Integrity, Standardization, and Security: A Sino-U.S. Comparative Approach to Addressing Pain Points in Healthcare Big Data

On the afternoon of August 21, the “Medical Big Data & Smart Healthcare” forum, part of the premium series hosted by Sinopharm Capital, was held in Shenzhen. The forum brought together representatives from government, hospitals, and enterprises to jointly discuss industry pain points, including how to break through bottlenecks in medical big data and smart healthcare.

The keynote speakers for this event include Li Wei, Founding Partner of Sinovest Capital; Wang Daping, Vice Chairman of the Shenzhen Municipal Committee of the Chinese People's Political Consultative Conference and Director of the Shenzhen Public Hospital Center; Wu Qingbin, Director of the Hospital Information Center at Jinan University; Lin Denan, Director of the Medical Information Center of the Shenzhen Health and Family Planning Commission; John Mattision, Chief Information Officer of the largest integrated healthcare and insurance organization in the United States; and Huang Yang, Chief Technology Officer of Hangzhou Shurui (MDQ).

VCBeat has compiled the key insights from this forum for our readers’ benefit:

First, openness is the norm; non-openness is the exception.

Open data access helps stimulate the momentum and vitality of deepening healthcare system reform; improves the efficiency and quality of medical and health services; expands resource supply to continuously meet the multi-level and diverse health needs of the public; and fosters new business models and economic growth drivers.

Second, standardized and orderly, safe and controllable.

Establish regulatory frameworks for the openness and protection of comprehensive health and medical big data; strengthen the development of standards and security systems; reinforce responsibilities for security management; and effectively safeguard personal privacy and information security.

Third, open integration and collaborative sharing.

The government and social forces should collaborate, integrate resources, foster broad-based support, unlock the dividends of data, and stimulate the vitality of mass entrepreneurship and innovation.

The primary source of medical data is the Electronic Health Record (EHR), specifically the Electronic Medical Record (EMR). An EMR refers to patient visit information recorded electronically by resident physicians, including medical orders and prescriptions. This definition originates from Construction of Hospital Information Platforms Based on Electronic Medical Records issued by the Health Statistics and Information Center, where it is defined within the foundational solutions for building hospital information platforms based on EMRs.

At the China Hospital Network Conference organized by the Professional Committee on Information Management of the Chinese Hospital Association, a survey on informatization status was conducted among more than 400 hospitals. The findings are as follows:

There are diverse approaches to applying medical big data within the policy environment. For instance, the coordinated development of healthcare, medical insurance, and pharmaceuticals (known as “Three-Medical Linkage”) can address the implementation challenges of data standardization. With substantial involvement from medical insurance authorities and data requirements imposed by government regional platforms, hospitals will be compelled to generate standardized data. A simple example is the current billing system based on the three major catalogs of medical insurance, which cover pharmaceuticals, diagnostic and treatment services, and medical consumables. Hospitals are required to convert their internal billing items into previously defined coding specifications in accordance with established rules.

Several opportunities in the process of hospital informatization deserve attention. For instance, there is still market demand for the foundational infrastructure of Hospital Information Systems (HIS), as this sector has not yet reached saturation. Attention should be paid to specialized data integration and application. High-level data integration, analysis, and application can be implemented in the management of conditions such as stroke, hypertension, and diabetes, as well as in more niche areas like depression. Additionally, the application of digital signatures and digital authorization in internet healthcare represents another key area of opportunity.

I. Shenzhen has established a four-tier data center architecture, primarily comprising the regional data center managed by the Medical Information Center, family planning data centers within administrative regions, and data centers distributed across various medical and health institutions, including family planning agencies. The four-tier regional health center aggregates data from 62 public hospitals, 604 community health service centers, and numerous other health institutions across the city.

II. Data, categorized by business domain, primarily encompasses six major areas: healthcare, public health, family planning, medical insurance, pharmaceutical management, and comprehensive administration. The four-tier data center infrastructure holds a total data capacity exceeding 5 terabytes (TB), comprising over 5 billion transactional records. Currently, approximately 15 million unique resident health records have been established within the electronic health record system.

III. Six Summary Principles of Shenzhen’s Data Openness

IV. Basic Technical Roadmap for Open Data Applications.

The establishment of the big data center should comply with big data architecture principles and be both secure and open. This center requires collaborative construction, followed by data preparation—including collection, cleaning, encryption, and de-identification—before conducting thematic data mining, analysis, and applications.

V. Operational Model.

The primary entities engaged in data research and application will be professional teams within the industry, including those from the healthcare, public health, and family planning sectors, as well as specialized teams from social enterprises, industry-specific operational teams, and big data analytics groups. These parties will collaborate on joint operations. Regarding funding, there are three models: government procurement (such as for public service products), investment by society and enterprises, and a hybrid model involving government, social, and enterprise partnerships. Each distinct funding approach will lead to three entirely different models of benefit sharing.

VI. The shortage of big data professionals in the industry and data security issues will be the two major pain points in data infrastructure development.

As sequencing costs decrease exponentially, an increasing number of people can rapidly obtain their own genetic testing results. Currently, approximately two million individuals in the United States have genetic testing data.

Meanwhile, the United States is also establishing large-scale biobanks to retain patients’ blood samples and sequencing results upon obtaining their consent. As the cost of gene sequencing drops from $1,000 to $300, it will become feasible to sequence all previously collected samples, thereby significantly enhancing the overall level of sequencing capabilities.

The U.S. government’s health insurance system employs three payment models: the first is traditional fee-for-service, where payment is made for each individual service provided during a medical visit; the second is the risk- and population-based payment model newly introduced under U.S. healthcare reform; and the third is an earlier model based on value-based or risk-based payment.

Ideally, the market or the country as a whole will see 10 to 20 institutions emerge as winners that are paid based on value and quality, with sufficient competition among projects so that the market can clearly identify which healthcare organizations are truly value-based. Physician groups will appear among the list of 20 healthcare institutions that succeed in this competitive landscape.

The Health Insurance Portability and Accountability Act (HIPAA), enacted in the United States, was specifically established to address privacy protection issues concerning medical health data. A deeper implication of this legislation is to ensure patients’ right to access their own electronic medical records. This enables patients to transfer their electronic medical records to other healthcare institutions and insurance providers, which explains the inclusion of the term “Portability” in the Act’s name.

This legislation has now become the new international standard for privacy protection of health and medical information, and its adoption will have a profound impact on fostering the U.S. healthcare R&D and data markets.