LinkDoc Unlocks the 'Black Box' of Oncology Care with Research-Grade Data Standards

Due to factors such as high industry entry barriers, long lead times for data accumulation, strong demand for customization, and difficulties in market validation, big data in healthcare is generally regarded as one of the most challenging areas within big data applications. The integrated application of the healthcare industry and big data still appears to have a long way to go.

As one of the early enterprises in China to explore the application of medical big data, LinkDoc Technology has provided big data solutions to more than 500 Grade III Class A general and specialized hospitals, deeply processed nearly 2 million oncology research-grade medical records, helped integrate and clean over 10 million regional medical data entries, and processed more than 10 million medical imaging data sets.

After accumulating extensive research-grade data, LinkDoc Technology began expanding into additional dimensions in 2017, launching offerings such as clinical research solutions, imaging-assisted diagnosis and treatment, industry insight reports, decision-support services for the healthcare sector, and financial payment consulting. These initiatives aim to address the multifaceted medical needs of cancer patients, including accurate diagnosis, appropriate medication use, affordability, and risk minimization.

As is well known, China’s healthcare big data has long faced three major challenges: severe data silos, a lack of unified data standards, and ineffective utilization of big data technologies. Breaking down data silos requires top-down promotion, while the initial step for startups is to achieve standardization and structuring of data, which lays the foundation for the application of healthcare big data.

The most direct application of structured medical data is to provide scientific research services for hospital physicians. Companies in China, such as Xinyu Technology, SiPai Network, Yidu Cloud, Yiming Data, Boshi Medical Cloud, and Yihaoxian, have all made attempts in this field.

The precision of clinical medical data determines its value to the pharmaceutical industry. LinkDoc’s advantage lies in its acquisition of high-quality clinical data that adheres to research standards from the outset. Since its establishment in 2014, LinkDoc has focused on the oncology sector, collaborating with hospitals to structure medical record data. By entering the market through research services and integrating post-discharge patient follow-up and rehabilitation data with clinical data, LinkDoc generates structured data with significant scientific research value.

Chinese hospitals never lack patients, but they do lack high-value data distilled from patient medical records. Because clinical departments often lack tools to comprehensively integrate multi-source medical data, treatment plans, medication regimens, and therapeutic outcomes, it is difficult to conduct more in-depth research. “Our goal is to help clinicians transition from a state of ‘having patients but no data’ to one where ‘every patient generates data,’” said Zhang Tianze, Founder and CEO of LinkDoc Technology.

Lingke employs a combination of manual precision standards and automated, intelligent machine processing to complete medical record entry and structuring, while assisting physicians in research protocol design and data statistical analysis, ultimately yielding scientific research outcomes. This business segment has been operational for three years, covering more than 500 Grade A tertiary general and specialized hospitals.

Zhang Tianze stated that, due to the public-welfare nature of scientific research projects, they are not suitable for commercialization. However, given the extensive practical experience accumulated in data processing and application, there is significant potential to further expand and promote their value in scientific research applications. For instance, the recent collaboration between LinkDoc and AstraZeneca on a project concerning lung cancer brain metastases aims to explore treatment regimens and analyze efficacy in real-world settings. The initial analysis results from this study were selected for poster presentation at the 2018 European Lung Cancer Conference.

Building upon research-grade medical data, it has become inevitable to further extend into intelligent solutions for clinical decision support and diagnosis and treatment.

ZeroKrypton Technology’s development path bears similarities to that of Flatiron Health in the United States. While strategically positioning itself for the long term with intelligent clinical decision support solutions, the company has also expanded into the artificial intelligence (AI) sector since 2017—a move that may be more closely aligned with commercial monetization opportunities.

It is understood that LinkDoc has initially established capabilities in determining the benign or malignant nature of pulmonary nodules, predicting prognosis for skip metastasis and dissection of lymph nodes, anticipating adverse drug reactions, providing commercial market insights, and supporting medical insurance applications. In April 2017, LinkDoc Technology officially launched the Hubble Medical Big Data Clinical Decision Support System, which provides auxiliary support to hospitals and physicians across three domains: hospital management, scientific research, and clinical practice. Currently, this system has been applied in clinical departments and in real-world studies conducted by pharmaceutical companies, and its clinical decision support solutions are set to expand their scope of application within clinical departments this year.

“We are able to see not just one or several steps in the diagnosis and treatment process, but deeper and more comprehensive overall outcomes. This is based not only on our robust data accumulation and technical team, but also on our professional clinical medical team that aligns with specific needs,” said Zhang Tianze. LinkDoc’s advantage lies in the longitudinal continuity of oncology big data: from genetic data, imaging data, to pathological data, surgical plans, postoperative responses, and patient follow-ups, forming a coherent dataset for cancer patients. Therefore, it does not merely start from the single domain of imaging diagnosis, but provides comprehensive services to both medical technology departments and clinical departments, offering decisions on treatment strategies, relevant policies, and related business models. Additionally, through collaborations with insurance companies and DTP (Direct-to-Patient) pharmacies, LinkDoc achieves “affordability” and “low risk.”

In addition to hospitals, LinkDoc is also expanding into pharmaceutical companies. Currently, the demand for healthcare big data in the pharmaceutical industry focuses on several aspects:

First, during the drug R&D phase, how to leverage data to enhance the efficiency of clinical trials and accelerate drug registration.

Second, after a drug is launched, how can it be incorporated into clinical guidelines to become the gold standard for physician diagnosis and treatment? How can safety evaluations be obtained? How can indications be further expanded to extend the drug’s lifecycle? And how can pharmacoeconomic recognition be secured to facilitate inclusion in medical insurance coverage, among other considerations?

The efficacy of tumor treatment often exhibits significant individual variability, with the same class of drugs or therapeutic modalities yielding markedly different outcomes across patients—a phenomenon particularly pronounced in immunotherapy. Consequently, pharmaceutical companies seek to leverage healthcare big data to identify responsive patient populations, while insurance companies aim to engage in the medical management of policyholders covered for critical illnesses such as cancer, thereby providing optimal healthcare solutions for oncology patients. Achieving these objectives requires not only general patient data but, more importantly, case-based data grounded in China’s real-world clinical practice.

Zhang Tianze stated, “By meticulously analyzing these big data sets, we aim to unlock the ‘black box’ of oncology diagnosis and treatment, providing optimal solutions for patients, healthcare institutions, pharmaceutical companies, and insurance providers, thereby realizing the commercial value of medical big data enterprises.”

Source: 36Kr