Reasons Why Healthcare Should Have Data Science

Big data has made great inroads into a slew of industries. Healthcare, being one of the big and complex industries in the US, it is ideal for big data initiatives.

While many major players in the healthcare segment have embraced big data in the US, by and large, the adoption by the total industry is pretty slow, precisely for a slew of reasons. There are few important ways, where healthcare industry could opt big data, which is hitherto not utilized to its fullest potential.

Medication Error Prevention

First and foremost, medication errors are a major issue amongst healthcare firms, as the humans have the propensity to make errors, though not all the time. For instance, healthcare organizations tend to choose the wrong medication in a pull-down menu, thus the patients face the potential hazard of ending up with wrong medication, eventually proving much harm or even death.

Just to avoid the medication error big data plays a critical role to help reduce these errors by analyzing the patient’s records with all medications prescribed, and ultimately flagging anything that seems out of place. The sad part is that there are more stumbling blocks to the widespread adoption of big data software. Unfortunately, due to the age of many healthcare IT systems, the adoption process of these devices can be very slow to catch on. What is more, healthcare data is hypersensitive and the healthcare players should be extra conscious about security and compliance with federal regulations.

High-risk Patients

Majority of healthcare systems have to cope up with high rates of patients perennially using the emergency department, which not only triggers healthcare costs but also will not lead to better care or outcomes for the patients. Few hospitals have succeeded in reducing the number of ER visits by identifying high-risk patients and offering customized patient-centric care using predictive analytics. Lack of data is the major roadblock to overcome in identifying high-risk patients, as the availability of data points are between few and far, essentially making it cumbersome to get a precise picture of the potential risks as well as the reasons for these risks.

Reduction of Cost and Waiting Period

There is tremendous potential for cutting costs with big data in the healthcare segment, besides reducing the waiting times, something that costs everyone a fortune. The budgets of hospitals are complex, while the return on investment potential is pretty high many of them show reluctance in investing in big data. In turn, many hospitals replace the existing equipment with the state of the art ones and spend more elsewhere, blissfully unaware of the fact that if they invest in big data, they would actually be saving a phenomenal amount of money.

Use of HER

Overall, the healthcare industry will be able to save as much as $400 billion by properly using big data. There are reasons to cheer about as most of the hospitals in the US have at last started using Electronic Health Records, which is making it easier for healthcare professionals’ swift access to data. This is certainly a major initiative towards implementing big data platforms. If more and more healthcare firms join the bandwagon of adopting big data, these practices will become the norm rather than the exception.

Data Breach

According to one study, the healthcare industry is 200% more likely to experience a data breach than other industries, simply because the personal data is so valuable. With this in mind, some organizations have used big data to help prevent fraud and security threats. For example, The Centers for Medicare and Medicaid Services were able to prevent a staggering $210.7 million in fraud in just one year using big data analytics. Unfortunately, in addition to the preventative benefits of big data, there are also some big security risks. Many organizations are wary of making themselves more vulnerable than they already are which is understandable considering federal patient information regulations.

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Did you know?
By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.

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