U.S. Expert Advises Hospitals to Focus on 'Small Data' Over Big Data Investments

Over the past decade, internet search volume for “big data” has surged exponentially, rising by nearly 100% between 2011 and 2016 alone. This trend has rendered the vision championed by big data enthusiasts far less elusive: while extracting insights from statistical data and numbers has historically been a labor-intensive endeavor, in the future, all such tasks can be effortlessly accomplished with the aid of electronic devices.

Big data not only enables people to visualize previously inaccessible images but also reveals scenarios that were once unimaginable. Against this backdrop, a big data fever has emerged both in China and abroad, with many startups and investment firms directing their business models toward medical big data analytics. Hospitals, too, have been actively establishing big data centers, either independently or through collaborations.

But for hospitals, is investing in big data truly all benefit and no harm? VCBeat interprets the views of Munzoor Shaikh, Head of Healthcare Transaction Services and Population Health at West Monroe Partners in Chicago, USA.West Monroe Partners is one of the leading consulting and high-tech R&D application firms in the U.S. healthcare sector,Let’s listen to the voices from the industry.

Munzoor Shaikh

Munzoor Shaikh is the leader of the company’s healthcare practice, specializing in rehabilitation health, health insurance, and demographic analysis. With over 15 years of experience in healthcare management and technology, he has been instrumental in uncovering new models for business transformation at West Monroe. He believes that even modest transformations can be revolutionary for hospitals. It would be a costly mistake for most healthcare institutions to miss out on valuable insights that could transform medical services and improve population health by focusing too heavily on the “big” in big data while overlooking the “small” shifts that may bring countless opportunities. In his view, hospitals should not get lost in big data but should instead focus on small data.

Shaikh noted that a big data mindset can leave hospitals overwhelmed by data. For the vast majority of healthcare systems, it is impractical to extract patient records, financial records, population health profiles, and data from wearable devices and fitness trackers from vast datasets, and then import them into massive digital repositories for sorting and aggregation. Implementing big data initiatives requires not only cross-organizational collaboration but also the development of data warehouses.

They are extremely large, complex, and costly; historical experience suggests that data warehouse initiatives often end in failure. Therefore, the expert believes that while most hospitals may be able to afford building such massive technical infrastructure in the future, it is more advantageous for both patients and hospitals today to break down data projects into smaller components with more measurable objectives.

For example, for a CIO or hospital data manager, a $15 million, five-year project requiring the construction of a data warehouse is clearly more challenging than a data-driven disease management program that requires only a $250,000 investment and can be launched in just three months. Although the latter may seem less ambitious, it is easier to implement from both financial and logistical perspectives, and its immediate impact is more readily apparent.

In Shaikh’s view, the most promising opportunities today are based on collections of small problems and small datasets. He cited another example involving a behavioral health data experiment: a healthcare company attempted to identify predictors of depression by combining biometric and behavioral data, but the experimental results fell far short of the company’s expectations. They had hypothesized that social and demographic factors would be the most significant predictors of depression; however, their research indicated that individuals with depression also showed strong correlations with certain clinical markers.

This unexpected insight is highly valuable and can be leveraged by hospitals to improve the health outcomes of target populations. This example demonstrates that if enterprises can launch such clearly targeted data initiatives and prove their practical value, they will lay a solid foundation for addressing subsequent “small data” challenges, enabling progressive advancement.

Identifying objectives for smaller projects can also help hospitals grasp the fundamentals of managing large volumes of data. Today, people are often drawn to flashy, trendy buzzwords such as “analytics” and “big data,” while overlooking essential principles like proper data management, data integration, and data quality.

Shaikh believes that what hospitals truly need is a clear strategy driven by their own clinical and operational demands. Many healthcare organizations, in their eagerness to invest in big data infrastructure, often fail to consider the timeframe within which their financial investments will yield quantifiable returns. Yet, this is precisely the question every enterprise should ask itself before making such investments. The vision for big data is real and attainable, and the path forward is correct and promising. However, hospitals should not ignore reality in pursuit of scale; instead, they should remain grounded, starting from the basics and proceeding according to their specific circumstances.