JMIR Publications

ABSTRACT

Background:

Electronic medical records (EMRs) contain a considerable amount of information about patients.

Objective:

We investigated whether the readily available longitudinal EMRs data including nursing records could be utilized to compute the risk of inpatient falls and its accuracy when compared to existing fall risk assessment tools.

Methods:

Two study cohorts from two tertiary hospitals with different EMR systems and located near Seoul, South Korea were used. The modeling cohort included 14,307 admissions (122,179 hospital-days) and the validation cohort comprised 21,172 admissions (175,592 hospital-days) from each of 6 nursing units. A probabilistic Bayesian network model was used, and patient data were divided into windows with a length of 24 hours. Data on existing fall risk assessment tools, nursing processes, Korean Patient Classification System groups, medications, and administration data were used as model parameters. Model evaluation metrics were averaged using 10-fold cross validation.

Results:

The initial model showed an error rate of 11.7% and a spherical payoff of 0.91 with a c-statistic of 0.96, which represent far superior performance compared to that for the existing fall risk assessment tool (c-statistic = 0.69). The cross-site validation revealed an error rate of 9.3% and a spherical payoff of 0.92 with a c-statistic of 0.87, compared to a c-statistic of 0.65 for the existing fall risk assessment tool. The calibration curves for the model displayed more reliable results than the risk assessment tools. Nursing intervention data showed potential contributions to reducing the variance in the fall rate as did the risk factors of individual patients.

Conclusions:

A risk prediction model that considers longitudinal EMR data including nursing interventions can improve the ability to identify individual patients at a high risk of falling.