Prescribe data culture in the modern healthcare sector
Healthcare enterprises have now started to invest in advanced systems capable of turning the enormous quantities of data they produce into a trustable and valuable resource.
Rulex provides healthcare organizations with specific solutions that unlock data potential with explainable models, whilst complying with regulatory standards.
Managing data-driven insight with confidence helps healthcare operators provide quality care and drive advanced medical research.
Why healthcare operators choose Rulex
Thanks to our eXplainable AI platform, healthcare enterprises can work with reliable and trusted data. Its human-centered technology provides users with insights that are transparent and easy to understand. Healthcare providers can trust data and use them for taking decisions confidently.
Rulex’s no-code, user-friendly software empowers healthcare experts with data-driven insight to speed up the decision-making process. Blending clinical data from multiple sources allows experts to access complete patient history, enabling them to deliver an increasingly personalized healthcare experience.
Our platform allows medical researchers to collect and blend clinical data from multiple sources. Health research can benefit from AI analytics as they help researchers identify correlations within large amounts of data.
What we do for healthcare
Our no-code platform is both versatile and flexible, meaning that it can be applied to multiple case scenarios.
Accelerate medical research
Health research can benefit from AI analytics since they help clinical experts identify patterns or correlations within large amounts of data. One of the more recent examples is COVID-19: a new virus with vast amounts of statistical data, which may be difficult to wade through, and which require rapid response times. Rulex can play an important role in all the phases of the data value chain, from reading to preparing data, from data cleansing to the extraction of intelligible rules using eXplainable AI algorithms. With Rulex, people involved in research can trust the results extracted from data, interpret them, and achieve maximum results.
Correcting data in medical records
Errors in medical records can have numerous consequences for healthcare companies. Sometimes these errors are minor, such as billing or admin blips, whilst other times mistakes can be major, such as incomplete or incorrect diagnoses, or delays in scheduling surgical interventions. Rulex’s no-code platform offers a full range of functions to avoid those errors, thanks to Rulex’s proprietary eXplainable AI algorithm, and numerous data preparation and data cleansing tools. These tools drastically improve the quality and consistency of data, and displays them in the simplest and most readable way. Improved data quality improves the quality of medical records, with a knock-on positive effect for healthcare admin, and patients’ well-being.
Diagnosis and prognosis support
Artificial intelligence will never replace doctors in the provision of medical diagnosis, but may act as a powerful technological aid in the decision-making process. In addition to excellent performance levels and extreme accuracy, one of the unique advantages of Rulex is its eXplainable AI algorithm, which produces intelligible rules that can be understood and interpreted by doctors, and quickly explained to patients whenever necessary. This enables artificial intelligence and human experience to combine to make the best decisions for patients. Rulex has proven its worth in various contexts, such as the fight against various forms of cancer and diabetes.
Rulex took my research team beyond the scope of standard statistics. Together, we applied their artificial intelligence algorithms in novel ways for my patients with Down syndrome.
I particularly appreciate working with Rulex technology because it is user-friendly and exploits explainable AI. The combination of these two features makes communication easy among colleagues with different areas of expertise. Rulex accelerated the discovery process of new candidate biomarkers for clinical research.
We applied the Rulex RDC algorithm to the hospitalizations data repository. We were able to analyze the errors detected by the algorithm, consequently creating a model that automatically detects incorrect encoding in the Hospital Discharge Form. That was quite an achievement!
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