As the aging population grows, coupled with a shortage of healthcare personnel, the demand for innovative solutions becomes imperative. Digital tools, such as medicine dispensers, offer promising avenues for remote healthcare delivery, alleviating the workload on professionals. Nonetheless, home care organizations encounter challenges in implementing and scaling these tools, ranging from a lack of awareness about available options to difficulties in selecting the most suitable tool for specific situations. This study investigates a recommendation methodology for a medicine dispenser based on Omaha profiles from Electronic Patient Dossier (EPD). Using the CRISP-DM methodology, we designed a Positive-Unlabeled learning-based algorithm. We added Explainable Artificial Intelligence (XAI) techniques, showing a feature importance representation based on Shapley values, to enrich the transparency and reliability of suggested interventions. The solution was evaluated with healthcare professionals from two healthcare organizations. Although the technical performance of the algorithm was decent (recall: 0.9), they stated the data is not detailed enough to conclude whether a medicine dispenser could be used, showing the need for human evaluation during the process. This study addresses challenges like a sparse dataset lacking detailed data and iteratively involving users during development when performing research in a real life situation.