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Traditional AACC assessments involve extensive paperwork, leading to delays and increased administrative workload. The Digital CHC platform digitises the entire AACC process – from referrals to assessments and care package approvals. This automation reduces manual data entry, minimises errors, and accelerates decision-making. For instance, Cheshire and Merseyside ICB reported an increase in patients receiving CHC eligibility decisions within 28 days from 68% to 82% since implementing the platform, surpassing the national targets.

When we look at the bigger picture facing the ICBs, we can see the significant scale and inefficiencies in the administration process of AACC. £5.5 billion is spent annually on this type of care in the UK, supporting around 160,000 patients. However, a notable concern is that up to 15% of this funding, approximately £825 million each year is not used for direct patient care, instead consumed by administrative tasks and paperwork. This underlines a substantial opportunity to improve efficiency and redirect funds towards frontline services.

Manual workflows are another area in All Age Continuing Care that not only consumes time, but also results in significant financial costs. AACC teams incur administrative expenses ranging from £30 to £50 per hour, with inefficient processes such as duplicated data entry and disjointed record-keeping contributing to hundreds of thousands of pounds in avoidable spending.

IEG4’s Digital CHC platform revolutionises the entire All Age Continuing Care process by streamlining workflows and eliminating inefficiencies. It reduces duplication of effort, ensures greater clarity and consistency across cases, and significantly accelerates decision-making. Built in close collaboration with NHS professionals, the platform is purposefully designed to meet the real-world needs of frontline teams. This supports them in delivering faster, more accurate assessments and improving outcomes for patients. By aligning technology with NHS priorities, it empowers staff to focus more on care and less on administrative complexity.

The patient is always at the heart of AACC, but outdated processes cause delays and frustration at an often already challenging time. By digitising and simplifying the AACC process, the transparency of each case is enhanced, waiting times are reduced and there is a significant improvement in communication between care teams and patients. Faster decisions not only reduce uncertainty for patients and their families but also contribute to better health outcomes.

The platform aligns with the NHS framework and supports integration with systems such as NHS Spine. This compatibility ensures that ICBs can maintain compliance with national standards while benefiting from a unified, efficient AACC processes.

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For years, digital inclusion has framed much of our national and local response to connectivity gaps. We focused rightly on access: devices, data, and basic digital skills. It was a vital first step. But now, in a climate of austerity, accelerating AI adoption, and widening inequality, inclusion alone is not enough. We must move towards digital equity  a justice-driven approach that ensures people not only access digital tools but benefit from them equitably.

Digital equity is about outcomes, not just inputs. It’s about power, participation, and sustainability. At DAISI, we’ve reoriented our entire model to reflect this and it’s transforming how services are co-designed and delivered.

What Does Digital Equity Look Like in Practice?

Let me be clear: equity does not mean everyone gets the same. It means recognising structural disadvantage  and actively designing services that account for it.

At DAISI, our approach includes:

• Device and data banks triaged using lived experience, not just postcode or income.
• Digital safeguarding pathways embedded in NHS Virtual Wards to address risks such as Domestic Abuse (DA).
• Cyber safety and digital training tailored for Deaf users and people with sensory disabilities.
• The use of open-source platforms to build agency, not lock-in.

These aren’t just ‘inclusion’ projects. They are structural correctives interventions that question who services are failing, and what must change.

Case Study: Virtual Wards – From Risk to Recovery

One of the most powerful examples of equity-led digital design is our contribution to the Gloucestershire Virtual Wards programme.

Through our Digital First Community Support initiative, DAISI influenced how early discharge is managed, particularly where digital health tools interface with complex social realities. We flagged that many patients, especially survivors of Domestic Abuse (DA), were being discharged into environments where digital monitoring could increase risk — for example, if abusers could access devices or overhear consultations.

In response, we helped integrate DA considerations into the digital triage process, ensuring discharge decisions now consider digital safety, not just clinical metrics. It marked a fundamental shift treating digital harm as a safeguarding issue, not a user error.

But equity also means ensuring access to preventative digital health tools, not just crisis response. That’s why our work also included exploring virtual walks and immersive nature experiences for housebound patients interventions designed to reduce isolation, anxiety, and inactivity through low-cost, sensory-rich digital wellbeing tools.

This dual-pronged approach safeguarding risk while enabling recovery, reflects what equity truly demands: contextual, compassionate, co-designed care pathways.

“Being part of the DAISI co-design group gave me my voice back. I didn’t just get a device  I helped design the system that keeps others like me safe.”
– A DAISI participant with lived experience of domestic abuse and digital exclusion

Why This Matters to Councils and Public Services

Digital equity isn’t charity. It’s good governance and efficient design.

When systems are built around equity:

• Duplication and crisis re-entry are reduced
• Staff time is used more effectively
• Residents engage more meaningfully — whether in health, housing, or democratic life

For example, by embracing open-source software and co-designed tools, DAISI has saved over £70,000 annually  money that now funds delivery and logistics.

Councils facing shrinking budgets must understand: Digital equity is not an added cost. It’s a smarter investment.

Global Lessons: RUSTIK and Rural Innovation

Digital equity is not just a local issue. We’ve taken this work into the RUSTIK Horizon Europe project, which explores sustainable rural transformation across 12 regions.

As part of the UK cohort, GRCC is embedding community-led data governance through AI-powered rural dashboards — all co-produced with VCSE groups.

This project shows that digital justice must scale, and that rural areas are not passive recipients — they can be leaders in equitable innovation. The work across all regions can be found here https://rustik-he.eu/

Policy Implications: A Call to Action for Local Leaders

To move beyond tokenism, we must change how digital strategies are framed and funded. I propose the following steps:

• Embed equity impact assessments into all public digital transformation projects
• Fund digital triage roles within community organisations, particularly those trusted by marginalised groups
• Shift from “digital literacy” to “digital agency” — where users influence, not just navigate, systems
• Ensure interoperability by favouring open standards and non-proprietary platforms
• Recognise digital harm as a safeguarding issue, especially in housing, social care, and mental health

Councils and NHS bodies must see community-led digital equity as infrastructure, not outreach.

Tangible Takeaways

• Redesign with communities, not just for them: true co-production builds resilience and relevance
• Scrutinise your data: who is missing? Who benefits from your digital systems? What bias does it contain
• Prioritise sustainability: open tech and circular device schemes save money and increase reach
• View digital harm through the same lens as physical or financial safeguarding

Final Thoughts: Digital Equity as a Compass

Digital equity isn’t a trend, it’s a compass. It orients us towards justice, efficiency, and democratic renewal. It allows us to deliver smarter government, reduce waste, and ensure that no one is left behind as digital transformation accelerates.

I believe the future will not only be judged by how many people are online but by how equitably they can participate, decide, and thrive.

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In my experience as a researcher and service designer for various clients in the healthcare sector, I have learned that user research is vital for developing effective, user-centred healthcare solutions that work for everyone. Insights from my past studies show that understanding and addressing the needs of both patients and healthcare professionals leads to more efficient and compassionate care.

User expectations of healthcare are changing

From various user research studies I conducted, some trends in healthcare were identified. For example, as technology advances, patients are becoming increasingly aware of the potential for improved healthcare experiences through online self-service apps and other digital tools. This heightened awareness stems from greater access to medical information online, increased exposure to healthcare technologies, and a growing desire for convenience and control over their own healthcare journey. This shows a shift towards remote interfaces and increased patient empowerment. As technology and AI-assisted tools continue to advance, patients become more informed, and expect healthcare providers to cater to their individual needs. This includes the demand for intuitive software that involves automation and customisation, designing better experiences for patients who can use simplified apps to help, for example, booking appointments online. Other trends include a recognition of how cultural and gender differences can affect how people behave and what they may expect during treatment..

Previous research studies also emphasise the importance of empathy and emotions, whether for patients or healthcare professionals. Empathy-driven design ensures that systems support users’ tasks and workflows while accommodating common emotions such as stress, overwhelm, and frustration – emotions that are extremely common for those going through healthcare issues.

Prioritising user research ensures that healthcare products and services are not only effective, but also provide accurate data and are responsive to the emotional, psychological, and cultural needs of users. Well informed design from research can enhance healthcare delivery.

As user expectations of healthcare technology continue to evolve alongside technological advancements, ongoing research remains essential. So with this in mind, here are some points to consider when starting to plan research for healthcare products and services based on some of my past experiences.

Ethnographic research gives a holistic view

Ethnographic research involves observing people in their natural environment to understand their behaviours and experiences. It can offer a comprehensive perspective on the healthcare system by examining how various elements interact within it. This includes considering the devices and equipment used in practices and hospitals (hardware) and the applications and programs (software) that run it; the technological infrastructure, and the physical setting where care is provided where patients are located ie. waiting rooms, treatment rooms, theatre).

For example, understanding the layout of a patient treatment room, and the placement of equipment in relation to the patient, can highlight the impact of technical issues on the patient experience. If a clinician is troubleshooting behind a screen or door, the patient may be left alone, unaware of what is happening. This lack of communication can cause unnecessary stress and discomfort for the patient, while also taking the clinician’s time away from providing care which can be stressful for them too.

Mapping healthcare processes and the ecosystem

Through research, a product team can start to examine how various elements within the healthcare system are interconnected – such as network structures, information flow, and a patient’s end-to-end diagnostic and treatment journey – can provide valuable insights. From these insights, we can map these processes. Doing this allows us to understand how different components, including technology, staff interactions, and patient experiences, interact and impact one another.

For instance, analysing the pathway of a patient’s care journey can highlight bottlenecks or redundancies, such as delays in test results due to poor information flow between departments. By comprehensively understanding the healthcare ecosystem, we can identify opportunities for improvement and ensure that all elements work harmoniously to enhance healthcare delivery and patient outcomes. This holistic approach is essential in enhancing the overall functionality of healthcare systems.

Research methods that involve feedback from real users

When creating healthcare professional tools, software and process navigation, creating prototypes and testing them with users is essential for identifying discrepancies between solution designs and the actual needs of their intended users. Without testing, such misalignments can lead to a lack of trust in and adoption of the systems, prompting healthcare professionals to rely on supplementary solutions, which in turn creates inefficiencies and duplicated efforts.

For example, if electronic health record (EHR) systems are not intuitive or do not align with clinicians’ workflows, staff might resort to manual record-keeping or use additional applications to fill in the gaps. This not only wastes time but also increases the risk of errors. Therefore, rigorous user testing of prototypes helps ensure that healthcare tools are aligned with real-world needs, promoting efficiency and enhancing overall healthcare delivery.

One previous research study involving healthcare professionals using a tool to capture patient details during 1:1 conversations revealed that the tool’s linear script could not cater for a natural conversational flow – this meant that capturing important details around conversations that jumped around a timeline of events was impossible. This issue prevented healthcare workers from giving patients their full attention, as they were distracted by trying to navigate data input constraints of the tool.

This rigidity highlighted the need for tools that accommodate the nuances of human interaction, especially when discussing sensitive topics. In this case, staff used a pen and paper workaround, allowing them to focus on and be more empathetic towards the patient, which emphasised the importance of human contact during the data capture process.

Empathy and understanding the world of both patient and healthcare staff

When planning and conducting research in the healthcare sector, it is important to focus on building empathy. Empathy is essential in research and design, it is foundational in creating human-centred products and services. Empathy involves going beyond simply identifying and addressing user needs; it requires feeling what users go through, understanding and feeling the user’s experience. By practicing empathy, researchers and designers are reminded that their perspective is not the users’.

Consider conducting research where you can imagine the world from the perspective of patients and healthcare staff of all types, from doctors, clinicians, nurses to back end technical staff and admin. Conduct user interviews where you can practice active listening, where you are fully present – don’t challenge or correct, and listen more than speaking. Getting a better understanding of their world will help to draw out impactful insights that can inform the design at all stages of development.

Empathising with healthcare workers

To truly empathise with healthcare workers, it’s important to step into their roles: during interviews, engage in conversations with them, use active listening techniques to understand their experiences, and collaborate with them during testing phases. One should develop a genuine understanding of the challenges they encounter, such as inefficiencies in workflows and technology, and recognise how these challenges can impact patient care, for instance, when poor technology consumes valuable time that could be better spent with patients. Since patient care and well-being are top priorities for healthcare professionals, any hindrance in providing full attention can affect their emotional well-being and increase stress levels.

Empathising with patients

To truly consider the patient, focus on understanding both their emotional and physical needs throughout their diagnosis and treatment journey. Delve into their key challenges, patient fears and concerns, and gain a deeper understanding of their experiences – what design challenges do we face to ensure their comfort and overall well-being? What insights can we gain to help with ideas that can make their healthcare journey more compassionate and responsive to their emotional needs?

For example, research can help us get a better understanding of how a treatment environment might affect a patient’s mental state if systems or software breaks down. A patient arriving for treatment may already be anxious. The unfamiliar environment and technical equipment can feel intimidating. If a healthcare worker is distracted by technical issues, the patient might be left unattended without explanation. This lack of communication can lead to increased stress and discomfort. Even short delays might seem “endless” if they are uncomfortable or in pain, causing further upset, or even worse, they may worry that the technical issues are related to their health.

Every patient has unique needs and preferences, and insights from user research can inform flexible designs that accommodate personalised treatment and care.

Accessibility testing with users for healthcare products and services is crucial to ensure that all patients, including those with disabilities, have equal access to essential health information and services. Patients often experience temporary or situational disabilities, such as a broken limb or vision impairment that hinders typing or cognitive impairments due to pain, medication or extreme stress. This can significantly impact their ability to interact with healthcare products and services. By including accessibility user testing in our research, we can ensure that all patients, regardless of their physical or cognitive state, can effectively access healthcare resources, communicate their needs, and receive timely and appropriate care, ultimately enhancing patient safety and satisfaction.

A lack of bias is of utmost importance to ensure that people from all backgrounds receive the same level of care. By integrating user-centred design that incorporates user research, healthcare experiences can become more calming and accessible, leading to a reduction in patient anxiety and stress.

By adopting user-centred design and research, healthcare providers can develop systems, tools, and processes that truly address the needs of both patients and staff. This approach results in better patient experiences, higher quality care, and a more compassionate healthcare system.

Originally posted here 

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What are digital health interventions?

Digital health interventions support individuals, both the users and the workforce of the health(care) system, and address the needs and challenges of the overall health system by using digital technologies in the form of different applications and services.

The WHO’s latest publication adds that digital health interventions can target patients or users of health services, healthcare providers, health system managers, and data services — below we’ll mainly focus on the first group, although such interventions usually have wide-ranging impact across the health system, reaching other groups as well.

These interventions point to the the concept and practice of digital transformation in health which is the comprehensive combination of different innovative technologies, analytics and processes for the improvement of the healthcare system itself.

Why and when is digital health intervention useful?

Digital health interventions work at the intersection of health(care) and technology, in the field of, as the name suggests, digital health, and so play an important role in enhancing the way that all the members of the healthcare system approach health issues — from broadening access to the healthcare system to improving the services available.

Digital health interventions help patients manage their health via computers, smartphones, and even virtual reality, and can be used, for instance, to facilitate targeted communications to individuals through reminders and health promotion messaging in order to stimulate demand for services and broaden access to health information.

Patients, in general, need to do the following to manage their health:

• Gain insights into their health
• Understand their symptoms and know when they need to act on it
• Find the patient care they need
• Stick to the treatment that was recommended

One of the most well-known examples of digital health intervention are the symptom checker sites available online which help people identify what may cause their symptoms. The diagnostic accuracy of these platforms varies but ADA, for instance, has a 77% accuracy which is a solid start to give you more information on your health before you consult a healthcare professional, if needed.

3 AI-driven digital health intervention tools to improve patient experience

Digital health interventions when driven by AI have the ability to help patients manage their health in a much easier and more personalized way, thus helping improve patient experience.

This is necessary for a number of reasons. First, patients often find themselves lost in the vast amount of medical information available which might even end up being irrelevant or not specific enough to improve their health outcomes.

Besides the information overload, patients can have trouble understanding medical information and prioritising the necessary steps to start and follow new habits or to keep to a patient care plan, not to mention the lack of expert help that could be the vessel for motivation and encouragement on their health journey.

AI-driven digital health intervention can support patients:

• gather useful information,
• process it easily in a digestible format and
• apply it in a tailored way.

By providing this level and quality of support and guidance, AI can boost people’s motivation to keep track of their health and simplify the activities one has to do to maintain it.

Digital health intervention with AI #1: Tailor information and educate

AI-driven digital health interventions can analyze patient data, such as medical history and demographics, to understand their specific needs. Based on this analysis, AI can generate personalized educational materials on diagnoses, medications, and preventive care. This empowers patients with knowledge relevant to their situation, fostering better engagement.

Symptom assessment apps, such as the previously-mentioned ADA or Buoy Health are good examples of a digital product leveraging AI for personalized communication — both use chatbots to answer patients’ health questions, offering personalized information and connecting them with appropriate resources.

Digital health intervention with AI #2: Improve accessibility and communication

The way how support, guidance and mentoring is provided to patients is the key to success — success meaning a healthy life. Whether the patient will actually find that support useful and make them end up acting on it right, depends on the the format, the content and the timing of getting support, motivational messages or notifications. Therefore it should be personalized based on their individual needs and preferences — AI chatbots can offer a simple solution here.

AI chatbots as digital health interventions can:

• be available 24/7, addressing basic inquiries and scheduling appointments, freeing up healthcare staff for more complex tasks,
• translate languages, ensuring clear communication between patients and providers who don’t share a common language,
• can offer ongoing support for chronic conditions, answer questions, and track symptoms.

Digital health intervention with AI #3: Send proactive reminders and offer support

Patients can follow up on their treatment when it is easily adaptable to their daily life. The format of the knowledge source, the way the information is presented can change how easily or difficult it is for someone to process it — and the preferences could be different on an individual level.

The more personal and personalized these notifications are, the more effective they will be. Some of the related principles to follow are:

• Effortless automation: AI can send automated reminders to work out or track food consumption or sleep. It can also generate medication adherence reminders, prompting patients to take their medication on time.
• Tailored communication: The reminders can be tailored to the patient’s preferred communication method (text, email, phone call). The form, content and the timing of notifications defines the impact of the notification and defines whether the patient will actually act on it or not.
• Personalized motivation: An AI-driven digital health intervention tool can use supporting messages that are relevant for that individual, and truly have an impact on that person’s motivation, focusing on their personal drives and ambitions.

Digital health intervention led by experts

Patients can be supported in their health management by sufficient knowledge on how to manage their health and by getting guidance and mentoring to actually do what needs to be done.

Digital health intervention tools, especially when equipped with AI, can support both the information gathering and processing, and the execution of healthy habits and activities — leading the way to digital transformation in healthcare.

Look at more healthcare case studies about successful digital health interventions and see how AI-enabled healthcare app development works in partnership with Supercharge’s expert team, successfully navigating the business, technology and regulatory complexities of the healthcare industry.

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IBM’s Watson which leverages AI algorithms, could ingest more than 600,000 pieces of medical evidence, more than two million pages from medical journals, and search through up to 1.5 million patient records, when its successful diagnosis rate for lung cancer was 90%, compared to 50% for human doctors — statistics from Continuing the curve of success, the size of the generative AI market in healthcare is now projected to reach USD 22.1 billion by the end of 2032, with nearly 75% of major healthcare companies currently experimenting or planning to scale generative AI in their operations.

In this article, we’ll cover how generative AI is used in healthcare with a focus on reducing that administrative burden, and we’ll share five use cases that demonstrate the future potential of this technology.

What is generative AI in healthcare?

“Generative AI refers to deep-learning models that can take raw data… and ‘learn’ to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that’s similar, but not identical, to the original data,” as IBM explains

How is generative AI used in healthcare?

Generative AI is revolutionizing healthcare by providing innovative solutions that enhance diagnosis, treatment, and patient outcomes. It helps create personalized treatment plans by analyzing large volumes of patient data and predicting potential health risks. Additionally, generative AI supports advanced medical imaging tools, leading to faster and more accurate disease detection, particularly in radiology and pathology. Beyond clinical applications, it improves patient engagement through AI-driven chatbots and virtual health assistants, making healthcare more accessible and efficient.

Clinical trial optimization: Scientists are starting to use generative AI to manage clinical trials, including “the tasks of writing protocols, recruiting patients and analyzing data”.
“Self-servicing” and care guidance: With virtual assistants and chatbots the healthcare system has better ways to connect to and engage patients, and a better quality of care leads to improved patient adherence.

“The promising new discipline of precision medicine can offer personalized medical treatments tailored to each patient based on their health and lifestyle information. But this approach requires a comprehensive understanding of existing therapies, patient characteristics, and the complex biological mechanisms that connect them, making it a highly challenging task. Large Language Models (LLMs) can make sense of decades of biomedical research fragmented across publications by extracting and synthesizing data and knowledge, which is laborious and expensive for human experts to do.” – Benjamin M. Gyori, Director of Machine-assisted Modeling & Analysis, Harvard Medical School
Source: Generative AI in healthcare and its role in the future of the industry
Generative AI can bring the widespread adoption of AI solutions in everyday healthcare settings when the right AI model is pointed at the right datasets.

Large Language Models can make a substantial difference in healthcare

For instance, LLMs have the potential to transform and revolutionize all stages of health management, including customizing preventive care interventions, giving medical diagnoses and finding the most suitable treatment, and freeing up human resources in patient management by focusing on eliminating the staff’s administrative load.

Generative AI in healthcare and its role in the future of the industry, get a comprehensive guide to the transformative technology of gen AI and an overview of its potential applications throughout the healthcare management process.

Download the whitepaper

5 use cases of generative AI reducing the administrative burden in healthcare
Healthcare professionals know that administration is a non-eliminable task, and unfortunately, it’s also tedious, and time-consuming, causing lots of frustration that impacts patients as well. In fact, physician specialties spend an average of 15.5 hours per week on paperwork and administration.

If the administrative burden is removed from the healthcare system, it can result in:
Improved Quality of Care: When healthcare professionals are freed from excessive administrative tasks, they can focus on providing better care to their patients.
Increased Efficiency: Streamlining administrative processes can save time and money for everyone involved in the healthcare system.

Patient Satisfaction: Patients are more likely to have a positive experience if they can easily access care and don’t have to deal with frustrating administrative hurdles.
Let’s see 5 use cases of generative AI in healthcare that help reduce the pressing issue of administrative burden.

1. Patient intakeProblem:

When it comes to administration, healthcare professionals must navigate different health systems and unappealing digital forms and manual input of large amounts of information. It’s time-consuming at every stage, from learning how to use these forms and systems to routinely managing them.
Generative AI solution:

With speech-to-text conversion, healthcare professionals can save time on note-taking and data entry, while not only capturing all information and ensuring the factual accuracy of the generated text, with specific AIs recognizing medical terms as well, but allowing them to focus on patients, instead of the data.
AI can help transform text into medical forms and generate progress notes, discharge summaries, and referral letters, resulting in better-formated, more consistent, and standardized documents and lots of time saved for staff.

2. Consultation and information handover Problem:

Healthcare professionals often don’t have enough time to hand over information in person to patients while the amount of information can be overwhelming for those on the other end.
Generative AI solution:

Generative AI can create customized educational materials based on a patient’s diagnosis, health literacy, and preferred language. This improves patient understanding and engagement in their care plan.
Intelligent virtual assistants for patients can answer basic patient inquiries on appointment scheduling, insurance pre-approval, and medication refills through chatbots or virtual health assistants.

3. Administration for medical logs
Problem:
Healthcare professionals usually have very limited time to do administration in between seeing patients. On top of that, the different types of software they need to use are often not intuitive and some medical experts are not tech-savvy and can face technical difficulties during administration.

Generative AI solution:
Automated Documentation and Reporting: Generative AI can analyze doctor-patient interactions and EHR data to automatically generate progress notes, discharge summaries, and referral letters. This frees up time for doctors to focus on patient care.
4. Administration for insurance
Problem:

The tasks of assessing patients’ insurance information, completing insurance forms, and compiling documentation for claims are not only time-consuming but are also high-risk areas for human error.
Generative AI solution:
Streamlined Insurance Communication: Generative AI can handle repetitive tasks like prior authorization requests and claim coding, reducing errors and expediting insurance approvals.

5. Administration for research
Problem:
Being overwhelmed by large data sets is a key factor in the constant administrative burden in healthcare that leads to burnout and frustration among healthcare professionals. It negatively impacts their well-being, potentially the quality of care they can provide to patients and therefore the level of patient satisfaction.
Generative AI solution:
Research and Development Acceleration: Generative AI can analyze vast amounts of medical data to identify patterns and generate hypotheses for new treatments and therapies, accelerating research progress.

How will generative AI transform healthcare?

When it comes to generative AI in healthcare, like all other technologies, it has its limitations, so gen AI’s immediate integration into your healthcare and business processes should likely be avoided — instead, we recommend future-proofing your services by learning and experimenting with it.
It’s also smart to remember that there are several ways to address the administrative burden in healthcare, such as process standardization, simplifying regulations and authorizations (in cooperation with governments and regulatory bodies), and yes, implementing the right technology for the purpose. This can mean using user-friendly electronic health records (EHRs) and automating tasks like appointment scheduling and billing with generative AI, to free up staff time.

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