This paper looks at the use of predictive analytics in medicine and algorithms to assist physicians to come up with more precise and accurate diagnoses in health care facilities, especially hospitals.
Historically, the healthcare sector has generated massive data amounts focused on recordkeeping, regulatory requirements, compliance and patient care. Driven by potential and obligatory requirements to bolster the healthcare quality while reducing costs, the large data quantities have the capability of backing up various healthcares and medical functions including support of the clinical decision.
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Predictive analytics can take advantage of the available evidence to make better-informed clinical decisions. By synthesis and analysis of the big data, healthcare providers, as well as other stakeholders, can get insights from revealed patterns and trends to create a more insightful and thorough diagnosis and treatment resulting in better quality care with lower costs and better results.
Healthcare providers like physicians can use clinical data from large data of patient information to comprehend histories of patients and anticipate future outcomes. Close analysis of the best treatment can ensure that healthcare providers make more informed decisions on plans of treatment, hence, reducing patient readmissions and complications (Elton and Ural, 2014).
In instances such as when a patient with chest pain goes to the ER, it is hard to establish whether the patient needs hospitalization. If doctors could answer the patients inquiries and ones condition into a system having an accurate and tested algorithm, then it would be possible to examine the likelihood of sending the patient home safely, thus, aiding their clinical judgments. In the instance a patient has a genome marked for an early appearance of a condition such as Alzheimers disease that researchers have determined using predictive analysis, then the physician is aware of the conditions crucial for treating such early patients of Alzheimer (Winters-Miner, 2014).
Consequently, the physician can engage the patient in apps on exercise, brain games and good nutrition on his smartphone where the physician can atomically upload to the portal of the patient. Memory tests can be regularly administered and entered into an EMR (Electronic medical record) linked to the patients portal. The patient can add weekly data on his portal to track his diet, time, sleep, types of exercises including other variables the physician wishes to monitor.
According to Raghupathi and Raghupathi (2014), estimates show that large data analytics can result in savings of over $300 billion for the U.S healthcare with two-thirds of that amount creating a reduction of roughly 8% in the healthcare expenditures of the nation. Challenges encountered include veracity issues, creating methodologies, avoiding errors and use of a friendly interface. On the hand, applications of real-time-data can assist to reduce the morbidity and mortality of patients (McDonald, 2017) as well as avert hospital outbreaks. The field of big data analytics providing insight from massive data is a reassuring ground for giving insights to healthcare providers, and bolstering outcomes while minimizing costs but faces setbacks which need to be overcome.
Elton, J., & Ural, A. (2014). Predictive Medicine Depends on Analytics. Harvard Business Review. Retrieved 16 September 2017, from https://hbr.org/2014/10/predictive-medicine-depends-on-analytics.
McDonald, C. (2017). Five big data trends in healthcare. IT Pro Portal. Retrieved 16 September 2017, from http://www.itproportal.com/features/five-big-data-trends-in-healthcare/
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 3.
Winters-Miner, L. A. (2014). Seven ways predictive analytics can improve healthcare.
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