Organizational Workflow Analysis : Health Informatics

Organizational Workflow Analysis
Name of the Student
Name of the University
Author Note

Introduction
Health Informatics
• Sector dealing with patient information
and organizational data.
• A common issue in health informatics includes data protection leading to ethical and legal issues.
• Workflow and workforce management is responsibility of health informatics sector.
• A health informaticist focusses on data management and recommends strategies to improve the
data workflow in an organization (Ziebell et al., 2019).

Situational Analysis
• Health care data management is required for the
medical record management of the patients.
• Situational analysis helps to determine the condition of
management with the current workflow in place.
• It can be seen that the process of the health care data management has been done with the
help of the paper works and it has many flaws (Ekblaw et al., 2016).

• The process of the hardcopy management is very
much difficult as there are possibility of data breach,
misplacement of data and the process of the data
management would be needing bigger storage and
high amount of workforce and workflow as well
(Yang, Zheng & Tang, 2017).
• The process of the data management should be
changed from the hardcopy or the paper works from
the software based process.

Workflow
Requirements
• Workflow is the process of
execution and automation
of business process including task and the information passing from one source to
another recipient.
• The business of the facility is based on the customer that is the patient care and patient
satisfaction obtaining (Mandel et al., 2016).

• The EHR will be helpful in reducing the
workload and timely data retrieval
process.
• This is effective in helping the health care
professionals of the organization to
provide safe and advanced care to the
patients.

Architectural Recommendation
• Architecture of the EHR is developed considering the ease of access to
the data from the mother or principle storage of the data.
• The flaws are the data breaching possibility from the EHR and also the
incompetence in terms of the connectivity issues (Bellazzi et al., 2015).
• Upgrading health record management to Electronic Health System
would be the most suited architectural recommendation.

• The EHR architecture should be analyzed in a regular
basis for the change of the user interface in terms of more
improved processing of the EHR.
• The upgrade is solely dependent on the user feedback and
the analysis of the flaws of the EHR in terms of the
situation.

Provisional Recommendation
• Several EHR tools or software available in market and most of them are effective
in proper data management.
• The architecture of the software is developed based on the questionnaire based
feedback and data management process.
• The Epic software is most effective as the technology of the software is
segregated based on the data type and also the retrieval process can also be
marked as the most effective in terms of easy search process (Brand et al., 2015).

• The keyword search, patient screening automated data storing,
effective data management and also segregating data in different
sections based on the type of data are the factor found in the Epic
software.
• The Epic software is very much advanced in terms of the other
software of the market and thus the implementation of the software in
the health care facility would be very much profitable.

Conclusion
• Based on the above discussion it can be concluded that the EHR should be
implemented in the health care facility for the improvement of the care
procedure. On this context it can be stated that the effective data management
and data retrieval can be developed through proper segregated data storing in
the EHR systems. It also helps in reducing the workload as well as the reduction
of the health care errors. Moreover, the most effective upgrade of the EHR
would be addition of the tutorial for the users in order to use the EHR and
effectively segregate and retrieve the data from the EHR as well.

References
• Balas, E. A., Vernon, M., Magrabi, F., Gordon, L. T., & Sexton, J. (2015, January). Big Data Clinical Research: Validity, Ethics, and
Regulation. In MedInfo (pp. 448-452).4
• Bellazzi, R., Dagliati, A., Sacchi, L., & Segagni, D. (2015). Big data technologies: new opportunities for diabetes management. Journal
of diabetes science and technology, 9(5), 1119-1125.
• Ekblaw, A., Azaria, A., Halamka, J. D., & Lippman, A. (2016, August). A Case Study for Blockchain in Healthcare:“MedRec” prototype
for electronic health records and medical research data. In Proceedings of IEEE open & big data conference (Vol. 13, p. 13).
• Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016). SMART on FHIR: a standards-based, interoperable
apps platform for electronic health records. Journal of the American Medical Informatics Association, 23(5), 899-908.
• Yang, Y., Zheng, X., & Tang, C. (2017). Lightweight distributed secure data management system for health internet of things. Journal of
Network and Computer Applications, 89, 26-37.
• Ziebell, R. C., Albors-Garrigos, J., Schultz, M., Schoeneberg, K. P., & Perello-Marin, M. R. (2019). eHR Cloud Transformation:
Implementation Approach and Success Factors. International Journal of Intelligent Information Technologies (IJIIT), 15(1), 1-21.