• Written by Muhammed Abdulla NC, CEO

Despite the potential of enterprise AI to transform healthcare providers, many capable systems go unused, with clinicians and administrators often reverting to manual methods or workarounds. This is not due to a lack of technical expertise, but rather the unique constraints of clinical operations, where strict privacy obligations and complex workflows can hinder adoption.

Challenges in healthcare providers

Healthcare providers face a number of challenges that can make it harder to adopt new systems, including scheduling and utilisation, supply consumption variability, and documentation burden. Scheduling and utilisation are critical in healthcare, where resources are often scarce and demand is high. However, the complexity of scheduling and utilisation can make it difficult to integrate new systems, as they must be able to accommodate the nuances of clinical workflows.

Supply consumption variability is another challenge, as healthcare providers must manage a wide range of supplies, from medications to medical equipment. This can make it difficult to predict and manage supply chains, and new systems must be able to accommodate this variability. The documentation burden is also a significant challenge, as healthcare providers must maintain accurate and detailed records of patient care. This can be time-consuming and labor-intensive, and new systems must be able to streamline this process.

Attaching new capability to existing routines

One way to overcome these challenges is to attach new capability to a routine that already exists. For example, a shift supervisor checks the maintenance log to ensure that all equipment is functioning properly. By integrating a new system into this existing routine, the shift supervisor can quickly and easily access the information they need, without having to learn a new system or workflow. This can help to increase adoption and reduce the risk of errors or workarounds.

This approach can be particularly effective in healthcare, where clinicians and administrators are often busy and may not have the time or resources to learn new systems. By building on existing routines and workflows, new systems can be designed to be more intuitive and user-friendly, reducing the barriers to adoption.

Who needs to be able to ask

When designing new systems, it is not just a matter of who needs the answer, but also who needs to be able to ask. In healthcare, this may include clinicians, administrators, and other stakeholders, each with their own unique needs and requirements. For example, a category manager may need to compare two suppliers to determine which one offers the best value. By giving the category manager the ability to ask questions and access information, the healthcare provider can make more informed decisions and improve outcomes.

This requires a deep understanding of the different roles and workflows within the healthcare provider, as well as the ability to design systems that are flexible and adaptable. It also requires a commitment to training and support, to ensure that all users are able to effectively use the new system. To learn more about how AsscherAi can support your organisation, visit our website.

The first wrong answer and rebuilding trust

The first wrong answer can be a significant setback for a new system, as it can erode trust and confidence among users. However, this does not have to be a fatal blow, as trust can be rebuilt over time. This requires a commitment to transparency and accountability, as well as a willingness to listen to feedback and make improvements.

For example, if a user receives an incorrect answer, the system should be able to provide an explanation of how the answer was generated, and what steps are being taken to improve the system. This can help to rebuild trust and confidence, and demonstrate a commitment to quality and accuracy. If you have any questions or concerns about AsscherAi, please contact us to learn more.

Limitations and alternatives

While enterprise AI has the potential to transform healthcare providers, it is not a solution for every problem. There may be cases where other approaches, such as process improvement or workflow redesign, are more effective. Additionally, there may be cases where the costs and benefits of implementing a new system do not align, or where the risks and challenges outweigh the potential benefits.

In these cases, it is important to carefully consider the alternatives and limitations, and to make an informed decision about the best course of action. This may involve conducting a thorough analysis of the costs and benefits, as well as seeking input and feedback from stakeholders. By taking a thoughtful and nuanced approach, healthcare providers can make the most of enterprise AI and achieve their goals.