Let’s consider using the building blocks system for healthcare systems, on a healthcare problem that wants to monitor patient vital signs similar to Chen et al. (2010).
- The purpose that the new data will serve: Most hospitals measure the following vitals for triaging patients: blood pressure and flow, core temperature, ECG, carbon dioxide concentration (Chen et al. 2010).
- Functions should it serve: gathering, storing, preprocessing, and processing the data. Chen et al. (2010) suggested that they should also perform a consistency check, aggregating and integrate the data.
- Which parts of the data are needed to serve these functions: all
- Tools needed: distributed database system, wireless network, parallel processing, graphical user interface for healthcare providers to understand the data, servers, subject matter experts to create upper limits and lower limits, classification algorithms that used machine learning
- Top level plan: The data will be collected from the vital sign sensors, streaming at various time intervals into a central hub that sends the data in packets over a wireless network into a server room. The server can divide the data into various distributed systems accordingly. A parallel processing program will be able to access the data per patient per window of time to conduct the needed functions and classifications to be able to provide triage warnings if the vitals hit any of the predetermined key performance indicators that require intervention by the subject matter experts. If a key performance indicator is sparked, send data to the healthcare provider’s device via a graphical user interface.
- Pivoting is bound to happen; the following can happen:
- Graphical user interface is not healthcare provider friendly
- Some of the sensors need to be able to throw a warning if they are going bad
- Subject matter experts may need to readjust the classification algorithm for better triaging
Thus, the above problem as discussed by Chen et al. (2010), could be broken apart to its building block components as addressed in Burkle et al. (2011). These components help to create a system to analyze this set of big health care data through analytics, via distributed systems and parallel processing as addressed by Services (2015) and Mirtaheri et al. (2008).
Draw on a large body of data to form a prediction or variable comparisons within the premise of big data.
Fayyad, Piatetsky-Shapiro, and Smyth (1996) defined that data analytics can be divided into descriptive and predictive analytics. Vardarlier and Silaharoglu (2016) agreed with Fayyad et al. (1996) division but added prescriptive analytics. Depending on the goal of diagnosing illnesses with the use of big data analytics should depend on the theory/division one should choose. Raghupathi & Raghupathi (2014), stated some common examples of big data in the healthcare field to be: personal medical records, radiology images, clinical trial data, 3D imaging, human genomic data, population genomic data, biometric sensor reading, x-ray films, scripts, and traditional paper files. Thus, the use of big data analytics to understand the 23 pairs of chromosomes that are the building blocks for people. Healthcare professionals are using the big data generated from our genomic code to help predict which illnesses a person could get (Services, 2013). Thus, using predictive analytics tools and algorithms like decision trees would be of some use. Another use of predictive analytics and machine learning can be applied to diagnosing an eye disease like diabetic retinopathy from an image by using classification algorithms (Goldbloom, 2016).
Examine the unique domain of health informatics and explain how big data analytics contributes to the detection of fraud and the diagnosis of illness.
A process mining framework for the detection of healthcare fraud and abuse case study (Yang & Hwang, 2006): Fraud exists in processing health insurance claims because there are more opportunities to commit fraud because there are more channels of communication: service providers, insurance agencies, and patients. Any one of these three people can commit fraud, and the highest chance of fraud occurs where service providers can do unnecessary procedures putting patients at risk. Thus this case study provided the framework on how to conduct automated fraud detection. The study collected data from 2543 gynecology patients from 2001-2002 from a hospital, where they filtered out noisy data, identified activities based on medical expertise, identified fraud in about 906.
Summarize one case study in detail related to big data analytics as it relates to organizational processes and topical research.
The use of Spark about the healthcare field case study by Pita et al. (2015): Data quality in healthcare data is poor and in particular that of the Brazilian Public Health System. Spark was used to help in data processing to improve quality through deterministic and probabilistic record linking within multiple databases. Record linking is a technique that uses common attributes across multiple databases and identifies a 1-to-1 match. Spark workflows were created to help do record linking by (1) analyzing all data in each database and common attributes with high probabilities of linkage; (2) pre-processing data where data is transformed, anonymization, and cleaned to a single format so that all the attributes can be compared to each other for a 1-to-1 match; (3) record linking based on deterministic and probabilistic algorithms; and (4) statistical analysis to evaluate the accuracy. Over 397M comparisons were made in 12 hours. They concluded that accuracy depends on the size of the data, where the bigger the data, the more accuracy in record linking.
- Burkle, T., Hain, T., Hossain, H., Dudeck, J., & Domann, E. (2001). Bioinformatics in medical practice: What is necessary for a hospital?. Studies in health technology and informatics, (2), 951-955.
- Chen, B., Varkey, J. P., Pompili, D., Li, J. K., & Marsic, I. (2010). Patient vital signs monitoring using wireless body area networks. In Bioengineering Conference, Proceedings of the 2010 IEEE 36th Annual Northeast (pp. 1-2). IEEE.
- Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37. Retrieved from: http://www.aaai.org/ojs/index.php/aimagazine/article/download/1230/1131/
- Goldbloom, A. (2016). The jobs we’ll lose to machines – and the ones we won’t. TED Talks. Retrieved from https://www.youtube.com/watch?v=gWmRkYsLzB4
- Mirtaheri, S. L., Khaneghah, E. M., Sharifi, M., & Azgomi, M. A. (2008). The influence of efficient message passing mechanisms on high performance distributed scientific computing. In Parallel and Distributed Processing with Applications, 2008. ISPA’08. International Symposium on (pp. 663-668). IEEE.
- Pita, R., Pinto, C., Melo, P., Silva, M., Barreto, M., & Rasella, D. (2015). A Spark-based Workflow for Probabilistic Record Linkage of Healthcare Data. In EDBT/ICDT Workshops (pp. 17-26).
- Raghupathi, W. Raghupathi, V. (2014). Big Data Analytics in healthcare: promise and potential. Heath Information Science and Systems. 2(3). Retrieved from http://hissjournal.biomedcentral.com/articles/10.1186/2047-2501-2-3
- Services, E. E. (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, 1st Edition. [Bookshelf Online].
- Vardarlier, P., & Silahtaroglu, G. (2016). Gossip management at universities using big data warehouse model integrated with a decision support system. International Journal of Research in Business and Social Science, 5(1), 1–14. Doi: http://doi.org/10.1108/ 17506200710779521
- Yang, W. S., & Hwang, S. Y. (2006). A process-mining framework for the detection of healthcare fraud and abuse.Expert Systems with Applications, 31(1), 56-68.