International journal of medical informatics
-
The aim of this study is to evaluate the effectiveness and efficiency of privacy-preserving data cubes of electronic medical records (EMRs). An EMR data cube is a complex of EMR statistics that are summarized or aggregated by all possible combinations of attributes. Data cubes are widely utilized for efficient big data analysis and also have great potential for EMR analysis. For safe data analysis without privacy breaches, we must consider the privacy preservation characteristics of the EMR data cube. In this paper, we introduce a design for a privacy-preserving EMR data cube and the anonymization methods needed to achieve data privacy. We further focus on changes in efficiency and effectiveness that are caused by the anonymization process for privacy preservation. Thus, we experimentally evaluate various types of privacy-preserving EMR data cubes using several practical metrics and discuss the applicability of each anonymization method with consideration for the EMR analysis environment. ⋯ The utility of anonymized EMR data cubes varies widely according to the anonymization method, and the applicability of the anonymization method depends on the features of the EMR analysis environment. The findings help to adopt the optimal anonymization method considering the EMR analysis environment and goal of the EMR analysis.
-
This study examined the current prevalence of electronic health records (EHRs) in Korea and identified the factors that impede or facilitate the adoption of EHRs. ⋯ The rate at which EHR and CPOE for medications systems have been adopted by Korean tertiary teaching and general hospitals was higher than the rate of adoption by US hospitals. Financial aspects are reported to be the most important facilitators of and barriers to EHR adoption. Government financial support, especially to small hospitals, seems to be essential to promoting the adoption of EHRs by Korean hospitals.
-
Pain gained recognition as a vital sign in the early 2000s, underscoring the importance of accurate documentation, characterization, and treatment of pain. No prior studies have demonstrated the utility of the 0-10 pain scale with respect to discharge opioid prescriptions, nor characterized the most influential factors in discharge prescriptions. ⋯ Pain scale was significantly negatively correlated with discharge MMEs in the ED and positively correlated in the inpatient population. Individual prescriber characteristics were the more influential variable, with prolific high prescribers writing for the largest MME amounts. The inverse association of pain and MMEs at discharge in the ED, and the large effect pre-existing prescriber patterns exhibited, both improved methodology for assessing and appropriately treating pain, and effective prescriber-targeted interventions, must be a priority.
-
Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48h after ICU admission. ⋯ The results show that although there are many values missing in the first few hour of ICU admission, there is enough signal to effectively predict mortality during the first 6h of admission. The proposed framework, in particular the one that uses the ensemble learning approach - EMPICU Random Forest (EMPICU-RF) offers a base to construct an effective and novel mortality prediction model in the early hours of an ICU patient admission, with an improved performance profile.
-
Predicting insulin-induced postprandial hypoglycemic events is critical for the safety of type 1 diabetes patients because an early warning of hypoglycemia facilitates correction of the insulin bolus before its administration. The postprandial hypoglycemic event counts can be lowered by reducing the size of the bolus based on a reliable prediction but at the cost of increasing the average blood glucose. ⋯ The results demonstrated that it is feasible to anticipate hypoglycemic events with a reasonable false-positive rate. The accuracy of the results and the trade-off between performance metrics allow its use in decision support systems for patients who wear insulin pumps.