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As healthcare becomes a growing concern among many Americans, there is a potential to unlock new initiatives that help improve the well-being of many and possibly save lives. Big data has many uses, including identifying health issues as they appear, getting a better understanding of certain conditions and working out options for treatments. However, compared to other industries, the use of healthcare performance management that incorporates big data remains fairly low. Part of this may be due to hesitance on providers, but there is also an issue of bottlenecks that are commonplace in the industry. If predictive analytics is to make any headway in the field, it must address these bottlenecks.

A matter of regulation
There are various hold-ups that can prevent the effective development of a strong data stream at any touch point in healthcare, according to The most significant of these is government regulations regarding recordkeeping and patient privacy. Any solution will require compliance with HIPAA at the bare minimum, among other sets of laws often associated with the Affordable Care Act. There are also related factors such as the willingness of patients to actually provide this valuable data. While the rise of wearable technology enabled a greater degree of information to open up, many people still feel uncomfortable with the idea of giving away their health details, even when it's anonymous.

Big data in medicine remains held back by bottlenecks. Big data in medicine remains held back by bottlenecks.

Another problem that exists in the field of healthcare is data interoperability. There are currently many different standards in play that make it difficult to pass along details to different core constituents, whether they are primary care providers or insurers. One way around this is to implement unique workarounds that often involve methods such as crowdsourcing. The Harvard Medical School demonstrated this in a partnership with a community of more than 450,000 algorithm specialists and developers to analyze key elements the immune system. It believes that this could apply to other areas of healthcare as well.

The most distinguishing issue at this point, however, remains governance of data within each individual institution. The management of insurers and healthcare providers often fail to send data even within their own organizations, essentially creating data silos. Consequently, the most severe bottleneck is the lack of coordination of data at the managerial level. A healthcare management system can help manage this problem, but only if the institutions using it can offer a disciplined line of support. That means greatly expanding collaboration of data across stakeholders and others.