Interprofessional education (IPE) has grown in popularity over the years because it is effective in training future health professionals and serves as the engine of productivity in healthcare-related research. The research team, consisting of higher education faculty in various health profession disciplines, conducted a systematic review of existent IPE literature targeting both graduate and under-graduate students. The main purpose of the systematic review was to categorize research and analyze IPE studies to determine sub-constructs that entail both barriers and facilitators often related to occurrences in primary provider, learner, and researcher IPE initiatives. If identified, IPE training and related curriculum plans were also analyzed in this review. The research team wanted to identify any/all potential instances of the use of research activities in coordination with IPE training to identify underlying constructs to support future college and program-specific andragogy initiatives. Review results demonstrate three categorical constructs related to IPE initiatives in higher education health professions programs (provider, learner, and researcher stakeholder perspectives). Results further demonstrate provider IPE challenges surrounding limited resources and a willingness to participate, while both patient experience and teamwork foci were identified as common IPE session themes. Further, the learners’ perspectives included a challenge related to a willingness to participate, as well as the patient experience focus. Review constructs were further validated using supplementary topic modeling. To the best of our knowledge, this is the first study that combined systematic review with a supplementary topic modeling. Findings support ongoing pedagogy and research efforts related to IPE activities for health professions programs in higher education.

The purpose of this study is to examine the effectiveness of states’ online fiscal disclosure processes for municipal bond issuances. This study conducts a comprehensive review of the factors that impact public borrowing costs in municipal bond issuances and tries to find other “costless signaling.” One way to improve the performance of public agencies is to improve online fiscal disclosure, which could lower the public borrowing cost. This study evaluates the effects of two forms of online fiscal disclosure: online budget reports and online Comprehensive Annual Fiscal Reports (CAFRs). The empirical estimates show that in a sample of state general obligation bonds, online budget reports have a statistically significant effect on the cost of municipal bond issuances, while online CAFRs do not have such an effect.

Health care organizations need to adopt an electronic health record (EHR) system for compliance with the Health Information Technology for Economic and Clinical Health Act. How to budget the EHR implementation is a challenging issue to the chief financial officers in health care organizations. This article uses a mock hospital to illustrate how to prepare an EHR implementation budget step by step in a hospital setting. After the project budget baseline is set, the budgeting steps are as follows: (1) estimating the cost for each project element, (2) aggregating all costs, and (3) revising budget to meet the budget baseline. Several budgeting techniques are discussed, including analogue, parametric, and unit cost based on effort. The budgeting methodology can also be applied to a physician/clinic EHR implementation budget.

Objectives: Urinary tract infections (UTIs) are the most common healthcare-associated infections in Skilled Nursing Facilities (SNFs); they are associated with longer lengths of stay, higher levels of care, increased treatment costs, and higher mortality rates. This study aimed to develop a machine learning classification model to predict the risk of high catheter-associated urinary tract infection rates based on SNF characteristics. Methods: We analyzed 94,877 total SNF-year observations from 2019 to 2024, not unique facilities; thus, individual SNFs may appear in multiple years. The factor variables were average length of stay in days, number of staffed beds, total nurse and total physical therapy staffing hours per resident per day, facility ownership, geographic classification, facility accreditation, Accountable Care Organization affiliations, Centers for Medicare and Medicaid Services SNF Overall Star Rating, and the SNF-year of the observations. We utilized three machine learning models for this analysis: Random Forest, XGBoost, and LightGBM. We used Shapley Additive exPlanations to interpret the best-performing machine learning model by visualizing feature importance and examining the relationship between key predictors and the outcome. Results: We found that machine learning models outperformed traditional logistic regression in predicting UTIs in skilled nursing facilities. Using the best-performing model, Random Forest, we identified rural SNFs, and the number of staffed beds as the most influential predictors of high UTI rates, followed by average length of stay, and geographic location. Conclusions: This study demonstrates the value of using facility-level characteristics to predict the risk of UTIs in SNFs with machine learning models. Results from this study can inform infection prevention efforts in post-acute care settings.