Digital Health for Supporting Precision Medicine in Pediatric Endocrine Disorders: Opportunities for Improved Patient Care

Digital home care interventions and quality of primary care for older adults: a scoping review

BACKGROUND

Population aging is forcing the transformation of health care. Long-term care in the home is complex and involves complex communication with primary care services. In this scenario, the expansion of digital health has the potential to improve access to home-based primary care; however, the use of technologies can increase inequalities in access to health for an important part of the population. The aim of this study was to identify and map the uses and types of digital health interventions and their impacts on the quality of home-based primary care for older adults.

METHODS

This is a broad and systematized scoping review with rigorous synthesis of knowledge directed by the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The quantitative data were analyzed through descriptive statistics, and the qualitative data were analyzed through basic qualitative content analysis, considering the organizational, relational, interpersonal and technical dimensions of care. The preliminary results were subjected to consultation with stakeholders to identify strengths and limitations, as well as potential forms of socialization.

RESULTS

The mapping showed the distribution of publications in 18 countries and in the Sub-Saharan Africa region. Older adults have benefited from the use of different digital health strategies; however, this review also addresses limitations and challenges, such as the need for digital literacy and technological infrastructure. In addition to the impacts of technologies on the quality of health care.

CONCLUSIONS

The review gathered priority themes for the equitable implementation of digital health, such as access to home caregivers and digital tools, importance of digital literacy and involvement of patients and their caregivers in health decisions and design of technologies, which must be prioritized to overcome limitations and challenges, focusing on improving quality of life, shorter hospitalization time and autonomy of older adults.

Artificial intelligence in paediatric endocrinology: conflict or cooperation

Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Telehealth Fatigue: Is It Real? What Should Be Done?

This commentary article discusses the recent trends and changes in popularity of telehealth usage as well as the most recent efforts to redefine telehealth value and usability. Six strategies to improve the patient experience and increase telehealth acceptance by overcoming simultaneous barriers are presented, which include (1) creating a new healthcare paradigm using telehealth, (2) scheduling the telehealth visit, (3) preparing for the telehealth visit, (4) conducting the telehealth visit, (5) using data and biomarkers, and (6) providing digital equity. With the application of these strategies, we believe that the recent decline in the popularity of telehealth can be reversed.

Growth Hormone Injection Log Analysis with Electronic Injection Device for Qualifying Adherence to Low-Irritant Formulation and Exploring Influential Factors on Adherence

Introduction

Although the treatment success of long-term growth hormone therapy (GHT) is dependent on maintaining patients' adherence to treatment, marked variations in adherence levels among children with GHT (eg, 7-71% nonadherence) have been reported. Barriers to or promoters of GHT adherence have been discussed and investigated, and digital health technologies, such as electronic GH injection devices, may have the potential to assess adherence to GHT more accurately. Thus, we conducted a multicenter, retrospective cohort study using GH injection log analysis of an electronic GH device, GROWJECTOR®L, to qualify adherence and explore the factors influencing adherence.

Methods

This study enrolled 41 patients (median[range] age, 5.8[3.0 ~ 17.0] years) with short stature from nine Japanese medical institutions. The injection log data (12-48 weeks) were read by smartphones and collected into the data center through a cloud server.

Results

Although cumulative adherence rates remained higher than 95% throughout the observation period, five (12.2%) patients had low adherence (<85%). Subsequently, subgroup and logistic regression analyses for exploring factors affecting adherence revealed that self-selection of GH device and irregular injection schedule (ie, frequent injections after midnight) significantly affected adherence rate (p=0.034 and 0.048, respectively). In addition, higher rates of irregular injections significantly affected low adherence (median[range], 11.26[0.79 ~ 30.50]% vs 0.26[0.00 ~ 33.33]%, p = 0.029).

Discussion

Our study indicated that injection log analysis using an electronic GH device could detect irregular injection schedules due to a night owl or disturbance in lifetime rhythm affecting low adherence and had significant potential to encourage collaborative monitoring of adherence with healthcare providers and patients themselves/caregivers, along with growing autonomy and shared decision-making. Our study suggests the significance of narrative and personal approaches to adherence of patients with GHT and the usefulness of digital devices for such an approach and for removing various barriers to patient autonomy, leading to improvement and maintenance of adherence.

Precision Medicine 2.0: How Digital Health and AI Are Changing the Game

In the era of rapid IT developments, the health domain is undergoing a considerable transformation [...].

Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management of patients with obesity.

Methods

The perspectives of the authors were augmented by scientific support from published citations and integrated with information derived from search engines (i.e., Chrome by Google, Inc) and chatbots (i.e., Chat Generative Pretrained Transformer or Chat GPT).

Results

Artificial Intelligence (AI) is the technologic acquisition of knowledge and skill by a nonhuman device, that after being initially programmed, has varying degrees of operations autonomous from direct human control, and that performs adaptive output tasks based upon data input learnings. AI has applications regarding medical research, medical practice, and applications relevant to the management of patients with obesity. Chatbots may be useful to obesity medicine clinicians as a source of clinical/scientific information, helpful in writings and publications, as well as beneficial in drafting office or institutional Policies and Procedures and Standard Operating Procedures. AI may facilitate interactive programming related to analyses of body composition imaging, behavior coaching, personal nutritional intervention & physical activity recommendations, predictive modeling to identify patients at risk for obesity-related complications, and aid clinicians in precision medicine. AI can enhance educational programming, such as personalized learning, virtual reality, and intelligent tutoring systems. AI may help augment in-person office operations and telemedicine (e.g., scheduling and remote monitoring of patients). Finally, AI may help identify patterns in datasets related to a medical practice or institution that may be used to assess population health and value-based care delivery (i.e., analytics related to electronic health records).

Conclusions

AI is contributing to both an evolution and revolution in medical care, including the management of patients with obesity. Challenges of Artificial Intelligence include ethical and legal concerns (e.g., privacy and security), accuracy and reliability, and the potential perpetuation of pervasive systemic biases.

Translating Precision Health for Pediatrics: A Scoping Review

Precision health aims to personalize treatment and prevention strategies based on individual genetic differences. While it has significantly improved healthcare for specific patient groups, broader translation faces challenges with evidence development, evidence appraisal, and implementation. These challenges are compounded in child health as existing methods fail to incorporate the physiology and socio-biology unique to childhood. This scoping review synthesizes the existing literature on evidence development, appraisal, prioritization, and implementation of precision child health. PubMed, Scopus, Web of Science, and Embase were searched. The included articles were related to pediatrics, precision health, and the translational pathway. Articles were excluded if they were too narrow in scope. In total, 74 articles identified challenges and solutions for putting pediatric precision health interventions into practice. The literature reinforced the unique attributes of children and their implications for study design and identified major themes for the value assessment of precision health interventions for children, including clinical benefit, cost-effectiveness, stakeholder values and preferences, and ethics and equity. Tackling these identified challenges will require developing international data networks and guidelines, re-thinking methods for value assessment, and broadening stakeholder support for the effective implementation of precision health within healthcare organizations. This research was funded by the SickKids Precision Child Health Catalyst Grant.

Digital Transformation in Healthcare: Technology Acceptance and Its Applications

Technological innovation has become an integral aspect of our daily life, such as wearable and information technology, virtual reality and the Internet of Things which have contributed to transforming healthcare business and operations. Patients will now have a broader range and more mindful healthcare choices and experience a new era of healthcare with a patient-centric culture. Digital transformation determines personal and institutional health care. This paper aims to analyse the changes taking place in the field of healthcare due to digital transformation. For this purpose, a systematic bibliographic review is performed, utilising Scopus, Science Direct and PubMed databases from 2008 to 2021. Our methodology is based on the approach by Wester and Watson, which classify the related articles based on a concept-centric method and an ad hoc classification system which identify the categories used to describe areas of literature. The search was made during August 2022 and identified 5847 papers, of which 321 fulfilled the inclusion criteria for further process. Finally, by removing and adding additional studies, we ended with 287 articles grouped into five themes: information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine and security issues.

Monitoring chronic inflammatory musculoskeletal diseases mixing virtual and face-to-face assessments-Results of the digireuma study

Mobile health technology holds great promise for the clinical management of patients with chronic disease. However, evidence on the implementation of projects involving digital health solutions in rheumatology is scarce. We aimed to study the feasibility of a hybrid (virtual and face-to-face) monitoring strategy for personalized care in rheumatoid arthritis (RA) and spondyloarthritis (SpA). This project included the development of a remote monitoring model and its assessment. After a focus group with patients and rheumatologists, relevant concerns regarding the management of RA and SpA were raised, leading to the development of the Mixed Attention Model (MAM), which combined hybrid (virtual and face-to-face) monitoring. Then, a prospective study using the mobile solution Adhera for Rheumatology was conducted. Over a 3-month follow-up period, patients were given the opportunity to complete disease-specific electronic patient reported outcomes (ePROs) for RA and SpA with a pre-established frequency, as well as flares and changes in medication at any time. Number of interactions and alerts were assessed. The usability of the mobile solution was measured by the Net-Promoter Score (NPS) and through a 5-star Likert scale. Following the MAM development, forty-six patients were recruited to utilize the mobile solution, of whom 22 had RA and 24 SpA. There were 4,019 total interactions in the RA group, and 3,160 in the SpA group. Fifteen patients generated a total of 26 alerts, of which 24 were flares and 2 were medication-related problems; most (69%) were managed remotely. Regarding patient satisfaction, 65% of the respondents were considered to have endorsed Adhera for Rheumatology, yielding a NPS of 57 and an overall rating was 4.3 out of 5 stars. We concluded that the use of the digital health solution is feasible in clinical practice to monitor ePROs for RA and SpA. Next steps involve the implementation of this telemonitoring method in a multicentric setting.

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.

Adherence to r-hGH Therapy in Pediatric Growth Hormone Deficiency: Current Perspectives on How Patient-Generated Data Will Transform r-hGH Treatment Towards Integrated Care

Pediatric growth hormone (GH) deficiency is a licensed indication for replacement therapy with recombinant human growth hormone (r-hGH). Treatment, consisting of daily subcutaneous injections, extends from the time of diagnosis until cessation of linear growth at completion of puberty. Suboptimal adherence to r-hGH therapy is common and has been well documented to substantially impair the growth response and achievement of the optimal goal which is attainment of adult height within the genetic target range. The causes of poor adherence are complex and include disease-, patient-, doctor-, and treatment-related factors. Interventions for suboptimal adherence are important for a long-term successful outcome and can include both face-to-face and digital strategies. Face-to-face interventions include behavioral change approaches such as motivational interviewing and non-judgmental assessment. Medical and nursing staff require training in these techniques. Digital solutions are rapidly advancing as evidenced by the electronic digital auto-injector device, easypod® (Merck Healthcare KGaA, Darmstadt, Germany), which uses the web-based easypod® connect platform allowing adherence data to be transmitted electronically to healthcare professionals (HCPs), who can then access GH treatment history, enhancing clinical decisions. Over the past 10 years, the multi-national Easypod® Connect Observational Study has reported high levels of adherence (>85%) from up to 40 countries. The easypod® connect system can be supported by a smartphone app, growlink™, which facilitates the interactions between the patients, their care team, and patient support services. HCPs are empowered by new digital techniques, however, the human-digital partnership remains essential for optimal growth management. The pediatric patient on r-hGH therapy will benefit from these innovations to enhance adherence and optimize long-term response.

Automated growth monitoring app (GROWIN): a mobile Health (mHealth) tool to improve the diagnosis and early management of growth and nutritional disorders in childhood

OBJECTIVE

To assess the functionality and feasibility of the GROWIN app for promoting early detection of growth disorders in childhood, supporting early interventions, and improving children's lifestyle by analyzing data collected over 3 years (2018-2020).

METHODS

We retrospectively assessed the growth parameters (height, weight, body mass index [BMI], abdominal circumference) entered by users (caregivers/parents) in the GROWIN app. We also analyzed the potential health problems detected and the messages/recommendations the app showed. Finally, we assessed the possible impact/benefit of the app on the growth of the children.

RESULTS

A total of 21 633 users (Spanish [65%], Latin American [30%], and others [5%]) entered 10.5 ± 8.3 measurements (0-15 y old). 1200 recommendations were for low height and 550 for low weight. 1250 improved their measurements. A specialist review was recommended in 500 patients due to low height. 2567 nutrition tests were run. All children with obesity (n = 855, BMI: 27.8 kg/m2 [2.25 SD]) completed the initial test with a follow-up of ≥1 year. Initial results (score: 8.1) showed poor eating habits (fast food, commercially baked goods, candy, etc.), with >90% not having breakfast. After 3-6 months, BMI decreased ≥1 point, and test scores increased ≥2 points. This benefit was maintained beyond 1 year and was correlated with an improvement in BMI (r = -.65, P = .01).

DISCUSSION/CONCLUSIONS

The GROWIN app represents an innovative automated solution for families to monitor growth. It allows the early detection of abnormal growth indicators during childhood and adolescence, promoting early interventions. Additionally, in children with obesity, an improvement in healthy nutritional habits and a decrease in BMI were observed.

Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis

Digital health has seen rapid advancements over the last few years in helping patients and their healthcare professionals better manage treatment for a variety of illnesses, including growth hormone (GH) therapy for growth disorders in children and adolescents. For children and adolescents requiring such therapy, as well as for their parents, the treatment is longitudinal and often involves daily injections plus close progress monitoring; a sometimes daunting task when young children are involved. Here, we describe our experience in offering devices and digital health tools to support GH therapy across some 40 countries. We also discuss how this ecosystem of care has evolved over the years based on learnings and advances in technology. Finally, we offer a glimpse of future planned enhancements and directions for digital health to play a bigger role in better managing conditions treated with GH therapy, as well as model development for adherence prediction. The continued aim of these technologies is to improve clinical decision making and support for GH-treated patients, leading to better outcomes.