Managing insulin therapy for type 2 diabetes can be challenging due to the need for frequent dose adjustments. This study examined whether a voice-based AI assistant could help patients achieve better blood sugar control by adjusting their insulin dosages more effectively.

In an RCT presented by Ashwin Nayak et al in JAMA Netw Open. 2023;6(12): e2340232, titled Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes, is presented the effectiveness of voice-based conversational AI applications in facilitating swift optimization of basal insulin dosage for individuals with type 2 diabetes.

The study involved 32 adults with type 2 diabetes who either used the AI assistant or received standard care. Those who used the AI assistant reached the optimal insulin dose significantly faster (median of 15 days vs. over 56 days for standard care). Additionally, AI users had better insulin adherence (83% vs. 50% for standard care), improved glycemic control, and reduced diabetes-related emotional distress.

These findings suggest that voice-based AI assistants can be a valuable tool for type 2 diabetes patients, helping them achieve better blood sugar control and improve their overall well-being. By providing personalized and adaptive insulin dosing recommendations, voice-based AI assistants can empower patients to take control of their condition and achieve better health outcomes.

The ability to quickly reach the optimal insulin dose, enhance adherence, and reduce emotional distress highlights the transformative impact of this technology in improving the lives of individuals with type 2 diabetes.

As AI continues to advance, its role in healthcare is expected to expand significantly. Voice-based AI assistants like the one used in this study offer a promising approach to medication titration and personalized treatment guidance, holding the potential to revolutionize chronic disease management and improve patient outcomes.

Explore the VirT2D study for detailed insights into how Healthentia provides personalized coaching for individuals with type 2 diabetes, aiming to enhance patient care in diabetes management.

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Undoubtably, AI was the keyword that dominated technology and new product reviews across all industries for 2023. This comes as no surprise, as AI was offered to the masses like never before – mainly through ChatGPT.

AI has been characterized over the past many decades from several periods of high activity and outputs, followed by “AI winters.” Today we obviously experience a high activity period with lots of outputs and 2023 has been very AI intense, because everyone with Internet access was able to validate its capabilities.

Life Sciences is among the industries with the highest impact of AI, but at the same time one of the most regulated ones. Large Language Models (LLMs) like GPT-4, LLaMA and Gemini are already used for various health and clinical applications like drug discovery, clinical protocol design, documentation, image analysis, task automation, virtual assistant and many more and the expectation is that these will grow much in the coming years. At the same time, regulators around the world are working hard to create the appropriate framework to control AI, in a way to promote transparency, prevent discrimination, protect privacy, enforce safety, and foster responsible innovation. Recently, the AI Task Force of the eClinical Forum and the European CRO Federation (EUCROF), published a white paper presenting the state of play and providing information about AI and ML in clinical research around the globe, with emphasis in US and Europe. Sofoklis Kyriazakos, leader of the AI joint Task Force, says “This white paper is an important milestone for the industry that sees the benefits of AI and puts efforts to understand the environment and how to transform the challenges into opportunities. This has been a very nice team work that underlines the importance of joining forces to make a step forward and adopt AI technologies.”

The year 2024 will start with the stakeholders of the industry being widely exposed to the capabilities of generative AI, most of them having invested resources to investigate or even to deploy such solutions and being more realistic on other matters with emphasis on the feasibility and compliance. We expect that 2024 will be the year of design, development, and compliance for the industry to create solid grounds for the adoption of AI. In Innovation Sprint we perform high impact research and development to identify AI features that can add value to Healthentia and follow a thorough process for implementation to become commercially available in minimum time.

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Study Description

ONCODIR (Evidence-based Participatory Decision Making for Cancer Prevention through implementation) will identify risk factors associated with colorectal cancer (CRC) and will integrate multidisciplinary research methods and technologies (including health policy analytics, artificial intelligence, and decision support theories) to deliver evidence-based and personalized recommendations on colorectal cancer prevention.

Study Details

• Type: HORIZON-RIA, HORIZON-MISS-2022-CANCER-01-01
• Start: 1/6/2023
• End: 1/11/2026
• Duration: 42 months

Our Role

Innovation Sprint will be offering Healthentia for use by healthy citizens to collect data for CRC prevention. Healthentia will be offering advice on citizens’ behavior, based on the collected data and the resulting citizen understanding.

To that extend, Healthentia will be extended both in terms of functionality and content. The new functionality involves:

(1) the development of a new biomarker widget to display risk prediction results automatically collected from other modules of ONCODIR.

(2) the creation of a Kafka client, producing towards, and consuming from ONCODIR, to facilitate integration with the rest of the project.

(3) the introduction of a mechanism to wrap recommendation messages into a dialogue, facilitating both delivery and feedback towards clinicians and policy makers.

In terms of content, CRC-related questionnaires will be facilitated, and a dialogue for recommendation wrapping will be created. The resulting solution will be tested in terms of technology readiness in Laboratory Integration Test 2 and will be delivered to the large-scale pilots for use by the citizens.

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Study Description

HumAIne facilitates advanced and reliable collaboration of experts and AI towards hybrid decision making and support in a variety of industries. It delivers the HumAIne Operating System, built on four technological pillars: Active Learning (AL), Neuro-Symbolic Learning, Swarm Learning (SL), and eXplainable AI (XAI). The HumAine OS enables AI solution creators to build advanced Human-AI collaboration systems that outperform standalone AI and individual experts’ efforts.

Study Details

• Type: HORIZON-RIA, HORIZON-CL4-2022-HUMAN-02-01
• Start: 1/10/2023
• End: 1/9/2026
• Duration: 36 months

Our Role

Innovation Sprint leads the project’s efforts on defining the vision and specifications for human-AI collaboration, focusing on user requirements extraction and use case scenarios definition. These efforts lead on the needs to be covered by the technology developed in HumAIne. We also lead the healthcare pilot, where the HumAIne technology will be leveraged to enhance the AI modules in the patient understanding and advice delivery systems of Healthentia.

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The “AI state-of-play around clinical research” is a white paper recently published by the AI joint Task Force from the European CRO Federation (EUCROF) and the eClinical Forum (eCF). This is a position paper that provides information about AI and ML in clinical research around the globe, with emphasis in US and Europe.

The use cases presented include: AI to enhance clinical trial operations, feasibility exploration according to inclusion/exclusion criteria for recruitment, patient and site recruitment, applications to improve trial operations, risk-based monitoring and data management, task automation, applications for Real World Evidence (RWE), medical coding, and more.

The paper also presents clinical evaluation challenges for the development and validation of AI and concludes with the positions of the AI Task Force, including the promise for improving treatments and clinical research, the risks, and the challenge for using AI given the current state of evolving regulations.

Sofoklis Kyriazakos, leader of the AI joint Task Force, says “This white paper is an important milestone for the industry that sees the benefits of AI and puts efforts to understand the environment and how to transform the challenges into opportunities. This has been a very nice team work that underlines the importance of joining forces to make a step forward and adopt AI technologies”.

See publication: https://www.eucrof.eu/images/23_11_06_AI_State-of-Play_Around_Clinical_Research_15Sep23__vPR1_Approved_by_eCF_SC_for_Release.pdf

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A digital therapy pathway is a healthcare method that employs technology to provide support and therapy services to patients remotely.  These technologies can include wearables and a mobile app for collecting data, an online platform for visualization purposes, video conferencing and chatting for direct communication between clinicians and patients, and a virtual companion service that can support the patient by giving personalized advice [2].

Typically, the process of digital therapy includes many steps [3]. These can include:

1. A first assessment of each patient to determine the best therapy approach
2. Set of clear goals that are specific, measurable, and achievable defined by healthcare professionals
3. Progress monitoring with the use of questionnaires and other digital tools allows healthcare professionals to keep track of the effectiveness of the treatment and adjust it as required

For the last step, a digital evaluation method of the data collected can be implemented where the predefined goals are transformed into critical rules for the health progression. This performance assessment can automatically alert the patient through the mobile app when a measurable attribute is out of range or provide guidance, feedback, and support as patients work towards achieving their goals. This will be achieved with the use of a virtual companion service, that will trigger a dialogue on a regular basis in order to give advice in a personalized manner to ensure progress towards the goals.

To conclude, digital therapy pathways can be an effective way to deliver therapy services to patients who need continuous monitoring and have difficulty accessing traditional in-person therapy, such as those who have mobility issues and also it can thrive in areas where demand for traditional treatment outgrows its capacity [3].

Up to this point, digital therapy is mostly used to supply psychotherapy [4] and Healthentia can be used to provide such services as it incorporates all the aforementioned tools, aspiring to effectively support also other diseases than mental health conditions. Healthentia can provide feedback and in combination with a virtual coaching service, orchestrated by a predefined digital clinical pathway, it can create a personalized approach to improve someone’s health. However, it is important to always take into account patients’ mental health needs, as in some cases in-person therapy may be required.

References

[1] “Global Digital Therapeutics Market Report 2022: Analysis & Forecasts 2020-2026 – Market to Reach $12.1 Billion by 2026” (accessed May 15, 2023).

[2] C. Moore, “What Is Digital Therapy and How Does It Really Work?”, Quenza, Aug. 13, 2021. (accessed May 15, 2023).

[3] “Digital Therapeutics: How Software Can Treat Diseases” AltexSoft. (accessed May 15, 2023).

[4] A. Nwosu, S. Boardman, M. M. Husain, and P. M. Doraiswamy, “Digital therapeutics for mental health: Is attrition the Achilles heel?”, Frontiers in Psychiatry, vol. 13, 2022, Accessed: May 15, 2023. [Online].

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We are excited that Innovation Sprint is short-listed for the Innovation Radar Award on Disruptive Health, together with 9 other companies.

Since 2015 the European Commission has been awarding the annual Innovation Radar Prize to great EU-funded innovators, putting a spotlight on the high-potential innovations they are trying to get "out of the lab and into the market". Intelligence gathered by the Innovation Radar is used to identify innovators who can then compete for the prize. 

The top innovation finalists are grouped into 3 categories of 10 each, out of a tremendous number of applications for the 2022 Innovation Radar Prize. The 3 categories are: 

• Disruptive Health 
• Purpose-driven & Green 
• Kickstarter 

The Innovation Radar Prize 2022 includes voting from the public, which will assist the jury in their search for the best innovation of 2022.   

Innovation Sprint is one of the 10 candidates for the Innovation Prize of Disruptive Health and we are looking forward to receiving your vote here. 

Why have we been selected as finalists?  

Sofoklis Kyriazakos, CEO of Innovation Sprint, explains the reasons that brought Innovation Sprint to the final of the Innovation Radar Prize 2022.

Innovation is part of our company name, but foremost it is part of our DNA. Our purpose to improve health outcomes and outperform existing therapeutic care-paths using innovative technologies, has been an exciting and long journey from the day we started until where we are now commercializing our novel MedTech product in this highly regulated and challenging domain. Our product Healthentia, is a certified Software as Medical Device that uses best of breed of Internet, IoT, AI, and Big Data technologies together with clinical and regulatory knowledge to support healthcare professionals to monitor their patients, through digital clinical pathways addressing chronic conditions. Healthentia has been used so far in patient cohorts with cancer, heart failure, COPD, Covid-19, HIV, and other conditions. The clinical evidence that has been captured so far demonstrates increased adherence to treatment and Quality of Life, while we are currently running clinical studies, in which we monitor endpoints like mortality and rehospitalization. In Belgium, we are already at level 2 of the mHealthBELGIUM pyramid link, which paves the way towards reimbursement. We have market authorization for Europe and we have finalized the PreSub evaluation from FDA for 510(k) clearance.  This journey was strongly influenced by our research activities, many of them performed under EC R&D projects, which contributed to the inclusion of key innovations in Healthentia and bringing the product to another level; a level that is backed up by science. We are committed to continuously improving Healthentia to disrupt health, by outperforming traditional treatments, thus contributing to Good Health and Wellbeing, one of the key sustainability goals of the UN. In this trajectory, we will continue to improve Healthentia in a way to address demanding market needs, regulatory challenges, while implementing our purpose to use innovative technologies to improve health outcomes and outperform existing therapeutic care paths. 

 

What are the key innovations so far? 

Aristodemos Pnevmatikakis, R&D Director of Innovation Sprint, explains Healthentia smart services and the role of EU co-funded projects in implementing them. 

Healthentia is a secure big data platform for patient data, providing two applications to its two categories of end-users: a) Patients use the mobile app to facilitate the collection of their data and delivery of info and feedback, and b) healthcare professionals use the portal app to setup studies or programs to monitor their patients both as a cohort, or as individuals. Both applications are empowered by smart services: On the one hand, clinicians can handle patients in clusters and get insights from inference on models learnt on patients’ data. On the other hand, patients benefit from feedback on important attributes in their data, as these attributes are highlighted by explainable AI techniques. 

EU co-funded projects have been playing a dual role in Healthentia. Looking back at our origins, the company started as the exploitation output of the eWALL project. Many more recent and ongoing projects have been instrumental in the implementation of our smart services. They allow us to run studies across Europe, increasing our volume and diversity of subjects, thus reducing the bias in the models we can learn from the collected data. They also facilitate early testing of our models in prototypes of our smart services. Last but not least, they facilitate our scientific output, by providing us with collaboration opportunities and funding for publications.

At Innovation Sprint we commit to providing patients and professionals with continuously improved smart services, aiming at helping the former improve the life of the latter. 

 

How do we combine innovation with needs? 

Konstantina Kostopoulou, Chief Product Officer of Innovation Sprint, explains how Healthentia has evolved into the solution it is today by combining innovation with the different stakeholder’s needs from the market.

Healthentia has been developed through participatory design and evaluation from stakeholders. Both the application for the patients and the portal for the clinicians are co-designed with patient focus groups and workshops with clinicians. In this product development journey, we have done user studies and interviewed patient focus groups after asking them to accomplish a set of tasks on the mobile app. They have evaluated the easiness and experience of using Healthentia. For similar tasks their valuable feedback is collected for feature optimizations. Regarding the clinician portal, regular sessions are taking place to collect feedback from clinicians to address their needs and optimize the assessment of a patients’ health state. We work together with clinicians from customizing their dashboards or enhance their patient record with selected clinical date from a hospital’s EMR. The Healthcare market include a great range of Stakeholders including but not limiting to, patients, caregivers, doctors, nurses, patient organizations, government, insurance companies, communities and pharmaceutical firms. Our collaborations within this market influence our product roadmap to meet the needs and expectations in this changing environment of digital Health.

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Study Description

AZIMUTH model of care is delivered through Healthentia; a Software as a Service (SaaS) medical device via a mobile application for patients and a clinical dashboard for clinicians for Remote Patient Monitoring (RPM), Patient Support Programs (PSP) and Digital Therapeutics (DTx). This innovative scalable model of personalised remote heart failure care is based on good clinical practice standards and international guidelines for heart failure management and has been designed taking into consideration both the optimization of in-hospital care delivery models as well as the often unmonitored out-of-hospital and home settings

The first phase of the study has successfully validated the feasibility, patient acceptance and perceived value of the app-based model of care, demonstrating increased user engagement in patients that leads into improved adherence to treatment.

Dr. Domenico D’Amario

Senior Cardiologist at the Gemelli IRCCS Polyclinic Foundation

Study Details

• Type:  Interventional
• Disease:  Heart Failure – Chronic Ischemic Disease
• Sites: Fondazione Policlinico Gemelli
• Start:  Phase 1: Nov-March 2021, Phase 2: Jul-Jan 2023
• Population: 300 patients
• Duration: Multiphase project

Study Design & Method

Heart failure is the leading cause of death and hospitalisation in patients aged > 65 years and a major and growing medical and economic burden, with high prevalence and incidence rates worldwide. The recent pandemic crisis has made even more evident, some critical aspects in the management of complex chronic diseases, such as heart failure, and these could be exacerbated by the increased demand for care in the future. Therefore, a significant effort must be made to align services with patients’ care needs by identifying a shared model that can exploit the most advanced technologies to enhance disease deterioration, provide adequate integration between hospital and territorial services, increase appropriateness, and reduce waiting times for specialist services.

Testimonial

The care pathway for patients with heart failure includes several steps, from acute phase treatments to chronic phase patient follow-up. This requires a close dialogue between hospital cardiologists and community medicine, especially once the patient returns home, after being discharged from a third-level hospital. But at the moment this complex path is very fragmented.

Validated the feasibility, patient acceptance and perceived value of remote patient monitoring

Demonstrating increased user engagement in patients that leads into improved adherence to treatment

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Study Details

• Type: Research
• Disease: COPD and Comorbid Complex Chronic Conditions (CCCs)
• Sites: The Netherlands, Italy, Estonia
• Start: March 2021
• Population: 710 patients
• Duration: 48 months

Study Description

Shared-decision making for tailored Comorbid Complex Chronic (CCC) disease management, crossing care paths towards daily life, and early proper treatment to prevent CCC disease progression. Identify individual multi-morbid CCC exacerbations and develop tailored referrals to a multidisciplinary, adaptive virtual companionship program for COPD patients with CCCs.

The RWD from daily life measurements and existing medical knowledge-base will be integrated based on inclusive models representing e.g. patients, healthcare (providers), scientists, policymakers, and industry. The RE-SAMPLE study develop privacy-securing data management and AI prediction modeling to tailor local personalised treatment programs using accurate predictions of multi-morbid exacerbations and personalised interventions.

Study Design & Method

Patients will receive an activity tracker (i.e monitoring bracelet) that will collect at a daily basis Real-World Data like activity (i.e. steps per day), sleep, and vital signs. The device will be delivered during the recruitment, and each patient/participant will pair the device through the Healthentia application downloaded on the smartphone.

During the whole observation period, patients will report through weekly dedicated questionnaires for RWD collection and management mechanisms. Also, the study aims to implement and test the real-time predictions of the RE-SAMPLE-trained models applied to new data. 

RWD to monitor COPD symptoms beyond scheduled medical check-ups

Personalised treatment and a virtual companionship programme

Multidisciplinary approach (respiratory medicine, AI, RWD, privacy, ethics, data protection, health policy)

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A biomarker is a naturally occurring characteristic by which a pathological or physiological process can be identified. A digital biomarker comprises of objective, quantifiable physiological and behavioral data, measured utilising digital portable, wearable, implantable or digestible devices, to be used to predict and manage health-related outcomes.

Innovation Sprint has built a composite contextual biomarker-based οn multiple aspects of Real-World Data (RWD), collected from people unobtrusively, while following-up their normal living routine. It is composite in the sense that it is not based on a single measurement, but rather on multiple diverse measurements (objective RWD) and peoples’ reports (subjective RWD). It is contextual in the sense that not only the person is measured, but also the person’s lifestyle context: social and environmental aspects complement the more traditional physiological and psychological ones.

Our RWD

At Innovation Sprint we are strong advocates of the empirical knowledge that lifestyle is a strong determinant of health. Hence our biomarker is based on RWD spanning four important aspects of a person’s lifestyle:

 Physiological RWD quantifies physical behaviour (active vs sedentary lifestyle as measured by steps walked, floors climbed, activity types, minutes in different intensity levels or heart rate zones, resting heart rate, sleep characteristics) and includes body info (height, weight, gender, race), nutrition (water, other liquids, food) and symptoms (body temperature, cough, diarrhea, headache, nausea, pain, etc.).

 Psychological RWD quantifies at a simple level mood, and in more complex situations mental state collected via elaborate, domain-specific questionnaires. Measurements can also play a role, either directly e.g. facial expression recognition, or indirectly, e.g. weather where people are living).

 Social RWD quantifies social activity of people. This can be measured indirectly from the usage of the phone (diversity, duration, frequency of calls) and social media (diversity, number, frequency of interactions). More direct information can be reported using questionnaires on activities with friends, family or co-workers.

 Environmental RWD indicates the quality of life. Usually, reported by the users. Measurements of living or working environment quality are made with commercial devices (e.g. air quality meters).

AI for discovering our biomarker

Biomarker discovery at Innovation Sprint is done in three stages

Definition stage, where the domain experts select the clinically significant outcomes that need to be predicted by the biomarker(s).

Manual RWD selection stage, where domain knowledge is applied to refine our generic RWD selection into those lifestyle aspects that are relevant to the disease/condition in question.

Iterative design stage: Machine Learning/AI algorithms are used to train a proprietary classifier using the elected RWD to predict the selected clinically significant outcomes. The classifier is applied on new data  yielding predictions and insights leading to digital therapeutics.

Validating our approach

We employed RWD collected over 7 years to train a biomarker that predicts significant weight changes. Such a biomarker is important for patients with several diseases (e.g. NAFLD), as well as for the general population interested in well-being. We achieved over 80% or correct prediction of the outcome, while we also analysed the different RWD aspects that led each individual to positive or negative outcomes, in order to offer personalized coaching services.

As we speak, we are utilising the same approach in other therapeutic areas, e.g. cervical cancer, to predict low toxicity events. Starting from 2021 we will validate this hypothesis in much larger cohorts, targeting –among others- COPD patients with Cardiovascular Disease comorbidities.

We will keep you update on our observations and findings!

Aristodemos Pnevmatikakis
R&D Director, Innovation Sprint

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