jueves, 20 de diciembre de 2012

National Quality Measures Clearinghouse | Selecting Process Measures for Clinical Quality Measurement

National Quality Measures Clearinghouse | Selecting Process Measures for Clinical Quality Measurement


National Quality Measures Clearinghouse (NQMC)

Selecting Process Measures for Clinical Quality Measurement

NQMC uses an adaptation of the structure, process, outcome framework for quality measures created by Donabedian. This tutorial explores the process measure domain. In NQMC, a clinical process is defined as "a health care-related activity performed for, on behalf of, or by a patient." A process is, by definition, carried out by a health care worker or patient or is an activity in the health care system.

Process Measures in Perspective

Most process measures assess the activities carried out by health care professionals to deliver services. These activities are often guided by evidence-based clinical guidelines. The evidence supporting guidelines varies in strength. Users of measures may wish to carefully judge the evidence linking process measures with health outcomes. For example, the percentage of patients with a myocardial infarction who receive an aspirin prescription on discharge is a process measure based on strong evidence that aspirin can prevent future cardiovascular events.
Performance on many process measures may fail to achieve 100 percent for reasons other than failure to act in accordance with evidence-based clinical guidelines. For example, some individuals have contraindications to a treatment, and not all contraindications are reflected as exclusions in the measure. Other patients have conditions that prompt a different treatment approach or may prefer to forgo treatment. These situations can often be anticipated and measure specifications may be reviewed for exclusions and exceptions when selecting process measures. NQMC contains this and other information about each measure, enabling users to determine whether a measure is suitable for their purposes.

Using Process Measures

Process measures have many uses (see NQMC's Uses of Quality Measures tutorial). They have been used to compare performance between providers, to guide value-based purchasing programs, to set pay-for-performance incentives, and for public reporting. In some situations, process measures possess advantages over outcome measures. Process measures may be easier and less costly to measure than outcome measures. Process measures can also be useful when outcomes of interest are rare or sample sizes small. Well-specified process measures that include key exclusions do not need case-mix or risk adjustment. Finally, quality improvement may be easier to guide using process measures.
Disadvantages of process measures include the level of detailed clinical data sometimes necessary to exclude patients who should not receive treatment. Sample sizes may be very small for some subsets of patients defined in a process measure. For example, assessing the use of radiation treatment for rectal cancer patients with a specific stage of disease and anatomy may focus on a subset of patients who will rarely be seen, even in a large cancer center, during the course of one year. Finally, when used in public reporting programs, process measures may require a level of knowledge that can make them challenging for many people to understand.

Summary

Overall, process measures provide a detailed look at activities performed by professionals and staff. To the extent that processes are closely associated with outcomes, changes to those processes can improve outcomes. Process measures can be very useful in quality improvement as well as performance-based incentive programs. In selecting process measures, users ought to consider sample sizes for denominators, exclusion criteria, and alternative processes or care pathways that may exist for some processes. For more information on the uses of quality measures, see NQMC's Uses of Quality Measures tutorial.

Questions to Consider When Selecting a Measure of Process

  1. Is there strong evidence linking the process to outcomes?
  2. Is the execution of process affected by other events outside the control of the provider?
  3. Is the collection of the required level of data feasible? 

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