This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN).

This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0.

We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit.

KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

This study was done to develop a prediction model for postpartum depression by verifying the mediation effect of antepartum depression. A hypothesized model was developed based on literature reviews and predictors of postpartum depression by Beck.

Data were collected from 186 pregnant women who had a gestation period of more than 32 weeks and were patients at a maternity hospital, two obstetrics and gynecology specialized hospitals, or the outpatient clinic of K medical center. Data were analysed with descriptive statistics, correlation and exploratory factor analysis using the SPSS/WIN 18.0 and AMOS 18.0 programs.

The final modified model had good fit indices. Parenting stress, antepartum depression and postpartum family support had statistically significant effects on postpartum depression, and defined 74.7% of total explained variance of postpartum depression. Antepartum depression had significant mediation effects on postpartum depression from stress in pregnancy and self-esteem.

The results of this study suggest that it is important to develop nursing interventions including strategies to reduce parenting stress and improve postpartum family support in order to prevent postpartum depression. Especially, it is necessary to detect and treat antepartum depression early to prevent postpartum depression as antepartum depression can affect postpartum depression by mediating antepartum factors.

The study was designed to determine the discriminating ability of a Bayesian network (BN) for predicting risk for pressure ulcers.

Analysis was done using a retrospective cohort, nursing records representing 21,114 hospital days, 3,348 patients at risk for ulcers, admitted to the intensive care unit of a tertiary teaching hospital between January 2004 and January 2007. A BN model and two logistic regression (LR) versions, model-I and -II, were compared, varying the nature, number and quality of input variables. Classification competence and case coverage of the models were tested and compared using a threefold cross validation method.

Average incidence of ulcers was 6.12%. Of the two LR models, model-I demonstrated better indexes of statistical model fits. The BN model had a sensitivity of 81.95%, specificity of 75.63%, positive and negative predictive values of 35.62% and 96.22% respectively. The area under the receiver operating characteristic (AUROC) was 85.01% implying moderate to good overall performance, which was similar to LR model-I. However, regarding case coverage, the BN model was 100% compared to 15.88% of LR.

Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.