• Written by Sonali Subhadarsini, Product Manager

A typical Monday morning in a large hospital, and the shift supervisor is already dealing with the aftermath of a busy weekend, trying to catch up on paperwork and ensure that all patients are receiving the care they need, while also keeping an eye on the maintenance log to prevent any equipment failures, all of which can be supported by querying the organisation's own data in natural language using AsscherAi, a platform built by Harns Technologies.

Governance challenges in healthcare providers

Healthcare providers present a unique set of challenges when it comes to implementing enterprise AI governance, primarily due to the complexities of their operations, which include scheduling and utilisation of resources, such as operating theatres and patient beds, supply consumption variability, which can be affected by factors like patient flow and treatment protocols, and documentation burden, which can be overwhelming for clinical staff, and all of these factors can impact the ability to effectively govern the use of AI within the organisation.

The category manager, for instance, needs to balance the demand for medical supplies with the available stock, taking into account factors like lead times and supplier reliability, and this can be a difficult task, especially when dealing with multiple suppliers and different types of supplies, and the use of AI can help with this task, but it also requires careful governance to ensure that the AI system is being used effectively and efficiently.

The shift supervisor, on the other hand, needs to ensure that all patients are receiving the care they need, while also keeping an eye on the maintenance log to prevent any equipment failures, and this requires access to accurate and up-to-date information, which can be provided by AsscherAi, but also requires careful governance to ensure that the information is being used correctly.

Enforcing permission at retrieval

One of the key challenges in implementing enterprise AI governance in healthcare providers is enforcing permission at retrieval, rather than relying on instructions given to the model, which can be complex and difficult to manage, especially in a clinical setting where privacy obligations are strict, and the use of AI needs to be carefully controlled to ensure that sensitive patient information is not compromised.

By enforcing permission at retrieval, healthcare providers can ensure that only authorised personnel have access to sensitive patient information, and that the use of AI is carefully controlled to prevent any potential breaches of privacy, and this can be achieved through the use of role-based access controls, which can be integrated with the AsscherAi platform to ensure that only authorised users have access to the information they need.

Auditability

Auditability is a critical component of enterprise AI governance in healthcare providers, as it allows organisations to track what was asked, what was returned, and on what data, which is essential for ensuring that the use of AI is transparent and accountable, and that any potential errors or biases can be quickly identified and corrected.

The use of AsscherAi provides a clear audit trail, which can be used to track all interactions with the platform, including the questions asked, the responses provided, and the data used to generate those responses, and this can help healthcare providers to ensure that the use of AI is carefully controlled and managed, and that any potential risks or issues can be quickly identified and addressed.

Routing to a human

There are certain categories of questions or requests that should always be routed to a human, rather than being handled by an AI system, such as those that require complex clinical judgement or those that involve sensitive patient information, and these categories need to be carefully defined and managed to ensure that the use of AI is safe and effective.

For example, a question about a patient's treatment plan should always be routed to a human, such as a clinician or a nurse, rather than being handled by an AI system, as it requires complex clinical judgement and a deep understanding of the patient's individual needs and circumstances, and the use of AsscherAi can help to ensure that these types of questions are always handled correctly.

By routing these categories of questions to a human, healthcare providers can ensure that the use of AI is safe and effective, and that patients receive the high-quality care they need, and for more information on how AsscherAi can support this, please visit our website at www.asscher.ai.

Limitations of AsscherAi

While AsscherAi can provide significant benefits to healthcare providers, it is not a solution that can be used in all situations, and there are certain limitations and constraints that need to be carefully considered, such as the need for high-quality data and the potential risks associated with the use of AI in a clinical setting.

For example, AsscherAi is not a replacement for human clinical judgement, and it should always be used in conjunction with the expertise and knowledge of clinical staff, and it is also important to note that AsscherAi is not a decision-making system, but rather a tool that can provide information and support to healthcare providers, and for more information on how to get the most out of AsscherAi, please contact us at contact@asscher.ai.