The University of Southampton
University of Southampton Institutional Repository

Patient-centric health-care data processing using streams and asynchronous technology

Patient-centric health-care data processing using streams and asynchronous technology
Patient-centric health-care data processing using streams and asynchronous technology

This paper describes a system in detail, which takes in depersonalized data collected by a patient medication management system, carries out reformatting and basic calculations on the data, then stores the resulting information into a database for retrieval, visualization, and further analysis. An investigation is also carried out to create a new way of developing a data analysis application that is efficient and simple to create/code, while avoiding the use of overly complicated libraries to boost performance or investing in expensive hardware. A description of the technologies, algorithms, software tools, and software development process shall be provided. The results from the creation of this application will also be covered while indicating relevant uses for the application, pitfalls/ challenges, and future improvements that can be carried out to enhance and improve the system. This application makes use of data collected by an existing patient prescription tracking app that was built by a third-party company that provided dummy data that were used as a guide to develop the system. The application/system created consists of four main sections, a data model project that was used to create the database schema, a "cruncher" project that consists of scripts used to extract data from the existing database, then transforms it into the needed information to be stored, an application programming interface (API) that is used to easily query/retrieve information from the database, and lastly a set of interfaces that visualized the data stored once collected by the cruncher project for easy interpretation and investigation. Disclaimers: The system described in this paper was developed and tested locally and not in any production environment. All data used for the creation of this paper are dummy data and not real user data. Therefore, all results are simulated and, in no way, violate any real user's privacy. All functionality proposed and developed in this solution represent the potential applications of an analytics tool of this nature and do not represent how any collaborator in this project currently uses the developed system in any real-world/production environment.

Analytics, Data processing, Data visualization, Health-care data, Prescription tracking
1178-5608
1-18
Mbuthia, Kenneth
797dbbc8-8b69-42c7-991d-037745f3ea91
Dai, Jin
67741e8c-beb9-4b3e-817a-2492075ad07a
Zavrakas, Stavros
51f9cdac-cb37-4c9a-9c0a-00f563cb5611
Yan, Jize
786dc090-843b-435d-adbe-1d35e8fc5828
Mbuthia, Kenneth
797dbbc8-8b69-42c7-991d-037745f3ea91
Dai, Jin
67741e8c-beb9-4b3e-817a-2492075ad07a
Zavrakas, Stavros
51f9cdac-cb37-4c9a-9c0a-00f563cb5611
Yan, Jize
786dc090-843b-435d-adbe-1d35e8fc5828

Mbuthia, Kenneth, Dai, Jin, Zavrakas, Stavros and Yan, Jize (2018) Patient-centric health-care data processing using streams and asynchronous technology. International Journal on Smart Sensing and Intelligent Systems, 11 (1), 1-18. (doi:10.21307/IJSSIS-2018-003).

Record type: Article

Abstract

This paper describes a system in detail, which takes in depersonalized data collected by a patient medication management system, carries out reformatting and basic calculations on the data, then stores the resulting information into a database for retrieval, visualization, and further analysis. An investigation is also carried out to create a new way of developing a data analysis application that is efficient and simple to create/code, while avoiding the use of overly complicated libraries to boost performance or investing in expensive hardware. A description of the technologies, algorithms, software tools, and software development process shall be provided. The results from the creation of this application will also be covered while indicating relevant uses for the application, pitfalls/ challenges, and future improvements that can be carried out to enhance and improve the system. This application makes use of data collected by an existing patient prescription tracking app that was built by a third-party company that provided dummy data that were used as a guide to develop the system. The application/system created consists of four main sections, a data model project that was used to create the database schema, a "cruncher" project that consists of scripts used to extract data from the existing database, then transforms it into the needed information to be stored, an application programming interface (API) that is used to easily query/retrieve information from the database, and lastly a set of interfaces that visualized the data stored once collected by the cruncher project for easy interpretation and investigation. Disclaimers: The system described in this paper was developed and tested locally and not in any production environment. All data used for the creation of this paper are dummy data and not real user data. Therefore, all results are simulated and, in no way, violate any real user's privacy. All functionality proposed and developed in this solution represent the potential applications of an analytics tool of this nature and do not represent how any collaborator in this project currently uses the developed system in any real-world/production environment.

Text
10.21307_ijssis-2018-003 - Version of Record
Download (1MB)

More information

e-pub ahead of print date: 3 January 2018
Keywords: Analytics, Data processing, Data visualization, Health-care data, Prescription tracking

Identifiers

Local EPrints ID: 423247
URI: http://eprints.soton.ac.uk/id/eprint/423247
ISSN: 1178-5608
PURE UUID: ea86f7a0-f852-471a-87b6-5f3072a80e45
ORCID for Jize Yan: ORCID iD orcid.org/0000-0002-2886-2847

Catalogue record

Date deposited: 19 Sep 2018 16:30
Last modified: 09 Jan 2022 03:50

Export record

Altmetrics

Contributors

Author: Kenneth Mbuthia
Author: Jin Dai
Author: Stavros Zavrakas
Author: Jize Yan ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×