What is predictive analysis in healthcare?
In this section we will discuss what is predictive analytics in healthcare And what is the role of data analytics in healthcare? Predictive analytics in Healthcare is the use of data and algorithms to protect future outcomes. Predictive analysis in healthcare helps professionals make better decisions, improve patient care, and reduce overall costs of treatment. Now you must be wondering, how does predictive analysis and data analytics works in healthcare?
Data analytics in healthcare uses historical and current healthcare data and analyzes it to identify trends and risk factors after the exercise of data analytics is carried out on the historical data, certain probabilities and potential future outcomes are chartered out.
Benefits of Predictive Analytics in Healthcare
To understand what is predictive analytics we need to understand what is the role of data analytics in healthcare. The goal of data analytics in healthcare is to identify essential or useful patterns or trends in data – this information can help healthcare professionals to predict possible future health complications – healthcare professionals can now tweak their treatment procedure to suit the patient better.
Let’s discuss some benefits of predictive analytics in healthcare:
- One of the benefits of predictive health analytics is that only through analysis of historical patient data, such as electronic health records and diagnostic information, predictive analytics helps healthcare professionals make informed decisions about patient care, allocation of resources and creation of treatment plans.
- Predictive analytics in healthcare has a good scope and promises of revolutionizing patient care and outcomes of treatment, improve operational costs along with reducing overall costs for patients.
- Data analytics in healthcare has enable doctors or healthcare professionals to detect potential health issues early on and provide optimizable personalized healthcare.
- Predictive analytics in healthcare has enabled better allocation of resources for more efficient use. Thus helping healthcare professionals to proactively address health risks associated with the patient they are treating.
- Predictive analytics in healthcare is applicable across multiple domains including disease prevention, patient management, and hospital administration.
With the speed of the current technological advancements and increasing availability of plethora of data, predictive healthcare analytics is continuously advancing, contributing to a more data driven and patient centered approach in healthcare field.
Top 3 use cases of AI Predictive analytics in healthcare
There are several use cases for AI, predictive analytics and data analytics in healthcare.
- Preventing hospital readmissions. The cost of hospital readmissions are rather very high. Healthcare predictive analytics and data analytics can identify characteristics in patients which indicate possibility of readmission so that doctors or professionals can direct additional resources to such patients.
- Population health management: citizen healthcare or data analytics and healthcare. Is crucially, in identifying potential population health threats, many public health journals like Lancet Public Health Journal. Use predictive analysis and data analytics and healthcare to identify public health trends.
- Enhancing cybersecurity: Data analytics in healthcare can positively help in securing Patient data.
Real world examples of predictive analytics in healthcare
Predictive analysis in healthcare enhances decision making. It helps in streamlining operations and significantly improves patient outcomes. Now let’s discuss some real world examples of predictive analysis and healthcare:
- Diagnosing diseases: Predictive analytics in healthcare help identify subtle patterns in electronic health records of the patient that may or may not escape human observation and detect medical conditions in the nascent stage when most treatable.
- Personalized medicine and treatment: Predictive analytics and healthcare Got it. Identify patients individual profiles containing of medical history and other types of data to create more personalized.
Factors Affecting Growth of Predictive Analytics in Healthcare
Electronic Health Records
The widespread and still robustly growing use of electronic health records across many countries has provided a huge rich data set for predictive analytics technology to be trained upon. Once, we try to make use of the gold mine of data available to train these emerging and robustly growing technologies, patient care would be transformed.
Personalized Medicine
Rising drug resistance to common drugs that people use extensively has spooked healthcare professionals across the globe. With growing advancements in data analytics and predictive Analytics in healthcare, patients can be given drugs that they really need instead of common drugs they might not require and instead develop resistance.
Good Health and Well Being
The global focus on combating major health problems being encountered by humans and reaching the Sustainable development Goal 3 of Good Health and Well being has prompted and will prompt more attention and resources being poured into fulfilling the said ideal.
Rising Chronic Ailments
The increasing prevalence of chronic diseases, many of which are caused by lifestyle issues, are plaguing humankind across the globe. Major country like India currently shares 20% of the global burden of diseases even though India only contributes 17% to the world’s population. It has become even more important for major countries like India, USA and China to pour their talent and resources into making the technology of predictive analysis in healthcare much better and accessible to the common folks.
Technological Innovation
Integration of predictive analytics and data analytics with wearable devices and other technologies such as Internet of Things (IoT) and blockchain, enhances the capabilities of predictive analysis and data analytics.
How is the Market Looking for Predictive Analytics?
As discussed in the previous section the wide adoption of Electronic Health Record (EHR) across the globe has made available tons of patient data that can be use to train predictive analytics models upon. The valuable insights and wealth of information enable healthcare professionals to make decisions which are data driven and facts based. This positive growth is set to revolutionize the healthcare industry.
According to some projections the CAGR of the predictive analytics industry withing the Healthcare vertical is set to be 24% form the year 2024 to 2034. These projections say that the market size of predictive analytics was worth USD 17.99 Billion in year 2024 but by the year 2034 the market cap of the predictive analytics industry is supposed to be set to USD 154.61 Billion.
Market Dynamics
The increasing adoption of electronic health records significantly boosts the demand for healthcare predictive analytics. As the healthcare industry is turning away from paper to documentation being completely turned digital, a vast amount of unstructured and structured data has been becoming available. With this vast amount of data becoming available there are valid concerns of patients data violating privacy and data security mandates or standards.
How can Neuronimbus help you in between?
Neuronimbus with it’s more than 20 years of experience is a leading healthcare software development company, got expertise in protecting patient data and making applications that enable easy management of electronic health records and other healthcare-based applications. Neuronimbus’s expertise is suitable for making data driven decisions, application development, software engineering, prototyping and MVP development can help provide nitro to the digital healthcare revolution and if you have got something alike that we can help you with, get in touch with us.
Conclusion
Predictive health analytics sector is set to boom around globe. Thanks to the recent advancements in Artificial Intelligence and Machine Learning, developing more advanced methodologies to improve patient care. Just like predictive health analytics has a huge potential to cut down cost of healthcare for many patients who might not be able to afford it, Neuronimbus is working with many such organizations to help make a better, safer, sustainable and affordable world. From application development, software engineering and data and privacy management, Neuronimbus is at the forefront of pioneering digital technological revolution.
