Medical big data is an innovative technology that collects and analyzes vast amounts of medical information to support the development of new treatments, early detection of diseases, and the realization of personalized medicine. This article will explain the benefits and drawbacks of medical big data, the challenges and issues associated with it, as well as market size and usage examples by companies.
What is Medical Big Data?
Medical big data refers to the massive collection of data in the medical field, including electronic medical records, insurance claims, and health checkup results, among other information held by medical institutions. Medical big data plays an essential role in improving medical technology, such as developing new treatments and drugs and providing personalized medical care tailored to individual patients. With the advancement of AI technology, the utilization of medical big data is further accelerated. For example, AI-based image diagnostics can accurately detect lesions that might be missed by the human eye, significantly contributing to early disease detection and treatment.
Usage of Medical Big Data
As mentioned above, the usage of medical big data can be mainly divided into…
- Primary Use
- Secondary Use
Each will be explained below.
Primary Use
Primary use refers to the utilization of medical big data by medical institutions for purposes directly related to patient diagnosis and treatment. This includes patient diagnosis, treatment planning, monitoring disease progression, and evaluating prognosis. For example, using electronic medical records to check a patient’s medical history and allergies to select the most appropriate medication is an instance of primary use. Additionally, analyzing health checkup results to take preventive measures against high-risk diseases is another example of primary use.
This enables medical institutions to provide appropriate and prompt responses to patients, maximize treatment effects, and maintain patient health.
Secondary Use
Secondary use refers to using medical big data outside medical institutions for purposes other than the original intent of diagnosis and treatment, such as research, policy-making, and product development. This includes clinical research, public health surveillance, healthcare policy formulation, and development of pharmaceuticals and medical devices. For instance, analyzing data from numerous patients to investigate trends in disease occurrence or evaluating the efficacy of new treatments in clinical research falls under secondary use. Additionally, using medical big data to develop policies for disease prevention and health promotion is within the scope of secondary use.
This is expected to enhance the quality of healthcare, reduce medical costs, and promote the development of new medical technologies, improving overall public health.
Market Size of Medical Big Data
The market size of medical big data…
- Global Market
- Domestic Market
…varies between these segments.
Global Market
According to a market research report published by Global Information Inc. in 2021, the global medical big data analytics service market reached 36.8 billion USD in 2022. The market is expected to continue growing at a double-digit pace, with the US market being particularly significant. In 2022, the US market was estimated to reach 14 billion USD. Additionally, the global market is projected to reach 80.8 billion USD by 2028, with a compound annual growth rate (CAGR) of 13.6% between 2023 and 2028.
This growth is driven by the advancement in medical technology, cost reduction, and improved patient outcomes resulting from the increased utilization of medical big data.
Domestic Market
According to a market research report published by Fuji Keizai in March 2023, the medical big data analytics service market in Japan reached 16.2 billion yen in 2022. This market is expected to grow by 69.4% compared to 2022, reaching 27.8 billion yen by 2025.
In particular, the market for secondary use-related services reached 17.8 billion yen in 2022 and is projected to reach 23.1 billion yen by 2025. Secondary use-related services involve analyzing collected medical data and selling it to pharmaceutical companies and medical institutions.
Additionally, the data holder market is also expected to grow, with the market size reaching 43.2 billion yen in 2022 and projected to reach 54.7 billion yen by 2025. Data holders are services that securely and appropriately manage medical data held by medical institutions and local governments.
Benefits of Medical Big Data
The benefits of medical big data are diverse. Here are some main ones…
- Early Disease Detection
- Improved Treatment Methods
- Personalized Medicine
- Medical Cost Reduction
- Public Health Improvement
- Promotion of Clinical Research
- Quality Improvement of Medical Services
- Medical Device Development
- Education and Training
- Real-Time Monitoring
…each will be explained below.
Early Disease Detection
The utilization of medical big data enables the analysis of vast amounts of data, allowing diseases to be identified at an early stage when symptoms are mild. This allows patients to receive early treatment before the disease progresses, maximizing the effectiveness of treatment. For example, analyzing electronic medical records and health checkup data to detect subtle abnormalities enables the early detection of serious diseases such as cancer and heart disease. Early detection enhances treatment success rates, improves patient survival rates, and contributes to cost reduction in treatment.
Furthermore, regular data monitoring allows for preventive measures to be taken for high-risk patients, potentially preventing disease onset.
Improved Treatment Methods
Utilizing medical big data allows for detailed analysis of patient response patterns, leading to the development of more effective treatment methods. By analyzing large clinical data sets, it is possible to determine which treatments are most effective for specific patient groups, enabling the optimization of treatment methods. For example, analyzing data on the use of chemotherapy drugs to identify the optimal dosage with minimal side effects. This improves the quality of treatment and enhances patients’ quality of life.
Additionally, it aids in the development of new treatment methods, promoting the advancement of medical technology beyond the limits of existing treatments.
Personalized Medicine
The utilization of medical big data enables the creation of customized treatment plans based on individual patients’ genetic information and lifestyles. This maximizes treatment effectiveness and minimizes side effects. For example, using genetic analysis data to select the most suitable drugs for patients with specific genetic mutations. Additionally, creating treatment plans that consider patients’ lifestyles and environmental factors promotes the realization of personalized medicine.
This allows for the provision of optimal treatment tailored to each patient, improving the quality of healthcare.
Medical Cost Reduction
Utilizing medical big data enables the identification of efficient treatment methods and the reduction of waste, thereby lowering medical costs. For example, analyzing past medical data to eliminate ineffective treatments and unnecessary tests reduces the waste of medical resources. This alleviates the financial burden on patients and improves the efficiency of the overall medical system. Additionally, the promotion of early detection and preventive medicine can prevent the onset of serious diseases requiring expensive treatments, contributing to long-term cost reduction.
Furthermore, the introduction of remote monitoring and telemedicine can reduce the number of hospital visits, saving transportation costs and time.
Public Health Improvement
Analyzing medical big data allows for the understanding of disease prevalence trends and the formulation of preventive measures and public health policies. This improves the overall health standards of the community. For example, monitoring the occurrence of infectious diseases in real-time allows for early response to prevent the spread of pandemics. Additionally, analyzing data on lifestyle diseases to develop effective health promotion programs can improve public health.
Moreover, policymakers can implement evidence-based public health measures, raising national health awareness and enhancing disease prevention consciousness.
Promotion of Clinical Research
Utilizing medical big data accelerates clinical research using large data sets, promoting the development of new drugs and treatment methods. This accelerates medical advancements and expands treatment options for patients. For example, analyzing data from millions of patients to identify the most effective drugs for specific patient groups. This streamlines the design of clinical trials and enables the rapid approval of treatments. Additionally, it aids in the research of rare diseases, promoting the development of treatments for previously untreatable conditions, offering hope to patients.
Furthermore, promoting data sharing and collaboration facilitates international research projects, contributing to the global advancement of healthcare.
Quality Improvement of Medical Services
Utilizing medical big data aims to improve patient satisfaction and provide higher quality medical services. Analyzing patient feedback and clinical data allows for the identification of areas for improvement in medical services and prompt response. For example, shortening waiting times and optimizing clinical processes reduce patient stress and improve satisfaction. Additionally, providing services tailored to individual patients promotes the realization of personalized medicine. This allows for the provision of optimal care for each patient, improving the quality of healthcare.
Furthermore, it aids in the education and training of medical staff, enhancing the overall medical service provision system.
Medical Device Development
Utilizing medical big data enables the development of more precise and effective medical devices and diagnostic tools. This improves diagnostic accuracy and enhances the effectiveness of patient treatment. For example, analyzing diagnostic imaging data to develop new algorithms for early detection of lesions. Additionally, conducting simulations based on patient data promotes the development of surgical support robots and wearable devices, improving the efficiency of medical practices. This enhances the quality of healthcare and improves patient outcomes.
Moreover, the introduction of new technologies enhances the market competitiveness of medical devices, contributing to economic ripple effects.
Education and Training
Utilizing medical big data allows for the use of actual patient data in the education and training of medical professionals. This enhances practical skills in the medical field and enables the provision of higher quality medical services. For example, using patient data in simulation education to recreate actual clinical situations, allowing medical professionals to acquire practical skills. Additionally, through the education of data analysis techniques, medical professionals can acquire the latest medical technologies, improving the quality of clinical care. This enhances the overall level of the medical service provision system, improving patient care quality.
Moreover, continuous education and training improve the motivation of medical professionals and enhance the work environment in the medical field.
Real-Time Monitoring
Utilizing medical big data enables real-time monitoring of patients’ health conditions, allowing for prompt response when necessary. This enables early response to sudden health changes, allowing for appropriate treatment before severe conditions develop. For example, implementing a system where data from wearable devices is monitored in real-time, sending notifications to medical professionals when abnormalities are detected. This allows patients to live their daily lives with peace of mind, improving the quality of healthcare. Additionally, continuous monitoring of patients with chronic diseases prevents disease progression and maximizes treatment effectiveness.
Furthermore, remote monitoring improves medical access, providing high-quality medical services to patients in remote areas.
Drawbacks and Challenges of Medical Big Data
While there are many benefits to using medical big data, there are also several drawbacks and challenges. Some of the main ones include…
- Privacy Concerns
- Data Security
- Data Quality and Consistency
- Data Interpretation and Utilization
- Technical Challenges
- Ethical Issues
- Legal Regulations
- Unequal Data Access
- Data Standardization
- Cost
Each will be explained below.
Privacy Concerns
Medical big data contains highly sensitive information such as individual health data and medical histories. If a data breach occurs, there is a risk of personal privacy being violated. For example, hacking or unauthorized use of information by insiders can significantly compromise patient privacy. Privacy breaches can lead to a loss of trust among patients, making them hesitant to provide information to medical institutions.
To address this, strict information management and legal frameworks for privacy protection are necessary.
Data Security
Ensuring the security of medical big data requires advanced security measures. Medical data is vulnerable to cyberattacks, posing risks of data tampering and unauthorized access. For example, ransomware attacks can encrypt data in medical institutions, rendering it unusable. To mitigate these security risks, the implementation of the latest security technologies and regular security checks are necessary.
Additionally, security education for medical professionals is important, and everyone needs to be conscious of securing data safety.
Data Quality and Consistency
Maintaining data quality and consistency is a significant challenge in utilizing medical big data. Variations in data notation, errors in entries, and different recording formats make accurate analysis difficult. For example, inconsistencies in the notation of the same disease name can hinder data integration and analysis. To solve this problem, standardization and quality control of data are necessary.
Implementing standardized recording methods and building systems to reduce errors during data entry are required.
Data Interpretation and Utilization
Extracting useful information from medical big data and appropriately interpreting it requires specialized knowledge. Even with data analysis, understanding the results accurately and applying them in clinical practice requires advanced expertise. For example, physicians need to understand AI algorithms and limitations to correctly interpret AI-based diagnostic results and apply them to treatment.
To address this, cooperation between medical professionals and data scientists is essential, and education programs to share mutual knowledge are important.
Technical Challenges
As AI and data analysis technologies advance, there are technical challenges in efficiently utilizing them. For example, analyzing big data requires high-performance computing resources, necessitating infrastructure development to secure them. Additionally, AI algorithms rely on massive datasets, requiring appropriate data collection and preprocessing.
Addressing technical challenges requires continuous research and development and the training of technical personnel.
Ethical Issues
The use of medical big data involves ethical issues. In research and policy-making, improper handling of data risks infringing on patients’ rights. For example, if informed consent is not properly obtained, patients may provide their data without knowing how it will be used. Ensuring transparency in data usage and strictly adhering to processes for obtaining patient consent are necessary to avoid this.
Addressing ethical issues requires establishing ethics committees and formulating ethical guidelines.
Legal Regulations
The use of medical data is subject to legal regulations that must be adhered to. For example, laws such as personal information protection laws and regulations concerning medical data exist, and violating them can result in legal sanctions. To meet legal requirements for data collection, storage, and sharing, advice from legal experts familiar with the regulations is necessary. Additionally, international data sharing requires dealing with different countries’ regulations, requiring complex adjustments.
Adhering to legal regulations ensures the safe use of medical data and secures reliability.
Unequal Data Access
In utilizing medical big data, not all researchers and medical professionals have equal access to the data. Unequal data access can result in certain groups benefiting while others are excluded. For example, large-scale research institutions with financial resources may have easier access to data, while smaller institutions find it difficult.
Solving this problem requires building a fair data-sharing framework that ensures equal access to data for all researchers.
Data Standardization
Standardizing data collected by different medical and research institutions is necessary. Variations in data formats and recording methods make data integration and analysis difficult. For example, differences in disease codes and test result notations compromise data consistency, making accurate analysis impossible. To address this, unified data recording based on international standards is required. Additionally, establishing a technical foundation for ensuring data interoperability is important.
Standardization allows the integration of different data sources, enabling more comprehensive analysis.
Cost
Collecting, storing, and analyzing medical big data incurs costs. High-performance hardware, software, and securing specialized personnel require significant investment. For example, operating large data centers and training AI require substantial expenses. To reduce these costs, efficient data management systems and the use of cloud services can be considered. Additionally, long-term cost reduction can be expected by achieving results in data utilization.
Balancing costs and benefits is essential for sustainable data utilization.
Usage Examples of Medical Big Data
Examples of the utilization of medical big data include…
- Improvement of Diagnostic Imaging Accuracy
- Advancement of Genomic Medicine
- Promotion of Preventive Medicine
- Reduction of Medical Costs
- PHR (Personal Health Records)
Each will be explained below.
Improvement of Diagnostic Imaging Accuracy
Utilizing medical big data significantly contributes to improving the accuracy of diagnostic imaging using AI. AI can learn from vast amounts of image data and accurately detect subtle lesions that might be missed by the human eye. For example, analyzing chest X-rays and CT scans can enable the early detection of lung cancer and other diseases. This promotes early disease detection and treatment initiation, improving patient survival rates.
Moreover, improved diagnostic accuracy reduces the burden on physicians, contributing to improved diagnostic efficiency.
Advancement of Genomic Medicine
The analysis of medical big data is expected to advance genomic medicine. Analyzing genetic information big data enables the development of customized treatments based on individual genetic characteristics. For example, identifying optimal treatments for patients with specific genetic mutations, enabling effective cancer treatment. The advancement of genomic medicine contributes to the realization of personalized medicine, providing optimized treatments for each patient and maximizing treatment effectiveness.
Additionally, the development of new gene therapies and gene editing technologies accelerates, evolving treatment methods for intractable diseases.
Promotion of Preventive Medicine
Utilizing medical big data promotes preventive medicine. Analyzing big data can identify disease occurrence trends and risk factors, enabling the implementation of preventive measures. For example, identifying individuals at risk for lifestyle diseases and providing early health guidance and preventive programs can prevent disease onset. This reduces medical costs and improves patients’ quality of life (QOL).
Furthermore, it leads to the development of effective preventive drugs, making preventive medicine increasingly important.
Reduction of Medical Costs
Utilizing medical big data reduces medical costs. Early disease detection and improved treatment methods allow for appropriate response before costly treatments become necessary. For example, detecting cancer early and treating it at an early stage avoids the high costs of late-stage treatment. Additionally, analyzing data to eliminate unnecessary tests and treatments enables efficient medical provision.
This improves cost efficiency throughout the medical system and reduces the financial burden on patients.
PHR (Personal Health Records)
PHR (Personal Health Records) is a system that allows patients to digitally collect and store integrated information related to health, medical care, and nursing care. This enables patients to centrally manage their health information and share it with medical institutions and care facilities. For example, consolidating medical history, test results, and prescription information in PHR helps physicians make prompt and accurate clinical decisions. The introduction of PHR improves patients’ self-management capabilities and enhances the quality of healthcare.
Additionally, in emergencies, patients can receive appropriate medical care quickly, ensuring their safety.
Companies Utilizing Medical Big Data
Companies utilizing medical big data include the following examples:
- M3, Inc.
- JMDC
- Medical Data Vision Co., Ltd. (MDV)
- NTT DATA
- Carenet Corporation
- GlaxoSmithKline plc
- Roche
- MedPeer, Inc.
- TechMatrix Corporation
Each will be explained below.
M3, Inc.
M3, Inc. operates “m3.com,” a site specializing in medical professionals, with over 300,000 registered doctors, making it one of Japan’s largest sites for healthcare professionals. “m3.com” aims to improve the quality of healthcare through information sharing among doctors and providing the latest medical information. M3 also utilizes medical big data to analyze physicians’ clinical behaviors and drug use trends, providing valuable insights to pharmaceutical companies and medical institutions.
This enhances pharmaceutical companies’ marketing strategies and the operational efficiency of medical institutions, overall improving healthcare quality.
JMDC
JMDC started as a subsidiary of Olympus and grew by leveraging accumulated medical data. JMDC analyzes vast medical data provided by insurers and medical institutions, contributing to healthcare cost optimization and the promotion of preventive medicine. For example, providing data-driven solutions for disease prevention and health promotion supports the efficiency of healthcare. Additionally, data-driven medical service provision contributes to the realization of personalized medicine.
This enables the provision of optimal treatment for each patient, improving healthcare quality.
Medical Data Vision Co., Ltd. (MDV)
Medical Data Vision Co., Ltd. (MDV) engages in diagnostic data business and accumulates health insurance data. MDV analyzes electronic medical record data and health insurance data, providing valuable information to medical institutions and pharmaceutical companies. For example, offering hospital management support based on diagnostic data and marketing support services to pharmaceutical companies. This enhances the operational efficiency of medical institutions and optimizes pharmaceutical companies’ market strategies.
Additionally, it contributes to medical innovation by supporting new drug development based on data analysis.
NTT DATA
NTT DATA actively engages in the utilization of health medical big data and real-world data (RWD). NTT DATA analyzes vast data provided by medical institutions and insurers, contributing to healthcare quality improvement and cost optimization. For example, offering management support to medical institutions and health promotion programs to insurers based on data analysis. Additionally, it engages in new drug development support and treatment effectiveness monitoring using real-world data.
This is expected to enhance healthcare efficiency and quality.
Carenet Corporation
Carenet Corporation develops new businesses that analyze and utilize medical big data. Carenet aims to improve the quality of healthcare through educational content and information provision services for healthcare professionals. Carenet analyzes medical big data to provide insights into clinical behavior and treatment effectiveness to medical institutions and pharmaceutical companies. This enhances the operational efficiency of medical institutions and optimizes pharmaceutical companies’ marketing strategies.
Additionally, it engages in developing new medical services based on data analysis.
GlaxoSmithKline plc
GlaxoSmithKline plc (GSK) contributes to medical research by publishing clinical trial data. GSK provides its clinical trial data to researchers, promoting the development of new treatments and improving healthcare quality. For example, re-evaluating the efficacy and safety of drugs based on clinical trial data can lead to the creation of new treatment guidelines. This enhances the transparency of healthcare and increases trust among patients.
Additionally, data sharing promotes collaboration with other researchers, accelerating medical research.
Roche
Roche publishes clinical trial data for its approved drugs, contributing to the advancement of medical research and treatment improvement. For example, new treatments and new indications for existing drugs are developed based on published data. This expands treatment options for patients and improves the quality of healthcare.
Additionally, data publication enhances research transparency and improves the reliability of healthcare.
MedPeer, Inc.
MedPeer, Inc. has access to real-world data (RWD) on a scale of about 5 million people and holds “Insight Data,” a collective intelligence of doctors. MedPeer promotes information sharing and treatment improvement among doctors through its community. For example, creating treatment guidelines and evaluating new drugs based on insight data. This enables doctors to perform clinical care based on the latest medical information, improving treatment effectiveness for patients.
Additionally, data analysis contributes to improving the quality of medical services.
TechMatrix Corporation
TechMatrix Corporation boasts high achievements in medical information cloud services and provides services specializing in big data analysis. TechMatrix supports secure management and analysis of data for medical institutions and pharmaceutical companies. For example, providing an environment where medical data can be securely stored in the cloud and accessed promptly when needed. This reduces data management costs and enables efficient data utilization.
Additionally, advanced analysis technologies provide valuable insights to medical institutions and pharmaceutical companies, contributing to healthcare quality improvement.
コメント