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| 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.
Some previous works have also tackled the design of intelligent healthcare management systems. Recently Jalal et al.[5] have proposed an intelligent, depth video-based human activity recognition system to track elderly patients that could be used as part of a healthcare management and monitoring system. However, the paper does not explore this integration. Also, Ghamdi et al.[6] have proposed an ontology-based system for prediction of patients’ readmission within 30 days so that those readmissions can be prevented.
Regarding the impact of data in a healthcare management system, the importance of data-driven approaches has been addressed by Bossen et al..[7] Roberts et al.[8] have explored how to design healthcare management systems using a design thinking framework. Basole et al.[9] propose a web-based game using organizational simulation for healthcare management. Zeng et al.[10] have proposed an enhanced VIKOR method that can be used as a decision support tool in healthcare management contexts. A relevant work from Mohapatra[11] explores how a hospital information system is used for healthcare management, improving the KPIs; and a pilot has been conducted in Kalinga hospital (India), turning out to be beneficial for all stakeholders.
Some works have also explored how to increase patients’ satisfaction. For example, Fortenberry and McGoldrick[12] suggest improving the patient experience via internal marketing efforts, while Minniti et al.[13] propose a model in which patient feedback is processed in real time, driving rapid cycle improvement.
To place this work into its context, what we have developed is a data-driven intelligent healthcare management system. Because of the volume and velocity of big data, we have used a big data architecture based on the one proposed by Baldominos et al.[14], but updating the tools to use Apache Spark for the sake of efficiency. Also, a pilot has been conducted to evaluate the performance of the proposed system.
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.
- ↑ Jalal, A.; Kamal, S.; Kim, D. (2017). "A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems". International Journal of Interactive Multimedia and Artificial Intelligence 4 (4): 54–62. doi:10.9781/ijimai.2017.447.
- ↑ Ghamdi, H.A.; Alshammari, R.; Razzak, M.I. (2016). "An ontology-based system to predict hospital readmission within 30 days". International Journal of Healthcare Management 9 (4): 236–244. doi:10.1080/20479700.2016.1139768.
- ↑ Bossen, C.; Danholt, P.; Ubbesen, M.B. et al. (2016). "Challenges of Data-driven Healthcare Management: New Skills and Work". 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing: 5. http://pure.au.dk/portal/da/publications/challenges-of-datadriven-healthcare-management-new-skills-and-work(fd56833b-db7b-44ed-b4fd-15882b382271).html.
- ↑ Roberts, J.P.; Fisher, T.R.; Trowbridge, M.J.; Bent, C. (2016). "A design thinking framework for healthcare management and innovation". Healthcare 4 (1): 11–14. doi:10.1016/j.hjdsi.2015.12.002. PMID 27001093.
- ↑ Basole, R.C.; Bodner, D.A.; Rouse, W.B. (2013). "Healthcare management through organizational simulation". Decision Support Systems 55 (2): 552–563. doi:10.1016/j.dss.2012.10.012.
- ↑ Zeng, Q.L.; Li, D.D.; Yang, Y.B. (2013). "VIKOR method with enhanced accuracy for multiple criteria decision making in healthcare management". Journal of Medical Systems 37 (2): 9908. doi:10.1007/s10916-012-9908-1. PMID 23377778.
- ↑ Mohapatra, S. (2015). "Using integrated information system for patient benefits: A case study in India". International Journal of Healthcare Management 8 (4): 262–71. doi:10.1179/2047971915Y.0000000007.
- ↑ Fortenberry Jr., J.L. (2015). "Internal marketing: A pathway for healthcare facilities to improve the patient experience". International Journal of Healthcare Management 9 (1): 28–33. doi:10.1179/2047971915Y.0000000014.
- ↑ Minniti, M.J.; Blue, T.R.; Freed, D.; Ballen, S. (2016). "Patient-Interactive Healthcare Management, a Model for Achieving Patient Experience Excellence". Healthcare Information Management Systems. Springer. pp. 257–281. doi:10.1007/978-3-319-20765-0_16. ISBN 9783319207650.
- ↑ Baldominos, A.; Albacete, E.; Saez, Y.; Isasi, P. (2014). "A scalable machine learning online service for big data real-time analysis". 2014 IEEE Symposium on Computational Intelligence in Big Data. doi:10.1109/CIBD.2014.7011537.
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.
