By: Noah Bennett
In the fast-paced fields of pharmaceutical R&D and clinical care, healthcare data acts like a hidden “blueprint” — only when interpreted by professional analysts can it be transformed into a critical force that drives the development of life-saving drugs and optimizes treatment plans. Li Ran, a SAS-certified expert with years of hands-on experience in healthcare data, currently serves as a Statistical Programming Analyst at Savio Group Analytics. She has established herself as a leading professional in this critical area of data interpretation. Leveraging her solid technical skills and industry insight, she oversees data processing and quality control in high-stakes corporate collaboration projects, injecting professional momentum into the precise advancement of the healthcare sector. Recently, our reporter conducted an exclusive interview with Li Ran to hear about her journey of deepening expertise in the field and to gain her insights into its value and trends.
“The core value of healthcare data lies in supporting reliable decisions through precise analysis,” Li Ran stated at the start of the interview, clarifying the essence of her work. Within her workspace, Guidelines for Healthcare Data Compliance and SAS programming manuals — filled with highlighted notes — lay open, while her screen displayed clinical trial data tables. Though specific project details were not disclosed, her focused demeanor alone was enough to highlight the rigor and importance of her work. “Many people think data analytics is just ‘working with numbers,’ but healthcare data is tied to drug safety, patient treatment outcomes, and even whether a new drug can ultimately reach the patients who need it,” Li Ran explained. A discrepancy in any single dimension of a dataset, she noted, could impact critical judgments. “That’s why our work requires not only ‘accurate calculations’ but also ‘clear communication’ — every data-driven conclusion must be evidence-based and traceable.”
When talking about her career choice, Li Ran mentioned that her direction was guided by the belief that “data drives medical progress.” With a master’s degree in Statistics from the U.S., she built a strong foundation in statistical analysis — and from there, she steadily mastered key skills like clinical trial design, healthcare data modeling, and regulatory compliance through dedicated, ongoing learning. It was during this journey of skill-building that she realized healthcare data isn’t just a bunch of cold numbers — it’s a core cornerstone of pharmaceutical innovation. To turn theory into real-world impact, she focused on mastering SAS as a key technical goal: starting with basic data cleaning and variable definition, she worked her way up to advanced macro programming and dataset construction. Every step was centered on “serving real healthcare scenarios.” That effort eventually earned her the SAS Certified Specialist credential — a highly respected certification in the healthcare data field. It doesn’t just require proficiency with tools like SAS/Base and SAS/STAT; it demands a deep understanding of healthcare data’s uniqueness and rigor. “I once spent weeks breaking down case codes just to master the logic behind automated clinical trial data analysis — until I could handle the entire process from integration to output on my own,” Li Ran recalled. That level of dedication is what let her tackle complex projects with confidence later in her career.
Today, at Savio Group Analytics, Li Ran plays a key role in multiple Phase 3 clinical trial data analytics projects conducted in collaboration with international pharmaceutical companies, covering critical therapeutic areas such as bacterial infections. These projects span dozens of research centers worldwide, involve hundreds of patients, and include data dimensions like clinical symptom assessment, laboratory indicator monitoring, and safety records. All work must strictly adhere to international clinical data exchange standards to meet regulatory review requirements across different regions. “The core challenge is the ‘standardized integration of multi-source data,’” Li Ran revealed. Data recording practices and unit formats often vary between centers — for example, some record concentration in mmol/L (millimoles per liter), while others use mg/dL (milligrams per deciliter), and decimal places aren’t consistent either. “Our work, at its core, is about picking out valid, usable data — and letting that data tell the ‘story’ we need. We sift through scattered data points to find meaningful insights, making sure every valid piece contributes to an accurate conclusion.” Thanks to her technical expertise, the analytics work she leads has consistently passed multiple rounds of quality audits by both collaboration partners, making her a trusted technical core recognized by all members of the project teams.
Balancing “precision” and “efficiency” has long been an industry-wide challenge, but Li Ran found an optimal solution through technological innovation. She noticed that repetitive tasks in traditional analytics — such as generating tables, listings, and figures (TLFs) required for clinical trial reports — are both time-consuming and prone to human error. To address this, she developed customized SAS macros to automate these processes. Work that once took a team several days to complete can now be significantly shortened using these macros, with error rates reduced to zero. “Automation isn’t just about saving labor; it frees up the team’s energy to focus on ‘data insights,’” Li Ran gave an example: in safety analytics, after macros quickly generate basic statistical tables, the team can concentrate on uncovering potential correlations in the data to inform drug safety guidelines. Additionally, she established a “dual-validation” mechanism to ensure the accuracy of results through cross-verification. In previous projects, she has corrected data discrepancies multiple times, earning her the title of “data gatekeeper” within her team.
Looking ahead to the future, Li Ran predicts the industry is moving toward “stricter compliance” and “greater efficiency.” “Regulatory authorities around the world are imposing increasingly high requirements for the traceability and compliance of healthcare data — we now need to submit not just results, but a complete record of the entire data processing workflow,” she explained. At the same time, the expanding scale of clinical trials has led to a surge in data volume and complexity, placing higher demands on efficiency. “This means we need to understand technology, rules, and basic medical knowledge,” Li Ran said, using an example: in data processing, analysts must identify which data dimensions require key annotation and which analytical methods comply with regulatory zards. “Only by balancing technology, rules, and healthcare scenarios can data truly realize its value.” She is optimistic about the “human-AI collaboration” model: “AI can handle basic tasks like data cleaning and trend identification, but healthcare data is tied to human life and health — final judgments still need to be led by humans. For instance, distinguishing whether a data anomaly is caused by a drug’s effects or a patient’s underlying condition requires clinical context, which algorithms cannot yet replace.”
In Li Ran’s view, the ultimate meaning of healthcare data analytics is “putting data to work for people.” In some of the projects she has participated in, data analytics results have helped drive breakthroughs in specific indications for drugs, indirectly providing more treatment options for patients. “I’ve never met the patients I’ve helped, but knowing that the data I processed can make a difference in their treatment makes all the effort worthwhile,” she said. This “hidden value” keeps her passionate about her career. For young professionals looking to enter the field, she offered three pieces of advice: “First, build a solid dual foundation in ‘statistics + healthcare’ — master analytical tools while also understanding clinical trial processes and basic medical knowledge. Second, cultivate an extreme sense of rigor; there’s no room for carelessness in healthcare data. Third, maintain a habit of continuous learning — the industry evolves rapidly, and only by constantly improving yourself can you keep up.”
She emphasized that behind every successful analytics project is teamwork: ‘Data analysis is not a solo effort — collaboration among statisticians, programmers, and clinicians is what ensures every conclusion is both statistically sound and clinically meaningful.’
As the interview concluded, Li Ran had already returned to work — the data analytics interface on her screen refreshed continuously as she prepared for dataset validation in the next project. “In healthcare data analytics, there’s no ‘finished state’; every dataset is a new beginning,” she said, her resolve impressive. From her in-depth study of statistics to her professional leadership in projects, and her clear understanding of the industry’s future, Li Ran has proven through her actions that healthcare data analysts are far more than just “data processors” — they are the “critical bridge” connecting data to medical breakthroughs. In an era where healthcare is pursuing precision and high-quality development, it is countless professionals like Li Ran who safeguard the value of data with their skills and responsibility, paving the way for pharmaceutical innovation and protecting patient health.
