What is Healthcare Big Data?
Healthcare Big Data Definition
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers. As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions. Faced with the challenges of healthcare data volume, velocity, variety, and veracity, health systems need to adopt technology capable of collecting, storing, and analyzing this information to produce actionable insight.
Benefits of Healthcare Big Data
- Create holistic, 360-degree views of consumers, patients, and physicians.
- Improve care personalization and efficiency with comprehensive patient profiles.
- Inform physician relationship management efforts by tracking physician preferences, referrals, and clinical appointment data.
- Boost healthcare marketing efforts with information about consumer, patient, and physician needs and preferences.
- Analyze trends within a single hospital or greater a healthcare network to benefit research and care procedures for enhanced population health outcomes.
- Identify patterns in health outcomes, patient satisfaction, and hospital organization.
- Predict health outcomes and create preventive care strategies with data analysis.
- Optimize growth by improving care efficiency, effectiveness, and personalization.
Common Questions About Healthcare Big Data
- What is Driving the Adoption of Big Data in Healthcare?
The adoption of big data is in response to three major shifts in the healthcare industry: the vast amount of data available, government regulations, and a focus on personalized care.
- Volume of Healthcare Data: When health records went digital, the amount of virtual data health systems had to handle rose steeply. In addition to EHRs, vast amounts of data come from other sources, such as wearable technology, mobile applications, digital marketing efforts, social media, and more. All of this adds up to an incredible amount of information, spurring health systems to adopt big data techniques and technologies to effectively collect, analyze, and take advantage of this information.
- Government Regulations: The past 20 years have seen rapid growth in healthcare costs. Today, healthcare expenses account for around 18 percent of GDP, totaling about $600 billion. Such high costs were straining patients and led to government acts aimed at lessening this burden, most notably the Affordable Care Act (ACA). As a result, more health systems are collecting and analyzing big data in the hopes of improving performance and efficiency.
- Desire for Personalized Care: Consumers in all industries expect exceptional, personalized service, also known as “The Amazon Experience”. Healthcare is no different – customers want valuable, personalized care, making it the new standard to which health systems must rise. This type of care focuses on quality, engagement, and retention. Health systems are turning to healthcare big data to provide the insight necessary for providing this type of care.
- How Can Marketing Take Advantage of Healthcare Big Data?
Healthcare marketers can utilize big data to create propensity models, improve communication personalization, and integrate communication efforts.
- Propensity models are a subset of big data statistical analysis used to predict the likelihood of a specific event to occur. Marketing departments can use propensity models to score potential targets and identify those most likely to respond to specific campaigns and messaging. With more accurate campaign targeting, marketers are able to improve response rates and reduce wasted spend.
- Communication personalization is a critical initiative for healthcare marketers in a value-based care landscape. With more relevant and personalized marketing communication, customers are more likely to form or continue an ongoing relationship with the healthcare organization. Healthcare big data is a collection of consumer and patient information which marketers can draw from to inform personalization initiatives.
- Integrated communication helps create holistic customer experiences throughout the care continuum. Big data is critical for transforming marketing communication platforms, including call centers, email, patient portals, and more, into strategic engagement entities. For example, call center representatives with easy access to customer and patient data are able to have more informed and personalized conversations based on that customer’s previous interactions with the health system. The result of creating holistic experiences across care touchpoints is improved customer engagement and satisfaction.
- What Challenges Arise with Healthcare Big Data?
A major challenge with healthcare big data is the sorting and prioritizing of information. Data capacities are so vast that oftentimes it can be difficult to determine which data points and insights are useful and which are not.
Another challenge is ensuring that the right access to big data insights and analysis is given to the right people, so they can do their jobs smarter. Even though healthcare data is pulled from many different systems, organizations need to make sure critical personnel across the industry have easy access to the information.
There are also a number of challenges to effective data analysis resulting from heterogeneous or missing claims data. Each healthcare institution files claims, with the data coming from their other Hospital Information Systems (HIS) or input by hospital personnel at the time of the encounter or shortly thereafter. The data becomes even more complex when you factor in all the ambulatory places of service types. As a result, there are five challenges to overcome in order to obtain accurate claims data:
- Billing systems are fragmented and dated: Data is often very “noisy”…practices, groups, and even practicing specialties can be inconsistent. The key is to consider directional data in combination with your local market knowledge; in other words, data should augment interactions and focused outreach to physicians, not replace it.
- Patients do not have unique patient identifiers: If every patient had a unique identifier, data matching would not be required. Until, and if, that happens, data matching mechanisms are required to look for these data anomalies and put the right patient claims together.
- Diagnosis and procedure codes can be unclear: Even industry-standard grouper tools can obscure, or just plain mis-map, physician activity. Perfect data and perfect insights are very hard to achieve, so you have to advocate for, and learn to work with, directional data.
- Claims data is highly inconsistent: With claims data, any field data that is not required for payment has a low probability of being filled in or completed accurately. In fact, among the few required fields for payment, along with patient, diagnosis, and procedure information, is the “rendering physician” via the NPI1 for that provider.
- It’s difficult to identify the referring physician: The “referring physician” field on available third-party claims is often inconsistent, incorrect, or, most often, not filled. In fact, some clearinghouses don’t even provide the “referring physician” filed because of these inconsistencies.’
- What is the Future of Healthcare Big Data?
In the future, healthcare organizations will adopt big data in greater numbers as it becomes more crucial for success. Healthcare big data will also continue to help make marketing touchpoints smarter and more integrated. Additionally, the amount of data available will grow as wearable technology and the Internet of Things (IoT) gains popularity. Constant patient monitoring via wearable technology and the IoT will become standard and will add enormous amounts of information to big data stores.