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Purpose of Review In this article, we review a range of digital technologies for possible application in heart failure patients, with a focus on lessons learned. We also discuss a future model of heart failure management, as digital technologies continue to become part of standard care. Recent Findings Digital technologies are increasingly used by healthcare professionals and those living with heart failure to support more personalised and timely shared decision-making, earlier identification of problems, and an improved experience of care. The COVID-19 pandemic has accelerated the acceptability and implementation of a range of digital technologies, including remote monitoring and health tracking, mobile health (wearable technology and smartphone-based applications), and the use of machine learning to augment data interpretation and decision-making. Much has been learned over recent decades on the challenges and opportunities of technology development, including how best to evaluate the impact of digital health interventions on health and healthcare, the human factors involved in implementation and how best to integrate dataflows into the clinical pathway. Summary Supporting patients with heart failure as well as healthcare professionals (both with a broad range of health and digital literacy skills) is crucial to success. Access to digital technologies and the internet remains a challenge for some patients. The aim should be to identify the right technology for the right patient at the right time, in a process of co-design and co-implementation with patients.

Purpose of Review Application of deep learning (DL) is growing in the last years, especially in the healthcare domain. This review presents the current state of DL techniques applied to electronic health record structured data, physiological signals, and imaging modalities for the management of heart failure (HF), focusing in particular on diagnosis, prognosis, and re-hospitalization risk, to explore the level of maturity of DL in this field. Recent Findings DL allows a better integration of different data sources to distillate more accurate outcomes in HF patients, thus resulting in better performance when compared to conventional evaluation methods. While applications in image and signal processing for HF diagnosis have reached very high performance, the application of DL to electronic health records and its multisource data for prediction could still be improved, despite the already promising results. Summary Embracing the current big data era, DL can improve performance compared to conventional techniques and machine learning approaches. DL algorithms have potential to provide more efficient care and improve outcomes of HF patients, although further investigations are needed to overcome current limitations, including results generalizability and transparency and explicability of the evidences supporting the process.

Purpose of the Review We aimed to provide an overview of telemedical monitoring and its impact on outcomes among heart failure (HF) patients. Recent Findings Most HF readmissions may be prevented if clinical parameters are strictly controlled via telemedical monitoring. Predictive algorithms for patients with cardiovascular implantable electronic devices (e.g., Triage-HF Plus by Medtronic or HeartLogic by Boston Scientific) were developed to identify patients at significantly increased risk of HF events. However, randomized control trial-based data are heterogeneous regarding the advantages of telemedical monitoring in HF patients. The likelihood of adverse clinical outcomes increases when pulmonary artery pressure (PAP) rises, usually days to weeks before clinical manifestations of HF. A wireless monitoring system (CardioMEMS™) detecting changes in PAP was proposed for HF patients. CardioMEMS™ transmits data to the healthcare provider and allows to institute timely intensification of HF therapies. CardioMEMS™-guided pharmacotherapy reduced a risk of HF-related hospitalization (hazard ratio [HR]: 0.72; 95% confidence interval (CI) 0.60–0–0.85; p  < 0.01). Summary Relevant developments and innovations of telemedical care may improve clinical outcomes among HF patients. The use of CardioMEMS™ was found to be safe and cost-effective by reducing the rates of HF hospitalizations.

Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital measures (DMs) they generate offer significant potential to accelerate the drug development timeline, decrease clinical trial costs, and improve access to care. However, choosing an appropriate statistical methodology when conducting analytical validation (AV) of a DM is complicated, particularly for novel DMs, for which appropriate, established reference measures (RMs) may not exist. More understanding of, and a standardized approach to, AV in these scenarios is needed. In a prior simulation study, 3 statistical methods were tested for their ability to estimate a simulated relationship between a sDHT-derived DM and several clinical outcome assessment (COA) RMs. The aim of this work was to assess the feasibility of implementation of these methods in real data and to examine the impact of AV study design factors on the relationships estimated. Four real-world datasets, captured using sDHTs, were used to prepare hypothetical AV studies representing a range of scenarios with respect to 3 key study design properties: temporal coherence, construct coherence, and data completeness. The datasets analyzed were as follows: Urban Poor (comparing nighttime awakenings to measures of psychological well-being), STAGES (comparing daily step count to psychological and fatigue measures), mPower (comparing daily smartphone screen taps to measures of function in Parkinson's disease), and Brighten (comparing smartphone communication activity to measures of psychological well-being). For each hypothetical AV study, 3 statistical methods were leveraged: the Pearson correlation coefficient (PCC) between DM and RM, simple linear regression (SLR) between DM and RM, multiple linear regression (MLR) between DMs and combinations of RMs, and 2-factor, correlated-factor confirmatory factor analysis (CFA) models. Performance measures were the PCC magnitudes (for PCC), R2 and adjusted R2 statistics (for SLR and MLR, respectively), and factor correlations (for CFA). Most of the CFA models exhibited an acceptable fit according to the majority of the fit statistics employed, and each model was able to estimate a factor correlation. For each model, these correlations were greater than or equal to the corresponding PCC in magnitude. Correlations were the strongest in the hypothetical studies with strong temporal and construct coherence. The performance of the selected statistical methods shown in this work supports their feasibility when implemented in real-world data. Our findings, in particular, support the use of CFA to assess the relationship between a novel DM and a COA RM. The observed impact of AV study design factors on the relationships estimated allowed the authors to determine practical recommendations for study design in AV of novel DMs. By using a standardized methodology for evaluating novel DMs, sDHT developers, biostatisticians, and clinical researchers can navigate the complex validation landscape more easily, with more certainty, and with more tools at their disposal.

Background Almost the entire World is experiencing the Coronavirus-Disease-2019 (COVID-19) pandemic, responsible, at the end of May 2020, of more than five million people infected worldwide and about 350,000 deaths. In this context, a deep reorganization of allergy clinics, in order to ensure proper diagnosis and care despite of social distancing measures expose, is needed. Main text The reorganization of allergy clinics should include programmed checks for severe and poorly controlled patients, application of digital medicine service for mild-to-moderate disease in well-controlled ones, postponement of non urgent diagnostic work-ups and domiciliation of therapies, whenever possible. As far as therapies, allergen immunotherapy (AIT) should not be stopped and sublingual immunotherapy (SLIT) fits perfectly for this purpose, since a drug home-delivery service can be activated for the entire pandemic duration. Moreover, biologic agents for severe asthma, chronic spontaneous urticaria and atopic dermatitis should be particularly encouraged to achieve best control possible of severe disease in times of COVID-19 and, whenever possible, home-delivery and self-administration should be the preferred choice. Conclusion During COVID-19 pandemic, allergists have the responsibility of balancing individual patients’ needs with public health issues, and innovative tools, such as telemedicine and digital medicine services, can be helpful to reduce the risk of viral spreading while delivering up-to-date personalized care.

Over the last 10-15 years, US health care and the practice of medicine itself have been transformed by a proliferation of digital medicine and digital therapeutic products (collectively, digital health tools [DHTs]). While a number of DHT classifications have been proposed to help organize these tools for discovery, retrieval, and comparison by health care organizations seeking to potentially implement them, none have specifically addressed that organizations considering their implementation approach the DHT discovery process with one or more specific outcomes in mind. An outcomes-based DHT ontology could therefore be valuable not only for health systems seeking to evaluate tools that influence certain outcomes, but also for regulators and vendors seeking to ascertain potential substantial equivalence to predicate devices. This study aimed to develop, with inputs from industry, health care providers, payers, regulatory bodies, and patients through the Accelerated Digital Clinical Ecosystem (ADviCE) consortium, an ontology specific to DHT outcomes, the Digital medicine Outcomes Value Set (DOVeS), and to make this ontology publicly available and free to use. From a starting point of a 4-generation-deep hierarchical taxonomy developed by ADviCE, we developed DOVeS using the Web Ontology Language through the open-source ontology editor Protégé, and data from 185 vendors who had submitted structured product information to ADviCE. We used a custom, decentralized, collaborative ontology engineering methodology, and were guided by Open Biological and Biomedical Ontologies (OBO) Foundry principles. We incorporated the Mondo Disease Ontology (MONDO) and the Ontology of Adverse Events. After development, DOVeS was field-tested between December 2022 and May 2023 with 40 additional independent vendors previously unfamiliar with ADviCE or DOVeS. As a proof of concept, we subsequently developed a prototype DHT Application Finder leveraging DOVeS to enable a user to query for DHT products based on specific outcomes of interest. In its current state, DOVeS contains 42,320 and 9481 native axioms and distinct classes, respectively. These numbers are enhanced when taking into account the axioms and classes contributed by MONDO and the Ontology of Adverse Events. DOVeS is publicly available on BioPortal and GitHub, and has a Creative Commons license CC-BY-SA that is intended to encourage stakeholders to modify, adapt, build upon, and distribute it. While no ontology is complete, DOVeS will benefit from a strong and engaged user base to help it grow and evolve in a way that best serves DHT stakeholders and the patients they serve.

The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research.

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.