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Operation management is essential for any organization to ensure effective administration and maximize operating profit. Supply chain management and the value chain of any industry require planning and management of production, logistics, and collaboration with partners (Yanamandra, 2018). Indeed, supply chain management was proven to be helpful in cost reduction in various spheres of business (Yanamandra, 2018). The healthcare value chain is considered more complex than in other organizations (Aldrighetti et al., 2019). This value chain incorporates five main participants: producers, providers, purchasers, products, and fiscal intermediates (Yanamandra, 2018). Providers are hospital physicians, nurses, and pharmacists whose work is multifaceted; thus, it demands significant financial resources. Healthcare organizations need specific diagnostic and surgical equipment, pharmaceuticals, and human resources to provide appropriate patient care. One of the main challenges in healthcare is that service cost reduction should not affect health outcomes in patients (Borges et al., 2019). Therefore, one possible way to reduce healthcare costs and increase diagnostic efficiency is automation and digitalization of patient assessment using artificial intelligence to interpret physical findings and imaging results.
Partial automation of the diagnostic process can reduce physicians workload and decrease healthcare costs. For example, applying artificial intelligence for image processing of a patients nails can help diagnose various illnesses from anemia and pulmonary disease to liver problems and cancer (Foster & Johansyah, 2019). Similarly, eye sclera, skin color, and patients gate can be helpful for the establishment of the diagnosis (Foster & Johansyah, 2019). Although this approach may slightly reduce physicians income, they will have more time for other tasks. Still, this system will require training to achieve the highest possible accuracy in image processing and representation. Furthermore, a certain amount of financial investment will be necessary to implement the new diagnostic software in hospitals. Nevertheless, diagnostics automation can reduce overall costs in the future, which is beneficial to patients who are also participants in the healthcare value chain.
The next step in provider cost reduction is introducing artificial intelligence programs to analyze radiological images to assist doctors in increasing the efficiency and accuracy of diagnostic procedures. Increasing precision in diagnosis will contribute to the healthcare value chain by maximizing customer satisfaction and minimizing costs (Foster & Johansyah, 2019). Implementing the new image processing system will consist of a series of six specific steps. The first two steps will be data input and processing of the provided data by the software program (Foster & Johansyah, 2019). During the third step, thresholding, the image will be adjusted to reduce errors and misinterpretation due to poor quality (Foster & Johansyah, 2019). The fourth and fifth steps are image segmentation and analysis using a neural network (Foster & Johansyah, 2019). Finally, the program will suggest the primary and differential diagnoses based on the provided information (Foster & Johansyah, 2019). This technique can markedly reduce diagnostic costs once system training achieves high accuracy and precision.
To sum up, the value chain is a critical component of operations management aimed at cost reduction and increasing work efficiency in an organization. Healthcare value chain management is complex and expensive because it requires the collaboration of multiple players to provide high-quality care to patients. Therefore, to reduce healthcare costs and increase efficiency, patient assessment and diagnosis automation can be introduced in hospitals. This approach may slightly affect healthcare workers salaries, but it will give them more time to complete other tasks. Furthermore, the new system will require multiple training to reach maximum accuracy, but the outcome will be lower healthcare costs, patient satisfaction, and more free time for physicians.
References
Aldrighetti, R., Zennaro, I., Finco, S., & Battini, D. (2019). Healthcare supply chain simulation with disruption considerations: A case study from Northern Italy. Global Journal of Flexible Systems Management, 20(1), 81-102.
Borges, G. A., Tortorella, G., Rossini, M., & Portioli-Staudacher, A. (2019). Lean implementation in healthcare supply chain: A scoping review. Journal of Health Organization and Management, 33(3), 304-322.
Foster, B. & Johansyah, M. D. (2019). Image processing as a value chain to enhance competitiveness in the healthcare retail business. International Journal of Supply Chain Management, 8(6), 87-91.
Yanamandra, R. (2018). Development of an integrated healthcare supply chain model. Supply Chain Forum: An International Journal, 19(2), 1-12.
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