READ CAREFULLY ORIGINAL WORK DUE JULY 16

USW1.45935.202070 - NURS-6051N-49,Transforming Nursing & HC.2020 Summer Qtr 06/01-08/23-PT27

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Monica Castelao on Mon, Jul 27 2020, 10:31 PM 42% highest match Submission ID: 897a4db8-2a9d-47d6-911a-70f9552bbac3

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7 Role of Electronic Health Record in Managing Cardiovascular Diseases Monica Castelao Walden University Role of Electronic Health Record in Managing Cardiovascular Diseases Cardiovascular diseases claim many lives around the world every year. While healthcare professionals are using different approaches to managing and treating cardiovascular diseases, the role of information technology is worth much consideration. Today, many healthcare facilities use electronic health records in understanding and managing health and diseases. They are using electronic data for research and other activities beyond their main purpose. The aim is to promote evidence-based practice founded on data in healthcare setting. A major development worth noting is the use of electronic health records in order to explore translational avenues from discovery phase to implementation phase. Past studies have explored the role that electronic health records play in supporting and treating cardiovascular patients. Thus, this annotated bibliography discusses the role of electronic health record in managing cardiovascular diseases. A summary of the Selected Studies The role that electronic health records plays in prediction and management cost of healthcare is worth significant consideration. 1 Asaria et al. Suspected Entry: 82% match Uploaded - Week8castelaom.extention.docx Asaria et al Source - Another student's paper According to Asaria et al (2016) explored how electronic health records helps in determining lifetime costs and health outcomes of persons suffering from stable coronary artery disease based on their future risk for cardiovascular cases. The researchers also focused on cost effectiveness of treatments aimed at persons with stable coronary artery disease. For this study, the Asaria et al used approximately 95,000 participants with stable coronary artery disease living in England between 2001 and 2010. They identified the research population in electronic health record sources and applied Markov modelling to estimate lifetime costs and the quality adjusted life years arranged in accordance with risk of cardiovascular disease. For the lowest risk persons with stable coronary artery disease, the remaining lifetime cost of healthcare was62,210 Sterling Pounds and quality adjusted life years was 12 years. For the highest risk patient, the lifetime costs were 35,549 Sterling Pounds and quality adjusted life years were 2.9 years. Asaria et al found that a new treatment method with risk minimization of 20 percent for cardiovascular diseases and other heart related diseases would be cost effective if priced below 646 Sterling Pounds for high-risk individuals and 72 Sterling Pounds for low risk individuals. 1 The research findings show that hospitals can use electronic health records to estimate the lifetime cost of healthcare as well as health outcomes of individuals with stable coronary artery disease. Suspected Entry: 66% match Uploaded - Week8castelaom.extention.docx The research findings show that hospitals can use electronic health records to estimate the lifetime cost of healthcare as well as health outcomes of individuals with stable coronary artery disease Source - Another student's paper (2018), electronic health records can be used to estimate lifetime healthcare costs and outcomes of patients diagnosed with stable coronary artery disease Numerous electronic health records data are currently expanding research studies in healthcare settings. Hemmingway et al. 2 (2018) decided to assess potential challenges and benefits of big data across early and late stages of translational cardiovascular disease research. Suspected Entry: 73% match Uploaded - Week8castelaom.extention.docx (2018) decided to assess potential challenges and benefits of big data across early and late stages of translational cardiovascular disease research Source - Another student's paper We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research They identified various concerns of electronic health record data for early and late phases of translational cardiovascular research including data quality, knowledge about data existence, and legal plus ethical framework for their use. Other concerns include data sharing, enhancement of public trust, equipping scientific workforce, and development of disease definition standards. Nonetheless, big data provides various opportunities such asricher health and disease profiles, better understanding of disease progression, and enhanced understanding of health in healthcare systems and population, as well as determination of new treatment methods. Hemmingway et al.also identified several exemplars such as correlation between exome sequence and electronic health record and the need to integrate precision medicine into electronic health record for effective pre-emptive pharmacogenomics. The findings demonstrate the impact of electronic health data in understanding the nature of cardiovascular care and supporting research studies in healthcare settings. For instance, providers can use big data to understand disease causation and take appropriate actionable analytics for improvement of health and healthcare. Healthcare organizations have, over the last decade, embraced importance of electronic health record for enhanced healthcare delivery. 3 In this article, Kalra et al analyzed the American College of Cardiology’s PINNACLE and India Quality Improvement Program data to determine association between electronic health record and India’s quality of cardiovascular disease healthcare outcome. Suspected Entry: 67% match Uploaded - Week8castelaom.extention.docx In this article, Kalra et al analyzed the American College of Cardiology’s PINNACLE and India Quality Improvement Program data to determine association between electronic health record and India’s quality of cardiovascular disease healthcare outcome Source - Another student's paper Data were analyzed from the American College of Cardiology's PINNACLE India Quality Improvement Program to evaluate the association between EHR use and quality of cardiovascular disease care in India (Ankur Kalra, Deepak L To do so, they collected data on performance measures for individuals with coronary artery diseases and other heart related complications from 17 participants in India Quality Improvement Program. More than 19,300individuals had coronary artery disease, more than 9,300 patients had heart failure, and more than 1,120 patients had atrial fibrillation. The researchers found that documentation of comorbidity burden in patients with coronary artery disease was significantly lower in hospitals with electronic health record hypertension, diabetes, and hyperlipidemia. 4 Hospitals with electronic health records also had higher documentation of treatment prescription in coronary artery disease patients. Suspected Entry: 63% match Uploaded - Week8castelaom.extention.docx Hospitals with electronic health records also had higher documentation of treatment prescription in coronary artery disease patients Source - Another student's paper Electronic health records can also be used to predict costs of patients with coronary artery disease Moreover, healthcare facilities with electronic health records had higher documentation of patients with coronary disease receiving different types of drugs such as ACE-i or ARBs, beta blockers+ACE-i or ARBs, and beta blockers. Among patients with atrial fibrillation, healthcare practices with electronic health records had higher documentation of oral anticoagulation prescription. The results indicate that hospitals with electronic health records better documentation of guideline-directed treatment in heart related complications than healthcare organizations without electronic health records. The research findings demonstrate the significance of electronic health records in management of cardiovascular diseases. Today’s healthcare systems recognize role of electronic health data in predicting events and determining disease onset. 5 Ng et al. Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx Ng et al Source - Another student's paper Ng et al (2016) examined the compromise achieved between healthcare data requirements and electronic health record utility. Specifically, they used a longitudinal study of electronic health records data in order to examine performance of machine learning technologies that seek to detect pre-diagnosis heart failure. They looked into model performance visa viedata requirements by focusing on prediction window length, data diversity, and data quantity. They also observed window length and data density. The researchers used at least 1680 incident heart failures and more than 13,500 sex, clinic, plus age-category controls for modeling. 5 Ng et al. Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx Ng et al Source - Another student's paper Ng et al found that model performance improves with decrease in length of prediction window and increase in observation window length. This happens when length of prediction window is less than 2 years and observation window length is greater than 2 years. They researchers also found that while many healthcare organizations used diverse data, a combination of diagnosis, treatment order, as well as hospitalization data proved more effective. They also found that data were limited to persons with cardiovascular disease who had more than 10 phone or face-to-face interactions in 2 years. 6 The research findings provide possible guidelines for the type and amount of data needed to use electronic health record data in training disease onset predictive technologies. Suspected Entry: 63% match Uploaded - Week8castelaom.extention.docx The research findings provide possible guidelines for the type and amount of data needed to use electronic health record data in training disease onset predictive technologies Source - Another student's paper The observed findings suggest possible guidelines for the minimum amount and type of data needed to train effective disease onset predictive models using longitudinal electronic health records data (Steinhubl et al., 2016) Rogers (2018) assessed differences in relations between main functions of electronic health records on provision of recommended preventive health services for cardiovascular disease. Among the core functions included public health, care coordination, and quality improvement, as well as patient engagement. The researcher focused on the differences in primary care visits for adults at risk of cardiovascular disease with private insurance and Medicaid. Rogers found that healthcare providers caring for persons with private insurance were more likely to use electronic health record technology compared to primary care providers caring for patients under Medicaid. Medicaid patients were less likely to receive blood pressure screening compared to Private insurance patients. The researcher also found significant correlations between cardiovascular disease preventive services and public health management. There was also a correlation between cardiovascular disease preventive services and patient coordination and care coordination. Research findings show significant gap in how primary care providers manage cardiovascular disease between persons with private insurance and Medicaid beneficiaries. In addition, the findings show that electronic health record can affect cardiovascular disease preventive service provision at healthcare organizations. References 4 Asaria, M., Walker, S., Palmer, S., Gale, C. Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx Asaria, M., Walker, S., Palmer, S., Gale, C Source - Another student's paper Asaria, M., Walker, S., Palmer, S., Gale, C P., Shah, A. Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx P., Shah, A Source - Another student's paper P., Shah, A D.,. .. & Sculpher, M. (2016). 4 Using electronic health records to predict costs and outcomes in stable coronary artery disease. Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx Using electronic health records to predict costs and outcomes in stable coronary artery disease Source - Another student's paper Using electronic health records to predict costs and outcomes in stable coronary artery disease 1 Heart, 102,755-762. Suspected Entry: 86% match Uploaded - Week8castelaom.extention.docx Heart, 102,755-762 Source - Another student's paper Heart, 102(10), 755-762 2 Hemmingway, H., Asselbergs, F. Suspected Entry: 70% match Uploaded - Week8castelaom.extention.docx Hemmingway, H., Asselbergs, F Source - Another student's paper Hemingway, H., Asselbergs, F 7 W., Danesh, J., Dobson, R., Maniadakis, N.,. Suspected Entry: 90% match Uploaded - Week8castelaom.extention.docx W., Danesh, J., Dobson, R., Maniadakis, N., Source - Another student's paper W., danesh, j., dobson, r., maniadakis, n., maggioni, a., .. & Denaxas, S. (2018). 2 Big data from electronic health records for early and late translational cardiovascular research: Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx Big data from electronic health records for early and late translational cardiovascular research Source - Another student's paper Big data from electronic health records for early and late translational cardiovascular research Challenges and potential. Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx Challenges and potential Source - Another student's paper challenges and potential European Heart Journal, 39, 1481-1495. Suspected Entry: 90% match Uploaded - Week8castelaom.extention.docx European Heart Journal, 39, 1481-1495 Source - Another student's paper European Heart Journal, 39(16), 1481-1495 Kalra, A., Bhatt, D. 8 L., Wei, J., Anderson, K. Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx L., Wei, J., Anderson, K Source - Another student's paper L., Wei, J., Anderson, K L., Ryokowski, S., Kerkar, P.G.,. Suspected Entry: 65% match Uploaded - Week8castelaom.extention.docx L., Ryokowski, S., Kerkar, P.G., Source - Another student's paper L., Rykowski, S., Kerkar, P . & Virani, S. S. (2018). 9 Electronic health record and outpatient cardiovascular disease care delivery: Suspected Entry: 87% match Uploaded - Week8castelaom.extention.docx Electronic health record and outpatient cardiovascular disease care delivery Source - Another student's paper Electronic health records and outpatient cardiovascular disease care delivery Insights from the American College of Cardiology’s PINNACLE India quality improvement program (PIQIP). Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx Insights from the American College of Cardiology’s PINNACLE India quality improvement program (PIQIP) Source - Another student's paper Insights from the American College of Cardiology’s PINNACLE India Quality Improvement Program (PIQIP) 3 Indian Heart Journal 70 -750-752 Suspected Entry: 90% match Uploaded - Week8castelaom.extention.docx Indian Heart Journal 70 -750-752 Source - Another student's paper Indian Heart Journal, 70(5), 750–752 2 Ng, K., Steinhubl, S. Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx Ng, K., Steinhubl, S Source - Another student's paper Ng, K., Steinhubl, S R., deFilippi, C., Dey, S., & Stewart, W. Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx R., deFilippi, C., Dey, S., & Stewart, W Source - Another student's paper R., deFilippi, C., Dey, S., & Stewart, W F. (2016). 2 Early detection of heart failure using electronic health records: Suspected Entry: 100% match Uploaded - Week8castelaom.extention.docx Early detection of heart failure using electronic health records Source - Another student's paper Early detection of heart failure using electronic health records Practical implications for time before diagnosis, data diversity, data quantity, and data density. Suspected Entry: 99% match Uploaded - Week8castelaom.extention.docx Practical implications for time before diagnosis, data diversity, data quantity, and data density Source - Another student's paper practical implications for time before diagnosis, data diversity, data quantity, and data density Circulation: 2 Cardiovascular Quality & Outcomes, 9, 649-658. Suspected Entry: 89% match Uploaded - Week8castelaom.extention.docx Cardiovascular Quality & Outcomes, 9, 649-658 Source - Another student's paper Cardiovascular Quality and Outcomes, 9(6), 649-658 Rogers, C. K. (2018). 1 Impact of core electronic health record functionalities on cardiovascular disease preventive health services for underserved patients. Suspected Entry: 99% match Uploaded - Week8castelaom.extention.docx Impact of core electronic health record functionalities on cardiovascular disease preventive health services for underserved patients Source - Another student's paper Impact of Core Electronic Health Record Functionalities on Cardiovascular Disease Preventive Health Services for Underserved Patients Journal of Medicine and Disease Prevention, 4(4), 1-10. Suspected Entry: 80% match Uploaded - Week8castelaom.extention.docx Journal of Medicine and Disease Prevention, 4(4), 1-10 Source - Another student's paper Journal Of Family Medicine And Disease Prevention, 4(4) DOI: 1 10.23937/2469-5793/1510092 Suspected Entry: 75% match Uploaded - Week8castelaom.extention.docx 10.23937/2469-5793/1510092 Source - Another student's paper https://doi.org/10.23937/2469-5793/1510092