Towards Future Reliable Pervasive Healthcare with Adherence Strategy Engineering

Despite more than a decade with a focus on creating technical solutions for supporting self-care efforts and securing increased patient autonomy, data quality remains to be a challenge, especially when moving from the supervised clinical setting and into the unsupervised self-care setting. In the unsupervised self-care setting, healthcare professionals cannot actively monitor and guide their patients’ use of medical devices nor track the use of prescribed medications, diets, and rehabilitation exercises. This lack of compliance with given medical advice and procedures results in reduced data quality of healthcare measurements and a diminished understanding of treatment effects, leading to faulty diagnoses of patients and resulting in treatment errors. If we wish to realize the future pervasive healthcare vision of reliable automated decision making systems, freed from the involvement of healthcare staff, we must have reliable high quality data as a basis. Thus, we must become able to identify and overcome data quality issues. The aim of this position paper is to discuss data quality related issues in pervasive healthcare and suggest how to meet these challenges in the future. Specifically, the paper addresses data quality issues stemming from a lack of patient adherence, and provides recommendations on how to move the field forward by using adherence strategy engineering as a design strategy.