Managing Large-Scale Standardized Electronic Health Records

Shivani Batra () and Shelly Sachdeva ()
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Shivani Batra: Jaypee Institute of Information Technology University, Department of Computer Science and Engineering
Shelly Sachdeva: Jaypee Institute of Information Technology University, Department of Computer Science and Engineering

A chapter in Big Data Analytics, 2016, pp 201-219 from Springer

Abstract: Abstract Electronic health records (EHRs) contain data about a person’s health history. Increasingly, EHRs have the characteristics of big data in terms of their volume, velocity, and variety (the 3 “V”s). Volume is a major concern for EHRs especially due to the presence of huge amount of null data, i.e., for storing sparse data that leads to storage wastage. Reducing storage wastage due to sparse values requires amendments to the storage mechanism that stores only non-null data, and also allows faster data retrieval and supports multidimensional heterogeneous data. Another area of concern regarding EHRs data is standardization. Standardization can aid in semantic interoperability that resolves the discrepancies in interpretation of health records among different medical organizations or persons involved. Various proposals have been made at the logical layer of relational database management system for managing large-scale standardized records in terms of data volume, velocity, and variety. Every proposed modification to logical layer has its pros and cons. In this chapter, we will discuss various aspects of the solutions proposed for managing standardized EHRs, and the approaches to adopt these standards. After efficient management of EHR data, analytics can be applied to minimize the overall cost of healthcare.

Keywords: Electronic health records (EHRs); Entity attribute value model; Storage schema; Access speed (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-81-322-3628-3_11

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