Introduction
Electronic health record (EHR) is a technological advancement in the medical field, bearing real-time and patient-centered records accessible to authorized personnel within the medical fraternity (The Office of the National Coordinator for Health Information Technology, 2019). I chose EHR technology due to its numerous contributions to improving health quality. For example, EHR has dramatically improved health outcomes due to the readily available and accessible patients’ information on secure databases within healthcare facilities. Patient information is easily transferred across departments or facilities, facilitating care. EHR technology has resulted in three significant achievements in the medical sector: affordable care, efficiency in the treatment process, and patients’ data privacy. During my research on the technology, I utilized qualitative analysis of databases such as the HealthIT.gov database, which presented EHR’s meaning, implementation, benefits, and future in improving healthcare quality. I used search terms such as healthcare technology, electronic health records, patient data privacy, and health information technology.
Annotation Elements
Ehrenfeld, J. M., Gottlieb, K. G., Beach, L. B., Monahan, S. E., & Fabbri, D. (2019). Development of a natural language processing algorithm to identify and evaluate transgender patients in electronic health record systems. Ethnicity & Disease, 29(Suppl 2), 441. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6604788/pdf/ethndis-29-441.pdf
The source gives information on how to use EHR systems to assist transgender patients. Transgender patients are increasingly becoming vulnerable to healthcare services due to bias and discrimination. Consequently, this group of patients is limited to specific health services since they are considered minorities, which act as barriers to accessing healthcare—assisting such patients requires collecting relevant information on gender identity and sexual orientation. However, the article presents a significant challenge in collecting such data. Based on previous studies presented in the article, there have not been successful processes using the EHR to collect gender identity and sexual orientation information. However, the modern updated EHR systems provide for patient gender identity and sexual orientation data collection.
Consequently, transgender patients can enjoy health benefits without discrimination and bias, courtesy of EHR. Even though previous studies presented by the article shows only twenty-one cases of records of transgender information on the EHR, the medical fraternity in the healthcare sector can ensure full implementation of the transgender information collection program. Mandating the collection of information on gender identity and sexual orientation would help in solving the problem of discrimination and biases in the treatment of transgender patients.
Gamal, A., Barakat, S., & Rezk, A. (2021). Standardized electronic health record data modeling and persistence: A comparative review. Journal of Biomedical Informatics, 114, 103670.
The article addresses a significant challenge facing the EHR: the problem of handling and managing big data. The continuous use of EHR is progressively attracting numerous heterogeneous data. Handling such data creates risk factors as far as the use of the technology is concerned. EHR system is not owned by an individual and contains data from various patients. The data is shared across several systems, which causes integration problems that may confuse if such information is not handled with care. Interoperability is, therefore, required to enhance the quality, efficiency, and effectiveness of such data. The article suggests the most appropriate strategy to address the growing data due to the increasing use of EHR. With its numerous advantages to the health sector, EHR has presented massive data, which may require time and advanced technology to handle. However, the article gives a solution to the big data problem presented by the technology. Standardization of the data is a sure way, suggested in the article, for use to handle big data. Healthcare providers should, therefore, use interoperability as a standardized technique for organizing, retrieving, and analyzing the patients’ data without confusion or errors.
Momenipour, A., & Pennathur, P. R. (2019). Balancing documentation and direct patient care activities: A mature electronic health record system study. International Journal of Industrial Ergonomics, 72, 338-346.
The research article presents a study on the electronic health record system at Midwestern Academic Hospital, a medical facility that implemented the EHR eleven years ago. The findings show the strategies put in place by most US states to fully embrace a mature health information system for the facility care process. Challenges to the documentation process in healthcare facilities form a pivotal aspect addressed in the article. The findings are meant to help manage multiple documentation challenges facing healthcare facilities, not only in the US but across the globe. According to the report, medical error is still a significant issue in the US medical sector. However, the prevalence of the problem is linked to the inefficiency in accessing accurate patient data. The treatment process requires access to complete and precise patient information such as medical history, allergies, and drug use, which forms the basis of subjective evaluation during the treatment process. The inability to access complete and accurate patient information is a significant cause of errors in treatment. Documentation of patients’ data is critical in managing the issues surrounding the quality treatment process. Therefore, utilizing the article in addressing the technology in nursing would present insightful information on the need to implement the technology and adopt its full maturity in various medical facilities to avoid errors in treatment and to attain an overall healthcare quality improvement.
Thompson, M., Hill, B. L., Rakocz, N., Chiang, J. N., Geschwind, D., Sankararaman, S., & Halperin, E. (2022). Methylation risk scores are associated with a collection of phenotypes within electronic health record systems. NPJ Genomic Medicine, 7(1), 1-11.
The article presents an investigation of the potentiality of epigenetic information for improving phenotypic inference in combined biobank- EHR systems. DNA methylation involves adding a methyl group to the DNA structure, which can repress or inhibit transcription factors to the given DNA structure. However, the methylation process is affected by several factors, including genetic and environmental aspects. In DNA methylation, the genetic and ecological information recorded on the biobanks- EHR systems is necessary for designing the most pleasurable phenotypic traits during the process. Analysis of the effects of environment and genetic factors on an individual’s DNA is paramount before the methylation process. Methylation risk scores indicate a reduced rate when the information is collected from electronic health records. Risk reduction associated with the process, when used alongside EHR, is due to the availability of all vital patient information on easily accessible and analyzable patients’ databases. EHR is, therefore, a fundamental factor in improving phenotypic elements during DNA methylation. With all patients’ information, including genetic factors, medical history, allergic reactions, and environmental factors, scientists can control the methylation process to improve phenotypic inference during the DNA methylation process.
Summary of Recommendation
Even though EHR is faced with several problems, such as the big data and inadequacy in collecting transgender information, its contribution to healthcare improvement is commendable. The adoption of the technology has increased healthcare efficiency due to easily accessed patients’ data. The data are also transferrable upon demand to various healthcare facilities, which increases the efficiency and effectiveness of the treatment process. However, it is essential to address the shortcomings of the technology to improve the quality of the treatment process further. Standardization programs are highly recommended when dealing with big data from EHRs. The standardization would ensure the data is easily integrated and analyzed to avoid treatment errors. Besides, caregivers should mandate the collection of transgender data during the treatment process. Consequently, there would be minimal issues of discrimination and biases in the treatment of transgender patients.
References
Ehrenfeld, J. M., Gottlieb, K. G., Beach, L. B., Monahan, S. E., & Fabbri, D. (2019). Development of a natural language processing algorithm to identify and evaluate transgender patients in electronic health record systems. Ethnicity & Disease, 29(Suppl 2), 441. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6604788/pdf/ethndis-29-441.pdf
Gamal, A., Barakat, S., & Rezk, A. (2021). Standardized electronic health record data modeling and persistence: A comparative review. Journal of Biomedical Informatics, 114, 103670.
Momenipour, A., & Pennathur, P. R. (2019). Balancing documentation and direct patient care activities: A mature electronic health record system study. International Journal of Industrial Ergonomics, 72, 338-346.
The Office of the National Coordinator for Health Information Technology. (2019). HealthIT.gov. https://www.healthit.gov/faq/what-electronic-health-record-ehr
Thompson, M., Hill, B. L., Rakocz, N., Chiang, J. N., Geschwind, D., Sankararaman, S., & Halperin, E. (2022). Methylation risk scores are associated with a collection of phenotypes within electronic health record systems. NPJ Genomic Medicine, 7(1), 1-11.
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