From code to clinic – what is holding healthcare back?

5 minute read 1 Aug 2021
5 minute read 1 Aug 2021
Related topics AI Digital Trust

Why the healthcare ecosystem should embrace data-driven technology as a way to improve outcomes for all.

In brief
  • Healthcare is not yet fully embracing the potential of AI
  • The entire healthcare ecosystem stands to benefit from emerging technologies – but should learn to collaborate first
  • Collaboration and a tech mindset are vital 

Like many other industries, healthcare has been keen to explore the promise of artificial intelligence to capitalize on data and boost quality. Despite operating in an inherently innovative environment, though, many healthcare players are struggling to shake off old processes and structures to truly embrace emerging technologies. While the need to invest upfront in new infrastructure, tools and approaches can seem daunting when margins are already squeezed, healthcare companies have little choice if they want to deliver on their pledge to make a difference.

At the same time, megatrends like demographic change are colliding with technological breakthroughs to accelerate transformation in the healthcare ecosystem. Agility and innovation are key if healthcare organizations – from pharmaceutical companies and physicians to new tech entrants – want to thrive amid expiring patents, growing privacy concerns and digital healthcare solutions. How do pharmaceutical companies, physicians and patients stand to benefit from AI, and how can tech companies find their place in the healthcare ecosystem? 

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Chapter 1

AI - a breakthrough in innovation

Potential for speed, relevance and efficiency in pharmaceutical R&D

It typically takes


years to get a drug to market

Besides being time-consuming, drug development is an expensive and risky business, with a clinical trial failure rate of up to 90% and average cost of USD 2.6 billion.

While AI cannot replace clinical studies as a vital step in safe drug development, it can help focus resources onto the most promising compounds, which accelerates processes, reduces risk of failure and cuts costs.  AI-based drug-discovery applications use machine learning to identify candidates and predict their interactions in vivo, enabling the research team to better prioritize their efforts.

Once a product is ready to move from bench to bedside, AI can help select sites and support the process of identifying the right patients for a clinical trial.  A world leading analytics provider in healthcare, has seen enrolment rates to clinical trials increase more than 20% and automatic processing of adverse drug reactions in 70% of cases.

AI isn’t just being used in early research and to plan clinical studies. Natural language processing (NLP) is also increasingly finding its way into medical applications. NLP can be used to search, analyze and interpret large amounts of patient data. Data sources can be as diverse as discussions in public health forums, social media posts or electronic health records. For example, large pharmaceutical companies already gained a label expansion for the drugs, partially based on NLP analysis. Data for the analysis was sourced from electronic health records and three databases containing post-marketing reports of real-world use of the drug in male patients. Real-world evidence is also supporting clinical studies. By serving as a synthetic control arm, it’s especially useful in the case of rare conditions where traditional patient recruitment would take too long. Speeding up the clinical trial benefits both patients and drug developers.

Considering the huge impact that AI can have on R&D and the large investments made by pharma companies, it’s surprising that pharmaceutical companies are generally hesitant adopters. A 2019 review paper found that just 18 AI-related patent applications had been submitted by the top 20 pharma companies (by revenue) in recent years, compared to thousands by technology companies. In terms of investment in AI, the healthcare industry will rank behind banking, manufacturing, retail and the public sector in 2021 according to Atos, a global leader in digital transformation.

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Chapter 2

Physicians plus AI – a partnership for patients

Taking AI from R&D to clinical practice and better patient outcomes

When it comes to technology in healthcare, much effort and budget is concentrated in processes and customer service, where tools like speech recognition and computer vision are boosting the customer experience. But the core business of frontline healthcare – diagnostics, treatment and monitoring – is lagging behind other industries. 

Machines will not replace physicians, but physicians using AI will soon replace those not using it.
Prof. Antonio Di Leva
in The Lancet

While adoption may be slow, it is more a question of when, not if, uptake will increase. The experience and expertise of the physician will remain a valuable resource, but AI has the potential to enhance care through:

  • Quicker diagnostic procedures
  • More accurate clinical decision-making
  • Better patient experience and outcomes
  • Improved cost management

An example covering all of these aspects is the software solution SubtleMR™, which improves the quality of accelerated head, spine and neck MRI protocols using super resolution and denoising. Patients spend less time being scanned, improving their experience, and more patients can benefit from scanning facilities in any one day. Better quality images reduce the need for repeat scans, and can also help limit the dosage of contrast agents.

In the new healthcare ecosystem, data-driven technology like AI can be the glue that connects two parties to create exciting new applications and opportunities. Here are some more examples of how patients benefit from tech-augmented healthcare when their physicians embrace new tools and technology:

  • Better treatment management

    An AI system to monitor Parkinson’s patients remotely was created by Chinese tech company Tencent Holdings and European data expert Medopad. With the help of wearables and a smart phone app, a patient’s movements and symptoms can be tracked non-invasively over time. The real-world data is shared with a health professional, enabling an assessment of disease progression and higher-quality, faster insights.

  • Improved drug adherence of patients

    Drug adherence can be a major factor in patient outcomes. Using a facial and image recognition algorithm of the AiCure mobile SaaS platform, Abbvie created a novel way to monitor adherence. Using their smartphone camera, patients film themselves taking their medication. The AI-powered platform then confirms that the right person took the right pill. Close monitoring saw adherence increase by up to 90%.

  • Early diagnosis

    Bayer and MSD, a trade name of Merck & Co., co-developed and launched a pattern-recognition artificial intelligence software that flags Chronic Thromboembolic Pulmonary Hypertension (CTEPH) – particularly important given that the condition is relatively unusual and shares symptoms with other diseases. The software relies on deep learning methodology to support radiologists by identifying signs of CTEPH in CTPA scans.

  • Connecting the dots

    EY PointellisTM, is a solution that reimagines the biopharma supply chain, and its processes and tools, to help provide patients with groundbreaking cell and gene therapies, in the timeliest way possible. The platform collects and shares data in a way that benefits all participants in the cell and gene therapy ecosystem, and in an environment they can all trust.

  • Novel insights

    In traditional gene sequencing, the sheer volume of data – often up to 1 terabyte – is a hurdle to manual processing. Machine learning or deep learning opens up possibilities to identify the mutations/risks that would go unnoticed in more traditional analytical techniques. And once they’ve been identified, innovative gene editing technologies can address the root cause, creating enormous value.

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Chapter 3

The data challenge

Data plus algorithms equals successful machine learning. But which data?

To gather data, healthcare companies need to transform deeply to become data-driven organizations. Pharmaceutical companies are built to collect and use data, but in a very traditional way and with a defined purpose. As a result, data silos are often created, and companies need to break these down and integrate all of their data across countries, trials, applications and assets. They should also strive to leverage data that is generated outside the organization. Truly data-driven companies transform from within. That means embracing new technology, entering into new partnerships and shedding the fear of the unfamiliar. If they really want to leverage the potential of tech, there’s no way round the need to invest: in infrastructure, tools and training. Forward-thinking CEOs acknowledge the importance of this despite any concerns about initial outlay. Having a data mindset, where IT is seen as a driver rather than a cost center, helps in integrating data collection along the value chain.

Healthcare stakeholders need to learn to gather data from the entire health ecosystem. That applies to pharmaceutical companies but also the surrounding healthcare systems including payers and patients. Capturing data more widely is beneficial to all the players involved but relies on collaboration in the healthcare ecosystem. Even if healthcare organizations have the data infrastructure required to collect adequate data to support machine learning, they often lack the in-house expertise to truly extract value. Many fail to ask the right questions or struggle to explore exciting new use cases. Strategic partnerships with disruptive market entrants like technology companies can prove mutually beneficial. An outside view can also help distill information and derive value for all stakeholders. For example, EY helped to develop a strategic business case for a clinical AI company in the UK to identify the commercial value of data.

Data integrity and confidence is key – not just between patients and their healthcare providers, but within the entire healthcare ecosystem. Targeted external assurance can help. EY has supported clients in the healthcare space to identify and prove AI use cases and develop market maturity based on real medical data.

Finally, one last major challenge to realizing the full potential of AI technology in healthcare is legal concerns. Software that informs diagnosis or treatment decisions is the most heavily regulated category of medical devices, and developers are required to prove reliability adequately to meet rigorous standards. The process can prove prohibitively expensive. Then there’s the issue of liability: who is responsible if the algorithm makes a mistake? This is a rapidly evolving topic with no consensus answer to date.

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Chapter 4

Subjects vs. stakeholders

Who owns data?

For most people, the intuitive answer would be the patient. In an opinion piece from 2020, the British Medical Journal proposes a definition of ownership as being “the ability to utilise information (i.e. reproduce, sell etc), control the flow of that data for use or restrict it to preserve privacy, and the responsibility to avoid harmful information release”. If this is true, then there are multiple owners. In fact, all stakeholders that invest time, funding or resources in processing and protecting data can claim some degree of ownership in the outcome. As the data subject, then, the patient co-owns data with organizations, governments and society. This doesn’t mean that patient data doesn’t deserve – and require – the highest level of privacy and security.

A rigorous data privacy policy should always be embedded in the tech strategy and applied without exception. This is true even when patients themselves are not that careful with their own data. For example, if a patient posts health information on social media, that data may be used as a valuable resource provided it is anonymized and included in databases in accordance with relevant laws. Existing data protection legislation like the GDPR in Europe is necessary, but the high cost of compliance can mean that well-funded incumbents may have an unfair advantage over start-ups. A common data schema for storage and transfer of healthcare data could make it easier for all parties in the healthcare ecosystem, e.g., through a generalized data infrastructure or commercially available cloud storage.

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Chapter 5

Preparing for the potential of AI

Four steps toward the future

Huge breakthroughs in personalized medicine – and ultimately better patient care and outcomes – are significantly enabled by the potential of data analytics and digitally enabled business models. For unprecedented data volumes and emerging technologies to deliver on their promise, the healthcare ecosystem needs to include data acquisition and retention into strategy, and ensure that this is underpinned by confidence. The healthcare ecosystem stands to benefit hugely from digital developments - if it can get the foundations right. EY has identified four key steps on this journey:

  • Build an ecosystem based on partnerships that bring together life sciences expertise and AI know-how
  • Embrace a data mindset, seeing IT as a driver rather than a cost center and integrating data collection to all sides of the business
  • Embed ethical aspects in AI design by safeguarding transparency, avoiding bias and ensuring accuracy and appropriateness in an individual context
  • Establish strong processes to ensure data confidence, and reinforce trust through third-party assurance
  • Article Reference

    1. Rashid et al., 2020, Artificial Intelligence Effecting a Paradigm Shift in Drug Development. SLAS Technology, 1-13
    2. Schuhmann et al. 2020, The upside of being a digital pharma player, Drug Discovery Today ( 
    3. Reports and Data: Artificial Intelligence (AI) in Healthcare 2021
    4. Schuhmann et al. 2020, The upside of being a digital pharma player, Drug Discovery Today (
    5. Source: Artificial Intelligence - Atos
    6. Pharma Industry in the Age of Artificial Intelligence: The Future is Bright – Healthcare Weekly
    7. Reports and Data: Artificial Intelligence (AI) in Healthcare 2021
    8. Pharma Industry in the Age of Artificial Intelligence: The Future is Bright – Healthcare Weekly
    9. Reports and Data: Artificial Intelligence (AI) in Healthcare 2021
    11. Who owns patient data? The answer is not that simple - The BMJ


Although the healthcare ecosystem recognizes the potential of AI, many players still struggle to embrace change, develop the necessary data landscape and redefine structures. Faced with high costs and complex data protection questions, it can be difficult to break down silos and truly transform. Healthcare players need to connect and collaborate more and see technological disruption as the transformative opportunity it is.


Special thanks go to Laura Jochem, Nadja Bekele and Josselin Meylan for their valuable contribution to this point of view article.

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