Technological advancements in healthcare have revolutionized patient care approaches intending to ensure patient safety and enhance patient experiences. Data and information management are essential in supporting decision-making due to the enormous information sharing involved in patient care. The massive amount of information that healthcare facilities handle daily necessitates an increase in the speed of data generation. Data collected and stored is essential for medical education and medicine, and data analysis can facilitate the discovery of new values such as evidence-based medicine. Case and utilization management has also influenced decision-making on patient care activities, enhancing the discovery of cost-saving approaches. Hence, associating the benefits influence of data mining on case management and utilization is resourceful in developing patient care plans.
Differences between Evidence-Based Practice and Research
Discovering new and innovative methods of enhancing patient management enhances patient management, facilitating better patient outcome achievement. Effective innovation in medicine follows effective implementation to facilitate significant outcome achievement (Mackey & Bassendowski, 2017). Research involves a planned and logical process to analyze relationships between events or outcome predictions while utilizing orderly scientific methods. The significant differentiating factor of evidence-based practice from research is its utilization of evidence for decision-making rather than deriving evidence through logical processes (Mackey & Bassendowski, 2017). Hence, evidence-based practice’s main aim is to develop and execute innovations or inventions that can enhance goal attainment.
Best evidence associated with evidence-based practice develops from information and practices that are valid and relevant to patient care. The research aims at conducting investigations with the significant objective of summing up the existing evidence without making further recommendations to improve patient care. However, evidence-based practice searches evaluate and analyze the best evidence provided by research (Grove & Gray, 2018). Research results provide a significant difference as its outcome aligns with the objective at the beginning of the study. The endpoint is essential in providing suggestions and recommendations for future research without changing practice (Mackey & Bassendowski, 2017). Evidence-based practice makes clinical decisions that aim to change or improve practice using valid and relevant evidence. Research does not focus on current evidence and relies on previous research evidence to develop a justifiable framework for investigations (Grove & Gray, 2018). The evidence-based practice relies on current and other research evidence, including individual patient needs and preferences. Hence, aligning research and evidence-based research is crucial in developing relevant and appropriate patient-centred care.
Importance and Application of Health Care Information, Data Mining and Importance in Patient Care Outcomes
Big data has immensely contributed to patient self-management, especially for patients with chronic illnesses using mobile devices to monitor their progress and vital signs. Health care information facilitates significant parties to deliver the best outcomes to patients. Data mining has contributed a great deal to cancer treatment by analyzing various components of the cancer patients’ situations and the essence of identifying their vulnerabilities (Parva et al., 2017). In cancer treatment, scientists monitor the big data derived from genetic analyses and develop a forecasting system based on examinations and imaging precisions (Parva et al., 2017). This aspect will prevent an opportunity for healthcare professionals to identify aspects that can lead to cancer development and provide remedial actions to prevent exacerbations. Hence, data mining is resourceful in detecting associations between factors leading to cancer development.
Data mining has led to advances in understanding disease mechanisms and body interactions. According to Parva et al. (2017), data mining can successfully predict neurodegenerative disease studies through genetic algorithms from healthy individuals, facilitating early preventive measures. Experimental data from various studies obtained through data mining can be crucial in identifying viable factors that characterize depressed patients and developing appropriate interventions in such cases. Laboratory investigations as health care information can be essential through data mining techniques by providing associations with developing depression risks among individuals (Parva et al., 2017). Hence, this approach can enhance patient outcomes through effective decision-making approaches obtained from data mining systems.
Influence of Data Mining Interpretation on Case Management and Utilization
Case management is in developed countries on various continents due to the healthcare costs arising from individuals with complex health needs and frequent users of healthcare services, including individuals with chronic illnesses, multimorbidity, and psychiatric comorbidities. Therefore, case management is a collaborative method in evaluating, planning, facilitating, and executing care coordination to meet complex health needs and health system goal achievement. It involves overwhelming data handling activities necessitating data mining and interpretation to facilitate seamless processes. Big data systems are crucial in analyzing unique patients’ medical backgrounds, needs, and preferences and developing medical reports from data mining to develop rational decisions and evidence-best practices suitable for patients (Cozzoli et al., 2021). Hence, data mining is resourceful in medical background checks on individualized patients.
Utilization management is a crucial aspect of patient care and a cost-saving approach to realizing best practices that suits patients. Utilization management involves various methods utilized by health care benefit users to manage spending by conducting case assessments to influence patient care decisions. Data mining has influenced patient care decision-making by managers by providing information related to the supply chain, such as pharmaceuticals or reasonable costs associated with various procedures, to facilitate cost containment, which is crucial in utilization management (Cozzoli et al., 2021). Data mining and interpretation provide direction to management strategies to enhance healthcare services supplies in utilization management, influencing patient-care decision-making. Additionally, data mining and interpretation facilitate modifying treatment techniques to enhance quality standards, informing decision-making to improve case management and utilization management (Cozzoli et al., 2021). Hence, data mining and interpretation play a significant role in enhancing case management and utilization management through data analysis and decision-making.
Managed Care and Importance of Quality Care Initiatives and Performance Indicators
Healthcare service utilization in America leads to overspending on patient care and services, leading to the development of managed care. Managed care involves insurance measures to incorporate healthcare service financing to facilitate cost containment while offering appropriate patient services (Giardino & De Jesus, 2020). Individuals should take the initiative in managed care as it provides various alternatives and pools with a network of providers. Some provide discounted rates to offer services to a large group of patients, reducing healthcare spending. Managed care provides sustainable and substantial cost-saving mechanisms that reduce hospitalization and mortality and lengthen hospital stay rates (Giardino & De Jesus, 2020). Hence, healthcare professionals should advocate for communities and populations to adopt managed care to improve healthcare quality.
Healthcare facilities should provide high-quality patient care services to improve their performance indicators and quality care initiatives. The significant performance indicators in healthcare include inpatient, prevention, patient safety, and pediatric care (AHRQ, 2022). The performance indicators, through various checks, enhance outpatient service access to communities and identify unmet needs within communities (AHRQ, 2022). Additionally, performance indicators can be resourceful in assessing potential issues associated with quality improvement and comparative public reporting that facilitates improved patient care activities (AHRQ, 2022). Hence, healthcare facilities must include performance indicators for quality improvement.
Conclusion
Therefore, the evidence-based practice should focus on methods that facilitate cost savings and quality improvement. The result of evidence-based practice is to facilitate practice change, while research focuses on providing recommendations and suggestions for further studies but does not authenticate clinical changes. Additionally, research utilizes previously researched evidence to support its framework, while evidence-based practice utilizes current evidence, including patient data, to influence practice changes. Data mining enhances patient care outcomes through various approaches, including improving cancer management by focusing on data such as laboratory investigations. Additionally, data mining allows healthcare facilities to organize information, analyze medical information backgrounds for different patients, and align their needs and preferences. Utilization management and data mining coexist by influencing decision-making through modifying treatment techniques and providing information on the supply chain. Hence, healthcare facilities should diversify their portfolio and influence communities to appreciate managed care to facilitate cost savings.
References
AHRQ. (2022). Quality improvement and monitoring at your fingertips. https://qualityindicators.ahrq.gov/
Cozzoli, N., Salvatore, F. P., Faccilongo, N., & Milone, M. (2021). How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review. BMC Health Services Research, 22. https://doi.org/10.1186/s12913-022-08167-z
Giardino, A. P., & De Jesus, O. (2020). Managed Care. In StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK564410/
Grove, S. K., & Gray, J. R. (2018). Understanding nursing research: Building an evidence-based practice (7th ed.). Elsevier Health Sciences.
Hudon, C., Chouinard, M. C., Bisson, M., Brousselle, A., Lambert, M., Danish, A., … & Sabourin, V. (2022). Case Management Programs for Improving Integrated Care for Frequent Users of Healthcare Services: An Implementation Analysis. International Journal of Integrated Care, 22(1). https://www.ijic.org/articles/10.5334/ijic.5652/
Mackey, A., & Bassendowski, S. (2017). The history of evidence-based practice in nursing education and practice. Journal of Professional Nursing, 33(1), 51-55. https://doi.org/10.1016/j.profnurs.2016.05.009
Parva, E., Boostani, R., Ghahramani, Z., & Paydar, S. (2017). The Necessity of Data Mining in Clinical Emergency Medicine; A Narrative Review of the Current Literature. Bulletin of Emergency & Trauma, 5(2), 90-95. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5406178/
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