Hello Everyone , So today we will let you know about Importance of Health Care Data Analytics or What is Health Care Data Analytics . Can big data save lives? Advocates of the emerging field of health care analytics certainly believe it can.
Big data analytics is transforming the way organizations do business in dozens of industries. But the impact may ultimately be most profound in the field of medicine, where advanced health care analytics holds the potential to revolutionize patient care.
Big data is already having positive effects on many areas of health care, including:
- Advancements in telemedicine
- Enhanced patient engagement
- Wearables that provide real-time alerts
- Disease prevention/population health
- Improving/refining treatment standards
- Potential to help cure diseases
- Improved staffing efficiency
- Prevention of opioid abuse
As the nation’s top technology innovators partner with health care organizations to leverage valuable insights from the immense amount of healthcare data being generated and collected each day, the search is on for new ways to transform rapidly expanding databases into improved outcomes for patients and entire populations.
Understanding the analytics progression and starting in the right place will help to guarantee success with advanced analytics and lead to AI utilization.
There are four types of analytics, Descriptive, Diagnostic, Predictive, and Prescriptive. The chart below outlines the levels of these four categories. It compares the amount of value-added to an organization versus the complexity it takes to implement.
The idea is that you should start with the easiest to implement, Descriptive Analytics. In this blog, we will review the four analytics types and an example of their use cases, and how they all work together.
Descriptive Data Analytics revolves around data and findings related to what has happened already in the past. It does not involve making any inferences or predictions with the data. It is rather a type of analytics that forms the base for further analysis and other types of data analysis. It basically involves collecting historical data and presenting the data in an organized manner for easy understanding. Very basic levels of statistical methods such as average, percentage change calculations, Mean etc. are used in Descriptive Analytics.
Predictive Analytics is the type of data analysis where probabilities are used to forecast possible future events and outcomes. In predictive analytics, complex statistical techniques are used to predict the outcomes. For example- Statistical Data Modeling, Data Mining etc. Predictive Analytics is rather a step towards Machine Learning and Artificial Intelligence which involve predictive models.
Prescriptive Analytics is the stage in data analysis where the learnings from Descriptive and Predictive Analytics are used to suggest the best actionables for the business problem or challenges. This type of data analytics uses rather complex statistical tools and techniques to figure out the right course of action. Complex algorithms based on internal as well as external data are used to achieve the results. Because of this reason, Prescriptive Analytics is not a routine practice adopted by organizations as it requires specialized tools and statistical processes.
What is the future of health care data analytics?
Technology and digital transformation define the future healthcare. As more and more patient and clinical data is collected, healthcare organizations will be able to expand their knowledge and take action to improve patient experiences and, ultimately, health outcomes. Thus, new digital technologies that utilize healthcare analytics are being developed with the goal of improving global health.
What role does the government play in Health Care Data Analytics?
The government plays an important role in healthcare analytics. Concerns over how healthcare organizations gather, store, share, and use personal information have prompted numerous pieces of legislation at the federal and state level in order to protect patient privacy.
In 1996, President Bill Clinton signed the Health Insurance Portability and Accountability Act (HIPAA) to ensure data confidentiality and security for medical information. Title II of HIPAA also requires healthcare organizations to secure their electronic access to health data and remain compliant with privacy regulations. More recently, the Office of the National Coordinator for Health Information Technology (ONC) issued the Federal Health IT Strategic Plan 2015-2020 to protect the privacy and security of health information and increase public confidence in the safety of health IT.
Organizations that carry out healthcare analytics must comply with these regulations to, first and foremost, function legally, but also to prioritize patient data security. The information used in health analytics is personal and oftentimes sensitive in nature. It is therefore of extreme importance that healthcare organizations performing health analytics attend to the legislation surrounding their operations.
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