Abstract

Electronic health record systems work beyond just recording patients` health data. They have multiple secondary functionalities, such as data reporting and clinical decision support. As each of these systems` workloads has contradictory different needs, managing a multipurpose electronic health record is a challenge. This paper proposes a unified healthcare data framework that can simplify health information system infrastructure. It investigates the suitability of the document-based NoSQL persistence mechanism, storing electronic health records data as a design choice for managing varied complexity ad hoc queries used in operational business intelligence. The performance of the most popular two document-based NoSQL back-ends, Couchbase Server and MongoDB, is compared according to the size of the database and query execution time. Results showed that while MongoDB can execute simple single-document queries nearly in milliseconds. It does not provide satisfactory response time for unplanned complex queries spanning multiple documents. By utilizing its analytics services and multi-dimensional scaling architecture, Couchbase Server multi-node cluster outperforms the response times of MongoDB for both simple and complex healthcare data access patterns. The primary advantage of the proposed tightly coupled EHRs processing framework is its flexibility to manage workload according to changing requirements.

Highlights

  • Electronic health records (EHRs) system is a quintessential part of healthcare information system (HIS)

  • Our experiment investigates the performance of the document-oriented database to store Fast Healthcare Interoperability Resources (FHIR) [48] compliant EHRs data

  • This paper evaluates how document-oriented database management systems (DODBMSs) store and retrieve EHRs in the context of storage space and response time

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Summary

Introduction

Electronic health records (EHRs) system is a quintessential part of healthcare information system (HIS) It is an ecosystem for maintaining a long-term patient‟s health record. This system usually has multiple core functionalities. It is used for storing and retrieving individual patient records for healthcare purposes. These EHRs data could be used in clinical decision support (CDS) to suggest the steps for treatment or predict future conditions trends by analyzing transactional data [1]. EHRs data are not possessed by any particular healthcare provider To interconnect these different healthcare practitioners, EHRs need to be interoperable through following certain standards to facilitate health information exchange (HIE) and sharing [2]. HIS workload usually encompasses two main practices: clinical use, which is regarded as a transactional workload, and research use which is dedicated to analytical workload

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