The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes, and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this article, we provide a structured and comprehensive overview of data mining techniques for modeling EHRs. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.

The guideline recommendations showed that shorter delays indicates better outcomes. There was no evidence that 3 hours is safe; even very short delays adversely impact outcomes. Findings demonstrated a new approach to incorporate time t when analyzing the impact on outcomes and provide new evidence for clinical practice and research.

SummarySummary: To conduct an independent secondary analysis of a multi-focal intervention for early detection of sepsis that included implementation of change management strategies, electronic surveillance for sepsis, and evidence based point of care alerting using the POC AdvisorTM application. Methods: Propensity score matching was used to select subsets of the cohorts with balanced covariates. Bootstrapping was performed to build distributions of the measured difference in rates/ means. The effect of the sepsis intervention was evaluated for all patients, and High and Low Risk subgroups for illness severity. A separate analysis was performed patients on the intervention and non-intervention units (without the electronic surveillance). Sensitivity, specificity, and the positive predictive values were calculated to evaluate the accuracy of the alerting system for detecting sepsis or severe sepsis/ septic shock. Results: There was positive effect on the intervention units with sepsis electronic surveillance with an adjusted mortality rate of -6.6%. Mortality rates for non-intervention units also improved, but at a lower rate of -2.9%. Additional outcomes improved for patients on both intervention and non-intervention units for home discharge (7.5% vs 1.1%), total length of hospital stay (-0.9% vs -0.3%), and 30 day readmissions (-6.6% vs -1.6%). Patients on the intervention units showed better outcomes compared with non-intervention unit patients, and even more so for High Risk patients. The sensitivity was 95.2%, specificity of 82.0% and PPV of 50.6% for the electronic surveillance alerts. Conclusion: There was improvement over time across the hospital for patients on the intervention and non-intervention units with more improvement for sicker patients. Patients on intervention units with electronic surveillance have better outcomes; however, due to differences in exclusion criteria and types of units, further study is needed to draw a direct relationship between the electronic surveillance system and outcomes.