Information systems in my organization represent a well-balanced and convenient set of applications that is supposed to maximize performance and increase the quality of life for practitioners and their patients. This paper will explain the SI background, communication software, database, electronic health product choice, enterprise resource planning, business intelligence, and cyberthreat awareness considerations. This information is essential to comprehend the value information systems will provide after their implementation.
IS’ areas of concern can be categorized into design, data, cost, and operations. In particular, a poor design might fail to capture organizational requirements, the data involved might be inaccurate, the high cost might not be justified by the value, and the system might be overall operationally inadequate (Rainer & Prince, 2021). However, properly established IS’ strengths manifest themselves in data evaluation’s interconnection, accessibility, and simplicity.
When it comes to the choice of communication software, the company can support the choice of its employees as long as the software uses end-to-end encryption (E2EE). Such communication software allows for swift information exchange with convenient design and almost no additional costs, whereas E2EE will provide solid data protection (Jelovčan et al., 2020). Consequently, it covers all four main IS concerns inside an organization.
The amount of data in a healthcare setting is growing exponentially beyond the capacity of traditional relational SQL databases. It motivates organizations to switch to NoSQL cloud options (Tomar et al., 2019). On the one hand, it can unite various data locations, such as electronic medical records (EMR) or electronic health records (EHR). On the other hand, low internet speed and high query times might negatively affect the database performance.
An enterprise resource planning (ERP) system can prove invaluable for the organization’s functioning. It allows for efficient process structuring, clearly defining roles, and assigning duties to employees, resulting in increased productivity and efficiency (Rainer & Prince, 2021). Its implementation has six stages: planning, design, development, testing, deployment, and maintenance. Overall, the process is resource- and time-consuming; if implemented correctly, it will reduce the likelihood of significant performance issues later.
Considering the choice of a specific EHR product, cost and information security balance becomes the essential concern. In this context, the Epic EHR system serves as a solid decision. According to Liu et al. (2019), Epic possesses encryption capabilities while being tightly integrated with native clinical applications, allowing for easier implementation. However, the Epic system lacks customization options and the ability to integrate with third-party systems.
The implementation process will occur as an installation of a built upon a native EHR clinical base. In the National Quality Strategy framework, acts and incentive programs, such as the Affordable Care Act or HITECH Act, encourage organizations to use EHRs (McBride & Tietze, 2018). Consequently, Epic will serve as a messaging module inside the native application, while incentives and implementation aid will cover the associated costs.
Business intelligence (BI) applications in a healthcare setting can be categorized into technical and business solutions. The former relates to increasing the users’ quality of life by providing managerial and data mining tools (Lee, 2018). The latter emphasizes data analysis tools, allowing for more reliable analytics (Lee, 2018). However, using BI risks information leaks due to cyberattacks, which incentivizes cybersecurity training, continuous monitoring, and endpoint protection.
In summary, SI represents the case of a double-edged sword for healthcare organizations. They significantly contribute to the quality of delivered service and increase the quality of life through various data manipulation possibilities. Conversely, poor SI implementation can cause performance issues while exposing organizations to cyber threats (Abraham et al., 2019). Nevertheless, the patient’s well-being and satisfaction have the upper hand. Thus, the solution lies in thorough implementation and increased awareness of consequences.
To conclude, SI possesses great potential for organizational performance improvement. The use of communication software, cloud database, and EHR will simplify information processing inside the organization. In the meantime, thoroughly planned ERP and BI implementation and awareness of associated cyber threats will ensure system robustness and data protection. Ultimately, SI will improve healthcare service delivery, which will result in better patient well-being and consequent satisfaction.
Abraham, C., Chatterjee, D., & Sims, R. R. (2019). Muddling through cybersecurity: Insights from the US healthcare industry. Business Horizons, 62(4), 539-548.
Jelovčan, L., Fujs, D., Vrhovec, S., & Mihelič, A. (Eds.). (2020). The role of information sensitivity in the adoption of E2EE communication software. In Proceedings of the European interdisciplinary cybersecurity conference (pp. 1-2). ACM Digital Library.
Lee, S. Y. (2018). Architecture for business intelligence in the healthcare sector. In IOP Conference series: Materials science and engineering (p. 012033). IOP Publishing. Web.
Liu, X., Sutton, P. R., McKenna, R., Sinanan, M. N., Fellner, B. J., Leu, M. G., & Ewell, C. (2019). Evaluation of secure messaging applications for a health care system: A case study. Applied Clinical Informatics, 10(1), 140-150.
McBride, S., & Tietze, M. (2018). Nursing informatics for the advanced practice nurse: Patient safety, quality, outcomes, and interprofessionalism (2nd ed.). Springer Publishing Company.
Rainer, R. K., & Prince, B. (2021). Introduction to information systems. John Wiley & Sons.
Tomar, D., Bhati, J. P., Tomar, P., & Kaur, G. (2019). Migration of healthcare relational database to NoSQL cloud database for healthcare analytics and management. In N. Dey, A. S. Ashour, & S. J. Fong (Eds.). Healthcare data analytics and management (pp. 59-87). Academic Press.