Tele-Monitoring System for Chronic Diseases Management: Requirements and Architecture

Smart home healthcare for chronic disease management: A scoping review

Objective

Chronic disease is the leading cause of morbidity and mortality on a global scale. Recent use of intelligent home environments for remote health monitoring has allowed patients to feel safe at home. By encouraging patient engagement and, if necessary, care delivery, smart home environments may be a useful method for managing chronic diseases at home. The purpose of this study is to synthesize the evidence on the usage of smart healthcare in the home for chronic illness management.

Methods

We conducted a scoping review using the Joanna Briggs Methodology and searched three databases from 2017 to 2022 for original research papers on smart healthcare, smart home technology, home-based technology, home monitoring, and physiological monitoring for chronic illness management. We did a descriptive study on the pertinent data we collected, as well as an analysis of whether the devices met its objectives.

Results

The final analysis included nine papers, the majority of which were randomized controlled trials. All of the studies were carried out in developed countries. The gadgets or smart healthcare in these studies are categorized based on the technology used and the outcomes measured. Respiratory, weight, and ballistocardiograph measurements, as well as changes in questionnaire ratings, hospitalization, activity monitoring, device acceptability, medication adherence, exercise capacity, and body function, were all measured.

Conclusion

Smart healthcare applications boost health by monitoring health and wellness, recording physical activity and rehabilitation, and improving overall quality of life. Not all smart home applications, however, served their intended purpose. As a result, more research into the efficacy of smart healthcare is needed to improve its application for chronic illness treatment.

Definition of a Method for the Evaluation of Telemedicine Platforms in the Italian Context

Background: The COVID-19 outbreak led to the diffusion of several telemedicine solutions. The choice of the correct platform is crucial for ensuring the release of effective assistance. However, there is a lack of an objective method for the assessment of technical features. Objective: This study proposes a methodology for the evaluation of functional requirements of telemedicine platforms. This approach also permits the comparison of solutions in the Italian market by means of defined parameters, thus directing the choice of health care professionals. Methods: The study is divided into three phases. First, a mapping of the telemedicine platforms operating in Italy is performed. Then, the available platforms are selected based on the offered telemedicine activity. Finally, a method for evaluating the investigated platforms is defined. Results: Thirty-three (n = 33) technological systems were identified through an accurate investigation on the web and interviews with IT companies. Fifteen parameters were defined and organized into three categories: (1) usability of the telemedicine platform, (2) security, and (3) technological and organizational aspects. A score between 1 and 4 was assigned to each parameter, proportionally to the completeness of the platform. In particular, 62.96% of platforms reached an average score between 3.01 and 4 points; 33.33% of them had scores between 2.01 and 3, while the remaining 3.70% of solutions obtained a result between 1.01 and 2. Conclusions: The study provides an evaluation approach that is easily usable by health professionals to select the most suitable platform. The number of solutions and quality of information could be updated to obtain a complete tool.

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

A Digital Health Service for Elderly People with Balance Disorders and Risk of Falling: A Design Science Approach

In this study, a design science research methodology was used aiming at designing, implementing and evaluating a digital health service to complement the provision of healthcare for elderly people with balance disorders and risk of falling. An explanatory sequential mixed methods study allowed to identify and explore the dissatisfaction with electronic medical records and the opportunity for using digital health solutions. The suggested recommendations helped to elaborate and develop “BALANCE”, a digital service implemented on the METHIS platform, which was recently validated for remote monitoring of chronic patients in primary healthcare. “BALANCE” provides clinical and interactive data, questionnaire pre and post-balance rehabilitation, tutorial videos with balance exercises and patient-recorded videos of the exercises. This digital service was demonstrated, including five elderly patients with clinical recommendations for balance rehabilitation at home. Finally, the authors conducted two focus groups with the participants and their caregivers as well as with physicians. The focus groups aimed at exploring their satisfaction level, needs of adjustment in the “BALANCE” service and strategies for applicability. The digital healthcare service evaluation revealed a significant potential for clinical applicability of this digital solution for elderly people with balance disorders and risk of falling.

Artificial Intelligence in Orthodontic Smart Application for Treatment Coaching and Its Impact on Clinical Performance of Patients Monitored with AI-TeleHealth System

BACKGROUND

Treatment of malocclusion with clear removable appliances like Invisalign^® or Spark™, require considerable higher level of patient compliance when compared to conventional fixed braces. The clinical outcomes and treatment efficiency strongly depend on the patient's discipline. Smart treatment coaching applications, like strojCHECK^® are efficient for improving patient compliance.

PURPOSE

To evaluate the impact of computerized personalized decision algorithms responding to observed and anticipated patient behavior implemented as an update of an existing clinical orthodontic application (app).

MATERIALS AND METHODS

Variables such as (1) patient app interaction, (2) patient app discipline and (3) clinical aligner tracking evaluated by artificial intelligence system (AI) system-Dental monitoring^® were observed on the set of 86 patients. Two 60-day periods were evaluated; before and after the app was updated with decision tree processes.

RESULTS

All variables showed significant improvement after the update except for the manifestation of clinical non-tracking in men, evaluated by artificial intelligence from video scans.

CONCLUSIONS

Implementation of application update including computerized decision processes can significantly enhance clinical performance of existing health care applications and improve patients' compliance. Using the algorithm with decision tree architecture could create a baseline for further machine learning optimization.