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.

The post Patients discussing with Healthentia appeared first on Healthentia.

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

The post SaMD & mHealth apps: Status and promising results for improvement of health outcomes appeared first on Healthentia.

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.

The post Oncology Virtual Ward appeared first on Healthentia.

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.

The post Discover the Nutrition widget appeared first on Healthentia.