NIT6130 Complexities in EHR management

NIT6130 Complexities in EHR management

1.1 Broad Scan

In the broad scan, I have selected my topic through conducting in-depth research on internet through typing different key words. I have searched different database platform from IEEE, Google scholar, Argosy library database and RMIT library. I have found various journals regarding the selected issues and selected twenty from the overall twenty-onejournals.

1.1.1 Research Journal

1.1.2 Filling System 

1.1.3 Bibliographic file from broad scan 

Adler-Milstein, J., & Jha, A. K. (2017). HITECH Act drove large gains in hospital electronic health record adoption. Health Affairs36(8), 1416-1422.

Andrus, M. R., Forrester, J. B., Germain, K. E., & Eiland, L. S. (2015). Accuracy of pharmacy benefit manager medication formularies in an electronic health record system and the Epocrates mobile application. Journal of managed care & specialty pharmacy21(4), 281-286.

Arndt, B. G., Beasley, J. W., Watkinson, M. D., Temte, J. L., Tuan, W. J., Sinsky, C. A., & Gilchrist, V. J. (2017). Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. The Annals of Family Medicine15(5), 419-426.

Barak-Corren, Y., Castro, V. M., Javitt, S., Hoffnagle, A. G., Dai, Y., Perlis, R. H., … & Reis, B. Y. (2016). Predicting suicidal behavior from longitudinal electronic health records. American journal of psychiatry174(2), 154-162.

Bayer, R., Santelli, J., & Klitzman, R. (2015). New challenges for electronic health records: confidentiality and access to sensitive health information about parents and adolescents. Jama313(1), 29-30.

Castillo, E. G., Olfson, M., Pincus, H. A., Vawdrey, D., & Stroup, T. S. (2015). Electronic health records in mental health research: a framework for developing valid research methods. Psychiatric Services66(2), 193-196.

Chan, K. S., Kharrazi, H., Parikh, M. A., & Ford, E. W. (2016). Assessing electronic health record implementation challenges using item response theory. Am J Manag Care22(12), e409-e415.

Cifuentes, M., Davis, M., Fernald, D., Gunn, R., Dickinson, P., & Cohen, D. J. (2015). Electronic health record challenges, workarounds, and solutions observed in practices integrating behavioral health and primary care. The Journal of the American Board of Family Medicine28(Supplement 1), S63-S72.

Curtis, J. R., Sathitratanacheewin, S., Starks, H., Lee, R. Y., Kross, E. K., Downey, L., … & Lindvall, C. (2018). Using electronic health records for quality measurement and accountability in care of the seriously ill: Opportunities and challenges. Journal of palliative medicine21(S2), S-52.

Gellert, G. A., Ramirez, R., & Webster, S. L. (2015). The rise of the medical scribe industry: implications for the advancement of electronic health records. Jama313(13), 1315-1316.

Gephart, S., Carrington, J. M., & Finley, B. (2015). A systematic review of nurses’ experiences with unintended consequences when using the electronic health record. Nursing administration quarterly39(4), 345-356.

Kuhn, L., Reeves, K., Taylor, Y., Tapp, H., McWilliams, A., Gunter, A., … & Dulin, M. (2015). Planning for action: the impact of an asthma action plan decision support tool integrated into an electronic health record (EHR) at a large health care system. The Journal of the American Board of Family Medicine28(3), 382-393.

Mason, P., Mayer, R., Chien, W. W., & Monestime, J. P. (2017). Overcoming Barriers to Implementing Electronic Health Records in Rural Primary Care Clinics. The Qualitative Report22(11), 2943-2955.

Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics22(5), 1589-1604.

Sinard, J. H., Castellani, W. J., Wilkerson, M. L., & Henricks, W. H. (2015). Stand-alone laboratory information systems versus laboratory modules incorporated in the electronic health record. Archives of Pathology and Laboratory Medicine139(3), 311-318.

Wilkerson, M. L., Henricks, W. H., Castellani, W. J., Whitsitt, M. S., & Sinard, J. H. (2015). Management of laboratory data and information exchange in the electronic health record. Archives of Pathology and Laboratory Medicine139(3), 319-327.

Wu, P. Y., Cheng, C. W., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2017). –omic and electronic health record big data analytics for precision medicine. IEEE Transactions on Biomedical Engineering64(2), 263-273.

Xhafa, F., Li, J., Zhao, G., Li, J., Chen, X., & Wong, D. S. (2015). Designing cloud-based electronic health record system with attribute-based encryption. Multimedia Tools and Applications74(10), 3441-3458.

Yadav, P., Steinbach, M., Kumar, V., & Simon, G. (2018). Mining electronic health records (EHRs): a survey. ACM Computing Surveys (CSUR)50(6), 85.

Zahabi, M., Kaber, D. B., & Swangnetr, M. (2015). Usability and safety in electronic medical records interface design: a review of recent literature and guideline formulation. Human factors57(5), 805-834.

Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2017). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal11(1), 88-95.

NIT6130 Complexities in EHR management 1.2.2 Updated Bibliographic file from focused review 

Adler-Milstein, J., & Jha, A. K. (2017). HITECH Act drove large gains in hospital electronic health record adoption. Health Affairs36(8), 1416-1422.

Andrus, M. R., Forrester, J. B., Germain, K. E., & Eiland, L. S. (2015). Accuracy of pharmacy benefit manager medication formularies in an electronic health record system and the Epocrates mobile application. Journal of managed care & specialty pharmacy21(4), 281-286.

Arndt, B. G., Beasley, J. W., Watkinson, M. D., Temte, J. L., Tuan, W. J., Sinsky, C. A., & Gilchrist, V. J. (2017). Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. The Annals of Family Medicine15(5), 419-426.

Barak-Corren, Y., Castro, V. M., Javitt, S., Hoffnagle, A. G., Dai, Y., Perlis, R. H., … & Reis, B. Y. (2016). Predicting suicidal behavior from longitudinal electronic health records. American journal of psychiatry174(2), 154-162.

Bayer, R., Santelli, J., & Klitzman, R. (2015). New challenges for electronic health records: confidentiality and access to sensitive health information about parents and adolescents. Jama313(1), 29-30.

Castillo, E. G., Olfson, M., Pincus, H. A., Vawdrey, D., & Stroup, T. S. (2015). Electronic health records in mental health research: a framework for developing valid research methods. Psychiatric Services66(2), 193-196.

Chan, K. S., Kharrazi, H., Parikh, M. A., & Ford, E. W. (2016). Assessing electronic health record implementation challenges using item response theory. Am J Manag Care22(12), e409-e415.

Cifuentes, M., Davis, M., Fernald, D., Gunn, R., Dickinson, P., & Cohen, D. J. (2015). Electronic health record challenges, workarounds, and solutions observed in practices integrating behavioral health and primary care. The Journal of the American Board of Family Medicine28(Supplement 1), S63-S72.

Curtis, J. R., Sathitratanacheewin, S., Starks, H., Lee, R. Y., Kross, E. K., Downey, L., … & Lindvall, C. (2018). Using electronic health records for quality measurement and accountability in care of the seriously ill: Opportunities and challenges. Journal of palliative medicine21(S2), S-52.

Gellert, G. A., Ramirez, R., & Webster, S. L. (2015). The rise of the medical scribe industry: implications for the advancement of electronic health records. Jama313(13), 1315-1316.

Gephart, S., Carrington, J. M., & Finley, B. (2015). A systematic review of nurses’ experiences with unintended consequences when using the electronic health record. Nursing administration quarterly39(4), 345-356.

Kuhn, L., Reeves, K., Taylor, Y., Tapp, H., McWilliams, A., Gunter, A., … & Dulin, M. (2015). Planning for action: the impact of an asthma action plan decision support tool integrated into an electronic health record (EHR) at a large health care system. The Journal of the American Board of Family Medicine28(3), 382-393.

Mason, P., Mayer, R., Chien, W. W., & Monestime, J. P. (2017). Overcoming Barriers to Implementing Electronic Health Records in Rural Primary Care Clinics. The Qualitative Report22(11), 2943-2955.

Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics22(5), 1589-1604.

Sinard, J. H., Castellani, W. J., Wilkerson, M. L., & Henricks, W. H. (2015). Stand-alone laboratory information systems versus laboratory modules incorporated in the electronic health record. Archives of Pathology and Laboratory Medicine139(3), 311-318.

Wilkerson, M. L., Henricks, W. H., Castellani, W. J., Whitsitt, M. S., & Sinard, J. H. (2015). Management of laboratory data and information exchange in the electronic health record. Archives of Pathology and Laboratory Medicine139(3), 319-327.

Wu, P. Y., Cheng, C. W., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2017). –omic and electronic health record big data analytics for precision medicine. IEEE Transactions on Biomedical Engineering64(2), 263-273.

Xhafa, F., Li, J., Zhao, G., Li, J., Chen, X., & Wong, D. S. (2015). Designing cloud-based electronic health record system with attribute-based encryption. Multimedia Tools and Applications74(10), 3441-3458.

Yadav, P., Steinbach, M., Kumar, V., & Simon, G. (2018). Mining electronic health records (EHRs): a survey. ACM Computing Surveys (CSUR)50(6), 85.

Zahabi, M., Kaber, D. B., & Swangnetr, M. (2015). Usability and safety in electronic medical records interface design: a review of recent literature and guideline formulation. Human factors57(5), 805-834.

 1.3 Complexities in the EHR programming

In the current period, primary care physicians are spend nearly 2 hour on the electronic health record tasks. Due to increased demand of non face-to-face care, the technological advancement is visualized in the patient communication as well as assessment (Wilkerson et al., 2015). On the other hand the success of this record system is positive if the pathologists and laboratories can depict strong effort as well as initiative on the information management and accounting. In thi case, section 1.3.1 emphasizes the topic background through the support of systematic methodology and approaches. The section 1.3.2 presents the employed theoretical framework, which are use to sum up different field of concept as well as practical attributes. In the section 1.3.3, overview of the current technological system has been discussed for understanding the perspective of both users and the clients. Through these factors I have got an idea about the pros and cons to adopt the Electronic health record system in the healthcare organization.

1.3.1 Background

Due to rapid advancement over technology, wide adoption of electronic health record system is evident and it has maintained strong attribute towards the fast data accumulation. As per the opinion of Wu et al., (2017), voluminous complex data contains abundant information in case of medication and clinical assessment. In this case, big data analytics can extract such knowledge for improving the overall quality of healthcare. The EHR benefitshave been visualized in various healthcare services, where medical practitioners have gained positive functional achievement on clinical assessment and ongoing surveillance. In the year of 2009, the issues of technological advancement. Adler-Milstein & Jha (2017) have commented that short term acute care hospitals are not capable to operant different form of EHR in the healthcare setting. The organization soften not adopt such technologies due to lower capability on the financial status and the lack of employee knowledge, which are the main two negative consequences to adopt such system. If the employee have not proper understanding to operate such technologies, healthcare organizationcannotfoster positive outcome by means of quality care.

1.3.2 Theoretical framework and method

CAS theory or complex adaptive system theory has been applied to support the understanding of components of healthcare system. CAS principles have been applied to understand the unpredictable nature of policy development. Through application of this, the implementation of changes in the healthcare delivery system can be understood. Organizations often consider Temporal Abstraction Framework for applying the patterns of EHR data.

This theoretical framework has been used here to facilitate a structured algorithm towards the EHR system.

1.3.3 Overview of the current technological system

EHR system has been used in the current task as medical concept extraction, disease interference and patient trajectory modeling. Over the last few years, most of the technologies are HER based, where availability of support vector machines, logistic regression is evident. Due to rise in the popularity of deep learning approaches, the emergence of electronic health record system has been visualized. In this case, application of EHR data has yield better performance than the traditional method, where less time consuming process is the main positive point.

In the given figure, it has been visualized that the deep learning in the EHR process is gradually increasing from 2012 to 2017, where the organization have got strong positive attribute. In this case, positive consequences to implement this system are depicting standard representation, phenotyping, prediction and assessment. In the articles the researchers have applied both primary and the secondary research method to align both conceptual as well as practical procedures. In this case, ANOVA test has been applied for secondary research. Through proper data interpretation, the authors have displayed both positive and negative impact of technology in healthcare practice.

1.3.4 Discussion

Positive patient identification is the essential part of the care life cycle, where proper accountability can depict standard process on ordering, testing, billing, transmission and care attributes. Through application of the EHR system, the healthcare professionals can foster standard approach on clinical assessment. It has been visualized that patient identification of error rate has been increased, where positive implication of EHR process has been visualized. From this standpoint, the presented chart depicts that increase in EHR adoption is evident at all level.

1.4 Approach on EHR technology

The first area presents the positive point of HER adoption but still there is a gap about the emerging issues in the technological advancement. Therefore, the following paper presents the associated negative consequences on the adoption of EHR. The following section depicts the approach of EHR, its mechanism. The experimental process has been discussed to current operational challenges.

1.4.1Concept of EHR

Electronic health record system has been promoted as an opportunity to rationalize the healthcare services, where medical practitioners can get proper accountable dimension. EHR creates new reality in which patients can get greater access, which are facilitated by the internet portals. The medical societies are focused on the adolescent health strategy for supporting the confidential and standard care. Through application of the electronic health record system, both clients and the medical practitioners has get standard access to care and the associated facilities. Medical practitioners can get the real time data through using the electronic health record system data. In the cloud based medium, the healthcare professionals can secure the patient data for longer period. In this case, real time access, identification and interpretation can make EHR system useful in the healthcare process.

 1.4.2 Issues of EHR technology

However, EHR also poses several issues in the clinical management process. Evolving challenges in the EHR programming. As commented by (), EHR process has potential impact to improve the overall healthcare. The issue of financial initiatives has been visualized. The organizational employees have not proper understudying regarding the advancement of HER program. The employees are facing issue to operate to handle the HER programs (Wilkerson et al., 2015). They think that offline process was more suitable than this current technology. However, the EHR program is time consumable and easy access service at all level. However, due to lack of proper knowledge the issue of data loss can be major concern. Due to these factors the organizations are facing issues to adopt the HER in the current healthcare setting.

1.4.3 Conclusion

It can be deduced that due to increased demand of non face-to-face care, the technological advancement is visualized in the patient communication as well as assessment. It has been understood that voluminous complex data contains abundant information in case of medication and clinical assessment. In this case, big data analytics can extract such knowledge for improving the overall quality of healthcare. It is evident that EHR creates new reality in which patients can get greater access, which are facilitated by the internet portals. The medical societies are focused on the adolescent health strategy for supporting the confidential and standard care. It has been understood that CAS principles have been applied to understand the unpredictable nature of policy development. Through application of this, the implementation of changes in the healthcare delivery system can be understood. In the dataset, it has been visualized that deep learning in the EHR process is gradually increasing from 2012 to 2017, where the organization have got strong positive attribute.

 1.5 Final outline of literature review

Introduction

2.0 Complexities in the EHR programming

2.1 Background

2.2 Framework and Method

2.3 Overview of the technology

2.4 Discussion

  1. Approach on EHR technology

3.1 concept of EHR

3.2 Issues in EHR system

3.6 Conclusion

1.6 Introduction

In this review, I have selected the topic of complexities in the electronic health record system, which is now a severe concern to maintain standard quality in the healthcare process. In the current healthcare setting, the issues are evident due to lack of financial support, employee knowledge and proper initiatives. The literature review depicts the existing theoretical demonstration, which hasbeen practiced in the healthcare organization over last years. Through the support of literature review, the researchers will evaluate the current practical issues in regards to theoretical understanding.

References

Castillo, E. G., Olfson, M., Pincus, H. A., Vawdrey, D., & Stroup, T. S. (2015). Electronic health records in mental health research: a framework for developing valid research methods. Psychiatric Services66(2), 193-196.

Wilkerson, M. L., Henricks, W. H., Castellani, W. J., Whitsitt, M. S., & Sinard, J. H. (2015). Management of laboratory data and information exchange in the electronic health record. Archives of Pathology and Laboratory Medicine139(3), 319-327.

Wu, P. Y., Cheng, C. W., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2017). –omic and electronic health record big data analytics for precision medicine. IEEE Transactions on Biomedical Engineering64(2), 263-273.

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