Business Challenges
The healthcare industry heavily relies on Electronic Health Records (EHR) systems to manage patient data efficiently. EHR systems digitize patient information, providing healthcare professionals with access to comprehensive medical histories, treatment plans, and clinical notes. In today’s healthcare landscape, EHR systems play a crucial role in improving patient care quality, enhancing communication among healthcare providers, and streamlining administrative processes. Efficient EHR systems facilitate timely access to patient information, enabling healthcare professionals to make informed decisions and deliver personalized care. Despite the benefits of EHR systems, healthcare organizations often face challenges related to clinical documentation.
Lengthy, unstructured clinical notes can hinder workflow efficiency, result in incomplete records, and pose challenges for data analysis and interpretation.
AI Solution
The implementation of AI-driven solutions in EHR systems involves several key components. An AI model, such as a Large Language Model (LLM), is trained on a large corpus of anonymized clinical notes and structured data elements. This model incorporates medical terminology, clinical context, and note-taking patterns to enhance the efficiency and accuracy of clinical documentation. Data used for training AI models is sourced from various departments within the healthcare organization, ensuring a diverse range of clinical scenarios and documentation styles are represented. Before training, the data is anonymized and cleaned to remove any sensitive or personally identifiable information.
During data preparation, clinical notes are structured and standardized to facilitate AI analysis. This involves cleaning, organizing, and categorizing clinical data elements to ensure consistency and accuracy in documentation. Through iterative training processes, AI models learn to recognize patterns, extract relevant information, and generate meaningful insights from clinical notes. These techniques enable AI models to understand and interpret complex medical language, extract relevant information, and generate actionable insights to support clinical decision-making.
Expected Outcome
AI-driven solutions address the challenges associated with clinical documentation by offering innovative features integrated into EHR systems. These features enhance workflow efficiency, improve documentation accuracy, and support clinical decision-making processes. By leveraging AI-powered features such as real-time note suggestion, automatic summarization, and standardized coding, the solution enhances clinical documentation efficiency and accuracy. Clinicians can now spend less time on documentation tasks, resulting in more time for direct patient care.
The benefits of AI implementation in EHR systems yield several benefits for healthcare organizations. Clinicians report a 20% reduction in time spent writing notes, allowing more time for direct patient care. AI-powered features streamline documentation processes, reduce turnaround time, and enhance overall workflow efficiency. Moreover, the percentage of complete notes increases by 15%, ensuring comprehensive documentation of clinical encounters. AI-driven features facilitate structured and standardized documentation practices, leading to more accurate and meaningful patient records. Additionally, automatic coding achieves 90% accuracy compared to manual coding, reducing errors and streamlining billing processes. AI-powered features extract relevant information from clinical notes and suggest appropriate medical codes, improving coding accuracy and efficiency.
Key performance indicators (KPIs) such as documentation turnaround time, note completion rates, and coding accuracy are used to measure the success of AI implementation. Healthcare organizations track these metrics to assess the impact of AI-driven solutions on workflow efficiency and patient care quality. The success of AI implementation is measured by improvements in workflow efficiency, documentation accuracy, and coding accuracy. Healthcare organizations observe tangible benefits such as reduced documentation time, increased note completion rates, and improved billing processes.
While AI plays a central role in enhancing clinical documentation processes, human oversight and input remain essential for ensuring the quality and relevance of patient records. AI augments the capabilities of healthcare professionals by automating routine documentation tasks, allowing them to focus more time and attention on direct patient care. AI-powered features within EHR systems enhance the capabilities of healthcare professionals by providing real-time assistance with documentation tasks. Clinicians can leverage AI-driven suggestions and summaries to improve the efficiency and accuracy of clinical documentation, ultimately leading to better patient care outcomes. The integration of AI into EHR systems has positive implications for the healthcare industry as a whole. By enhancing workflow efficiency, improving documentation accuracy, and supporting clinical decision-making processes, AI-driven EHR enhancements contribute to the growth and innovation of the healthcare industry. Healthcare organizations that adopt AI-powered EHR solutions gain a competitive edge by improving patient care quality, reducing administrative burden, and enhancing overall efficiency. AI adoption fosters innovation and drives positive outcomes for both healthcare providers and patients.
Our Team Members
AI TRANSFORMATION LEAD
Dr. Mikhail Krasnov
Based in Switzerland, Mikhail is an esteemed leader with a Ph.D. in Economics and recognized as a Certified Director by the London Institute of Directors. With a 30-year tenure in the IT sector, he co-founded Verysell Group in 1990 and now drives strategy and innovation across the entire organization. He staunchly believes that AI should be a catalyst for all businesses, not a threat, enhancing and transforming companies. To him, AI is a powerful tool for businesses to harness for their advantage.
Head of Applied AI Lab
Yurii Lozinskyi
Based in England, Yurii is the driving force behind Verysell Applied AI Lab. With 25 years of experience in Healthcare, Insurance, and Finance, he specializes in Business Process & Digital Transformation. Yurii excels in creating neural networks for diverse business automation, from medical diagnostics to machinery anomaly detection. Beyond tech, he’s a proficient software delivery manager and project leader. His leadership has fueled remarkable growth, achieving up to 60% CAGR by guiding 20+ Agile Teams globally.
Chief AI Scientist
Dr. Hung Dao
Based in Vietnam, Dr. Hung leads a dynamic Consulting Team, driving AI innovation across diverse sectors, from industrial visual inspection to medical image segmentation at our AAI Lab. With a PhD in Engineering from Keio University (2014), his expertise extends to groundbreaking research published in renowned conferences such as FG, BMVC, ICASSP, and ACCV, alongside multiple granted patents. With a distinguished track record at FPT, VinBrain & Nautilus AI, he’s a prominent figure in the AI landscape.
STRATEGIC ADVISOR ON AI
Dr. Alexey Minin
Prof. Dr. Alexey Minin, an esteemed expert in the digital economy and artificial intelligence implementation, boasts over 19 years in high-tech. Rising from Siemens engineer to associated partner at MHP – A Porsche Company, Germany, Alexey excels in management consulting, exponential technologies, and creating businesses based on modern tech. A Doctor of Science in AI, Honored Professor, and consultant for several boards of directors, Alexey specializes in digital transformation, AI application, and startup guidance. He co-founded startups, lectures globally, and is a sought-after speaker in digital conferences.
Business Development Manager
Muin Pirzada
Muin brings to the table an extensive background of over 13 years in the fields of Robotic Process Automation (RPA), Test Automation, and Artificial Intelligence (AI). This vast experience has not only equipped him with a profound knowledge of these cutting-edge technologies and deep passion for business growth and operational efficiency, but also with an acute understanding of their capability to revolutionize industries and streamline operations.
Marketing Manager
Ha Nguyen
Based in Hanoi, Ha spearheads marketing endeavors, leveraging over a decade of prowess in the global technology arena. She currently serves as AAI Lab’s Marketing Manager, leveraging her expertise in B2B marketing, branding, content strategies, and Agile methodologies to propel the company’s growth. Her practical know-how equips her to craft and execute effective marketing strategies for AAI Lab and other entities within the Verysell Group.
Software Development Automation Lead
Hieu Tran
Hieu, from Danang, Vietnam, heads the automation team at SmartDev. With a decade of experience in software development, he’s skilled in tech strategy and research. Known for boosting daily operations, Hieu shines in project coordination and customer happiness. Currently, he’s the top engineer at SmartDev and also leads the AAI Lab Software Development Automation.
Head of Project Management
Richard Sharp
Richard, based in Danang Vietnam, has over 40 years of IT experience. He has had many roles including Client Manager, Supplier Manager, and Service Management Program Manager. He has designed, developed and implemented advanced service management solutions for leading organizations across the technology, government, health, telecoms, oil & gas and financial services sectors.
HEAD OF AI EDUCATION
Soan Duong
Dr. Soan is a lecturer at the Computer Science Department within Le Quy Don Technical University. She is also a senior research scientist with keen interest in computer vision and medical imaging. Her research work has been published at prestigious venues, including ISBI, ICIP, ICASSP, BMVC, and CVPR. Soan plays an active role in the academic community by serving as a dedicated reviewer for esteemed publications such as: IEEE Access and NMR in Biomedicine.