Predictors of Influenza Vaccination Compliance Among Union and Nonunion Workers in a Pennsylvania Health Care System

To improve U.S. residents\u27 health, advocates are focusing their efforts on workplace health. Researchers have found that unionization is a positive influence on workers\u27 participation in health promotion programs relating to smoking and obesity prevention. However, the effect of union membership on other health promotion initiatives, such as influenza vaccination compliance among health care workers, has not been examined. The purpose of this quantitative study was to address this knowledge gap between a union and a nonunion health care facility in the U.S. state of Pennsylvania. The health belief model was used to determine if different domains of influenza vaccination perception predicted vaccination behaviors among union and nonunion health care workers. A secondary analysis was performed on the 2013-2014 Influenza Vaccination Survey, which was completed by 2,480 health care workers. While a chi-square analysis showed that vaccination compliance was not statistically different between facilities, a binary logistic regression revealed a significant difference in predicted vaccination behaviors for each domain of influenza vaccination perceptions. Among union health care workers, perceived barriers yielded the highest positive predictability of vaccination compliance, whereas perceived benefits were positively associated with vaccination compliance among nonunion workers. These study findings affect social change by identifying vaccine compliance predictors among union and nonunion health care workers. By focusing on these predictors, health care facilities may be able to improve levels of vaccination compliance and achieve the Joint Commissions\u27 vaccination goal of 90% compliance amongst all healthcare workers

Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections

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).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.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%.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