Rashmeet Toor
Abstract
The key mission of Healthcare industry is improving lives through better healthcare solutions. Technical innovations in the last decade have led to solutions that are safe, cost effective, high-quality and easily accessible. A wide variety of computational techniques, storage techniques, softwares and tools are already shaping the future of healthcare. In this paper we have systematically reviewed the emerging trends of Information Technology (IT) in healthcare. Further, this paper elaborates on the impact of healthcare data, technological transformations and tools which will eventually merge and culminate into user-centric healthcare in near future. A total of 108 papers were analyzed, out of which 40 papers were identified to be relevant and further we classified 19 papers into four broad categories according to the technologies used. This paper also reveals issues in the current approaches and suggests possible future outcomes which will help researchers to gain ideas for further research.
- Afgan, E., Baker, D., Chilton, J., Coraor, N., Taylor J., and Galaxy Team. 2014. Galaxy cluster to cloud-genomics at scale. In Proceedings of the 9th Gateway Computing Environments Workshop, IEEE Press. DOI= 10.1109/GCE.2014.13 Google Scholar
Digital Library
- Ahmed, K., Abdullah-Al-Emran, A. A. E., Jesmin, T., Mukti, R. F., Rahman M., and Ahmed F. 2013. Early detection of lung cancer risk using data mining. Asian Pacific Journal of Cancer Prevention 14, 1, 595--598. DOI= http://dx.doi.org/10.7314/APJCP.2013.14.1.595.Google ScholarCross Ref
- Baral, C., Gonzalez, G., Gitter, A., Teegarden, C., Zeigler A., and Joshi-Topé, G. 2007. CBioC: beyond a prototype for collaborative annotation of molecular interactions from the literature. ComputSyst Bioinformatics Conf. 6, 381--4.Google Scholar
- Brohee, S., and Van Helden J. 2006. Evaluation of clustering algorithms for protein-protein interaction networks BMC bioinformatics 7, 1, 1. DOI= 10.1186/1471-2105-7-488Google Scholar
- Carson, M. B., and Lu, H. 2015. Network-based prediction and knowledge mining of disease genes. BMC medical genomics 8, 2, S9. DOI= 10.1186/1755-8794-8-S2-S9Google Scholar
- Castillo-Davis, C. I., and Hartl, D. L. 2003. GeneMerge---post-genomic analysis, data mining, and hypothesis testing Bioinformatics 19, 7, 891--892. DOI= 10.1093/bioinformatics/btg114Google Scholar
- Chen, Y. C., Ke, W. C., and Chiu, H. W. 2014. Risk classification of cancer survival using ANN with gene expression data from multiple laboratories. Computers in biology and medicine 48, 1--7. DOI= 10.1016/j.compbiomed.2014.02.006Google Scholar
- Deepika, T., and Porkodi, R. 2015. A Survey on Microarray Gene Expression Data sets in Clustering and Visualization Plots. International Journal of Emerging Research in Management and Technology 4, 3, 56--66.Google Scholar
- Fahim, M., Fatima, I., Lee, S., and Lee, Y. K. 2012. Daily life activity tracking application for smart homes using android smartphone. In Proceedings of 14th International Conference on Advanced Communication Technology. ICACT '12, IEEE.Google Scholar
- Freudenberg, J., and Propping, P. 2002. A similarity-based method for genome-wide prediction of disease-relevant human genes. Bioinformatics, 18, 2, S110--S115. DOI= 10.1093/bioinformatics/18.suppl_2.S110Google ScholarCross Ref
- Fu, Li M., and Youn, E S. 2003. Improving reliability of gene selection from microarray functional genomics data. IEEE Transactions on Information Technology in Biomedicine 7, 3, 191--196. DOI= 10.1109/TITB.2003.816558 Google ScholarDigital Library
- Guo, X., Gao, L., Wei, C., Yang, X., Zhao, Y., and Dong, A. 2011. A computational method based on the integration of heterogeneous networks for predicting disease-gene associations. PloS one 6, 9, e24171. DOI= http://dx.doi.org/10.1371/journal.pone.0024171Google ScholarCross Ref
- Hans A., and Kalra, S. 2014. Comparitive analysis of various cloud based biomedcial services. 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom). DOI= 10.1109/MedCom.2014.7006038Google ScholarCross Ref
- Leroy, H., and Auffray, C. 2013. Participatory medicine: a driving force for revolutionizing healthcare. Genome medicine 5, 12, 1--4. DOI= 10.1186/gm514Google Scholar
- Huang M., Zhu X., Hao, Y., Payan, D. G., Qu, K., and Li, M. 2004. Discovering patterns to extract protein--protein interactions from full texts. Bioinformatics 20, 18, 3604--3612. DOI= 10.1093/bioinformatics/bth451 Google ScholarDigital Library
- Juen, J., Cheng, Q., and Schatz, B. 2014. Towards a natural walking monitor for pulmonary patients using simple smart phones. In Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. DOI= http://dx.doi.org/10.1145/2506583.2512362 Google ScholarDigital Library
- Jung, K., Grade, M., Gaedcke, J., Jo, P., Opitz, L., and Becker, H. 2010. A new sensitivity-preferred strategy to build prediction rules for therapy response of cancer patients using gene expression data. Computer methods and programs in biomedicine 100, 2, 132--139. DOI= http://dx.doi.org/10.1016/j.cmpb.2010.03.016 Google ScholarDigital Library
- Karimi, S., Wang, C., Metke-Jimenez, A., Gaire, R., and Paris, C. 2015. Text and data mining techniques in adverse drug reaction detection. ACM Computing Surveys (CSUR), 47, 4, 56. DOI= http://dx.doi.org/10.1145/0000000.0000000 Google ScholarDigital Library
- Koufi, V., Malamateniou, F., and Vassilacopoulos, G. 2012. An android-enabled mobile framework for ubiquitous access to cloud emergency medical services. In IEEE Second Symposium on Network Cloud Computing and Applications (NCCA). DOI= 10.1109/NCCA.2012.30 Google ScholarDigital Library
- Kutlu M., and Agrawal, G. 2014. Cluster-based SNP calling on large-scale genome sequencing data. In 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). DOI= 10.1109/CCGrid.2014.111Google Scholar
- Ma, J., Peng, C., and Chen, Q. 2014. Health Information Exchange for Home-Based Chronic Disease Self-Management--A Hybrid Cloud Approach. In 5th IEEE International Conference Digital Home (ICDH). DOI= 10.1109/ICDH.2014.54 Google ScholarDigital Library
- Meireles, A., Figueiredo, L., Lopes, L. S., and Almeida, A. 2014. Portable decision support system for heart failure detection and medical diagnosis. In Proceedings of the 18th ACM International Database Engineering & Applications Symposium. DOI= 10.1145/2628194.2628204 Google ScholarDigital Library
- Mork, J., Peters, L., Jimeno-Yepes, A., Aronson, A. R., Bodenreider, O. MetaMap in the CALBC Workshop II.Google Scholar
- Mukherjee, S., Dolui, K., and Datta, S. K. 2014. Patient health management system using e-health monitoring architecture. In IEEE International Advance Computing Conference (IACC). DOI= 10.1109/IAdCC.2014.6779357Google Scholar
- Özgür, A., Vu, T., Erkan, G., Radev, D. R. 2008. Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics, 24, 13, i277--i285. DOI= 10.1093/bioinformatics/btn182 Google ScholarDigital Library
- Pal, S. K., Bandyopadhyay, S., and Ray, S. S. 2006. Evolutionary computation in bioinformatics: A review. In IEEE transactions on Systems, man, and cybernetics, Part c: Applications and reviews 36, 5, 601--615. DOI= 10.1109/TSMCC.2005.855515 Google ScholarDigital Library
- Parthibanand, L., and Subramanian, R. 2008. Intelligent heart disease prediction system using CANFIS and genetic algorithm. International Journal of Biological, Biomedical and Medical Sciences 3, 3. DOI=10.1.1.451.9421Google Scholar
- Reddy, B. E., Kumar, T. V. S., and Ramu, G. 2012. An efficient cloud framework for health care monitoring system. In IEEE International Symposium on Cloud and Services Computing (ISCOS). DOI= 10.1109/ISCOS.2012.11 Google ScholarDigital Library
- Shah, S. C., and Kusiak, A. 2004. Data mining and genetic algorithm based gene/SNP selection. Artificial intelligence in medicine 31, 3, 183--196. DOI= 10.1016/j.artmed.2004.04.002 Google ScholarDigital Library
- Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., and Wang, J. T. 2003.Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13, 11, 2498--2504. DOI= 10.1101/gr.1239303Google Scholar
- Sheng, J., Li, F., and Wong, S. T. C. 2015. Optimal drug prediction from personal genomics profiles. In IEEE Journal of Biomedical and Health Informatics 19, 4, 1264--127. DOI: 10.1109/JBHI.2015.2412522Google ScholarCross Ref
- Sirota, M., Dudley, J. T., Kim, J., Chiang A. P., and Morgan, A. A. 2011. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Science translational medicine 3, 96, 96ra77. DOI: 10.1126/scitranslmed.3001318Google Scholar
- Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P. L., and Ideker, T. 2011. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 3, 431--432. DOI= 10.1093/bioinformatics/btq675 Google ScholarDigital Library
- Soni, S. R., Khuntetaand, A., Gupta., M. 2011. A review on intelligent methods used in medicine and life science. In Proceedings of the ACM International Conference & Workshop on Emerging Trends in Technology. DOI= 10.1145/1980022.1980173 Google ScholarDigital Library
- Tong, W., Harris, S. C., Fang, H., Shi, L., and Perkins, R. 2007. An integrated bioinformatics infrastructure essential for advancing pharmacogenomics and personalized medicine in the context of the FDA's Critical Path Initiative. Drug Discovery Today: Technologies 4, 1, 3--8. DOI= 10.1016/j.ddtec.2007.10.008Google ScholarCross Ref
- Venkatraman, S., Bala, H., Venkatesh, V., and Bates, J. 2008 "Six strategies for electronic medical records systems," Communications of the ACM 51, 11, 140--144. DOI= 10.1145/1400214.1400243 Google ScholarDigital Library
- Yadav, V. P., and Kumari, M. 2014. Name Entity Conflict Detection in Biomedical Text Data Based on Probabilistic Topic Models. In Proceedings of the ACM International Conference on Information and Communication Technology for Competitive Strategies. DOI= 10.1145/2677855.2677916 Google ScholarDigital Library
- Bhattacharyya, M. 2015. Disease dietomics. XRDS: Crossroads, The ACM Magazine for Students 21, 4, 38--44. DOI= 10.1145/2788508 Google ScholarDigital Library
- Razavian, N. 2015. Advancing the frontier of data-driven healthcare. XRDS: Crossroads, The ACM Magazine for Students 21, 4, 34--37. DOI= 10.1145/2788506 Google ScholarDigital Library
- Kaur P. D., and Chana. I. 2014. Cloud based intelligent system for delivering health care as a service. Computer methods and programs in biomedicine 113, 1, 346--359. DOI= 10.1016/j.cmpb.2013.09.013 Google ScholarDigital Library
Index Terms
- Application of IT in healthcare: a systematic review
Recommendations
Data Mining in Healthcare and Biomedicine: A Survey of the Literature
As a new concept that emerged in the middle of 1990's, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare ...
Healthcare IT
Adopting information technology for healthcare continues to present new opportunities and challenges as we move into an era of universally accessible electronic medical records and telemedicine. Efforts at healthcare reform have created new incentives ...
Mobile healthcare application with EMR interoperability for diabetes patients
With digitalization of medical information and rapid distribution of smart devices, currently, healthcare service is actively planned and developed based on smart devices. By 2015, 500 million smartphone users are expected to use a mobile health ...
Reviews
Lalit P Saxena
Information technology (IT) is expanding its scope from communications to other fields, including engineering, the environment, healthcare, and so on. Healthcare is an emerging field for the development of medical applications using IT computational techniques. This paper reviews IT applications in healthcare. The authors present a comparison of current and future trends in health monitoring by doctors and patients. They gather healthcare data under genomic, proteomic, drug-related, and clinical data. The authors further highlight the use of various technologies or tools in medical applications, including (1) data mining, (2) network analysis and similarity-based measures, (3) text mining, and (4) cloud-based services. They focus on NCBI, Google Scholar, IEEE Xplore, ACM Digital Library, and XRDS: Crossroads , the ACM Magazine for students, as sources of knowledge in their study. According to the authors, the survey comprises 19 research papers distributed into the four areas mentioned above. They provide tables listing the publicly available databases, the kind of data (however, Table 7 is missing in the paper), and type, purpose, and special features of some popular tools. This paper is an interesting read for those who are working in the area of IT application development in healthcare. The tabular representation of the common issues and future outcomes of the presented technologies in healthcare applications makes it a worthwhile read. Online Computing Reviews Service
Waldemar W. Koczkodaj
Toor and Chana present a cogent overview of emerging trends in information technology in the broader healthcare sector, distilled from 108 papers that were analyzed and ultimately classified into four broad categories according to the technologies used. Given the staggering number of patients who die yearly due to medical errors (reported by the authors as 98,000 in 1995), the incredible costs of fixing these errors (reported as $29 billion), and the fact that three of out four errors could have been eliminated if better information systems would have been used to make required information readily available, the importance of accurate and reliable IT systems in the healthcare sector cannot be overstated. The introduction provides an overview of the history of electronic health record systems (EHRs), but unfortunately glosses over the many technical issues related to integration and standards, not to mention cost, that invariably slows the adoption of EHRs, especially within smaller healthcare centers. Clinical decision support systems (CDSS) are referenced, particularly as they arise out of the implementation of EHRs. It should be noted that the review by Toor and Chana aims at summarizing the numerous fields of IT providing medical solutions and thus leans more toward an exposé of CDSS style solutions. The research questions and motivations of the authors are covered in sufficient detail, as are their search methodologies. The results provide interesting insight into the emerging trends in healthcare information technology research as they relate to decision support systems for a period covering 2002 to 2015. Toor and Chana classify medical data into four broad categories (genomic, proteomic, drugs, and clinical data), which are leveraged via the use of four technologies (network analysis, data mining, text mining, and cloud-based services). The information is high level, and the resulting sections limit their discourses to summary-level information. Of particular value is a set of tables that provide cross-references between the tools and techniques and the papers the authors reviewed. Overall, this paper provides a decent introduction to a number of important emerging trends in medical data-focused information technology. The paper provides a good entry point for researchers interested in pursuing and implementing clinical decision support systems in the healthcare IT field. Online Computing Reviews Service
Access critical reviews of Computing literature here
Become a reviewer for Computing Reviews.
Comments