Getting it right, first time around, is critical in the disciplines of healthcare and medicine. In this increasingly data driven world, it is little surprise that Big Data and analytics are playing increasingly important roles in this field. This is how data science is bringing costs down and boosting efficiency across healthcare.

Search Out and Destroy Inefficiencies in Healthcare Processes

Why does any business or any organization use data? Usually, it is to try to boost efficiency and introduce streamlined processes. In healthcare, the methodology is no different.

The provision of healthcare is a complex field, with many different aspects coming together and being expected to work in harmony. As a result of this complexity, the structure of healthcare is prone to inefficiencies, which can translate to major expenses. In 2015, US News reported on the Cleveland Clinic, who had turned their attention towards a comprehensive program of data capture and collection.

This analytic approach led to patient satisfaction increasing at the clinic from 66% to 82% in only six years.

Ease the Strain on Hospital Infrastructure

A significant proportion of the work handled by the healthcare structure in the US takes place in hospitals or other large medical institutions. If large numbers of patients need hospital treatment – either as a result of climatic conditions, epidemic illnesses and conditions in a society, or simply over-population in the area – this puts these vital institutions under a great deal of strain.

This high volume of patients can push costs through the roof, drag efficiency down, and even put lives at risk. By applying data analytics, hospital administrators can begin to understand how many of these hospital visits are actually unnecessary, and begin to define ways in which care can be provided outside of medical institutions, reducing the pressure these institutions find themselves under.

Getting a Handle on the Data

In almost all cases, the issue is not that there is not enough data, but that there is too much, and that such data volumes are difficult to control. The phrase “drowning in data, starving for insight” has been kicked around the field of data science for several years now, and it certainly rings true in many areas.

The concept of, semi-literally, drowning in data has put many business owners off data science altogether. However, in healthcare – where understanding and insight can truly be a matter of life and death – this is not the answer; what is needed instead is a different approach.

Data solutions providers are working towards this, creating innovative new storage and processing architectures with healthcare institutions in mind. Protocols such as the Network File System also enables fast dissemination of insight across a network, all of which contributes to the overall agility of the hospital’s data strategy.

Predicting the Future

One of the beautiful things about data science is that it removes the need for trial and error. You have an idea that you want to test and – rather than taking risky gambles on its efficacy – you simply apply the necessary data and model the outcome on a computer.

In a field such as healthcare, accurate predictions and a solid platform of understanding are vital. Medical institutions cannot afford to waste time and money on poor analytic predictions, and doing so may even result in diminished quality of healthcare.

Instead, they need to be able to rely on the data they collect. They need systems and protocols which they can trust, data sets which have been proven as accurate; only then can they trust the outcomes they receive from data modelling. A serious investment in Big Data and analytics can provide all three of these components, and save serious money in the long term.

Direct Patient Care

Writing for MAPR in 2016, Carol McDonald was eager to point out how effective analytics and data understanding can be in the direct application of patient care. We tend to think of analytics and Big Data as supporting the role of a doctor or direct healthcare provider, giving them the resources required to stay in the field for longer.

However, McDonald identified how the data gathered from patient monitoring systems was playing an active role in healthcare processes. This data is accessible to doctors and nurses around the clock, but this is nothing new; what are new, and certainly very exciting from both a data science and a medical point of view, are the algorithms which are being used to process this data and make it more usable. These algorithms are helping to enhance the insight that a doctor has, reducing the cost of the treatment provided and, crucially, giving the patient a better chance at recovery.

Prioritization

The running of a successful healthcare institution requires warmth, empathy and a caring attitude towards our fellow humans. However, conversely, it also requires a staunch sense of practicality and pragmatism. It is in this sense that data science provides the most profound advantage.

In the past, hospitals and other institutions have wasted vast amounts of money on illogical processes of treatment. A patient arrives and they need treatment, provided there is no emergency patient who needs life-saving care, as they are treated first. Other patients must wait, potentially worsening their condition and lengthening the period of treatment they require.

By applying analytics, institutions can quickly assess the level of care needed and the level of urgency. Then, preliminary treatments can be provided as and when they are required, while emergency patients are prioritized and key treatment decisions are supported by reliable data. The result is a dramatic reduction in costs, and also a major enhancement of the service level the institution can offer.

This idea of healthcare representing a balance of empathy and pragmatism has been around since the days of Hippocrates and those early pioneers. Now, through the application of data and understanding, we are learning how to optimize this balance, reduce costs, and maximize the level of care the patient experiences.

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