Novel Approach to Inpatient Fall Risk Prediction and Its Cross-Site Validation Using Time-Variant Data.

BACKGROUND: Electronic medical records (EMRs) contain a considerable amount of information about patients. The rapid adoption of EMRs and the integration of nursing data into clinical repositories have made large quantities of clinical data available for both clinical practice and research. OBJECTIVE: In this study, we aimed to investigate whether readily available longitudinal EMR data including nursing records could be utilized to compute the risk of inpatient falls and to assess their accuracy compared with existing fall risk assessment tools. METHODS: We used 2 study cohorts from 2 tertiary hospitals, located near Seoul, South Korea, with different EMR systems. 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. In addition, data on existing fall risk assessment tools, nursing processes, Korean Patient Classification System groups, and 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 with that for the existing fall risk assessment tool (c-statistic=0.69). The cross-site validation revealed an error rate of 4.87% and a spherical payoff of 0.96 with a c-statistic of 0.99 compared with a c-statistic of 0.65 for the existing fall risk assessment tool. The calibration curves for the model displayed more reliable results than those for the fall risk assessment tools alone. In addition, 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 likely to fall.

Automatic population of eMeasurements from EHR systems for inpatient falls.

Objective: Representing nursing data sets in a standard way will help to facilitate sharing relevant information across settings. We aimed to populate nursing process and outcome metrics with electronic health record (EHR) data and then compare the results with event reporting systems. Methods: We used the "eMeasure" development process of the National Quality Forum adopted by the American Nurses Association. We used operational definitions of quality measures from the American Nurses Association and the US Institute for Healthcare Improvement and employed concept mapping of local data elements to 2 controlled vocabularies to define a standard data dictionary: (1) Logical Observation Identifiers Names and Codes and (2) International Classification for Nursing Practice. We assessed feasibility using the nursing data set of 7829 and 8199 patients from 2 general hospitals with different EHR systems. Using inpatient falls as a use case, we compared the populated measures with results from the event reporting systems. Results: We identified 17 care components and 118 unique concepts and matched them with data elements in the EHRs. Including suboptimal mapping, 98% of the assessment concepts mapped to Logical Observation Identifiers Names and Codes and 52.9% of intervention concepts mapped to International Classification for Nursing Practice. While not all process indicators were available from event reporting systems, we successfully populated 9 fall prevention process indicators and the fall rate outcome indicator from the 2 EHRs. We were unable to populate the falls with an injury rate indicator. Conclusions: EHR data can populate fall prevention process measure metrics and at least one inpatient fall prevention outcome metric.

Performance analysis of hospital information system of the national health insurance corporation ilsan hospital.

OBJECTIVES: This study evaluated the qualitative and quantitative performances of the newly developed information system which was implemented on November 4, 2011 at the National Health Insurance Corporation Ilsan Hospital. METHODS: Registration waiting time and changes in the satisfaction scores for the key performance indicators (KPI) before and after the introduction of the system were compared; and the economic effects of the system were analyzed by using the information economics approach. RESULTS: After the introduction of the system, the waiting time for registration was reduced by 20%, and the waiting time at the internal medicine department was reduced by 15%. The benefit-to-cost ratio was increased to 1.34 when all intangible benefits were included in the economic analysis. CONCLUSIONS: The economic impact and target satisfaction rates increased due to the introduction of the new system. The results were proven by the quantitative and qualitative analyses carried out in this study. This study was conducted only seven months after the introduction of the system. As such, a follow-up study should be carried out in the future when the system stabilizes.