María Consuelo Company-Sancho1, Marcos Estupiñán-Ramírez2, Hilda Sánchez-Janáriz3, Rita Tristancho-Ajamil2. 1. Servicio de Atención Primaria, Dirección General de Programas Asistenciales, Servicio Canario de la Salud, Las Palmas de Gran Canaria, España. Electronic address: ccomsan@gobiernodecanarias.org. 2. Servicio de Atención Primaria, Dirección General de Programas Asistenciales, Servicio Canario de la Salud, Las Palmas de Gran Canaria, España. 3. Servicio de Evaluación de la Calidad Asistencial y Sistemas de Información, Dirección General de Programas Asistenciales, Servicio Canario de la Salud, Las Palmas de Gran Canaria, España.
Abstract
AIM: The health service invests up to 75% of its resources on chronic care where the focus should be on caring rather than curing. Nursing staff focuses their work on such care. Care requires being redorded in health histories through the standardized languages. These records enable useful analyses to organisational and healthcare decision-making. Our proposal is to know the association of between nursing diagnosis and a higher total expenditure on health. METHOD: An observational cross-sectional analytical study was performed based on data from electronic health records in Primary Care (Drago-AP), hospital discharges (CMBD-AH) and prescriptions (REC-SCS) of patients over 50 from 2012-2013 in the Canary Islands. A descriptive, bivariate and multivariate analysis was undertaken to create a predictive model on the use of resources. INDEPENDENT VARIABLES: Sociodemographic (age, sex, type of health-care affiliation, type of prescription charge) and nursing diagnosis (ND) recorded in late 2012. Dependent variables: Resources consumed in 2013. RESULTS: 582,171 patients met the criteria for inclusion. 53.0% of them were women with an average age of 64.3 years (SD 10.8years). 53.2% were pensioners. 49% of the included population had an ND, with an average of 2.1ND per patient. The average costs per patient were 1824.62€ (with a median of 827.5€) 25 and 27 percentiles of 264.1€ and 1824.7€, respectively. The bivariate analysis showed a significant correlation between these expenses and all the demographic variables; the expenses increased when a nursing diagnosis has been made (Spearman's rank=0.37: the more diagnoses, the more expenses). In the multivariate analysis, a first linear regression with the sociodemographic variables as independent variables explains 13.7% of the variability of the logarithm of the full costs (R2=0.137). If we add to this model the presence of nursing diagnoses, the explanatory capacity reaches 19.77% (R2=0.1977). CONCLUSION: Compared with a model that only consists of sociodemographic variables, nursing diagnoses can enhance the explanatory capacity of the use of healthcare resources.
AIM: The health service invests up to 75% of its resources on chronic care where the focus should be on caring rather than curing. Nursing staff focuses their work on such care. Care requires being redorded in health histories through the standardized languages. These records enable useful analyses to organisational and healthcare decision-making. Our proposal is to know the association of between nursing diagnosis and a higher total expenditure on health. METHOD: An observational cross-sectional analytical study was performed based on data from electronic health records in Primary Care (Drago-AP), hospital discharges (CMBD-AH) and prescriptions (REC-SCS) of patients over 50 from 2012-2013 in the Canary Islands. A descriptive, bivariate and multivariate analysis was undertaken to create a predictive model on the use of resources. INDEPENDENT VARIABLES: Sociodemographic (age, sex, type of health-care affiliation, type of prescription charge) and nursing diagnosis (ND) recorded in late 2012. Dependent variables: Resources consumed in 2013. RESULTS: 582,171 patients met the criteria for inclusion. 53.0% of them were women with an average age of 64.3 years (SD 10.8years). 53.2% were pensioners. 49% of the included population had an ND, with an average of 2.1ND per patient. The average costs per patient were 1824.62€ (with a median of 827.5€) 25 and 27 percentiles of 264.1€ and 1824.7€, respectively. The bivariate analysis showed a significant correlation between these expenses and all the demographic variables; the expenses increased when a nursing diagnosis has been made (Spearman's rank=0.37: the more diagnoses, the more expenses). In the multivariate analysis, a first linear regression with the sociodemographic variables as independent variables explains 13.7% of the variability of the logarithm of the full costs (R2=0.137). If we add to this model the presence of nursing diagnoses, the explanatory capacity reaches 19.77% (R2=0.1977). CONCLUSION: Compared with a model that only consists of sociodemographic variables, nursing diagnoses can enhance the explanatory capacity of the use of healthcare resources.
Authors: Maria Consuelo Company-Sancho; Víctor M González-Chordá; María Isabel Orts-Cortés Journal: Int J Environ Res Public Health Date: 2022-04-01 Impact factor: 3.390