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How Client-Reported Data Can Help Care Workers Make Better Health Care Decisions


The voice of a client is essential to understanding whether or not home care services are making a difference. Client-centered care has taken the spotlight when it comes to discussions of quality; home care agencies are adapting to providing care that is responsive and respectful of individual client preferences and values, which in turn, guides clinical decisions [1].

Now-a-days with advanced technology and digitization of the home care industry, agencies have the potential to generate and capture a significant amount of meaningful data on client-reported outcomes (CROs), which can be used to provide a higher quality of care.

In order for client-centered care to flourish, agencies rely heavily on communication.

Client-reported information gives providers access to insights on the effectiveness of their care from the clients’ perspective, and enables them to adjust their methods to maximize the quality of care. By transforming the voice of a client into meaningful, valuable information, relationships can improve, as well as the overall outcomes.

With the help of electronic health records, harnessing and managing CROs and ADL tracking has become much more efficient. Care workers can now effortlessly submit information via tablets, mobile devices, and wearables resulting in copious amounts of data and enhanced communication.

While there is no shortage of data, many agencies lack the capability to analyze these records. According to a recent study, only 3 percent of potentially useful data is tagged and even less is analyzed, and 95 percent of healthcare CEOs are exploring better ways to harness, manage and analyze data [2].

The potential for CROs and big data analytics in home health care to lead to better outcomes exists across many scenarios, for example: applying analytics to client profiles such as segmentation or predictive measures to identify clients who would benefit from specific treatments or care plans; or analyzing client outcomes to identify predictive events and support care worker decisions or prevention initiatives [3]

Imagine clients could ask, “how will this treatment decision affect someone like me?” and care workers could respond with, “based on the outcomes of one hundred clients who are the same age with a similar condition, so-and-so treatment would be a suitable approach for you.”

AlayaCare has taken active steps in tackling data analytics with machine learning; using algorithms to “learn” from data in an iterative fashion and produce reliable, repeatable alerts to aid care workers in their decision-making processes.

For home health care providers, amongst the vast amount and array of data, is opportunity. While there are logistical challenges associated with implementing and interpreting valuable client-reported data, it is the most direct approach to gaining insights from patients that can dramatically improve the quality of care and the way home care is delivered.