The Revolutionary Christian Health administrator
The power of influence is a crucial attribute that a healthcare administrator can possess because it can help the administrator secure, worthy deals for the organization. The revolutionary healthcare administrator is able to use influence to get well-wishers who can financially support the organization. Well-wishers may help the healthcare organization in acquiring expensive but important machines and medical equipment like cancer screening machines, CT scanners and surgical robots. Such machines help the healthcare organization to perform specialized activities like chemotherapy procedures, screening for patients with cancer and conducting surgical operations whenever necessary. Provision of such services improves the quality of healthcare in an organization and is likely to attract more patients while retaining the existing ones (Covey, 1992). More patients will increase the returns from the hospital.
Additionally, an influential Christian healthcare administrator is able to secure crucial partnerships. Effective partnerships may facilitate the sharing of essential services and resources. For instance, the healthcare administrator may secure partnerships with nearby healthcare organizations and share resources like ambulances and key expensive medical appliances or services like expertise and internet. Such partnerships improve the quality of healthcare in the involved organizations, and they may be likely to win more clients. Besides, the administrator may use influence to reach giant competitors and learn from them through the concept of benchmarking. Through benchmarking, the administrator is able to rank the organization in the market, learn what the competitor is doing differently, identify what is not working in their organization and take corrective measures (Purushotham, Meng, Che & Liu, 2018).
References
Covey, S. (1992). Principle-centred leadership. Los Angeles, CA: Fireside Press.
Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018). Benchmarking deep learning models on large healthcare datasets. Journal of biomedical informatics, 83, 112-134.