[Evaluation of medication self-administration feasibility in a university hospital: Clinical audits and recommendations]. / Évaluation de la faisabilité du dispositif d'auto-administration des médicaments par le patient dans un centre hospitalier universitaire : audits de pratique et recommandations pour une mise en œuvre sûre.

The French Health Authority recently published guidelines about patient self-administration of medications for voluntary hospitalized patients under medical supervision. This study aimed to assess medication management practices in our hospital and provide recommendations for self-administration medication. A prospective monocentric study was performed from January to June 2023, involving patient and nurse surveys based on the guidelines from the French Health Authority. A total of 207 patients participated in the survey, with a mean age of 59.6years. Among them, 56% were inclined to self-manage treatments initiated during hospitalization. Among patients with regular treatments, 62% were inclined to self-manage them in the hospital. In weekday hospitalization units, 92% of patients were inclined to self-manage their regular treatments, and 75% of those initiated during hospitalization. Among the 26 surveyed nurses, 71% reported patient autonomy for taking drugs in narrative transmissions, and 88% verified medication intake through self-administration, while 96% digitally traced it. The concept of self-administration of medication appears promising, especially within weekday hospitalization units, particularly for patients with a good understanding of their treatment. Nurses currently assess patient autonomy without specific monitoring tools. Collaborative efforts among healthcare professionals, with pharmacists playing a central role, are essential for the success of this innovative approach.

Identifying homogeneous healthcare use profiles and treatment sequences by combining sequence pattern mining with care trajectory clustering in kidney cancer patients on oral anticancer drugs: A case study.

OBJECTIVE: We evaluated the ability of a coupled pattern-mining and clustering method to identify homogeneous groups of subjects in terms of healthcare resource use, prognosis and treatment sequences, in renal cancer patients beginning oral anticancer treatment. METHODS: Data were retrieved from the permanent sample of the French medico-administrative database. We applied the CP-SPAM algorithm for pattern mining to healthcare use sequences, followed by hierarchical clustering on principal components (HCPC). RESULTS AND CONCLUSION: We identified 127 individuals with renal cancer with a first reimbursement of an oral anticancer drug between 2010 and 2017. Clustering identified three groups of subjects, and discrimination between these groups was good. These clusters differed significantly in terms of mortality at six and 12 months, and medical follow-up profile (predominantly outpatient or inpatient care, biological monitoring, reimbursement of supportive care drugs). This case study highlights the potential utility of applying sequence-mining algorithms to a large range of healthcare reimbursement data, to identify groups of subjects homogeneous in terms of their care pathways and medical behaviors.