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Predictive analytics for home and hospice care: What you should know

At MatrixCare, we’re always working to provide better tools for care delivery for our valued clients and customers. One of the most important of those tools is predictive analytics, which can help predict the needs and care trajectory for seriously ill patients on an individual and population level.

By giving you the data to better predict the level of care a patient will need at a given time—or when you can expect to have to transition them to a different setting—predictive analytics can help you maximize care in a way that also reduces costs. It also helps you better predict, manage, and maintain resources. Here’s what you should know.

What are the main benefits of predictive analytics?

Long used to achieve cost reductions and care improvements in hospitals and health centers, predictive analytics are now being adapted to better suit the needs of home and hospice care providers. The main benefits are in the management of:

  • Patient risk for rehospitalization
  • Resource utilization
  • Population health

A better way to assess and manage risk

Perhaps the most significant benefit offered by predictive analytics is managing patient risk for rehospitalization with a more accurate assessment of the individual risks for each patient. This is key for home health providers, whose reimbursement and reputation often rests on the ability to keep patients at home and out of the hospital.

By also helping you map risks for other events like falls, wound care, vital sign readings, and assessment data, predictive analytics go hand-in-hand with cost savings and bolstering revenue. It also helps your reputation and your ability to earn future referrals by reducing damage to your relationship with referring hospitals.

Predictive analytics also help manage risk involved with transitions. For example, if a patient is at a level of recovery that allows them to be discharged from home health, it’s likely they might need palliative care, and ultimately hospice care. Identifying and projecting these trends can keep people within your ecosystem, which can be important from an organizational standpoint.

Improving resource utilization

Predictive models can also help with resource utilization by assessing questions like how many nursing visits will be needed or whether patient care can be delivered with a telemonitoring device (leading to fewer visits). In other words, what tools can keep the care quality high for your patients, but do so with the minimum resource investments?

This also extends into nuts-and-bolts resource management. For instance, predictive analytics can help you maximize your supply, ordering, and management. It can give you a much clearer picture into ensuring you’re using only necessary supplies.

With this focus on resources, predictive analytics can both enable the best possible patient care as well as streamline efficiencies within the operation itself. However, it’s also important to make sure that any resource utilization, suggestion, and action is still aligned with the primary goal of maximizing patient care.

Managing population health

Integrating these predictive models more broadly also gives you the ability to manage care not just for a particular patient, but for an entire population. This leveraging of predictive analytics at a macro level helps you further optimize your ability to provide care. In turn, this can make your organization more appealing to referral sources.

Better understanding patient care across populations also helps you reorganize roles within your own platform, as well as within a broader ecosystem. It might mean participating in programs with larger health systems to get insight into how patients in your region track in terms of key indicators.

For instance, you may operate in an area with a high rate of smoking. That’s likely to mean that you’ll need the ability to provide care for patients with emphysema, cancer, or COPD. Knowing that helps you tailor your organization to meet that need, including hiring staff members with the ability to provide the specific care that population needs today and in the future.

Leveraging the power of predictive analytics with MatrixCare

In conclusion, predictive analytics can help you better manage the tactical as well as the broader aspects of patient health. In both cases, it’s about tailoring the patient experience in a way that maximizes care while offering the best margin for the resources invested. It helps ensure that your organization has the power to provide effective care for the populations that you serve.

Through partnerships with organizations like Medalogix and Aclivity, MatrixCare is working to enhance the predictive analytics available to our home and hospice partners, as well as to integrate this important tool into a fully functional, forward-looking EHR system.

Learn more about predictive analytics


Chris Pugliese
Chris Pugliese

Chris Pugliese is a Senior Product Manager of Integration and Interoperability for MatrixCare. Chris has spent the last decade working with post-acute technology and EMRs, and the last 5 years focused on interoperability. His strength is enabling technology, as well as educating on the growing importance of interoperability and its benefits to the post-acute care settings. In a short time, Chris has become a leader, spearheading integration and interoperability initiatives within and outside of MatrixCare. Recent industry committee roles and responsibilities include: Leadership Team Member for the Post Acute Interoperability Work Group (PACIO), Technical Lead for the Functional Status Subgroup for the PACIO initiative – developing FHIR Profiles for Functional Status, CommonWell Health Alliance Use Case Committee member, CommonWell Health Alliance Specification Workgroup member


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