Don't Miss the Launch of DeHealth Revolutionary AI-Powered Product!

Many researchers believe that the most significant advancements in medicine occur in emergency circumstances, with pandemics and wars being the most prominent examples. And indeed, it is the case: whether it's typhoid, the Spanish flu, or COVID, the primary goal has always been to develop the most universal and safe vaccine, which serves as a starting point for a range of necessary medications. In this array, every infected individual or those in need of preventive measures can find the right medicine for them. 

Nonetheless, a personalized approach to treatment is increasingly becoming a priority from the outset, rather than a logical extension. This very strategy allows us to overcome human error and randomness. The probability that certain specific tests and analyses won't be conducted, given the initial availability of all the necessary information, gradually approaches zero. The most compelling motivation for this is, unsurprisingly, the statistics from the WHO. According to their data, a significant portion of deaths result from medical errors, with the majority being consequences of haste or a lack of essential information. More than 1.5 million people die as a result of these errors. According to global statistics, medical errors in prescribing medications are a more common cause of fatalities than road traffic accidents and cancer. 

Steps confirming the critical need for a personalized approach were taken during the recent pandemic: COVID-19 and all its derivatives provided a significant boost to the development of biotechnology and the entire field of medicine as a whole. 

Look at the diagram of the IDG research results: 55% of companies that haven't transitioned to digital platforms have significantly accelerated work on many projects. In turn, 37% explicitly state that they either already use or are natively integrating advanced technologies, while a total of 54% position themselves as fully transformed. 39% are creating specific strategies for working with embedded digital technologies. 

APPLICATION OF TECHNOLOGIES IN PRACTICE 

One of the many practical cases demonstrating the usefulness of such an approach is the virus spread model proposed by medical researchers from Mount Sinai, predicting the mortality risk of individual COVID-19 patients. In this study of developing and validating a predictive model, scientists applied machine learning methods to clinical data from a large group of COVID-19 patients receiving treatment within the Mount Sinai healthcare system in New York, to forecast mortality. Data on patients were analyzed at the individual level, collected in the Mount Sinai data repository for individuals diagnosed with COVID-19, who sought healthcare services between March 9 and April 6, 2020. For the initial analysis, patient data from March 9 to April 5 were used, and patients were randomly split (80:20) into a development dataset or test dataset 1 (retrospective). Data from patients who encountered the virus on April 6, 2020, were used in the prospective test dataset. 

The outcome was predictive models based on clinical features and patient characteristics during healthcare encounters to forecast mortality using development data. After evaluating the models in terms of the area under the receiver operating characteristic curve (AUC) in the test datasets, the following conclusions were drawn: using the development dataset (n = 3841) and a systematic machine learning approach, it is possible to develop a predictive model for mortality from any disease (in this case, COVID-19), which demonstrates high accuracy (AUC = 0.91) when applied to retrospective test datasets (n = 961). This model was based on three clinical characteristics: patient age, minimum oxygen saturation during healthcare encounters, and patient type (inpatient compared to outpatient and telemedicine visits). 

In interpreting the long-term implications of this research, an accurate and cost-effective predictive model for mortality from a specific disease, based on even a minimal set of characteristics, can be beneficial in clinical settings for managing and forecasting patients suffering from that disease. External validation of each such predictive model in multiple populations is, of course, required, but similar valuable experiences have already been adapted by specialists from the Cleveland Clinic, whose model allowed for determining the likelihood of a positive COVID-19 test result for individuals and forecasting not only the number of patients but also the availability of intensive care, the number of beds, the presence of ventilators, etc. 

Likewise, specialists from the University of Chicago, whose prognostic model is designed to accurately forecast the spread of COVID-19 and the subsequent weekly count of cases; scientists from the University of Cologne, in collaboration with experts from Bartz & Bartz GmbH, developed a tool called "BaBSim.Hospital," which helps hospitals plan capacity and resources in advance. In the case of COVID-19, this assisted in predicting medical supply needs and planning accordingly. 

 
As you can see, Data Science specialists are making research more efficient, diagnosis faster, and treatment methods more effective in the field of healthcare. Machine learning and data analysis are enabling the creation of data repositories and services, the updating and optimization of registry infrastructures, and pioneering research in evidence-based medicine, pharmaceuticals, and pharmacology. 

However, it's worth noting that this field is more demanding when it comes to specific personal health data, which patients may not always be willing to share due to its confidential nature. 

Nevertheless, there is already a noticeable trend towards personalized medicine. For instance, Deloitte, an international consulting company, highlights several trends in a recent analytical study that will characterize the future of medicine. Data plays a crucial role in most of these trends. MedTech companies are poised to lead the entire biomedicine industry, and the development of software capable of analyzing medical data is becoming a top priority. Big Data will infiltrate R&D departments, and advancements in artificial intelligence, nanotechnology, and bioinformatics will significantly enhance clinical diagnostics for many diseases. 

By analyzing the historical and scientific experiences of our predecessors, we have decided to create something that can provide a comprehensive source of information for healthcare professionals worldwide while ensuring the complete security of patient data. To satisfy both sides of this social contract, we had to revolutionize the world of medical data analysis! 

WHAT WE OFFER 

DeHealth's latest software product is set to revolutionize your perception of healthcare. Imagine that the interaction between clinics and patients, burdened with numerous complexities and challenges, suddenly becomes remarkably comfortable for both parties. Sounds good, right? The key to this product is artificial intelligence. As a result of fully integrating medical data, its subsequent utilization, clinic operation optimization, and the natural increase in profitability are no longer a coincidence but rather a logical continuation of our collaboration. 

So, what's the secret? How does this product work, and why is work optimization so effortless? We're not keeping it a secret: last year, we launched one of our projects - a Web3 application that currently holds more than 17 million medical records across 200 specialties. The application not only allows users to securely store their data but also offers an opportunity to monetize it effectively. 

In turn, the new AI product processes this data using advanced machine learning algorithms and statistical analysis. Thanks to this, DeHealth provides personalized recommendations for your clinic and your patients. Imagine having to put together a set of tests for an appointment in three months or determine whether a preventive check-up is needed upon reaching a certain age. It's routine work, but it still requires time and effort. Now you can entrust this task to the powerful artificial intelligence of DeHealth! The software product will also help your clinic conduct an analysis of patients' conditions before and after medical interventions. 

The significant advantages are simply evident. First and foremost, you will be able to provide your patients with higher quality and more reliable care because you now have data based on specific personalized recommendations. The next noticeable advantage is the optimization of your resources. Knowing exactly what your patients need and where to allocate resources minimizes unnecessary actions. 

And finally, expanding your client base and, as a result, your revenue! Patients who see that the clinic actively monitors their health and provides personalized recommendations are more likely to remain loyal and attract new clients, leading to a "win-win" strategy for your collaboration. This point complements the fact that all data obtained when using the product will be stored and processed in strict compliance with data protection and privacy legislation. The product is constantly evolving and improving: in the next stage of collaboration with clinics, we plan to expand DeHealth's functionality and offer an extended market for the clinic's products and services through DeBooking. This will allow DeHealth users from 80 countries to access clinic services. DeHealth contributes to enhancing clinic efficiency and ensuring better patient care, which is in the interest of all parties.