Applications of Real-time Forecasting
In several facets of the healthcare sector, payers make projections based on real-time data. For instance, real-time data analytics can help payers anticipate changes in membership. When the economy is unstable, payers may experience more churn between Medicaid, Medicare, the Affordable Care Act marketplaces, and employer-sponsored health plans. This switch between health plans may impact various payer functions, including medical loss ratios. Understanding how demand is changing so you can better build the proper capabilities around it is another area where real-time data proves useful. If payers can identify when a specific service is in greater demand, they can innovate more accurately to meet customer needs. They can use this information to determine how much to reimburse for specific services, including telehealth. Payers will be better able to determine whether remote patient monitoring and virtual care reduce healthcare costs and if so, provide the right reimbursements after they obtain more of this real-time data. Monitoring prescription selections serves as another application for real-time data analytics. It ensures that members don’t fill prescriptions that aren’t suitable for their condition or that interfere with any other medications they might be taking at the same time.Real-time Predictive Data Technologies
Tools for data analysis and technologies for data collection are both required. Real-time sensors will serve as some of the technologies used to collect data. In remote patient monitoring, these sensors might be the ones nearest to the member. For example, a blood glucose monitor communicates with a member and sends member data via Bluetooth to an APP. The app then collects that information. However, it necessitates that payers have unrestricted access to sensitive patient data, the transition between data collection and analysis presents one of the biggest obstacles. Since a lot of data is often trapped in EHRs, interoperability has become a hot concern lately. One aspect of technology is the ability to combine and extract data in real-time.Real-time Predictive Data Challenges
Working with your business users on how to approach machine learning and artificial intelligence, as opposed to traditional analytics, involves some change management. Machine Learning and AI models require users to specify a broad objective and then instruct the model on how to accomplish it, in contrast to traditional analytics models that require users to manually instruct the model on what to do. Therefore, using machine learning artificial intelligence models for real-time and predictive analytics can alter how a payer views its forecasting objectives and success measures. One significant procedural obstacle that may prevent health plans from obtaining real-time predictive data is the data capture procedure. The first step in enabling real-time analytics is to create a data acquisition strategy and architecture that guarantees you receive the most recent data. An organization may need to invest in new technologies, such as an API management platform, to obtain external data sources, and consider replication technologies that enable real-time data replication from its online transaction processing (OLTP) systems into its analytics platform. This undertaking can be expensive. Your data-gathering approach must serve as the foundation of your entire real-time analytics plan because you may also need data streaming technologies for Internet of Things (IoT) devices, which play an essential role in any remote monitoring strategy in the healthcare industry. The last real-time analytics difficulty that payers need to be aware of is that any predictive analytics model has its limitations.Partner Relationship Changes as a Result of Employing Real-time Data
Collecting data in real time requires teamwork. To accurately portray each member’s community, collaborations among various stakeholders are necessary. To provide that information, payers must work with providers, technology firms, and pharmaceutical companies. Comprehending a community’s cost profile and needs, even down to the zip code level, is crucial. Regional radars are often required. These radars compile information from payers, hospitals, life sciences firms, and health information exchanges. These systems draw from sources that provide behavioral data, demographic data, and local economic data, in addition to data from healthcare stakeholders. Interoperability plays a crucial role in realizing the deployment of real-time predictive data via regional radars. Stakeholders require an interoperable gateway for data sharing, just as payers need an interoperable framework to access patient data. The healthcare community must make real-time data a collective effort because a delay in data transfer in one corner of the industry can lead to delays across the entire chain of data exchange. Partnerships outside the healthcare industry are also necessary for real-time predictive analytics. Payers must seek input from the legal community on their real-time data-gathering initiatives due to the legal ramifications of predictive analytics.Prospects for Real-time Predictive Modeling in the Future
The goal of creating an interoperable, real-time database that can accurately inform healthcare practitioners’ decisions at the zip code level will take time and effort. Consumers will drive one of the main forces behind these developments. This has been true for the growth of care coordination and the use of virtual care. It goes beyond technology. The goal is to simplify things for customers by creating the right community, services, and capabilities around them. Payers can strengthen their forecasting capabilities overall by improving both their real-time data analytics and more conventional predictive analytics procedures by paying attention to how data can be used most effectively in the consumer’s favor.Read Whitepaper Use Of Real-Time Analytics In Population Health
