Introduction:
The realm of health care does not undermine the use of technology, and in particular, for breast cancer detection, technology has become paramount. This essay offers a practical analysis of the problem of business implementation of artificial intelligence (AI) in mammography screening, with the case of Cincinnati’s TriHealth organization as an example. Breast cancer is one of the most significant issues of global health, and the use of AI solutions in this scope has provided both advantages and potential dilemmas. TriHealth is a local not-for-profit health system that is renowned for its offerings of total health care solutions. TriHealth operates multiple hospitals, speciality care centres, and primary care facilities(TriHealth, n.d.). TriHealth has recently integrated AI into its breast cancer screening processes, which conforms to the organization’s commitment to leading and remaining on the cutting edge of healthcare.
Challenges
The primary business problem involves integrating AI-driven computer-aided diagnosis (CAD) systems into mammography screening. CAD systems were implemented initially to address the problem of a shortage of resources in mammography screening. Still, the systems could not achieve the desired result because of issues like repeated marking and high false favourable rates. These challenges, thus, have occasioned increased fees, duplication of processes and a need for more confidence in radiologists.
TriHealth, being aware of the inability of traditional CAD systems to detect breast cancers at an early stage, decided to consider using deep-learning-based software, Lunit INSIGHT MMG, to improve mammography screening(Kasthuri & Meeradevi, 2022). The organization’s limitations are combating radiologists’ scepticism, gathering experience to enhance the accuracy of the sort, and coordinating the CAD fixes into the existing systems.
Change Management
TriHealth is now adjusting to the loss of traditional practice processes due to the rampant adaption of AI in mammography screening. The organization realizes that the conventional way is no longer suitable and uses modern deep-learning-based software (Lunit INSIGHT MMG) in its processes. It is underlined that radiologists need to critically assess the relative strengths and limitations of AI algorithms before full-scale adoption. Although it necessitates a proactive approach to change management, it ensures that the incorporation of AI goes hand in hand with the expertise and needs of the professionals involved.
Implementing the change management in TriHealth involves the extensive introduction of the radiologists to the techniques and procedures to be used. These programs are developed to increase radiologists’ grasp of AI algorithms and their clinical implications(Gao et al., 2023). Through training on the differences in algorithms’ performance based on radiologist experience, TriHealth takes measures that prepare the organization’s medical professionals for the revolution that AI brings.
Recommendations
In analyzing the case, several recommendations can be put forth for TriHealth: In analyzing the case, several recommendations can be put forth for TriHealth:
Comprehensive Training Programs: Develop training programs for radiologists to effectively relate to and understand the AI algorithms and their implementation into clinical practice. This includes taking into account those variations in the performance of the algorithms affected by the radiologist’s experience.
Continuous Evaluation: Set up a feedback system to monitor AI algorithms after their implementation by using the experience of radiologists and, if needed, adjusting the algorithms. This iterative process leads to the evolution of technology, capable of fulfilling the specific needs of healthcare professionals who are usersusing the same technology(Rossi et al., 2017).
Collaborative Decision-Making: Support a collaborative decision-making process that will take place between health staff and technologists(Shukla et al., 2022). This guarantees that the implementation of AI is keyed in with the organizational policies and that radiologists have an active part in defining how the technology is consumed.
Patient Education: Develop educational material for patients to acquaint them with the AI integration in breast cancer screening. This fosters trust and understanding within the population, an effort to deal with possible fears or notions.
Conclusions
The key findings to remember when brainstorming include constant assessment, differentiated training and involvement of all stakeholders. AI utilizing mammography is a roadmap of the AI journey of TriHealth as other healthcare organizations get involved in the intersection of technology and patient care. As the healthcare sector keeps on changing, the examples of successful integration of AI depend on an organization’s ability to manage change efficiently, enabling that technology to help rather than miss the purpose of improving of quality care. AI adoption is strategically and ethically done through comprehensive training, ongoing evaluations, sharing of decisions, and patient education, the way TriHealth does. The organisation’s approach to work through the challenges of change management is likely to be a reference for other healthcare organizations in the field of using technology to improve patient results.
References
Gao, S., Xu, Z., Kang, W., Lv, X., Chu, N., Xu, S., & Hou, D. (2023). Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors evaluation in lung cancer screening.https://doi.org/10.21203/rs.3.rs-3020750/v1
Kasthuri, N., & Meeradevi, T. (2022). AI‐Driven Healthcare Analysis. Smart Systems for Industrial Applications, 269-285.https://doi.org/10.1002/9781119762010.ch11
Rossi, E. C., Kowalski, L. D., Scalici, J., Cantrell, L., Schuler, K., Hanna, R. K., … & Boggess, J. F. (2017). A comparison of sentinel lymph node biopsy to lymphadenectomy for endometrial cancer staging (FIRES trial): a multicentre, prospective, cohort study. The Lancet Oncology, 18(3), 384-392.
Shukla, P. K., Zakariah, M., Hatamleh, W. A., Tarazi, H., & Tiwari, B. (2022). AI-DRIVEN novel approach for liver cancer screening and prediction using cascaded fully convolutional neural network. Journal of Healthcare Engineering, 2022.https://doi.org/10.1155/2022/4277436
TriHealth. (n.d.). Breast care. Retrieved from https://www.trihealth.com/services/womens-services/breast-care
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