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The market is growing and in three years data analytics tools will be worth almost 8 billion USD globally. Promising opportunities and clear direction for improvements, right?
We live in an era of unprecedented technological advancements. And we are generating more data on an annual basis than we have for most of history. This is why the future of data analytics in healthcare is undeniable.
When we think about the opportunities that are afforded by this generation of exorbitant amounts of information we can start to understand and interact with our world in a better way. In the USA they spend nearly 3 trillion USD a year on chronic conditions like heart diseases or diabetes. If we can figure out ways to incorporate lifestyle changes or adjustments that reduce the need for medication or procedures, we can not only save money for the system but we can improve our life.
The great news is that we have tons of data at our fingertips. And we can use this data to help us better understand and make small tweaks to a person’s health plan in a way that has a really big impact.
Although health information and health data seem synonymous, they are not the same thing. Data refers to raw facts such as a blood pressure reading, a lab value or a temperature. Information is a collection of data that has a context. It is usually organized and has some meaning. This could be a trend of increased postoperative infections. The last level of the DIK theory is knowledge. This is a combination of information and relationships that are useful for decision-making.
Although it’s not important to define these concepts, it is important to be careful about the quality and the source of the raw data.
Where does the data come from? Most of it starts from EMR, and there are multiple sources for it like medical imaging and x-ray results, progress notes, and more which are in a narrative form. There are also lab tests, problem lists, provider orders, flowsheets for vital signs, ect. These are more structured ones and can be abstracted more easily but may not provide the context or meaning without the narrative notes.
In order to be reimbursed, the healthcare organization or provider practice must abstract data from the notes and documentation in accordance with guidelines and submit the claim to the payer. This is done more easily through the use of a natural language processor or NLP.
What Is Big Data Analytics In Healthcare?
We associate the world data mostly with bar charts and line graphs and spreadsheets of numbers. And what’s so inspiring or powerful about the impact of big data analytics on healthcare is that blending these new data files with the current medical achievements helps in speeding up the growth in studying and seeing how the external aspects and human biology are being tied and correlated. It is a truly transformational vision of individual care pathways. Data and AI rule everything here. Big data and predictive analytics in healthcare adds up and accumulates huge amounts of PHI coming from all possible sources mentioned above. With all that treasure, doctors can find out a lot of precious things like potential diseases due to patients’ lifestyles, or how different treatments will affect them, whether they are likely to miss appointments, and much more.
How Can I Use Healthcare Data Analytics? Some Examples
Global big data analytics in the healthcare market already has its marvelous solutions that changed medicine tremendously.
Making cybersecurity stronger, for example, as it is always one of the top preoccupations, unfortunately, and what is better to cope with data safety than a cool innovative tech able to spot any little signs of an upcoming threat?
Or preventing readmissions. It is not a cheap thing, to be honest, with tens of billions of USD spent each year. Analytic tools can easily pick out persons with traits showing a chance of readmission. This way, medical staff can fix extra measures for such outcomes and prevent themselves from penalties.
In the case of managing health, the solutions help in defining chronic issues, and make people at risk safer with early preventive treatment leaving more chances for recovery.
Healthcare data analytics degree defines how effective patient engagement is. Proper care means active involvement and responsibility from both sides of the treatment process. Having the information on patients’ behavior, helps doctors greatly.
Making the experience personalized and individual thanks to the modern tech tools makes the whole process optimized and impactful.
Analytics apps can also make insurance claims be processed faster. The main benefit here is bringing mistakes in documentation to a minimum.
Healthcare big data analytics companies also contribute greatly to estimating and predicting suicide attempts via a model based on EHR to study persons’ strong possibilities for committing a suicide. Foreseeing missed or ignored appointments is one more important application. It may sound like a minor problem compared to the previous one but the no-shows cost a lot in the USA, they have a crazy amount of 150 billion USD that are lost annually due to this reason. Thanks to healthcare data analytics and management tools, it is possible to improve this frustrating situation by effective predictions.
What Do Healthcare Data Analytics Companies Offer?
This partnership will bring your business a chance of lowering expenses on missed appointments and readmission fees, first of all, as well as make you properly prepared for the next health tendencies. Moreover, a lot of organizational and regulatory workflows like insurance claims will become easier and faster. Finally, data safety gets enhanced with the newest protective technology and advertising campaigns become more efficient.
On the other hand, there are some things you should think over. Apart from all the obvious benefits, big data analytics in medicine and healthcare tools are fragile to some risks that hospitals will have to manage before making the most of their full power.
- Medical ethics and moral risks. There is a danger that clinicians might put all the decision-making responsibility to analytics. But these tools are only suggestive and doctors still need to ponder and study them thoroughly and discuss with colleagues.
- It is not always easy to gain doctors’ cooperation and approval. Finding the balance between patient care and data collection during appointments takes effort. To make it work, doctors can be engaged in the creation of the methods to ensure the solution is focused on their needs.
- Lack of regulations is an issue. There are no clear rules to manage algorithm development, unfortunately, and routine audits is what is necessary here to ensure the current relevance of methods.
We hope this article was useful. Contact us today for more information on data analytics and healthcare. Our team will create a perfect solution to upgrade and innovate your services. Good luck!