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What is the next evolution of EHRs?

Electronic health records (EHRs) in home health and hospice are becoming far more than just a repository of an episode of care in an isolated care setting. To provide the highest level of care, it’s important to understand the full history and context of the patient and their health challenges. In the future, the patient’s health record will move from episodic to more of a continuum across their lifetime as a full longitudinal record.

In this blog, we explore the evolution of EHRs and what we can expect in the future of healthcare technology.

A broadened health record

As the patient’s health record evolves, it will broaden to include subjective and objective information, and become far more personal as it includes lifestyle, behavioral, and even family and genetic information. In addition to broadening, it will also become more specific and searchable.

Today’s information is often document-based, where things like lab results come in the form of a lab report. Evolution in document management and machine learning will allow caregivers and patients to search for discrete information that could come from multiple sources, including documents.

A decision support system

As the EHR evolves, it becomes more of a decision support system that can recognize trends and possible adverse outcomes based on individual and population-level comparisons. These changes will bring along higher degrees of identity matching and far more integrations across multiple repositories, which are linked in real time.

This virtual aggregation will allow for a lifetime health record, accessible to both professional and family caregivers that allow people to age well and move from reactive to more proactive management of their own health.

A passive, proactive and predictive future

The future will be about bridging episodes of care and the moments in between, to create a full longitudinal view of the patient through technology allowing those interactions to be more passive.

Furthermore, technology will drive care to be more proactive by capturing both subjective information (“How are you feeling?”) as well as objective data (i.e., heart rate, sleep patterns, etc.). This will help providers make more informed decisions about patient care.

Providers will also use reporting, analytics and insights that are driven through machine learning to be more predictive. More visibility into how a patient is doing in the relative general population can provide insight into how they are trending — allowing clinicians to then predict how and when to engage with patients and families.

An intelligent approach to care delivery

Machine learning in healthcare is a type of artificial intelligence (AI) that uses data and algorithms to imitate performance and carry out tasks. Here are a few ways machine learning can impact an organization:

  • Build a pre-visit summary: Information can be pulled from PDFs, documents and charts to automate pre-visit summaries.
  • Populate the patient record: Machine learning in post-acute care can decode voice-enabled documentation and identify missed information.
  • Build a personalized plan of care: Machine learning can also assist in post-visit tasks by creating a personalized care plan for patients.

Connect with us today and learn how MatrixCare can keep you ahead of EHR trends

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Tim Smokoff

Tim is the General Manager, Home Health and Hospice, at MatrixCare. He is a seasoned healthcare leader, keenly focused on advancing data-driven technology solutions, driving operational excellence, and fostering strategic innovation. Building on more than 25 years of extensive success leading software operations, Tim guides the overall strategic direction and operational execution of MatrixCare's Home Health and Hospice business vertical.

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