UNDERSTANDING HEALTHCARE ANALYTICS

What are Healthcare Analytics?

Healthcare analytics give benefits professionals unparalleled insight into hidden patterns and population health trends, and a data-driven view of how health benefits programs are impacting employee productivity, satisfaction, and costs.
Sophisticated data analytics also allows employers to understand what’s behind high utilization, engagement and cost trends at an individual and group level within their member population.
When decision-making directly impacts employee health, you can’t settle for data that is just “good enough.” While there are multiple data analytics vendors available, it’s important to note that data quality standards and accuracy vary significantly.

THE BENEFITS OF PREDICTIVE HEALTHCARE ANALYTICS

Our cutting-edge technology offers organizations of all sizes levels of predictive analysis that were available only for large companies. With the most cost-effective system available in the predictive analytics industry, you can experience benefits such as:

  • Overall improvement in member health
  • Generally lower annual cost for operations
  • Easy access to electronic health records
  • Improved patient outcomes with early interventions
  • Identification of high-risk members
  • Cost reduction through preventative measures
  • Treatment and medication compliance tracking

WHAT IS DATA ANALYTICS?

Data analytics is the process of analyzing, interpreting, and transforming raw data into useful insights that can be used to make better decisions. It involves using algorithms, statistical methods, and artificial intelligence (AI) to identify patterns and trends in large data sets. Data analytics is used in various industries, including finance, insurance, marketing, retail, and healthcare.

Various types of data analytics are used depending on the objective of the analysis. These are identified as some of the common types of data analytics:

Descriptive Analytics:

Descriptive analytics is the most basic form of analytics, which provides an overview of past events and summarizes historical data

  • This type of analytics is used to describe what has happened in the past, and it helps to identify trends, patterns, and correlations
  • An example of descriptive analytics in healthcare would be analyzing member records to determine the number of members who have been diagnosed with a particular disease

Diagnostic Analytics:

Diagnostic analytics involves identifying the cause of a particular event or outcome

  • This type of analytics helps to determine why something happened in the past
  • An example of diagnostic analytics in employee benefits would be analyzing employee turnover rates to determine why employees are leaving the organization

Predictive Analytics:

Predictive analytics uses statistical models and machine learning algorithms to predict future outcomes

  • This type of analytics is used to forecast future events based on past data
  • An example of predictive analytics in healthcare would be using member data to predict the likelihood of a member developing a particular disease

 

Prescriptive Analytics:

Prescriptive analytics is the most advanced type of analytics, which recommends a course of action based on the anticipated outcomes of predictive analytics

  • This type of analytics helps decision-makers to take action to prevent future events or optimize outcomes
  • An example of prescriptive analytics in healthcare would be recommending a treatment plan for a member with hypertension based on gaps in care, medication compliance, life-style issues, future risk and willingness to change.