scholarly journals AB1189 PICASO - THE PLATFORM FOR IMPROVED PERSONAL AND COORDINATED CARE OF CHRONICALLY ILL – SIX MONTHS RESULTS FROM A PROOF-OF-CONCEPT STUDY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1885.1-1885
Author(s):  
J. G. Richter ◽  
G. Chehab ◽  
M. Tomczak ◽  
C. Schwartz ◽  
E. Ricken ◽  
...  
Keyword(s):  
Health Care ◽  
Chronic Diseases ◽  
Health Care System ◽  
Health Monitoring ◽  
User Satisfaction ◽  
User Requirements ◽  
Proof Of Concept ◽  
Concept Study ◽  
Management Processes ◽  
Remote Health

Background:Cross-sectoral coordination of treatment plans and efficient management of patients with chronic diseases and co-morbidities are of great importance. In rheumatoid arthritis (RA) it is essential to orchestrate information available for a patient at various locations, to allow (cost) efficient data use, to optimize management processes and to avoid redundant diagnostics. The information and communication platform developed in the Horizon2020 funded PICASO project (www.picaso-project.eu) supports the management of patients and their data along the continuum of care, consisting of hospitals, outpatient departments, practices, other health service providers via remote health monitoring. The platform might empower patients to improve their self-management of their illnesses.Objectives:What technological expertise and resources do RA patients and physicians have, who are willing to participate in a proof-of-concept study using a modern ICT platform? What is the user satisfaction? What are platform`s clinical implications?Methods:PICASO pursued a user-centered design approach. Platform`s user requirements were determined through workshops and interviews with physicians from various disciplines, patients and other stakeholders in the health care system (e.g. data protection officers). The development was accompanied by so-called “expert walkthroughs” to ensure a user-friendly design. An evaluation concept assessing the usability of the applications, user satisfaction and clinical relevance of the platform was part of the 6-month proof-of-concept study with RA patients and their physicians (rheumatologists and family doctors). A positive ethics vote was obtained.Results:111 user requirements were identified and used to develop the platform. Conformity with the GDPR as well as national regulations were precisely adhered to. All developments are based on the new ‘Fast Healthcare Interoperability Resources’ standard enabling data exchange with other software systems in the healthcare sector. This offers many advantages, e.g. a semantic model for describing the smallest units in the health care system (e.g. medication intake times, diagnostic procedures). Thus information can be linked and made available across sectors. Data can remain with the data owner and role-specific data access is ensured.30 RA patients (80% female) participated, mean age was 58.6±10.8 years, disease duration 12.6±8.5 years, DAS28 2.6±0.9, average number of comorbidities 3.0±1.6. Patients’ IT-experience was heterogeneous. After 6 months evaluations showed a good platform acceptance with an overall rating of 2.3±1.1 (n=27, Likert scale (LS) 1-6) and evaluation of ‘ease of use’ at 2.3±1.2 (n=27, LS 1-6). Usability tests showed that for patients the presentation of (1) tasks to be performed for the management of their disease, (2) results from their remote health monitoring, and (3) patient-reported outcome instruments in a dashboard was clear and easy to understand. Time required for documentation and daily tasks was rated as appropriate by 75.9% of the patients. No major technical problems or impairments due to RA where experienced when using the dashboard. 8 physicians (37.5 % female) participated in the evaluation; overall the platform was rated at 2.2±0.5 (LS 1-6).Conclusion:The platform offers cross-sectoral orchestration of patient data and thus innovative capabilities for modern management processes (e.g. treat-to-target, tele-monitoring). The PICASO platform is available for RA patients as well as for other chronic diseases.Acknowledgments:This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689209.Disclosure of Interests:None declared

scholarly journals Remote health care system for elderly people with chronic diseases

Ariadna ◽  
2013 ◽  
pp. 39-52 ◽  
Author(s):  
Isabel Martí Ruiz ◽  
Juan Pablo Lázaro Ramos ◽  
Alejandro Aracil Ramón
Keyword(s):  
Health Care ◽  
Chronic Diseases ◽  
Elderly People ◽  
Health Care System ◽  
Remote Health ◽  
Care System

scholarly journals Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study

10.2196/16669 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16669
Author(s):  
Marcus D'Souza ◽  
Caspar E P Van Munster ◽  
Jonas F Dorn ◽  
Alexis Dorier ◽  
Christian P Kamm ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Data Privacy ◽  
Neurological Diseases ◽  
Weighted Kappa ◽  
Relevant Information ◽  
Machine Learning Algorithms ◽  
Motor Dysfunction ◽  
Proof Of Concept ◽  
Concept Study

Background In chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase the reliability of severity ratings. When these recordings are automated, quantitative disability assessments by machine learning algorithms can be created. Creation of these algorithms involves non–health care professionals, which is a challenge for maintaining data privacy. However, autoencoders can address this issue. Objective The aim of this proof-of-concept study was to test whether coded frame vectors of autoencoders contain relevant information for analyzing videos of the motor performance of patients with MS. Methods In this study, 20 pre-rated videos of patients performing the finger-to-nose test were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. The original and decoded videos were shown to 10 neurologists at an academic MS center in Basel, Switzerland. The neurologists tested whether the 200 videos were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-Expanded Disability Status Scale definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between the original and decoded videos. Results In total, 172 of 200 (86.0%) videos were of sufficient quality to be ratable. The intrarater agreement between the original and decoded videos was 0.317 (Cohen weighted kappa). The average difference in the ratings between the original and decoded videos was 0.26, in which the original videos were rated as more severe. The interrater agreement between the original videos was 0.459 and that between the decoded videos was 0.302. The agreement was higher when no deficits or very severe deficits were present. Conclusions The vast majority of videos (172/200, 86.0%) decoded by the autoencoder contained clinically relevant information and had fair intrarater agreement with the original videos. Autoencoders are a potential method for enabling the use of patient videos while preserving data privacy, especially when non–health-care professionals are involved.

scholarly journals Factors associated with seasonal influenza immunization in people with chronic diseases

2021 ◽  
Vol 72 (2) ◽  
pp. 19-25
Author(s):  
Slađana Arsenović ◽  
Tatjana Gazibara
Keyword(s):  
Health Care ◽  
Influenza Vaccine ◽  
Influenza Vaccination ◽  
Chronic Diseases ◽  
Health Care System ◽  
Vaccination Coverage ◽  
Seasonal Influenza ◽  
Seasonal Influenza Vaccination ◽  
Influenza Immunization ◽  
Factors Associated

Annually, at a global level, 3 to 5 million people present severe clinical forms of seasonal influenza and up to 650 000 people die of influenza-related complications. People with chronic diseases, such as cardiovascular, pulmonary, renal, hepatic, neurologic, hematologic and metabolic diseases or those reciveing immunosuppressive therapy, constitute a high-risk population group for the development of influenza-related complications, more severe clinical course and poorer health-related outcomes. Due to all of the above, people with chronic diseases are of high priority to receive the influenza vaccine. Immunization represents the key strategy to prevent influenza both in terms of effectiveness and health care costs. Based on the World Health Organization (WHO) recommendations, adequate seasonal influenza immunization coverage among people with chronic diseases is set at 75%. However, few countries achieve this threshold. Understanding predictive factors of vaccination, at different levels of health care delivery (such as individuals, service providers, health policy), is essential to secure acceptance of influenza immunization and achieve the recommended level of vaccination coverage. In this mini review, all the available evidence regarding seasonal influenza vaccination coverage is summarized, alongside factors associated with vaccine uptake in people with chronic diseases as a whole, as well as according to specific diseases such as: cardiovascular and pulmonary disorders, diabetes and cancer. Based on the reviewed empirical evidence, a wide spectrum of factors associated with immunization against influneza was found in people who have chronic diseases. Although diverse, these factors can be systematized into 4 distinctive groups: socio-demographic characteristics, individual attitudes and beliefs, health promoting behaviors and factors related to the health care system. Further efforts are needed to improve the seasonal influenza vaccination coverage. The immunization strategy needs to include the health care system and the community to support people with chronic diseases to continously accept the influenza vaccine.

Editorial on Remote Health Monitoring: from chronic diseases to pandemics

2021 ◽  
Vol 41 (10) ◽  
pp. 100401
Author(s):  
Joachim A Behar ◽  
Chengyu Liu ◽  
Yaniv Zigel ◽  
Pablo Laguna ◽  
Gari D Clifford
Keyword(s):  
Chronic Diseases ◽  
Health Monitoring ◽  
Remote Health Monitoring ◽  
Remote Health
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