Following the recent announcement of increased funding for artificial intelligence within the NHS by the Chancellor in the Spring 2024 programme highlights the importance of stabilising the healthcare system and securing the leading position in healthcare delivery on a global scale. In order to maximise the patient benefit and staff satisfaction through the full utilisation of the use of AI, it is useful to develop an action plan with proactive next steps.
Step 1: Focus on the right priorities
There are many high-impact use cases such as waiting list management. By developing AI algorithms and data-driven decision support tools to predict patient demand, optimize scheduling, and efficient allocation of resources, healthcare facilities can significantly reduce wait times and enhance patient satisfaction. Through our work with NHS Trusts it has been shown that the development of AI-based programming tools has resulted in An increase in patient preference within imaging. Artificial intelligence can also help to enhance the support of office services. Experience so far has shown a significant reduction in the time to review a contract with the use of artificial intelligence.
Step 2: Cultivating staff acceptance
An important aspect of any AI initiative is cultivating trust and acceptance from frontline staff. As clinicians focus on refining their clinical judgment and providing personalized care it makes sense that they approach the integration of AI with caution. To avoid this hesitation it is important that strict trials of an evidence-based implementation. Furthermore, the syndrome of ”that it was not manufactured here” prevents and forces individual executives to actively engage and convince their clinical communities of tangible evidence of AI adoption. One approach to fostering staff buy-in is active involvement from the outset in the design of the programme design. The use of the “Net Promoter Score”, which measures staff enthusiasm for a particular technology, is a strong incentive for engagement and could potentially help. Also in cases where staff know that the technology is supported by their colleagues, after a pilot project, it is more more likely to want to participate themselves. Another useful approach is for the clinician to present his or her own prediction alongside any AI prediction, allowing the staff to make their own prediction to immediately see how the AI compares. Using this method, we can show that an AI algorithm was more predictive of the day a patient would be ready to leave than the clinician’s own estimate.
Step 3: Integrating the technology into the standard
Considering a user-driven approach to process redesign has the dual impact of bring the affected parties – for example, staff, patients and carers – together on the journey, while also helping to ensure that change is not just a digitisation of existing processes. THE NHS will need to fund the effort so that by supporting staff to trust and find the budget to deliver the necessary change programme.
Looking forward: A collaborative imperative
Vital to the whole effort will be the collaboration between government, the NHS leadership, frontline staff and the technology partners. Latest research has shown that 74% of organisations recognize that AI can make their operations more efficient, and a staggering 84% do not believe they are equipped to do so to make it a reality. While the Chancellor’s investment in AI represents a significant moment, the result is testament to how capable we are of navigating the complexities of implementation effectively and provide frontline organisations with the support they need.
Read more: Transforming through Data,page 116 Article from Jenny Lewis under the title ” Unlocking the Potential of AI in the NHS: A Path Forward’