndromic surveillance systems that incorporate electronic free-text data have primarily focused on extracting concepts of interest from chief complaint text, emergency department visit notes, and nurse triage notes. Due to availability and access, there has been limited work in the area of surveilling the full text of all electronic note documents compared with more specific document sources. This study provides an evaluation of the performance of a text classifier for detection of influenza-like illness (ILI) by document sources that are commonly used for biosurveillance by comparing them to routine visit notes, and a full electronic note corpus approach. Evaluating the performance of an automated text classifier for syndromic surveillance by source document will inform decisions regarding electronic textual data sources for potential use by automated biosurveillance systems. Even when a full electronic medical record is available, commonly available surveillance source documents provide acceptable statistical performance for automated ILI surveillance.