What is Healthcare Data Analytics?
Healthcare data analytics is a method of systematic data analysis that allows healthcare professionals to find opportunities for improvement in health system management, patient engagement, spending, and diagnosis. Healthcare data analytics combines real-time and historical data analysis to predict trends, reveal actionable insights, and drive long-term growth.
Benefits of Healthcare Data Analytics
Frequently Asked Questions About Healthcare Data Analytics
- What is the Role of Big Data Analytics in Healthcare?
Big data, according to Gartner, is “data that contains greater variety arriving in increasing volumes and with ever-higher velocity.” Big data analytics has become increasingly important to the healthcare industry in recent years, with new tools and technology emerging to capture and make use of the wealth of information on patients, procedures, diagnoses, and medical claims that health systems process every day. While the industry still has a long way to go in terms of the percentage of available data that is actually put to use, healthcare data analytics has opened doors for organizations to gain a holistic understanding of their patients and make strategic improvements to operations.
Big data analytics helps healthcare organizations with a variety of initiatives, including disease surveillance and preventive care efforts, the development of diagnostic and clinical techniques, and the creation of personalized, impactful healthcare marketing campaigns. Organizations that put their data to use strategically are better able to capture market share and grow their brand, all while maintaining a high standard of patient care.
- What is Public Health Analytics?
Public health analytics is the process of obtaining, reviewing, and managing health-related data of entire populations, typically carried out by public health organizations in an effort to maintain population health. Public health data may include mortality reports, demographic data, socioeconomic data, procedural and diagnostic data, and medical claims data, among others. Public health organizations may utilize analytics to monitor disease trends and determine patterns in certain populations, guide the implementation of disease control programs, and set priorities for allocating health resources to populations in need.
- What is Predictive Analytics in Healthcare?
Predictive analytics is an advanced statistical technique that takes into account both real-time and historical data in order to make predictions about a particular outcome. By applying predictive analytics to patient, consumer, or claims data, healthcare professionals can forecast trends or patterns that can then be leveraged to improve outreach initiatives or patient care.
This type of analysis also recommends appropriate communication channels based on calculated preferences, their propensity for particular diseases, likely payer type, etc. Leveraging insights from predictive models allows healthcare professionals to align their efforts more closely with a preferred outcome, make better decisions on how to invest resources and budgets, and better understand the patients they serve.
- How Can Healthcare Data Analytics Identify Growth Opportunities for Health Systems?
Healthcare data analytics doesn’t provide value to health systems in a vacuum. In order to derive insights that promote the attainment of organizational goals, it’s important to start with a business question around which to center your data initiative. These questions should be specific and tied to a high-level organizational goal within a targeted market, service line, or demographic. For instance: “How do I grow market share by five percent?” Or, “How do I acquire and retain one million new patients in the next two years?”
One of the main barriers to successful data analysis and interpretation within the healthcare setting has to do with the fact that clinical data, demographic information, consumer data, and market claims data tend to exist in silos. Using an Enterprise Data Warehouse (EDW), health systems can begin to consolidate and overlay these datasets in a way that enables them to answer pertinent business questions. When combined with a business intelligence solution, this consolidated data becomes even more actionable.
With Evariant Insights Business Planner, for example, healthcare professionals put healthcare analytics to work to uncover top opportunities for organizational growth. The platform integrates data from the HCRM, PRM, and Engagement Center solutions to recommend Best Next Actions – whether it’s supporting a high-value service line, improving network utilization and planning within a priority market, or improving patient engagement initiatives within a specific demographic.
- What Should I Consider When Selecting a Healthcare Analytics Solution?
Many healthcare organizations have begun to grasp the importance of a robust healthcare analytics solution in order to maximize the patient and consumer data they collect. When considering an analytics provider, time-to-value is the first thing that health systems should consider. Choosing a solution that provides a rapid time-to-value keeps implementation costs down and offers quick access to reliable data.
Additionally, it’s important to consider a health analytics provider’s experience and proven success. Since regulations are constantly shifting around healthcare data collection, it’s best to work with an experienced, flexible solutions provider that can help your organization adapt to change.
- What are the Challenges Involved in Implementing Healthcare Data Analytics?
There are a number of challenges to consider when implementing a healthcare data analytics solution. The first is making sure that the data you are looking to collect is clean, complete, accurate, and formatted correctly for use across multiple systems. Ensuring that your electronic health records (EHR) are optimized is a good way to prevent data capture problems.
The second key challenge to consider has to do with data storage. Costs involved in storing the ever-increasing quantities of healthcare data can be difficult to manage. Cloud storage is a popular option for rectifying this problem.
Finally, security is another important factor to consider when implementing healthcare data analytics. Data security in healthcare is extremely important – organizations must prioritize compliance with HIPAA security regulations. Best practices for maintaining data security include the use of up-to-date anti-virus software, firewalls, data encryption, and multi-factor authentication.
- What is the Future of Healthcare 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 Healthcare 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.