What Healthcare Analytics Can Teach The Rest of Us - InformationWeek

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Data Management // Big Data Analytics
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11/17/2016
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Lisa Morgan
Lisa Morgan
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What Healthcare Analytics Can Teach The Rest of Us

Analytics are taking on a lead role in all phases of the healthcare system. Those personal fitness devices? Their value is in what they say about us, not the data itself.

Healthcare analytics is evolving rapidly. In addition to using traditional business intelligence solutions, there is data flowing from hospital equipment, medical-grade wearables, and FitBits.

The business-related data and patient-related data, sometimes combined with outside data, enable hospitals to triage emergency care patients and treat patients more effectively, which is important. For example, in the U.S., Medicare and Medicaid are embracing "value-based care" which means hospitals are now being compensated for positive outcomes rather than on the number of services they provide, and they're docked for "excessive" readmissions. Similarly, doctors are increasingly being compensated for positive outcomes rather than the number of patients they see. In other words, analytics is more necessary than ever.

Credit: Pixabay
Credit: Pixabay

There are a couple of things the rest of us can learn from what's happening in the healthcare space, and there are some surprises that may interest you. The main message is to learn how to adapt to change, because change is inevitable. So is the rise of machine intelligence.

The Effect of the IoT

Medical devices are becoming connected devices in operating rooms and hospital rooms. Meanwhile, pharmaceutical companies are beginning to connect products such as inhalers to get better insight into a drug's actual use and effects, and they're experimenting with medical-grade (and typically application-specific) devices in clinical trials to reduce costs and hassles while getting better insights into a patient's physical status. Surgeons are combining analytics sometimes with telemedicine to improve the results of a surgical procedures. Slowly but surely, analytics are seeping into just about every aspect of healthcare to lower costs, increase efficiencies, and reduce patient risks in a more systematic way.

One might think devices such as FitBits are an important part of the ecosystem, and from a consumer perspective they are. Doctors are less interested in that data because it's unreliable, however. After all, it's one thing for a smartwatch to err in monitoring a person's heart rate. For a medical-grade device, faulty monitoring could lead to a heart attack and litigation. At this point, doctors are more interested in the fact that someone wears a FitBit because it indicates health consciousness.

Not surprisingly, predictive analytics is important because mitigating or avoiding healthcare-related episodes is preferable to dealing with an episode after the fact. From an IoT perspective, there is a parallel here with equipment and capital asset management. One way to reduce the risk of equipment failure is to compare the performance of a piece of equipment operating in real-world conditions against a virtual representation that is operating normally under the same conditions. Similarly, patient "signatures" will make it easier to spot complications earlier, such as weight gain which is an indicator of congestive heart disease or fluid retention which may indicate the likelihood of a heart attack. Imagine if the same predictive concept were applied in your industry or business.

Machine Intelligence Reveals Insights

"Insights" is a oft-misused term. It is related to analytics but not synonymous with analytics. Insights means new knowledge, and that is precisely the reason why machine learning is gaining traction in the healthcare space. Traditional medical research has been hypothesis-driven. Machine learning doesn't necessarily have a theory or the same veil of human biases.

Take diabetes research, for example, a machine learning-based research project found that a person who has been hospitalized is at greater risk for subsequent hospitalization, which comes as no surprise to doctors. However, several more interesting factors were unearthed, the biggest surprise of which was flu shots. It turns out the diabetics who did not get flu shots were more likely to be hospitalized and following hospitalization their health became unstable or unmanageable.

The lesson here is one of adaptation: as machine learning becomes mainstream, more of us will have to get comfortable with insights we hadn't anticipated. In a business context, machine learning may reveal opportunities, risks, or competitive forces you hadn't considered. In short, more of us will have to embrace an ethos of exploration and learning.

 

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