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The healthcare industry collects a lot of information from their clients and different insurance firms; especially data that regulate ethical conduct in the healthcare system. Data mining would be increasingly useful to hospitals, especially since the data generated in healthcare transactions do not rely on traditional methods of data management for reasons such as security and data complexity. Data mining provides solutions that ensure effective treatment, patient safety, proper healthcare management, efficient client relationships, and data security. Healthcare systems mainly use Knowledge Discovery in Databases as part of data mining such that they are able to extract useful information relevant to medical care(Wang, Li, &Perrizo, 2015). It mainly adopts the criterion of using mathematical analysis to find existing trends in data patterns. Using traditional algorithms is pointless mainly because the data to be processed is often complex.
The healthcare industry uses data mining in patient safety by checking trends of different diseases and establishing diagnoses before the disease outbreak. It is useful in evidence based practices, providing effective measures in medical research, medical tech advancements and in the pharmaceutical industry. A practical example of the use of data mining is in detecting fraud and abuse in healthcare(Abouelmehdi, Beni-Hessane, &Khaloufi, 2018). To properly evaluate such conditions, data mining often identifies different patterns and detects anomalies in medical claims made by healthcare practitioners. Using this method makes it easy to detect insurance fraud and even increased medical charges on different patients.
In hospital management, data mining helps in determining the cases of disease outbreak within their area, evaluate high-risk patients, and develop suitable treatment schemes that would tackle issues such as hospital admissions and insurance claims(Fox, Aggarwal, Whelton, &Johnson, 2018). Due to its flexibility, data mining benefits all stakeholders in healthcare; patients, insurance companies, stakeholders, medical practitioners, patients, and non-profit organizations. It would assist in reducing cost and increasing on efficiency, especially in managing patient data and personal information, while maintaining high standards of quality healthcare.
References
Abouelmehdi, K., Beni-Hessane, A., &Khaloufi, H. (2018). Big healthcare data: preserving security and privacy. Journal of Big Data, 5(1), 1. Retrieved from https://doi.org/10.1186/s40537-017-0110-7
Fox, F., Aggarwal, V. R., Whelton, H., & Johnson, O. (2018).A data quality framework for process mining of electronic health record data.In 2018 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 12-21).IEEE. Retrieved from DOI: 10.1109/ICHI.2018.00009
Wang, B., Li, R., &Perrizo, W. (Eds.).(2015). Big data analytics in bioinformatics and healthcare.Medical Information Science Reference. Retrieved from DOI: 10.4018/978-1-4666-6611-5
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