Big data is going to play a big part in the healthcare industry in the future. In fact, it’s being used right now, such as to improve patient care. Big data and analytics is a massive industry itself, being implemented across businesses all over the world to gain insights into customers and making appropriate changes to processes and systems.
This article will explain the huge role big data will play in healthcare in the years to come. It will show how big data has already been adopted as well as insights into how it is going to help the healthcare industry overall.
Healthcare And Fitness
The fitness industry has been one of the biggest benefactors of the big data movement. For example, some of the most recent advancements in wearable technology like smartwatches use big data to keep an eye on patient health. In some cases, this data is fed back to their doctor for further analysis.
Two of the main players in the smartwatch space are Apple with the Apple Watch and Fitbit. They can track steps and other daily activities taken by the wearer, as well as track sleep and even body weight. From the millions of people that wear these devices, the data is analyzed by medical professionals to try and connect it to health.
For example, according to Digital Authority Partners, Fitbit, has more than 90 billion hours of heart rate data that can help cardiovascular clinicians, researchers, and even payers devise preventive measures and care.
While the data may not be totally reliable, there’s no denying the fact that big data is having a big impact on the healthcare industry already. Fitness trackers and smartwatches are one of the biggest sources of personalized healthcare available right now. Going into the future, it’s likely that they will play an even greater role than today.
As technology advances, the data will become more accurate and reliable, meaning that it becomes more valuable to doctors and healthcare companies looking to discover new treatment methods, or pharma companies in drug discovery.
Now, while this all sounds great, it’s worth mentioning data privacy. As more data is collected, healthcare companies must ensure that their security systems are capable of keeping it safe. After all, as technology advances, so do hacking methods and fraud attempts so the industry must be equipped to deal with these issues in the years to come.
Identify High-Risk Group
Some patients are constantly in the emergency room through no fault of their own. Not only does their illness or disease force them to seek urgent care, but they also end up spending a lot of money on life-saving healthcare. 17% of patients are responsible for nearly 75% of all health care expenditures.
Big data in healthcare will play an important role in the years to come for patients with high-risk conditions. It will help doctors determine why they keep coming back and predict if and when they next return to the hospital. This will also help to provide the personalized care that these patients require.
While there isn’t enough data to paint a clear picture of high-risk patients right now, there will certainly be a big improvement in the future. As long as the data is reliable, there’s no reason why results can’t improve.
One of the biggest issues that the healthcare industry faces is costs and how to reduce them – and big data can help.
According to a report by the Society of Actuaries, 47% of healthcare organizations are already using predictive analytics. The report also shows that more than 57% of healthcare sectors believe that predictive analytics will save organizations at least 25% in annual costs.
Big data will help to solve business models, cost models and calculate expenses. This means that it is very valuable to healthcare organizations to study their current situations and save on costs.
Furthermore, big data sets can be analyzed to create data models for predicting how healthcare staff can be allocated based on how busy the hospital has been from historical data. For example. During the winter months, it’s a lot more likely that they will be dealing with skiing accidents compared to the summertime.
Therefore, the use of big data in healthcare will mean that organizations will be able to allocate their money to the appropriate areas when and where it’s needed.
This will have a waterfall effect. For example, if hospital beds are utilized more efficiently and staff allocation is improved, insurance companies will also save money.
Preventing Human Error
Finally, human error is a serious problem that healthcare practices and hospitals face every day. For example, providing a patient with the wrong prescription can be fatal – and the mistake could simply be a misread of a label.
Humans are always prone to mistakes but in the healthcare industry, it could literally mean the difference between life and death. Software as a medical device and big data analytics in healthcare can be used to reduce the number of errors made by doctors, pharmacists and other professionals. It is particularly useful for practitioners that deal with a lot of patients every day which increases the chance of making a mistake.
One application in the future could be used to scan a patient’s medical history, identify if any errors have been made and inform their doctor is anything seems out of the ordinary.
In 2017, researchers at Harvard Medical School demonstrated technology that could reduce medication error. The study showed that from almost 800,000 patients that were analyzed, just under 16,000 errors were flagged, of which 75% were actually validated. According to that same report, prescription drug errors cost the US more than $20 billion annually, showing how important it is to ensure the correct medication is prescribed.
These are just some of the many ways that big data will be used in the healthcare industry for years to come, from improving patient care to reduce the number of mistakes made. The only issue that stands in the way of big data is that it will require a large-scale upgrade of the existing computer systems that hospitals are running on. Old computers are not capable of running this kind of software so it’s up to the key decision-makers to decide how and when they are going to go all-in on big data.
This article was contributed by Julian Gnatenco @ JGBilling