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E -Health Patient R ecords Analysis. By Gian Frez (el13gcf) and Matthew Hughes (ed10m2jh). E-Health Patient Records Overview. Electronic health records (EHRs) are electronic health data recorded by health care professionals , as well as patients, including:
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E-Health Patient Records Analysis By Gian Frez (el13gcf) and Matthew Hughes (ed10m2jh)
E-Health Patient Records Overview • Electronic health records (EHRs) are electronic health data recorded by health care professionals, as well as patients, including: • Medical history and medication records • Daily charting, diagnosis and test results • Nursing notes and care plan • Purpose: • To increase the availability of information across healthcare systems/practices • To improve the efficiency and effectiveness of the healthcare system • To support research into new diseasesand intervention trials • NHS Connecting for Health (UK), the Personally Controlled Electronic Health Record (Australia), e-Health Exchange (USA)
Case Study Data and Text Mining the Electronic Medical Record to Improve Care and to Lower Costs1 1 P. Cerrito and J.C. Cerrito, “Data and Text Mining the Electronic Medical Record to Improve Care and to Lower Costs,” in Proc. SUGI 31, 2006, paper 077-31.
Data Description • Examine EHRs to determine the treatment of patients in the Emergency Department (ED) • EHRs over a 6-month period were analysed using SAS software • Data contains triage information, final outcome (patient disposition), medication and treatment time
Medication Clusters • All medications for each patient were transposed and concatenated into one text string • SAS Text Miner defined clusters of medication treatments, resulting in 13 different clusters
Final Disposition and Medications • Relationship between final disposition and medications • Cluster 7 has the highest proportion of home discharges • Clusters 3 and 13 have a majority of patients admitted to hospital
Transactional Time Series Analysis • SAS PROC HPF used to examine patient time series data • 3,300 patient visits to ED were examined over 3 months • Data accumulated by the hour and then averaged to determine changes over a 24-hour period
Average Treatment Time • Average treatment time (LOS) over 24 hours:
Explanation of Results • The peak in treatment time was due to an increase in the number of patient visits • Statistically significant, r2 = 74%
High Performance Forecasting (HPF) • HPF to determine whether there is a regular and predictable pattern in patient treatment time (LOS) • Optimal prediction was seasonal/periodic
Conclusion • EHRs can be mined and analysed to improve the healthcare system e.g. the ED • SAS Text Miner compared final disposition and medications • SAS PROC HPF used to examine patient time series data • Analyses can be extended e.g. tracking demand for other hospital services to optimise facility use, develop protocols, improve scheduling and reduce waiting time
References • Denny JC (2012) Chapter 13: Mining Electronic Health Records in the Genomics Era. PLoSComputBiol 8(12): e1002823. doi:10.1371/journal.pcbi.1002823 • Blog.withings.com. How e-health data mining can help scientific research. | Withings blog. [Online] Available from: http://blog.withings.com/en/2012/04/05/how-e-health-data-mining-can-help-scientific-research/ [Accessed 30 Nov 2013]. • Research.microsoft.com. eHealth - Microsoft Research. [Online] Available from: http://research.microsoft.com/en-us/collaboration/global/asia-pacific/programs/ehealth.aspx [Accessed 30 Nov 2013]. • P. Cerrito and J.C. Cerrito, “Data and Text Mining the Electronic Medical Record to Improve Care and to Lower Costs,” in Proc. SUGI 31, 2006, paper 077-31.