• Written by Muhammed Abdulla NC, CEO

Can we really cut costs in healthcare without compromising patient care, and if so, where should we even start looking? This question comes up in every meeting about cost optimisation in healthcare providers, and it is hard to answer because the constraints of clinical operations with strict privacy obligations make it difficult to identify areas where costs can be reduced without affecting the quality of care.

Healthcare provider complexities

Healthcare providers face unique challenges that make cost optimisation more complicated than in other industries. Scheduling and utilisation of resources, such as operating rooms and medical equipment, are critical to ensuring that patients receive timely and effective care. However, variability in supply consumption and the documentation burden imposed by regulatory requirements can make it difficult to identify areas where costs can be reduced. For example, a shift supervisor checks the maintenance log to ensure that equipment is properly maintained, but this process can be time-consuming and may not always be effective in reducing costs.

The clinical operations of healthcare providers are also subject to strict privacy obligations, which can limit the ability to share data and collaborate with other organisations to reduce costs. This means that cost optimisation efforts must be carefully designed to ensure that they do not compromise patient confidentiality or the quality of care.

Unit cost vs total cost

One of the key challenges in cost optimisation is understanding the difference between unit cost and total cost of an AI workflow. Unit cost refers to the cost of a single instance of a process or service, while total cost refers to the overall cost of the entire workflow. In healthcare, the total cost of a workflow can be much higher than the unit cost, due to the complexity of the processes involved and the need for multiple stakeholders to be involved. For example, the cost of a single medical imaging procedure may be relatively low, but the total cost of the entire diagnostic workflow, including the cost of equipment, personnel, and facilities, can be much higher.

This means that cost optimisation efforts must focus on reducing the total cost of the workflow, rather than just the unit cost. This requires a detailed understanding of the entire workflow, including all of the processes and stakeholders involved, and the ability to identify areas where costs can be reduced without compromising the quality of care.

Real levers for cost optimisation

There are three real levers for cost optimisation in healthcare providers: caching, batching, and model routing. Caching involves storing frequently-used data or results in a easily-accessible location, to reduce the need for redundant calculations or queries. Batching involves grouping similar tasks or processes together, to reduce the overhead of individual transactions. Model routing involves using AI algorithms to optimise the flow of patients and resources through the healthcare system, to reduce wait times and improve efficiency.

For example, a category manager compares two suppliers of medical equipment, and uses caching to store the results of the comparison, so that they do not have to be recalculated every time a new purchase is made. This can help to reduce the cost of procurement and improve the efficiency of the supply chain. To learn more about how AsscherAi can help with cost optimisation, visit our website at www.asscherai.com.

Token cost is not the dominant line

Token cost, which refers to the cost of a single unit of a service or process, is often not the dominant line in the cost structure of a healthcare provider. Other costs, such as personnel, equipment, and facilities, can be much higher, and may be more important to focus on when it comes to cost optimisation. For example, the cost of a single medical imaging procedure may be relatively low, but the cost of the equipment and personnel required to perform the procedure can be much higher.

This means that cost optimisation efforts must take a holistic approach, and consider all of the costs involved in the delivery of healthcare services. It also means that cost optimisation efforts must be carefully designed to ensure that they do not compromise the quality of care, and that they are aligned with the overall goals and objectives of the healthcare provider. If you have any questions about how AsscherAi can help with cost optimisation, please contact us at contact-us@asscherai.com.

Limitations of AI cost optimisation

AI cost optimisation is not a silver bullet, and it is not suitable for all healthcare providers or situations. It requires a significant amount of data and computational resources, and it may not always be possible to implement in a way that is consistent with clinical operations and privacy obligations. Additionally, AI cost optimisation may not always be able to identify the most effective areas for cost reduction, and it may require significant expertise and resources to implement and maintain.

Therefore, healthcare providers must carefully consider their own unique needs and circumstances before implementing AI cost optimisation, and must be careful not to over-rely on technology as a solution to their cost challenges. By taking a careful and nuanced approach to cost optimisation, healthcare providers can reduce their costs and improve the quality of care, while also ensuring that they are complying with all relevant regulations and obligations.