Measuring Patient Engagement

Of course, caregivers on their path to value-based care delivery, where patient participation is critical, will be dissatisfied by this circumstance. In the world of healthcare data analytics, hopefully, nothing is unquantifiable. Caregivers may turn an abstract idea into a quantitative value that will aid them in making fact-based decisions by helping them to elicit the most effective channels, understand what attracts patients' attention, and see the true picture of patient involvement. Let's have a look at how we can accomplish this.

Rules for Evaluating Patient Engagement

Data analytics must harness the following four information flows to provide caregivers with significant insights into patient involvement levels:

It is feasible to categorize patients based on their amount of engagement by studying each flow independently and in combination. It may, for example, be an ABC rating, with the 'A' group representing the highest level of participation. As a result, under-engaged patients are assigned to groups 'B' and 'C.'

Connecting Patient Engagement Levels to Dimensions: Opportunities for Improvement

Here are a few possibilities for tying patient engagement to different patient health profiles. Providers looking for the most effective channel for engaging patients can compare equal dimensions from the list above, such as the frequency of logging in to patient portals and launching mobile apps within their "A" level. Additionally, these measures can be applied to a facility or a department, which can demonstrate preferences among patients with different conditions. As each new criterion set is added, data analytics become even more complex and give rise to new insights that can be applied to improving care delivery and patient health outcomes.

Similarly, when caregivers elaborate on significant measures for health outcomes analytics (such as the quality of life, exacerbation rates, blood pressure control, and more) under CMS reporting policy or for internal performance evaluation, the possibility of tracking multiple dependencies arises. Despite the common assumption that higher engagement leads to better health outcomes, results can be unexpected and can provide a hint on areas for further improvement.

When the Kingdom comes, being a King is a Great Honor.

We need to take a broader view. Value-based care is gradually replacing fee-for-service care. As long as patients remain healthy, prevent exacerbations, admissions, and readmissions, and improve health outcomes, programs and models that are aimed at easing the transition (such as ACOs) continue to use FFS payments. The engagement of the patient is an important link in this chain, especially when measured.

In light of this, analyzing patient engagement and integrating it with other healthcare data analytics dimensions that providers already use means taking the lead and reaping its benefits before competitors.