Prevention Quality Indicators
The PQIs represent the current state of the art in assessing quality of health services in local communities using inpatient discharge data. These indicators measure the outcomes of preventive care for both acute illness and chronic conditions, reflecting two important components of the quality of preventive care—effectiveness and timeliness. For example, with effective drug therapy in the outpatient setting, hospital admissions for hypertension can be prevented. Likewise, accurate diagnosis and timely access to surgical treatment will help reduce the incidence of perforated appendix. The PQIs are thus valuable tools for identifying potential quality problems in outpatient care that help to set the direction for more in-depth investigation. Because the PQIs are based on readily available data—hospital discharge abstracts—resource requirements are minimal. With uniform definitions and standardized programs, the PQIs will allow comparisons across States, regions, and local communities over time.
The PQI module contains 14 indicators that measure hospital admissions for ambulatory care sensitive conditions (ACSC) across geographic areas. ACSCs represent conditions for which hospitalization could be avoided if the patient receives timely and adequate outpatient care. Many factors influence the quality of outpatient care, including access to care and adequately prescribed treatments, once care is obtained. In addition, patient compliance with those treatments and other patient factors may play a role. In general, areas with lower social-economic status tend to have higher admission rates for ACSCs than areas with higher social-economic status. As with utilization indicators, there are no “right rates” of admission for these conditions. Very low rates could signal inappropriate underutilization of health care resources while very high rates could indicate potential overuse of inpatient care.
Therefore, hospital admission for ACSCs is not a measure of hospital quality but a potential indicator of outpatient and community health care need. For example, if an area has a relatively high hospital admission rate for diabetes complications, the local health care providers should work with the community to identify reasons and strategies to address the problem.
Except for perforated
appendix and low birth weight, each indicator is measured as the number of
hospital admissions for a particular ACSC per 100,000 residential population in a county.
The data required for measuring these indicators comes from hospital discharge abstracts or billing claims (administrative data) which are readily available in many states. The residential population data are from the U.S. Census Bureau.
The observed and risk-adjusted
rates for each indicator at the county level have been reported. Observed rates are the raw rates. Risk-adjusted rates are derived from applying
the average casemix of a baseline file that reflects
a large proportion of the
Despite the unique strengths of the PQIs, there are several issues that should be considered when using these indicators. First, for some PQIs, differences in socioeconomic status have been shown to explain a substantial part—perhaps most—of the variation in PQI rates across areas. The complexity of the relationship between socioeconomic status and PQI rates makes it difficult to delineate how much of the observed relationships are due to true access to care difficulties in potentially underserved populations, or due to other patient characteristics, unrelated to quality of care, that vary systematically by socioeconomic status. For some of the indicators, patient preferences and hospital capabilities for inpatient or outpatient care might explain variations in hospitalizations. In addition, environmental conditions that are not under the direct control of the health care system can substantially influence some of the PQIs. For example, the COPD and asthma admission rates are likely to be higher in areas with poorer air quality.
Second, the evidence related to potentially avoidable hospital admissions is limited for each indicator, because many of the indicators have been developed as parts of sets. Only five studies have attempted to validate individual indicators rather than whole measure sets. A limitation of this literature is that relatively little is known about which components represent the strongest measures of access and quality. Most of the five papers that did report on individual indicators also used a single variable, such as median area-specific income or rural residence, for construct validation. All but one of these papers included adjustment only for demographic factors (e.g., age, sex, and race).
Third, despite the relationships demonstrated at the patient level between higher quality ambulatory care and lower rates of hospital admission, few studies have directly addressed the question of whether effective treatments in outpatient settings would reduce the overall incidence of hospitalizations. The extent to which the reporting of admission rates for ambulatory care sensitive conditions may lead to changes in ambulatory practices and admission rates also is unknown. Providers may admit patients who do not clinically require inpatient care or they may do the opposite—fail to hospitalize patients who would benefit from inpatient care.