Addressing the Health Needs of an Aging America: New Opportunities for Evidence-Based Policy Solutions

This report systematically maps research findings to policy proposals intended to improve the health of the elderly. The study identified promising evidence-based policies, like those supporting prevention and care coordination, as well as areas where the research evidence is strong but policy activity is low, such as patient self-management and palliative care. Future work of the Stern Center will focus on these topics as well as long-term care financing, the health care workforce, and the role of family caregivers

Status and recommendations of technological and data-driven innovations in cancer care:Focus group study

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects. Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations

Exploring Interactive Survivorship Plans: Patient Perceived Value, Acceptance and Usability Evaluation of an Online Breast Cancer Survivorship Tool

Introduction: Having recently been discharged from the hospital, several breast cancer survivors find themselves unable to adjust to the transition and take charge of their own health, away from the confines of the hospital. With the rapid advancement in treatment methods and techniques, the rate of breast cancer survivors has grown exponentially. It is crucial to provide adequate means to support cancer survivors in an active manner. This includes regular monitoring for recurrence (or occurrence of new cancers), handling any related and non-related comorbidities, provide recommendations for preventive care as well as dealing with any long term side effects from the treatment. The specific objective of this research is to design and develop a personalized web application to support breast cancer survivors after treatment (chemotherapy and/or radiation), as they deal with post-treatment challenges, such as comorbidities and side-effects of treatment. Methodology: I used an iterative design and development approach to produce a web application for breast cancer survivors that help them monitor their quality of life, provide them with personalized alerts based on their breast cancer related medical history as well as timely alerts, to remind them of follow up visits. Finally, I utilized a combination of qualitative methodology (thematic analysis), as well as user task analysis to assess the acceptability and usability of the prototype among a group of breast cancer survivors. User feedback was gathered on their perceived value of the application, and any user-interface issues that may hinder the overall usability among lay users were identified. Results: Fifteen breast cancer survivors participated in the acceptability and usability testing of the prototype. The prototype was found to be perceived as unique and valuable among the participants, in its ability to utilize personalized breast cancer related medical history. The application’s portability and capability of organizing their entire breast cancer related medical history as well as the at-home tracking of various quality of life indicators were perceived to be valuable features. The application had an overall high usability, however certain sections of the application, such as viewing observations history were not as intuitive to locate. While participants appreciated the visual and graphical elements of the website, the overall experience of the application would benefit from incorporating some sociable elements that exhibit positive re-enforcement within the end user and provide a friendlier and fun experience. Conclusion: The results of the study showcase the need to provide more personalized tools and resources to breast cancer survivors to support them in self-management after completion of treatment. It also demonstrates the ability to integrate breast cancer survivorship plans from diverse providers and paves the way to add further value-added features in consumer health applications, such as personal decision support. The feedback received from end-users will be used in order to further improve the prototype and address any existing user-interface issues. It is hoped that making such tools more accessible could help in engaging survivors to play an active role in managing their health and also encourage shared-decision making with their providers

Artificial Intelligence and Patient-Centered Decision-Making

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient

Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare:State-of-the-Art and Future Prospects

In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare

The Use of Artificial Intelligence (AI) in the Radiology Field: What Is the State of Doctor–Patient Communication in Cancer Diagnosis?

Simple Summary Artificial Intelligence (AI) has been increasingly used in radiology to improve diagnostic procedures over the past decades. The application of AI at the time of cancer diagnosis also creates challenges in the way doctors should communicate the use of AI to patients. The present systematic review deals with the patient's psycho-cognitive perspective on AI and the interpersonal skills between patients and physicians when AI is implemented in cancer diagnosis communication. Evidence from the retrieved studies pointed out that the use of AI in radiology is negatively associated with patient trust in AI and patient-centered communication in cancer disease. Background: In the past decade, interest in applying Artificial Intelligence (AI) in radiology to improve diagnostic procedures increased. AI has potential benefits spanning all steps of the imaging chain, from the prescription of diagnostic tests to the communication of test reports. The use of AI in the field of radiology also poses challenges in doctor-patient communication at the time of the diagnosis. This systematic review focuses on the patient role and the interpersonal skills between patients and physicians when AI is implemented in cancer diagnosis communication. Methods: A systematic search was conducted on PubMed, Embase, Medline, Scopus, and PsycNet from 1990 to 2021. The search terms were: ("artificial intelligence" or "intelligence machine") and "communication" "radiology" and "oncology diagnosis". The PRISMA guidelines were followed. Results: 517 records were identified, and 5 papers met the inclusion criteria and were analyzed. Most of the articles emphasized the success of the technological support of AI in radiology at the expense of patient trust in AI and patient-centered communication in cancer disease. Practical implications and future guidelines were discussed according to the results. Conclusions: AI has proven to be beneficial in helping clinicians with diagnosis. Future research may improve patients' trust through adequate information about the advantageous use of AI and an increase in medical compliance with adequate training on doctor-patient diagnosis communication

P5 eHealth: An Agenda for the Health Technologies of the Future

This open access volume focuses on the development of a P5 eHealth, or better, a methodological resource for developing the health technologies of the future, based on patients’ personal characteristics and needs as the fundamental guidelines for design. It provides practical guidelines and evidence based examples on how to design, implement, use and elevate new technologies for healthcare to support the management of incurable, chronic conditions. The volume further discusses the criticalities of eHealth, why it is difficult to employ eHealth from an organizational point of view or why patients do not always accept the technology, and how eHealth interventions can be improved in the future. By dealing with the state-of-the-art in eHealth technologies, this volume is of great interest to researchers in the field of physical and mental healthcare, psychologists, stakeholders and policymakers as well as technology developers working in the healthcare sector

Development of an updated, standardized, patient-centered outcome set for lung cancer

BACKGROUND: In 2016, the International Consortium for Health Outcomes Measurement (ICHOM) defined an international consensus recommendation of the most important outcomes for lung cancer patients. The European Health Outcomes Observatory (H2O) initiative aimed to develop an updated patient-centered core outcome set (COS) for lung cancer, to capture the patient perspective of the impact of lung cancer and (novel) treatments using a combination of patient-reported outcome (PRO) instruments and clinical data as a means to drive value-based health-care. MATERIAL AND METHODS: An international, expert team of patient representatives, multidisciplinary healthcare professionals, academic researchers and pharmaceutical industry representatives (n = 17) reviewed potential outcomes generated through literature review. A broader group of patients/patient representatives (n = 31), healthcare professionals / academic researchers (n = 83), pharmaceutical industry representatives (n = 26), and health authority representatives (n = 6) participated in a Delphi study. In two survey rounds, participants scored the relevance of outcomes from a preliminary list. The threshold for consensus was defined as ≥ 70 % of participants scoring an outcome as 'highly relevant'. In concluding consensus-meeting rounds, the expert multidisciplinary team finalized the COS. RESULTS: The preliminary list defined by the core group consisted of 102 outcomes and was prioritized in the Delphi procedure to 64. The final lung cancer COS includes: 1) case-mix factors (n = 27); 2) PROs related to health-related quality of life (HRQoL) (n = 25); 3) clinical outcomes (n = 12). Patient-reported symptoms beyond domains included in the ICHOM lung cancer set in 2016 were insomnia, nausea, vomiting, anxiety, depression, lack of appetite, gastric problems, constipation, diarrhoea, dysphagia, and haemoptysis. CONCLUSIONS: We will implement the lung cancer COS in Europe within the H2O initiative by collecting the outcomes through a combination of clinician-reported measures and PRO measures. The COS will support the adoption and reporting of lung cancer measures in a standardized way across Europe and empower patients with lung cancer to better manage their health care