<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
<p>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.</p>
</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.

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

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper describes a system in detail, which takes in depersonalized data collected by a patient medication management system, carries out reformatting and...

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

Article Category: Research-Article

Online veröffentlicht: 03. Jan. 2018

Seitenbereich: 1 - 18

DOI: https://doi.org/10.21307/ijssis-2018-003

Schlüsselwörter
data processing, prescription tracking, health-care data, analytics, data visualization

© 2018 Kenneth Mbuthia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Article Category: Research-Article

Online veröffentlicht: 03. Jan. 2018

Seitenbereich: 1 - 18

DOI: https://doi.org/10.21307/ijssis-2018-003

Schlüsselwörterdata processing, prescription tracking, health-care data, analytics, data visualization

© 2018 Kenneth Mbuthia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
data processing, prescription tracking, health-care data, analytics, data visualization