In the preceding half of the blog (First part), we mentioned that big data is fueling the healthcare sector with exciting trends that include machine learning, predictive analysis and more. We also took a detailed look at how hospitals were prioritizing individual patient care with real time monitoring of events. We also spoke about how algorithms helped reduce fraud, waste and abuse across all units that highly benefit the industry. Let’s look at other big data trends that are changing the healthcare industry.
Healthcare And Internet Of Things
In data technology, internet of things or IoT is the new buzz word. There is an exponential growth in the data of connected devices as this network of internet-connected computing devices collect and exchange data using fixed or embedded sensors. These devices can be used in the healthcare industry to monitor a wide range of patient behavior. Though this data is unstructured, change in actions due to glucose level, heart function or even blood pressure can be supervised.
Spending on healthcare IoT is estimated to reach around $120 billion
in the next couple of years.
When combined with big data and machine learning, smart devices can also be used to communicate with each other. This could potentially lead to lowering frequent or unnecessary doctor visits and be replaced with a telephonic conversation. Another example of healthcare IoT is smart medicine dispensers that can detect if medicines are being taken as prescribed. If a patient is noncompliant or skips a dose, the smart device is capable of initiating a phone call and reminds the patient to take the medication at an approved time. This just proves that the abilities to improve patient care with big data solutions and healthcare IoT are limitless.
Predictive Analytics To Improve Outcomes
Patient data is exploding with the adoption of (EHR) electronic health records. It is clear that sharing of patient information across agencies and organizations is encouraged so that there may be better speedy diagnosis and patient outcomes. The accuracy of analyzing unstructured and structured data among multiple sources is beneficial to help diagnose the right patient condition, match treatments with outcomes and predict the risk a patient may experience for a disease.
Predictive modeling enables for early diagnosis of various illnesses thereby reducing the mortality rate. The foremost spending for any disease is accounted by a congestive heart failure. The early stage heart failure is generally known to be the most neglected and missed. For a healthcare condition such as this, complications can be avoided if it is diagnosed early. The big data trend such as using machine learning algorithms can look at alternate factors in patient charts and substantially help identify patients who are more capable of experiencing a congestive heart attack.
Predictive analytics uses real-time EHR data such as respiratory rate, heart rate, temperature and blood cell count that can identify various conditions
Similarly, doctors and patients may tend to neglect most early symptoms due to various factors. Many conditions can be treated and be kept under control if diagnosed early. Normally, the lack of information makes it challenging to take a call by looking at physical copies or notes. Machine learning accounts for all factors that could have been otherwise missed. Big data combined with predictive analysis, the algorithm distinguishes patients that experience such conditions and need to be treated early.
In Conclusion
The increasing costs of healthcare, the aging population and rise in chronic conditions are universal challenges that the healthcare industry and countries face. Through big data and analytics, there is a huge scope to improve care and delivery while reducing the overall expense. The potential use of big data solutions across the healthcare ecosystem is possible with data-driven tools and technologies. The fundamental shift is in how healthcare data is being used to develop the end-to-end resolutions taking complete advantage of emerging technologies. The use of artificial intelligence and machine learning has accelerated the prevalent integration of big data capabilities for major hospitals and companies. As technology continues to develop in this field, clear direction to health systems, leadership and organizational efforts will lead the new way of delivering clinical excellence.
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Author Bio – Matt Wilson – A Healthcare Expert, is working with Aegis Health Tech as senior developer from last 5 years. He has extensive experience in patient management system, EMR & EHR Development, Implementation and Integration.
