Unit 4 Assignment – Assessment Paper
Due Jun 6 by 11:59pm
- Describe and evaluate the differences between evidence based practice and research.
- Describe the importance and application of health care information, data mining, and importance to application in patient care outcomes
- Discuss how data mining and interpretation influences case management and utilization
- Describe participation in managed care and the importance of quality care initiatives and performance indicators (remember to use AHRQ as a resource)
- 1000-1500 words not including the cover page and references (total, not for each topic)
- Follow the APA 7th edition for references and citations
- Include a minimum of 6 scholarly references (does not include text or websites)
- Demonstrate analysis, evaluation and synthesis of information
Assessment Paper due Sunday at 11:59 PM.
The Differences between Evidence-Based Practice (EBP) and Research.
The differences between evidence-based practice and research originate from definitions. There are numerous definitions of research that have been provided by scholars over time Carnwell (2016), defines research as a logical and planned process undertaken to analyze relationships between events or predict outcomes. On the other hand, Fitzpatrick (2016) defines research as the utilization of scientific methods and systematic inquiry to solve problems and provide answers. Some scholars have also defined research as looking for information to prove or disprove a given hypothesis or to contribute to understanding the world (Carnwell, 2016).
According to Fitzpatrick (2016), evidence-based practice is the explicit, positive, and utilization of current evidence to make informed decisions related to the care of specific patients. The utilization of evidence to make decisions in contrast to coming up with evidence in the research process is one of the fundamental differences between research and evidence-based practice. On the other hand, Li et al. (2019) suggest that evidence-based practice is taking account of all current and valid information when making patient care decisions. Therefore, the best evidence varies between patients, with the best evidence being the one that is most valued and relevant to a given patient. Finally, Carnwell (2016) refers to evidence-based practice as the need to collect, integrate, and interpret applicable valid and essential research derived and clinically observed evidence.
The significant differences between research and evidence-based practice, therefore, include research being well planned and systematic investigation while evidence-based practice is systematic search and appraisal of best evidence (Fitzpatrick, (2016), Research and evidence-based practice also differ in that research specifies the problem to be investigated evidence-based practice utilizes already collected evidence mainly from research processes to make clinical decisions. On the other hand, research involves a statement of predetermined outcomes, while evidence-based practice entails taking account of patients’ individual needs and research-based evidence. Finally, the research contributes to the understanding of the world. Evidence-based practice enhances changes in practice (Carnwell, 2016).
Importance and Application of HealthCare Information, Data Mining
As a result of the introduction of health information technology which includes technology and infrastructure that analyses, records, and shares patient health data, healthcare information has proliferated in healthcare settings. Various healthcare technologies such as electronic health records and personal health tools, including applications and smart devices, provide a lot of healthcare information that needs to be analyzed and understood (Alotaibi & Federico, 2017). Healthcare information is therefore crucial in that it helps improve the quality of care provided to patients and achieve health equity. Healthcare information technology allows the recording of patient data, with such data being important in health care delivery and analysis by different agencies to implement policies to curb the spread of diseases and improve quality of service (Alotaibi & Federico, 2017). Health information, therefore, enhances the quality of healthcare delivery and decreases medical errors. This helps to boost patient safety. Health information also strengthens the interaction between health caregivers and patients. Accurate health information helps healthcare providers to have a precise history of patients, which increases the accuracy of treatments and prevents errors such as prescription and medication errors. The benefits of health information include improving different aspects of patient care such as effectiveness, safety education, patient-centeredness, communication efficiency, timeliness, and equity (Alotaibi & Federico, 2017).
On the other hand, the proliferation of healthcare information technology such as electronic health records has resulted in the availability and increased access to large amounts of patient data. Health care providers utilize data mining to optimize the quality and efficiency of their services. In healthcare, data mining is therefore applied in areas such as customer relationship management, predictive medicine, management of healthcare, detection of abuse and fraud, and measuring the effectiveness of given treatments (Yang et al., 2020). Data mining provides useful and understandable patterns through data analysis, with such patents being important in predicting information or industry trends (Yang et al., 2020). Data mining can be applied to improve patient outcomes by helping to measure treatment effectiveness. Data mining helps to compare and contrast symptoms, causes, and treatment therapies to determine an ideal course of action for certain conditions and illnesses. Data mining can therefore help in the standardization of treatment therapies for diseases and making the diagnosis and treatment of such illnesses easier and quicker (Yang et al., 2020). On the other hand, data mining can be used to detect abuse and fraud in healthcare facilities. This entails establishing standard patterns of medical claims by clinics and flagging any unusual patterns of such claims. Therefore, data mining can be used to identify inappropriate prescription referrals, fraudulent medical claims, and insurance fraud (Yang et al., 2020).