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==State of the art== | ==State of the art== | ||
Because healthcare services are very complex and life-critical, many works have tackled the design of healthcare management systems, aimed at monitoring metrics in order to detect undesirable behaviors that decrease their satisfaction or even threaten their safety. | |||
Discussion on the design and implementation of the healthcare management system is not new. In the 2000s, Curtright ''et al.''<ref name="CurtrightStat00">{{cite journal |title=Strategic performance management: Development of a performance measurement system at the Mayo Clinic |journal=Journal of Healthcare Management |author=Curtwright, J.W.; Stolp-Smith, S.C.; Edell, E.S. |volume=45 |issue=1 |pages=58–68 |year=2000 |pmid=11066953}}</ref> described a system to monitor KPIs, summarizing them in a dashboard report, with a real-world application in the Mayo Clinic. Also, Griffith and King<ref name="GriffithChampion00">{{cite journal |title=Championship management for healthcare organizations |journal=Journal of Healthcare Management |author=Griffith, J.R. |volume=45 |issue=1 |pages=17–30 |year=2000 |pmid=11066948}}</ref> proposed to establish a “championship” where those healthcare systems with consistently good metrics would help improve decision making processes. | |||
Some of these works explore the sensing technology that enable proposals. For instance, Ngai ''et al.''<ref name="NgaiDesign09">{{cite journal |title=Design of an RFID-based Healthcare Management System using an Information System Design Theory |journal=Information Systems Frontiers |author=Ngai. E.W.T.; Poon, J.K.L.; Suk, F.F.C.; Ng, C.C. |volume=11 |issue=4 |pages=405–417 |year=2009 |doi=10.1007/s10796-009-9154-3}}</ref> focus on how RFID technology can be applied for building a healthcare management system, yet it is only implemented in a quasi real-world setting. Ting ''et al.''<ref name="TingCritical11">{{cite journal |title=Critical elements and lessons learnt from the implementation of an RFID-enabled healthcare management system in a medical organization |journal=Journal of Medical Systems |author=Ting, S.L.; Kwok, S.K.; Tsang, A.H.; Lee, W.B. |volume=35 |issue=4 |pages=657–69 |year=2011 |doi=10.1007/s10916-009-9403-5}}</ref> also focus on the application of RFID technology to such a project, from the perspective of its preparation, implementation, and maintenance. | |||
==References== | ==References== | ||
Revision as of 22:41, 23 May 2018
| Full article title | DataCare: Big data analytics solution for intelligent healthcare management |
|---|---|
| Journal | International Journal of Interactive Multimedia and Artificial Intelligence |
| Author(s) | Baldominos, Alejandro; de Rada, Fernando; Saez, Yago |
| Author affiliation(s) | Universidad Carlos III de Madrid, Camilo José Cela University |
| Primary contact | Email: abaldomi at inf dot uc3m dot es |
| Year published | 2018 |
| Volume and issue | 4(7) |
| Page(s) | 13–20 |
| DOI | 10.9781/ijimai.2017.03.002 |
| ISSN | 1989-1660 |
| Distribution license | Creative Commons Attribution 3.0 Unported |
| Website | http://www.ijimai.org/journal/node/1621 |
| Download | http://www.ijimai.org/journal/sites/default/files/files/2017/03/ijimai_4_7_2_pdf_16566.pdf (PDF) |
 |
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Abstract
This paper presents DataCare, a solution for intelligent healthcare management. This product is able not only to retrieve and aggregate data from different key performance indicators in healthcare centers, but also to estimate future values for these key performance indicators and, as a result, fire early alerts when undesirable values are about to occur or provide recommendations to improve the quality of service. DataCare’s core processes are built over a free and open-source cross-platform document-oriented database (MongoDB), and Apache Spark, an open-source cluster computing framework. This architecture ensures high scalability capable of processing very high data volumes coming at rapid speeds from a large set of sources. This article describes the architecture designed for this project and the results obtained after conducting a pilot in a healthcare center. Useful conclusions have been drawn regarding how key performance indicators change based on different situations, and how they affect patients’ satisfaction.
Keywords: Architecture, artificial intelligence, big data, healthcare, management
Introduction
When managing a healthcare center, there are many key performance indicators (KPIs) that can be measured, such as the number of events, the waiting time, the number of planned tours, etc. Often, keeping these KPIs within the expected limits is vital to achieving high user satisfaction.
In this paper we present DataCare, a solution for intelligent healthcare management. DataCare provides a complete architecture to retrieve data from sensors installed in the healthcare center, process and analyze it, and finally obtain relevant information, which is displayed in a user-friendly dashboard.
The advantages of DataCare are twofold: first, it is intelligent. Besides retrieving and aggregating data, the system is able to predict future behavior based on past events. This means that the system can fire early alerts when a KPI is expected to have a future value that falls outside the expected boundaries, and it can provide recommendations for improving the behavior and the metrics, or prevent future problems with attending events.
Second, the core system module is built on top of a big data platform. Processing and analysis are run over Apache Spark, and data are stored in MongoDB, thus enabling a highly scalable system that can process large volumes of data coming in at very high speeds.
This article will discuss many aspects of DataCare. The next section will present context for this research by analyzing the state of the art and related work. After that an overview of DataCare’s architecture will be presented, including the three main modules responsible for retrieving data, processing and analyzing it, and displaying the resulting valuable information.
After the architecture has been explained, the subsequent three sections will describe the preprocessing, processing, and analytics engines in further detail. The design of these systems is crucial to providing a scalable solution with an intelligent behavior. After discussing those engines in detail, the article will then describe the visual analytics engine and the different dashboards that are presented to users.
Finally, the penultimate section will describe how the solution has been validated, and the last section will provide some conclusive remarks, along with potential future work.
State of the art
Because healthcare services are very complex and life-critical, many works have tackled the design of healthcare management systems, aimed at monitoring metrics in order to detect undesirable behaviors that decrease their satisfaction or even threaten their safety.
Discussion on the design and implementation of the healthcare management system is not new. In the 2000s, Curtright et al.[1] described a system to monitor KPIs, summarizing them in a dashboard report, with a real-world application in the Mayo Clinic. Also, Griffith and King[2] proposed to establish a “championship” where those healthcare systems with consistently good metrics would help improve decision making processes.
Some of these works explore the sensing technology that enable proposals. For instance, Ngai et al.[3] focus on how RFID technology can be applied for building a healthcare management system, yet it is only implemented in a quasi real-world setting. Ting et al.[4] also focus on the application of RFID technology to such a project, from the perspective of its preparation, implementation, and maintenance.
References
- ↑ Curtwright, J.W.; Stolp-Smith, S.C.; Edell, E.S. (2000). "Strategic performance management: Development of a performance measurement system at the Mayo Clinic". Journal of Healthcare Management 45 (1): 58–68. PMID 11066953.
- ↑ Griffith, J.R. (2000). "Championship management for healthcare organizations". Journal of Healthcare Management 45 (1): 17–30. PMID 11066948.
- ↑ Ngai. E.W.T.; Poon, J.K.L.; Suk, F.F.C.; Ng, C.C. (2009). "Design of an RFID-based Healthcare Management System using an Information System Design Theory". Information Systems Frontiers 11 (4): 405–417. doi:10.1007/s10796-009-9154-3.
- ↑ Ting, S.L.; Kwok, S.K.; Tsang, A.H.; Lee, W.B. (2011). "Critical elements and lessons learnt from the implementation of an RFID-enabled healthcare management system in a medical organization". Journal of Medical Systems 35 (4): 657–69. doi:10.1007/s10916-009-9403-5.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar has been updated for clarity. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version — by design — lists them in order of appearance.
