Developing modular training components to support home hospital digital solutions: Results of a Delphi panel.

BACKGROUND: Home hospitalization (HH) has demonstrated to be a cost-effective alternative with respect ti traditional hospitalization. Digital technologies, such as remote monitoring, have the potential to contribute to its expansion. Tailored educational content is a need to ensure patient safety during the whole admission. PURPOSE: The objective of this study was to systematically obtain consensus on patients with HH using training in the digital monitoring system. The goal of this work was to develop an adaptable modular and personalized training program for patients to support quality and safety care for HH. METHODS: The methodological approach for developing the proposed training content followed a modified Delphi technique with a multidisciplinary group of experts with significant knowledge of health informatics and HH protocols in Spain. The study comprised two rounds of training material description and gathering were completed. In Round 1, the experts received 58 predefined items obtained from the literature review and protocol selection. 20 items were rejected for different reasons and 25 new items were proposed. In Round 2, the experts selected the final items to build on the training content for every type of user and illness. RESULTS: A total of 21 experts completed rounds 1 and 2. The consensus was reached at the end of Round 2 with the inclusion of 53 items to build the training material. This included 17 treatment procedures, 4 diagnosis procedures, 22 additional support content, and 10 content features that describe how to build and deliver customized training content. CONCLUSIONS: Participants agreed on the type of content, its structure, and delivery methods to build modular training materials that support patients when they are hospitalized at home with the help of digital monitoring tools. This information can be used to create HH training programs that support new HH protocols and provide a standard for evaluating the quality of existing educational materials and programs.

Design of a Training Model for Remote Management of Patients Hospitalized at Home.

PURPOSE: Hospitalization at Home (HaH) has proven to be more efficient and effective than conventional one, but it also requires a higher number of resources and specialised personnel. Information technologies can make this process scalable and allow physicians and nurses to deliver remote healthcare services for patients hospitalized at home. However, a correct and satisfactory usage of technology requires an adequate training of professionals and patients. This paper describes a new model for training healthcare professionals on managing remote ICT-based services for Hospitalization at Home. METHODS: The model was defined based on mix-method that combined the PICO model and a User Centred Design methodology, oriented to identify and discover the healthcare professionals needs and the training instruments in the literature that directly involved these professionals. These aspects were used in the definition and development of the assessment framework of the proposed training model. RESULTS: A training model for healthcare professionals focused on achieving an effective uptake of complex digital interventions such as Hospitalization at Home was defined. The selected mix-method led to the identification of four different blocks, that were considered as the main areas to include in a training programme. The model identifies measurable elements for assessing acceptability, workability increment and integration into daily clinical practice outcomes, as well as for evaluating the proposed training content and its outcomes. CONCLUSIONS: The proposed training model highlights the key aspects of training health professionals to favour an effective and successful implementation of complex technological healthcare interventions in the context of ICT-based HaH ICT.

A Mobile Solution for Rhythmic Auditory Stimulation Gait Training.

Recent studies showed that Parkinson's disease (PD) patients improved their gait parameters while walking with rhythmic auditory stimulation (RAS). They achieved a longer stride length, a reduced stride time variability and a higher walking speed. Combining RAS with mobile gait analysis would allow continuous monitoring of RAS effects and gait in natural environments. This paper proposes a mobile solution for home-based assessment of RAS by combining RAS gait training and a mobile system for data acquisition. Existing datasets were used to investigate the cadence of PD patients and to propose suitable frequencies for RAS gait training. The cadence calculation was implemented using a peak detection algorithm, which uses the time difference between two mid-swing events as stride time values. We validated our system as a whole using a cohort of 13 PD patients who performed RAS gait training. The algorithms were also validated against the eGaIT system, a state-of-the-art system, and achieved a mean F1 score for detected strides of 97.57 % ± 0.86 % and a mean absolute error for the cadence of 0.16 spm ± 0.09 spm. This study lays the ground work for further clinical studies investigating the effectiveness of mobile RAS within a home environment.

Monitoring of motor and non-motor symptoms of Parkinson's disease through a mHealth platform.

Parkinson's disease (PD) is a complex, chronic disease that many patients live with for many years. In this work we propose a mHealth approach based on a set of unobtrusive, simple-in-use, off-the-self, co-operative, mobile devices that will be used for motor and non-motor symptoms monitoring and evaluation, as well as for the detection of fluctuations along with their duration through a waking day. Ideally, a multidisciplinary and integrated care approach involving several professionals working together (neurologists, physiotherapists, psychologists and nutritionists) could provide a holistic management of the disease increasing the patient's independence and Quality of Life (QoL). To address these needs we describe also an ecosystem for the management of both motor and non-motor symptoms on PD facilitating the collaboration of health professionals and empowering the patients to self-manage their condition. This would allow not only a better monitoring of PD patients but also a better understanding of the disease progression.