Within just 2.5 years, Generative AI has disrupted entire industries. For a time, its potential was constrained by the materials the technology was exposed to; then the ability to understand this in context. But with escalating momentum those limitations are being overcome. This is presenting a challenging duality: a future that is already here, yet still largely unknown. In life sciences, where an advantage lost could mean patients missing out, companies are wondering how on earth they move forwards?

The unstoppable force of AI

GenAI is the branch of artificial intelligence that uses everything that is known already to create something new. From early conversational capabilities, through reasoning, the technology is already delivering ‘agentic’ capabilities (goal-driven abilities to act independently and make decisions with human intervention only where needed).

There are early signs too that “innovating AI” is emerging. That’s as AI becomes capable of creating novel frameworks, generate fresh hypotheses, and pioneer new approaches. This creative potential pushes AI from merely processing information to actively shaping the future of scientific discovery, applying it to problems yet to be solved.

At the core of the latest GenAI advances is the accelerated pace of large language model (LLM) development. These deep learning models, trained on extensive data sets, are capable of performing a range of natural language processing (NLP) and analysis tasks, including identifying complex data patterns, risks and anomalies. A growing movement towards open-source GenAI models, meantime, is making the technology more accessible and customisable (alongside proprietary models).

Reimagining scientific discovery and deep research

In life sciences, there are persuasive reasons to keep pace with and harness latest developments as they evolve. GenAI is poised to become a gamechanger in scientific discovery and new knowledge generation – at speed and at scale.

In human intelligence terms, we have already reached and surpassed human expertise levels[1]. Recent advancements in Agentic AI models have even led to the need for a new benchmark[2].

The advanced reasoning promise, a highlighted benefit of DeepSeek’s latest AI model, has enormous scope in science (enabling logical inferences and advanced decision-making). Google and OpenAI both have Deep Research agents that go off and perform their own searches, combining reasoning and agentic capabilities. As reasoning capabilities continue to improve, and as the technology becomes more context-aware, the potential to accelerate scientific discovery becomes real through the creation of new knowledge. The ability to project forward, and consider “What if?” and “What next?”.

Already OpenAI’s Deep Research is optimised for intelligence gathering, data analysis and multi-step reasoning. It employs end-to-end reinforcement learning for complex search and synthesis tasks, effectively combining LLM reasoning with real-time internet browsing.

Meanwhile Google has recently introduced its AI co-scientist[3], a multi-agent AI system built with Gemini 2.0 as a “virtual scientific collaborator”. Give it a research goal, and off it will go – suggesting novel hypotheses, novel research and novel research plans.

Which way now?

With all of this potential, the strategic question for biopharma R&D becomes one of how to keep pace with all of these technology developments and build them into the business-as-usual; how to prepare for a future that is simultaneously already here yet continuously changing shape?

Up to now, most established companies have experimented with GenAI to see how it might address everyday pain points in Safety/Pharmacovigilance, Regulatory, Quality and some Clinical and Pre-Clinical processes. These activities been largely about becoming familiar with the technology, and assessing its trustworthiness and value. Others have gone further, creating lab-like constructs for experimentation.

Yet the hastening pace of technology development, and the intangibility around what’s coming, means that the industry now needs to embed AI more intrinsically within its infrastructure and culture. This is about proactively becoming AI-ready rather than simply “receptive to” what the technology can do.

Being discerning as “experts” hover

In the past, a popular approach to a hyped new technology or business change lever has been to “pepper” associated champions across the business. In this case, some organisations are taking a venture-capital like approach of bringing in non-native AI talent to key roles – visionaries and master-crafters from other industries. But AI is moving so quickly, and its likely impact is so fundamental to life sciences, that experts need to be “neck deep” in it to be of strategic value.

One of the biggest challenges now is the duality companies are now grappling with: the simultaneous need to be ready for and get moving with deeper AI use today, while gearing up for a tomorrow that is likely to look very different. This has widespread “change” implications: at a mindset and method level; and from a technical and cultural perspective – both today and tomorrow.

For this reason, strategic partnerships are proving a safer route – with tech companies that are fully up to speed with the latest developments, are enmeshed in it and its expanding application, and are actively building sector-specific solutions. Even so, companies will need to choose their AI advocates wisely, as “AI washing” is commonplace among consultants and service providers now, as new converts to the technology inflate their credentials in the field.

The good news is that internal IT and data teams are well versed in AI technology today, and have high ambitions for it. The challenge is bringing the technology’s potential to fruition where it could make a difference strategically. This is likely to require involve sitting with an organisation’s real problem areas, and understanding if and how emerging iterations of AI might offer a solution.

About the author

Jason Bryant is Vice President, Product Management for AI & Data at ArisGlobal, based in London, UK. A Data Science Actuary, he has built his career in fintech and healthtech, and specialises in AI-powered, data-driven, yet human-centric product innovation.

[1] Measuring Massive Multitask Language Understanding, Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021): https://arxiv.org/pdf/2009.03300

[2] Humanity’s Last Exam, November 2024, https://agi.safe.ai/

[3] Accelerating scientific breakthroughs with an AI co-scientist, Google Research blog, February 2025: https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/

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Despite these advancements, the sector still faces challenges in fully realising the potential of digital maturity compared to other industries.

Looking ahead to 2026, several key trends will shape the future of biotech, driven by the integration of digital technologies and advanced analytics.

Artificial Intelligence (AI) in Biotech and Drug Discovery

AI is accelerating the discovery of novel therapeutics by streamlining the identification of promising drug candidates.

Machine learning algorithms analyse vast biological datasets to identify viable molecules, significantly reducing R&D costs and timelines. AI-powered platforms enhance target identification, lead optimisation, and preclinical testing, improving efficiency in biotech research.

Cloud Computing for Biotech Innovation

Cloud and edge computing are revolutionising the scalability and innovation potential of biotech firms.

With enhanced data sharing, real-time collaboration, and seamless AI integration, cloud computing enables faster drug development cycles and robust data security. Companies leveraging cloud-based platforms will gain a competitive advantage in operational efficiency and scientific breakthroughs.

Machine Learning (ML) for Drug Development

Industrialised machine learning is transforming every stage of drug development. From predictive modelling in clinical trials to optimising biologics formulations, ML enhances data-driven decision-making. Advanced algorithms refine predictions, minimise trial failures, and accelerate regulatory approval processes for new therapies.

Real-Time Analytics in Clinical Trials

The demand for more efficient and effective clinical trials has led to greater adoption of real-time data analytics. AI-powered data processing enables biotech companies to monitor patient responses, detect anomalies early, and optimise trial designs. This trend is particularly critical in rare disease research, where patient recruitment and retention remain key challenges.

Investment in Digital Health Technologies

Venture capital is flowing into digital health solutions, particularly those that enhance patient engagement, remote monitoring, and commercialisation strategies. Biotech firms are increasingly partnering with health tech start-ups to develop wearable devices, mobile applications, and AI-powered telemedicine solutions that improve patient outcomes and treatment adherence.

Data-Driven Decision Making

Biotechnology companies are leveraging big data to optimise research, clinical development, and commercial operations. Advanced analytics provide deep insights into patient behaviour, biomarker discovery, and market dynamics, enabling more precise business and scientific strategies. Organisations that successfully utilise data-driven decision-making will drive innovation and maintain industry leadership.

Synthetic Biology and Precision Medicine

Synthetic biology is rapidly emerging as a disruptive field for engineering novel biological systems. By designing customised treatments for genetic disorders, regenerative medicine, and vaccine development, synthetic biology offers unprecedented potential for addressing unmet medical needs with precision and efficiency.

Decentralised and Virtual Clinical Trials

The shift towards virtual and decentralised clinical trials is improving patient accessibility, recruitment, and trial efficiency. AI-driven analytics, remote monitoring tools, and telemedicine solutions allow biotech companies to conduct trials with greater flexibility while ensuring data integrity and regulatory compliance. This trend is redefining the clinical trial landscape, making drug testing more patient-centric.

Quantum Computing in Drug Discovery

Quantum computing is poised to become a game-changer for biotech. By simulating molecular interactions at an unprecedented scale, quantum computers could dramatically accelerate drug discovery. While still in its early stages, this technology holds immense promise for solving complex chemical and biological challenges beyond the capabilities of traditional computing.

AI-Powered Diagnostics and Personalised Medicine

AI is transforming diagnostics by enabling early disease detection and precision medicine. AI-driven imaging, pathology analysis, and predictive algorithms are revolutionising how diseases are diagnosed and treated. As healthcare shifts towards personalised medicine, AI-powered diagnostics will play a crucial role in advancing targeted therapies and improving patient outcomes.

AI-Driven Scientific Research Assistants

AI-powered research assistants are becoming indispensable tools in biotech and life sciences. These digital assistants automate data analysis, literature reviews, and experiment documentation, significantly enhancing productivity. By integrating with cloud computing and real-time analytics, AI-driven assistants foster collaboration, accelerate discoveries, and reduce the workload for human researchers.

Conclusion

As we move towards 2026, the integration of digital and AI-driven solutions in biotech is not just a trend—it is a necessity. Companies that invest in these innovations will lead the charge in scientific and medical advancements, driving faster drug development, improving patient care, and optimising research operations. The future of biotechnology is digital, and those who embrace this transformation will be at the forefront of innovation and discovery.

Kevin Cramer, CEO, Sapio Sciences

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To address these challenges, Resonance Health developed FerriScan®, a system where MRI centres worldwide could send image data scanned either by 1.5T or 3T scanners to expert analysts for accurate LIC measurement. This system has been invaluable for hospitals treating patients and pharmaceutical companies developing and accessing drug efficacy with conditions like thalassemia, who require frequent and precise iron measurements.

Introducing FerriSmart®: The Future of Automated MRI Iron Measurement

In a significant leap forward, Resonance Health has harnessed artificial intelligence to create FerriSmart®, an automated system for calculating LIC from MRI images. This system simplifies the process: radiologists can upload image data into a secure portal hosting the software, which then provides a LIC report within seconds. FerriSmart® maintains the high standards of FerriScan® and is compatible with a wide range of MRI scanners, ensuring broader accessibility.

Liver iron concentration measurement through MRI is a powerful tool in diagnosing and managing conditions like hereditary hemochromatosis and thalassemia. With the advent of FerriSmart®, we are making this technology more accessible, offering rapid, reliable results at a lower cost. As we continue to expand the availability of these advanced imaging solutions, we remain committed to enhancing patient care and supporting clinicians with precise, non-invasive diagnostic tools.

Measuring Iron in Other Organs

MRI technology is also capable of assessing iron in the heart using the CardiacT2* technique which has regulatory approval for routine clinical practice. This method is frequently used in patients with thalassemia to monitor cardiac iron levels. Similarly, for investigational purposes, MRI can measure iron in the bone marrow, another suspected target organ. While not many studies have focused on this, there is evidence that patients with hereditary hemochromatosis can have excess iron in their bone marrow.

The spleen plays a crucial role in recycling iron from old or damaged red blood cells. Excess iron can be found in the spleen, due to different conditions, such as, hemochromatosis, and regular blood transfusion dependent patients, which can lead to several health issues. Accurate spleen iron measure can be important for proper diagnosis and treatment. Early intervention can help manage the condition and prevent complications.  The pancreas is another organ under investigation. Historically, hereditary hemochromatosis has been associated with diabetes, suggesting a link to pancreatic iron overload. Although MRI techniques for measuring pancreatic iron are still investigational, they hold promise for future clinical use. Additionally, iron accumulation in the spleen is typically not expected in hereditary hemochromatosis but is a significant marker in ferroportin disease, which can initially present similarly to hemochromatosis.

The Role of MRI in Assessing Liver Fat

An important complementary measurement to liver iron concentration (LIC) is the assessment of liver fat with HepaFatSmart®. Using MRI, we can quickly determine the presence of fatty liver, which can influence serum ferritin levels. This dual measurement can clarify ambiguities in diagnosing the cause of elevated ferritin, providing a clearer picture of a patient’s condition.

What to Expect During a FerriScan® or FerriSmart® MRI

For patients, the procedure is completely non-invasive, with no need for injections or contrast agents. Patients lie on a scanner bed with a radio antenna placed on their abdomen, wearing earmuffs or headphones to protect against the scanner’s noise. The data acquisition takes about 8 minutes, during which patients must remain still and breathe gently.

We understand that some patients may feel nervous or claustrophobic. To mitigate this, the scanner tube is open at both ends, with fresh air circulating, and patients can always communicate with the radiographer. Patients can also hold a device to alert the radiographer if they feel uncomfortable, and they can enter the scanner feet first if preferred. Closing eyes or using a blindfold, along with listening to relaxing music, can also help ease anxiety.

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Ask Google’s Gemini assistant how many articles have been published on AI in the UK over the past year, and the response is that the data is “too vast and dispersed” to say, but “it’s a very active area of discussion.”

Or, as Jeremy Nettle, chair of the Highland Marketing advisory board, said it’s almost impossible to avoid news about AI and new uses of AI just now: and not all of it is good.

“There has been some pretty horrific AI coming out of the US about Trump, and Gaza,” he said, referring to a widely circulated video showing a golden statue of the US president in a Las Vegas-style Gaza strip.

Putting the full weight of the British state behind AI  

This has not stopped the government making a big bet on AI. Last July, UK science secretary Peter Kyle asked tech entrepreneur Matt Clifford to conduct an AI ‘opportunities review’ and in January the government accepted its recommendations.

These range from creating the power and water infrastructure needed for new data centres, to setting up a library of public data sets to train new models, to creating ‘growth zones’ and running ‘scan, pilot, scale’ projects’ in public services.

Prime minister Sir Keir Starmer made a speech promising to put “the full weight of the British state” behind the plans. And a week later, the government published a ‘blueprint for modern, digital government’ with a big role for AI – and a suite of tools for civil servants, called Humphrey.

But haven’t we been here before? 

While Sir Keir’s speech made a splash, consultant and former NHS CIO Neil Perry pointed out that we’ve heard similar things before. “Previous governments have talked about being leaders in technology and investing in new UK companies, and we’re not seeing it come through,” he said.

As a case in point, the advisory board considered the development of AI in the NHS, which has so far mainly focused on clinical decision support tools and automated reporting for x-rays and scans.

Entrepreneur Ravi Kumar, whose company CyberLiver works on digital therapies for advanced liver disease, said this requires a huge amount of development work, regulatory compliance, and liaison with clinicians.

“Getting AI into a practical, implementable state to work in a clinical setting is very expensive,” he said. “So, when the government talks about creating a home-grown industry, we have to be realistic about the number of companies that will be able to persist long enough to make the grade.”

NHSX, the digital unit set up by former health secretary Matt Hancock, recognised this problem. It set up a National Artificial Intelligence Lab to “bring together academics, specialists and health tech companies” to “work on the biggest challenges” facing the system.

While it did some interesting work, advisory board members felt its record was spotty when it came to growing UK businesses. Some prominent health-AI companies have lost out to over-seas competitors or left for bigger markets themselves. And there’s no money on the table for anything similar, today.

Decision support tools: company and clinician experience has been mixed 

From a clinical perspective, radiology expert Rizwan Malik also felt mistakes had been made. “Millions have gone into AI in the NHS, particularly in the diagnostic imaging space,” he said. “But we gave a lot of companies money to develop things without asking what the business case was.

It was: ‘AI is the answer, now what’s the question?’” As an example, he said AI is good at detecting abnormalities in chest x-rays, but so are radiologists, who now have to check both the chest x-ray and the AI’s interpretation of it. Which adds to their workload, for unclear benefit.

“That’s why I say we need business cases,” he said, underlining that those business cases need to think about how AI can help individuals to work smarter, not just harder.

“At the moment, it’s always: ‘how many more scans will you be able to do with this, Rizwan?’ It’s never: ‘how will this make your practice safer’? Or: ‘how will it improve outcomes?’ Or: if it can do those things, is it worth the price tag?”

Coming soon: generative AI 

The AI that everybody is talking about right now, though, is generative AI or the large language models like OpenAI’s ChatGPT, Google’s Gemini, and the open-source Llama.

These take large-scale data inputs and use them to predict what character is most likely to follow another character, creating (or generating) new outputs in the process, such as a response to a web query, or a block of code, or a social media post.

Jason Broch, a GP and CCIO, said he was worried about putting LLMs into clinical spaces. “We have used Microsoft Copilot for administrative work,” he said. “Some of the people who take minutes at meetings have tried it, and they have found it can cut the time involved in producing a report from three hours to one hour.

“But that is because they are experts at producing reports. In a clinical setting, we don’t know whether the output from an LLM is good, or not.”

People are using AI assistants because they are free or, increasingly, built into consumer software packages. But they’re not transparent. “We don’t really know what data [a model] has been trained on,” he said. “It produces an output, but we don’t really know how it does that.

“If you run a prompt again, it can come up with a completely different output. We need guardrails for the use of LLMs. Or we need healthcare specific models, because if we are going to scale the use of these tools in the NHS, we need to be able to trust them.”

Sam Neville, a nurse and CNIO, agreed. “Trust is an important word,” she said. “Staff do not trust this technology, and patients don’t trust it either.

“If we tell patients that we are going to put their information into a third-party system like a patient portal, they don’t like it. If we tell them that the NHS is looking at AI, they think Trump video. They think we are just going to make things up.”

Where’s the regulation, where are the guardrails? 

David Hancock, a consultant and interoperability expert, said he is worried that the government is paying far too little attention to these issues, in its dash for growth and productivity gains.

The EU, he pointed out, has passed legislation (the EU AI Act) to ban certain uses of AI and encourage transparency and labelling. Whereas the UK’s approach does not have the same level of emphasis on human rights protections.

“The UK government has said that it sees not being in the EU as an opportunity, so it sounds as if it is not going to go down the same route,” he said. “It looks as if it will allow this to be more commercially driven, as it is in the US.”

Nicola Haywood-Cleverly, a former CIO and consultant who also works as an NHS non-executive director, felt the NHS also needs to think much harder about the data that is being fed into these tools. “We all know there is a lot of concerns regarding data quality out there,” she said. “If we want to train good models, we need better data to train them on.”

The NHS will also need better infrastructure, she added, to make sure new tools are properly embedded into clinical workflows, and clinicians are clear about when they are using AI outputs.

Neil Perry said this raised the question of how the NHS can make sure new AI tools are implemented safely. “I have just joined one of NHS England’s panels looking at refreshing DCB 0129 and 0160 [clinical risk management standards for companies and organisations looking to roll-out digital systems].

“One of the first questions asked was: is the standard fit for AI? And the answer is: not really. In fact, it’s not really fit for two-week sprints [software development cycles]. When DCB 0129 and 0160 were written, the NHS was lucky if it got a system update yearly. We need to refresh methodologies. And we need to educate and include clinicians and patients.”

Jane Brightman, a social care expert who works at Skills for Care, said social care staff and people drawing on care also need to be brought into the picture. The social care sector is doing some work with the University of Oxford on the “ethical use of AI” that should lead to some basic principles for its development and deployment.

Time to think clearly on NHS AI

Jason Broch also suggested that the NHS needs to avoid some of the mishaps that it has made with AI to date by thinking clearly about what LLMs are good at and where that can resolve some of the challenges that the NHS is facing.

“We need to get cleverer about language,” he said. “We talk about LLMs as if they generate meaning. But they don’t. We talk about ‘hallucinations.’ But the LLM isn’t hallucinating. It’s doing what it’s meant to do, it’s just that we don’t like the output. So, we need to understand that these things are a great language tool, but they are not a cognition tool.”

Following on from this, he suggested the best uses of generative AI in the NHS might be in helping with language tasks, such as summarising a mass of patient records before an appointment, or generating communications.

Advisory board members had many other ideas for using AI alongside other technologies. Sam Neville said she is looking at an AI tool that can review trends in outpatient appointments to identify patients who may be at risk of ‘DNAing’ or not attending appointments.

David Hancock said the NHS could usefully run something similar over its patient reported outcomes or PROMS data, to find out what it is getting for its money.

Highland Marketing chief executive Mark Venables said it is working with an AI firm that can take vital signs information from patients waiting for admission and alert clinical teams to signs of deterioration.

Neil Perry suggested that similar technology could be used in A&E, to make long waits safer. “We can argue about whether all of this is AI, or whether it is just technology,” he said. “The point is that it automates what we do anyway, accurately enough to trigger an alert that leads to a human decision.”

Build out, take people with you 

The biggest problem, he said, is that in the current NHS financial environment projects like this are difficult to implement. He argued that instead of making big statements about NHS AI, the government should focus on where it could address the big “volume” issues and use its buying power to secure solutions for the whole system.

“Back in the days of the national programme for IT we used to talk about ‘ruthless standardisation’,” he said. “Perhaps we could do a bit of that now. Build AI tools into the NHS App and 111 services to detect and diagnose conditions, or read vital signs from a selfie and direct patients to the most appropriate service.

“The technology is available; we need to make it meaningful, useful and used at scale.” Meanwhile, Rizwan Malik argued there were some good things to have come out of the faltering start that has been made on AI so far.

“The upside is that we have experience of decision support tools,” he said. “So, perhaps we can start talking about the best way to use them. Instead of sending everybody for an MRI or CT scan we can start talking about which patients really need them. Or which patients need to go first.

“We could make incremental improvements. For the millions invested so far, we cannot say we are at the forefront of AI in healthcare in the UK, or that we are supporting UK plc. But we do have a workforce ready to have meaningful conversations going forward.”

About the Highland Marketing advisory board        

The Highland Marketing advisory board includes: Jeremy Nettle (chair), formerly of Oracle and techUK; Cindy Fedell, regional chief information officer at North western Ontario Hospitals, Canada; Nicola Haywood-Cleverly, a former integrated care system chief information officer, non-executive director for NHS foundation trusts, and health tech strategist and advisor; Andy Kinnear, former director of digital transformation at NHS South, Central and West Commissioning Support Unit and now consultant at Ethical Healthcare; Ravi Kumar, health tech entrepreneur and chair of ZANEC; Dr Rizwan Malik, consultant NHS radiologist and director of SMR Health Tech Consultancy; James Norman, EMEA health and life science director, Pure Storage; Ian Hogan, CIO at the Leeds and York Partnership NHS Foundation Trust; Neil Perry, former director of digital transformation at Dartford and Gravesham NHS Trust and now director at Synergy Digital Health Innovation; David Hancock, digital health strategist specialising in interoperability; Jane Brightman, director of workforce strategy at Skills for Care; Jason Broch, GP and CCIO at Leeds Health and Care Partnership.  

About Highland Marketing           

Highland Marketing is a specialist marketing, communications, market access and consultancy agency, focusing on the health tech and med tech industries. We offer an integrated range of services, covering all elements of the marketing mix, to help organisations achieve their goals by ensuring their messages are heard, understood, and acted upon by their chosen audiences. Our highly experienced and well-connected team has deep knowledge of health and care technology, strong contacts in the industry, and is well-versed in delivering effective campaigns and content. We support clients across the NHS and EMEA healthcare markets and work with clients looking to expand from the UK into international markets, and with overseas companies looking to enter the UK market.       

Website: www.highland-marketing.com  X: @HighlandMarktng

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• Taking notes to record interactions such as meetings and phone calls with clients.
• Updating and managing a client’s case file and records.
• Preparing reports for supervisors or for legal purposes.
• Completing general paperwork or forms for legal and administrative purposes.
• Scheduling meetings and visits on a daily basis.

And much more. However, from things like digital case management systems, to telehealth and online counseling, to being able to complete online degrees, like an online MSW degree, technology has come a long way in transforming the social work sector, and we now have access to technologies that could potentially cut the time that social workers spend at their desks. By integrating AI into social work, we have the opportunity to create more efficient and accurate systems that reduce the administrative burden, freeing up social workers to do more of what they do best—spend time helping people.

AI administrative support for social workers

At the moment, 28 councils in England are using a specialized AI tool called Magic Notes that records meetings between clients and social workers and produces a meeting summary file. The AI tool works much like other AI meeting-summary assistants (you might be familiar with tools like Microsoft’s CoPilot, Google Gemini’s “Take notes for me” function, and Zoom’s AI-assistant), by listening to the session and producing a complete transcript, before using AI to summarize that transcript into easily digestible notes. At the end of the session, Magic Notes emails the practitioner a transcript, summary and AI-powered suggestions for what to include in case notes.

Early results show that implementation of the Magic Notes software has resulted in huge gains in efficiency and reduction in time spent on administrative tasks, with one council noting that the AI reduced admin time by a dramatic 48%. Not only that, but the AI has helped social care workers produce reports that are more accurate and evidence-based, leading to a higher quality of output that more efficiently communicates findings to the supervisor or other parties reading the reports. While Magic Notes is still in testing stages in the UK, and is yet to reach our shores here in the US, its success overseas shows promise that it, or something like it, might soon come to America.

Things to keep in mind

While the growth of AI in the social care space has potential to reduce practitioners’ workloads, it must also be used with caution, to reduce errors and other potential problems.

Data privacy and security

Social care is often also healthcare, and with healthcare rapidly becoming a top target for cyber criminals, it’s more important than ever that confidential patient data used by AI tools and companies is securely and privately held. This means that AI companies that offer note-taking or schedule-supporting AI tools should also reassure clients that data is held ideally onshore here in the States, and that that data is used only for a temporary period of time to generate relevant artefacts that are then emailed out to practitioners, before the initial unprocessed data is deleted.

Accuracy

AI, like any other tool, is fallible, and can make mistakes like any human. It also can “hallucinate” or make up information that doesn’t exist, but sounds accurate. It’s crucial that practitioners not totally rely on AI, but either use AI to support their own work (i.e., using the summary notes to create their own reports, or using AI-suggested templates to make their report writing faster), or read and closely check over AI-generated reports before submission.

Biases

AI is trained on historical data, and that data can reflect existing biases related to race, gender, status, or disability. When using AI, practitioners should note that AI tools could unfairly prioritize or de-prioritize specific clients—for example, an AI algorithm used by a Pittsburgh-area child protective services agency was accused of disproportionately flagging families with disabled members for investigation. Other potential biases include an AI lacking crucial context for a certain case, and therefore creating irrelevant or overly simplistic results.

At the end of the day, although AI has the potential to change the lives of many social workers, the technology is still new and should be used carefully.

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The final rule, an extension of the ACA’s original non-discrimination provisions, sets new, particularly pertinent standards as healthcare evolves in the digital age. For healthcare providers, this new regulation signals a shift towards inclusivity and accessibility in healthcare delivery systems, especially as telemedicine becomes an increasingly central part of patient care.

Telemedicine Accessibility: Meeting New Standards

Telemedicine has proven vital in making healthcare more accessible, particularly in remote or underserved areas. However, as its use has become more widespread, it has become clear that additional steps are needed to ensure that telehealth services are accessible to all individuals, including those with LEP and disabilities.

Under the new rule, telemedicine programs and telehealth services must be accessible to individuals with LEP. Telehealth platforms must incorporate language services like spoken language interpretation into their systems. Accessibility features like closed captioning, sign language interpretation, and other adaptive technologies are required for individuals with hearing impairments or other disabilities to ensure these platforms are fully usable and practical for all patients.

The expansion of telemedicine brings with it a responsibility to ensure that it remains inclusive. Healthcare providers must now adapt their telehealth platforms to meet these new standards, providing accessibility for individuals previously marginalized or excluded from digital healthcare systems. These requirements include ensuring that virtual visits, follow-up consultations, and digital health services are all available to those who need them, regardless of language proficiency or disability.

Language Assistance: A Critical Mandate

Language is often the most significant barrier to adequate healthcare. Misunderstandings caused by language barriers can result in missed diagnoses, incorrect treatments, and overall poor health outcomes. The new rule seeks to mitigate these risks by mandating that covered entities provide cost-free, timely, and accurate language assistance services to individuals with LEP.

Under the regulation, healthcare providers, insurers, and other covered entities must offer qualified linguists for in-person and telemedicine consultations, including follow-up care, discharge instructions, and written communications. Healthcare organizations must ensure they train all staff members to use these services effectively and to notify patients of their right to language assistance at no charge.

The rule further ensures that individuals with LEP can access materials and information in a language they understand, including critical medical documents such as consent forms, prescriptions, and instructions for care.

This provision clearly recognizes that effective healthcare depends on accurate communication, which is only possible when language barriers are eliminated.

This requirement is a significant step forward for healthcare providers, who must guarantee equal care to all patients, regardless of their primary language. At Ad Astra, we work with healthcare providers to implement systems that ensure consistent and effective language access services, helping patients navigate the complexities of medical environments with clarity and confidence.

In addition to language assistance services, the new rule addresses integrating technology into healthcare communication.

LLMs in Translation: A Balanced Approach

As technology plays an increasingly important role in healthcare, the new rule acknowledges the role of LLMs while recognizing the technology’s limitations in accuracy and nuance—particularly in the medical field, where precision is paramount.

The rule stipulates that any LLM used in healthcare must be reviewed and corrected by a qualified human linguist to ensure the accuracy of the information. This requirement is crucial because even minor errors in medical translation—such as misinterpreting dosage instructions or medical terminology—can have severe consequences for patient safety and health outcomes.

Artificial Intelligence has significantly advanced in recent years but still cannot fully replicate a human linguist’s cultural and contextual understanding. By requiring human oversight, the regulation ensures that healthcare providers use technology to enhance care while maintaining the high standards of quality and safety that human linguists possess.

Key Takeaways and Implications for Healthcare Providers

Now that the July 2024 implementation date has passed, healthcare providers must begin preparing to meet these new requirements. Critical aspects of the rule that affect healthcare organizations include:

1. Telemedicine Accessibility: Telehealth platforms must integrate language assistance and adaptive technologies to accommodate LEP individuals and those with disabilities. This stipulation may require updates to telemedicine software, including the addition of real-time interpretation services and accessibility features like closed captioning.
2. Comprehensive Language Assistance: Healthcare providers must ensure that all their patients, regardless of language, can access professional interpretation and translation services. This requirement includes providing verbal and written communications in the patient’s preferred language and ensuring timely access to qualified linguists for telemedicine consultations.
3. AI Oversight: While AI-assisted translation is permissible, healthcare providers must implement systems to ensure that such translations are reviewed and corrected by qualified human linguists to safeguard the accuracy and safety of medical information.
4. Implementation Timeline: Providers must implement these changes within a year of the rule’s effective date. This timeline means that healthcare organizations should act now to ensure compliance, including staff training, software updates, and readily available language assistance services.

Ethical Challenges and Technological Solutions

As we embrace new technologies like AI in healthcare, it is critical to balance innovation with accessibility. There are several ethical considerations to address:

• Bias in AI: If they are not carefully developed and monitored, AI systems can inadvertently reinforce biases. Therefore, it is crucial to ensure that AI tools are designed to provide accurate, culturally competent translations and recommendations.
• Ensuring Human Oversight: While technology can aid in efficiency, human oversight remains essential to ensure that technology does not replace the empathy, context, and cultural understanding that human linguists provide; this is vital in healthcare, where the stakes are high.
• Balancing Access and Equity: These regulations ensure that all patients, regardless of language or disability, receive the care they need. However, as healthcare organizations implement these new systems, providing equal access to services for all patients–including those in rural or underserved areas who may face challenges in accessing telemedicine platforms–is essential.

The Future of Healthcare: Inclusivity Through Technology

The new Section 1557 regulations set a bold vision for a healthcare system where accessibility, inclusivity, and accuracy are paramount. These changes represent a critical step forward in the ongoing effort to eliminate disparities in healthcare and ensure that all patients—regardless of language, ability, or background—receive the highest standard of care.

At Ad Astra, we are excited to support healthcare providers as they adapt to these new standards, leveraging our expertise in interpretation, translation, and telemedicine accessibility. As we move toward a more inclusive healthcare future, we are committed to ensuring that all patients, regardless of their background, receive care that is not only accessible but also accurate, timely, and compassionate.

By Lena Petrova, CEO of Ad Astra

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The pharmaceutical cold chain is becoming a cornerstone of global supply chains, shaped by new regulations, innovation, and the need for resilience.

This year, the industry witnessed remarkable innovations across the broader pharmaceutical distribution landscape. One standout development was the rapid growth of direct-to-patient logistics. Pharmaceutical companies have made strides in delivering treatments directly to patients’ homes. These advancements not only enhance patient access but also reflect a wider trend towards personalised, patient-centric supply chains.

Hard on the heels of a year shaped by innovation, sustainability, and security, 2025 now promises the pharmaceutical industry even greater transformation.

Strengthening supply chain resilience

Over this past year, global tensions and geopolitical conflicts have created ongoing logistical disruptions and bottlenecks. These challenges can vary from port closures to sanctions or trade restrictions, all of which impact delivery efficiency and cost. Such issues expose vulnerabilities in the pharmaceutical supply chain, leading to significant delays or, in worse cases, spoiled or cancelled shipments.

In response, companies in 2025 will need to diversify logistics strategies and focus on working with partners who are able to quickly adapt in response to changing circumstances or risks in the supply chain. Cold chain solutions will also evolve, with a growing emphasis on redundancy and alternative routes to avoid disruptions and support the reliable delivery of critical medicines.

Elevating sustainability standards

Following on from COP29, the call for sustainable practices in pharmaceutical delivery is louder than ever. This summit marked a pivotal moment, as countries are now urged to integrate health considerations into their Nationally Determined Contributions, which will significantly impact the pharmaceutical industry.

This increased pressure will lead pharmaceutical companies to evaluate and adapt their ESG requirements, making sustainability a non-negotiable factor for suppliers. Building on this expectation, advanced cold chain providers are shifting their focus from merely avoiding waste to finding the most sustainable way to distribute essential medicines. This includes both reusable equipment and optimised single-use solutions that are lighter, use fewer raw materials, or are biodegradable, enabling effective resource use even when disposability is necessary.

As environmental awareness continues to grow, sustainability standards across the industry will continue to rise. The next step for the industry is to review and address supply chain emissions, which are often the hardest to reduce. Companies can achieve this independently, but submitting emissions targets to the Science Based Targets initiative provides a public demonstration of their commitment to taking meaningful action.

Expanding into emerging markets

Despite global tensions and geopolitical conflicts, the pharmaceutical industry is increasingly expanding its reach into underserved markets – and this reach will continue to grow next year. This push is essential for bringing critical medicines to new communities, yet logistics and storage infrastructure in these regions can create additional complications.

Cold chain logistics rely on stable, temperature-controlled environments to protect sensitive medicines. However, outdated infrastructure in emerging markets often means smaller storage spaces, unreliable electrical supplies, and no return logistics for reusable packaging. These factors make it problematic for cold chain providers to maintain the necessary conditions consistently.

To overcome these challenges, market expansion will require specialised packaging and logistics solutions that address local infrastructure limitations. These adaptations will be key to enabling successful market entry and supporting long-term growth.

Rising mergers, acquisitions, and strategic partnerships

We have witnessed an increase in merger and acquisition activity in the pharmaceutical industry in 2024, and we expect to see this continue to rise in 2025. Pharmaceutical companies are also increasing their strategic partnerships with CMOs and CDMOs to accelerate innovation and reduce time-to-market. By pooling resources and expertise, companies will be able to streamline the R&D process, paving the way for faster production and delivery of high-demand drugs.

The beneficial outcomes will be set to multiply if companies integrate cold chain logistics into these new partnerships. Not only will this support the rapid scale-up of new therapies, but it will also help build a robust supply chain for new markets.

Adapting to small-batch shipments

As the demand for smaller, high-value pharmaceutical shipments grow, cold chain logistics will need to become more agile and adaptable to meet these evolving needs. These solutions are essential to ensure highly sensitive medicines, such as personalised treatments, are stored and transported under precise temperature-controlled conditions.

Additionally, AI-driven clinical trials are accelerating drug development, leading to faster production cycles that also require efficient, responsive cold chain solutions to handle smaller but high-value, sensitive shipments.

Finally, the growing prevalence of decentralised clinical trials adds to the demand for robust cold chain logistics. These trials, which involve direct delivery of experimental therapies to patients’ homes, rely on precision cold chain networks to maintain the integrity of highly sensitive medicines.

Optimising pharmaceutical logistics cold chain with technology

The potential of AI will extend beyond R&D to supply chain management next year. Organisations will begin to use the technology to minimise risk, reduce costs and boost efficiency. Cold chain providers can learn from tech giants like Amazon and Alibaba, who use AI and data-driven insights to improve logistics and access to treatments. While their focus isn’t exclusively on the cold chain, their innovations may offer valuable ideas for delivering sensitive medicines more efficiently.

Finally, blockchain’s potential to increase supply chain transparency through decentralised, immutable records makes it a key technology to watch. Deloitte’s case studies already demonstrate its practical application in ensuring product integrity, underscoring the importance of blockchain in advancing pharmaceutical logistics.

2024 has seen steady progress in pharmaceutical logistics, with innovations continuing to shape the future. As we move into 2025, the focus will be on smarter packaging, agile supply routes, and solutions that address the growing complexity of global healthcare demands. The year ahead holds significant opportunities for companies willing to adapt, collaborate, and lead the charge in delivering medicines to underserved communities, improving availability, and saving lives.

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So what’s the solution? It isn’t only about hiring more staff at practices, there’s a bigger picture at play. It’s about making better use of the tools and technology already available to ease the load on existing teams. It’s an area where AI can help, but in order to maximise benefits and ensure use and adoption at scale, we must make sure there is clinician and patient trust in the tools.

Why is AI in digital telephony so important?

Use of the digital front door is ever-increasing, but despite advancements, over two thirds (68%) of patients continue to contact their GP practice via telephone as the first port of call. The shift from analogue to digital is something we all know is coming in the 10 year health plan, and as the most used element of the digital front door, it is clear that primary care has a fantastic opportunity to embrace the use of AI in telephony. It’s also something that the Social Market Foundation has touched on recently in their report ‘In the blink of an AI’, with the recommendation of further integrating AI and automation into user-facing workstreams.

What benefits could AI digital telephony bring?

The integration of AI in digital telephony for primary care can offer transformative benefits, addressing some of the numerous pressing challenges. Integrating technologies such as cloud telephony, unified communications, and AI-enabled healthcare tools, means practices can alleviate the pressure on GPs and other clinicians. By integrating AI-powered features such as; voice agents, call routing, speech-to-text, and automated signposting, practices can alleviate the inbound pressure on call handlers whilst at the same time improving access, and speeding up a patient’s time-to-care by offering an assessment of need at the first point of contact, in line with NHS national priorities.

By removing the notorious 8am rush, AI-powered systems can enable more efficient call handling, prioritising urgent cases and directing patients to other appropriate services, such as pharmacy, and community without delay. This enhanced accessibility not only improves patient satisfaction but also fosters better health outcomes by ensuring timely care. Additionally, these streamlined processes reduce administrative burdens and repetitive tasks for staff, creating a more manageable workload and mitigating burnout. Together, these advancements pave the way for a more sustainable, patient-centered approach to primary care.

How do we get patients and clinicians onboard?

The challenge is how do we ensure that patients and clinicians are harmonised with the progress to build their trust?

To build trust in patients is not a simple process, particularly when you consider digital poverty which brings many disparities and makes it harder for people to access the very tools that are designed to help them. Patients value human interaction, especially when discussing health concerns, so AI tools must prioritise empathy in design and simplify communication. Data monitoring is key here, and should be used to help evidence that effective digital tools will also improve access for all, including those less digitally-abled who require human contact.

Patients should not feel intimidated by the use of AI, and it needs to be introduced in a straightforward way, focusing on the benefits in relatable terms. Transparency of data usage is also vital otherwise we risk the further creation of a two-tier system for those that trust the data and those that do not, also creating further burden for GP teams.

Patient empowerment means putting AI in their hands and allowing them to self-serve for non-urgent needs, allowing clinicians to focus on more complex patient needs and preventative care. If we can remove the demand before it flows into the practice then it eases the burden immediately.

For clinicians and practice staff, it needs to be clear that AI is an opportunity to reduce the pressure, not a threat to remove jobs. Clinicians are more likely to trust tools that they understand how to use, so time dedicated to training can demystify the AI’s capabilities and limitations. The tech may be amazing but it needs the support around it to build in training for staff in order to make best use of systems already in place and integrate additional tools such as Surgery Assist.

Take for example Tudor Lodge, a practice in South-West London that is an early adopter of AI tools. The implemented Surgery Assist, a digital assistant, as part of a wider Access Optimisation Service and the practice has experienced 54% fewer calls in the 8am rush as a direct result. Applied nationally it is estimated that this service could result in 9.1 million fewer calls received per month by GP surgeries.

Will AI live up to the hype?

One of the questions asked to the discussion panel at X-on Health’s recent AI in primary care event was ‘will it live up to the hype?’ AI is by no means a magic bullet, and it could be said that it is currently not up to the hype, but applied correctly AI has the potential to move primary care forward beyond all expectations.

As referenced by an ICB contacts at the recent X-on AI in primary care event, AI is a tool, not a solution and must be viewed as such. To my mind it’s the correct approach and AI is just one of the arsenal of tools available to reduce the burden. There is a crisis knocking on the door of primary care and the technology is needed now to help practices survive. Technology cannot simply be layered over inefficient processes, instead the two need to be addressed hand in hand so we can build trust and preserve the NHS into the AI era.

What are the next steps?

Whilst some GP partners have pushed on, giving lots of their time to self-appraising AI products in the pursuit of improved efficiencies to support their staff, there have been calls for the formation of an AI advisory board or list of approved AI suppliers to expedite procurement and adoption. To further build trust the technology testing needs rubber stamping at a national level and the creation of a framework of consistency is something that is essential. The ‘In the blink of an AI ’ report supports the creation of a strong Digital Centre of Government in the Department for Science Innovation and Technology (DSIT) and recommends that it becomes a one stop shop for all public sector AI and automation needs, highlighting tools that are already working and have been successfully implemented.

One thing is clear – if the NHS doesn’t work out how to become agile enough to embrace the technology and build trust quickly then Google will do, as is clear from the Public First report, AI and the public sector, that was recently commissioned by Google Cloud.

By Max Gattlin, Commercial Director, X-on Health

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Sir Keir Starmer wants the tech industry to thrive and become more profitable. With AI technologies alone projected to boost UK GDP by up to 10.3% by 2030, the Prime Minister wants businesses to focus on responsible innovation, particularly in public services.

However, the NHS and other public services are under increasing pressure from rising demand, an ageing population, and financial constraints. It is well documented that the NHS has been starved of capital and the result is crumbling infrastructure that is hindering productivity. The backlog maintenance bill now stands at more than £11.6bn and a lack of capital means that there are too many outdated systems, too little automation, and that some parts of the NHS are still to enter the digital era.

Bring these elements together and this is where progress through responsible innovation is essential to drive forward the health service in line with the upcoming 10 Year Health Plan. Yet, economic growth driven by digital health will be meaningless if it exacerbates existing inequalities, does not consider equity of access, or leaves vulnerable populations behind. Rural areas, low-income communities, and the elderly must not be overlooked in the rush to embrace technology.

Taking a responsible approach to innovation in digital health

A great example of responsible innovation in digital health is the recent opening of the community diagnostic centre (CDC) in the north-east’s MetroCentre shopping centre; a joint service between Gateshead Health NHS Foundation Trust and The Newcastle upon Tyne Hospitals NHS Foundation Trust.

It is all too easy to become distracted by the latest solutions available and think that bringing in something new is the only way to tackle capacity problems. However, for Gateshead and Newcastle, their priority was to innovate using technology they already had in order to release diagnostic capacity quickly.

The approach of placing clinical services in a shopping centre with over 550,000 people able to reach it by public transport in under 30 minutes is delivering equity of access and care across the region. Clinicians now understand the diagnostic need at a regional level so supply can be aligned to demand, making further improved use of time and resources.

Responsible innovation is not solely about advancing technology, but ensuring that progress is equitable, inclusive, and sustainable for all. While digital technologies present a powerful tool to help address pressures in the health service, their implementation must be done thoughtfully and digital solutions should be designed with the NHS values at the core.

Unfortunately, responsible practice and innovation in healthcare don’t always go hand in hand. Using AI as an example; the advancement in capability has created much anticipation and excitement in healthcare, but also a heightened sense of opportunity among technology providers. Unfortunately, much of the national funding made available to date has encouraged lots of individual siloed development projects, rather than evaluating the benefits of AI at scale.

Broader ecosystem

Sustainable economic growth in healthcare requires careful attention to the broader ecosystem in which these technologies are deployed. Data security, privacy, and ethical considerations should be central to innovation, ensuring that the economic benefits of digital health are not outweighed by risks such as data misuse or inequality in access.

As highlighted in the recent report by Prof. Cathie Sudlow, ‘Uniting the UK’s Health Data: A Huge Opportunity for Society’, aligning these goals with the government’s agenda for long-term sustainable growth requires collaboration between tech companies, healthcare providers, and policymakers to establish robust regulations and frameworks that protect both individuals and institutions.

A great example is the increasing use of federation in the Secure Data Environments programme. While the rich datasets held in NHS systems are a huge and largely untapped source of value for health research, failed projects show that patients and the public are uncomfortable with large health record datasets being built and held centrally. By joining health records from hospitals and GPs across individual regions and providing Trusted Research Environments to researchers, data analysis can be done under locally-defined privacy and security controls without having to hand over records to third party organisations.

Answer Digital’s work with several regional SDEs has focused on aligning their datasets with common data standards and structures, allowing researchers to run the same analysis across environments so that they can still gain access and consistent results across a large patient population while under regional controls. Access to a broad base of patient data is particularly important, for example when researching rare disease or training AI in a way that avoids bias.

Aligning with NHS values

On a business and employer level, responsible innovation is central to everything we do at Answer Digital. Championing diversity and ensuring that every member of our team feels valued, respected, and heard, working for the benefit of the community, and aligning our values to those of the NHS. As an employee-owned digital consultancy this is an approach that we take seriously, embodied through our Answer Academy. We currently have approximately 120 people working in the team, and over half of these have joined through our academy which has given more people the opportunity to work in tech, no matter where they started their career.

The best outcomes in healthcare often come from a combination of human empathy and technological innovation. As digital health solutions continue to evolve, it is essential that all stakeholders—government, healthcare providers and tech companies work together to ensure that progress is both responsible and sustainable.

Following the industrial strategy consultation in late 2024, I look forward to seeing if Labour’s desire for responsible innovation is followed by the subsequent action when the Invest 2035 industrial strategy and the highly anticipated 10 Year Health Plan are both published in spring.

By Paul Wye, Head of AI, Answer Digital

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1. Understand the Organization’s Current Tech Structure

IT professionals must begin by mapping out the facility’s existing technical infrastructure and how workers engage with it. Once they diagram it out and study the present workflows, it’ll be easier to understand how AI might fit in and how it could help with health worker shortages. These steps can become more time-consuming in large organizations, but completing them can pay off by highlighting unmet needs and compelling opportunities.

A Nebraska-based health system with two hospitals and nearly 70 medical centers began using an AI tool to reduce turnover among first-year nurses and other health worker staff. The chosen solution combines various human resource systems into a single cloud-based platform, making the associated information more accessible to managers.

A representative from the health system’s tech provider said this offering could save six hours per week and help users prioritize their actions to make the most impact. The tech implementation reduced nurse turnover by 47%, and executives noticed significant retention improvements within eight months.

Similar results are most likely to occur elsewhere when hospital administrators and other involved parties work closely with IT professionals to learn more about successful utilization strategies for the present systems.

2. Listen to Providers’ Insights About Planned Tech Implementations

It is also important to hear about how health care providers would like support with their current workflows. Shortages can happen when people leave their positions because they feel overstretched and believe no relief exists. Although some individuals believe AI will replace their roles, the best implementations usually involve the technology supplementing humans’ expertise. That reality can mean it eases work-related burdens.

One investigation showed that radiologists were 95% accurate when using an AI tool to find cancer. Allowing professionals to use artificial intelligence in their work that way is especially important when in-demand medical specialists feel frequently stressed due to their heavy workloads. Being consistently under pressure can make people more likely to overlook aspects of patient charts or imaging files or misread those resources. When IT professionals hear about providers’ current challenges, it is easier for them to evaluate the best ways to insert tech into their processes.

Another strategy is to get details from potential users about patient engagement and how technology could enhance it. Medical professionals may mention feeling too distracted while typing notes with screen-based devices during face-to-face conversations. In those cases, AI medical scribe tools can address that downside. They allow providers to maintain eye contact while listening carefully and practicing empathetic communication. Then, patients are more likely to trust medical professionals and feel satisfied with the interactions.

IT professionals should rely on user feedback before, during and after tech rollouts. The things cdfc

health care providers mention can help the process go more smoothly and become increasingly fulfilling for everyone.

3. Examine Platforms That Use AI to Train Health Worker Staff Faster

Many individuals see health care industry careers as options for getting into better-paying jobs and finding their skills almost perpetually needed. However, a notable downside for many of them is the long training programs that they must enroll in to get the required skills. Many AI tech companies have focused on making workplace education more accessible, and some specialize in platforms for aspiring health care workers.

One startup currently raising funding has an AI platform that needs only four months to train students for entry-level medical industry positions. The average cost is just $2,500. That’s a stark difference compared to programs at trade schools or community colleges that often take years and cost as much as $20,000.

Another component of the company’s process is a partnership with 8,000 clinics and hospitals. Learners attend those after completing the online component of their coursework to receive one or two months of hands-on training.

A startup representative noted that health care employers like this arrangement because it helps them address hiring needs. The brief period gives them time to get to know the students, see if they like them and gauge how well they perform in the role. Then, if they like them, they can hire them without the lengthy process of placing ads, screening candidates and scheduling interviews to fill positions.

IT professionals should look for similar programs their health care organizations could participate in to fill labor gaps. Those opportunities could be easier ways to reap the benefits of AI to tackle worker shortages before upgrading the internal tech infrastructure to use the technology more directly.

Assess the Top Needs Before Finding Solutions

In addition to following these suggestions, IT professionals should encourage administrators, people in patient care roles, floor managers and others to detail their typical daily shifts. How does technology work well for them, and in which ways does it fall short?

Finding the sources of friction and frustration could serve a dual purpose by highlighting how to deploy AI and revealing the best ways to support employees who feel under too much pressure — and may consider leaving if things don’t improve. Health care workers have dozens of potential ways to benefit from AI. Identifying those use cases early is a practical way to raise the likelihood of meaningful success.

By Zac Amos, ReHack

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