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.

Due to an ever-increasing amount of data generated in healthcare each day, healthcare professionals are more and more challenged with information. Predictive models based on machine learning algorithms can help to quickly identify patterns in clinical data. Requirements for data driven decision support systems for health and care (DS4H) are similar in many ways to applications in other domains. However, there are also various challenges which are specific to health and care settings. The present paper describes a) healthcare specific requirements for DS4H and b) how they were addressed in our Predictive Analytics Toolset for Health and care (PATH). PATH supports the following process: objective definition, data cleaning and pre-processing, feature engineering, evaluation, result visualization, interpretation and validation and deployment. The current state of the toolset already allows the user to switch between the various involved levels, i. e. raw data (ECG), pre-processed data (averaged heartbeat), extracted features (QT time), built models (to classify the ECG into a certain rhythm abnormality class) and outcome evaluation (e. g. a false positive case) and to assess the relevance of a given feature in the currently evaluated model as a whole and for the individual decision. This allows us to gain insights as a basis for improvements in the various steps from raw data to decisions.

Heart failure (HF) is one of the biggest concerns for health care systems in developed countries. To support the long-term treatment of HF patients, the Austrian Institute of Technology implemented a HF telehealth network called "HerzMobil". While most data within this network are stored in a structured format, health care professionals can also communicate via clinical notes in free text format. These notes are hardly ever analyzed automatically, even though a large number contains valuable information for the patient's treatment process. With currently more than 20,000 notes stored in the system, an automatic approach is beneficial to spare manual screening time. One important step in this process concerns the extraction of time references from the notes. This information could, for example, be used to match the time references with events from the same note. Therefore, two Python scripts were developed to: extract time references from the notes (Script A) and subsequently calculate the corresponding dates (Script B). Script A was compared to an already existing Python library and achieved superior results for all calculated key figures. The time calculation algorithm of Script B achieved an accuracy of 75.34%. These scripts could be implemented in the HerzMobil network to provide additional information for the treatment process and further improve the telehealth system.