Patient and caregiver experience with telepharmacy in cancer care to support beginning systemic therapy: A qualitative study.

INTRODUCTION: Although the COVID-19 pandemic spurred telehealth adoption for many specialties and care team roles, the patient and caregiver experience for telepharmacy visits has been relatively understudied. To our knowledge, there is a paucity of studies that have attempted to qualitatively evaluate this. This study aimed to qualitatively assess the patient and caregiver experience of telepharmacy visits in a cancer center. METHODS: Semistructured interviews were conducted with 21 patients with cancer and seven caregivers that had attended a telepharmacy visit between December 1, 2021, and May 24, 2022. The interviews assessed visit content, overall satisfaction, system experience, visit quality, and future preferences for pharmacy visits as telehealth versus in-person. We used both deductive and inductive coding to identify themes. RESULTS: Telepharmacy delivery was generally well-received. Reasons for having the telepharmacy visit included reviewing chemotherapy procedures, side effects to expect during treatment, providing education on recently prescribed medications, offering dietary recommendations (e.g., avoiding grapefruit juice), and performing medication reconciliation. Participants were receptive to having pharmacy visits through telehealth due to the perceived lack of a need to have a physical exam and prior relationship with the pharmacist. Participants also highlighted the main reason for the telepharmacy visits was primarily to provide patient education, which participants felt was suitable for telehealth. CONCLUSIONS: The patient and caregiver experience of telepharmacy is influenced by several factors, such as ease of connectivity, communicating effectively with the pharmacist, and timing of the telepharmacy visit (e.g., immediately after picking up medications from the pharmacy). Participants' recommendations to improve telepharmacy delivery included health systems raising awareness of telepharmacy services and providing a list of questions to patients to guide discussions.

Machine Learning as a Support for the Diagnosis of Type 2 Diabetes.

Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual's health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual's risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics' (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.

Comparison of Nausea and Vomiting Associated With Amino Acid Formulations Coinfused With Peptide Receptor Radionuclide Therapy: Commercial Parenteral Nutrition Formulas Versus Compounded Arginine/Lysine.

OBJECTIVES: Positively charged amino acids (AA) such as arginine/lysine are coinfused with radiolabeled somatostatin analogs to reduce rates of nephrotoxicity. In the phase 3 NETTER-1 trial, commercial AA formulations were used in association with 177Lu-DOTA-0-Tyr3-Octreotate (DOTATATE). These formulations were also used in an early-access program (EAP) before regulatory approval of 177Lu-DOTATATE. Our program transitioned to compounded l-arginine 2.5%/l-lysine 2.5% in 0.9% NaCl after commercial approval of 177Lu-DOTATATE. We sought to compare rates of nausea/vomiting with arginine/lysine versus commercial parenteral AA formulations. METHODS: Rates of nausea/vomiting of all 20 EAP patients who received commercial AAs (15% Clinisol) were compared with the first 29 patients to receive 177Lu-DOTATATE after commercial approval and coinfused with arginine/lysine. Other parameters reviewed included infusion rates, need for PRN nausea medications, and other toxicities. RESULTS: Seventeen percent of patients who received compounded arginine/lysine experienced nausea, compared with 100% of patients in the EAP group (P < 0.0001). Infusion-related reactions occurred in 3% of the arginine/lysine cohort versus 35% in the EAP group. Infusion durations were substantially shorter in the arginine/lysine cohort (reduced by 61%). CONCLUSIONS: Coinfusions of arginine/lysine with radiolabeled somatostatin analogs result in substantially lower rates of nausea/vomiting compared with commercial AA formulations designed for parenteral nutrition.