From the January/February 2016 issue of HealthCare Business News magazine

Do the patient risk-assessment guidelines put forth by AHA, ADA and GOLD directly correlate with patient outcomes, and are there additional metrics not covered by these institutions that provide better predictive assessments toward the same end?

Multi-focal data sets: providing better predictive assessments
The first step, correlation of extant institutional guidelines with patient outcomes, is readily addressed via regression of the appropriate risk assessment measurements with emergency health events associated with the chronic conditions at hand. While this sounds simple enough at first glance, there are two avenues of approaching this risk analysis that one ought to take into account. As patients are enrolled in a digital care management program following diagnosis with a chronic condition, these individuals will already have data that cast them into a corresponding level of risk.
However, as multiple studies demonstrate, patient measurements change favorably over time under digital care management, meaning that a second measurement for assessment must be sought: the new equilibrium measurement following the initiation of treatment. In concert with the measurement and risk categorization at diagnosis, the assessment of the new patient equilibrium may prove a more effective predictor of patient outcome as it becomes the “new normal” for the individual.

Furthermore, as multiple measurements play a role in assessing patient risk, the use of these multifocal data sets for prediction must incorporate not only the individual, but also interactive contributions of each of these measures to the long-term patient result. In line with the testing of the above extant treatment guidelines, the increase in characterized patient risk will directly correlate with patient outcomes as determined by maintenance of long-term metric values, greater hospitalization rates, greater condition-associated mortality rates and lower patient satisfaction.

Beyond the current published guidelines for patient risk assessment may stand even more effective metrics, particularly within a digital care management setting, where patient measurements are taken much more frequently. This improvement from the traditional clinical care model allows for more fine-tuned analysis of patient response to treatment. With regard to the new patient equilibrium mentioned above, daily data will offer a view into the stochastics inherent in the day-today state of the patient. This, along with the amount of time required to achieve treatment equilibrium, may provide insights into the sensitivity of each patient’s condition and thus, a way of determining which patients require tighter management.

January/February 2016 Magazine

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