Clinical healthcare performance improvement strives for the perfect conclusion data. These are the outcome measures, and they traditionally help formulate the core response plan for streamlining activities. The industry as a whole is head-over-heels in love with outcome measures because they inherently have more value. They act as the assistors in making final decisions that can reshape the institution budget, force changes nurses have been begging for, and generally make sincere weaves in the hospital's processes.

The Outcome of a Rock Band

The irony is that quality and performance improvement in healthcare is shaped by the process measures. Health data is not functional based fully on the outcome data. The outcome measures, despite getting all the fame and notoriety as a lead singer in a rock band, could not exist without the backing group. The outcome measures are reliant on the quality and showmanship of the process measures. Who comes to the live show? The lead singer would be lost without the bassist and outcome measures examples drummer processing the whole event.

The Outcome Measure's Valuation

Every system is perfectly designed to get the results it gets. The outcome measure is only as strong as the system which provided that measure. Diabetic care is a controversial topic because it is infamously costly and highly regulated. The outcome measure from a patient was poor. The patient was disappointed with the service. To make matters worse, he returned three times in the last two months for revisitation that may have been avoidable. It cost the hospital more money, and the fault lies in the process. The outcome is only a suggestion of the process that led to that result.

Diabetes and Process Reviews

The outcome measure states that the patient was disappointed, which would ultimately enforce the outcome of additional oversight to the nurse who treated him. Unfortunately, it is the process which is broken. Implementing healthcare performance improvments does not have a place to retain data on blood work and diabetic review during patient visits. The hospital is not actually checking for diabetes in their initial test runs when a patient visits due to swollen ankles. In this example, the process is fallible.

It does not store and track for initial visits that could suggest diabetic treatment. The outcome is distorted because it suggests the fault lies on the nurse's treatment. The real problem is that the institution does not have quality initiatives in healthcare process for qualifying diabetic care early on, and that also affects re-visits from diabetic symptoms.