Clinically Actionable Analytics

Problems: Payors want their policy holders to be assured that the care that they receive is of the highest quality. Provider profile analytics created by payors will give them a level of confidence that if complex conditions present during an exam, they will be identified and managed properly by the provider. Because complex conditions occur infrequently, the measure that is most useful in assessing quality of care is identification and documentation of more common conditions. This measure strongly suggests that the rare but more complex conditions will be properly identified.
The analytics in this registry include:

1. The percentage of time the diagnosis of Hypertension is recorded in the Problem List
   It is a reasonable assumption that when paying for a routine eye exam, that a payor expects the ocular side effects of hypertension to be evaluated. If a patients’ systemic conditions are not listed in the Problem List, it is quite probable that the ocular side effects were not accurately evaluated.
   To a payor, if this analytic scores well, it indicates that the process of obtaining the history is of high quality, and that the provider is aware of the patient’s systemic conditions.
2. The percentage of time patients with Hypertension are diagnosed with Hypertensive Retinopathy
   Some providers who have not recorded systemic conditions in the Problem List, may feel justified for not doing so because it was recorded in the History, they are still performing the necessary evaluations and have looked for abnormalities.
   If these assumptions are correct, then it may be possible to have low performance on the above analytics but still perform well on this measure. Knowing the correlation in these analytics helps providers better understand best practices and helps payors have a better indication as to thoroughness of the internal retina evaluation. This measure requires that the provider closely evaluate the retinal vasculature in a way that results in a consistent diagnosis.
   To a payor, if this analytic scores well, it likely indicates that the evaluation of the retina is of high quality and that any retinal condition would be detected.
3. Percentage of time that anisocoria is recorded in the Problem List
   This measure addresses two important considerations for payors. The first is that abnormal pupils are rare. The published incidence of a Horner’s Syndrome is 1/6,250 patients. It is not possible to accumulate data for Horner’s due to the low incidence, however, payors want to be confident that if a Horner’s pupil presents, it will be identified. Missing this finding is associated with a potentially high risk. The published incidence of physiological anisocoria 20%. It requires specialized equipment to detect very subtle differences in pupil size. Registry data indicates that the clinical incidence of physiological anisocoria detected via swinging flashlight evaluation of pupils is around 6%.
   The second payor consideration is that if anisocoria is present but not identified, then PERRL or PERRLA has likely been recorded for the pupil examination. This error is unfortunate, especially during a time when information blocking is resulting in examination findings being increasingly shared with other practitioners. The sharing of inaccurate data results in increased costs to payors. Payors have a definite financial incentive to ensure that inaccurate data is not being recorded and shared.
   To a payor, if this analytic scores well, it likely indicates that if an abnormal pupil presents, it will likely be detected and that the rest of the external evaluations are also being completed at a high level.

Purpose:

1. To help providers understand the type of analytics that payors will be creating to evaluate the quality of care that they deliver and to determine which providers to include (or exclude) on their provider panels
2. To provide analytics to registry users that allow them to act on these issues in a way that improves their outcomes and results

Goals:

1. Payors will likely use registry data in a variety of ways to ensure that their providers are performing well on these basic functional analytics.
   These are basic analytics on which every provider should want to perform well regardless of payor’s involvement.
2. These analytics will give providers valuable data in order to negotiate with payors to increase their reimbursements.

Problems:

1. Providers do not have data on OrthoK efficacy on slowing axial elongation from their patient population. They currently rely on studies done on Asian populations in Asia.
2. Providers have no mechanism to compare visual quality, patient satisfaction, impact on vision-related quality of life or efficacy of slowing axial elongation in their practice compared to other practices.
3. Providers do not appreciate the potential income that an OrthoK patient can bring to their practice.

Purpose:

1. To provide data on efficacy of OrthoK (compared to other modalities) on slowing of axial elongation in their patient population.
2. To Provide data on visual acuity, patient satisfaction, vision-related quality of life and efficacy of slowing axial elongation with OrthoK in their practice compared to other practices.
3. To provide data on patient retention/attrition with OrthoK.

Goals:

1. To use outcome-based data to prescribe the most efficacious OrthoK design.
2. To use data from a practitioner’s EHR to compare OrthoK outcomes vs. other practices.
3. To use attrition data to retain OrthoK patients and demonstrate its overall benefits to your patients and practice.

Under construction.