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Gelila TilahunYesterdayAug 7 at 12:32pmManage Discussion EntryHello class and professor,A data mining approach identifies patterns in data by using computational methods and large, complex data sets. The use of data mining in healthcare has significant implications since enormous amounts of electronic health record data can be mined to answer questions regarding patient care delivery and outcomes (Berger & Berge, 2004). Based on the complexity of healthcare informatics, it appears that data mining has been growing rapidly in nursing over the past few years and will be a key tool in the future to generate nursing knowledge (Xiao et al., 2019). Natural language processing, machine learning, and artificial intelligence are all common approaches to data mining in nursing (Berger & Berger, 2004).The use of data mining may improve care quality and patient outcomes by generating new information and understanding relationships between healthcare variables that cannot be identified any other way due to data complexity. For example, researchers have applied machine learning, a form of data mining, to social media posts to identify the risk of suicide (Coppersmith et al., 2018). Data mining has been applied to electronic health records in the US Department of Veteran Affairs to identify patients at risk for suicide (Coppersmith et al., 2018). If predictive models could be developed from information inputs (e.g., social media content, electronic health record text), it might be possible to flag patients who are at risk and intervene, such as what is being explored for suicide. Data mining is becoming increasingly popular in healthcare, if not essential. The enormous amounts of data generated by healthcare EDI transactions cannot be processed and analyzed using traditional methods because of the complexity and volume of the data. Data mining in healthcare is used mainly for predicting various diseases, diagnosing, and advising doctors in making clinical decisions. But, the potential of data mining is much more significant – it can provide question-based answers, anomaly-based discoveries, more informed decisions, probability measures, predictive modeling, and decision support. For the purpose of evidence-based practice and clinical reasoning, data mining may also be used to generate new knowledge from large or complex data sets. In the face of large amounts of health data, providers may find it difficult to sort through the volume of information in order to identify what is most important in the patient’s medical history. Data mining tools and applications can assist clinicians in making sense of information and making informed clinical decisions.References:Berger, A. M., & Berger, C. R. (2004). Data mining as a tool for research and knowledge development in nursing. CIN: Computers, Informatics, Nursing, 22(3), 123-131.Coppersmith, G., Leary, R., Crutchley, P., & Fine, A. (2018).Natural language processing of social media as screening for suicide risk. Biomedical informatics insights, 10, 1178222618792860.Xiao, Q., Wang, J., Wang, Y., & Wu, Y. (2019). Data Mining in Nursing: A Bibliometric Analysis (1990–2017). In MEDINFO 2019: Health and Wellbeing e-Networks for All(pp. 1616-1617). IOS Press. 

Grace ViloriaYesterdayAug 7 at 4:28pmManage Discussion EntryWhat Is data mining? Discuss how EHR is related to data mining. What is the potential of healthcare data mining? How can it benefit or improve patient outcomes? Finally, explain why knowledge work and data mining are important for clinical reasoning and evidence-based practice.An EHR is an electronic version of a patient’s medical history that is maintained by the provider over time and may include all of the key administrative clinical data relevant to that person’s care under a specific provider. A patient’s EHR includes demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data, and radiology reports. The EHR can also directly or indirectly assist other care-related activities via various interfaces, including evidence-based decision support, quality monitoring, and outcomes reporting. EHR is said to minimize medical errors through delivering improved accuracy and clarity of medical records and making health information available which in turn avoids duplication of tests, thus reducing delays in treatment plans (Centers for Medicare & Medicaid Services, 2023). Data mining as an analytic process is aimed to extract meaningful facts, patterns, and trends from huge amounts of data utilizing techniques such as clustering, classification, association, and regression. Prediction is the end goal of data mining. Data mining solutions can benefit all stakeholders in the healthcare industry. It can assist healthcare insurers in detecting fraud and abuse, clinicians in identifying successful treatments and best practices, and patients in receiving better and more economical healthcare services. Traditional approaches cannot process or interpret the massive amounts of data created by healthcare transactions because they are too complicated and extensive. Data mining provides the methodology and technology to turn large amounts of data into meaningful information for healthcare decision-making. With today’s healthcare system using EHR, the huge amount of medical data containing pertinent patient information like results of diagnostic tests, clinical pieces of information such as blood pressure, blood sugar levels, cholesterol levels, or other blood works including physician interpretations, are used in data mining. Knowledge and regularities can be extracted from the data using mining techniques. The obtained knowledge can then be applied to medical data to improve working efficiency and assist medical practitioners in making decisions. This opens up limitless options for symptom trend detection, earlier detection of sickness, and DNA trend analysis, which can enhance healthcare efficiency (Ekwonwune et al., 2022) An example that I could think of in my current workplace is when the system automatically generates an order upon completing the ault admission history. The software embedded in our system uses the patient’s medical history which lessens the amount of time to be spent in checking previous information manually. If the data that we input includes symptoms of stroke, elevated LDL, positive diagnostics, and medical risk factors, our system automatically orders neuro checks and NIH, suggestions to start Aspirin and statin, and so forth. Data mining generates ideas about patterns and relationships. These patterns and links must then be interpreted and evaluated before they can be labeled knowledge. It is considered the computer process of examining data from many viewpoints, dimensions, and angles and summarizing it into relevant information (Ekwonwune et al., 2022). Clinical reasoning and EBPs rely on analyzed data before they can be implemented. The process they utilize depends mainly on the analyzed facts and data that they collect. Thus, knowledge work and data mining play a vital role in EPB.References:Centers for Medicare & Medicaid Services. (2023, 02 13). Electronic Health Records. CMS.gov. https://www.cms.gov/medicare/e-health/ehealthrecor…Ekwonwune, E. N., Ubochi, C. I., & Duroha, A. E. (2022, September). Data Mining as a Technique for Healthcare Approach. Scientific Research An Academic Publisher, 15(9). https://www.scirp.org/journal/paperinformation.asp…