An analysis of the relationship among patient profile variables in predicting home care resource utilization and outcomes

Abstract

The study sought to identify patient profile variables that explain variation in resource utilization and outcomes and to explore the most frequently used nursing diagnoses and the related types of nursing interventions for home health care. The conceptual framework was based on Donabedian's quality care elements (structure, process, and outcome) and the Nursing Minimum Data Set (NMDS). Patient profile variables included in this study were age, sex, race, marital status, living status, payment source, medical diagnoses, nursing diagnoses, and prognosis. Home health care resource utilization was measured by the episode of care, home care visits, visit duration, and nursing interventions. Home health care outcomes were measured by the discharge reason, discharge condition and discharge disposition. A retrospective descriptive design with 244 patient records was used and data were obtained from a home health care agency located in Washington D.C. A series of stepwise and discriminant analyses were conducted to explore the relationship among variables of patient profile, resource utilization, and outcomes. Nursing diagnoses was based on the NANDA taxonomy system and nursing activities were grouped into three major nursing intervention categories: assessment, instruction and other. The results indicated that patient profile variables explained the largest amount variance in resource use of the total nursing interventions (R2 = 0.69, p<0.001) and the smallest amount of explained variance was in the utilization measure of total home visits (R{2 = 0.06, p<0.001). The number of nursing diagnoses (R2 = 0.27) and two nursing diagnoses (alteration in mobility, R2 = 0.26, and knowledge deficit in IV therapy, R2 = 0.38) were strong predictors of overall resource use. While only three medical diagnoses were identified as significant predictors, ten nursing diagnoses were found to significantly explain variance of resource use. Prognosis (discriminant function coefficients (greater than or equal to 0.81) proved to be the strongest predictor of all three discharge outcomes. Age, sex, race and living status were not related to either resource use or patient outcomes. The six most frequently used nursing diagnoses in describing home care patient problems were alteration in mobility, alteration in cardiac status, alteration in comfort (pain), knowledge deficit regarding IV therapy, alteration in breathing pattern, and alteration in nutrition. In addition, for most diagnoses, instructions were the most frequently used interventions, followed by assessment and "other" interventions. The results of this study indicated that data related to nursing diagnoses and nursing interventions can provide valuable information in predicting resource use. Prognosis made by nursing judgement was also sensitive in predicting patient outcomes. It is recommended that these critical data elements be included in describing home health patient characteristics and related resource utilization and care outcomes.