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Role of Data Analysis and Interpretation in modeling health outcomes and economic impacts

What do you mean by data analysis and interpretation?

Data analysis and interpretation is a process where collected data is examined, cleaned, analyzed and interpreted to gain fruitful insights. There are various steps involved in this process which are data collection, data cleaning & exploration, data transformation and analysis, data interpretation, validation and verification.

Are there different types of data analytics?

Yes, there are mainly 3 types of data analytics:

Descriptive Data Analytics:

Past or historical data is used in descriptive data analysis

Predictive Data Analytics:

Predictive analysis involves utilization of complex statistical techniques to predict future outcomes. It involves data mining/modeling, cruising towards use of AI and ML in predictive analytics.

Prescriptive Data Analytics:

Here in predictive data analytics, the combined data from descriptive and predictive analysis are utilized to derive best actionable insights.

How does data analysis and interpretation is utilized in healthcare?

Availability of data has never been a problem in the healthcare industry. Instead, collecting, analyzing and interpreting data from various sources has been the main challenge. Data analysis and interpretation has the ability to store enormous amounts of data, interpret it accurately and provide valuable insights healthcare patterns and help in informed decision making.

How is data analytics helping healthcare organizations?

Healthcare organizations are leveraging various types of data analytics such as descriptive, predictive, prescriptive and diagnostics to detect anomalies in scans, minimize cost, predict illness outbreak, boost business intelligence and make better decisions for patients. Data analytics is also being utilized in enhancing preventative care, predicting future illness, and lower insurance costs.

How is data analytics being utilized in enhancing healthcare?

Various tools and methods from data analytics are being utilized to interpret large populations’ healthcare needs. Effective data visualization can detect and predict communities and high-risk patients, their adherence, social condition and enhance healthcare delivery and decrease health inequalities. Data analytics and interpretation plays a vital role of providing personalization and precision in public health by identifying the right patient for right intervention in a cost-effectiveness method.

What is the role of data analytics in health economics?

Latest technological models and methods have been transforming healthcare. Utilization of data science has opened the door for using various applications such as EHR and geocoding, digital public health, mobile health and many more. These applications allow extraction and interpretation of data from various groups of population, integrate it and provide cost-effective drugs and therapies for specific people.

Digital information and novel technological advancements provide the potential to collect, integrate and utilize large portions of data, such as social media, EHR, smartphone, GPS-enabled devices. This data is then utilized to identify economical, effective and accurate methods for disease prevention.

How is data analytics impacting HEOR?

The goal of HEOR has always been to provide best and cost-effective healthcare to the needy patients. HEOR works for better utilization and allocation of resources in healthcare and maximize the beneficial impact on every patient. This approach has been further complemented with the use of data analytics and interpretation. The efficiency and cost-effectiveness of healthcare and healthcare related services have been optimized with the help of big data analytics, artificial intelligence and machine learning. Fruitful insights from data analytics and interpretation have led to providing tailored drug/therapies to specific populations, lower operating costs and avoiding wastage of resources.

Utilization of real-world data through various data tools and applications has enabled the healthcare service providers to manufacture cost-effective drugs and medical devices and provide targeted public health services. The availability of cost-effective drugs/therapies/devices and value-based healthcare models has resulted in great reduction in healthcare inequalities.