The way one leads their life is considered an important factor in health. In this paper we propose a system to provide risk assessment based on behavior for the health insurance sector. To do so we built a platform to collect real-world data that enumerate different aspects of behavior, and a simulator to augment actual data with synthetic. Using the data, we built classifiers to predict variations in important quantities for the lifestyle of a person. We offer a risk assessment service to the health insurance professionals by manipulating the classifier predictions in the long-term. We also address virtual coaching by using explainable Artificial Intelligence (AI) techniques on the classifier itself to gain insights on the advice to be offered to insurance customers.

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

Background: Unhealthy lifestyle behaviors have been identified as a major cause of numerous health issues, with a steady global increase in their prevalence. Addressing this challenge requires comprehensive behavioral changes to promote the adoption of a sustainable healthier lifestyle. However, despite the prevalent need, cost-effective and successful digital coaching for health-related behavior change remains scarce. Objective: This study aimed to present a holistic framework for designing, modeling, and executing behavior change strategies through a multiagent reasoning system that selected optimal digital coaching techniques based on individual assessments and integrated data-driven decision-making. Methods: Behavioral change theories have been explored to design a multiagent system aimed at achieving sustainable lifestyle changes. This system selected behavior change techniques based on individual user assessments, prioritizing those with the strongest impact on key behavioral components. The framework incorporated evidence-based practices stemming from behavioral change science and integrated them into Healthentia’s behavioral change coaching scheme. Healthentia, a certified software as a medical device, implemented this framework in its non-medical modules that aim for lifestyle behavioral change and wellbeing specifically for chronic disease management, serving as an eHealth solution that advances decentralized care by enabling remote monitoring, data-driven content selection, and personalized digital coaching that adjusts to patient progress and engagement patterns. Results: This study explored the application of the Healthentia behavioral change coaching scheme in patients with type 2 diabetes. Behavioral attributes have been evaluated in 9 patients, yielding notable results in terms of fasting glucose dropping by an average of –17.3 mg/dL (Cohen d=1.5; P=.002), further underscored by a narrow 95% CI (–26.1 to –8.43), and in terms of weight and BMI, with mean reductions of –2.89 kg and –1.05 kg/m², respectively. These changes yielded large effect sizes (Cohen d approximately 1.05) and were statistically significant (P=.01). The positive outcomes were at least partly attributed to the personalized delivery of content, 71.66% (1125/1570) of which was well received by the patients. Conclusions: Our study of this multiagent system, which was tested through simulated patient behavior and preliminary, limited behavior observations of patients with type 2 diabetes, promises improved health outcomes using personalized digital coaching strategies. Future directions include optimizing the multiagent selection process; further exploring the type 2 diabetes program; conducting an in-depth evaluation of its results, including glycated hemoglobin measurements; and expanding its applications to other chronic conditions.

The health emergency linked to the SARS-CoV-2 pandemic has highlighted problems in the health management of chronic patients due to their risk of infection, suggesting the need of new methods to monitor patients. People living with HIV/AIDS (PLWHA) represent a paradigm of chronic patients where an e-health-based remote monitoring could have a significant impact in maintaining an adequate standard of care. The key objective of the study is to provide both an efficient operating model to “follow” the patient, capture the evolution of their disease, and establish proximity and relief through a remote collaborative model. These dimensions are collected through a dedicated mobile application that triggers questionnaires on the basis of decision-making algorithms, tagging patients and sending alerts to staff in order to tailor interventions. All outcomes and alerts are monitored and processed through an innovative e-Clinical platform. The processing of the collected data aims into learning and evaluating predictive models for the possible upcoming alerts on the basis of past data, using machine learning algorithms. The models will be clinically validated as the study collects more data, and, if successful, the resulting multidimensional vector of past attributes will act as a digital composite biomarker capable of predicting HIV-related alerts. Design: All PLWH > 18 sears old and stable disease followed at the outpatient services of a university hospital (n = 1500) will be enrolled in the interventional study. The study is ongoing, and patients are currently being recruited. Preliminary results are yielding monthly data to facilitate learning of predictive models for the alerts of interest. Such models are learnt for one or two months of history of the questionnaire data. In this manuscript, the protocol—including the rationale, detailed technical aspects underlying the study, and some preliminary results—are described. Conclusions: The management of HIV-infected patients in the pandemic era represents a challenge for future patient management beyond the pandemic period. The application of artificial intelligence and machine learning systems as described in this study could enable remote patient management that takes into account the real needs of the patient and the monitoring of the most relevant aspects of PLWH management today.

BACKGROUND Unhealthy lifestyle behaviors have been identified as a major cause of numerous health issues, with a steady global increase in their prevalence. Addressing this challenge requires comprehensive behavioral changes to promote the adoption of a sustainable healthier lifestyle. However, despite the prevalent need, cost-effective and successful digital coaching for health-related behavior change remains scarce. OBJECTIVE This study aimed to present a holistic framework for designing, modeling, and executing behavior change strategies through a multiagent reasoning system that selected optimal digital coaching techniques based on individual assessments and integrated data-driven decision-making. METHODS Behavioral change theories have been explored to design a multiagent system aimed at achieving sustainable lifestyle changes. This system selected behavior change techniques based on individual user assessments, prioritizing those with the strongest impact on key behavioral components. The framework incorporated evidence-based practices stemming from behavioral change science and integrated them into Healthentia’s behavioral change coaching scheme. Healthentia, a certified software as a medical device, implemented this framework in its non-medical modules that aim for lifestyle behavioral change and wellbeing specifically for chronic disease management, serving as an eHealth solution that advances decentralized care by enabling remote monitoring, data-driven content selection, and personalized digital coaching that adjusts to patient progress and engagement patterns. RESULTS This study explored the application of the Healthentia behavioral change coaching scheme in patients with type 2 diabetes. Behavioral attributes have been evaluated in 9 patients, yielding notable results in terms of fasting glucose dropping by an average of –17.3 mg/dL (Cohen d=1.5; P=.002), further underscored by a narrow 95% CI (–26.1 to –8.43), and in terms of weight and BMI, with mean reductions of –2.89 kg and –1.05 kg/m², respectively. These changes yielded large effect sizes (Cohen d approximately 1.05) and were statistically significant (P=.01). The positive outcomes were at least partly attributed to the personalized delivery of content, 71.66% (1125/1570) of which was well received by the patients. CONCLUSIONS Our study of this multiagent system, which was tested through simulated patient behavior and preliminary, limited behavior observations of patients with type 2 diabetes, promises improved health outcomes using personalized digital coaching strategies. Future directions include optimizing the multiagent selection process; further exploring the type 2 diabetes program; conducting an in-depth evaluation of its results, including glycated hemoglobin measurements; and expanding its applications to other chronic conditions.

The healthcare domain is increasingly adopting IoT and Electronic Health Record (EHR) systems, generating vast volumes of healthcare data. This shift is driven by the growing need of delivering the right information to the right individuals, at the right time. The latter underscores the importance of adopting a comprehensive strategy for efficiently collecting, utilizing, and analyzing health-related data to not only enhance overall healthcare management but also for the provision of timely and personalized prevention strategies. The latter is of highest importance especially in scenarios where lack of effective treatments or poor survival rates (such in pancreatic cancer) renders typical healthcare strategies ineffective. In this article, we introduce an innovative and integrated platform that is specifically designed and developed for accessing, processing, and analyzing data in challenging healthcare scenarios, such as dealing with pancreatic cancer. This platform, called iHelp, combines multidisciplinary technologies and provides healthcare professionals reliable risk modelling, analysis, and prediction techniques so that individuals (at risk of developing pancreatic cancer) can be provided with timely, reliable, and personalized prevention and intervention measures. A key innovation in the iHelp platform is the standardized data management approach called Holistic Health Records (HHRs) that facilitate the capturing of all health determinants in a standardized and well-structured way for processing towards the provision of health risk detection and personalized healthcare decision support. In the development of iHelp platform, the HHRs are evaluated through different real-world healthcare datasets, including datasets coming from hospital systems, data from wearables, questionnaires, and mobile applications.