How AI is Transforming Healthcare Diagnostics

Healthcare systems worldwide, including the NHS, are under immense pressure to improve productivity, reduce waiting times, and deliver better patient outcomes. Diagnostics, a critical component of patient care, is one area where AI is poised to make a transformative impact. From streamlining administrative tasks to enhancing diagnostic accuracy and speed, AI offers numerous opportunities to optimise processes and improve patient care.

However, barriers such as disparate systems across healthcare providers and unclear decision-making structures can hinder the widespread implementation of AI. Despite these challenges, the need to improve efficiency and care quality has never been greater. Now is the time to adopt AI to drive meaningful change in healthcare diagnostics.

In this blog, we’ll explore the key steps in the diagnostic process, highlight specific AI use cases that can help transform healthcare diagnostics, and outline critical success factors for successful AI implementation.

The Diagnostic Process in Healthcare

A typical diagnostic pathway in healthcare involves several key stages:

1. Referral & Appointment Booking
2. Pre-Assessment & Preparation
3. Diagnostic Testing & Results Analysis
4. Reporting & Documentation

AI Use Cases in Healthcare Diagnostics

Let’s explore how AI can be applied at each stage of the diagnostic process to enhance efficiency and improve patient outcomes:

1. Referral & Appointment Booking

• Automating CT Scan Scheduling for Nodule Surveillance
  AI can automate the scheduling of follow-up CT scans for patients requiring ongoing nodule surveillance. Intelligent automation ensures that appointments are prioritised and scheduled efficiently, reducing delays and manual workload.
• AI-Powered Diagnostic Pathway Suggestions for GPs
  AI can recommend the most appropriate diagnostic pathways for GPs based on patient history and medical data. This reduces unnecessary referrals, improves decision-making, and ensures patients are directed to the right tests at the right time.

2. Pre-Assessment & Preparation

• Automating Pre-Assessment Form Completion
  AI can pre-populate pre-assessment forms using patient data from electronic health records (EHR). This allows patients to verify or update their information, streamlining the process and reducing administrative burdens.
• AI Integration with Wearable Devices
  AI can extract and analyse data from wearable health devices, providing real-time insights into a patient’s health status. This data can be integrated into pre-assessment to provide a more comprehensive view of the patient before diagnostic testing.

3. Diagnostic Testing & Results Analysis

• AI-Assisted Vetting of Radiology Requests
  AI can vet and prioritise radiology requests based on urgency, ensuring that critical cases are processed first. This automation helps healthcare providers manage workloads more effectively and reduces delays in diagnosis.
• AI-Driven Image Analysis
  AI tools can assist in the analysis of diagnostic images, such as CT scans, MRIs, and X-rays. These tools improve the speed and accuracy of diagnosis, allowing healthcare professionals to focus on more complex cases and make faster, more informed decisions.

4. Reporting & Documentation

• AI-Generated Outcome Letters for Patients
  AI can automate the creation of outcome letters for patients following diagnostic tests or treatments. This ensures consistency and speed, helping patients receive clear, timely information about their health and next steps.
• Speech-to-Text AI for Clinical Coding
  AI-powered speech-to-text tools can transcribe conversations between healthcare professionals and patients, automatically converting them into clinical codes and updating patient records. This reduces manual documentation and improves the accuracy of health records.

Critical Success Factors for AI in Healthcare Diagnostics

For AI to be successfully integrated into healthcare diagnostics, several critical success factors must be considered:

1. Interoperability Across Systems
   Different healthcare providers, including NHS Trusts, often operate on disparate systems. Ensuring that AI tools are interoperable across these systems is crucial for seamless integration and data sharing, enabling broader adoption of AI solutions.
2. Clear Decision-Making Structures
   To avoid delays and confusion, healthcare providers must establish clear decision-making processes for AI adoption. Defining who has the authority to approve and implement AI technologies ensures a smooth and efficient rollout.
3. Staff Training and Engagement
   AI implementation requires more than just the technology—it requires staff buy-in. Providing comprehensive training on AI tools and ensuring that healthcare professionals understand their value is key to achieving successful integration.
4. Data Quality and Governance
   AI relies heavily on high-quality data. Ensuring that data governance practices are robust, and that electronic health records (EHR) are accurate and up to date, is critical for AI to deliver optimal results in diagnostics.

Conclusion: The Time for AI in Healthcare Diagnostics is Now

As healthcare systems worldwide, including the NHS, continue to face increasing demand and rising expectations, AI offers a clear path to improving diagnostics efficiency and patient outcomes. From automating appointment scheduling to assisting with image analysis, AI has the potential to revolutionise healthcare diagnostics. By addressing key barriers and ensuring that critical success factors are met, healthcare providers can unlock the full potential of AI and provide faster, more accurate care for patients.

Challenge

An NHS department recognised a substantial opportunity to integrate Artificial Intelligence (AI) into their services, aiming to improve patient outcomes and streamline administrative tasks. However, they faced challenges in developing AI literacy among their team members and identifying practical applications for AI technology. This foundational work was essential for building a compelling business case for transforming the a key function with AI at its core.

Solution

To address these challenges, Hudson & Hayes partnered with an NHS departmenr to enhance the team's understanding of AI and its applications. We engaged with 21 members of the Digital Transformation team and Integrated Care System (ICS) representatives, including clinicians, to deliver targeted training sessions focused on:

• AI Fundamentals: Providing foundational knowledge about AI technologies and their potential impact on healthcare.
• Governance: Educating staff on the ethical and regulatory considerations surrounding AI implementation in healthcare settings.
• Productivity Tools: Introducing workplace productivity tools such as Microsoft Co-Pilot, which can automate repetitive tasks and enhance workflow efficiency.

As part of the training sessions, we included multiple demonstrations and case studies on practical applications of AI in healthcare, covering areas such as disease prediction, medical large language models, tele dermatology and skin analytics, and robotic process automation (RPA). This hands-on approach helped the team to understand the real-world impact of AI technologies on improving patient care and operational efficiency.

In addition, we supported the development of an opportunity pipeline and a curated list of use cases that leverage various AI and automation capabilities, specifically designed to address the operational challenges faced by the organisation.

Key Outcomes

Through our collaboration, the NHS achieved significant milestones:

• 41 AI and Automation opportunities were identified, each with tangible benefits and a clear ROI, paving the way for transformative changes within the organisation.
• 21 staff members gained valuable training, enhancing their skills and understanding of AI applications in healthcare.

Conclusion

The partnership between Hudson & Hayes and the NHS not only fostered AI literacy but also equipped the organisation with the tools necessary for successful AI integration. This initiative positions the NHS to enhance patient outcomes and improve operational efficiency, laying the groundwork for a data-driven future in healthcare.

Client Information:

The client was a leading Integrated Care System (ICS) within the NHS, renowned for its scale and complexity.

The Challenge:

The ICS had recently centralised its Procurement function across nine Trusts, revealing prospects to standardise and streamline business processes, thereby ensuring consistency and unlocking operational efficiencies. A key issue was the manual supplier onboarding process, which involved nine distinct process versions and set-up forms, conducted primarily via email and Word documents. At the back-end, variations in Finance systems and databases further complicated the process.

Our Task: Our objective was to design a future-state process and a transformation plan aimed at standardising the process, minimising rework and waste, and implementing automation and digitalisation, thereby reducing risk and enhancing compliance with EDI and sustainability standards.

Our Approach:

1. Initiation: We developed a comprehensive project plan, a communication strategy, and a stakeholder engagement plan. Engagement at the Trust level was pivotal to ensure widespread buy-in. Utilising existing policies and processes, we created a detailed view of the current state and established project governance for effective oversight and communication.
2. Current-State Assessment: We meticulously mapped the supplier onboarding process across the nine Trusts, identifying areas of waste, rework, and potential improvements. A detailed inventory of tools and systems used at each step was compiled to pinpoint digitalisation and integration possibilities. A RACI matrix was created to clarify roles and responsibilities across the Trusts.
3. Future-State Design and Transformation Plan: We devised an interim future state, considering existing organisational and technological constraints. This included a digital front-end using low-code workflow solutions integrated with RPA for data input into Finance systems. We refined the onboarding form, focusing on EDI, sustainability, and eliminating redundant fields. Our end-state vision incorporated eProcurement, supplier master data management, and automated third-party checks. We then structured these changes into a transformation roadmap, aiming for iterative process maturity and benefit realisation.

The Outcome:

Identification of the following potential benefits:

• Identified a 50% reduction in time required for supplier onboarding.
• Opportunity to consolidate eight onboarding processes and forms into a single streamlined procedure.
• Calculated a 40% decrease in effort, allowing a shift towards more value-adding activities.
• Opportunity to transition from an Email & Word-based system to a more efficient, digitalised, and automated front-end.
• Enhanced visibility of onboarding performance metrics, including timeframes, volume, and instances of rework.