Advantages Of Electronic Health Record (EHR) System And Enterprise Information Architecture

Discussion

Major issues have cropped up in the health care system because of the latest adversities. Due to the impending risks from certain diseases and demonstration of workers, these issues have grown. Moreover, the shortage of health care workers have also elevated these issues. Most of them are worried about taking care of the people in the cities more than sharing their expertise with the people living in the rural areas. Due to these impending threats and challenges, the government is forced to invest in the healthcare systems and ICT or Information and Communication Technology techniques (Adenuga, Kekwaletswe and Coleman 2015). But still the implementation of the new technology was not a successful one. This led to the creation of EHR system or Electronic Health Record system where the health records of different individuals were stored and used until a new set of technologies were devised (Wang, Kung and Byrd 2018).

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In the following assignment, the EIA or Enterprise Information Architecture is analysed to understand how the health care data are stored to be used in the health care business. The report will further discuss the implementation and benefits of the EIA RA technology along with a diagram. The report will also include the Information Management and Integration System and the challenges it creates for data integration and management. 

Enterprise Information Architecture Reference Architecture

The EIA RA model can be divided into four classifications:-

  • Zachman architecture model
  • Department of defence architecture model
  • Federal enterprise architecture model
  • The orient group model

The EIA architecture technology has the advantage of saving the patient data in the framework database so that it can be used in the future (America C. o. 2009). The mentioned architecture also has the added advantage of increasing the efficiency of the database information storage in the database management system (Viceconti, Hunter and Hose 2015). The data can be also used as a reference medium to enhance the decision making aspect of the organization. The EIA RA system also allow the organization to make proper management strategy that will help the organization to manage costs regarding the data. The Enterprise Information Architecture also allows proper management of employees within the company that allows the company to maintain a high turnover rate of employees.

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The next benefit enjoyed by the organization due to the Enterprise Information Architecture is that the return of investment in implementing the architecture comes with high efficiency rates, if the technology is implemented properly (Smith et al. 2015). Moreover, the EIA RA technology also has an added advantage of enhancing the communication methodology of the organisation by having an infrastructural communication system (Birkhead, Klompas and Shah 2015). This helps the organization to maintain transparency of information among its employees which results in efficient communication between the employees irrespective of the hierarchy that is maintained in the organization (Wager, Lee and Glaser 2017).

Enterprise Information Architecture Reference Architecture

For gathering information and providing it to the organization, the EIA or Enterprise Information Architecture is used. The bottom up approach of technical strategy is used in this aspect. This allow the organization to maintain, use and create the data for the database of the organization (Brennan 2017). The Enterprise Infrastructure architecture checks the source of the information that is used by the organization. The source is used as an authentication medium for the information.

Benefit 1

The first benefit of EIA RA or Enterprise Information Architecture Reference Architecture includes improved data quality. The EIA RA helps to maintain consistency of data and improves the accuracy of information that is provided to the customers both outside and inside the organization. The enhanced data quality is provided by eliminating the data’s program specific view of the consolidated data sources. Also, the data redundancy and improper data location are reduced due to the EIA RA system. The EIA RA system also helps to improve data accuracy by following the business rules associated with aggregating and linking data. Moreover, the system checks the user needs to devise schedules for refreshing the published data.

Benefit 2

The second benefit is that the system provides for the organization is that it protects the data of the clients and keeps it safe from being accessed by imposters, from being lost and being modified (White, Dudley-Brown and Terhaar 2016). This reflects on the decisive decisions taken by the organization. The investment on the technology allows the clients to gain a goodwill on the organization knowing that the data that they are providing to the organization will be kept secure that raises the loyalty base of the organization (Dinev et al. 2016).  This in turn helps the organization to give a goodwill from the clients which in turns makes the system procedure of the organization more efficient. The database of a particular disease can be also maintained with this system that helps in the efficient working of the organization.

Benefit 3

The third benefit provided by the new implemented architecture is that the storing of data in the database allows the data to be used in future technologies as an initial reference (Sagha Zadeh, Xuan and Shepley 2016). It can be used as a tracking point for any medical issues that has occurred. 

Disparate Nature of the Data

The nature of data that is taken in the healthcare management system is complex with respect to the data characteristics and data taxonomy. The data collection and system integration is dependent on the disparate nature of data for maintaining the healthcare data analytics (Ginter 2018). The data is normally collected from several sources to determine its authentication but several healthcare providers disagree with this proposition as they believe such a procedure is not always conducted. The data storage and collection process is increasingly prone to miscalculations, should be clean and accurate which makes this process very intricate (Crowley, Hinchliffe and McDonald 2017). A number of systems are used in a healthcare organization where several data collection methods are used. As the data is disparate in nature, the use of multiple systems is justifiable in a single institute. A single device cannot be used for integration as well as collection of data in the institute (Zhang et al. 2017). The strategy of storing such a huge amount of data in a single go is also advantageous for the institute. In a study, it was found that the patient and the EHR data matched by 23% only which shows that such a disagreement of data is common in this scenario (Hitz et al. 2015). This is mainly caused due to the disparate nature of the data.

Benefits of Implementing The EHR System

Structured and Unstructured Data

 The EHR system that is implemented in the health care institutes acts just like big data in the healthcare sector.  The data is integrated and analysed to find a cure for an existing solution (Manogaran et al. 2017). The data is unstructured as well as structured. The data that is collected from lab testing done to the patients is used as structured data. This data is usually organized and is entered through a radio button, checkbox and drop box during the data collection phase. The structured data usually stays in pre designed fields and is consistent with the rest of the patient data (Manogaran et al. 2017). The data which remains unorganized consists of the unstructured data. The data has irregularity as well as ambiguity. The data are normally text heavy.

Studies have shown that 94% of the data that is available is mostly unstructured in the health care scenario (Payne and Ediche 2016). The data that is provided by the EHR systems are mostly unstructured in nature. The data are very difficult to analyse and natural language processing tools are used normally to predict a pattern in this unstructured data for better analysis. 

The challenges that are faced in integration and management of data sensitivity are written as follows:-

  • Mobile device compatibility
  • HIPAA technology compliance
  • Client data sharing with the healthcare organizations
  • Absence of integration of data between the administrative systems as well as clinical institutions
  • Operational analytics for better productivity and profitability (Including the EHRs)
  • Absence of analytics talent in this scenario as the expertise that is needed for handling an ICT technology is far different from the expertise needed for a medical person

The first recommendation is the consideration that needs to be undertaken regarding the implication faced by the data evaluation. A development of a new culture can be ushered if the system is implemented correctly with regular improvements and procedures. The studies needs to be conducted precisely so that the negative results can be understood and proper actions can be taken against that. The procedure is however very complex and tricky so proper care needs to be taken. Also, aims of the EHR system implementation in EIA reference architecture approach can be overlooked due to the evaluation results, so care needs to be taken in this aspect. For example, the alert systems need to be comprehensive. A fast alert system would not be accepted by the personnel working in the institutions. Hence, an action plan needs to be formally documented which is accepted by all the stakeholders and the responsibilities of each and everyone needs to be precisely mentioned for identifying everyone’s responsibilities and timescales (Raghupathi and Raghupathi 2014). Proper action communication needs to be identified and necessary adjustments needs to be considered. For proper action planning and shared learning, negative actions need to be reviewed from time after time. The healthcare institutions also needs to be prepared for the worst case scenario in case the new system fails. A backup data needs to be working if the HER system malfunctions. 

Information Management and Integration

Conclusion

To conclude the report, it can be stated that the information that is stored in the data base of the health care institutions are used for the EIA RA system. In this report, the benefits of using the data in the health care systems are evaluated for proper collection and analysis of data. Tis is used for the benefit of the organization as well as the clients. The report also discusses how the system utilizes the healthcare records for processing in the framework. The volume of unstructured as well as structured data and the data entry natures are processed for the Integration and processing of the database present in the Information system. The report also discusses the data management system and data integration in detail. . Any kind of data, that does not form into a similarity index or do not fall into a particular pattern that has been detected in the systems can clearly be used as a reference to make out the differences and form a new set of pattern. This helps the health care analysts to detect a disease which has been undetected for a long time and understand the symptoms that are caused due to the issue. The EHR and EIA RA implementation has thus revolutionised the way that electronic data is integrated and managed in a health care system. 

Adenuga, O. A., Kekwaletswe, R. M. and Coleman, A., 2015. eHealth integration and interoperability issues: towards a solution through enterprise architecture. [Online] Available at: https://doi.org/10.1186/s13755-015-0009-7  

America, C. o., 2009. HITEC Act. [Online] Available at: https://www.healthit.gov/sites/default/files/hitech_act_excerpt_from_arra_with_index.pdf#%5B%7B%22num%22%3A10%2C%22gen%22%3A0%7D%2C%7B%22name%22%3A%22Fit%22%7D%5D

Birkhead, G.S., Klompas, M. and Shah, N.R., 2015. Uses of electronic health records for public health surveillance to advance public health. Annual review of public health, 36, pp.345-359.

Brennan, P., 2017. Is the EHR the New Big Data?. [Online] Available at: https://datascience.nih.gov/BlogIsTheEHR

Crowley, S.L., Hinchliffe, S. and McDonald, R.A., 2017. Invasive species management will benefit from social impact assessment. Journal of Applied Ecology, 54(2), pp.351-357.

Dinev, T., Albano, V., Xu, H., D’Atri, A. and Hart, P., 2016. Individuals’ attitudes towards electronic health records: A privacy calculus perspective. In Advances in healthcare informatics and analytics (pp. 19-50). Springer, Cham.

Ginter, P.M., 2018. The strategic management of health care organizations. John Wiley and Sons.

Hitz, P.J., Juusola, M., Waring, S.C. and Haller, I.V., 2015. Natural Language Processing of the Unstructured Electronic Health Record Data Using Regular Expressions and SAS Hash Objects. Journal of Patient-Centered Research and Reviews, 2(2), pp.118-119.

Lacey, A., Lyons, J., Akbari, A., Turner, S.L., Walters, A.M., Fonferko-Shadrach, B., Pickrell, O., Rees, M.I., Lyons, R.A., Ford, D.V. and Middleton, R.M., 2017. Codifying unstructured data: A Natural Language Processing approach to extract rich data from clinical letters. International Journal for Population Data Science, 1(1).

Manogaran, G., Thota, C., Lopez, D., Vijayakumar, V., Abbas, K.M. and Sundarsekar, R., 2017. Big data knowledge system in healthcare. In Internet of things and big data technologies for next generation healthcare (pp. 133-157). Springer, Cham.

Payne, J.D., Ediche, Llc, 2016. System and method for data management. U.S. Patent 9,454,748.

Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), p.3.

Sagha Zadeh, R., Xuan, X. and Shepley, M.M., 2016. Sustainable healthcare design: Existing challenges and future directions for an environmental, economic, and social approach to sustainability. Facilities, 34(5/6), pp.264-288.

Smith, M.W., Ash, J.S., Sittig, D.F. and Singh, H., 2015. INCREASING RESILIENCE IN AN EHR-ENABLED HEALTHCARE ORGANIZATION. SAFER Electronic Health Records: Safety Assurance Factors for EHR Resilience, p.383.

Viceconti, M., Hunter, P. and Hose, R., 2015. Big data, big knowledge: big data for personalized healthcare. IEEE journal of biomedical and health informatics, 19(4), pp.1209-1215.

Wager, K.A., Lee, F.W. and Glaser, J.P., 2017. Health care information systems: a practical approach for health care management. John Wiley & Sons.

Wang, Y., Kung, L. and Byrd, T.A., 2018. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, pp.3-13.

Wang, Y., Kung, L., Ting, C. and Byrd, T.A., 2015, January. Beyond a technical perspective: understanding big data capabilities in health care. In System sciences (HICSS), 2015 48th Hawaii international conference on (pp. 3044-3053). IEEE.

White, K.M., Dudley-Brown, S. and Terhaar, M.F. eds., 2016. Translation of evidence into nursing and health care. Springer Publishing Company.

Zhang, Y., Qiu, M., Tsai, C.W., Hassan, M.M. and Alamri, A., 2017. Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), pp.88-95