How technology is revolutionizing healthcare

Hello everyone! It has been far too long since I’ve been fortunate enough to speak to you all. Six years ago, Health News invited me to speak on how technology is revolutionizing healthcare. So much has changed since then, which is part of the reason I’m back here today. Although I can only speak to you virtually, rest assured that will change as I soon plan to visit China for the first time since the start of the pandemic.

Entering the new world of COVID was a tough time for us all. Exiting it is proving to be just as hard. Few could have predicted that we would go from travelling and collaborating globally to locked inside in a matter of weeks. Even fewer could have predicted all the suffering we would have to endure to get back to where we are now.

I am proud to say that Elsevier, for its part, has been vital in disseminating COVID information throughout the medical community. When the world was still unsure of what COVID was, Elsevier employees took the initiative to create a central platform for all our COVID resources — for free!

After recognizing the unique needs of our Chinese colleagues, we recently released a Chinese version of the information center. This was then delivered to members of the Belt and Road Health Professionals Development Alliance of 180 hospitals and research institutions. Initiatives like this are only a small, but important part of Elsevier’s involvement in China.

The reality of burnout

But despite remarkable scientific advancements like these, I have seen a troubling trend growing these past three years that clinicians everywhere face: burnout. Burnout is neither new nor complex; anyone in a demanding role has grappled with extreme stress and fatigue.

However, the severity and quantity of challenges healthcare workers face make them more prone to burning out, leading to worse outcomes for them and their patients. So, while I can’t deny all the wonderful things we saw in 2022, I would like to share a real-world story about burnout.

Imagine a child decided from a young age that she wanted to become a doctor. She goes through years and years of brutal schooling, all in the hopes that one day she could positively impact the lives of those around her. The tradeoffs are immense: instead of spending precious time with her loved ones, she studies for exams and shadows doctors and does whatever it takes to get into medical school.

Even then, graduating from medical school is only one step in her long journey to save lives. She completes her residency, a process that takes anywhere from 3-7 years depending on her specialty. Throughout her training, she never leaves her patients’ side, making herself available at all times, day or night, because she is so personally invested in their health outcomes.

After many years of internships, schooling and residency, it seemed that she finally accomplished her childhood dream. But this accomplishment came at such a high cost. Instead of fulfillment, she found inefficiency in the hospitals and burning frustration with the industry she thought she loved. Tasks that once brought her happiness now fill her with dread and even the simple things have become too draining to complete.

She finds that now that she has “made it,” she doesn’t have the strength to go on.

You may be wondering why I haven’t named this doctor. The sad reality is that this story describes millions of healthcare workers worldwide – how could I pick just one?

People like Dr Anita Gadgilv(opens in new tab/window) in India and Dr Danielle Ofri(opens in new tab/window) in the US have all bravely spoken out about their burnout, and they are not alone. Many people you know, and indeed many people watching this video, continue to suffer.

Stories like this affect me personally because these are the people that we serve at Elsevier every day, and in turn, these are the people who take care of us when we’re not well. We depend on them. That’s why the current situation is so upsetting.

The simple fact is: clinicians are burned out and struggle to keep up with the changing healthcare landscape. This has only gotten worse during the pandemic. Surveys conducted by Elsevier show that clinicians work very long hours and don’t have the opportunity for balance between their jobs and personal lives.

Overall, the numbers are depressing — an astounding 43% of clinicians believe that they cannot achieve a good work-life balance in their industry. Nowhere is this more apparent than in the Asia Pacific region, where clinicians work an average of 48 hours a week. This is compared to the global average of 44 hours a week.

With statistics like these, is it any surprise that clinicians are more likely to burn out than almost any other profession? But there is hope.

Hope with the rise of big data and digital health technologies

The same study, Clinician of the Future, revealed that clinicians believe the rise of big data and digital health technologies will help them with future treatment decisions.

I encourage everyone to read the study, but here are some numbers that stood out to me.

80% of clinicians agree that big data will be integral to managing population health. With this transition, we will see a shift from symptom treatment to holistic, preventative health. Imagine a future where healthcare issues are solved before they even happen!

Robust data is the key to these advancements. For instance, 79% of clinicians believe that electronic medical records will soon rely on data from multiple sources, allowing for more accurate and easier diagnoses and treatment decisions. Another 67% agree that real time patient analytics will be critical to personalized care, leading to less frequent, but more meaningful clinician-patient interaction.

If all of this sounds too far away to picture, you might be surprised that much of this technology already exists! In the past year alone, I have been fortunate enough to learn about many new advancements that will have a great impact in 2023 and beyond. These breakthroughs are relevant at each part of a clinician’s journey, from their education to their administrative duties, and their research.

Let’s start with medical education.

Innovations in medical education

Two months ago, Imperial College London hosted the Transform MedEd(opens in new tab/window) conference. More than 50 medical institutions from around the world came together to explore innovative improvements to medical education. One of the key proposals was for a more evidence-based, humanistic teaching approach for future clinicians. Such an approach would take the current realities of the healthcare system into account, preparing clinicians to better empathize with their patients and themselves.

In anticipation of this changing landscape, researchers are calling for healthcare educators to implement AI education into their curriculum. In October, England’s National Health Service(opens in new tab/window) released research recognizing that curricula must adopt and embrace AI education. This will prepare clinicians to take advantage of all the advancements big data and digital health are bringing to their jobs.

Medical schools are already implementing digital health technology into their teaching curricula. For example, Johns Hopkins recently created a virtual reality simulator(opens in new tab/window) that lets nurses practice critical skills like resuscitation and pediatric critical care — all from the comfort of a VR headset. Honing their skills in a stress-free environment, nurses can focus on getting better at what they love doing.

We at Elsevier are proud to say that we are also playing a vital role in using technology to train future clinicians. Early last year, one of our products, Complete Anatomy, released fully interactive 3D models of the female body. With it, clinicians no longer have to rely on male-based models that don’t accurately represent the female body.

Just last month, Complete Anatomy went even further by adding diverse skin colors, faces and complexions to reflect patients more accurately. This isn’t just important for representation; it also helps doctors recognize how conditions like melanoma and Lyme disease appear on different skin tones. Just think — clinicians that are trained using better data will make fewer mistakes when dealing with patients, lowering frustration for everyone involved! More than that, it will help save more lives.

But ensuring our clinicians are equipped with the knowledge to deal with an ever-changing world is only one part of the equation. We must integrate AI into their workflows, too.

Integrating AI into the clinical workflow

When we think about healthcare workers, we imagine them spending a lot of their time doing the work that matters — saving lives. But that’s simply not the case. So much of a clinician’s time is spent doing important, but repetitive, administrative work that they lose valuable time to interact with patients.

Many companies have begun tackling these inefficiencies, and the results are remarkable. One US-based company, Trusted Health(opens in new tab/window), has developed a platform that automates the amount of manual labor involved in scheduling nurses. Another US company, Paradox(opens in new tab/window), uses AI to lower the administrative hassles in the hiring process, giving prospective nurses more time to determine if a hospital’s culture is right for them.

Nurses are the bridges between doctors and patients. While they work tirelessly doing their job, we must do ours to ensure that they have healthy work environments.

Meanwhile, bigger companies are working to connect consumers with licensed clinicians who can diagnose, treat and prescribe medication for a range of health conditions. Telehealth providers became a common part of life during the pandemic for millions around the world. Now that we’ve adopted telehealth, it’s up to us to make it better.

To do this, Amazon has created virtual clinics(opens in new tab/window) to help people with common ailments like allergies and hair loss. Not only do these people get easier access to treatment, it also frees up resources so clinicians can invest more time into patients that truly need the extra attention. Sometimes that attention can be the difference between life and death!

Elsevier has also invested heavily to decrease the time clinicians spend diagnosing patients, especially when they don’t have all relevant context on hand. This has been a massive problem for generations, but our solutions are already helping countless people.

Let me tell you about ongoing projects in two of our biggest markets: the US and India.

In October, our US Commercial Health team launched Gravitas(opens in new tab/window), a digital token that uses existing datasets in a way that both benefits patients and maintains their privacy. As more datasets are added to Gravitas, clinicians will be able to conduct high-impact studies focused on rare diseases, health equity in underserved communities and more. With tools like these, holistic, preventative healthcare is finally turning into a reality!

I am also proud of the life-changing work being done in India, which is in the middle of an unprecedented healthcare crisis. How can you hope to get treatment when the current patient to physician ratio is eleven thousand to one?

Our answer to this question is a mobile, AI-based application that trained workers can use to pre-screen patients and alert them if they need to visit a doctor. The app also collects health data, ensuring doctors have all relevant information to make an accurate diagnosis. This past summer, the Indian government gave us the green light to expand this project across the country, so I’m excited to see what developments the new year brings!

Finally, as you all are aware, clinical research is the critical factor in developing the ground-breaking treatments of tomorrow. For many in medicine, the research they undertake is how they best serve their patients. However, with new advancements in AI and Language Learning models, you don’t have to be a clinician to be a researcher. In fact, you may not even need to be human!

I’m sure all of you heard about ChatGPT. I would be here for a long time trying to list even a fraction of the news articles theorizing whether this Large Language Model will take all our jobs. So for now, let’s focus on what it can do: make our jobs easier.

A big part of dealing with patients is educating them on what is and isn’t going on in their bodies. ChatGPT, with its ability to summarize large amounts of text, can explain complicated medical diagnoses to patients in ways they understand. Because these models use everyday language, they are free from confusing and sometimes frightening medical jargon.

While ChatGPT’s uses remain broad for now, there is another large language model fine-tuned for clinicians: Med-PaLM(opens in new tab/window). Preliminary studies have shown that we can use Med-PaLM for clinical decision support, summarization of key findings in studies, and triaging patients’ primary care concerns.

I know everyone likes to have an assistant when they conduct research. Think of all the possibilities if that aid was trained on millions of datasets! By integrating new LLLMs into their daily workflow, doctors and researchers will have more time to focus on developing innovative and life-saving treatments.

Medicine must serve society ...

Thank you, and I wish every success to the the Health Technology News Conference for this year and many years to come.