Healthcare industry is continuously aggregating a large volume of data on patient diagnostics, treatment plans, payment and insurance coverage- something which has attracted attention of clinicians as well as scientists. However, despite established credibility over the utility of such data, the application of data on the practical front remains largely limited.
US healthcare expenses have increased by more than 100% between 2010 and 2015 (Islam, et al., 2018). Moreover, some of the inefficient and value-added tasks such as readmissions, inept use of antibiotics and fraud forms nearly 21%-47% of the entire expenditure. Research by Islam et al (2018) has found that close to 251,454 patients die due to medical error in the USA. Using better decision making through efficient use of available data can help in mitigating these challenges.
Solution through Data Analytics
In the current era, mobile communication systems, big data, Internet of Things (IoT) and wearable computing technologies are quickly being endorsed by healthcare sector (Ma, et al., 2018). Advanced data analytics tools can contribute a great deal in critical fields such as healthcare diagnostics.
Medical images are an important set of data usually utilized for diagnosis, therapy assessment and planning. The medical image data collected through techniques such as Computed tomography (CT), magnetic resonance imaging (MRI), X-ray, molecular imaging, ultrasound, photoacoustic imaging, fluoroscopy and positron emission tomography-computed tomography (PET-CT) ranges from few megabytes to hundreds of megabytes per study (Belle, et al., 2015). Such data needs high storage capacity and complex algorithms to support quicker decision making. In diseases such as cancer, the integration of multiple images from various sources and physiological data can enhance the accuracy of diagnosis and outcome prediction.
Case of AMIGO
Systems such as Advanced Multimodal Image-Guided Operating (AMIGO) suite
have angiographic X-ray system, MRI, 3D ultrasound, and PET/CT imaging
integrated at one place (Belle, et al., 2015). AMIGO has recorded
improvement in localization and targeting disease tissue among cancer patients.
Belle, A. et al., 2015. Big Data Analytics in Healthcare. BioMed Research International.
Islam, M. S. et al., 2018. A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining. Healthcare (Basel), VI(2), p. 54.
Ma, X. et al., 2018. Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing. Wireless Communications and Mobile Computing.
Categories: Data Analytics