Medication management needs information and communications technology‐based approaches, including telehealth and artificial intelligence

Abstract

Life expectancy is rising in most parts of the world as is the prevalence of chronic diseases. Suboptimal adherence to long‐term medications is still rather the norm than the exception, although it is well known that suboptimal adherence compromises the therapeutic effectiveness. Information and communications technology provides new concepts for improving adherence to medications. These so‐called telehealth concepts or services help to implement closed‐loop healthcare paradigms and to establish collaborative care networks involving all stakeholders relevant to optimising the overall medication therapy. Together with data from Electronic Health Records and Electronic Medical Records, these networks pave the way to data‐driven decision support systems. Recent advances in machine learning, predictive analytics, and artificial intelligence allow further steps towards fully autonomous telehealth systems. This might bring advances in the future: disburden healthcare professionals from repetitive tasks, enable them to timely react to critical situations, and offer a comprehensive overview of the patients' medication status. Advanced analytics can help to assess whether patients have taken their medications as prescribed, to improve adherence via automatic reminders. Ultimately, all relevant data sources need to be collated into a basis for data‐driven methods, with the goal to assist healthcare professionals in guiding patients to obtain the best possible health status, with a reasonable resource utilisation and a risk‐adjusted safety and privacy approach. This paper summarises the state‐of‐the‐art of telehealth and artificial intelligence applications in medication management. It focuses on 3 major aspects: latest technologies, current applications, and patient related issues.

Predictive models of medication non-adherence risks of patients with T2D based on multiple machine learning algorithms

“…The development of patient’s medication adherence predictive models facilitates individualized diabetes education and care 19 20. In our research, 30 machine learning-based modeling algorithms were developed for adherence prediction, which at most 16 variables were used to build the model.…”

Compliance and Adherence to Enteral Nutrition Treatment in Adults: A Systematic Review

“…The authors did not present adherence figures; however, this system made it possible to receive alerts such as that the patient “has not taken the oral nutritional supplement”, “did not like the taste of the oral nutritional supplements” or “took only a little bit”. In this sense, “telehealth” and the use of telematic means is a way of maintaining regular contact with health professionals and facilitating the monitoring of adherence to prescribed treatments by collecting vital signs and data on intake and sharing information among patients, caregivers and professionals involved so that everyone has access to information in real time [51], thus allowing the implementation of early interventions. In any case, the limitations of this form of EN monitoring should be assessed because the authors reported difficulties in recruiting patients in the study because they did not understand the importance of monitoring EN or were not familiar with electronic devices.…”

Demographics in the 2020s—Longevity as a challenge for pharmaceutical drug development, prescribing, dispensing, patient care and quality of life

“…The development of information and communication technology (ICT) in recent years has created a new level of networking between healthcare professionals and patients and in particular the interaction between patients and physicians 36 . Eggerth et al summarizes the actual status of progress in this emerging field of science such as diagnostic sensors connected to a smartphone in order to provide physicians with real time health parameters 37,38 . By effectively and continuously monitoring health parameters in patients, the service providers will be able to focus on preventive and proactive therapeutic measures by physicians rather than reactive measures in the event of a health crisis.…”