Providing a suitable rehabilitation at home after an acute episode or a chronic disease is a major issue as it helps people to live independently and enhance their quality of life. However, as the rehabilitation period usually lasts some months, the continuity of care is often interrupted in the transition from the hospital to the home. Relieving the healthcare system and personalizing the care or even bringing care to the patients’ home to a greater extent is, in consequence, the superior need. This is why we propose to make use of information technology to come to participatory design driven by users needs and the personalisation of the care pathways enabled by technology. To allow this, patient rehabilitation at home needs to be supported by automatic decision-making, as physicians cannot constantly supervise the rehabilitation process. Thus, we need computer-assisted patient rehabilitation, which monitors the fitness of the current patient plan to detect sub-optimality, proposes personalised changes for a patient and eventually generalizes over patients and proposes better initial plans. Therefore, we will explain the use case of patient rehabilitation at home, the basic challenges in this field and machine learning applications that could address these challenges by technical means.

As integrated care is recognized as crucial to meet the challenges of chronic conditions such as Parkinson’s disease (PD), integrated care networks have emerged internationally and throughout Germany. One of these networks is the Parkinson Network Eastern Saxony (PANOS). PANOS aims to deliver timely and equal care to PD patients with a collaborative intersectoral structured care pathway. Additional components encompass personalized case management, an electronic health record, and communicative and educative measures. To reach an intersectoral consensus of the future collaboration in PANOS, a structured consensus process was conducted in three sequential workshops. Community-based physicians, PD specialists, therapists, scientists and representatives of regulatory authorities and statutory health insurances were asked to rate core pathway-elements and supporting technological, personal and communicative measures. For the majority of core elements/planned measures, a consensus was reached, defined as an agreement by >75% of participants. Additionally, six representatives from all partners involved in the network-design independently assessed PANOS based on the Development Model for Integrated Care (DMIC), a validated model addressing the comprehensiveness and maturity of integrated care concepts. The results show that PANOS is currently in an early maturation state but has the potential to comprehensively represent the DMIC if all planned activities are implemented successfully. Despite the favorable high level of consensus regarding the PANOS concept and despite its potential to become a balanced integrated care concept according to the DMIC, its full implementation remains a considerable challenge.

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Providing a suitable rehabilitation after an acute episode or a chronic disease helps people to live independently and enhance their quality of life. However, the continuity of care is often interrupted in the transition from hospital to home. Virtual coaches (VCs) could help these patients to engage in personalized home rehabilitation programs. These coaching systems need also to be fed with procedural precepts in order to work as intended. This, in turn, relates both to properly represent the clinical knowledge (as the VC somehow replaces the formal caregivers that cannot be fully present) as well guide the patient correctly (in order to follow the medically desired procedures given the need for personalisation according to individual needs). Therefore, we outline our technical approach to deal with this. In particular, clinical pathways in terms of semi-formal procedure models in combination with machine learning components processing and powerful user interfaces providing these pathway information and feeding the VC are presented. The system is currently under testing in a participatory design phase called Living Lab. Thus, initial user feedback for further improvements is about to come.

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