“A year of ending and beginning, a year of loss and finding and all of you were with me through the storm. I drink your health, your wealth, your fortune for long years to come, and I hope for many more days in which we can gather like this.”
C.J. Cherryh, American Author
For my first blog post of 2022, I wanted to review the material that readers found to be the most interesting and received the most impressions (over 13,000 in under six months) across all social media platforms. First, a big thank you to all who have read, shared, and commented on the material I published last year. Your comments, suggestions (more on those in a bit), and encouragement have been greatly appreciated.
Looking back –
In analyzing the data, the area of interest that generated the most engagement were the posts from my “Straight Talk” series on technologies in health care. These were intended to provide a snapshot of the current development of key technologies with examples of how they were being used to improve care delivery. Those posts (and associated links) are listed below:
As I mentioned in my end-of-the-year post on Disruptors to Watch in 2022, and based on readers’ feedback on what you would like to see more of this year, I’m going to focus a lot on the topic “It’s About Time.” The ongoing pandemic has accelerated the deployment of technologies to support care delivery and remote care options. The associated workforce issues have created a dynamic tension between continuing to innovate while dealing with the stresses placed on the care team as the waves of patients needing care hit the system. So, while I’m going to continue to report on technology developments and applications in health care, I want to explore how the implementation of certain technologies can save time for patients, families, and providers in navigating our complex, fragmented, and confusing health care system. Watch for the first post in that series next week.
I’m also going to step back from discussing just the technology and share some examples of companies and organizations that I’ve personally interacted with who I believe are driving significant and sustainable change in the delivery of care in our country. Up until now, I’ve taken a “vendor agnostic” approach to posts on this blog. And while that will still be a part of my research and reporting, I think that sharing some personal examples of companies and organizations that are pushing the envelope in technology development and care delivery would be valuable to readers. I’d appreciate your feedback on these posts and recommendations on other companies and organizations to highlight throughout the year.
Finally, many readers commented that they enjoy the weekly health care technology news posts that hit the blog on Saturdays. This year I’m refining that weekly post to add an infographic of the week from either research or reading that I’ve come across. And I’ve been invited to participate in a beta test of a Zoom platform application called “On-Zoom.” So, for registered users on the WordPress blog site, you’ll be able to register for a weekly 15-minute live Zoom event that will provide an early look at the top technology news stories in health care that week. These will be on Fridays at Noon ET (one day before the blog post goes live), so subscribers will get the stories first. You can register on the Talking Healthcare Technology WordPress site here: https://healtech.blog and enter your email in the subscription section as shown below:
Registered subscribers will receive an email invitation with the link to the On-Zoom sessions. You can choose to subscribe to a single session or the entire series by clicking on the link provided.
Hopefully, you’ll find this experiment interesting and valuable. As always, comments and suggestions for improvement are greatly appreciated.
“Artificial Intelligence will help everyone become a better doctor in the future by eradicating waiting time, prioritizing emails, finding relevant information or making hard decisions rational.“
Bertalan Mesko, M.D. – Director, The Medical Futurist Institute
It’s important to separate the hype from the reality in our quest to incorporate A.I. into care delivery.
In a contributed article to Mobihealthnews on July 1st, Dr. Liz Kwo highlights her “Top 10 Use Cases for AI in Healthcare”. This comprehensive overview outlines the current major categories that health care providers are exploring to incorporate AI into the clinical care continuum. I applaud Dr. Kwo for synthesizing this information and share her enthusiasm for the potential of AI in supporting care teams in their daily work.
However, I do think it is important to balance the tremendous potential of the technology with the current reality. There’s an old saying that goes: “We overestimate the impact of technology in the short-term, and underestimate the impact in the long-term.” That certainly applies to AI in health care. The amount of “digital ink” devoted to the topic would fill several data warehouses.
CB Insights conducted a survey at the end of last year and asked which areas of healthcare would be impacted most by AI. The results are shown in the graphic below:
Every year, Gartner publishes their “Hype Cycle” analyses across multiple industries. Their methodology helps us to separate the hype from reality when considering whether (and when) we decide to incorporate AI into clinical workflow. If you are not familiar with the Gartner Hype Cycle model, this is the best book that I’ve found on the subject: “Mastering the Hype Cycle: How to Choose the Right Innovation at the Right Time.”
Below is the Gartner Hype Cycle for Healthcare Providers 2020.
We can see that, according to Gartner, AI is approaching the “Peak of Inflated Expectations”, and is not expected to reach “The Plateau of Productivity” for five to ten years. With that information as our baseline, let’s examine each of Dr. Kwo’s use cases and where we stand today.
1. AI supports medical imaging analysis – AI is used as a tool for case triage. It supports a clinician reviewing images and scans. This enables radiologists or cardiologists to identify essential insights for prioritizing critical cases, to avoid potential errors in reading electronic health records (EHRs), and to establish more precise diagnoses.
Where we are – The best assessment of where we stand in the implementation of AI in imaging can be found in an Aunt Minnie article by Michael Cannavo (akaPACSMAN) here. To quote him: “Unless you perform a large volume of studies that would benefit from the use of a CT algorithm stroke protocol, lung CT algorithm, or others, you may be hard-pressed to justify the purchase of AI software without obtaining additional revenue. There are significant advantages to using AI, but no clear path for how it will pay for itself in hard dollars.”
2. AI can decrease the cost to develop medicines – Supercomputers have been used to predict from databases of molecular structures which potential medicines would and would not be effective for various diseases. By using convolutional neural networks, a technology similar to the one that makes cars drive by themselves, AtomNet could predict the binding of small molecules to proteins by analyzing hints from millions of experimental measurements and thousands of protein structures.
Where we are – While many pharmaceutical companies and academic medical centers are exploring the use of supercomputers to help identify and develop new drug targets, the benefits are as of yet unproven. The introduction of “high throughput screening,” using robots to test millions of compounds rapidly, generated mountains of leads in the early 2000s but notably failed to solve inefficiencies in the research process. When it comes to AI, big pharma is treading cautiously. The technology has yet to demonstrate it can successfully bring a new molecule from computer screen to lab to clinic and finally to market.
3. AI analyzes unstructured data – In many cases, health data and medical records of patients are stored as complex unstructured data, which makes it challenging to interpret and access. AI can seek, collect, store and standardize medical data regardless of the format, assisting repetitive tasks and supporting clinicians with fast, accurate, tailored treatment plans and medicine for their patients instead of being buried under the weight of searching, identifying, collecting, and transcribing the solutions they need from piles of paper formatted EHRs.
Wherewe are – The author’s assertion that AI can collect, aggregate, and standardize all of this data regardless of the format is much easier said than done. The lack of data standards in healthcare continues to be the key stumbling block to an accurate, longitudinal patient record. And, collecting disparate data sets and placing that data in the proper context for review is a daunting challenge that has yet to be overcome.
4. AI builds complex and consolidated platforms for drug discovery – AI algorithms can identify new drug applications, tracing their toxic potential as well as their mechanisms of action.
Where we are – See the comments under point number two above.
5. AI can forecast kidney disease – In 2019, the Department of Veterans Affairs (VA) and DeepMind Health created a ML tool that can predict Acute Kidney Injury (AKI) up to 48 hours in advance. The AI tool was able to identify more than 90% of acute AKI cases 48 hours earlier than with traditional care methods.
Where we are – The partnership between VA and DeepMind Health continues. Its next target is to identify how this ML tool can be installed in medical units. A user-friendly platform is also targeted to support clinicians in their treatment decisions that would improve the quality of life for Veterans suffering from AKI.
6. AI provides valuable assistance to emergency medical staff – During a sudden heart attack, the time between the 911 call to the ambulance arrival is crucial for recovery. For an increased chance of survival, emergency dispatchers must recognize the symptoms of a cardiac arrest to take appropriate measures. AI can analyze both verbal and nonverbal clues to establish a diagnostic from a distance.
Where we are – Implementing these types of AI-assisted tools in the EMS is an expensive proposition. That, coupled with the fact that most EMS departments are run at the local town or city level, makes it difficult to achieve scale in deploying these tools. Also, one must consider the training requirements for proper implementation as well.
7. AI contributes to cancer research and treatment, especially in radiation therapy. In some cases, radiation therapy can lack a digital database to collect and organize EHRs, making the study and treatment difficult. To assist clinicians in making informed decisions regarding radiation therapy for cancer patients, a platform has been developed that collects the relevant medical data of patients, evaluates the quality of care provided, optimizes treatments, and offers specific oncology outcomes, data, and imaging.
Where we are – In a classic case of hype versus reality, consider IBM’s Watson and cancer care. Watson’s entry into cancer care and interpretation of cancer genomics was, just like its appearance on Jeopardy!, highly hyped, with overwhelmingly positive press coverage and little in the way of skeptical examination of what, exactly, Watson could potentially do and whether it could improve patient outcomes. An article in STAT looked at Watson for Oncology’s use, marketing, and actual performance in hospitals around the world, interviewing dozens of doctors, IBM executives, and artificial intelligence experts and concluded that IBM released a product without having fully assessed or understood the challenges in deploying it and without having published any papers demonstrating that the technology works as advertised, noting that, as a result, “its flaws are getting exposed on the front lines of care by doctors and researchers who say that the system while promising in some respects, remains undeveloped.” Quoting the STAT authors:
Perhaps the most stunning overreach is in the company’s claim that Watson for Oncology, through artificial intelligence, can sift through reams of data to generate new insights and identify, as an IBM sales rep put it, “even new approaches” to cancer care. STAT found that the system doesn’t create new knowledge and is artificially intelligent only in the most rudimentary sense of the term.
STAT – Casey Ross & Ike Swetlitz
8. AI uses data collected for predictive analytics – Turning EHRs into an AI-driven predictive tool allows clinicians to be more effective with their workflows, medical decisions, and treatment plan. NLP and ML can read the entire medical history of a patient in real-time, connect it with symptoms, chronic affections, or an illness that affects other family members. They can turn the result into a predictive analytics tool that can catch and treat a disease before it becomes life-threatening.
Where we are – The buzzword fever around predictive analytics will likely continue to rise and fall. Unfortunately, lacking the proper infrastructure, staffing, and resource to act when something is predicted with high certainty to happen, we fall short of the full potential of harnessing historical trends and patterns in patient data. In other words, without the willpower for clinical intervention, any predictor – no matter how good – is not fully utilized.
9. AI accelerates the discovery and development of genetic medicine – AI is also used to help rapidly discover and develop medicine with a high rate of success. Genetic diseases are favored by altered molecular phenotypes, such as protein binding. Predicting these alterations means predicting the likelihood of genetic diseases emerging. This is possible by collecting data on all identified compounds and on biomarkers relevant to specific clinical trials.
Where we are – If you live in the U. S. you’ve undoubtedly seen various cancer treatment centers talking about their personalized therapy plans, and especially how they’ll tailor things to your DNA sequence and so on. You would get the impression that we have an arsenal of specifically targeted cancer therapies, waiting for patients to get their tumors sequenced so they can be paired with the optimal treatment. That’s not true. I wish it were, but it just isn’t.
There are estimates that only about 15% of patients total are currently even eligible (under FDA guidelines) to have their tumors sequenced in the hope of matching with a targeted therapy. About one-third of those may actually benefit from the process in the end. This is not exactly what you’d expect if all you knew about this stuff was what you heard on TV. The thing is, that’s actually a significant advance because the number used to be zero in both categories. We really are making progress, and the people who can benefit really can benefit. It’s just that there aren’t nearly as many of them as we’d like, not yet.
10. AI supports health equity – Those responsible for applying AI in healthcare must ensure AI algorithms are not only accurate but objective and fair.
Where we are – Unfortunately, as has been demonstrated in many of the AI algorithms that exist today, we cannot assume that all relevant factors were applied in the training set of the AI algorithms. The medical datasets openly available for use by AI researchers are notoriously biased, especially in the US. It’s not a secret: Healthcare data is extremely male and extremely white, which has real-world impacts. (For a deeper discussion on this topic, check out Amber M. Hamilton’s article in Slate’s Future Tense Silicon Valley Pretends That Algorithmic Bias Is Accidental. It’s Not.) Questions that need to be addressed include: Is the selection of the training factors evidence-based? Are race, gender, and ethnicity data included in the training data set?
Adding a link here to an excellent, insightful blog post from John Halamka, M.D. titled “Learning from AI’s Failures. I love his concluding paragraph:
If we are to learn from AI’s failures, we need to evaluate its products and services more carefully and develop them within an interdisciplinary environment that respects all stakeholders.
John Halamka. M.D., President, Mayo Clinic Platform
AI holds great potential in improving care delivery by optimizing the use of scarce resources and eliminating repetitive and non-value-adding work for the care team. However, AI adoption in healthcare continues to have challenges, such as a lack of trust in the results delivered by an ML system and the need to meet specific requirements. It is essential to take a realistic approach when considering the implementation of AI in your organization. At this time, the best Return on Investment (RoI) case for AI involves operational use cases (e.g., bed management, staffing management, supply chain optimization, etc.) Certain clinical use cases like imaging, dermatology, and pathology can improve workflow and prioritize work lists for those disciplines.
I listed seven ways to prepare for incorporating AI in health care organizations during one of my presentations in 2017. I think they are still relevant today.
If you are interested in digging a bit deeper into the topic of AI in health care, I highly recommend this online learning course from my friend and colleague Tom Giordano. His “Plain and Simple” series of courses are excellent.
“Reading is to the mind what exercise is to the body.” — Richard Steele
I’m certainly no Bill Gates. But with the Independence Day Holiday weekend upon us, I’m going to poach his idea of a “Think Week,” albeit in shorter form, to dig into some books that have been piling up since the beginning of the year. There are a wealth of options to choose from, but here are some of my top recommendations.
I will read anything that Walter Isaacson publishes. I loved his biographies of Albert Einstein and Leonardo DaVinci. So it is no surprise that one of my top recommendations is his book The Code Breaker, his biography of CRISPR pioneer Dr. Jennifer Doudna. The development of CRISPR by Nobel Prize Winner Doudna and her colleagues launched a revolution that will have dramatic implications for science and medicine in the future. Isaacson makes the science understandable and probes other ethical concerns that the development of this tool creates.
Dr. Robert Pearl’s latest book, “Uncaring: How the Culture of Medicine Kills Doctors & Patients” is another must-read. In this critical and timely book, Dr. Robert Pearl shines a light on the unseen and often toxic culture of medicine. Today’s physicians have a surprising disdain for technology, an unhealthy obsession with status, and an increasingly complicated relationship with their patients. All of this can be traced back to their earliest experiences in medical school, where doctors inherit a set of norms, beliefs, and expectations that shape almost every decision they make, with profound consequences for the rest of us. To get a sense of the importance of this book, you can view this YouTube interview with Dr. Pearl by Dr. Zubin Damania (aka ZDOGGMD) here.
From former head of Obamacare Andy Slavitt, Preventable is an inside account of the United States’ failed response to the Coronavirus pandemic. Slavitt chronicles what he saw and how much could have been prevented – an unflinching investigation of the cultural, political, and economic drivers that led to unnecessary loss of life. A sobering account of the response to the COVID-19 pandemic from someone on the front lines. A link to Slavitt’s interview with The Washington Post is here.
Dr. Makary is a professor at the Johns Hopkins School of Medicine and Bloomberg School of Public Health and prolific writer on medicine and healthcare policy. One in five Americans now has medical debt in collections and rising health care costs today threaten every small business in America. Dr. Makary shows how so much of health care spending goes to things that have nothing to do with health and what you can do about it. Dr. Makary challenges the medical establishment to remember medicine’s noble heritage of caring for people when they are vulnerable. He and his team have testified in court proceedings for patients & their families who have been sued for outstanding charges and won victory after victory. Dr. Zubin Damania interviews Makary about his book here.
Scott Galloway is a professor at the NYU Stern School and an entrepreneur, podcaster, and critically acclaimed writer. In this book, Galloway argues that the pandemic has not been a change agent so much as an accelerant of trends already well underway. In Post Corona, he outlines the contours of the crisis and the opportunities that lie ahead. Some businesses, like the powerful tech monopolies, will thrive as a result of the disruption. Other industries, like higher education, will struggle to maintain a value proposition that no longer makes sense when we can’t stand shoulder to shoulder. And the pandemic has accelerated deeper trends in government and society, exposing a widening gap between our vision of America as a land of opportunity and the troubling realities of our declining wellbeing. He co-hosts the podcast Pivot with New York Times tech reporter Kara Swisher. You can find their episodes here.
Although not technically a “tech” book, Michael Lewis’s book The Premonition highlights the work of a band of medical visionaries against the wall of ignorance that was the official response to the outbreak of COVID-19. A great read.
Cybersecurity is a huge issue in healthcare. Every week we hear of new attacks on health systems, insurance companies and other care providers. Millions of dollars have been paid to bad actors to regain access to health record systems that have been held hostage, delaying care and exposing the protected health information of millions of patients. Frankly, this book scared the !$%^ out of me. New York Times cybersecurity reporter Nicole Perlroth’s in-depth reporting exposes the dark underworld of “zero-day” exploits, revealing the urgent threat faced by us all if we cannot bring the global cyber arms race to heel.
I hope you find these books as enjoyable as I have. If I’ve missed one of your favorites, or you have suggestions for additional reading, please leave them in the comments to this post. Happy reading!
“There is no frigate like a book to take us lands away….”
Emily Dickinson Poem
In case you are looking for some last minute holiday gifts for a colleague or friend, I thought I might share some of the books I’ve read over the year that have shaped my thinking in 2019.
First up is Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol, MD. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. Unfortunately, AI has become a buzzword in healthcare these days. Every vendor has a different definition of what it is, how it works, and the benefits it provides. Dr. Topol lays out the landscape of AI in healthcare clearly, succinctly, and with a minimum of technical jargon. If you want to understand how this technology can REALLY impact healthcare, this is a must read.
Augmented Health: The End of the Beginning by Lucien Engelen is a great read. It provides some insights on what is happening to health(care) and how you might best prepare for the digital future that’s coming. It addresses all levels: physicians, nurses, patients, IT, board members & governments.
I’m not a healthcare economist, but I have always enjoyed reading anything by the late Uwe Reinhardt. His last book Priced Out: The Economic and Ethical Costs of American Health Care was eye opening. He analyzes why there is no American political consensus on a fundamental question other countries settled long ago: to what extent should we be our brothers’ and sisters’ keepers when it comes to health care? In typical Reinhardt fashion he dispels the confusion, ignorance, myths, and misinformation that hinder effective reform.
Reframing Healthcare: A Roadmap for Creating Disruptive Change by Zeev Neuwirth, MD was another of my favorite reads. In my work with Sg2, we were very fortunate to have Zeev join us as a keynote speaker for our Innovation Summit in San Francisco last July. His presentation outlined the roadmap for healthcare organizations that wish to thrive in a customer-centric, community-oriented, value-based healthcare system. A great read by itself, but you can take advantage of the ongoing developments in this critical area by listening to Zeev’s podcast series Creating A New Healthcare where he interviews clinical leaders from around the country who are putting these tools into practice.
I have always followed Jane Sarasohn-Kahn on social media and enjoy her blog Health Populi. Her book, Health Consuming: From Health Consumer to Health Citizen is a terrific read. The book explains how HealthConsuming has come to be: how consumers are playing growing roles in making health for themselves, their families and friends, and in their communities, facing ever-growing financial health risks; peoples’ growing use of mobile platforms and broadband connectivity, and the promise of digital health for wellness, prevention, self-care and chronic medical care; expanding access for retail health in our communities; the overwhelming evidence for investing in social determinants of health; growing challenges of personal health information privacy and security; and, ultimately, whether Americans have the prospect of becoming full health citizens like peers enjoy in the rest of the developed world.
Another of my favorite people to follow online and across multiple social platforms is Peter Diamandis. I regularly read his blog, watch his videos from Singularity Univerity online, and read his books. Peter and his co-author Steven Kotler always push the envelope and drive creative thinking across multiple industries. Their book Bold: How to Go Big, Create Wealth and Impact the World was one of my favorites this year. If you are interested in the exponential technologies that are disrupting today’s Fortune 500 companies and enabling upstart entrepreneurs like 3D printing, artificial intelligence, robotics, networks and sensors, and synthetic biology, this is the book for you. I’m excited to be part of the launch team for their new book The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives which will be released at the end of January, 2020. I’m sure that it will make my list of recommendations next year.
Finally, I was blown away by The Price We Pay: What Broke American Health Care–and How to Fix It by Marty Makary, MD. Drawing from on-the-ground stories, his research, and his own experience, The Price We Pay paints a vivid picture of price-gouging, middlemen, and a series of elusive money games in need of a serious shake-up. The book offers a roadmap for everyday Americans and business leaders to get a better deal on their health care, and profiles the disruptors who are innovating medical care. I can also recommend a video of Dr. Makary being interviewed by ZDogg MD, Dr. Zubin Damania on his You Tube Channel. You can find the interview here.
I hope that you find one or more of these titles interesting. If you have other book recommendations that you’ve enjoyed this year, please share them in the comments to this post. I’m always looking for another great read.