Enhancing Home-centered Care with the Right Technologies

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Mark HeinemeyerAdvocates of home-centered care understand the potential of home care programs to deliver high quality outcomes and reduce costs. In-home care is typically less expensive than other care options and usually results in higher patient satisfaction when compared to treatment at inpatient or long-term care facilities. In addition, the delivery of routine and preventative care at home helps keep individuals healthier so they can avoid more expensive treatment in the emergency department or as a hospital patient.

When home-centered programs include integrated technologies such as in-home medical devices, communication tools, and analytics, then the effectiveness and efficiency of care is enhanced even further. Routine tasks, like taking vital signs, checking a patient’s weight and collecting condition-specific symptoms/insights, can be automated and results analyzed using machine learning and artificial intelligence; risk scores can also be electronically communicated to remote caregivers to provide insights into an individual’s well-being. For low-risk patients, remote monitoring can optimize the value and frequency of at-home visits. For higher-risk individuals, caregivers are able to stay on top of an individual’s condition between visits and take early action if necessary.

Consider some of the additional ways that technology-enabled home-centered care programs benefit individuals and care providers:

  • In-home monitoring, when coupled with strong care team collaboration and communication, provides care that similar in quality to an inpatient setting. Providers can stay on top of clinical issues while patients enjoy the comforts of home and community.
  • Participants have improved quality of life while receiving care at home. Most seniors prefer to age in place at home and to avoid long inpatient stays and moves to settings like long term care or senior living homes. In-home technology allows them to continue living in their own homes and in the community, without sacrificing care.
  • Costs are reduced. Technology-enabled in-home care is more cost efficient than other options for all stakeholders.

Predictive analytics and machine-learning technologies

The use of technologies such as predictive analytics and machine-learning have already been proven to impact the quality of care for the post-acute care segment. For example, a 2015 study published in the Journal of the American Medical Informatics Association found that the incorporation of EHR data into predictive analytics algorithms increases the accuracy of identifying patient at high risk of harmful falls by more than 10%.

In home-centered care, the use of remote care solutions that integrate machine-learning and predictive analytics with remote patient management technologies are also helping improve chronic disease management, in-home and remote care delivery, staffing predictions, and population health risk assessment.

The addition of analytics provides valuable insights into the health of an individual based on collected data and contextual information. Over time, as additional data is captured and available for analysis, the insights become more precise, which is critical for predicting the likelihood of adverse events and providing caregivers adequate time to enact proactive measures to enhance outcomes.

Care plan adherence: a key consideration

When evaluating home-centered care solutions, one of the most important considerations is the potential impact on participant adherence.  Adherence is a challenge across the healthcare spectrum. However, adherence to prescribed care plans is essential for the achievement of quality outcomes, regardless of whether care is delivered in the individual’s home, within the four walls of a hospital, or at a long term care facility.

Most provider organizations struggle to successfully engage people in healthy behaviors. However, by deploying technologies that leverage machine-learning, predictive analytics, and behavior assessment, caregivers are better equipped to drive patient adherence and sustained consumer engagement.

Consider the potential impact of customized home-centered solutions that integrate machine-learning and predictive analytics with remote participant management technologies. Remote monitoring tools can automate routine tasks, while the addition of analytics considers collected data and contextual information. Providers have better insights into the health of an individual and are able to predict adverse events earlier and with more precision.

In order to accurately identify potential non-adherence, home-centered solutions should also integrate digital health tools and apps that assess the unique personalities of each individual. Behavioral-based tools recognize the distinctiveness of each patient and allow caregivers to individualize communications, treatments and medication plans. Equipping caregivers with important insights into the individual’s personality and behavior helps them to establish a much more effective and sustaining rapport. Care is delivered in a manner that optimizes patient engagement, encourages adherence and improves health outcomes.

Selective buying required

Not all home-centered care technology suites are created equal, so buyers must be selective. Consider the proliferation of smart wearable health devices and remote monitoring systems in recent years. Despite the great promise, the sum total preventative impact of these technologies has been underwhelming in terms of preventing adverse events and readmissions among individuals with chronic conditions. Patients, payers and providers must choose technology wisely to ensure that selected in-home care solutions are beneficial and cost-effective.

The success of home-centered care programs requires technology solutions that are proven to monitor and encourage patient engagement and adherence. They should promote the prescriptive utilization of collected data and the empowered personalized engagement of participants, as well as leverage remote monitoring, machine-learning and predictive analytics. Ideally the selected solution will be cost-effective and facilitate high-quality outcomes for patients.

Mark Heinemeyer

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