Delayed diagnosis is a significant healthcare challenge that can lead to worsened health outcomes, increased mortality, and higher healthcare costs. Globally, millions of patients experience diagnostic delays each year. In cancer care, it is estimated that delays in diagnosis can affect up to 20% of patients, according to a study published in the International Journal of Cancer.
In response to this critical issue, Labplus was established in 2018 with the mission to enhance the timeliness and accuracy of medical diagnostics. Based in Wrocław, Poland, Labplus was founded by a diverse team of experts: Bartłomiej Bartoszewicz, Jakub Gorowski, Siddarth Agrawal, and Szczepan Czyczerski. Their combined expertise and innovative approach have poised Labplus to tackle the challenges of diagnostic delays through advanced AI-driven technologies, contributing to improved patient care and outcomes.
Pain point addressed
Labplus addresses a critical pain point in the healthcare sector: the complexity and time consumption inherent in the analysis of laboratory test results. Traditionally, this process requires significant human expertise and can be subject to human error, leading to delays in diagnosis and treatment, which can be detrimental to patient outcomes. Moreover, the volume of data generated by modern diagnostic tests can be overwhelming for laboratory personnel to interpret efficiently.
By leveraging AI algorithms, Labplus’s technology automates this process, offering a swift and reliable interpretation of lab results. This not only accelerates the diagnostic pathway, allowing for quicker patient treatment but also enhances the accuracy of test result analysis, potentially reducing the risk of diagnostic errors. In doing so, Labplus significantly eases the burden on healthcare professionals and improves the overall efficiency of healthcare delivery systems.
Type of solution
Labplus offers a type of solution that falls predominantly into the category of software, particularly leveraging the power of Artificial Intelligence (AI). The company’s product is an API-based IT solution that seamlessly integrates into the existing infrastructure of diagnostic laboratories, hospitals, and clinic systems. This software-centric approach revolves around a proprietary diagnostic engine that interprets lab test results. It is designed to deliver individualized disease probabilities and further testing recommendations based on the analysis. By providing a software solution that can be embedded into current systems, Labplus ensures a high degree of adaptability and ease of use for healthcare providers.
Type of input data leveraged
- Laboratory test results: including blood cell counts, hormone levels, enzyme activities, and biochemical measurements.
- Historical health data: previous medical tests and outcomes.
- Demographic information: age, sex, and other relevant patient demographics.
- Medical histories: patient’s past health issues, family, genetic history, and treatments.
Key technology involved
- Machine learning/deep Learning: Labplus employs these techniques to process and analyze the vast amounts of data that are part of lab test results. Through machine learning algorithms, the system can identify patterns and correlations in the data that might be indicative of certain health conditions. Over time, as it processes more data, the system learns and improves, becoming more accurate in its diagnostic suggestions.
- Natural language processing (NLP): Lab test results often include unstructured text, such as observations or notes from technicians and pathologists. Labplus uses NLP to extract meaningful information from this text. NLP algorithms can process natural language data, understand the context, and convert it into structured data that the diagnostic engine can use.
Key applications of solution
Labplus’s technology is adeptly designed for key applications in diagnostic assistance, which involves a multi-faceted approach:
- Interpreting test results: The AI algorithms analyze the numerical and textual data from lab tests to detect patterns indicative of specific diseases. This is where deep learning excels, as it can handle complex, multi-layered data and derive meaningful insights.
- Individual disease probability: The system calculates the likelihood of diseases based on the lab results. It considers the levels of various biomarkers and compares them with known disease profiles to estimate disease probabilities, personalizing the diagnosis based on the patient’s unique data.
- Streamlining the diagnostic pathway: By automating these steps, Labplus’s technology expedites the entire diagnostic process. Faster interpretation of results means quicker decision-making for subsequent tests or treatments, reducing the time patients spend in uncertainty and potentially improving their outcomes.
Implications of key stakeholders
- Patients: Labplus improves patient care by reducing the waiting time for test results, which can alleviate anxiety and lead to earlier interventions. Their AI system’s precision can also minimize misdiagnoses, enhancing patient experience and health outcomes by ensuring appropriate treatment is received promptly.
- Healthcare providers: The technology affects healthcare providers by integrating into their existing workflows, thereby reducing the manual burden of interpreting test results. This efficiency allows providers to allocate more time to direct patient care, potentially improving the quality of services offered. The AI’s recommendations for further testing can also assist providers in making more informed decisions, thereby increasing the efficacy of treatments prescribed.
- Insurers: For insurers, Labplus’s accurate and efficient diagnostic processes can result in cost savings by reducing unnecessary tests and procedures, leading to more streamlined healthcare spending. Additionally, the detailed data provided by the AI system can enhance risk assessment models, enabling insurers to better predict healthcare costs and improve premium pricing accuracy.
- Regulatory bodies: Labplus’s adherence to medical device classifications and data protection laws (like GDPR) means that regulatory bodies have a framework to ensure that the technology is safe and respects patient privacy. If proven consistently effective, as AI medical solutions evolve, regulatory bodies could use solutions like Labplus’s to benchmark for policy development, ensuring that new technologies entering the market maintain similar standards of quality and safety.
Current impact
- Labplus has successfully integrated its AI-driven diagnostic engine into the healthcare system, enhancing the ability of providers to interpret complex lab test results efficiently and accurately.
- The company has secured significant funding, indicating confidence in its technology and potential for growth.
Potential future impact
- As Labplus continues to evolve its AI technology, it could become a standard tool for diagnostic labs globally, further speeding up and improving the accuracy of diagnostic processes.
- There’s potential for expansion into other areas of healthcare, such as patient monitoring and treatment planning, leveraging the data analytics capabilities of its AI.
- With its focus on automation and precision, Labplus could lead to substantial cost savings across the healthcare sector by reducing the need for redundant testing and minimizing diagnostic errors.
- As Labplus scales, it may also influence healthcare policy, particularly regarding the use of AI in medical diagnostics and patient data management.
Business model
Labplus operates on a B2B model (business to business), providing AI-driven diagnostic tools that integrate into the IT infrastructure of healthcare entities like providers, labs, and hospitals. These institutions, guided by IT and operations managers along with medical staff input, are the ones that enter into subscription or licensing agreements to use Labplus’s services, aiming to enhance their diagnostic capabilities.
Advantages of the business model
- Focused development: Serving a professional and institutional clientele allows Labplus to tailor its development efforts towards the specific needs of healthcare providers, enhancing the value and efficacy of its product.
- Strategic partnerships: The B2B model opens doors for strategic partnerships with other healthcare technology providers, enabling the creation of an integrated diagnostic ecosystem.
- Compliance and trust: Working directly with healthcare providers may support them in navigating through regulatory landscapes and building trust with these institutions, given the sensitive nature of healthcare data.
Funding and key investors
In 2019, the company received a 5 million Polish złoty grant, affirming its potential to contribute significantly to the healthcare sector. Building on this momentum, Labplus attracted a substantial pre-seed investment of 7 million złoty from LT Capital in December 2021, underscoring strong investor confidence. These financial milestones have empowered Labplus to advance its research and development, attract skilled professionals, and enhance its technological capabilities, thereby reinforcing its vision to transform the interpretation of laboratory results and streamline the diagnostic process.
Competitive differentiator
Labplus stands out in the health tech industry with its unique API-based diagnostic engine that delivers personalized disease probabilities, offering seamless integration for healthcare providers. This innovative approach simplifies the adoption of new technologies in medical diagnostics, emphasizing ease of use, accuracy, and efficiency in patient care services.
Relevant regulatory and compliance requirements
Labplus’s compliance with the General Data Protection Regulation (GDPR) can promote data protection and privacy, a critical aspect given the sensitive nature of medical data. This compliance is not only a legal requirement for operating in the EU but also serves as a trust signal to customers and partners regarding Labplus’s dedication to data security.
Partnerships and collaborations
Labplus has cultivated partnerships with key scientific and medical institutions to enhance the development of its diagnostic engine. Collaborating with The National Centre for Research and Development, Medical University of Silesia, and Wrocław Medical University, Labplus taps into a wealth of academic research and clinical expertise.
These collaborations are pivotal, as they combine Labplus’s technological prowess with cutting-edge medical research and validation from reputable universities. Such alliances not only enrich the company’s R&D capabilities but also bolster its credibility and the efficacy of its diagnostic solutions in the healthcare market.
Areas for continuous improvement
- Enhancing the precision of the diagnostic engine through ongoing machine learning and data analysis is crucial for maintaining technological edge and patient outcomes.
- User experience improvements could involve simplifying the interface and making the API more developer-friendly to encourage wider adoption.
- Streamlining integration processes ensures that new clients can easily incorporate Labplus’s solutions into their systems.
References
https://www.likeminded.care/en/home
https://www.who.int/publications/i/item/9789240053052
https://www.crunchbase.com/organization/likeminded
Disclaimer: Please note that the opinions, content, and analysis in my posts are entirely my own and do not reflect the views of any current or past employers or institutional affiliations. These posts, based solely on publicly available information, are for informational purposes and should not be taken as professional advice. All insights and conclusions are my viewpoints and should not be considered representative of any organizations I am or have been associated with. This content is not endorsed by, nor does it represent the stance of any affiliated entity.
