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Description
Sqilline Business Solutions Ltd. is a forward-looking software and consulting company with expertise in advanced analytical platform solutions, focusing on "Big Data" processing and machine learning in the healthcare sector. They are the first Bulgarian partner of SAP AG for Eastern Europe and have a track record of developing SAP mobile solutions. Furthermore, Sqilline is venturing into cutting-edge analytical platforms with Responsive Web Design (RWD) and embedded Artificial Intelligence (AI) algorithms, specializing in fields such as cardiology, oncology, hematology, and rare diseases.
The project''s core objective is to pioneer an innovative welfare technology solution that addresses the multifaceted challenges posed by an aging population and patients dealing with prostate and breast cancer. Simultaneously, it will serve as a robust support tool for healthcare professionals providing high-quality care services. Sqilline will develop causal machine learning models by utilizing standardized electronic health records (EHR) from a patient cohort dataset of up to 50,000 individuals. Their approach involves constructing a Dynamic System Adaptation (DSA) guided by Clinical Practice Guidelines (CPGs) with a focus on breast and prostate cancers. Sqilline''s methodology integrates real-world clinical data, input from expert clinical oncologists, and state-of-the-art technology to bridge gaps and rectify discrepancies within existing CPGs. The project''s main innovation lies in its capacity to provide oncology healthcare professionals with precise and cohesive CPGs, cultivated from real-world "Big Data" analyses facilitated by advanced machine learning algorithms. It aims to benefit patients with prostate and breast cancer by ensuring they receive the highest standard of data-driven healthcare.
Summary of project results
Sqilline Business Solutions Ltd. faced several significant challenges in its pursuit of growth and operational efficiency. As a small software and consulting company specializing in analytical platforms for “Big data” processing and machine learning in healthcare, Sqilline struggled to navigate the complexities of expanding its market presence and adapting to the rapidly evolving healthcare IT sector.
A primary challenge was the inconsistent use and adherence to Clinical Practice Guidelines (CPGs) across different healthcare practices, particularly in oncology, where compliance rates varied between 33% and 61%. This posed difficulties in ensuring that the company''s software solutions effectively addressed the needs of healthcare professionals striving to maintain high standards of patient care. Despite compelling evidence that adherence to CPGs resulted in better patient outcomes—demonstrated by a 30% reduced risk of mortality over five years for patients treated according to guidelines—the uptake remained inconsistent, limiting the full impact of Sqilline’s solutions.
Additionally, Sqilline needed to overcome the barriers of market entry and competition within key regions such as Germany, Nordic countries, and Eastern Europe. While the company had successfully established a German-based subsidiary and was operational, increasing its market share and ensuring the effective deployment of innovative products like the Decision Supportive Application (DSA) tool required strategic efforts. This tool aimed to enhance clinical decision-making but needed to be developed and tailored to meet the stringent requirements of healthcare providers across various international markets.
The company also faced the challenge of maintaining its competitive edge by integrating cutting-edge technologies like Artificial Intelligence (AI) into its platforms while ensuring the solutions adhered to rigorous healthcare regulations and standards. The complexity of embedding AI algorithms in a way that delivered both efficiency and valuable insights for patient treatment and clinical trials posed a significant technical and operational challenge.
In summary, Sqilline Business Solutions Ltd. grappled with inconsistent adoption of clinical guidelines, the challenge of expanding its presence in competitive European markets, and the technical demands of integrating advanced AI into its healthcare-focused platforms.
The project aimed to develop an effective and efficient IT solution that would aid healthcare specialists by providing reliable and consistent access to CPGs for the care of breast and prostate cancer. By combining clinical expertise with advanced AI and real-world data analytics, the project sought to enhance the overall quality of care and facilitate more informed decision-making among healthcare providers.
In summary, the project created an innovative decision support application that not only integrated AI-driven analyses of real-world data but also established a new open standard for guideline representation and offered patient-focused tools. It ultimately aimed to improve clinical outcomes in breast and prostate cancer care by equipping specialists and patients with better, evidence-backed resources.
The project titled "Experimental development for creation of decision support application (Danny DSA) for breast and prostate cancer care" undertook a range of strategic activities and produced specific outputs to advance clinical practices in oncology through technology. Below is an overview of what the project did:
1. Development of a Decision Support System (Danny DSA)
The project developed a comprehensive decision support system specifically designed for breast and prostate cancer care. This system utilized advanced AI tools to analyze and contrast clinical practice guidelines (CPGs) with large-scale real-world data.
- Output: The decision support application was created to provide healthcare professionals with enhanced access to accurate, up-to-date, and consistent clinical guidelines for treatment and decision-making.
2. Integration of Advanced AI Tools
Leveraging sophisticated AI technologies, the project incorporated semantic and causal analyses to evaluate real-world patient data. This approach aimed to align and enhance the clinical guidelines by reflecting practical, real-life scenarios and improving their applicability in clinical settings.
- Output: The integration of these AI tools ensured that the decision support system could provide insights that are evidence-based and tailored to the nuances of individual patient cases.
3. Development of an Open Standard for Guideline Representation
The project aimed to propose an open standard for the representation of clinical practice guidelines. This involved creating a methodology and a toolset that could convert clinical guidelines into a computer-readable format, making them accessible and usable within oncology-specific applications.
- Output: A structured framework and tools were developed to enable seamless conversion of CPGs into a standardized format that could be easily incorporated into digital health solutions.
4. Patient-Centric Support Tools
The project included the creation of tools designed to provide support directly to patients diagnosed with breast or prostate cancer. These tools were intended to improve patient engagement, education, and empowerment by providing them with relevant, understandable information about their care and treatment options.
- Output: The development of user-friendly, patient-centric applications that facilitate informed decision-making and enhance the overall patient experience throughout their cancer care journey.
5. Establishment of a Large, Real-World Dataset
A significant effort was made to compile and establish a comprehensive dataset of real-world data specifically related to breast and prostate cancer care. This dataset was used for training, validating, and refining the decision support application.
- Output: The dataset served as a critical foundation for the project, enabling the AI tools to perform meaningful analyses that would improve the relevance and accuracy of the provided clinical guidance.
The project achieved substantial results with wide-ranging impacts for multiple stakeholders in the oncology sector:
Enhanced Clinical Decision-Making:
The development of Danny DSA, an AI-based decision support system, significantly improved clinicians'' compliance with Clinical Practice Guidelines (CPGs) for therapeutic decisions. This outcome directly impacted patient care by promoting more consistent, evidence-based treatment choices that reduced variation in clinical practices, leading to better patient outcomes and optimized use of healthcare resources.
Bridging Gaps in CPGs:
By leveraging real-world data from electronic health records (EHRs), the project addressed gaps and discrepancies in existing CPGs. This enhanced the comprehensiveness and applicability of clinical guidelines, allowing for more robust decision-making processes that fill in the content weaknesses of traditional CPGs.
Timely Patient Information:
The platform provided patients with relevant, timely information at the point of care, supporting informed decision-making during complex, fast-paced treatment scenarios. This empowerment fostered patient-centric care and improved patient participation throughout their treatment journey.
Identification of Innovative Treatments:
The project facilitated insights into new, effective, and safer drug treatment options for cancer, aiding clinicians in exploring and adopting potential novel therapies that could improve patient outcomes.
Support for Policy-Makers:
Policy-makers benefited from Danny DSA by gaining tools to ensure that healthcare providers adhered to the best clinical practices. This enabled better oversight and evaluation of whether patients received optimal care, aligning policy decisions with evidence-based practices.
Industry Insights:
The project’s guidelines-based platform provided the industry with comprehensive information on drug recommendations, dosing, and criteria for treatments. This transparency helped industry stakeholders understand clinical decision-making and opened avenues for drug repurposing and the potential use of off-label drugs.
Research Advancements:
Researchers gained valuable access to data that facilitated hypothesis generation and answers to critical biomedical questions. This empowered research teams to build on the project’s findings and contribute to the overall body of knowledge in oncology.
Beneficiaries:
Patients: Benefited through more informed and patient-centric care, contributing to better treatment adherence and outcomes.
- Clinicians: Gained a powerful tool to enhance compliance with clinical guidelines, make evidence-based decisions, and optimize patient care.
- Policy-Makers: Used the system to monitor and ensure that the most effective treatment protocols were implemented.
- Industry: Gained insights into clinical decision-making processes that could inform drug development, repurposing, and strategic planning.
- Researchers: Had access to valuable data for innovative studies and the development of new hypotheses.
Long-term Impact:
Danny DSA empowered the medical community to standardize clinical practices through comparative analysis of different guidelines, promoting guideline homogenization. The project also contributed to the creation of an accessible, maintainable digital format for CPGs, fostering continuous improvement and adaptation of clinical practices. The outcomes underscored a significant step forward in aligning healthcare delivery with real-world data, leading to a more efficient, effective, and patient-focused oncology care model.