Health Insurance And Rehabilitation Services: European Models

Health Insurance And Rehabilitation Services: European Models – Perceived Stigma as a Contextual Barrier to Early Use of HIV Testing, Treatment Initiation, and Disclosure; the Case of Patients Admitted with AIDS-Related Diseases in a Rural Hospital in South Africa

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Health Insurance And Rehabilitation Services: European Models

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Value Based Health Care In Four Different Health Care Systems

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Primary Eye Health Services For Older Adults As A Component Of Universal Health Coverage: A Scoping Review Of Evidence From High Income Countries

By Matthias Klumpp Matthias Klumpp Scilit Preprints.org Google Scholar 1, 2, * , Marcus Hintze Marcus Hintze Scilit Preprints.org Google Scholar 1 , Milla Immonen Milla Immonen Scilit Preprints.org Google Scholar 3 , Francisco Ródenas-Rigla Francisco Ródenas-Rigla Preprints .org Google Scholar 4 , Francesco Pilati Francesco Pilati Scilit Preprints.org Google Scholar 5 , Fernando Aparicio-Martínez Fernando Aparicio-Martínez Scilit Preprints.org Google Scholar 6 , Dilay Çelebi Dilay Çelebi Scilit Preprints.org Google Scholar 7 , Thomas Liebig Thomas Liebig Scilit Preprints.org Google Scholar 8, 9 , Mats Jirstrand Mats Jirstrand Scilit Preprints.org Google Scholar 10 , Oliver Urbann Oliver Urbann Scilit Preprints.org Google Scholar 1 , Marja Hedman Marja Hedman Scilit Preprints.org Google Scholar 11 , Jukka A. Lipponen Jukka A. Lipponen Scilit Preprints.org Google Scholar 12 , Silvio Bicciato Silvio Bicciato Scilit Preprints.org Google Scholar 13 , Anda-Petronela Radan Anda-Petronela Radan Scilit Preprints.org Google Scholar 14 , Bernardo Valdivieso Bernardo Valdivieso Scilit Preprints. Scholar 15 , Wolfgang Thronicke Wolfgang Thronicke Scilit Preprints.org Google Scholar 16 , Dimitrios Gunopulos Dimitrios Gunopulos Scilit Preprints.org Google Scholar 17 and Ricard Delgado-Gonzalo Ricard Delgado-Gonzalo Scilit Preprints.org Google Scholar 18

Heart Center, Kuopio University Hospital and Institute of Clinical Medicine, University of Eastern Finland, Ritva Jauhiainen-Bruun, 70029 Kuopio, Finland

Interdepartmental Center for Stem Cells and Regenerative Medicine (CIDSTEM), Department of Life Sciences, University of Modena and Reggio Emilia, Via Gottardi 100, 41125 Modena, Italy

Received: 18 May 2021 / Revised: 28 June 2021 / Accepted: 3 July 2021 / Published: 29 July 2021

The Evolution Of The Italian National Health Service

(This article belongs to Special Apparel Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent issue of research and management. Especially for hospitals, the expectations regarding improved efficiency and effectiveness with the introduction of new AI applications are enormous. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible areas of application and benefits of hospital AI technologies. This is structured as a review article and current opinion from a diverse range of researchers and health care professionals. This contributes to important improvement options also for the challenges of pandemic crises, e.g., the current situation of COVID-19. The expected advantages as well as the challenges regarding data protection, privacy, or human acceptance are reported. Overall, the diversity of application cases is a key feature of AI applications in hospitals, and this requires a specific approach for successful implementation in the healthcare sector. This may include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects.

Research into the applications of artificial intelligence (AI) in healthcare and hospitals is a crucial area of ​​innovation [1]. Intelligent healthcare with the support of AI technologies, such as Machine Learning (ML), is necessary due to specific challenges in providing medical support in European countries as well as in the rest of the world. It is not only the outbreak of the COVID-19 pandemic that reveals the current problems and challenges facing European hospitals. The success in the science of medicine in the last decades has had the effect of making patients older, frail, and multi-morbid due to a longer life expectancy [2].

This is accompanied by the fact that medical care and diseases are becoming more and more complex. Because of this medical complexity, medical staff are becoming increasingly specialized, which generally cannot be fully provided by smaller hospitals in rural areas. With this there is also the demographic change that is already emerging in Europe, e.g., the population of elderly people over 80 years old in the EU27 will double from 6.1% in 2020 to 12.5% ​​in 2060 [ 3]. Therefore, more elderly people with their specific health problems will use the health care system. In contrast, the number of well-trained young medical personnel is currently decreasing and a shortage of skilled personnel, such as doctors and nurses, is already emerging in many European nations [4].

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The challenges of the simultaneous increase of elderly and multi-morbid patients with complex diseases and the lack of skilled staff are also hampered by the increasing economic constraints on hospitals. An increase in chronic diseases due to aging populations and the lack of medical specialists results in a scarcity of resources and challenges of medical sustainability. In order not to endanger the living and health standards of the European nations it will be necessary to develop applied AI solutions to ease the burden of increased workload as well as be instrumental in providing care of efficient, effective and high quality health.

Adaptability and agility in hospitals are key prerequisites in this context, and reducing the application of AI to optimization alone misses the point in many cases. By opening up a wider range of actionable options, from personalized medical diagnosis and treatment to options in areas of care, sourcing and logistics, AI applications will provide avenues of support more important than just efficiency improvements [5, 6]. In addition, multiple benefits regarding the ongoing COVID-19 pandemic can also be expected and should be further explored, especially regarding data analysis and prevention of unnecessary patient contact for staff of health care in hospitals as centers of the fight against the viral disease [7].

AI can also contribute to the fight against pandemics like COVID-19, helping hospitals focus resources on treating pandemic patients in current as well as possible future situations. In this sense, most AI applications are directed towards non-contact analysis, diagnosis and treatment (e.g., self-treatment and prevention), reducing the number of personal contacts and visits in hospitals, thus reducing the potential spread of COVID-19 and other viral pandemics. . AI in particular offers great potential for improving medical care and supporting medical staff. The state of the art and challenges regarding AI applications in hospitals and the healthcare sector are outlined for specific application areas in Figure 1.

Regarding the introduction of AI applications in hospitals, two specific questions arise, with the answers to them as the central contributions of this paper: First, what are the requirements and hospital setups for AI applications? For this purpose, the authors carried out a survey of different European hospitals and identified relevant projects in this field. As a result, the main areas of application of AI for hospitals are found to be care, diagnosis, and logistics. The hospitals surveyed saw the greatest medical and economic potential in these three areas through the use of AI. Building on this, the paper describes a total of 11 use cases in 9 hospitals across Europe, and informs how AI can contribute to agility and efficiency in hospitals, improve health care from resource efficiency as well as service quality and choice side, aligned with the core. hospital workflow and value-adding processes. The second question is: How can a basic structure be established for the different AI use cases to avoid the mistake of developing isolated solutions that are difficult to transfer across hospitals? The authors propose three basic areas of support that help ensure a holistic approach to the implementation and transfer of AI application within the paper.

A Digital Health Decade: From Ambition To Action

The document is structured as follows: The following section is describing the use case methodology applied to the presented analysis. The next section is describing the specific descriptions of the use cases and the expectations of hospitals towards AI applications. The following section presents a discussion of the benefits and possible challenges as well as the objects of the concept

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