Artificial intelligence and digital medicine for integrated home care services in Italy: Opportunities and limits

Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review

BACKGROUND

Artificial intelligence is experiencing rapid growth, with continual innovation and advancements in the health care field.

OBJECTIVE

This study aims to evaluate the application of artificial intelligence technologies across various domains of respiratory care.

METHODS

We conducted a narrative review to examine the latest advancements in the use of artificial intelligence in the field of respiratory care. The search was independently conducted by respiratory care experts, each focusing on their respective scope of practice and area of interest.

RESULTS

This review illuminates the diverse applications of artificial intelligence, highlighting its use in areas associated with respiratory care. Artificial intelligence is harnessed across various areas in this field, including pulmonary diagnostics, respiratory care research, critical care or mechanical ventilation, pulmonary rehabilitation, telehealth, public health or health promotion, sleep clinics, home care, smoking or vaping behavior, and neonates and pediatrics. With its multifaceted utility, artificial intelligence can enhance the field of respiratory care, potentially leading to superior health outcomes for individuals under this extensive umbrella.

CONCLUSIONS

As artificial intelligence advances, elevating academic standards in the respiratory care profession becomes imperative, allowing practitioners to contribute to research and understand artificial intelligence's impact on respiratory care. The permanent integration of artificial intelligence into respiratory care creates the need for respiratory therapists to positively influence its progression. By participating in artificial intelligence development, respiratory therapists can augment their clinical capabilities, knowledge, and patient outcomes.

Integrating Telemedicine in Botulinum Toxin Type-A Treatment for Spasticity Management: Perspectives and Challenges from Italian Healthcare Professionals

(1) Background: Telemedicine is a vital tool for enhancing healthcare accessibility and outcomes at reduced costs. This study aimed to assess the usability of the Maia Connected Care telemedicine platform for managing spasticity in patients receiving botulinum toxin type-A, focusing on the perspectives of Italian physiatrists with expertise in this treatment. (2) Methods: Conducted from March 2023 to June 2023, this multicenter survey involved 15 Italian physicians who used the platform for teleconsultations. Data collected included demographic details, responses to the Telemedicine Usability Questionnaire, and physician insights on patient satisfaction, treatment effectiveness, and implementation challenges in telehealth. (3) Results: The platform demonstrated high usability, with strong physician satisfaction due to its user-friendly interface and quality of interactions. A majority expressed willingness to continue telehealth for spasticity management, noting its effectiveness in improving patient satisfaction and outcomes. Challenges included replicating the depth of in-person consultations and addressing issues like reimbursement and telehealth standardization. (4) Conclusions: This study highlights telemedicine's potential for spasticity management and clinician satisfaction, while underscoring the need for improvements in simulating in-person experiences and addressing systemic issues. The absence of patient perspectives represents a limitation, advocating for future research to optimize telemedicine practices and evaluate long-term clinical impacts.

Healthcare professionals' beliefs, attitudes, and thoughts toward cardiopulmonary telerehabilitation: A mixed-methods study

Healthcare professionals' opinions, attitudes, and thoughts toward new digital healthcare are often overlooked. However, they play a crucial role in accepting new digital care strategies. This research aimed to understand the perceptions of cardiopulmonary rehabilitation professionals towards telerehabilitation by conducting a mixed-method study at various Fondazione Don Carlo Gnocchi centers in Italy. A total of 14 healthcare workers, including 7 experts in telerehabilitation, participated through surveys and semi-structured focus groups, covering 12 thematic areas derived from the Theoretical Domains Framework (knowledge; professional role; beliefs about capabilities; beliefs about consequences; optimism; reinforcement, goals; memory, attention, and decision process; environmental context and resources; social influences; emotions; ideal patient's profile). Participants (mean age = 45.00 ± 9.06; M:F = 3:11) shared diverse experiences and views on telerehabilitation. All participants had a good knowledge of telerehabilitation. While non-experts indicated technological expertise and preserved cognitive level as a prerequisite for telerehabilitation use, experts believed that no ideal patient exists and that all people can benefit from it. They converged in defining it as a new delivery path of rehabilitation with the same objective of face-to-face rehabilitation services, enhancing the patient's quality of life. Environmental, infrastructural, and institutional resources are needed to enhance accessibility. Positive attitude, optimism, and expectation were reported, but uncertainties about how to manage safety issues and increased workload were mentioned. The study showed a complex picture of staff rehabilitation beliefs about telerehabilitation. Overall, telerehabilitation was considered a great opportunity for patients who face barriers to in-person clinical interventions.

Impact mechanism and spatial spillover effect of the digital economy on the high-quality development of undertakings for the aged in China

BACKGROUND

Digitalization and population aging have had a profound impact on the development of undertakings for the aged, which brings challenges as well as opportunities for elder care system. This study examines the impact of the digital economy on the high-quality development (HQD) of undertakings for the aged in China.

METHODS

Based on the panel data of 31 provinces in mainland China from 2013 to 2021, this study explores the influence mechanism of the HQD of undertakings for the aged driven by the digital economy and its spatial spillover effects. The benchmark regression model is used to investigate the impact of the development level of the digital economy on the HQD of undertakings for the aged. The mediation effect model is used to explore the indirect effects of the digital economy on the HQD of undertakings for the aged through the influence of the intermediary variable. The spatial panel model is then used to analyze the spatial spillover effect of the digital economy on the HQD of undertakings for the aged.

RESULTS

The digital economy has a positive effect (coefficient = 0.1530, P-value < 0.01) on the local HQD of undertakings for the aged and a negative effect (coefficient = - 0.1012, P-value < 0.01) on the level of the HQD of undertakings for the aged in neighboring areas after controlling for other variables. The mediation effect of the proportion of the tertiary industry in GDP accounts for 6.6% of the total effect of the digital economy on the HQD of undertakings for the aged.

CONCLUSIONS

The digital economy can significantly promote the HQD of undertakings for the aged by transforming the development of the tertiary industry. The improvement in the digital economy has a significant spatial spillover effect. This research enriches the existing body of literature by suggesting effective ways to enhance the HQD of undertakings for the aged through the digital economy and the tertiary industry.

Perception of Utility and Efficacy of Implementation of TEC-MED Model of Care for Frail Older People and Their Caregivers: A Qualitative Study

INTRODUCTION

The global population is ageing, and healthcare systems continue to adopt outdated social models of ageing that do not respond to older people's needs. The aim of this study was to explore the experiences of participants in the implementation of the Transcultural social-ethical-care (TEC-MED) model for integrated community care.

METHODS

A qualitative descriptive research study was conducted. Qualitative data were collected through individual interviews and focus groups with purposive sampling.

RESULTS

We gathered experiences from five older people, five informal caregivers, two training agents (nurses), six healthcare professionals and eight stakeholders (senior management of businesses, public administrators, researchers and educators). Four themes were extracted: TEC-MED as a new model of home care, TEC-MED model outcome, key role of training agent and platform and resources. Overall, all the participants were satisfied with the model and various positive outcomes were found. The TEC-MED model of care was inclusive and personalised and bridged the communication and integration gaps between different services for the care of dependent older people and their caregivers in the community. Recommendations were made for improvements to the model.

CONCLUSION

New models of care that are inclusive, personalised and integrated are necessary to respond to the multiple needs of the older people. A model that integrates the multiple skills of healthcare professionals is an optimum solution in the care of the older people and their caregivers in Mediterranean countries. Similar research is imperative for other healthcare systems to help them prepare adequately to respond effectively to the needs of present and new generations of older people.

IMPLICATIONS FOR PRACTICE

The TEC-MED model presents a promising approach to addressing the complex care needs of older people and their caregivers by fostering inclusivity, personalisation and integration across services. For nursing practice, this model emphasizes the importance of multidisciplinary collaboration and the role of nurses in facilitating the adoption of new care strategies. Implementing such models in everyday practice could improve the quality of care provided to older adults, enhancing communication between healthcare providers and ensuring that care is more aligned with the individual needs of patients. Furthermore, integrating digital platforms and targeted resources, as highlighted in the TEC-MED model, can aid in overcoming existing barriers in healthcare systems, improving the coordination of care at the community level.

Artificial intelligence in andrology - fact or fiction: essential takeaway for busy clinicians

ABSTRACT

Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.

The Rise of Virtual Health Care: Transforming the Health Care Landscape in the Kingdom of Saudi Arabia: A Review Article

Background: The rise of virtual healthcare underscores the transformative influence of digital technologies in reshaping the healthcare landscape. As technology advances and the global demand for accessible and convenient healthcare services escalates, the virtual healthcare sector is gaining unprecedented momentum. Saudi Arabia, with its ambitious Vision 2030 initiative, is actively embracing digital innovation in the healthcare sector. Methods: In this narrative review, we discussed the key drivers and prospects of virtual healthcare in Saudi Arabia, highlighting its potential to enhance healthcare accessibility, quality, and patient outcomes. We also summarized the role of the COVID-19 pandemic in the digital transformation of healthcare in the country. Healthcare services provided by Seha Virtual Hospital in Saudi Arabia, the world's largest and Middle East's first virtual hospital, were also described. Finally, we proposed a roadmap for the future development of virtual health in the country. Results and conclusions: The integration of virtual healthcare into the existing healthcare system can enhance patient experiences, improve outcomes, and contribute to the overall well-being of the population. However, careful planning, collaboration, and investment are essential to overcome the challenges and ensure the successful implementation and sustainability of virtual healthcare in the country.

Challenges experienced by health care providers working in both hospital and home-based palliative care units in Dhaka city: A multi-center based cross-sectional study

BACKGROUND

Palliative care is paramount in the modern clinical field worldwide. However, in Bangladesh, its acceptance is limited compared to other related sectors, despite the country suffering from a huge burden of life-limiting diseases. Besides, PC teams and their approach to care are entirely different from the conventional clinical approach. This study aimed to explore the challenges faced by healthcare providers working in the palliative care unit in Bangladesh, including all groups.

DESIGN

This was a cross-sectional descriptive survey involving palliative care providers.

METHODS

A self-administered pre-tested questionnaire was used for data collection. Data was analyzed using descriptive statistics and Chi-square at p <0.05.

RESULT

The mean age of the respondents was 33.59 ± 8.05 years, and barely most (82.5%) had served for 7-9 years. More than half (51%) of doctors and 31% of nurses claimed patient agitation as a challenge. Almost all groups of respondents exhibit ethical dilemma as a barrier, although a significant relationship was found between professional level and ethical dilemma. More than half of doctors (51%), 41.5% of nurses, and 29.5% of PCA-ward staff mentioned the lack of telemedicine facilities as a challenge. Nearly half (47.1%) of doctors and nurses claimed that patients' families had made patient care difficult, on the other hand, PCA-ward staff (70%) group ignorance of family did the same thing. Opioid phobia of other health professionals restricted the growth mentioned by the majority of all four groups of respondents. A significant relationship was found between limited dose formulation and experience of HPs (p<0.07). At the institutional level, 93.3% of nursing staff agreed that the lack of supporting staff was a drawback. A significant relationship was also found between the type of institution and the lack of a support system to conduct home-based care (p<0.002). Moreover, the majority (83.3%) of PCA-WS exhibit a lack of career development opportunities (p<0.001) as a barrier, besides, more than 7 out of 10 doctors (7.2%) felt social discrimination as a challenge(p<0.001).

CONCLUSION

Introducing new concepts comes with obstacles, but proper planning and awareness can make it necessary. Incorporating it into primary healthcare can create new job opportunities and increase familiarity among the general population. Training healthcare professionals on opioid handling can also increase its acceptance.

Robotics and AI into healthcare from the perspective of European regulation: who is responsible for medical malpractice?

The integration of robotics and artificial intelligence into medical practice is radically revolutionising patient care. This fusion of advanced technologies with healthcare offers a number of significant benefits, including more precise diagnoses, personalised treatments and improved health data management. However, it is critical to address very carefully the medico-legal challenges associated with this progress. The responsibilities between the different players concerned in medical liability cases are not yet clearly defined, especially when artificial intelligence is involved in the decision-making process. Complexity increases when technology intervenes between a person's action and the result, making it difficult for the patient to prove harm or negligence. In addition, there is the risk of an unfair distribution of blame between physicians and healthcare institutions. The analysis of European legislation highlights the critical issues related to the attribution of legal personality to autonomous robots and the recognition of strict liability for medical doctors and healthcare institutions. Although European legislation has helped to standardise the rules on this issue, some questions remain unresolved. We argue that specific laws are needed to address the issue of medical liability in cases where robotics and artificial intelligence are used in healthcare.

Disability 4.0: bioethical considerations on the use of embodied artificial intelligence

Robotics and artificial intelligence have marked the beginning of a new era in the care and integration of people with disabilities, helping to promote their independence, autonomy and social participation. In this area, bioethical reflection assumes a key role at anthropological, ethical, legal and socio-political levels. However, there is currently a substantial diversity of opinions and ethical arguments, as well as a lack of consensus on the use of assistive robots, while the focus remains predominantly on the usability of products. The article presents a bioethical analysis that highlights the risk arising from using embodied artificial intelligence according to a functionalist model. Failure to recognize disability as the result of a complex interplay between health, personal and situational factors could result in potential damage to the intrinsic dignity of the person and human relations with healthcare workers. Furthermore, the danger of discrimination in accessing these new technologies is highlighted, emphasizing the need for an ethical approach that considers the social and moral implications of implementing embodied AI in the field of rehabilitation.

Artificial Intelligence Needs Data: Challenges Accessing Italian Databases to Train AI

Population biobanks are an increasingly important infrastructure to support research and will be a much-needed resource in the delivery of personalised medicine. Artificial intelligence (AI) systems can process and cross-link very large amounts of data quickly and be used not only for improving research power but also for helping with complex diagnosis and prediction of diseases based on health profiles. AI, therefore, potentially has a critical role to play in personalised medicine, and biobanks can provide a lot of the necessary baseline data related to healthy populations that will enable the development of AI tools. To develop these tools, access to personal data, and in particular, sensitive data, is required. Such data could be accessed from biobanks. Biobanks are a valuable resource for research but accessing and using the data contained within such biobanks raise a host of legal, ethical, and social issues (ELSI). This includes the appropriate consent to manage the collection, storage, use, and sharing of samples and data, and appropriate governance models that provide oversight of secondary use of samples and data. Biobanks have developed new consent models and governance tools to enable access that address some of these ELSI-related issues. In this paper, we consider whether such governance frameworks can enable access to biobank data to develop AI. As Italy has one of the most restrictive regulatory frameworks on the use of genetic data in Europe, we examine the regulatory framework in Italy. We also look at the proposed changes under the European Health Data Space (EHDS). We conclude by arguing that currently, regulatory frameworks are misaligned and unless addressed, accessing data within Italian biobanks to train AI will be severely limited.

A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China

PURPOSE

To explore the feasibility and validity of machine learning models in determining causality in medical malpractice cases and to try to increase the scientificity and reliability of identification opinions.

METHODS

We collected 13,245 written judgments from PKULAW.COM, a public database. 963 cases were included after the initial screening. 21 medical and ten patient factors were selected as characteristic variables by summarising previous literature and cases. Random Forest, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) were used to establish prediction models of causality for the two data sets, respectively. Finally, the optimal model is obtained by hyperparameter tuning of the six models.

RESULTS

We built three real data set models and three virtual data set models by three algorithms, and their confusion matrices differed. XGBoost performed best in the real data set, with a model accuracy of 66%. In the virtual data set, the performance of XGBoost and LightGBM was basically the same, and the model accuracy rate was 80%. The overall accuracy of external verification was 72.7%.

CONCLUSIONS

The optimal model of this study is expected to predict the causality accurately.

Evidence-based position paper on Physical and Rehabilitation Medicine (PRM) professional practice on telerehabilitation. The European PRM position (UEMS PRM Section)

INTRODUCTION

The evidence on the utility and effectiveness of rehabilitation interventions delivered via telerehabilitation is growing rapidly. Telerehabilitation is expected to have a key role in rehabilitation in the future.

AIM

The aim of this evidence-based position paper (EBPP) is to improve PRM physicians' professional practice in telerehabilitation to be delivered to improve functioning and to reduce activity limitations and/or participation restrictions in individuals with a variety of disabling health conditions.

METHODS

To produce recommendations for PRM physicians on telerehabilitation, a systematic review of the literature and a consensus procedure by means of a Delphi process have been performed involving the delegates of all European countries represented in the UEMS PRM Section.

RESULTS

The systematic literature review is reported together with the 32 recommendations resulting from the Delphi procedure.

CONCLUSIONS

It is recommended that PRM physicians deliver rehabilitation services remotely, via digital means or using communication technologies to eligible individuals, whenever required and feasible in a variety of health conditions in favor of the patient and his/her family, based on evidence of effectiveness and in compliance with relevant regulations. This EBPP represents the official position of the European Union through the UEMS PRM Section and designates the professional role of PRM physicians in telerehabilitation.

Building community engagement with caregivers through online interaction and a salutogenic approach in a period of isolation

Background

Informal caregivers are essential figures that deal with the effects of dependence in the elderly. However, they suffer from poorer health-related quality of life, particularly regarding mental health. Social support is crucial, but this was suspended or dramatically reduced during the Covid-19 pandemic. Salutogenesis theory explores the contributing factors for the promotion and maintenance of health. Considering all these, we offered caregivers the opportunity to join a participatory project aimed at creating communication spaces where they could share experiences, think together about potential solutions, and explore which salutogenic actions they used in their daily basis and how they had changed during Covid-19 restrictions.

Methods

We used a qualitative methodology with a socio-constructivist and phenomenological approach and purposive sampling. We organized two focus groups consisting of online semi-structured discussions with seven participants in total. Conversations were videotaped and transcribed and we conducted content thematic analyses using the NVivo software.

Results

Caregiving in our setting are primarily women with high levels of education that do not always feel comfortable with this load because it interferes with their personal and professional lives. The pandemic increased caregivers feelings of loneliness, resignation, and burden, directly affecting their mental health. Furthermore, the disappearance of prevention programs and the difficulties to access healthcare services produced negative consequences on the already fragile elderly and their family caregivers.

Conclusion

The pandemic and its restrictions exacerbated the problematics affecting informal caregivers. Although these people are aware of their situation and have valued knowledge of how to improve their health, they cannot always put it into practice. We call policymakers to reframe interventions aimed at caregivers by introducing the voice of the community in the planning and to rethink the management of vulnerable people and their carers in other potential health crises.

Rewiring care delivery through Digital Therapeutics (DTx): a machine learning-enhanced assessment and development (M-LEAD) framework

BACKGROUND

Digital transformation has sparked profound change in the healthcare sector through the development of innovative digital technologies. Digital Therapeutics offer an innovative approach to disease management and treatment. Care delivery is increasingly patient-centered, data-driven, and based on real-time information. These technological innovations can lead to better patient outcomes and support for healthcare professionals, also considering resource scarcity. As these digital technologies continue to evolve, the healthcare field must be ready to integrate them into processes to take advantage of their benefits. This study aims to develop a framework for the development and assessment of Digital Therapeutics.

METHODS

The study was conducted relying on a mixed methodology. 338 studies about Digital Therapeutics resulting from a systematic literature review were analyzed using descriptive statistics through RStudio. Machine learning algorithms were applied to analyze variables and find patterns in the data. The results of these analytical analyses were summarized in a framework qualitatively tested and validated through expert opinion elicitation.

RESULTS

The research provides M-LEAD, a Machine Learning-Enhanced Assessment and Development framework that recommends best practices for developing and assessing Digital Therapeutics. The framework takes as input Digital Therapeutics characteristics, regulatory aspects, study purpose, and assessment domains. The framework produces as outputs recommendations to design the Digital Therapeutics study characteristics.

CONCLUSIONS

The framework constitutes the first step toward standardized guidelines for the development and assessment of Digital Therapeutics. The results may support manufacturers and inform decision-makers of the relevant results of the Digital Therapeutics assessment.

Knowledge, Attitudes, and Practices of Parents in the Use of Antibiotics: A Case Study in a Mexican Indigenous Community

The rise and spread of antibiotic-resistant bacteria have become a global health problem. At the community level, bacterial resistance has been linked to antibiotic misuse practices. These practices are related to social factors such as education level, poverty, ethnicity, and use of traditional medicine. Through a survey, this study aims to analyse the knowledge, attitudes, and practices (KAP) of antimicrobial use, in an indigenous community in the south of Oaxaca, Mexico. It was observed that the population had a low socioeconomic profile, poor access to healthcare services, low academic level, little knowledge of antibiotics, the use of traditional medicine, and proper attitudes and practices regarding antibiotics use. Therefore, social factors are related to bacterial resistance only if they make the population prone to the use of antimicrobials. Lack of medical access and cultural factors drives this population to use ancestral alternatives such traditional medicine to treat conditions that in other contexts could be treated with antibiotics. This is an example of how the population can reduce the consumption of antimicrobials in infections if they have a reliable alternative that improves their symptoms.

Application possibilities of artificial intelligence in facial vascularized composite allotransplantation-a narrative review

Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgical treatment of patients with severe facial disfigurements. While conventional reconstructive strategies were previously considered the goldstandard for patients with devastating facial trauma, FVCA has demonstrated promising short- and long-term outcomes. Yet, there remain several obstacles that complicate the integration of FVCA procedures into the standard workflow for facial trauma patients. Artificial intelligence (AI) has been shown to provide targeted and resource-effective solutions for persisting clinical challenges in various specialties. However, there is a paucity of studies elucidating the combination of FVCA and AI to overcome such hurdles. Here, we delineate the application possibilities of AI in the field of FVCA and discuss the use of AI technology for FVCA outcome simulation, diagnosis and prediction of rejection episodes, and malignancy screening. This line of research may serve as a fundament for future studies linking these two revolutionary biotechnologies.

Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers

Artificial intelligence (AI) and machine learning (ML) span multiple disciplines, including the medico-legal sciences, also with reference to the concept of disease and disability. In this context, the International Classification of Diseases, Injuries, and Causes of Death (ICD) is a standard for the classification of diseases and related problems developed by the World Health Organization (WHO), and it represents a valid tool for statistical and epidemiological studies. Indeed, the International Classification of Functioning, Disability, and Health (ICF) is outlined as a classification that aims to describe the state of health of people in relation to their existential spheres (social, family, work). This paper lays the foundations for proposing an operating model for the use of AI in the assessment of impairments with the aim of making the information system as homogeneous as possible, starting from the main coding systems of the reference pathologies and functional damages. Providing a scientific basis for the understanding and study of health, as well as establishing a common language for the assessment of disability in its various meanings through AI systems, will allow for the improvement and standardization of communication between the various expert users.

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic's effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.