Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study – A Critical Review of Article

This is a critical appraisal of a manuscript outlining additional indicators used in the United States to augment traditional disease surveillance tools. The article went through the peer-review process. Therefore, it may be considered as objective and unbiased. The structure of the article is coherent, and it was published in a journal for digital medicine, health, and health care in the internet age. The article has contributed to the literature and provides a basis for strengthening existing surveillance systems to improve public health outcomes. However, it is suggested that whenever new indicators are being developed, their essential components must be fully defined.

Corona hotels in Israel: Care and abandonment under the auspices of digital medicine

Following the onset of the COVID-19 pandemic, Israel established a number of ‘corona hotels’ – hybrid spaces that were neither fully treatment-oriented nor fully incarcerational, in which people known or suspected to be infected with the coronavirus were confined, sometimes for prolonged and indefinite periods. This paper describes the experience of 25 people who were confined in corona recovery and isolation hotels between March and July 2020. The corona hotels exemplify how remote medical technology and digital medicine together enable a new ‘technogeography of care’, where care and abandonment are inextricably linked. The paper adds to the growing number of critical studies on digital health by showing how the employed technologies impact the concepts of human embodiment, subjectivity and social relations, as well as how the occupants negotiated the meaning of these technologies and resisted their effects.

Chatbots for Smoking Cessation: Scoping Review (Preprint)

BACKGROUND Despite significant progress in reducing tobacco use over the past two decades, tobacco still kills over 8 million people every year. Digital interventions such as text messaging have been found to help people quit smoking. Chatbots, or conversational agents, are newer digital tools that mimic instantaneous human conversation and therefore could extend the effectiveness of text messaging. OBJECTIVE This scoping review aims to assess the extent of research in the chatbot literature for smoking cessation and provide recommendations for future research in this area. METHODS Relevant studies were identified through searches conducted in MEDLINE, APA PsycINFO, Google Scholar and Scopus as well as an additional search on JMIR, Cochrane Library, Lancet Digital Health, and Digital Medicine. Studies were identified if they were conducted with tobacco smokers, were conducted between 2000 to 2021, were available in English and included a chatbot intervention. RESULTS Of the 323 studies identified, 10 studies were included in the review. Some studies noted an improvement in smoking cessation measures. However, the number of studies was limited and most had methodological or quality concerns. CONCLUSIONS More research is needed to make a firm conclusion of the efficacy of chatbots for smoking cessation. Researchers need to provide a more in-depth description of the chatbot functionality, mode of delivery, and theoretical underpinnings. CLINICALTRIAL na

Assessing a Digital Medicine System in Veterans with Severe Mental Illness: A provider-randomized clinical trial (Preprint)

BACKGROUND Suboptimal medication adherence is a significant problem for patients with serious mental illness (SMI). Measuring medication adherence through subjective and objective measures can be challenging, time consuming and inaccurate. OBJECTIVE We evaluated a digital medicine system (DMS) compared to treatment as usual (TAU) on adherence to oral aripiprazole and patient and provider perspectives on the feasibility and acceptability of a DMS. METHODS This open-label, 2-site, provider-randomized trial assessed aripiprazole refill adherence in Veterans with schizophrenia, schizoaffective disorder, bipolar disorder, or major depressive disorder. We randomized 26 providers such that their patients either received TAU or DMS for a period of 90 days. Semi-structured interviews with patients and providers were used to examine feasibility and acceptability of using the DMS. RESULTS We enrolled 46 patients across 2 Veterans Affairs (VA) sites: (21 in DMS and 25 in TAU). There was no difference in medication refill over 3 and 6 months, respectively (82% and 75% DMS vs. 86% and 82% TAU). The DMS arm had 85% days covered during the period they were engaged with the DMS (144 days on average). Interviews with patients (n=14) and providers (n=5) elicited themes salient to using the DMS. Patient themes included: pre-enrollment adherence strategies and interest in the DMS, positive impact on medication adherence, system usability challenges, support needs, and suggested design/functionality improvements. Provider themes included: concerns for patient medication adherence and interest in the DMS, concerns with the DMS, DMS dashboard usability, challenges of the DMS, and suggestions to increase provider use. CONCLUSIONS There was no observed difference in refill rates. Among those who engaged in the DMS arm, refill rates were relatively high (85%). The qualitative analyses highlighted areas for further refinement of the DMS. CLINICALTRIAL NCT03881449

Analyzing Patient Trajectories With Artificial Intelligence

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery.

Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management

Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care.

Integration Environment Software Module for Accumulation and Exchange of Digital Medical Data

Introduction. The development of effective digital medicine tools is an intensive and complex process that requires the interdisciplinary efforts of a wide range of experts, from scientists and engineers to ethics experts and lawyers. Digital medicine products have great potential for improving medical measurement, diagnosis and treatment. One of the main challenges for the healthcare sector is to address the issue of fast, convenient and secure exchange of information about patients’ health. Service-oriented architectures of such products may accomplish many of the challenges facing healthcare systems. The purpose of the paper is to develop an information and software module ExchangeDMD to ensure the accumulation, storage and exchange of diagnostic medical data in accordance with modern medical information standards to maintain the interoperability function as one of the leading principles of digital medicine. Results. A special adaptive architecture of digital medicine infrastructure has been developed, which enables an integrated solution of data exchange between participants of providing medical services, which is carried out with the help of web services. The specifics of different types of medical information are analyzed and taken into account in accordance with the access regime for its processing. The module structure has been developed and implemented in software, which has three main levels: central virtual storage (virtual data center to implement certain functions), remote administration segment (technical support and administration network) and user segment (mobile devices and user-patient applications). Conclusions. The ExchangeDMD information and software module is designed to ensure the accumulation of patient data, integration between the various units within the system, as well as to ensure the management of this data by health care personnel. The ExchangeDMD module is built using the international standard HL7 CDA, which enables formalizing electronic medical records using RIM (information model links) to attract the necessary directories and classifiers when creating medical records and documents.

Digital conversion in health care system and daily activities of a clinician

The article represents the issues of information and analytical support of clinicians. The article deals with the theory and practice of creating a digital subsystem of medical support for the population and a modern system for examination and treatment, the collection, storage and use of medical information in the databases of medical organizations, as well as data processing centers and situational control centers of the federal level or subjects of the Russian Federation. The basic principles of digital medicine, such as continuous development, mobility of doctors and patients, as well as the interaction of sensors and executive devices are discussed.