BHA FPX 2106 Assessment 2 Managing Change

Managing Change

Student Name

Capella University

BHA FPX2106

Professor name

Submission Date

The use of advanced technologies, which boost the outcomes of their clients, improve efficiency, and enable them to deliver evidence-based care, is gradually becoming a common practice in healthcare organizations. The Mount Sinai Health System in New York is one of the top healthcare institutions as far as innovation and the use of technology in enhancing its service delivery is concerned. The discussion in this paper is related to the implementation of the Artificial Intelligence-based Clinical Decision Support System at the Mount Sinai Health Records (Mount Sinai, 2026). Adopting this new technology is crucial in relation to health information management for several reasons.

Affected databases, Systems, and IT Applications

There will certainly be several changes in the subsequent health care information technologies applied at Mount Sinai as the AI-based CDSS will be put into practice. Firstly, the EHR system (Epic) will undergo significant changes due to its use as a source of information by 48,000 employees who will be operating within Mount Sinai (Mount Sinai, 2026). In addition, the CDSS is expected to communicate with other types of clinical data, i.e., the data that is contained in clinical databases including LIS, RIS, and Pharmacy Information Systems.

Another type of technologies which are likely to experience a change upon deployment of the new system is data warehousing and large data analytics, as they guarantee the aggregation of large amounts of data needed in training algorithms that are used in the AI system. In addition to that, one can also refer to Health Information Exchange systems (HIE) that do the data transfer and interoperability (Gomez-Cabello et al., 2024). Finally, the APIs, in particular the FHIR APIs, will also be involved in the process. In addition, the cybersecurity system and cloud data storage will need to be designed to accommodate massive amounts of data.

Stages in the Information Life Cycle

The emergence of the CDSS, which works on the principles of artificial intelligence, affects every step of information management in the healthcare industry. As regards the data production process, it is also worth noting that the patient himself is a data producer when composing the medical record as well as when carrying out the diagnosis. This step is also rather crucial as the quality of the input data directly determines the quality of the generated data. Concerning information storage, it is linked with securing the data since the patient’s data should be stored with the help of such techniques as electronic health records and cloud computing using encryption technologies.

It becomes easier for the doctors to use data as they have to rely on the results obtained by artificial intelligence (Veernapu, 2023). Sharing of data is an essential stage since the data of the patient is being shared among organizations. Lastly, archiving and disposing are also quite crucial since they entail storage of data to be used later in other modeling and disposing of the same.

Law and Rules

To implement CDSS, utilizing AI technologies at the Mount Sinai Health System, several rules would need to be complied with. The regulations would guarantee the safety, confidentiality, and security of personal data concerning patient health care information and privacy of the patient according to US healthcare laws. With respect to the HIPAA rule, there must be reasonable protection and privacy, i.e., security measures, physical, administrative, and technical protection, e.g., access controls, audit trails, and staff training (Pham, 2025). On the one hand, the HITECH Act adds to the HIPAA laws with penalties and requires notice in the case of a security breach. Finally, the 21st Century Cures Act introduces limitations on and the provision of health IT interoperable products, in which the consumer can access their health information by using API like FHIR (Phelan et al., 2024).

In addition, all the software created through the application of artificial intelligence technology in any medical field is considered to be a medical device, and therefore will require validation regarding its effectiveness and efficiency (US. 2025 Food and Drug Administration. The integrity and responsibility of the organizations they are required to adhere to are accompanied by a lot of legal and ethical requirements. The organizations will be advantageous as they will be adhering to the healthcare demands in the USA.

Patient Data and Clinical Knowledge Management Representation

Implementation of an AI-based CDSS system brings about a paradigm shift in how information about patient healthcare and clinical knowledge is managed in the Mount Sinai hospital setting. This system offers real-time analysis of large amounts of structured and unstructured data to produce evidence-based recommendations to improve diagnosis and treatments (Elhaddad & Hamam, 2024). This will certainly have an effect on personalized medicine.

Simultaneously, the new system will prove to be extremely efficient in terms of knowledge management in the organization, as recent scientific discoveries, clinical guidelines, and practices will be continuously incorporated into the process. The tool assists in population health management by identifying trends and risks in patients. However, its performance depends mostly on the quality of the information that is provided since false information can result in biased AI-based recommendations (Gomez-Cabello et al., 2024). Moreover, critical thinking skills of clinicians could also be at risk since clinicians may rely on automated recommendations more frequently. In turn, the need to balance human judgment and technological assistance must be addressed. On the whole, it allows converting raw information into knowledge.

Difficulties of implementing technology changes

Despite the advantages of using a CDSS, several potential issues tied to the use of this kind of technology can impact the project in a negative manner, unless addressed properly. First, the resistance to change on the part of health care practitioners might arise because of their concerns regarding AI’s ability to recommend the most optimal action (Saikali et al., 2026). Absence of training and non- involvement by end-users is yet another aspect that will not make the project successful. A number of technical challenges, such as system integration and data standardization issues, and inadequate infrastructure, will also be a big threat. The lack of proper leadership and resource distribution will probably result in obstacles to accomplishing the project successfully.

Technology Change Implementation Challenges

To effectively cope with the above challenges, Mount Sinai Hospital must come up with change management strategies. Having proper leadership and communication are vital elements of the process, as they assist in aligning people towards the general vision (Saikali et al., 2026). They will also have to develop training programs that will ensure that users acquire skills. It is strongly suggested to have clinicians involved in the design and implementation phases, as this enhances its acceptance and ease of use. Staged implementation alongside pilot-testing is the other aspect that can be helpful since it enables issues to be spotted and solved in advance.

Conclusion

The introduction of an AI-based Clinical Decision Support System in the Mount Sinai Health System is a new innovation that will change the sphere of information management in the healthcare sector. The use of this new technology will impact various systems as well as all the stages of the life cycle of information management. Yet, the new technology poses some threats regarding how quality information is handled, user acceptance is guaranteed, and how there is smooth integration with the other applications. The success of the project will be mainly dependent on effective change management.