How Big Data Truly Lands in Hospitals: Insights and Solutions from Leading Hospital Executives

Content Compiled by: Wang Xiaoxing / Zhou Mengya / Yang Juan

With the introduction of the concept of national health and medical big data, hospitals’ information management urgently needs to move beyond the traditional model focused solely on IT technical services and process reengineering. The entire continuum from “data → information → knowledge → action” will serve as the entry point for this transformation. As a powerful tool for advancing clinical practice, scientific research, and industry development, big data platforms will play a significant role.

Recently, at the 2017 Summit on Medical Big Data and Precision Medicine, hosted by Chinese Hospital CEO Magazine and co-organized by Boshi Medical Cloud,Zhi Xiuyi, Executive Vice President and Secretary-General of the Chinese Association of Thoracic Surgeons、Zhang Fengzhen, President of the 731 Hospital of China Aerospace Science and Industry Corporation、Yu Hongxia, President of Beijing Yanhua Hospital, each shared their insights on the practical application of big data platforms in hospitals. The following are the highlights of expert opinions compiled by VCBeat (WeChat Official Account: vcbeat):

"Many situations in the medical diagnosis and treatment process generate data, such as automatically recorded pathological data, personalized treatment plans, medical research, new drug development, etc."

These foundational data hold significant value, but hospitals must address how to integrate them with standardized and regulated data infrastructure to enable health management, disease prevention, and early diagnosis and treatment, rather than merely providing single-disease treatment.

Standardized management of these data facilitates on-demand retrieval by physicians during scientific research, thereby saving time and effort.

Leveraging a disease-specific information platform enables the rapid implementation of seven core functions:

1. Standards and Specifications: Achieve data interoperability and break down data barriers between systems. Our data entry personnel must communicate with IT companies to enhance the practical value of the products.

2. Analytical Mining: Leveraging unsupervised and semi-supervised learning algorithms to facilitate a deeper understanding of healthcare big data, thereby uncovering new knowledge, patterns, and discoveries within emerging datasets.

3. Data Sharing: Establish data sharing mechanisms centered on disease types, involving various organizational forms such as disciplines, regions, and medical consortia. While requesting data, each organization must also improve its own database and contribute data for sharing.

4. Visual Interaction: Present data through diagrams to discover patterns more quickly and intuitively.

5. Medical Modeling: Create continuous medical models to facilitate the implementation of tiered diagnosis and treatment, promote the development of smart healthcare, and support the realization of precision medicine.

6. Translational Application: Accelerate the rapid circulation of translational medicine evidence and outcomes.

7. System Integration: Align with major scientific research initiatives to facilitate the advancement of medical science and cultivate talent in medical big data.

In terms of disease-specific information platforms, Boshi Medical Cloud, used by Xuanwu Hospital, serves as a prime example.

The advantages of Boshi Medical Cloud are as follows: Data is interconnected across systems, allowing physicians to enter patient information only once for use by multiple systems, thereby avoiding redundant data entry. Furthermore, mutual recognition of information can be achieved among hospitals.

For the same patient, each department only needs to complete its own portion of the records, eliminating the need to create a new medical file. By leveraging Boshi Medical Cloud, physicians can achieve data aggregation, search and retrieval, comprehensive filtering of medical records, target patient analysis, and patient follow-up.

Currently, all data platforms at Xuanwu Hospital are managed by professional personnel who manually input data. The hospital provides training to these staff members to ensure data quality. Thanks to the involvement of dedicated data entry specialists, Xuanwu Hospital can achieve comprehensive data management visibility across its mobile, tablet (PAD), and PC endpoints through the Boshi Medical Cloud.

The features of Boshi Medical Cloud include a structured data platform, real-time data analytics, patient tracking and follow-up, department management, multidisciplinary team (MDT) collaboration, and multi-center clinical trials.

Xuanwu Hospital focuses on pulmonary diseases, building a data platform centered around this specialty. Furthermore, as departmental data continues to be updated, these data assets can better serve the department’s needs.

My focus on respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and bronchial asthma, has led me to believe that information-based management can significantly aid in evaluating the efficacy of chronic disease treatments, establishing disease prevention models, and, in particular, facilitating patient follow-up.

In hospital management, we leverage the Boshi Medical Cloud to integrate and utilize data, effectively guiding managerial, operational, and financial decision-making. By collecting data to generate analytical trends, we provide robust support for administrators’ work.

Currently, the 731 Hospital of China Aerospace Science and Industry Corporation (CASIC) and Boshi Medical Cloud are primarily collaborating in the fields of thoracic surgery and respiratory medicine. Future cooperation will be explored in departments such as radiology, clinical laboratory, and orthopedics.

Health examination management data for the entire population within a region can guide disease prevention and decision-making for community-wide or regional population health management. By leveraging Boshi Medical Cloud to effectively integrate chronic disease management, big data, and health management, there is an opportunity to elevate Electronic Medical Records (EMR) to the level of Electronic Health Records (EHR) and even Personal Health Records (PHR). This ultimately enables early detection and early treatment of diseases.

The collaboration between Beijing Yanhua Hospital and Boshi Medical Cloud aims to cover the entire spectrum of oncology diagnosis and treatment, including pre-hospital early screening, in-hospital multidisciplinary team (MDT) implementation for oncology care, and post-discharge digital follow-up management.

Compared with European and American countries, China’s five-year cancer survival rate remains relatively low due to insufficient emphasis on early screening, early diagnosis, and early treatment. The application of big data, such as monitoring data throughout the entire process of cancer diagnosis and treatment, will provide a reference basis for health policy decisions. Beijing Yanhua Hospital is currently implementing a quality evaluation system for cancer diagnosis and treatment, which includes an indicator measuring the time interval from the identification of suspected cancer in patients to the initiation of treatment. It is hoped that valuable data collected through regional data aggregation and follow-up will inform health authorities’ decision-making.

The application of big data in oncology requiresPre-hospital Screening, In-hospital Diagnosis and Treatment, Post-hospital RehabilitationThree parts.

In terms of pre-hospital screening, the Yanshan area has a relatively stable population of approximately 80,000. The majority are retired employees of Yanshan Petrochemical Company, characterized by a unified payment system, consistent living standards, and standardized medical reimbursement mechanisms, making it suitable for population-based cohort studies. A recent questionnaire survey conducted among 8,000 individuals in this functional community revealed that the male smoking rate is significantly higher than that of the general male population in Beijing, reaching as high as 60%. There is an urgent need for smoking cessation interventions in the region, and further investigation into the potential incidence of lung cancer should be carried out with the support of information technology tools.

In-hospital Diagnosis and Treatment: From suspected diagnosis to treatment, whether in outpatient or inpatient settings, patients require multidisciplinary expert consultations at various stages to develop personalized diagnostic and therapeutic plans. For instance, in the diagnosis of small pulmonary nodules, leveraging imaging and big data analytics can provide physicians and patients with more precise and convenient diagnostic and treatment services.

In terms of post-discharge follow-up, tracking patient disease outcomes and monitoring the side effects of tumor radiotherapy and chemotherapy, if supported by big data, will play a positive role in the selection of future treatment methods and the continuous training of physicians.

In the future, Beijing Yanhua Hospital may explore comprehensive collaboration opportunities with Boshi Medical Cloud across the pre-hospital, in-hospital, and post-hospital stages.