What are the practical applications of AI for healthcare organisations?

18 July 2024

In the ever-evolving landscape of healthcare, the integration of artificial intelligence (AI) and machine learning technologies is reshaping the way professionals operate. Rather than replacing human expertise, these advancements are increasingly seen as tools to enhance human capabilities, leading to more efficient and effective outcomes across the board. 

Mohi Khan, business transformation expert, examines the range of use cases and benefits for healthcare organisations, along with guidance on how business leaders in the health and care sector can approach deploying AI in practice.

What are the main areas where AI can support the health and care industry?

Broadly, there are four main areas where AI can support the health and care sector: preventive care and self-care, service delivery, detection and diagnosis and administrational/operational processes. These can bring multiple benefits to the health and care organisation and ultimately to the patient. 

There are opportunities for AI to be used throughout the organisation, from the patient-facing front-end around the delivery of service and care, to the administrative and support functions. We explore these areas, highlighting examples of use cases where AI could be deployed.

In terms of technology uses, the healthcare industry is already deploying both predictive AI (reviewing, enhancing and analysing data to support decision making) and generative AI (supporting content, interaction and engagement). These technologies aren’t mutually exclusive and can be deployed effectively together.

So, how can AI support health and care organisations in the delivery of care and to improve health outcomes for their patients?

Preventive healthcare and self-care

With the focus on preventative care as well as the demands on healthcare budgets, AI can help with preventative and self-care. This can reduce the demand on healthcare systems and support both workforce and patients in a way that traditional healthcare systems may not have been able to. For example, there have been some successful use cases where healthcare apps are supporting children and adults with neuro divergent conditions. 

Self-care is about preventing problems, so AI can help people to better comprehend their conditions and treatment plans, with self-guidance and self-help. 

Service delivery: patient care, engagement and support 

AI has a wide variety of use cases in health and care service delivery, including improving patient engagement and supporting healthcare professional in their day-to-day activities. Examples include:

  • 24-hour monitoring of patients via wearable devices to collect data;
  • taking and analysing notes to support the clinician or carer;
  • supporting decisions around effective deployment of staff, to determine staff numbers and skills required across site locations;
  • education, for example, augmented with AR or VR technology to support training;
  • answering patient questions, such as the triage of symptoms and offering initial guidance for patients with less critical care needs;
  • enabling care at home, such as via virtual wards or virtual nursing assistants which can offer 24-hour access and support. (This can involve both predictive and generative AI. A predictive engine can answer questions combined with generative AI that can provide an avatar to deliver the content via a chat box); and
  • telehealth at home: AI has huge potential to evolve future service delivery and patient engagement. For example, AI can issue prompts or reminders for patients to improve medication adherence.

Detection and diagnosis

AI brings three important benefits – greater speed, volume, and accuracy. This allows a health or care organisation to scale up without the historically associated additional costs. It can make detection and diagnosis more accessible for a wider population and may allow budgets to go further as screening sooner potentially allows earlier and less costly interventions. 

Furthermore, it supports broader population samples and synthesises larger volumes of data. The focus is on enhancing human expertise rather than replacing it, with lab technicians double-checking data and being able to add more value to provide evidence-based recommendations to health and care professionals.

Examples of AI used in detection and diagnosis include:

  • developing personalised treatment approaches;
  • testing, scanning and analysing the results;
  • integrated with medical equipment with built-in AI to support clinicians. For example, a smart stethoscope can pick up a potential heart condition in real-time, providing an extra level of inspection for the doctor; and
  • wearable devices allowing 24-hour monitoring for data collection, analysis and intervention. For example, a pulse monitor measuring blood oxygen can be combined with AI to enable care from home and support virtual wards.

Back-office or administrative opportunities for efficiencies

AI and automation are revolutionising the way administrative and operational tasks are handled within the health and care ecosystem. These technologies are streamlining workflows and can support staff retention by reducing the heavy lifting and freeing up valuable time for more complex and rewarding tasks. 

Health and care organisations can look at every part of their business model and administrative workflow to find a broad range of opportunities where AI can bring greater efficiencies, including: 

Administrative tasks: AI can reduce the admin burden within any healthcare organisation’s administrative flows, such as appointment scheduling. It can also be used within the finance function to automate invoicing, payments, reimbursements or fraud checks. We are seeing that fraud is an increasing issue within many NHS and private healthcare settings

Supporting frontline staff: AI technology can assist carers or clinicians in documenting day-to-day patient notes. This builds up data points over time that can help the care team to work out what they need to do and when, as well as building a broader data set that can be used to support predictions for preventative healthcare. For example, analysing data around senior patients falling in the home setting or improving medication adherence.

Training and support: The integration of AI into medical training and education is another area of significant advancement. AI-powered systems are being used to generate synthetic patient data for training purposes, provide virtual tutoring and practice scenarios, and facilitate ongoing education for healthcare professionals. For example, AI can provide simulations and training for various procedures for social care or junior medical staff.

Recruitment of staff: In healthcare, and particularly in social care, where there can be a high volume of staff turnover, recruitment processes can be automated via AI rather than in-house teams sifting through applications. 

Key recommendations for AI adoption

Generally, across the broader health and care sector, we see that AI adoption is lower than other sectors, particularly among many small to medium-sized organisations. This is due to a number of reasons, including uncertainty about where to start, the fear of compromising the quality of patient care, the capabilities and cost needed to deliver AI, and the complex education needed to adopt AI within their organisations.

It is worth considering the following points regarding the application of AI within your health or care organisation.

The essential role of humans in health and social care delivery

No matter how advanced AI and technologies become, there is always going to be an important place for human interaction when delivering care. It is crucial to consider the essential roles played by doctors, nurses, clinicians or carers within your organisation. Also, consider what processes may be improved by technologies, such as automation or AI, to enhance the effectiveness of business activities and ultimately improve patient care. 

Consider use cases at all levels of your organisation

The potential application of AI can be considered at all levels of your organisation from leaders to middle management to service or care delivery. 

Leadership and culture 

For the successful adoption of AI within your organisation, it’s important that your leadership and culture adapt to support the deployment of AI. You need your leaders to create and foster an environment that embraces innovation and creativity within a safe space.

Education

It’s essential to understand and communicate what the AI can do and how it works, and crucially how it can work alongside your workforce. This will be important to defining your education needs.

Prioritising human oversight and governance

While the potential of AI in healthcare and life sciences is vast, it also comes with its share of challenges and risks. The importance of robust governance frameworks and human oversight must be emphasised, particularly for high-risk applications. This cautious approach ensures that AI technologies are implemented responsibly and ethically, with a focus on patient safety and well-being.

Capability

Consider whether you have existing skills within your organisation that can help you build AI to support and improve your processes. This will help identify where you need to train or build skills capability, or indeed to buy or leverage pre-built platforms and tools.

How we can help

Our technology consultants can help you navigate your digital journey towards optimised processes and enhanced visibility and insights. With our deep industry knowledge and technical expertise, we can help you identify the most suitable AI solutions for your organisation’s needs. 

To discuss the business transformation needs of your healthcare organisation, please contact Mohi Khan, Joel Segal, Suneel Gupta or your usual RSM contact.