Can somebody analyze a health care organization's needs and describe what type of measure (structure, process or outcome) one can implement and why?
Ans) Measures used to assess and compare the quality of health care organizations are classified as either a structure, process, or outcome measure. Known as the Donabedian model, this classification system was named after the physician and researcher who formulated it.
Structural Measures:
- Structural measures give consumers a sense of a health care
provider’s capacity, systems, and processes to provide high-quality
care.
For example:
Whether the health care organization uses electronic medical
records or medication order entry systems.
The number or proportion of board-certified physicians.
The ratio of providers to patients.
Process Measures:
- Process measures indicate what a provider does to maintain or
improve health, either for healthy people or for those diagnosed
with a health care condition. These measures typically reflect
generally accepted recommendations for clinical practice.
For example:
The percentage of people receiving preventive services (such as
mammograms or immunizations).
The percentage of people with diabetes who had their blood sugar
tested and controlled.
Process measures can inform consumers about medical care they may
expect to receive for a given condition or disease, and can
contribute toward improving health outcomes. The majority of health
care quality measures used for public reporting are process
measures.
Outcome Measures:
- Outcome measures reflect the impact of the health care service or
intervention on the health status of patients. For example:
- The percentage of patients who died as a result of surgery
(surgical mortality rates).
The rate of surgical complications or hospital-acquired
infections.
- Outcome measures may seem to represent the “gold standard” in
measuring quality, but an outcome is the result of numerous
factors, many beyond providers’ control. Risk-adjustment
methods—mathematical models that correct for differing
characteristics within a population, such as patient health
status—can help account for these factors. However, the science of
risk adjustment is still evolving. Experts acknowledge that better
risk-adjustment methods are needed to minimize the reporting of
misleading or even inaccurate information about health care
quality.
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