CATEGORY: Digital Public Health

SOURCE: Frontiers – Public Health, December 2024, Volume 12-2024; https://doi.org/10.3389/fpubh.2024.1488687

Benchmarking the clinical outcomes of Healthentia SaMD in chronic disease management: a systematic literature review comparison

Sofoklis Kyriazakos¹ , ^*Aristodemos Pnevmatikakis¹, Konstantina Kostopoulou¹, Laurent Ferrière² , Kyun Thibaut² , Erika Giacobini³ , Roberta Pastorino^3,4 , Marco Gorini⁵ , Peter Fenici⁵

¹Innovation Sprint srl, Clos Chapelle-aux-Champs, Brussels, Belgium

²COVARTIM, Watermael-Boitsfort, Belgium

³Section of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy

⁴Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy

⁵AstraZeneca SpA, Milano Innovation District (MIND), Milano, Italy

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Managing chronic obstructive pulmonary disease (COPD) alongside other chronic conditions (CCs) like depression requires a comprehensive and innovative approach. As part of the RE-SAMPLE research project, Healthentia, our advanced SaMD solution, addresses this challenge by integrating real-world data (RWD) to enhance disease management and improve daily life for patients. For more information about the intended use of the device and the medical modules, please consult: https://healthentia.com/medical-device/

This research-driven initiative bridges the gap between clinical research and real-world healthcare, supporting two phases of COPD and CCs management:

• Phase 1: Healthentia collects RWD to track disease progression, uncover patterns, and predict exacerbations. These insights enable proactive care, empowering healthcare providers to anticipate and address patient needs more effectively.
• Phase 2: Through its Virtual Companionship Program (VCP), the platform delivers tailored self-management tools, including lifestyle coaching, goal-setting, and real-time medical suggestions. Patients gain the confidence to manage their health actively, while healthcare providers benefit from actionable insights via a clinical dashboard. This enables personalized, adaptive care strategies, ensuring better outcomes.

Healthentia redefines the standard of COPD care in hospitals across Europe by seamlessly integrating real-world insights with technology-driven solutions. By prioritizing both patients and providers, it transforms the approach to managing not only COPD but also co-existing chronic conditions, helping patients regain control over their health and well-being.

This World COPD Day, Healthentia reaffirms its commitment to innovation, providing hope and support for millions living with COPD.

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World Diabetes Day is an essential reminder of the global impact of diabetes. Especially type 2 diabetes affects millions of lives and continues to grow at an alarming rate. Currently, type 2 diabetes makes up more than 90% of the over 450 million adults worldwide that are living with diabetes. By 2030, this number is expected to reach 578 million. This increase not only strains healthcare systems but places a significant burden on individuals who must learn to live with and manage this chronic condition.

At Innovation Sprint, we believe in empowering patients by providing Healthentia, the patient companion app they need to effectively manage their condition at home. Healthentia is designed for chronic disease management, offering a personalized approach to each patient. By collecting data on daily habits, physical activity, and health metrics, Healthentia offers customized support and advice that aligns with the patient’s health goals and current state. For more information about the intended use of the device and the medical modules, please consult: https://healthentia.com/medical-device/.

For type 2 diabetes patients, effective management is key to preventing serious complications like heart disease and nerve damage. While healthcare professionals are essential, most daily care happens at home, requiring regular monitoring, a balanced diet, physical activity, and adherence to treatments. Many patients find this challenging, especially without personalized guidance.

In our ongoing research study with type 2 diabetes patients, we are taking this a step further. By gathering detailed data, we can provide personalized recommendations that evolve alongside the patient. For instance, if a patient’s activity level drops or they struggle with diet management, Healthentia can recognize these patterns and suggest targeted interventions that encourage gradual improvements. This constant, personalized feedback loop helps patients feel supported and more capable of taking control of their health.

On this World Diabetes Day, let’s acknowledge not only the challenges but also the strides being made in digital health to enhance self-management for millions of people worldwide. With solutions like Healthentia, we’re creating a future where living with diabetes doesn’t have to be so daunting.

“In our new study with Diabetes Mellitus Type 2 patients in the University of Thessaly we are integrating Healthentia, a digital health solution, to evaluate how digital health tools can enhance traditional healthcare. The patients thus far responded positively, embracing the mobile app for their support at home, while the healthcare professionals benefit from the web portal, which provides real-time data monitoring and insight into the tailored advice each patient receives.” 

Tamouridis Stefanos MD, MSc
Clinic of Endocrinology and Metabolic Diseases, General University Hospital of Larissa
University of Thessaly

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At Innovation Sprint, we are advancing new features for Healthentia to improve patient analysis and care. One of our key initiatives is the development of models that provide both long-term and short-term predictions for patients. In the long-term, these models forecast a patient’s health progression based on their clinical, physiological, and behavioral data, while in the short-term, they predict the most suitable next steps for intervention. A critical focus is on ensuring these intervention techniques are personalized to each patient’s unique condition, both for long-term management and immediate care. By tailoring interventions to the individual, we aim to achieve more accurate results, enhance patient outcomes, and improve the overall care process.

Our experiments indicate that model quality strongly depends on the data, both its volume and quality. Data volume is determined by the number of patients having already used Healthentia for a particular pathology. Data quality is determined by each patient’s adherence to the data collection plan. Both data volume and quality are seldom ideal. At least during the first studies Healthentia is used for a particular pathology, there are simply not enough patients to give us a satisfying volume of data. Moreover, patients are of varying degrees of capability to collaborate with Healthetnia’s mobile app. Omissions and errors can happen, and then reduce data quality.

Training with suboptimal data volume and quality leads to suboptimal models, suffering from overfitting and bias. The alternative is to reside to the knowledge of the experts, the healthcare professionals. Their knowledge is captured within rule-based expert systems. That yield the necessary analysis of the patients. At Innovation Sprint we have been experimenting with ways to combine the information the data offers and the expertise the healthcare professionals offer. The HumAIne project offers Healthentia the novel tools to achieve this combination.

HumAIne is an EU co-funded project under the topic HORIZON-CL4-2022-HUMAN-02-01 that started in October 2023. 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, Neuro-Symbolic Learning, Swarm Learning, and eXplainable AI. The HumAine OS enables AI solution creators to build advanced Human-AI collaboration systems that outperform standalone AI and individual experts’ efforts.

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. More specifically, we will be employing Neuro-Symbolic Learning to learn models not simply on data, but on the combined information of data and healthcare professionals’ expertise. We will be comparing the models learnt traditionally to those employing Neuro-Symbolic Learning, both in terms of prediction metrics but also in terms of their effectiveness in the overall behavioral change framework of Healthentia.

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Healthentia tries to change patients’ behavior using a novel behavioral change framework and within it, different techniques like setting and monitoring goals, offering visualizations of their data and discussing aspects of their behavior with them.

While dialogue selection is the most important task of Healthentia’s behavioral change framework, keeping these dialogues personalized and interesting is also important in the long run. Dialogues need to be both to the point, to persuade the patients to go through them and have an impact, but also need to be interesting for the patients to keep working with them. Setting dialogue selection aside, in this blog post we discuss the two features of Healthentia dialogues that make them interesting to their recipients. We also mention a third, experimental feature based on LLMs, that is still on a lab level.

Healthentia dialogues are personalized with data from the patients. While the dialogues are selected when certain conditions arise, the content delivered is augmented with actual patient data, which could be simple – for example – static pieces of information like the patient’s name or sex. They can also be some personalized goals set for the patient by a doctor. Finally, they can also be the results of statistics on some data of the patient, like the average steps walked in the past week, or the frequency red meat is consumed in the past month. This way actual patient data can be compared to the personalized goals, while the patient is addressed by name.

Healthentia dialogues allow patients to voice their decisions and affect to some degree their therapy. While input variables augment dialogue nodes with data from the patient receiving the dialogue, output variables allow the patient to communicate some intent back to Healthentia. Such output variables can be simple feedback on whether the patient considered the dialogue useful. Or they can carry the result of some negotiation about a goal the patient has difficulty reaching. Some output variables are just stored to be part of the patient’s data, while others trigger processes that change elements of the therapy. An example of output variable creating new data is the feedback, with the dialogue selection utilizing accumulated feedback. An example of output variables triggering processes are the negotiations of goals or frequency of contact with more dialogues.

Future Healthentia dialogues can have dynamic nodes generated by LLMs. At a lab level, we experiment with two ways to add dynamically generated nodes to the dialogues. On the one hand, patient data summaries can be created using LLMs. On the other hand, LLMs can present data from reference material. In both cases, the advantage of using LLMs over manually creating the text is that the LLM text is quite variable across repetitions of the dialogues. The text variation has to do with the tone of voice requested, the data at hand and the inherent variability of the models themselves. Data summaries are simpler to create, utilizing the expressive power of a trained LLM. Selecting and presenting material from a reference library is more involved. While the expressive power of LLMs is still employed, the library of reference material is also set up and analyzed, while the LLM is restricted to get information from this library alone.

Employing the existing features in the dialogues and looking ahead to more dynamic options, Healthentia can keep the patients interested, since the dialogues are dynamic, personalized and offer two-way exchange of information, both from Healthentia to the patient, but also from the patient to Healthentia.

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Software as a Medical Devices (SaMD) and mobile health (mHealth) applications have revolutionized the healthcare landscape in the areas of Remote Patient Monitoring (RPM) and Digital Therapeutics (DTx). These technological advancements offer a range of benefits, from improved patient engagement and real-time monitoring to personalized treatment plans and enhanced clinical outcomes. During the continuous design, development, and validation of our medical device Healthentia, we have conducted a comprehensive systematic literature review to assess the current status and efficacy of these digital health interventions and benchmark the results with our finding from the clinical evidence we have collected from various studies. The results are rather positive, demonstrating how SaMDs like Healthentia can support the management of chronic diseases, satisfying patient safety and performance requirements.

The current landscape of SaMD and mHealth apps is characterized by rapid innovation and widespread adoption, especially after the Covid pandemic, which might have worked as a catalyst for digital transformation in digital health. SaMD refers to software intended to be used for medical purposes that perform these purposes without being part of a hardware medical device. mHealth applications, on the other hand, encompass a wide range of digital tools designed to support health and wellness through mobile devices. mHealth applications are not necessarily medical devices and therefore, they often don’t comply with the regulatory framework. Both SaMD and mHealth apps have seen significant advancements, driven by the increasing need for accessible, efficient, and patient-centric healthcare solutions. Regulatory frameworks have also evolved to keep pace with these innovations, ensuring that these technologies meet rigorous safety and efficacy standards. While the potential of SaMD and mHealth apps are immense, there are several challenges that need to be addressed to fully realize their benefits, such as data privacy concerns, integration with existing healthcare systems, and user adoption barriers.

The review involved a scientific process of identifying, selecting, and analyzing relevant studies that evaluate the effectiveness of SaMD and mHealth apps in managing chronic diseases, using the PICO (Population, Intervention, Comparison, Outcomes and Study) framework. The selected studies provide a robust evidence base, highlighting the positive impact of these technologies on patient outcomes.

This post aims to provide a comprehensive overview of the current status of SaMD and mHealth apps, highlight promising results, and discuss the potential these technologies hold for improving health outcomes and provides only an overview of a peer reviewed paper that we expect to publish in the coming months.

The safety and performances of such solutions for chronic disease patients is thoroughly discussed in various meta-analyses that have been retrieved and analyzed and include 14 meta-analyses for diabetes Type II, cardiovascular diseases, COPD and cancer. Those meta-analyses allow to identify the main clinical performances that could be expected for Healthentia to define the acceptance criteria for the risks/benefits acceptability. In addition, clinical studies originating from the selected systematic reviews were analyzed to check which features were put in place in the eHealth solutions under evaluation, allowing a comparison with those of Healthentia, resulting in prospective controlled studies on several chronic diseases:

• Diabetes Type II: 24 studies, about 1.200 patients in the intervention group
• COPD: 1 study, 36 patients
• Cancer: 3 studies, about 150 patients
• Heart diseases: 7 studies, 560 patients

Summarizing the results from these studies we provide some data both biomarkers and other clinical endpoints that demonstrate the potential:

• HbA1c level: Decreased in several of the studies.
• Physical activity & performance: Significant improvements in exercise capacity.
• Body fat: Significant improvements.
• Sleep: Significant improvements in sleep time.
• Self-care/management: Self-reported improved the self-monitoring of patients’ blood glucose levels, diet, exercise, other self-management skills, and knowledge of the disease.
• Depression & anxiety: Self-reported improved effect on their psychological status.
• 6MWD: Significant improvement.
• (HR)QoL: Difference between the DQoL–Social/Vocational Concerns subscale scores was statistically significant.
• Hospital readmissions: The 30-day hospital readmission rate was much lower, compared with the national readmission rates.
• Adherence: Significant improved medication adherence.

The list of results above is indicative and is only provided to showcase the potential and is not to be considered as a scientific outcome itself. Scientific results will be presented and analyzed in the Systematic Literature Review paper in the coming months, which will also include evidence and findings from studies that used Healthentia. By sharing our findings and experiences, we hope to contribute to the ongoing dialogue on digital health innovations and their role in transforming chronic disease management. Furthermore, the insights gained from the literature review and our benchmarking analysis may offer valuable guidance for healthcare providers, patients, and policymakers as they navigate the evolving landscape of digital health.

Looking ahead, the future of SaMD and mHealth applications appears promising, with the potential to significantly enhance the quality of care for patients with chronic diseases.

Sofoklis Kyriazakos, CEO

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Study Description

Virtual Ward study is leveraging Healthentia solution to monitor and interact with Ovarian Cancer Patients. The Phase 1 scope pertains monitoring and support through the platform for those patients who have been treated with surgery for ovarian cancer and covers the 30-days follow-up period after post-surgery discharge. This is a critical phase of the overall care pathway, with need for the patients to receive guidelines about daily habits, treatment adherence, use/state of post-surgical devices, check of wound heal, potential inflammations, etc. Likewise, the opportunity for clinical staff to have more frequent, focused remote interactions where they can capture any evolution on overall health status, mild and severe adverse symptoms, practical issues during the recovery stage, will result in better quality of care and more efficient management of the patients group.

Study Details

• Type: Virtual Ward
• Start: September 2023
• Phase 1: 30-days follow-up period, Phase 2: pre-surgery and post-surgery treatment period

Our role

Healthentia is the essential link between gynecological surgery patients and their healthcare team. It enables seamless communication, allowing patients to report symptoms, share vital data, and interact with professionals. With features like a Virtual Assistant, we provide guidance and support during the crucial post-operative phase, enhancing care effectiveness and patient experience. Through collaboration with healthcare facilities like the Gemelli Generator – Real World Data Facility, we contribute to innovative care models, ensuring personalized and timely interventions for patients.

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Among the many transformative solutions emerging in Healthcare, Virtual Wards have garnered considerable attention for their potential to revolutionize patient care. By leveraging digital tools and remote monitoring technologies, virtual wards offer a paradigm shift in how hospitals deliver services and how patients experience healthcare. In this blog post, we will highlight some benefits of virtual wards, exploring their impact on hospitals, patients, and the broader healthcare landscape and give details of our implementation of our recent use cases.

The biggest benefit of these wards are to facilitate continuous monitoring of patients’ health post discharge, reducing readmissions by providing post-discharge support and guidance. Access to care improves, especially for those in remote areas. Cost savings accrue through optimized resource utilization and reduced hospital stays. Patients feel empowered to manage their health with remote tools and education, enhancing their overall experience. Flexibility and convenience are paramount, allowing patients to receive care from home. Moreover, virtual wards support population health management by enabling effective monitoring and intervention based on collected data. Overall, they promise improved outcomes, cost-effectiveness, and patient satisfaction.

Healthentia has been used in two scenarios in Oncology. We will explore here the one from the Gynecology Oncology Unit that offers a direct line between surgeons and patients who will be able to report any symptoms or complications during convalescence.
The continuous observation and assistance to patients, monitoring their health condition outside the hospital, make management and interaction with the clinical team increasingly effective during the post-operative period, help research towards increasingly personalized care models. These are in summary the objectives of the Virtual Ward, a new App dedicated to gynecological patients undergoing surgery at the Gynecology Oncology Unit of the A. Gemelli IRCCS University Hospital, directed by Professor Giovanni Scambia.

The patient who decides to enter the Virtual Ward can download the Healthentia App for free on her mobile phone and thus begin to share numerous information that will help the clinical team to have a complete and updated picture of her health status. The Care Managers present the care path during the interview with the patient admitted to the ward and inform the doctor about the state of health and promptly notify him in case of need and urgency. The process begins at the time of discharge from the hospital and lasts approximately 30 days during which the patient receives a series of questionnaires which aim to evaluate the progress of the patient’s health condition with particular attention to signs and post-operative symptoms, state of the surgical wound and monitoring of any devices, such as catheters or drainage.
The patient also has the possibility of sharing numerous optional data such as vital parameters and multimedia content (photos, videos, screenshots of reports external to the facility), as well as being able to interact with healthcare professionals immediately and continuously.

“Virtual Ward is an integral part of our clinical practice – explains Professor Anna Fagotti , Director of the Ovarian Carcinoma Unit of the Agostino Gemelli IRCCS University Polyclinic Foundation. – This App is useful for patients to share, understand and resolve any health problems that arise while they are at home, accompanying them in the days following discharge from hospital. The home convalescence phase – continues Professor Fagotti – is a difficult emotional moment for patients, there are many uncertainties and many questions about the treatment path. Furthermore, patients may have little knowledge of the disease and difficulty recognizing signs and symptoms of clinical importance.”

The App can be an extremely useful tool, in fact it includes a Virtual Assistant who conveys useful information and advice, for example, relating to the lifestyle to follow in the post-operative period, thus covering an important educational role.
“This tool – concludes Professor Fagotti – is also very useful for us doctors. The data sent by the App allows us to immediately detect the onset of symptoms to correlate with possible complications related to the surgery which otherwise could emerge late. Through the App we can interact with the patient immediately and continuously and intervene in a timely and punctual manner.”

The close collaboration between the healthcare team and the Gemelli Generator – Real World Data Facility, coordinated by Dr. Stefano Patarnello , has made it possible to create a care model capable of integrating and supporting traditional post-operative monitoring means.
The experience of the clinical team made it possible to define an innovative assistance path: the questionnaires were defined to allow the patient an effective and pragmatic interaction; all the information collected allows a holistic view of the state of health; finally, the team of Care Managers dedicated to the development and management of the new care path is the focal point for guaranteeing the provision of the service on a daily basis.
The Gemelli Generator – Real World Data project team, coordinated by Dr. Alice Luraschi , worked together with Innovation Sprint company, manufacturer of the Healthentia medical device, to offer an intuitive service and tool of immediate benefit in clinical practice and in the patient’s daily life .

Source: https://www.policlinicogemelli.it/en/news-events/al-via-virtual-ward-app-per-monitorare-a[…]io-le-pazienti-ginecologiche-post-intervento-chirurgico/

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In the past many years, it became clear to the scientific community that nutrition affects everthing, from therapy outcomes to disease prevention and evolution. In our recent studies with Healthentia, we have seen an increase in the popularity of the nutrition feature. Driven by this insight, we present a holistic food tracking approach for patients, including meal photos, food category logs and dietary intake with separate meals calculating grams, nutrients, and calories.

Up to now, we have seen an interest from clinicians to track certain food categories throughout the week/day like logging fibers or sugary drinks for example and monitoring whether this habit has changed throughout a period.

The need was now clear, that clinicians were looking into the whole daily dietary intake of patients and therefore the widget had to support a bigger food database, portions, nutrient macros, and calories. Dieticians and clinicians need to monitor in more detail the number of meals, types of foods, combinations, and portions across the day. If there is no need for analytical food input but an image is sufficient than the patients can upload a food’s image.

Clinicians have the option to activate a nutrition widget for a specific study, allowing patients to choose food categories, portion sizes, and weights from a food database.

So, what exactly does our Nutrition Widget do? Let’s dive in.

Streamlined Food Tracking

From the App by pressing the nutrition widget patients can effortlessly add a meal from the daily view of the nutrition widget, inputting consumed food categories with serving sizes or weights, and categorizing the meal type from a predefined list (e.g., Breakfast, Breakfast Snack, Lunch, Dinner, etc.). They can save these meals as favorites for future use.

Last it is easy to edit your meals with ease. Made a mistake or decided to swap out that snack for something healthier? No problem. Simply edit your entries to reflect your choices accurately.

Picture 1: Home widget and Daily view of meals

Personalized Insights

Understanding your nutritional habits is key to making informed decisions about your diet. With our widget, you gain access to personalized insights into your eating patterns. Visualize your intake of different food groups, identify areas for improvement, and celebrate your successes along the way.

Picture 2: Weekly/monthly view (macros) and food categories

Clinicians Nutrition meal reports

Clinicians can see the calories and macro nutrients distribution across time for each patient but also investigate the specific food categories that were consumed and how these change overtime.

Picture 3: Nutrition meal report

We are very eager to see this feature being used across more studies.  Our Nutrition Widget is more than yet another tool for tracking food intake—it is part of your disease pathway and can guide you towards a healthier, more balanced nutrition that can help you improve health outcomes and achieve better results in therapy treatments like immunotherapy.

We are excited to empower your patients on your journey towards better nutrition and overall well-being. You can explore other features or areas of expertise through our Healthentia website.

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Our research paper titled “HIV Patients’ Tracer for Clinical Assistance and Research during the COVID-19 Epidemic: A Paradigm for Chronic Conditions” has been published in the Special Issue Reprint titled “Advances in AI for Health and Medical Applications“.

This publication shares insights derived from the INTERFACE study; a collaborative effort conducted by Healthentia that provided support to HIV patients during the 1^st wave of the Covid-19 pandemic.

The INTERFACE study focused on leveraging e-health and remote monitoring to enhance care for people living with HIV/AIDS (PLWHA), especially during the SARS-CoV-2 pandemic period. Using Healthentia, a mobile application with decision-making algorithms, the study collected data on symptoms, treatment adherence, and quality of life, processed through an innovative e-Clinical platform. Machine learning algorithms aimed to create a digital composite biomarker for HIV-related alerts. With over 1500 stable HIV patients in a university hospital, the study aimed to improve patient care beyond the pandemic, showing promising results in monthly data for learning predictive models.

INTERFACE, an interventional research initiative, collected real-world data (RWD) on patient outcomes. Voluntarily enrolled patients provide informed consent through the app or eConsent process. Operating within Gemelli Generator Real-World Data (G2 RWD), the study integrated Healthentia data into a tailored e-Clinical environment provided by Innovation Sprint. Healthentia combined traditional electronic patient-reported outcomes with lifestyle, behavioral, and health-related data, employing AI and machine learning to predict outcomes and generate prevention alarms, supporting clinical decision-making during trials.

This comprehensive approach proved crucial for managing HIV patients during the COVID-19 pandemic, addressing physical and mental health challenges. The e-health system identified health issues, tailored interventions, and served as a pre-screening tool for chronic patients during the epidemic. Preliminary evidence indicated effective prediction of alerts, with ongoing data collection and integration enhancing accuracy. Future development involves SHAP analysis, assessing the machine learning system’s impact on health indicators, and evaluating its application in advanced chronicity management. The study aimed to provide tailored and effective care for individuals living with HIV, especially in challenging times like the COVID-19 pandemic.

Explore the INTERFACE study for detailed insights into how Healthentia offered assistance to individuals with HIV during the Covid-19 pandemic.

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