A decade of digital medicine innovation

Feasibility of in-home monitoring for people with glaucoma: the I-TRAC mixed-methods study

Background

Glaucoma is a chronic disease of the optic nerve and a leading cause of severe visual loss in the UK. Once patients have been diagnosed, they need regular monitoring at hospital eye services. Recent advances in technology mean patients with glaucoma can now monitor their disease at home. This could be more convenient for patients and potentially reduce costs and increase capacity for the NHS. However, it is uncertain whether self-monitoring would be acceptable or possible for patients with glaucoma.

Objectives

The objectives were to: identify which patients are most appropriate for home monitoring; understand views of key stakeholders (patients, clinicians, researchers) on whether home glaucoma monitoring is feasible and acceptable; develop a conceptual framework for the economic evaluation of home glaucoma monitoring; and explore the need for and provide evidence on the design of a future study to evaluate the clinical and cost-effectiveness of digital technologies for home monitoring of glaucoma.

Design

In-home Tracking of glaucoma: Reliability, Acceptability, and Cost (I-TRAC) was a multiphase mixed-methods feasibility study with key components informed by theoretical and conceptual frameworks.

Setting

Expert glaucoma specialists in the UK recruited through professional glaucoma societies; study site staff and patient participants recruited through three UK hospital eye services (England, Scotland, Northern Ireland); and UK research teams recruited though existing networks.

Intervention

Home tonometer that measures intraocular pressure and a tablet computer with a visual function application. Patients were asked to use the technology weekly for 12 weeks.

Results

Forty-two patients were recruited. Retention and completion of follow-up procedures was successful, with 95% (n = 40) completing the 3-month follow-up clinic visits. Adherence to the interventions was generally high [adherence to both devices (i.e. ≥ 80% adherence) was 55%]. Overall, patients and healthcare professionals were cautiously optimistic about the acceptability of digital technologies for home monitoring of patients with glaucoma. While most clinicians were supportive of the potential advantages glaucoma home monitoring could offer, concerns about the technologies (e.g. reliability and potential to miss disease progression) and how they would fit into routine care need to be addressed. Additionally, clarity is required on defining the ideal population for this intervention. Plans for how to evaluate value for money in a future study were also identified. However, the study also highlighted several unknowns relating to core components of a future evaluative study that require addressing before progression to a definitive effectiveness trial.

Limitations

The main limitation relates to our sample and its generalisability, for example, the over-representation of educated persons of white ethnicity who were generally experienced with technology and research motivated.

Conclusions

The In-home Tracking of glaucoma: Reliability, Acceptability, and Cost study has demonstrated 'cautious optimism' when considering patients' and healthcare professionals' views on the acceptability of digital technologies for home monitoring of patients with glaucoma. However, the study also highlighted several unknowns relating to the research question and design of a future evaluative study that require addressing before progression to a randomised controlled trial.

Future work

Further research is required to determine the appropriate population (i.e. low vs. high risk of progression) and further refine the intervention components and delivery for planning of future evaluation studies.

Study registration

This study is registered as Research Registry #6213.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR129248) and is published in full in Health Technology Assessment; Vol. 28, No. 44. See the NIHR Funding and Awards website for further award information.

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.

Face the Future-Artificial Intelligence in Oral and Maxillofacial Surgery

Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) "OMFS" OR "oral and maxillofacial" OR "oral and maxillofacial surgery" OR "oral surgery" AND (2) "AI" OR "artificial intelligence". The search format was tailored to each database's syntax. To find pertinent material, each retrieved article and systematic review's reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding.

Failure to scale in digital agronomy: An analysis of site-specific nutrient management decision-support tools in developing countries

While many have extolled the potential impacts of digital advisory services for smallholder agriculture, the evidence for sustained uptake of such tools remains limited. This paper utilizes a survey of tool developers and researchers, as well as a systematic meta-analysis of prior studies, to assess the extent and challenges of scaling decision support tools for site-specific soil nutrient management (SSNM-DST) across smallholder farming systems, where "scaling" is defined as a significant increase in tool usage beyond pilot levels. Our evaluation draws on relevant literature, expert opinion and apps available in different repositories. Despite their acclaimed yield benefits, we find that SSNM-DST have struggled to reach scale over the last few decades and, with strong heterogeneity in adoption among intended stakeholders and tools. For example, the log odds of a SSNM-DST reaching 5-10 % of the target farmers compared with reaching none, decreases by ∼200% when a technical problem is stated as a reason for the tools' failure to be used at scale. We find a similar decrease in odds ratios when technical, socioeconomic, policy, and R&D constraints were identified as barriers to scaling by national extension and private systems. Meta-regression analysis indicates that the response ratio of using SSNM-DST over Farmer Fertilizer Practice (FFP) varies by non-tool related covariates, such as initial crop yield potential under FFP, current and past crop types, acidity class of the soil, temperature and rainfall regimes, and the amount of input under FFP. In general, the SSNM-DST have moved one step forward compared with the traditional 'blanket' fertilizer recommendation by accounting for in-field heterogeneities in soil and crop characteristics, while remaining undifferentiated in terms of demographic and socioeconomic heterogeneities among users, which potentially constrains adoption at scale. The SSNM-DSTs possess reasonable applicability and can be labeled 'ready' from purely scientific viewpoints, although their readiness for system-level uptake at scale remains limited, especially where socio-technical and institutional constraints are prevalent.

Evaluation of acceptance, attitude, and knowledge towards artificial intelligence and its application from the point of view of physicians and nurses: A provincial survey study in Iran: A cross-sectional descriptive-analytical study

Background and Aims

The prospect of using artificial intelligence (AI) in healthcare is bright and promising, and its use can have a significant impact on cost reduction and decrease the possibility of error and negligence among healthcare workers. This study aims to investigate the level of knowledge, attitude, and acceptance among Iranian physicians and nurses.

Methods

This cross-sectional descriptive-analytical study was conducted in eight public university hospitals located in Tehran on 400 physicians and nurses. To conduct the study, convenient sampling was used with the help of researcher-made questionnaires. Statistical analysis was done by SPSS 21 The mean and standard deviation and Chi-square and Fisher's exact tests were used.

Results

In this study, the level of knowledge among the research subjects was average (14.66 ± 4.53), the level of their attitude toward AI was relatively favorable (47.81 ± 6.74), and their level of acceptance of AI was average (103.19 ± 13.70). Moreover, from the participant's perspective, AI in medicine is most widely used in increasing the accuracy of diagnostic tests (86.5%), identifying drug interactions (82.75%), and helping to analyze medical tests and imaging (80%). There was a statistically significant relationship between the variable of acceptance of AI and the participant's level of education (p = 0.028), participation in an AI training course (p = 0.022), and the hospital department where they worked (p < 0.001).

Conclusion

In this study, both the knowledge and the acceptance of the participants towards AI were proved to be at an average level and the attitude towards AI was relatively favorable, which is in contrast with the very rapid and inevitable expansion of AI. Although our participants were aware of the growing use of AI in medicine, they had a cautious attitude toward this.

Embedding the Patient-Citizen Perspective into an Operational Framework for the Development and the Introduction of New Technologies in Rehabilitation Care: The Smart&Touch-ID Model

To date, at least 2.41 billion people with Non-Communicable Diseases (NCDs) are in need of rehabilitation. Rehabilitation care through innovative technologies is the ideal candidate to reach all people with NCDs in need. To obtain these innovative solutions available in the public health system calls for a rigorous multidimensional evaluation that, with an articulated approach, is carried out through the Health Technology Assessment (HTA) methodology. In this context, the aim of the present paper is to illustrate how the Smart&TouchID (STID) model addresses the need to incorporate patients' evaluations into a multidimensional technology assessment framework by presenting a feasibility study of model application with regard to the rehabilitation experiences of people living with NCDs. After sketching out the STID model's vision and operational process, preliminary evidence on the experiences and attitudes of patients and citizens on rehabilitation care will be described and discussed, showing how they operate, enabling the co-design of technological solutions with a multi-stakeholder approach. Implications for public health are discussed including the view on the STID model as a tool to be integrated into public health governance strategies aimed at tuning the agenda-setting of innovation in rehabilitation care through a participatory methodology.

System Integrated Digital Empowering and teleRehabilitation to promote patient Activation and well-Being in chronic disabilities: A usability and acceptability study

Introduction

Telerehabilitation systems represent a promising way for the management of chronic disability, delivering technology-enabled rehabilitation outside the hospital setting. However, usability and acceptability assessment with users represents a critical starting point when using digital healthcare solutions. This study aims at evaluating the user experience with a Telerehabilitation system (SIDERA^∧B) from the end-user side.

Methods

SIDERA^∧B consists of an asynchronous delivery of rehabilitation activities through multimedia digital contents and tele-monitoring of vital parameters with technological devices for individualized, home-based management of chronic conditions. Usability (with the System Usability Scale, SUS) and acceptability (using the Technology Acceptance Model, TAM - and The Service User Technology Acceptance Questionnaire, SUTAQ) data were analyzed from the dataset of the SIDERA^∧B project (N = 112 patients with Chronic Heart Failure, Parkinson's Disease and Chronic Obstructive Pulmonary Disease). The possible influence of five external factors (i.e., technological expertise, education, sex, age, and level of disability) on TAM domains was tested using Spearman's Correlation analysis.

Results

Results showed a satisfactory level of technological usability (SUS Median = 77.5) and good scores in usability and learnability SUS subdomains (mean scores > 2.5). Regarding technological acceptability, participants showed high scores (Median > 4) in "Behavioral Intention", "Perceived Usefulness", and "Perceived Ease of Use" TAM domains. Finally, results from the SUTAQ scale highlighted that the SIDERA^∧B system obtained optimal scores in all domains, especially in "Increased accessibility," "Care personnel concerns," and "Satisfaction." Age (rho = -0.291, p = 0.002) and disability level (WHODAS Total score: rho = -0.218, p = 0.021) were the two external factors inversely associated with the Perceived Ease of Use.

Discussion

The age of digital transformation requires everyone to understand, accept and master the changes affecting modern-day healthcare. The usability and acceptability of the SIDERA^∧B system were high across all end-users, despite the medium-low level of the technological expertise of the sample. These findings support the efficiency and the suitability of these digital solutions in the modern digital age transition of rehabilitation from inside to outside the clinic.

Digital Orthopedics: The Future Developments of Orthopedic Surgery

Digital medicine is a new type of medical treatment that applies modern digital information technologies to entire medical procedures [...].

Rehabilomics: A state-of-the-art review of framework, application, and future considerations

Rehabilomics is an important research framework that allows omics research built upon rehabilitation practice, especially in function evaluation, outcome prediction, and individualized rehabilitation. In the field of rehabilomics, biomarkers can serve as objectively measured indicators for body functioning, so as to complement the International Classification of Functioning, Disability, and Health (ICF) assessment. Studies on traumatic brain injury (TBI), stroke, and Parkinson's disease have shown that biomarkers (such as serum markers, MRI, and digital signals derived from sensors) are correlated with diagnosis, disease severity, and prognosis. Rehabilomics also examines a wide range of individual biological characteristics in order to develop personalized rehabilitation programs. Secondary prevention and rehabilitation of stroke have already adopted a rehabilomic approach to individualize treatment programs. Mechanisms of non-pharmacological therapies are expected to be unveiled in light of rehabilomics research. When formulating the research plan, learning from established databases is recommended and a multidisciplinary collaborative team is warranted. Although still in its infancy, the advancement and incorporation of rehabilomics has the potential to make a significant impact on public health.

Artificial intelligence for precision medicine in autoimmune liver disease

Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.

The use of E-health during the COVID-19 pandemic: a case study in China's Hubei province

Globally, the use of e-health has accelerated dramatically during the coronavirus pandemic. Based on both quantitative and qualitative data collected in China's Hubei province (i.e. the first epicentre of COVID-19), this research explores how the pandemic influences the practices of e-health from the perspective of users. Through analysis of 1,033 surveys and 14 in-depth interviews, we find that e-health has played a crucial role in residents' healthcare during the COVID-19 pandemic. Certain external factors influence the choice of digital health, including the high risk of infection outdoors, the shutting down of transport systems, and dysfunctional healthcare facilities that neglect non-COVID-19 patients' clinical demands. Against this backdrop, we argue digital health acts as a functional equivalent to traditional medical treatment and has largely satisfied patients and users in the crisis period. Additionally, the COVID-19 pandemic has unintentionally sped up the diffusion of digital medicine over the long term as respondents expressed their willingness to continue use of e-health in the post-COVID-19 phase. However, we assert that despite the increasing use of e-health, it cannot fully substitute traditional offline treatment. Thus, we suggest a combination of online and offline healthcare will be more commonly practiced in the future.

Medical Students and Their Perceptions of Digital Medicine: a Question of Gender?

Digital technologies play an essential role in the medical sector of today and the future. In a cross-sectional online survey at a German medical university, male students more frequently reported keeping themselves informed about digital medicine outside of their studies across all clinical years of study. While female students self-assessed their knowledge in different fields of digital medicine as worse than their male peers in the first clinical years of study, no more gender differences could be found towards the final year. However, students of both genders showed a strong desire for further education on the topic of digital medicine.

Enhancing Resilience in Family Caregivers Using an mHealth App

BACKGROUND

We previously developed a mobile health (mHealth) app (Roadmap) to promote the resilience of family caregivers during the acute phases of care in patients undergoing hematopoietic cell transplantation (HCT).

OBJECTIVE

This study explored users' perspectives on the uptake of Roadmap's multicomponent features and the app's utility in promoting resilience.

METHODS

Fifteen participants were randomized to the full version of the app that included resilience-building activities and the other 15 were randomized to the control version that included a limited view of the app (i.e., without any resilience-building activities). They were instructed to use the app for 120 days. Semistructured qualitative interviews were then conducted with users as part of an ongoing, larger Roadmap study (NCT04094844). During the interview, caregiver participants were asked about their overall experiences with the app, frequency of use, features used, facilitators of and barriers to use, and their perspectives on its utility in promoting resilience. Data were professionally transcribed, coded, and categorized through content analysis.

RESULTS

Interviews were conducted with 30 participants, which included 23 females and 7 males. The median age of the population was 58 years (range, 23-82). The four main themes that emerged included app use, ease of use, user experiences, and ability to foster resilience. The subthemes identified related to facilitators (convenience and not harmful), barriers (caregiver burden and being too overwhelmed during the acute phases of HCT care), resilience (optimism/positivity and self-care), and app design improvements (personalization and notifications/reminders).

CONCLUSION

The qualitative evaluation provided insights into which components were utilized and how one, or a combination of the multicomponent features, may be enhancing users' experiences. Lessons learned suggest that the Roadmap app contributed to promoting resilience during the acute phases of HCT care. Nonetheless, features that provided enhanced personalization may further improve longer-term engagement.

Smartphone camera oximetry in an induced hypoxemia study

Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO₂) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health. Towards this goal, we performed the first clinical development validation on a smartphone camera-based SpO₂ sensing system using a varied fraction of inspired oxygen (FiO₂) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG methods on a wider range of SpO₂ values (70–100%) than prior studies (85–100%). We built a deep learning model using this data to demonstrate an overall MAE = 5.00% SpO₂ while identifying positive cases of low SpO₂ < 90% with 81% sensitivity and 79% specificity. We also provide the data in open-source format, so that others may build on this work.

Weakly Supervised Captioning of Ultrasound Images

Medical image captioning models generate text to describe the semantic contents of an image, aiding the non-experts in understanding and interpretation. We propose a weakly-supervised approach to improve the performance of image captioning models on small image-text datasets by leveraging a large anatomically-labelled image classification dataset. Our method generates pseudo-captions (weak labels) for caption-less but anatomically-labelled (class-labelled) images using an encoder-decoder sequence-to-sequence model. The augmented dataset is used to train an image-captioning model in a weakly supervised learning manner. For fetal ultrasound, we demonstrate that the proposed augmentation approach outperforms the baseline on semantics and syntax-based metrics, with nearly twice as much improvement in value on BLEU-1 and ROUGE-L. Moreover, we observe that superior models are trained with the proposed data augmentation, when compared with the existing regularization techniques. This work allows seamless automatic annotation of images that lack human-prepared descriptive captions for training image-captioning models. Using pseudo-captions in the training data is particularly useful for medical image captioning when significant time and effort of medical experts is required to obtain real image captions.

Assessing the Clinical Robustness of Digital Health Startups: Cross-sectional Observational Analysis

BACKGROUND

The digital health sector has experienced rapid growth over the past decade. However, health care technology stakeholders lack a comprehensive understanding of clinical robustness and claims across the industry.

OBJECTIVE

This analysis aimed to examine the clinical robustness and public claims made by digital health companies.

METHODS

A cross-sectional observational analysis was conducted using company data from the Rock Health Digital Health Venture Funding Database, the US Food and Drug Administration, and the US National Library of Medicine. Companies were included if they sell products targeting the prevention, diagnosis, or treatment phases of the care continuum. Clinical robustness was defined using regulatory filings and clinical trials completed by each company. Public claims data included clinical, economic, and engagement claims regarding product outcomes made by each company on its website.

RESULTS

A total of 224 digital health companies with an average age of 7.7 years were included in our cohort. Average clinical robustness was 2.5 (1.8 clinical trials and 0.8 regulatory filings) with a median score of 1. Ninety-eight (44%) companies had a clinical robustness score of 0, while 45 (20%) companies had a clinical robustness score of 5 or more. The average number of public claims was 1.3 (0.5 clinical, 0.4 economic, and 0.4 engagement); the median number of claims was 1. No correlation was observed between clinical robustness and number of clinical claims (r2=0.02), clinical robustness and total funding (r2=0.08), or clinical robustness and company age (r2=0.18).

CONCLUSIONS

Many digital health companies have a low level of clinical robustness and do not make many claims as measured by regulatory filings, clinical trials, and public data shared online. Companies and customers may benefit from investing in greater clinical validation efforts.

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

BACKGROUND

Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery.

METHODS

We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases.

RESULTS

Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs.

CONCLUSIONS

We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Digital approaches to enhancing community engagement in clinical trials

Digital approaches are increasingly common in clinical trial recruitment, retention, analysis, and dissemination. Community engagement processes have contributed to the successful implementation of clinical trials and are crucial in enhancing equity in trials. However, few studies focus on how digital approaches can be implemented to enhance community engagement in clinical trials. This narrative review examines three key areas for digital approaches to deepen community engagement in clinical trials-the use of digital technology for trial processes to decentralize trials, digital crowdsourcing to develop trial components, and digital qualitative research methods. We highlight how digital approaches enhanced community engagement through a greater diversity of participants, and deepened community engagement through the decentralization of research processes. We discuss new possibilities that digital technologies offer for community engagement, and highlight potential strengths, weaknesses, and practical considerations. We argue that strengthening community engagement using a digital approach can enhance equity and improve health outcomes.

Effects and tolerability of exercise therapy modality on cardiorespiratory fitness in lung cancer: a randomized controlled trial

BACKGROUND

Poor cardiorespiratory fitness (CRF) is a cardinal feature of post-treatment primary lung cancer. The most effective exercise therapy regimen to improve CRF has not been determined.

METHODS

In this parallel-group factorial randomized controlled trial, lung cancer survivors with poor CRF (below age-sex sedentary values) were randomly allocated to receive 48 consecutive supervised sessions thrice weekly of (i) aerobic training (AT)-cycle ergometry at 55% to >95% of peak oxygen consumption (VO₂ peak); (ii) resistance training (RT)-lower and upper extremity exercises at 50-85% of maximal strength; (iii) combination training (CT)-AT plus RT; or (iv) stretching attention control (AC) for 16 weeks. The primary endpoint was change in CRF (VO₂ peak, mL O₂ ·kg^-1 ·min^-1 ). Secondary endpoints were body composition, muscle strength, patient-reported outcomes, tolerability (relative dose intensity of exercise), and safety. Analysis of covariance determined change in primary and secondary endpoints from baseline to post-intervention (Week 17) with adjustment for baseline values of the endpoint and other relevant clinical covariates.

RESULTS

Ninety patients (65 ± 9 years; 66% female) were randomized (AT, n = 24; RT, n = 23; CT, n = 20; and AC, n = 23) of the planned n = 160. No serious adverse events were observed. For the overall cohort, the lost-to-follow-up rate was 10%. Mean attendance was ≥75% in all groups. In intention-to-treat analysis, VO₂ peak increased 1.1 mL O₂ ·kg^-1 ·min^-1 [95% confidence interval (CI): 0.0, 2.2, P = 0.04] and 1.4 mL O₂ ·kg^-1 ·min^-1 (95% CI: 0.2, 2.5, P = 0.02) in AT and CT, respectively, compared with AC. There was no difference in VO₂ peak change between RT and AC (-0.1 mL O₂ ·kg^-1 ·min^-1 , 95% CI: -1.2, 1.0, P = 0.88). Favourable improvements in maximal strength and body composition were observed in RT and CT groups compared with AT and AC groups (Ps < 0.05). No between-group changes were observed for any patient-reported outcomes. Relative dose intensity of exercise was lower in RT and CT compared with AT (Ps < 0.05).

CONCLUSIONS

In the context of a smaller than planned sample size, AT and CT significantly improved VO₂ peak in lung cancer survivors; however, the tolerability-to-benefit ratio was superior for AT and hence may be the preferred modality to target impaired CRF in post-treatment lung cancer survivors.

Labeling self-tracked menstrual health records with hidden semi-Markov models

Globally, millions of women track their menstrual cycle and fertility via smartphone-based health apps, generating multivariate time series with frequent missing data. To leverage this type of data for studies of fertility or studies of the effect of the menstrual cycle on symptoms and diseases, it is critical to have methods for identifying reproductive events, such as ovulation, pregnancy losses or births. Here, we present a hierarchical approach relying on hidden semi-Markov models that adapts to changes in tracking behavior, explicitly captures variable and state dependent missingness, allows for variables of different type, and quantifies uncertainty. The accuracy on simulated data reaches 98% with no missing data and 90% with realistic missingness. On our partially labeled real-world time series, the accuracy reaches 93%. Our method also accurately predicts cycle length by learning user characteristics. Its implementation is publicly available (HiddenSemiMarkov R package) and transferable to any health time series, including self-reported symptoms and occasional tests.

Systems of conductive skin for power transfer in clinical applications

The primary aim of this article is to review the clinical challenges related to the supply of power in implanted left ventricular assist devices (LVADs) by means of transcutaneous drivelines. In effect of that, we present the preventive measures and post-operative protocols that are regularly employed to address the leading problem of driveline infections. Due to the lack of reliable wireless solutions for power transfer in LVADs, the development of new driveline configurations remains at the forefront of different strategies that aim to power LVADs in a less destructive manner. To this end, skin damage and breach formation around transcutaneous LVAD drivelines represent key challenges before improving the current standard of care. For this reason, we assess recent strategies on the surface functionalization of LVAD drivelines, which aim to limit the incidence of driveline infection by directing the responses of the skin tissue. Moreover, we propose a class of power transfer systems that could leverage the ability of skin tissue to effectively heal short diameter wounds. In this direction, we employed a novel method to generate thin conductive wires of controllable surface topography with the potential to minimize skin disruption and eliminate the problem of driveline infections. Our initial results suggest the viability of the small diameter wires for the investigation of new power transfer systems for LVADs. Overall, this review uniquely compiles a diverse number of topics with the aim to instigate new research ventures on the design of power transfer systems for IMDs, and specifically LVADs.

Defining the "New Normal" in Primary Care

Health care organizations in the United States have transformed at an unprecedented rate since March 2020 due to COVID-19, most notably with a shift to telemedicine. Despite rapidly adapting health care delivery in light of new safety considerations and a shifting insurance landscape, primary care offices across the country are facing drastic decreases in revenue and potential bankruptcy. To survive, primary care's adaptations will need to go beyond virtual versions of traditional office visits. Primary care is faced with a chance to redefine what it means to care for and support patients wherever they are. This opportunity to shape the "new normal" is a critical step for primary care to meet its full potential to lead a paradigm shift to patient-centered health care reform in America during this time when we need it most.

Technological readiness and implementation of genomic-driven precision medicine for complex diseases

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.

Digital Technologies and Data Science as Health Enablers: An Outline of Appealing Promises and Compelling Ethical, Legal, and Social Challenges

Digital technologies and data science have laid down the promise to revolutionize healthcare by transforming the way health and disease are analyzed and managed in the future. Digital health applications in healthcare include telemedicine, electronic health records, wearable, implantable, injectable and ingestible digital medical devices, health mobile apps as well as the application of artificial intelligence and machine learning algorithms to medical and public health prognosis and decision-making. As is often the case with technological advancement, progress in digital health raises compelling ethical, legal, and social implications (ELSI). This article aims to succinctly map relevant ELSI of the digital health field. The issues of patient autonomy; assessment, value attribution, and validation of health innovation; equity and trustworthiness in healthcare; professional roles and skills and data protection and security are highlighted against the backdrop of the risks of dehumanization of care, the limitations of machine learning-based decision-making and, ultimately, the future contours of human interaction in medicine and public health. The running theme to this article is the underlying tension between the promises of digital health and its many challenges, which is heightened by the contrasting pace of scientific progress and the timed responses provided by law and ethics. Digital applications can prove to be valuable allies for human skills in medicine and public health. Similarly, ethics and the law can be interpreted and perceived as more than obstacles, but also promoters of fairness, inclusiveness, creativity and innovation in health.

A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

Teaching and Learning Core Values in General Practice/Family Medicine: A Narrative Review

General Practice/Family Medicine (GP/FM) is a key discipline within primary health care and so by extension for the whole health care system. An essential condition for effective GP/FM care is a work force that is highly qualified. As society is changing rapidly, a revision of the GP/FM definition is ongoing, in addition to a recent movement of identifying related core values. In this paper, we want to give an overview on how these new paths and perspectives are currently reflected in GP/FM teaching and training. We selected four core values that fit in with possible future visions: person-centered care, continuity of care, cooperation in care, and community-oriented care. By a narrative review, we observed that GP/FM education toward core values is often built around overarching topics. Teaching and learning take place in specific contexts, most of all through placements within communities, primary care settings, or hospital wards. Mixed teaching- and training methods are used combining knowledge, skills, and attitude. Furthermore, collaboration with other health professionals and peers is stressed, in addition to the importance of role models, a holistic focus and the involvement of patients. Since these core values are important within GP/FM and rather few studies on the educational aspects and learning tools were found we advocate encouraging each other more to share good practices, certainly the innovative ones specifically related to GP/FM.

Constraints in Clinical Cardiology and Personalized Medicine: Interrelated Concepts in Clinical Cardiology

Systems biology is established as an integrative computational analysis methodology with practical and theoretical applications in clinical cardiology. The integration of genetic and molecular components of a disease produces interacting networks, modules and phenotypes with clinical applications in complex cardiovascular entities. With the holistic principle of systems biology, some of the features of complexity and natural progression of cardiac diseases are approached and explained. Two important interrelated holistic concepts of systems biology are described; the emerging field of personalized medicine and the constraint-based thinking with downward causation. Constraints in cardiovascular diseases embrace three scientific fields related to clinical cardiology: biological and medical constraints; constraints due to limitations of current technology; and constraints of general resources for better medical coverage. Systems healthcare and personalized medicine are connected to the related scientific fields of: ethics and legal status; data integration; taxonomic revisions; policy decisions; and organization of human genomic data.

Artificial intelligence in cancer research: learning at different levels of data granularity

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Leveraging Technology to Improve Diabetes Care in Pregnancy

Pregnant women with diabetes are at higher risk of adverse outcomes. Prevention of such outcomes depends on strict glycemic control, which is difficult to achieve and maintain. A variety of technologies exist to aid in diabetes management for nonpregnant patients. However, adapting such tools to meet the demands of pregnancy presents multiple challenges. This article reviews the key attributes digital technologies must offer to best support diabetes management during pregnancy, as well as some digital tools developed specifically to meet this need. Despite the opportunities digital health tools present to improve the care of people with diabetes, in the absence of robust data and large research studies, the ability to apply such technologies to diabetes in pregnancy will remain imperfect.

Advancing Oncology Nursing Practice Through the Adoption of Patient Monitoring Digital Tools

OBJECTIVE

The objective of this paper is to describe the use of oncology digital symptom monitoring and patient self-management coaching tools, how nurses and nurse practitioners (NPs) can optimize their use as an adjunct in improving oncology care and discuss issues and strategies needed for adoption within a variety of clinical settings.

DATA SOURCES

A review of the research literature regarding digital health in oncology symptom management in PubMed provided the foundation for this paper.

CONCLUSION

Digital symptom monitoring technology provides a variety of opportunities for oncology nurses and NPs to efficiently extend and improve symptom management in multiple settings including cancer patients at home between clinic visits, at clinic visits, and during inpatient stays. Digital monitoring and patient engagement make possible frequent symptom assessments, just-in-time personalized self-management reinforcement, and judiciously alert nurses and NPs about key times for follow-up with patients supported with evidenced-based guidelines. Oncology nurses at all levels have the opportunity to be leaders in the adoption and expansion of digital tools to enhance their practice.

IMPLICATIONS FOR NURSING PRACTICE

Oncology nurses and NPs can lead practice changes that improve patient outcomes through understanding and shaping the use of digital tools.

Big Data Approaches in Heart Failure Research

PURPOSE OF REVIEW

The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on "omics" and clinical data. We address some limitations of this data, as well as their future potential.

RECENT FINDINGS

Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

A multicenter clinical trial to assess the efficacy of the digital therapeutics for essential hypertension: Rationale and design of the HERB-DH1 trial

Digital therapeutics is a new approach to treat hypertension via using software programs such as smartphone apps and/or device algorithms. We develop a HERB system-new interactive smartphone app (HERB Mobile) with web-based patient management console (HERB Console)-to lower blood pressure (BP) based on an algorithm that helps users to promote lifestyle modifications in conjunction with medically validated non-pharmacological interventions. The app can assess the personalities, behavior characteristics, and hypertension determinants of each patient with hypertension to provide adequate guidance. To demonstrate the efficacy of the system, we designed a randomized, controlled, multicenter, open-label trial "HERB-DH1 (HERB digital hypertension 1)" to assess the efficacy of HERB system in patients with essential hypertension. The authors allocate patients to the intervention group (HERB system + standard lifestyle modification) or to the control group (standard lifestyle modification alone). In the intervention group, we provide the HERB Mobile for patients and the HERB Console for their primary physicians for 24 weeks. Both groups are instructed for standard lifestyle modifications based on the current recommendations in the Japanese Society of Hypertension 2019 guideline. The primary outcome is the mean change from baseline to 12 weeks in 24-hour systolic BP measured by ambulatory BP monitoring. We started this study in December of 2019, and the trial results will be expected in early 2021. We believe that this trial enables us to verify the efficacy of the HERB system in patients with essential hypertension.

Improving Patients' Medication Adherence and Outcomes in Nonhospital Settings Through eHealth: Systematic Review of Randomized Controlled Trials

Background

Electronic health (eHealth) refers to the use of information and communication technologies for health. It plays an increasingly important role in patients’ medication management.

Objective

To assess evidence on (1) whether eHealth for patients’ medication management can improve drug adherence and health outcomes in nonhospital settings and (2) which eHealth functions are commonly used and are effective in improving drug adherence.

Methods

We searched for randomized controlled trials (RCTs) on PubMed, MEDLINE, CINAHL, EMBASE, EmCare, ProQuest, Scopus, Web of Science, ScienceDirect, and IEEE Xplore, in addition to other published sources between 2000 and 2018. We evaluated the studies against the primary outcome measure of medication adherence and multiple secondary health care outcome measures relating to adverse events, quality of life, patient satisfaction, and health expenditure. The quality of the studies included was assessed using the Cochrane Collaboration’s Risk of Bias (RoB) tool.

Results

Our initial search yielded 9909 records, and 24 studies met the selection criteria. Of these, 13 indicated improvement in medication adherence at the significance level of P<.1 and 2 indicated an improved quality of life at the significance level of P<.01. The top 3 functions that were employed included mechanisms to communicate with caregivers, monitoring health features, and reminders and alerts. eHealth functions of providing information and education, and dispensing treatment and administration support tended to favor improved medication adherence outcomes (Fisher exact test, P=.02). There were differences in the characteristics of the study population, intervention design, functionality provided, reporting adherence, and outcome measures among the included studies. RoB assessment items, including blinding of outcome assessment and method for allocation concealment, were not explicitly reported in a large number of studies.

Conclusions

All the studies included were designed for patient home-based care application and provided a mechanism to communicate with caregivers. A targeted study population such as older patients should be considered to maximize the public health impact on the self-management of diseases.

Trial Registration

International Prospective Register of Systematic Reviews (PROSPERO) CRD42018096627; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=96627

Effects of an Innovative Telerehabilitation Intervention for People With Parkinson's Disease on Quality of Life, Motor, and Non-motor Abilities

Parkinson's disease (PD) often leads to multifactorial motor and non-motor disabilities with resultant social restrictions. Continuity of care in this pathology, including a tailored home rehabilitation, is crucial to improve or maintain the quality of life for patients. The aim of this multicenter study was to test in a pilot sample of PD patients the efficiency and efficacy of the Human Empowerment Aging and Disability (HEAD) program. The virtual reality HEAD program was administered in two consecutive phases: (1) in clinic (ClinicHEAD, 12 45-minutes sessions, 3 sessions/week); (2) at home (HomeHEAD, 60 45-minutes sessions, 5 sessions/week). Thirty-one PD outpatients were enrolled [mean age (SD) = 66.84 (9.13)]. All patients performed ClinicHEAD, and after allocation (ratio 1:2) were assigned to the HomeHEAD or the Usual Care (UC) group. Motor, cognitive and behavioral outcome measures were assessed at enrollment (T0), at hospital discharge (T1), at 4 (T2) and 7 (T3) months after baseline. After ClinicHEAD (T1 vs. T0 comparison) a significant (p < 0.05) improvement in functional mobility, balance, upper limb mobility, global cognitive function, memory, quality of life and psychological well-being was observed. After the HomeHEAD intervention there was an additional enhancement for upper limb mobility. At T3 follow-up, the UC group that did not continue the HEAD program at home showed a worsening with respect to the HomeHEAD group in balance and functional mobility. Furthermore, in the HomeHEAD group, a positive association was observed between adherence, mental and physical health (SF-12). A trend was also registered between adherence and positive affect. The digital health patient-tailored rehabilitation program resulted in improving motor and non-motor abilities and quality of life in clinical setting, enhancing the motor function in telerehabilitation at home, and maintaining the non-motor abilities and quality of life at follow-up. In the near future, people with PD can be supported also at home with individualized rehabilitation strategies for a better quality of life and wellbeing along with lower costs for society.

Venous Thromboembolism Research Priorities: A Scientific Statement From the American Heart Association and the International Society on Thrombosis and Haemostasis

Venous thromboembolism is a major cause of morbidity and mortality. The impact of the US Surgeon General's The Surgeon General's Call to Action to Prevent Deep Vein Thrombosis and Pulmonary Embolism in 2008 has been lower than expected given the public health impact of this disease. This scientific statement highlights future research priorities in venous thromboembolism, developed by experts and a crowdsourcing survey across 16 scientific organizations. At the fundamental research level (T0), researchers need to identify pathobiological causative mechanisms for the 50% of patients with unprovoked venous thromboembolism and to better understand mechanisms that differentiate hemostasis from thrombosis. At the human level (T1), new methods for diagnosing, treating, and preventing venous thromboembolism will allow tailoring of diagnostic and therapeutic approaches to individuals. At the patient level (T2), research efforts are required to understand how foundational evidence impacts care of patients (eg, biomarkers). New treatments, such as catheter-based therapies, require further testing to identify which patients are most likely to experience benefit. At the practice level (T3), translating evidence into practice remains challenging. Areas of overuse and underuse will require evidence-based tools to improve care delivery. At the community and population level (T4), public awareness campaigns need thorough impact assessment. Large population-based cohort studies can elucidate the biological and environmental underpinnings of venous thromboembolism and its complications. To achieve these goals, funding agencies and training programs must support a new generation of scientists and clinicians who work in multidisciplinary teams to solve the pressing public health problem of venous thromboembolism.

Decision analysis and reinforcement learning in surgical decision-making

BACKGROUND

Surgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.

METHODS

Cochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.

RESULTS

Requirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.

CONCLUSION

Traditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

SAFE@HOME: Digital health platform facilitating a new care path for women at increased risk of preeclampsia - A case-control study

OBJECTIVE

In women at risk of developing preeclampsia, we evaluated the use of a digital health platform for telemonitoring blood pressure and symptoms combined with a minimal antenatal visit schedule.

STUDY DESIGN

A case-control study for women with chronic hypertension, history of preeclampsia, or maternal cardiac or kidney disease. A care path was designed with reduced visits enhanced with a digital platform (SAFE@HOME) for daily blood pressure and symptom monitoring starting from 16 weeks of gestation. Home-measurements were monitored in-hospital by obstetric professionals, taking actions upon alarming results. This prospective SAFE@HOME group was compared to a retrospective control group managed without self-monitoring.

MAIN OUTCOME MEASURES

Primary: healthcare consumption (number of antenatal visits, ultrasounds, admissions and diagnostics), user experiences of the platform. Secondary: maternal and perinatal outcomes.

RESULTS

Baseline characteristics of the SAFE@HOME (n = 103) and control group (n = 133) were comparable. In the SAFE@HOME group, antenatal visits (mean 13.7 vs 16.0, p < 0.001) and ultrasounds (6.3 vs 7.4, p = 0.005) were lower compared to the control group. Admissions for hypertension or suspected preeclampsia were significantly fewer in the SAFE@HOME group (2.9% versus 13.5%, p = 0.004). Telemonitoring participants were highly satisfied using the platform. No differences were observed for maternal and perinatal outcomes.

CONCLUSIONS

Our care path including blood pressure telemonitoring for women at risk of preeclampsia allows fewer antenatal visits, ultrasounds and hypertension-related admissions. We observed no differences in perinatal outcomes. These results suggest that telemonitoring of blood pressure is feasible in a high-risk pregnant population and has the potential to profoundly change antenatal care.

Venous thromboembolism research priorities: A scientific statement from the American Heart Association and the International Society on Thrombosis and Haemostasis

Venous thromboembolism (VTE) is a major cause of morbidity and mortality. The impact of the Surgeon General's Call to Action in 2008 has been lower than expected given the public health impact of this disease. This scientific statement highlights future research priorities in VTE, developed by experts and a crowdsourcing survey across 16 scientific organizations. At the fundamental research level (T0), researchers need to identify pathobiologic causative mechanisms for the 50% of patients with unprovoked VTE and better understand mechanisms that differentiate hemostasis from thrombosis. At the human level (T1), new methods for diagnosing, treating, and preventing VTE will allow tailoring of diagnostic and therapeutic approaches to individuals. At the patient level (T2), research efforts are required to understand how foundational evidence impacts care of patients (eg, biomarkers). New treatments, such as catheter-based therapies, require further testing to identify which patients are most likely to experience benefit. At the practice level (T3), translating evidence into practice remains challenging. Areas of overuse and underuse will require evidence-based tools to improve care delivery. At the community and population level (T4), public awareness campaigns need thorough impact assessment. Large population-based cohort studies can elucidate the biologic and environmental underpinings of VTE and its complications. To achieve these goals, funding agencies and training programs must support a new generation of scientists and clinicians who work in multidisciplinary teams to solve the pressing public health problem of VTE.

The Role of Digital Devices in Dentistry: Clinical Trends and Scientific Evidences

In recent years, digital technologies have significantlychanged the clinical approach to medicine and dentistry. Innovative operative techniques and restorative materials have paved the way to a significant active boost towards full digital workflows. Particularly, novel dental materials offer undeniable advantages such as optimal mechanical resistance, excellent esthetic and optical properties, and reliable accuracy and precision, widening the clinical scenario and allowing for innovative and less invasive restorative solutions.

Computerization and Digital Workflow in Medicine: Focus on Digital Dentistry

Continuously evolving technologies make dentistry one of the most advanced sectors in the field of medicine. The digital improvements in recent years have brought many advantages to clinicians and patients, including reduced working times, lower costs and increased efficiency of performance. Some of the most important digital technologies introduced in the dental filed are cone beam computer tomography (CBCT) scan, Computer Aided Design/Computer Aided Manufacturing (CAD-CAM) systems, and intraoral scanners. All of these allow faster and more accurate rehabilitations, with the opportunity of pre-simulation of the final treatment. The evolution of computer science has brought significant advantages in the medical and dental fields, making the diagnosis and execution of even complex treatments, such as implantology and bone reconstruction, possible. The digital world is trying to supplant the traditional analog workflow, and over time, with the further advance of technologies, it should tend to be the treatment of choice of our patients.

Intelligent, Autonomous Machines in Surgery

Surgeons perform two primary tasks: operating and engaging patients and caregivers in shared decision-making. Human dexterity and decision-making are biologically limited. Intelligent, autonomous machines have the potential to augment or replace surgeons. Rather than regarding this possibility with denial, ire, or indifference, surgeons should understand and steer these technologies. Closer examination of surgical innovations and lessons learned from the automotive industry can inform this process. Innovations in minimally invasive surgery and surgical decision-making follow classic S-shaped curves with three phases: (1) introduction of a new technology, (2) achievement of a performance advantage relative to existing standards, and (3) arrival at a performance plateau, followed by replacement with an innovation featuring greater machine autonomy and less human influence. There is currently no level I evidence demonstrating improved patient outcomes using intelligent, autonomous machines for performing operations or surgical decision-making tasks. History suggests that if such evidence emerges and if the machines are cost effective, then they will augment or replace humans, initially for simple, common, rote tasks under close human supervision and later for complex tasks with minimal human supervision. This process poses ethical challenges in assigning liability for errors, matching decisions to patient values, and displacing human workers, but may allow surgeons to spend less time gathering and analyzing data and more time interacting with patients and tending to urgent, critical-and potentially more valuable-aspects of patient care. Surgeons should steer these technologies toward optimal patient care and net social benefit using the uniquely human traits of creativity, altruism, and moral deliberation.

Healthcare Digitalization and Pay-For-Performance Incentives in Smart Hospital Project Financing

This study aims to explore the impact of healthcare digitalization on smart hospital project financing (PF) fostered by pay-for-performance (P4P) incentives. Digital platforms are a technology-enabled business model that facilitates exchanges between interacting agents. They represent a bridging link among disconnected nodes, improving the scalable value of networks. Application to healthcare public-private partnerships (PPPs) is significant due to the consistency of digital platforms with health issues and the complexity of the stakeholder's interaction. In infrastructural PPPs, public and private players cooperate, usually following PF patterns. This relationship is complemented by digitized supply chains and is increasingly patient-centric. This paper reviews the literature, analyzes some supply chain bottlenecks, addresses solutions concerning the networking effects of platforms to improve PPP interactions, and investigates the cost-benefit analysis of digital health with an empirical case. Whereas diagnostic or infrastructural technology is an expensive investment with long-term payback, leapfrogging digital applications reduce contingent costs. "Digital" savings can be shared by key stakeholders with P4P schemes, incentivizing value co-creation patterns. Efficient sharing may apply network theory to a comprehensive PPP ecosystem where stakeholding nodes are digitally connected. This innovative approach improves stakeholder relationships, which are re-engineered around digital platforms that enhance patient-centered satisfaction and sustainability. Digital technologies are useful even for infectious disease surveillance, like that of the coronavirus pandemic, for supporting massive healthcare intervention, decongesting hospitals, and providing timely big data.

Effects of Exercise Therapy Dosing Schedule on Impaired Cardiorespiratory Fitness in Patients With Primary Breast Cancer: A Randomized Controlled Trial

BACKGROUND

Current exercise guidelines for clinical populations recommend an exercise therapy (ET) prescription of fixed intensity (moderate), duration (40-50 minutes per session), and volume (120-160 min/wk). A critical overarching element of exercise programming that has received minimal attention is dose scheduling. We investigated the tolerability and efficacy of 2 exercise training dose regimens on cardiorespiratory fitness and patient-reported outcomes in patients with posttreatment primary breast cancer.

METHODS

Using a parallel-group randomized trial, we randomly allocated 174 postmenopausal patients (2.8 years after adjuvant therapy) with impaired peak oxygen consumption (VO₂peak) to 1 of 2 supervised exercise training interventions delivered with a standard linear (LET) (fixed dose intensity per session for 160 min/wk) or nonlinear (NLET) (variable dose intensity per session for ≈120 min/wk) schedule compared with a stretching attention control group for 16 consecutive weeks. Stretching was matched to exercise dosing arms on the basis of location, frequency, duration, and treatment length. The primary end point was change in VO₂peak (mL O₂·kg^-1·min^-1) from baseline to after intervention. Secondary end points were patient-reported outcomes, tolerability, and safety.

RESULTS

No serious adverse events were observed. Mean attendance was 64%, 75%, and 80% for attention control, LET, and NLET, respectively. In intention-to-treat analysis, VO₂peak increased 0.6±1.7 mL O₂·kg^-1·min^-1 (P=0.05) and 0.8±1.8 mL O₂·kg^-1·min^-1 (P=0.07) in LET and NLET, respectively, compared with attention control. Change in VO₂peak ranged from -2.7 to 4.1 mL O₂·kg^-1·min^-1 and from -3.6 to 5.1 mL O₂·kg^-1·min^-1 in LET and NLET, respectively. Approximately 40% of patients in both exercise dosing regimens were classified as VO₂peak responders (ie, Δ ≥1.32 mL O₂·kg^-1·min^-1). NLET improved all patient-reported outcomes compared with attention control.

CONCLUSIONS

Short-term exercise training, independently of dosing schedule, is associated with modest improvements in cardiorespiratory fitness in patients previously treated for early-stage breast cancer.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov. Unique identifier: NCT01186367.

Translation of Digital Health Technologies to Advance Precision Medicine: Informing Regulatory Science

The proliferation of digital technologies and the application of sophisticated data analysis techniques are increasingly viewed as having the potential to transform translational research and precision medicine. While digital technologies are rapidly applied in innovative ways to develop new diagnostics and therapies, the ultimate approval and adoption of these emerging methods presents several scientific and regulatory challenges. To better understand and address these regulatory science gaps, a working group of the Clinical and Translational Science Awards Program convened the Regulatory Science to Advance Precision Medicine Forum focused on digital health, particularly examining gaps in the use, validation, and interpretation of data from sensors that collect and tools that analyze digital biomarkers. The key findings and recommendations provided here emerged from the Forum and include the need to enhance areas related to data standards, data quality and validity, knowledge management, and building trust between all stakeholders.

Artificial Intelligence in Medicine: Today and Tomorrow

Artificial intelligence-powered medical technologies are rapidly evolving into applicable solutions for clinical practice. Deep learning algorithms can deal with increasing amounts of data provided by wearables, smartphones, and other mobile monitoring sensors in different areas of medicine. Currently, only very specific settings in clinical practice benefit from the application of artificial intelligence, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or the diagnosis of disease based on histopathological examination or medical imaging. The implementation of augmented medicine is long-awaited by patients because it allows for a greater autonomy and a more personalized treatment, however, it is met with resistance from physicians which were not prepared for such an evolution of clinical practice. This phenomenon also creates the need to validate these modern tools with traditional clinical trials, debate the educational upgrade of the medical curriculum in light of digital medicine as well as ethical consideration of the ongoing connected monitoring. The aim of this paper is to discuss recent scientific literature and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on physicians, healthcare institutions, medical education, and bioethics.

Digital twins to personalize medicine

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.