Illuminating the dark spaces of healthcare with ambient intelligence.

Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.

LifeTime and improving European healthcare through cell-based interceptive medicine.

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.

Artificial intelligence in healthcare: why not apply the medico-legal method starting with the Collingridge dilemma?

Technology has greatly influenced and radically changed human life, from communication to creativity and from productivity to entertainment. The authors, starting from considerations concerning the implementation of new technologies with a strong impact on people's everyday lives, take up Collingridge's dilemma and relate it to the application of AI in healthcare. Collingridge's dilemma is an ethical and epistemological problem concerning the relationship between technology and society which involves two approaches. The proactive approach and socio-technological experimentation taken into account in the dilemma are discussed, the former taking health technology assessment (HTA) processes as a reference and the latter the AI studies conducted so far. As a possible prevention of the critical issues raised, the use of the medico-legal method is proposed, which classically lies between the prevention of possible adverse events and the reconstruction of how these occurred.The authors believe that this methodology, adopted as a European guideline in the medico-legal field for the assessment of medical liability, can be adapted to AI applied to the healthcare scenario and used for the assessment of liability issues. The topic deserves further investigation and will certainly be taken into consideration as a possible key to future scenarios.

Development and validation of the Trust in Multidimensional Healthcare Systems Scale (TIMHSS).

CONTEXT: The COVID-19 pandemic has reignited a commitment from the health policy and health services research communities to rebuilding trust in healthcare and created a renewed appetite for measures of trust for system monitoring and evaluation. The aim of the present paper was to develop a multidimensional measure of trust in healthcare that: (1) Is responsive to the conceptual and methodological limitations of existing measures; (2) Can be used to identify systemic explanations for lower levels of trust in equity-deserving populations; (3) Can be used to design and evaluate interventions aiming to (re)build trust. METHODS: We conducted a 2021 review of existing measures of trust in healthcare, 72 qualitative interviews (Aug-Dec 2021; oversampling for equity-deserving populations), an expert review consensus process (Oct 2021), and factor analyses and validation testing based on two waves of survey data (Nov 2021, n = 694; Jan-Feb 2022, n = 740 respectively). FINDINGS: We present the Trust in Multidimensional Healthcare Systems Scale (TIMHSS); a 38-item correlated three-factor measure of trust in doctors, policies, and the system. Measurement of invariance tests suggest that the TIMHSS can also be reliably administered to diverse populations. CONCLUSIONS: This global measure of trust in healthcare can be used to measure trust over time at a population level, or used within specific subpopulations, to inform interventions to (re)build trust. It can also be used within a clinical setting to provide a stronger evidence base for associations between trust and therapeutic outcomes.

Using the consolidated Framework for Implementation Research to integrate innovation recipients' perspectives into the implementation of a digital version of the spinal cord injury health maintenance tool: a qualitative analysis.

BACKGROUND: Despite advances in managing secondary health complications after spinal cord injury (SCI), challenges remain in developing targeted community health strategies. In response, the SCI Health Maintenance Tool (SCI-HMT) was developed between 2018 and 2023 in NSW, Australia to support people with SCI and their general practitioners (GPs) to promote better community self-management. Successful implementation of innovations such as the SCI-HMT are determined by a range of contextual factors, including the perspectives of the innovation recipients for whom the innovation is intended to benefit, who are rarely included in the implementation process. During the digitizing of the booklet version of the SCI-HMT into a website and App, we used the Consolidated Framework for Implementation Research (CFIR) as a tool to guide collection and analysis of qualitative data from a range of innovation recipients to promote equity and to inform actionable findings designed to improve the implementation of the SCI-HMT. METHODS: Data from twenty-three innovation recipients in the development phase of the SCI-HMT were coded to the five CFIR domains to inform a semi-structured interview guide. This interview guide was used to prospectively explore the barriers and facilitators to planned implementation of the digital SCI-HMT with six health professionals and four people with SCI. A team including researchers and innovation recipients then interpreted these data to produce a reflective statement matched to each domain. Each reflective statement prefaced an actionable finding, defined as alterations that can be made to a program to improve its adoption into practice. RESULTS: Five reflective statements synthesizing all participant data and linked to an actionable finding to improve the implementation plan were created. Using the CFIR to guide our research emphasized how partnership is the key theme connecting all implementation facilitators, for example ensuring that the tone, scope, content and presentation of the SCI-HMT balanced the needs of innovation recipients alongside the provision of evidence-based clinical information. CONCLUSIONS: Understanding recipient perspectives is an essential contextual factor to consider when developing implementation strategies for healthcare innovations. The revised CFIR provided an effective, systematic method to understand, integrate and value recipient perspectives in the development of an implementation strategy for the SCI-HMT. TRIAL REGISTRATION: N/A.

Potential of Large Language Models in Health Care: Delphi Study.

BACKGROUND: A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications. OBJECTIVE: The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care. METHODS: We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items. RESULTS: The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data. CONCLUSIONS: Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

And what about today? Burden and support needs of adolescent childhood cancer survivors in long-term follow-up care-A qualitative content analysis.

PURPOSE: Childhood cancer affects approximately 2000 children annually in Germany, and there is an increasing number of long-term childhood cancer survivors. Due to developmental tasks, adolescent survivors in long-term follow-up (LTFU) care may face specific challenges and perceive different burden due to their disease. The current study explored (a) the impact of cancer and burden regarding survivorship and (b) supportive needs of adolescent childhood cancer survivors in LTFU care. METHODS: Semistructured qualitative interviews were conducted with 18 adolescent childhood cancer survivors in LTFU care aged 14-18 years (average age 16.4 years). Interviews were transcribed verbatim and analysed using content analysis. RESULTS: Based on the exploratory research questions, two key categories were generated: (1) The impact and burden on survivors' lives during LTFU care and (2) support needs of adolescent childhood cancer survivors in LTFU care. The four subcategories that emerged regarding the impact and burden on survivors' lives during LTFU care were (1) physical consequences, (2) cognitive impairments, (3) difficulties in social interactions, and (4) psychosocial burden. Additionally, two subcategories, (1) practical and (2) emotional support needs of adolescent childhood cancer survivors were identified. CONCLUSIONS: Our results indicate that childhood cancer influences adolescent survivors' life in a negative way even many years after the end of treatment. Furthermore, parents seem to play a crucial role in the survivorship experience of childhood cancer survivors, as they remain keep responsible for most cancer-related concerns even during LTFU care, causing adolescents to persist in the child role. A family systemic approach to care is suggested to facilitate development-specific tasks and to enable adolescents to become autonomous adults. Still, the question remains as to who in the health care system could take over the family systemic tasks.

PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber-Physical Systems.

This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber-physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient's Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle's Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient's gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient's BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time.

CUSCO: An Unobtrusive Custom Secure Audio-Visual Recording System for Ambient Assisted Living.

The ubiquity of digital technology has facilitated detailed recording of human behaviour. Ambient technology has been used to capture behaviours in a broad range of applications ranging from healthcare and monitoring to assessment of cooperative work. However, existing systems often face challenges in terms of autonomy, usability, and privacy. This paper presents a portable, easy-to-use and privacy-preserving system for capturing behavioural signals unobtrusively in home or in office settings. The system focuses on the capture of audio, video, and depth imaging. It is based on a device built on a small-factor platform that incorporates ambient sensors which can be integrated with the audio and depth video hardware for multimodal behaviour tracking. The system can be accessed remotely and integrated into a network of sensors. Data are encrypted in real time to ensure safety and privacy. We illustrate uses of the device in two different settings, namely, a healthy-ageing IoT application, where the device is used in conjunction with a range of IoT sensors to monitor an older person's mental well-being at home, and a healthcare communication quality assessment application, where the device is used to capture a patient-clinician interaction for consultation quality appraisal. CUSCO can automatically detect active speakers, extract acoustic features, record video and depth streams, and recognise emotions and cognitive impairment with promising accuracy.

Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?

Importance: Since the introduction of ChatGPT in late 2022, generative artificial intelligence (genAI) has elicited enormous enthusiasm and serious concerns. Observations: History has shown that general purpose technologies often fail to deliver their promised benefits for many years ("the productivity paradox of information technology"). Health care has several attributes that make the successful deployment of new technologies even more difficult than in other industries; these have challenged prior efforts to implement AI and electronic health records. However, genAI has unique properties that may shorten the usual lag between implementation and productivity and/or quality gains in health care. Moreover, the health care ecosystem has evolved to make it more receptive to genAI, and many health care organizations are poised to implement the complementary innovations in culture, leadership, workforce, and workflow often needed for digital innovations to flourish. Conclusions and Relevance: The ability of genAI to rapidly improve and the capacity of organizations to implement complementary innovations that allow IT tools to reach their potential are more advanced than in the past; thus, genAI is capable of delivering meaningful improvements in health care more rapidly than was the case with previous technologies.

Advanced Selection Methodologies for DNAzymes in Sensing and Healthcare Applications.

DNAzymes have been widely explored owing to their excellent catalytic activity in a broad range of applications, notably in sensing and biomedical devices. These newly discovered applications have built high hopes for designing novel catalytic DNAzymes. However, the selection of efficient DNAzymes is a challenging process but one that is of crucial importance. Initially, systemic evolution of ligands by exponential enrichment (SELEX) was a labor-intensive and time-consuming process, but recent advances have accelerated the automated generation of DNAzyme molecules. This review summarizes recent advances in SELEX that improve the affinity and specificity of DNAzymes. The thriving generation of new DNAzymes is expected to open the door to several healthcare applications. Therefore, a significant portion of this review is dedicated to various biological applications of DNAzymes, such as sensing, therapeutics, and nanodevices. In addition, discussion is further extended to the barriers encountered for the real-life application of these DNAzymes to provide a foundation for future research.

Reducing ethnic and racial disparities by improving undertreatment, control, and engagement in blood pressure management with health information technology (REDUCE-BP) hybrid effectiveness-implementation pragmatic trial: Rationale and design.

BACKGROUND: While racial/ethnic disparities in blood pressure control are documented, few interventions have successfully reduced these gaps. Under-prescribing, lack of treatment intensification, and suboptimal follow-up care are thought to be central contributors. Electronic health record (EHR) tools may help address these barriers and may be enhanced with behavioral science techniques. OBJECTIVE: To evaluate the impact of a multicomponent behaviorally-informed EHR-based intervention on blood pressure control. TRIAL DESIGN: Reducing Ethnic and racial Disparities by improving Undertreatment, Control, and Engagement in Blood Pressure management with health information technology (REDUCE-BP) (NCT05030467) is a two-arm cluster-randomized hybrid type 1 pragmatic trial in a large multi-ethnic health care system. Twenty-four clinics (>350 primary care providers [PCPs] and >10,000 eligible patients) are assigned to either multi-component EHR-based intervention or usual care. Intervention clinic PCPs will receive several EHR tools designed to reduce disparities delivered at different points, including a: (1) dashboard of all patients visible upon logging on to the EHR displaying blood pressure control by race/ethnicity compared to their PCP peers and (2) set of tools in an individual patient's chart containing decision support to encourage treatment intensification, ordering home blood pressure measurement, interventions to address health-related social needs, default text for note documentation, and enhanced patient education materials. The primary outcome is patient-level change in systolic blood pressure over 12 months between arms; secondary outcomes include changes in disparities and other clinical outcomes. CONCLUSION: REDUCE-BP will provide important insights into whether an EHR-based intervention designed using behavioral science can improve hypertension control and reduce disparities.

Patient Factors Associated With Use of Adult Primary Care and Virtual Visits During the COVID-19 Pandemic.

BACKGROUND: The delivery of adult primary care (APC) shifted from predominately in-person to modes of virtual care during the COVID-19 pandemic. It is unclear how these shifts impacted the likelihood of APC use during the pandemic, or how patient characteristics may be associated with the use of virtual care. METHODS: A retrospective cohort study using person-month level datasets from 3 geographically disparate integrated health care systems was conducted for the observation period of January 1, 2020, through June 30, 2021. We estimated a 2-stage model, first adjusting for patient-level sociodemographic, clinical, and cost-sharing factors, using generalized estimating equations with a logit distribution, along with a second-stage multinomial generalized estimating equations model that included an inverse propensity score treatment weight to adjust for the likelihood of APC use. Factors associated with APC use and virtual care use were separately assessed for the 3 sites. RESULTS: Included in the first-stage models were datasets with total person-months of 7,055,549, 11,014,430, and 4,176,934, respectively. Older age, female sex, greater comorbidity, and Black race and Hispanic ethnicity were associated with higher likelihood of any APC use in any month; measures of greater patient cost-sharing were associated with a lower likelihood. Conditional on APC use, older age, and adults identifying as Black, Asian, or Hispanic were less likely to use virtual care. CONCLUSIONS: As the transition in health care continues to evolve, our findings suggest that to ensure vulnerable patient groups receive high quality health care, outreach interventions to reduce barriers to virtual care use may be warranted.

Barriers to Adoption of a Secure Text Messaging System: a Qualitative Study of Practicing Clinicians.

BACKGROUND: Secure text messaging systems (STMS) offer HIPAA-compliant text messaging and mobile phone call functionalities that are more efficient than traditional paging. Although some studies associate improved provider satisfaction and healthcare delivery with STMS use, healthcare organizations continue to struggle with achieving widespread and sustained STMS adoption. OBJECTIVE: To understand the barriers to adoption of an STMS among physicians and advanced practice providers (APPs). DESIGN: We qualitatively analyzed free-text comments that clinicians (physicians and APPs) across a large healthcare organization offered on a survey about STMS perceptions. PARTICIPANTS: A total of 1110 clinicians who provided a free-text comment in response to one of four open-ended survey questions. APPROACH: Data were analyzed using a grounded theory approach and constant comparative method to characterize responses and identify themes. KEY RESULTS: The overall survey response rate was 20.5% (n = 1254). Clinicians familiar with the STMS frequently believed the STMS was unnecessary (existing tools worked well enough) and would overburden them with more communications. They were frustrated that the STMS app had to be downloaded onto their personal mobile device and that it drained their battery. Ambiguity regarding who was reachable in the app led to missed messages and drove distrust of the STMS. Clinicians saw the exclusion of other care team members (e.g., nurses) from the STMS as problematic; however, some clinicians at hospitals with expanded STMS access complained of excessive messages. Secondhand reports of several of these barriers prevented new users from downloading the app and contributed to ongoing low use. CONCLUSIONS: Clinicians are reluctant to adopt an STMS that does not offer a clear and trustworthy communication benefit to offset its potential burden and intrusiveness. Our findings can be incorporated into STMS implementation strategies that maximize active users by targeting and mitigating barriers to adoption.

An implementation science approach to the systematic study of access to gynecologic cancer care.

INTRODUCTION: Barriers to access to cancer care are profoundly threatening to patients with gynecologic malignancies. Implementation science focuses on empirical investigation of factors influencing delivery of clinical best practices, as well as interventions designed to improve delivery of evidence-based care. We outline one prominent framework for conducting implementation research and discuss its application to improving access to gynecologic cancer care. METHODS: Literature on the use of the Consolidated Framework for Implementation Research (CFIR) was reviewed. Delivery of cytoreductive surgery for advanced ovarian carcinoma was selected as an illustrative case of an evidence-based intervention (EBI) in gynecologic oncology. CFIR domains were applied to the context of cytoreductive surgical care, highlighting examples of empirically-assessable determinants of care delivery. RESULTS: CFIR domains include Innovation, Inner Setting, Outer Setting, Individuals, and Implementation Process. "Innovation" relates to characteristics of the surgical intervention itself; "Inner Setting" relates to the environment in which surgery is delivered. "Outer Setting" refers to the broader care environment influencing the Inner Setting. "Individuals" highlights attributes of persons directly involved in care delivery, and "Implementation Process" focuses on integration of the Innovation within the Inner Setting. CONCLUSIONS: Prioritization of implementation science methods in the study of access to gynecologic cancer care will help ensure that patients are able to utilize interventions with the greatest prospect of benefiting them.