AI’s role in streamlining healthcare operations and services

AI is already having a transformative effect on UK healthcare in both the public and private sectors. Recent years have seen a host of exciting innovations, with no signs of this trend abating.

So how can you implement cutting-edge AI technology in your organisation for similar results? Let’s answer that question.

AI in daily healthcare operations

Artificial intelligence in healthcare environments has led to a range of operational gains, with ever-increasing levels of automation and efficiency predicted for the future.

Patient scheduling: AI-driven platforms prioritise patient issues, free up resources spent on triaging and reduce wait times.
Records management: AI can categorise and retrieve digital records and provide compliance oversight.
Data entry and sharing: AI automation refines rule-based protocols for data interoperability across healthcare databases.
Supply chain optimisation: AI-driven predictive analytics streamlines inventory management, forecasts medicine and equipment requirements, and identifies waste “drop off” points.

One interesting case study is the use of AI in an NHS healthcare setting is appointment scheduling in GP’s surgeries. The Mid and South Essex NHS Foundation Trust predicts that using AI to limit missed appointments will allow an extra 80,000 to 100,000 patients to be seen

AI in medical diagnosis and patient care

AI services have shown incredible promise in diagnosing and improving patient care. Specifically, healthcare providers have seen outcomes in screening and care personalisation, with potential applications to surgery.

Imaging: AI is being used to enhance images, interpret information and detect problems across various applications, including cancer screening and scar detection in individuals who have had a heart attack.
Predictive analytics: AI can process vast datasets to predict patient risks for early intervention. It can ascertain which cancer drugs will be most effective and the likelihood of degenerative illness.
Robotics in surgery: Although largely still an area of research, AI-driven robotic systems may help surgeons perform intricate surgical tasks and develop tailored patient approaches.
Virtual health assistants: AI-powered chatbots provide instant, personalised health advice for non-urgent issues. The private sector is very active in this area.

AI for enhanced patient wellbeing

In addition to operations diagnosis and treatment, AI is also positively impacting preventative medicine and general patient wellbeing.

Preventative monitoring: AI-based apps can collect and interpret data over the long term, flagging issues before they become serious.
Mental health: The NHS supports mental health apps like Wysa, which can improve patient mental well-being through self-care exercises.
Personalised care: AI can contribute to personalised care plans, especially for people living with long-term conditions like type 2 diabetes.

Challenges and ethical considerations

The long-term adoption of AI presents several technological challenges. There are also areas of ethical concern.

Human oversight: Ensuring AI systems are complemented by human decision-making can mitigate the risk of error and maintain trust in medical diagnoses and treatments. Traditional rule setting can also help to contain AI systems.
“Black box” algorithms: The lack of transparency in relation to AI decision-making processes has ramifications for accountability and the appropriate place of oversight.
Data privacy: AI creates new opportunities for data misuse, and patient confidentiality, consent and security must be prioritised.
Monetisation of healthcare tech: Ensuring that innovations are accessible to all patients irrespective of socio-economic status presents difficulty, especially in an industry often driven by profit.

Conclusion

AI can help healthcare leaders like you achieve improvements across all of your core metrics, including operational efficiency, expenditure and patient outcomes.

AI holds much promise, both for immediate gains and over the coming years and decades. And organisations that begin trialling, implementing and refining AI healthcare applications now are set to see the most significant results.