DB FPX 9801 Assessment 9

DB FPX 9801 Assessment 9
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LEVERAGING ARTIFICIAL INTELLIGENCE FOR STRATEGIC DECISION-MAKING IN HEALTH CARE

 

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Student Name

Professor Name

 

Maja Zelihic, PhD, Dean

Academics and Doctoral Studies

School of Business, Technology and Health Care Administration

 

A Capstone Work Presented in Partial Fulfillment

Of the Requirements for the Degree

Doctor of Business Administration

Strategy and Innovation

 

Capella University

Month & year of dean’s approval

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    Professional Development Plan

    I could be a doctoral researcher working in the field of AI implementation in healthcare, which is why my opportunities to develop according to Applied Research Competencies are high and multidimensional. The multifaceted nature of the healthcare AI adoption, in light of the fact that only 25% of executives admit to realising potential of AI, despite a 75 percent recognition (Bain & Company, 2024) states the necessity to establish sophisticated research capabilities. Of course, these three sub domains are qualitative research method, analysis of the results and the distribution of the research findings.

    As to particular research methodologies, I am looking forward to acquiring high-level skills in semi-structured interviews and thematic research, as they will play a critical role in the scenario in which the proposed topic is applied due to the knowledge of the technology and the nature of the organization (AlKuwaiti et al., 2023). As far as data analysis is concerned, I will build my competence in terms of the organizational data about AI deployment since most healthcare organizations report problems related to data integration; on average, 40 percent of these institutions list data standardization among the systems among their issues (Hummelsberger et al., 2023). The research dissemination subdomain aims to provide adequate dissemination and presentation of the findings to the audience of various other stakeholders as the authorities of healthcare need this information to design the appropriate strategies and implementation procedures (Steerling et al., 2023).

    Figure 1

    Professional Development Matrix

    Professional Development Matrix

    The strategies that I intend to implement involve creating a classical structure of my work that will encompass the academic part and the practical part that is focused on achieving the goals, mechanisms that will assist me to be motivated and conquer difficulties. To enhance the quality of this practice, I will engage in communication with more advanced researchers and enroll in special methodology classes, such as interview strategy and thematic analysis of the work as it is conducted in the latest researches in the sphere of healthcare technology (Rahman et al., 2024).

    Regarding data analysis competencies, I will seek job placements in healthcare organisations that are also implementing AI solutions to analyse data in order to improve the analytical skills of the person undertaking the process as demonstrated by Krishingan et al., 2023. To a certain degree this model of development has already impacted my professional practice, since it encouraged me to become more involved in various types of activities, e.g. working on AI-implementation projects, collaborating with healthcare-technology teams.

    These exercises assisted me in the process of getting acquainted with utilizing various data sets to examine and synthesize the relevant to the fact that the healthcare organizations implementing AI solutions decrease the costs of operations by 25-30 percent (Rojek et al., 2023). The framework has also improved my skill to develop viable solutions to significant challenges in healthcare AI deployment, in particular, the problems related to data integration and change management, which are some of the critical impediments to successful implementation as Dwivedi et al. (2021) recognize.

    Conclusion

    According to this professional development model, I have outlined the plan in terms of developing the applied research competence to deal with the challenge of AI application in healthcare. Such competencies of merging qualitative research techniques, data analysis, and research communication capabilities equip me with the task when applied in the area of healthcare technology adoption. These research competencies enhancements will prove very helpful when healthcare organizations move to the direction of AI development.

    These skills will be applicable in making sure that the knowledge acquired through the organizational practice is reflected back in the healthcare academia to be analyzed further. The accumulation of these skills as thoroughly organized as well as the practical training opportunities in practice are more than good in terms of enriching my academic along with professional training as a healthcare AI practitioner.

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      References for
      DB FPX 9801 Assessment 9

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        AlKuwaiti, A., Nazer, K., AlReedy, A., AlShehri, S., AlMuhanna, A., Subbarayalu, A. V., Al Muhanna, D., & AlMuhanna, F. A. (2023). A review of the role of artificial intelligence in healthcare. Journal of Personalized Medicine13(6), 951. https://doi.org/10.3390/jpm13060951

        Bain & Company. (2024). Majority of health system executives believe generative AI will reshape the industry, yet only 6% have a strategy in place. Bain. https://www.bain.com/about/media-center/press-releases/2023/majority-of-health-system-executives-believe-generative-ai-will-reshape-the-industry-yet-only-6-have-a-strategy-in-place/#:~:text=Press%20release-

        Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., & Medaglia, R. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management57https://doi.org/10.1016/j.ijinfomgt.2019.08.002

        DB FPX 9801 Assessment 9

        Hummelsberger, P., Koch, T., Rauh, S., Dorn, J., Lermer, E., Raue, M., Matthias, F., Schicho, A., Colak, E., Ghassemi, M., & Gaube, S. (2023). Insights on the current state and future outlook of artificial intelligence in healthcare from expert interviews. Journal of Medical Internet Research  Artificial Intelligencehttps://doi.org/10.2196/47353

        Krishnan, G., Singh, S., Pathania, M., & Dhar, M. (2023). Artificial intelligence in clinical medicine: Catalyzing a sustainable global healthcare paradigm. Frontiers in Artificial Intelligence6(6), 8–12. https://doi.org/10.3389/frai.2023.1227091

        Rahman, Md. A., Victoros, E., Ernest, J. T., Davis, R., Shanjana, Y., & Islam, Md. R. (2024). Impact of artificial intelligence (AI) technology in healthcare sector: A critical evaluation of both sides of the coin. Clinical Pathology17https://doi.org/10.1177/2632010×241226887

        Rojek, I., Piechowski, M., & Mikołajewski, D. (2023). An artificial intelligence approach for improving maintenance to supervise machine failures and support their repair. Applied Sciences13(8), 4–11. https://doi.org/10.3390/app13084971

        Steerling, E., Siira, E., Nilsen, P., Svedberg, P., & Nygren, J. (2023). Implementing AI in healthcare-the relevance of trust: A scoping review. Frontiers in Health Services3(3), 12–50. https://doi.org/10.3389/frhs.2023.1211150

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          • Bradly E. Roh.

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            Answer 2Developing applied research competencies for AI in healthcare.

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