What is Health Analytics?

Health Analytics Definition

Health analytics, also known as big data analytics, is the process of deriving insights from patterns and correlations found in healthcare big data and used to make better healthcare decisions. Health analytics extends beyond data management to finding meaning in real-time or historical data, and making predictions about the future to improve the probability of success.


Health Analytics Resources

patient relationship


Making Data Matter: Driving for Optimization

Watch Now


engagement center


Using Data Science and Actionable Models to Drive Campaign Outcomes

Download Now


patient engagement


How to Use Predictive Modeling for Healthcare


View Now

Benefits of Health Analytics

  • Interpret data to inform future interactions with patients, consumers, and populations.

  • Uncover the root cause of consumer response or lack of response to outreach.

  • Aid in predictive modeling by interpreting patient data from previous interactions.

  • Improve quality of clinical care by increasing healthcare organizations’ access to patient data.

  • Improve workflow across your organization by streamlining data-access processes.

  • Uncover the root cause of consumer response or lack of response to outreach.

  • Demonstrate ROMI with insights into successful and unsuccessful outreach campaigns.

  • Gain market share by improving patient care and reputation.

Common Health Analytics Questions

  • What Should I Consider When Selecting a Health Analytics Provider?

    Time-to-value is the first thing you should consider when selecting a health analytics provider. Choosing a solution that provides a rapid time-to-value keeps implementation costs down and offers your organization quick access to reliable data. Additionally, your organization should consider a health analytics provider’s experience and proven success. A vendor with lots of experience and a good reputation is likely to successfully deliver efficient, dependable service.

    Also look for a provider that can fit to your needs as an organization. Make sure the provider fully understands your requirements and that you communicate well with one another. The goal is to work with your health analytics provider as a partner. Provider flexibility is also important, since regulations around healthcare data collection are constantly changing. You need a health analytics provider that seamlessly adapts to these changes.


  • What are the Challenges Involved in Implementing Health Analytics?

    There are a number of challenges to consider when implementing health analytics. The first is making sure that the data you are looking to collect is clean, complete, accurate, and formatted correctly for use in multiple systems. Ensuring that your electronic health records (EHR) are optimized is a good way to prevent data capture problems.

    Another challenge to consider is lack of storage space. Costs involved in storing ever-increasing amounts of healthcare data can be difficult to manage. Cloud storage is a popular option for rectifying this problem.

    Security is also a concern when implementing health analytics. Data security in healthcare is extremely important – you need to make sure you are keeping up with HIPAA security regulations. When it comes to implementing health analytics and data security, be smart. Use up-to-date anti-virus software, set up firewalls, encrypt data, and use multi-factor authentication.


  • How Can Health Analytics Help with Predictive Modeling?

    By using health analytics to derive insights from patterns and correlations found in healthcare data, healthcare marketers can make predictions about which groups of people are the most likely candidates for certain conditions. They can also predict how those people will behave during their interactions with the healthcare organization, based on past data. Creating predictive models based on analytical data can save healthcare marketers time and money, since they can target their campaign efforts to the most likely prospects.


  • What is the Future of Health Analytics?

    Technology and digital are the future. As more and more data is collected about patients, healthcare organizations will be able to utilize that data in new and exciting ways. Thus, new digital technologies that utilize health analytics are being developed with the goal of improving global health. One such technology is telehealth.

    Telehealth is a collection of methods for enhancing health care, public health, and health education by using telecommunications technologies to deliver virtual medical, health, and education services. In this manner,healthcare organizations can provide care virtually, which is convenient for both physicians and patients. The data collected using telehealth technologies (blood pressure, physical activity, medication intake, etc.) and processes will be stored and analyzed with health analytics.

    Using health analytics in conjunction with technologies like telehealth, healthcare organizations will be able to provide faster identification of disease outbreaks, faster time-to-market for new drugs, personalized medicine based on DNA, and much more.


  • What is the Role of the Government in Health Analytics?

    The government plays an important role in health 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 1996, President Bill Clinton signed the Health Insurance Portability and Accountability Act (HIPAA) to ensure data privacy 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 confidence in the safety of health IT.

    Health analytics operations 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.


  • What’s the difference between concurrent analytics and predictive modeling?

    Predictive modeling is one type of analysis, whereas concurrent analytics is multiple analyses running at the same time. Basically, predictive modeling is just one piece of analysis marketers can perform within the context of using concurrent analytics. 

    Concurrent analytics encompass all different types of data analysis, including predictive modeling, descriptive statistics, exploratory polls, and more.