Data analytics has become essential in a lot of industries. It allows the user to conclude from large sets of data. We’re currently living in an age of massive data accumulation, but a lot of organizations are still struggling to manage it efficiently.
In medicine, the situation is no different. Hospitals, private practices, and other medical institutions have a lot of data to deal with. Various patient information, schedules, appointments, and other data sets provide the potential for more efficiency and consistent healthcare. Through data analytics, we provide better patient care and better resource utilization.
We rely on software solutions to help us organize and analyze these massive databases with more ease and less time wasted.
Why is data analytics important in healthcare?
Well, let’s see.
Evaluating Practitioner Performance
We’ve moved away from the classic healthcare approach that focuses on volume to a modern value-based approach. This means that the patient’s experience stands at the core of excellent healthcare service.
Being that feedback is more comfortable to leave now than ever before, we get a better insight into the performance of practitioners daily. Patients can now easily rate their physicians through online means. They can leave extensive comments that outline their experience.
Furthermore, we have access to robust, digitalized databases regarding the patient’s outcomes and practice patterns.
All in all, we can feed our data analytics software with all it needs to analyze the practitioners’ performance in real-time.
All organizations, including ones in healthcare, strive to decrease operating costs without harming performance quality. With value-based medical services, there is also a correlation with performance related to costs.
By following the overall outcome of the medical services provided, we get a more precise approach to reimbursement. Furthermore, through analytics, physicians get a better insight into patterns regarding general population health. Through this, we can avoid unnecessary costs by skipping patient care practices that are redundant.
Analytics are also useful for identifying individual patient costs. This helps with cost reductions. We can also use them to identify patients’ behavior outside healthcare institutions. Through this, we can help them establish healthy habits and reduce their needs for assistance.
Chronic diseases are the costliest health cases for the healthcare industry. We have parameters we can use to determine which patients within the population have a higher risk of developing a chronic disease.
Here are the parameters:
Through analytics, we can identify parts of the population that are more likely to experience these medical issues. This, in turn, gives us the ability to act preemptively, getting the situation under control before it fully develops. This cuts costs and ensures a healthier population.
Seven Ways in Which Data Analytics is Changing the Healthcare Industry
- Heath Tracking
One of the most significant advances in healthcare is big data, analytics, and the Internet of Things. These days, with devices attached to our body called wearables, we can track the state of our body continually. This allows physicians insight into how the body was performing over a long period. This also means that hospitalization is not necessary for monitoring which reduces costs significantly.
- Reducing Costs
Failing at evaluating how many hospital staffers needs to be, leads to either over or understaffing. Through analytics, we can be more precise with our evaluation. We can also improve staff allocation.
We can then precisely evaluate the staff needed for each institution. Furthermore, we can predict the number of beds required to accommodate the patients. This will prevent putting patients on waiting lists for hospitalizations. It will also prevent unnecessary vacancies.
Society of Actuaries has reported that 47% o healthcare institutions already use predictive analysis. They also say that 57% of healthcare sectors are sure that it will help them reduce annual costs by 25%.
- Assisting High-Risk patients
Analytics is perfect for evaluating high-risk and chronic patients’ needs. This can help physicians come up with better care approaches in the form of corrective measures to reduce the need for frequent hospital visits.
- Preventing Human Errors
In some situations, physicians handle large volumes of patients daily. This can lead to exhaustion which in turn leads to mistakes. Big data and software that controls it can help flag potential errors and avoid potentially devastating mistakes.
- Advancement in Healthcare Sector
These days, we can use Big Data analysis to search through massive databases and find answers to complex medical issues. The more we do research, the better these databases will become, and physicians will be able to find solutions to complex diagnostic problems in seconds.
Healthcare research will become more streamlined and applicable in less time than ever before.
- Using Wearable Data to Monitor and Prevent Health Problems
Our body produces about two terabytes of data every day. With current wearable technology, we can record most of it. A lot of major tech and IT brands have joined the race for medical monitoring through wearables.
The potential for application of these analytics is impressive. We can anticipate heart attacks, strokes, and other potentially lethal health issues and react on time. This means that we will be able to save lives, not just provide a diagnosis.
Furthermore, wearable tech can help patients change their lifestyles to keep potential and chronic health conditions under control.
- Improving Diagnostic Accuracy and Efficiency
Misdiagnosed patients are hardly a thing of the past. Despite our advancement in the way we approach patient diagnostics, 5% of patients in the US are incorrectly diagnosed every year. This amounts to 12 million patients each year. Mistakes during diagnostic procedures account for 10% of patients’ deaths.
Enlitic is a startup that aims to improve the accuracy of diagnosis through deep learning. This is made possible through an algorithm capable of analyzing imaging data. This includes CT scans, x-rays, and other imaging data.
This tech can also be targeted at preventing misdiagnosis of specific diseases that are usually missed.
Importance of Data Analytics in the Health Industry
The use of Data Analytics for health insurance is one of the most significant advancements in healthcare as a whole in recent years.
The first advancement is reflected in the better availability of structured and unstructured data. Through cloud computing, machine learning, and analytics, we have access to information like never before. The use of this technology reflects in better decision making, smaller costs, and broader healthcare availability.
The leadership of healthcare organization has realized the value of these tech advancements. There are three primary ways healthcare organization can use this kind of data management:
- Improved R&D pipeline development
- Improvement of diagnostic and therapeutic techniques
- Better prevention and surveillance management
There are six underlying layers of analytics relevant to the healthcare industry:
- Promotion of the trade
- Analytics of assets for medical devices
- Analytics of service for medical devices
- Social Media analytics
- Clinical Data Management
- Spend compliance
Big data is here to stay when it comes to the healthcare industry. It will be one of the main factors separating efficient and cost-effective organizations from the rest. There will be more room for data entry outsourced solutions to keep things moving. It still has some way to go, but the results we got so far are driving progress.