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 E-Health Patient Records Analysis By Gian Frez (el13gcf) and Matthew Hughes (ed10m2jh)

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Presentation on theme: " E-Health Patient Records Analysis By Gian Frez (el13gcf) and Matthew Hughes (ed10m2jh)"— Presentation transcript:

1  E-Health Patient Records Analysis By Gian Frez (el13gcf) and Matthew Hughes (ed10m2jh)

2 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 diseases and intervention trials  NHS Connecting for Health (UK), the Personally Controlled Electronic Health Record (Australia), e-Health Exchange (USA)

3  Case Study Data and Text Mining the Electronic Medical Record to Improve Care and to Lower Costs 1 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

4 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

5 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

6 Medication Clusters

7 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

8 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

9 Average Treatment Time  Average treatment time (LOS) over 24 hours:

10 Explanation of Results  The peak in treatment time was due to an increase in the number of patient visits  Statistically significant, r 2 = 74%

11 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

12 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

13 References  Denny JC (2012) Chapter 13: Mining Electronic Health Records in the Genomics Era. PLoS Comput Biol 8(12): e doi: /journal.pcbi  Blog.withings.com. How e-health data mining can help scientific research. | Withings blog. [Online] Available from: can-help-scientific-research/ [Accessed 30 Nov 2013].  Research.microsoft.com. eHealth - Microsoft Research. [Online] Available from: 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


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