Healthcare data analytics FAQ

Editor's Note: This blog was published prior to the transition to WebMD Ignite.

Frequently asked questions: What is healthcare data analytics?

Healthcare data analytics allows healthcare professionals to find opportunities to improve operations, patient engagement, care delivery, and clinical outcomes. Healthcare data analytics combines real-time and historical data to predict trends, reveal actionable insights, achieve medical advances, and drive long-term growth. WebMD Ignite provides comprehensive analytics solutions for healthcare organizations of all shapes and sized to strategically use healthcare data analytics.

Benefits of healthcare data analytics

• Improve quality of clinical care by increasing healthcare organizations’ access to patient data and allowing health systems to unlock important patterns and trends around diagnosis, treatment, and continued care.
• Access actionable insights that inform future interactions with patients, consumers, and populations.
• Inform population health initiatives and allow public health organizations to better manage the spread of disease, predict outbreaks, and allocate health resources to communities in need.
• Uncover the root cause of consumer response – or lack of response – to outreach and create personalized campaigns to improve patient engagement.
• Gain market share by uncovering what type of care your consumers want and where healthcare delivery can be optimized.
• Personalize the patient journey via informed contact center agents or even automatically via chatbots.

Building an effective healthcare analytics strategy

Before you start acquiring tools, data, and talent to support your healthcare analytics initiative, it’s important to iron out a strong strategy. Here are four considerations to help you get started:

1. Start with high quality data

Make sure that the data you want to collect is clean, complete, accurate, and formatted correctly for use across multiple systems. Healthcare data is primarily unstructured and available in many forms. This unstructured data typically contains the most important information about patients, staff, and overall performance. If this data is properly structured using healthcare data management technology, health systems will have immediate access to high-quality, relevant data.

Next, consider data silos. By pooling cleansed and standardized internal data with other third-party data in a data lake or similar data repository, you’ll discover top growth opportunities and pinpoint next best actions that create unprecedented value. Proper data management will be instrumental to overcoming both obstacles.

2. Search for and eliminate bias in your analysis

Across industries, implicit and explicit biases drag down the value of data analytics. Healthcare is no exception. With the critical role that your hospitals play in maintaining patient health, it is crucial to ensure your data analytics strategy accounts for and negates potential biases.

The best way to overcome this starts with people. Work closely with developers, care equity experts, and various patient segments to uncover what the real needs of your patients are, and make sure your insights align with the experience of experts and patients alike. If something is not lining up, take a step back and make sure you’re collecting plenty of high quality data from all segments of patients and consumers.

3. Keep patient privacy top of mind

To develop efficient health initiatives and optimal patient outcomes, HIPAA-compliant clinical data must be securely shared across multiple organizations like hospitals, physician practices, ancillary providers, research centers, and more. Unfortunately, with data sharing comes issues with data privacy.

Healthcare organizations need to work with various entities to get high quality, HIPAA-compliant data. This includes insurers, provider organizations, and healthcare-focused clearinghouses. Additionally, you could acquire helpful tools that are designed to derive de-identified data from sources like your EHR.

4. Bring in outside expertise when needed

Medical organizations collect massive amounts of data on a daily basis. However, data scientists are in high demand and out of reach for many hospitals that lack the resources and skill sets to get the most out of their data. Tools that make analytics accessible by providing insights and suggesting best next actions help fill that gap, as do advanced analytics services with healthcare experts.

Examples of how to use healthcare analytics

In the healthcare industry, data analytics often helps organizations derive insights regarding systemic waste of resources. Leveraging healthcare data and analytics is crucial to successful patient engagement strategies and can help inform how marketers engage with individuals and specific groups of patients. With this information, health systems can more efficiently allocate their resources to maximize revenue while improving patient care, physician performance, and population health.

1. Improving patient care

Value-based payment initiatives incentivize performance improvement, including efficiency. Understanding patient and total program costs also involves accounting for what happens to patients inside and outside of care. A major cost to the healthcare industry involves treatment of chronic diseases. Predictive analytics in particular can help cut costs greatly by identifying high-risk patients early and helping them manage their own care as much as possible.

2. Evaluating physician performance

Alongside the seismic shift away from volume-based to value-based care, the implementation of healthcare data analytics provides new methods for evaluating the performance and effectiveness of practitioners at the point of delivery. Ongoing performance evaluations and health data related to patient wellness can be used to give practitioners ongoing feedback.

3. Analyzing public health trends

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 use 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.

Frequently asked questions about healthcare data analytics

How is big data analytics used 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 use 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.

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?

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. With healthcare data management tools like enterprise data warehouses (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 a business insights tool, for example, healthcare professionals put healthcare analytics to work to uncover top opportunities for organizational growth. The tool integrates data from your customer relationship management (CRM), physician relationship management (PRM), and engagement center solutions to recommend your best next action – 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 and flexible healthcare analytics provider that can help your organization adapt to change.

What are the challenges involved in implementing healthcare data analytics?

• Data Quality: Make sure that the data you want to collect is clean, complete, accurate, and formatted correctly for use across multiple systems. Ensuring your EHRs are optimized is a great way to prevent data capture problems. Healthcare data is primarily unstructured and available in many forms. This unstructured data typically contains the most important information about patients, staff, and overall performance.
• Data Storage and Sharing: Next, consider data silos. By pooling cleansed and standardized internal data with other third-party data in a data lake or similar data repository,  you’ll discover top growth opportunities and pinpoint next best actions that create unprecedented value. Equally important, in order to develop efficient health initiatives and optimal patient outcomes, HIPAA-compliant clinical data must be securely shared across multiple organizations like hospitals, physician practices, ancillary providers, research centers, and more. Unfortunately, with data sharing comes issues with data privacy.
• Knowledge Availability: Medical organizations collect massive amounts of data on a daily basis. However, data scientists are in high demand and out of reach for many hospitals that lack the resources and skill sets to get the most out of their data. Tools that make analytics accessible by providing insights and suggesting best next actions help fill that gap, as do advanced analytics services with healthcare experts.

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.

How does healthcare analytics improve patient care?

In the healthcare industry, data analytics often helps organizations derive insights regarding systemic waste of resources. Leveraging healthcare data and analytics is crucial to successful patient engagement strategies, and can help inform how marketers engage with individuals and specific groups of patients. With this information, health systems can more efficiently allocate their resources to maximize revenue, patient care, and population health.

• Patient cost: Value-based payment initiatives incentivize performance improvement, including efficiency. Understanding patient and total program costs also involves accounting for what happens to patients inside and outside of care.
• Predicting risk: A major cost to the healthcare industry involves treatment of chronic diseases. Predictive analytics can help cut costs greatly by identifying high-risk patients early, and helping them manage their own care as much as possible.

Evaluating practitioner performances: Alongside the seismic shift away from volume-based to value-based care, the implementation of healthcare data analytics provides new methods for evaluating the performance and effectiveness of practitioners at the point of delivery. Ongoing performance evaluations and health data related to patient wellness can be used to give practitioners ongoing feedback.