One of the primary concerns for many governments is to reduce healthcare costs. Also, healthcare facilities are trying to come up with ways in which they can identify and assist patients with chronic diseases who require specialized care in order to manage their conditions in the most appropriate manner.

At the same time, vendors who supply these facilities with IT services are expanding their big data tools with the aim of helping the providers with data mining services.

Through this, the healthcare facilities can get a better understanding of patient results, performance analysis and know the areas where they can reduce costs and the methods they can use in the cost reduction process.

Many healthcare providers are moving from paper records to electronic records, thus they the services of health IT vendor in order to assess their patients more accurately and mine data which come help them predict outcomes.

The following are some of analytics tools that can be used to improve healthcare.

Descriptive Analytics

This is the common big data analytics tool used by many industries. Descriptive analytics focuses on areas which did not go as planned by trying to find the reasons why outcomes varied with the predicted results. Descriptive analytics cover almost everybody.

Descriptive analytics can help healthcare facilities have a better understanding of the current data assessments. For example, assisting the facilities to know if their patients should have received a particular kind of drug or how many hypertensive patients have their blood pressure under control.

Predictive Analytics

Most healthcare facilities use big data analytics to identify patterns, predict future patterns and avoid preventable events in order to reduce costs. The common question, especially in accountable care organizations is the percentage of their patients likely to be re-admitted to these facilities again.

The organizations also predict how many patients will use the emergency room. The predictive analytics tool to categorize patients based on their risk profiles.

Prescriptive Analytics

In the past few years, healthcare facilities have shown an increased interest in prescriptive analytics. A report generated by Gartner indicated that only 13% of businesses use predictive analytics but only 3% use prescriptive analytics.

However, the demand for this tool is increasing and there is still room for growth. Prescriptive analytics assist providers to find the total number of patients and then come up with the necessary plans to manage the patient population.

For example, Explorys is a big data analytics tool that allows hospitals to focus on obese patients and assess their cholesterol levels or other factors to determine the areas where they need to focus their attention.

Good data is mostly used when trying to change the cost curve, however when most providers receive the data, they find it a challenge when trying to figure out the areas they need to focus on.

Comparative Analytics

Healthcare facilities can use big data analytics to compare their performance with other healthcare facilities. Here we also focus on Explorys. The comparative analytical market has been expanded by this tool in the past few years using its National Benchmarks platform.

The National Benchmarking platform uses comparative metrics in over 90 billion clinical, financial and operational records across a range of healthcare facilities. By analyzing this data, the platform is able to shed more light into the patterns and trends in the concerned medical facilities.

Healthcare facilities can then use the insights from the data to compare their level of services with other medical facilities. The participating facilities are kept private and the patient information is de-identified in order comply with HIPAA regulations.

For example, hospitals can compare the LDL levels of their patients with other hospitals. The same criteria can be applied across the gender, age, race and geography. The facilities can then use the information to plan how they can improve performance.

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