How to Maximize Clinical Doctors' Experience? Huimei Technology Outlines Three Strategic Insights for the CDSS Industry
It is evident that clinical decision support systems (CDSS) serving clinicians have become a significant branch of healthcare informatization, playing an increasingly vital role in application scenarios such as pre-diagnosis decision-making, intra-diagnosis support, and post-diagnosis evaluation, with a trend toward greater specialization and expansion.
“This is the golden age for CDSS development! As CDSS products evolve from merely meeting accreditation requirements to becoming increasingly integrated into clinical workflows, the industry is poised for a second wave of significant growth. The degree of deep integration with clinical practice will become the key criterion for assessing the value of CDSS products.Taking into account factors such as current hospital accreditation in China, iterations of health information systems, and the supplementation of primary healthcare resources, Zhang Qi, CEO of Dr. Mayson, provided the following industry assessment of the CDSS sector.
Founded in 2015, Dr. Mayson is a leader in the CDSS industry, having participated in and witnessed the development of domestic CDSS from its inception to large-scale implementation.
Zhang Qi noted that since the National Health Commission launched initiatives in 2018 to further advance hospital informatization with electronic medical records (EMR) at its core, the rigid demand for clinical decision support systems (CDSS) among healthcare institutions has increased substantially, propelling the industry into a phase of explosive growth. Since then, CDSS vendors have been simultaneously refining their products to meet the accreditation requirements of healthcare institutions and exploring deeper application scenarios to develop next-generation CDSS solutions, thereby providing more precise support for clinical practice.
Although Clinical Decision Support Systems (CDSS) have reached an industry inflection point driven by rating policies, they still face numerous challenges and difficulties. The most significant factor constraining their development is the high threshold for clinical application in hospitals.
For a Clinical Decision Support System (CDSS) to provide practical assistance to clinicians, it is essential not only to build a comprehensive clinical knowledge base that encompasses vast amounts of data—including the latest clinical guidelines, evidence-based medicine, medical literature, and extensive electronic health records—but also to ensure excellent interactivity that aligns with clinicians’ workflow habits, enabling them to readily access desired information from the database. Furthermore, the database must be open, capable of continuously incorporating and updating valid information, and supporting data exchange or information sharing with other databases.
To this end, Dr. Mayson collaborates with leading specialties in Grade 3A hospitals to co-develop an evidence-based knowledge base that meets clinical needs. By leveraging continuous project experience, the company iteratively updates its knowledge graph, laying a solid “foundation” for the “superstructure” of AI applications and establishing high technical barriers in the CDSS sector. To cover as many application scenarios as possible, Dr. Mayson has constructed a three-tiered knowledge base:
First, a knowledge base constructed from objective data sources such as clinical guidelines and medical literature.Dr. Mayson transforms clinical knowledge, guidelines, and standards into a set of “computer-readable and interpretable” clinical reasoning frameworks, and collaborates with partner hospitals to refine specialty- and disease-specific knowledge bases by leveraging logic extraction methods from these clinical reasoning frameworks.
Secondly, the knowledge base trained on big data models serves as a powerful complement to guideline-based standard knowledge bases.Dr. Mayson extracts real-world information from medical record data to continuously train its AI “brain,” refine diagnostic models, and transform experts’ clinical reasoning and experience into diagnosis and treatment pathways that reflect the unique characteristics and strengths of each hospital.
For instance, Dr. Mayson has partnered with leading tertiary hospitals in their respective specialties to leverage information technology in building a risk identification model for acute kidney injury (AKI). This enables the Clinical Decision Support System (CDSS) to automatically analyze the probability of disease onset based on patients’ vital signs, laboratory and diagnostic test results, and clinical guidelines before the disease manifests. It also displays associated risk factors, thereby providing decision support for physicians in early diagnosis.
Third, Dr. Mayson assists hospitals in building their own knowledge bases.Achieving Level 6 in the Electronic Medical Record (EMR) Grading System imposes stringent requirements on Clinical Decision Support Systems (CDSS), mandating that hospitals establish a hospital-wide, multi-dimensional medical knowledge base system to provide advanced clinical decision support. To this end, Dr. Mayson provides hospitals with knowledge base management tools. Hospitals can supplement disease-related knowledge within the knowledge base based on actual clinical practices, and customize modules such as diagnostic rationality, laboratory and imaging examination guidelines, surgical contraindications, and medication contraindications. This enables the creation of a dynamic, real-time, and internalized knowledge base that aligns with the hospital’s unique diagnostic and treatment characteristics.
Huimei Technology has also established the AI Engineering Laboratory (Huimei AI Lab), built upon its data middle platform, to enable applications for data preprocessing, feature engineering, machine learning, model training, and model evaluation, thereby realizing the vision of “applying clinical data to scientific research and leveraging research insights to enhance clinical practice.”
Helping clinicians realize value is a key differentiator for Dr. Mayson compared to other CDSS vendors. Its core lies in making the CDSS product an indispensable tool in physicians’ daily practice.“Meeting this requirement achieves clinical value,” shared Zhang Qi.
From an application perspective, CDSS products must meet three criteria: First, the computational results of the CDSS model must be accurate. Second, they should be seamlessly integrated into clinical workflows to help clinicians handle repetitive tasks and alleviate their workload. Third, they should enable real-time interaction, rather than relying on retrospective error detection.
In response, Dr. Mayson adopts a four-step approach: needs assessment, model development, practical implementation, and gap analysis. To evaluate the real-world application and effectiveness of its Clinical Decision Support System (CDSS), Dr. Mayson’s CDSS not only enhances acceptance among hospital business departments by closely aligning with clinical needs but also establishes a “CDSS Usage Data Statistics Platform.” This platform enables hospitals to monitor data such as system alerts and user clicks in real time, providing an intuitive overview of in-hospital application status and outcomes.
Dr. Mayson is the core product of Huimei Technology, offering a medical quality control solution that spans the entire in-hospital diagnosis and treatment process and covers three key dimensions: hospital informatization, clinical research, and healthcare management.
Dr. Mayson is divided into two major application dimensions:One is the quality control of the diagnosis and treatment process by disease type., to meet national medical treatment assessment and reporting standards, thereby forming a closed-loop hospital quality management system encompassing pre-diagnosis identification, intra-diagnosis intervention, and post-diagnosis reporting;Second, medical quality control aimed at cost containment, such as running medical record quality control, quality control of the front page of medical records, DRG grouping, and cost prediction, to help hospitals achieve reasonable cost control and data governance.
Cost control and medical quality management are not mutually exclusive; rather, they should complement each other. Specifically, cost control serves as a lever to drive improvements in medical quality, thereby achieving the goal of high-quality hospital development. This is evident from the successive issuance of two policies: the Action Plan for Promoting High-Quality Development of Public Hospitals (2021–2025) and the Notice on the Three-Year Action Plan for DRG/DIP Payment Method Reform issued by the National Healthcare Security Administration.
Huimei’s medical quality control module effectively reduces the difficulty of cost containment for hospitals by incorporating Diagnosis-Related Groups (DRG). Through real-time management, it facilitates macro-level forecasting and control of medical expenses, providing a scientific and mutually comparable classification method for assessing medical quality. On the other hand, empowered by Huimei’s Clinical Decision Support System (CDSS) to establish a more measurable quality management system, hospitals are strongly motivated to enhance medical quality management. To maximize revenue, hospitals proactively reduce costs, shorten length of stay, and minimize inducement-driven medical expenditures, thereby contributing to effective cost control.
Empowered by Dr. Mayson’s AI-driven real-time cost control management via its Clinical Decision Support System (CDSS), a DRG pilot hospital in Zhejiang Province has seen its case grouping rate gradually rise to over 90%, with grouping accuracy reaching as high as 93%. The proportion of high-cost outlier cases decreased by 66.5%, and the medical insurance balance improved from a deficit of RMB 23,100 to a surplus of RMB 1.0304 million. While controlling costs, the hospital also enhanced the quality of care, laying a solid foundation for achieving its goal of high-quality development.
Currently,Dr. Mayson’s product portfolio covers the fields of oncology, respiratory diseases, and cardiovascular and cerebrovascular diseases. It encompasses 51 single-disease categories under key national monitoring, including venous thromboembolism (VTE), atrial fibrillation, and coronary heart disease. This has established a closed-loop system for in-hospital quality control management, featuring pre-diagnostic identification, intra-diagnostic intervention, and post-diagnostic reporting, thereby constructing an intelligent protective shield for medical quality and patient safety.Meanwhile, our extensive product portfolio provides comprehensive support for hospitals in iteratively upgrading their medical quality management toward intelligent, digital, and refined operations.
Regarding Huimei Technology’s next steps in product development, Zhang Qi stated that the company will focus more on clinical operational needs, expanding into areas such as perioperative care and innovative cancer therapies. It also aims to leverage AI technology to analyze CDSS data, ultimately reducing postoperative complications and improving patient outcomes.
Prior to 2018, Clinical Decision Support Systems (CDSS) were primarily deployed in healthcare institutions through free trials. During this period, CDSS vendors gathered frontline clinical requirements, refined their products and services, and explored viable business models. With the launch of the Electronic Medical Record (EMR) grading system, healthcare institutions developed a rigid demand for CDSS. Having matured through years of trial deployments, the CDSS industry gained the capability to deliver mature products. Since then, CDSS has entered the stage of commercialization.
Dr. Mayson’s products offer flexible deployment models, allowing hospitals to choose the deployment method based on their actual needs, with options for one-time payment or annual subscription. Zhang Qi also noted that due to the iterative nature of knowledge bases, CDSS vendors will continue to update their products and make new investments in the future. There is also a potential shift in the CDSS industry’s payment model from project-based fees to service-based fees.
Amid the accelerating commercialization of clinical decision support systems (CDSS), how can CDSS companies further expand their market presence? Zhang Qi highlighted three directions:
Hospitals that have not yet met the rating standards continue to “sprint,” while those that have achieved their targets strive for higher accreditation levels.In 2020, the average rating for the application level of electronic medical record (EMR) systems in hospitals across China reached 2.43. Among these, tertiary hospitals had an average rating of 3.46, while secondary hospitals averaged 2.03, both failing to meet policy requirements. Furthermore, some large hospitals have very high demands for information technology infrastructure, leading to a stable and continuous growth in the demand for higher EMR system ratings.
Forming a CDSS that fits fine-grained scenarios through personalized data from different departments of different hospitals.Large hospitals handle high patient volumes and have accumulated substantial clinical data through their health information systems. To fully leverage the value of this data in supporting clinical diagnosis and treatment, enhance overall diagnostic and therapeutic capabilities, or strengthen research capacity in specific specialties or disease areas, there is a growing demand for specialty-specific Clinical Decision Support Systems (CDSS).
Grassroots CDSS Holds Great Promise Under the Tiered Diagnosis and Treatment System.CDSS can help establish homogeneous and standardized clinical pathways at the primary care level, assisting primary care physicians in avoiding misdiagnosis and missed diagnosis of common diseases. Meanwhile, it facilitates scientific referral practices, contributes to improving the quality of primary healthcare services, promotes the effective implementation of the tiered diagnosis and treatment policy, and alleviates the current imbalance in the allocation of medical resources.
Reviewing the development trajectory of Dr. Mayson, at a micro level, the company has focused on addressing practical clinical challenges by enhancing the consistency and standardization of physicians’ diagnostic and treatment processes. Comprehensive business products and robust data support form the foundation for achieving this objective. At a macro level, Dr. Mayson aims to develop high-value big data resources driven by healthcare big data, explore additional scenarios requiring Clinical Decision Support System (CDSS) assistance, and realize its vision of making health benefits accessible to everyone.
In the future, while Dr. Mayson’s products and business model may evolve, its unwavering commitment to regarding “quality as life” and leveraging Clinical Decision Support Systems (CDSS) to enhance healthcare quality and safeguard patient safety remains the company’s steadfast direction and original aspiration.