Sentence examples for risk adjustment methodologies from inspiring English sources

Of note, the persistent age related differences found in the present study suggest that any risk adjustment methodologies stratify or adjust outcomes using age 85 reflecting that this group of oldest old have more positive outcomes for some kinds of measures (e. g. lower hospitalization rate for patients with COPD) and less positive outcomes for other kinds.

Although risk adjustment methodologies can mitigate the effects of differences among patient populations, transparency will sometimes lead to rankings of providers that are not fair.

There are differences in how the population is defined (i.e. who is included and excluded) and how the risk adjustment methodologies are applied [ 5].

Thus, the use of a data mining approach could identify the dependencies and interactions that influence outcomes so that risk adjustment methodologies can be improved in accuracy and actual benchmarks could be established.

From January 2008 the indicator has been included in the processing of Medicare claims [ 1]., but its use has been advocated for many years to support quality assurance activities and improve risk adjustment methodologies [ 2, 3].

On one hand, risk adjustment methodologies are essential for obtaining valid estimates; however, on the other hand, improper definition of confounders may introduce further unexpected biases and provide a distorted picture of reality.

For 13 of the 19 indicators that have risk adjustment, after applying the risk adjustment methodology, the difference between the adjusted rates in the two regions was reduced.

A previous simulation study has confirmed that the risk adjustment methodology in these reports introduced significant bias into the calculation of risk adjusted mortality rates.

This study assessed the Kaiser Permanente inpatient risk adjustment methodology for hospital mortality in a patient population distinct from that used for its derivation.

By taking into account the risk factors for a covered population, prospective risk adjustment methodologies--commonly referred to as "predictive models" (PMs --can be helpful for health budget planning and case profiling [ 3].

The risk adjustment methodology used by the Health Care Financing Administration, incorporates individual patients' age, gender, specific diagnoses and comorbidities, admission source, emergency or elective admission and the patient's risk group based on hospitalizations during the preceding 6 months [ 9].