It is feasible to integrate the programmatic, data collection, data transmission, and outcome enhancement components of OBQI into the day-to-day operations of home health agencies. The aggregate findings and the agency-level evidence available from site-specific communications suggest that OBQI had a pervasive effect on outcome improvement for home health patients. OBQI appears to warrant expansion and refinement in HHC and experimentation in other healthcare settings.
The Outcome and Assessment Information Set (OASIS) is used for outcome reporting, quality improvement, and case mix adjustment of per-episode payment for home health care. The research described here addresses interrater reliability of OASIS items and compares clinician time required to complete patient assessment with and without OASIS. Interrater reliability for OASIS data items was estimated using independent assessments by two clinicians for a sample of 66 patients. Incremental assessment time due to OASIS was estimated using interview data from two agency-matched groups of clinical care providers--one group who used OASIS in the assessment and a second group whose assessment did not include OASIS items. Interrater reliability is excellent (kappa > .80) for many OASIS items and substantial (kappa > 0.60) for most items. The reported time required to complete an assessment with OASIS did not differ from the time required for a comparable assessment without OASIS. The results of this study are being used to guide developmental efforts to improve OASIS items. They can also be informative to home health care agencies when interpreting OASIS-based outcome and case mix reports.
Increased use of home care services, and the provision of more sophisticated care to acutely ill patients, have prompted concern about quality assurance in home settings. A conceptual framework is proposed to assess home health-care quality based on two premises: outcome, process, and structural measures, first, are required to evaluate quality accurately; classification of patients into quality indicator groups (QUIGs), second, permits specification and use of more practical and valid quality measures. The framework may prove useful because measures are related directly to patient conditions, services rendered, and treatment objectives. Further reliability and validity testing of the QUIGs and a system of operational quality measures are currently underway.
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