Caregivers Attitude Detection From Clinical Notes.

Caregivers' attitudes impact healthcare quality and disparities. Clinical notes contain highly specialized and ambiguous language that requires extensive domain knowledge to understand, and using negative language does not necessarily imply a negative attitude. This study discusses the challenge of detecting caregivers' attitudes from their clinical notes. To address these challenges, we annotate MIMIC clinical notes and train state-of-the-art language models from the Hugging Face platform. The study focuses on the Neonatal Intensive Care Unit and evaluates models in zero-shot, few-shot, and fully-trained scenarios. Among the chosen models, RoBERTa identifies caregivers' attitudes from clinical notes with an F1-score of 0.75. This approach not only enhances patient satisfaction, but opens up exciting possibilities for detecting and preventing care provider syndromes, such as fatigue, stress, and burnout. The paper concludes by discussing limitations and potential future work.

Optimizing health facility location for universal health care: A case study from the Philippines.

Site selection of health facilities is critical in ensuring universal access to basic healthcare services. However, in many low and middle-income countries (LMICs) like the Philippines, site selection is traditionally based on political and pragmatic considerations. Moreover, literature that demonstrates the application of facility location models in the Philippine healthcare setting remains scarce, and their usage in actual facility planning is even more limited. In this study, we proposed a variation of cooperative covering maximal models to identify the optimal location of primary care facilities. We demonstrated the feasibility of implementing such a model by using open source data on an actual city in the Philippines. Our results generated multiple candidate locations of primary care facilities depending on the equity and efficiency parameters. This approach could be used as one of the critical considerations in evidence-based, multi-criterion health facility location decisions of governments, and can also be adapted in other industries, given the model's use of readily available open source datasets.