Role of Electronic Health Record in Managing Cardiovascular Diseases
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 rolethat electronic health records plays in prediction and management cost of healthcare is worth significant consideration. Asaria et al. (2016) exploredhow 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 atpersons with stable coronary artery disease.For this study, the Asaria et al usedapproximately 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. The research findingsshow 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.
Numerous electronic health records data are currently expanding research studies in healthcare settings. Hemmingway et al. (2018) decided to assess potential challenges and benefits of big data across early and late stages of translational cardiovascular disease research. They identified variousconcerns 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 ascorrelation 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. 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. 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 inhospitals with electronic health record hypertension, diabetes, and hyperlipidemia.
Hospitals with electronic health records also had higher documentation of treatment prescription in coronary artery disease patients. 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 higherdocumentation of oral anticoagulation prescription. The results indicate that hospitals with electronic health records better documentation of guideline-directed treatment in heart related complicationsthanhealthcare 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. 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.
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 yearsand 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. 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.
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 weremore 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 toPrivate insurance patients. The researcher also found significant correlations between cardiovascular disease preventive services and public health management. There was also a correlation betweencardiovascular 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 serviceprovision at healthcare organizations.
Asaria, M., Walker, S., Palmer, S., Gale, C. P., Shah, A. D.,……… & Sculpher, M. (2016). Using electronic health records to predict costs and outcomes in stable coronary artery disease. Heart, 102,755-762.
Hemmingway, H., Asselbergs, F. W., Danesh, J., Dobson, R., Maniadakis, N., ……….. & Denaxas, S. (2018). Big data from electronic health records for early and late translational cardiovascular research: Challenges and potential. European Heart Journal, 39, 1481-1495.
Kalra, A., Bhatt, D. L., Wei, J., Anderson, K. L., Ryokowski, S., Kerkar, P.G.,…… & Virani, S. S. (2018). Electronic health record and outpatient cardiovascular disease care delivery: Insights from the American College of Cardiology’s PINNACLE India quality improvement program (PIQIP). Indian Heart Journal 70 -750-752
Ng, K., Steinhubl, S. R., deFilippi, C., Dey, S., & Stewart, W. F. (2016). Early detection of heart failure using electronic health records: Practical implications for time before diagnosis, data diversity, data quantity, and data density. Circulation: Cardiovascular Quality & Outcomes, 9, 649-658.
Rogers, C. K. (2018). 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. DOI: 10.23937/2469-5793/1510092