Amos Cahan

Wearable Technologies and Telehealth in Care Management for Chronic Illness

Telehealth is the use of technology for remote patient monitoring and care. Wearables are small electronic devices that can seamlessly collect data about a patient for prolonged periods of time and support the implementation of telemedicine in the patient’s natural environment. In a reality where patients are becoming older and sicker, medicine is becoming more and more a multidisciplinary team work and healthcare resources are limited, telehealth holds promise as a way to improve patient care while cutting on costs. It may improve coordination between care providers, allow for bringing top notch expertise to remote, rural settings, provide a more complete picture of the patient’s condition and support independent living of the elderly and patients with chronic diseases. In this chapter, we review some of the related technology and application and portrait how they may be integrated in the near future in the healthcare delivery system.

Improving precision medicine using individual patient data from trials

Our first commitment as clinicians is to our patients as individuals. This understanding lies at the heart of the precision medicine initiative, which aims to customize treatments based not only on a patient’s clinical picture but also on their genetic, demographic and environmental profile.[1][1

Estimating the effects of second-line therapy for type 2 diabetes mellitus: retrospective cohort study

Objective Metformin is the recommended initial drug treatment in type 2 diabetes mellitus, but there is no clearly preferred choice for an additional drug when indicated. We compare the counterfactual drug effectiveness in lowering glycated hemoglobin (HbA1c) levels and effect on body mass index (BMI) of four diabetes second-line drug classes using electronic health records. Study design and setting Retrospective analysis of electronic health records of US-based patients in the Explorys database using causal inference methodology to adjust for patient censoring and confounders. Participants and exposures Our cohort consisted of more than 40 000 patients with type 2 diabetes, prescribed metformin along with a drug out of four second-line drug classes—sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 (DPP-4) inhibitors and glucagon-like peptide-1 agonists—during the years 2000–2015. Roughly, 17 000 of these patients were followed for 12 months after being prescribed a second-line drug. Main outcome measures HbA1c and BMI of these patients after 6 and 12 months following treatment. Results We demonstrate that all four drug classes reduce HbA1c levels, but the effect of sulfonylureas after 6 and 12 months of treatment is less pronounced compared with other classes. We also estimate that DPP-4 inhibitors decrease body weight significantly more than sulfonylureas and thiazolidinediones. Conclusion Our results are in line with current knowledge on second-line drug effectiveness and effect on BMI. They demonstrate that causal inference from electronic health records is an effective way for conducting multitreatment causal inference studies.