A Look Back at 2019

I've always been an optimist.   I believe humans are basically good and that the nice guy will win eventually.

After traveling 400,000 miles to 40 countries in 2019, helping government, academia, and industry, my view of the world has not changed.

Despite our focus on the negative 24x7 news cycle, 2019 has been the best year for humanity in history.

My best memories, looking back at 2019:

*Serving the Gates Foundation in South Africa and Northern India.  Experiencing the rollout of technology enabled platforms that reduced HIV disease burden and improved diagnosis/treatment of tuberculosis.

*Working with mayors and hospital presidents in China to create innovation centers in Shenzhen and Shanghai, enabling healthcare analytics platforms for population health and precision medicine.

*Helping government in Japan think about refinements to privacy policy that empower patients to be stewards of their own data.

*Discussing opportunities with government to enhance electronic health record and cloud adoption in Germany.

*Meeting numerous new colleagues in Northern Europe (Netherlands, Denmark, Norway, Finland and Sweden) while working together to harness past patient data for the benefit of future patients.

*Teaching National Health Service leaders in the UK (both England and Scotland) about a digital future that can transform workflow and the patient experience.

*Running courses with my Harvard colleagues all over the world to share lessons learned about technology policy and innovation.

*Mentoring the next generation of innovators in Massachusetts at Beth Israel Lahey Health and Mass Challenge Healthtech.

*Assisting with government policy development for data exchange as part of the Massachusetts Digital Health Council.

*Understanding the data needs of payers, providers, pharma, patients, and tech companies while defining the ethical uses of that data.

*Embracing a significant change for me personally - joining new colleagues at Mayo Clinic to build a global digital health platform for innovation. 

*Caring for 250 abandoned, abused, diseased, distressed, and unwanted animals at Unity Farm Sanctuary while building a self-sustaining community service destination for the Boston area.

In all these experiences, I saw forward progress as healthcare moved to the cloud, internet of things devices for health became mainstream and machine learning proved its value for diagnosis/treatment planning.   That even applies to Unity Farm Sanctuary where 103 internet of things devices help the staff deliver care.

Yes, I saw political unrest and divisiveness, the rise of populist movements, and a conservative shift in many governments.  To me, those were short term variations on a positive overall trajectory.    2019 set the stage for the next major leaps forward in digital health.

I'm honored to be a part of the 2020 journey that begins tomorrow.

Happy New Year!

Reinventing CDS Requires Humility in the Face of Overwhelming Complexity

Paul Cerrato and I have created a new book, Reinventing Clinical Decision Support, our first to be published about Platform thinking.   Although it is being published during my tenure at Mayo Clinic, it is not endorsed by Mayo Clinic and represents the personal opinions of Paul and me.  Below is the preface.

In our last book, on mobile health(1),  we wrote about the power of words such as cynicism, optimism, and transformation. Another word with powerful connotations is misdiagnosis. To a patient whose condition remains undetected, it is a source of frustration and anger. To a physician or nurse who has become a defendant in a malpractice lawsuit, it can likewise generate frustration and anger as they try to demonstrate that they did everything humanly possible to uncover the source of their patient’s symptoms.

The National Academy of Medicine’s report Improving Diagnosis in Health Care explains: “It is estimated that 5 percent of U.S. adults who seek outpatient care each year experience a diagnostic error. Postmortem examination research spanning decades has shown that diagnostic errors contribute to approximately 10 percent of patient deaths, and medical record reviews suggest that they account for 6 to 17 percent of adverse events in hospitals.”(2) An earlier report from the same group, To Err Is Human, came to a similar disturbing conclusion. The message between the lines of both reports is straightforward: Medical errors, including misdiagnoses, are often the consequences of being human. That same reality also comes across in a recent New England Journal of Medicine editorial: “The complexity of medicine now exceeds the capacity of the human mind.”(3)

Such complexity fosters humility—or at least it should. It requires humility for clinicians with years of experience successfully diagnosing patients’ ills to admit that they may be missing as many disorders as they catch. And the way the healthcare system is currently designed, that is a distinct possibility. When a patient is misdiagnosed by Dr. Jones, he often never goes back to him to say: You made a mistake, please try again. He is just as likely to move on to Dr. Smith in the hope that her diagnostic skills are more finely tuned. Humility is also required of clinicians to admit that the quantity of new research coming out in each specialty each year is so massive that it is virtually impossible for any one person to stay abreast of it. By one estimate, a new medical journal article is published once every 26 seconds, which translates to about 5,000 articles per day.(4)

Many diagnostic aids are now available to help address the epidemic of diagnostic errors we now face. Clinical decision support (CDS) systems, for example,are designed to help practitioners stay up to date on new developments without requiring them to spend their entire day reading the medical literature. Some CDS systems also offer symptom finders, decision trees, and other advanced features. But today’s digital tools only scratch the surface. Incorporating newly developed algorithms that take advantage of machine learning, neural networks, and a variety of other types of artificial intelligence (AI) can help address many of the shortcomings of human intelligence.

Fatima Paruk, MD, MPH, the chief medical officer at Allscripts, said it best: “[W]ith machine learning, clinical decision support can do so much more. We can transform systems laden with meaningless alerts to intelligent workflows and best practices driven by relevant patient history . . . Machine learning can enable clinical decision support based on multi-system analysis to understand which patients are at highest risk of a negative outcome, or to optimize treatment in real-time . . . Algorithms can parse available historical and current information to inform clinicians which patients are at risk for specific outcomes or deliver personalized treatment plans for patients with chronic conditions.”(5)

When will this next generation of CDS tools be available for clinicians in the trenches? When will we reinvent CDS? As the 8 chapters of this book point out, these tools are already emerging. Ignoring their value puts both clinicians and patients at risk.

This book begins with an examination of the diagnostic reasoning process itself, which includes how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods, cognitive mistakes such as availability bias, affective bias, and anchoring, and potential solutions such as the Human Diagnosis Project.

AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including the landmark Google study that demonstrated the value of deep learning in diagnosing diabetic retinopathy. Machine learning–enabled neural networks are also helping to detect melanoma, breast cancer, cancer metastasis, and colorectal cancer, and to manage severe sepsis. AI is even helping to address the opioid epidemic by reducing the number of pills being prescribed postoperatively. Each of these topics includes detailed references to the peer-reviewed medical literature.

With all the enthusiasm in the healthcare community about the role of AI and machine learning, it was also necessary to outline some of the criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed is the relative lack of hard scientific evidence supporting some of the latest algorithms and the “explainability” dilemma. Most machine learning systems are based on advanced statistics and mind-bending mathematical equations, which have made many clinicians skeptical about their worth. We address the so called black box problem, along with potential solutions, including educational tutorials that open up the black box.

This book devotes an entire chapter to commercial CDS systems, comparing legacy products to the latest software platforms. The evidence to show that these are having an impact on patient outcomes is mixed—an issue explored in depth in this book. On a more positive note, this chapter explores many of the innovative developments being launched by vendors such as DynaMed (EBSCO), VisualDX, UpToDate Advanced, and Isabel Healthcare.

The chapter on data analytics does a deep dive into new ways to conduct subgroup analysis and how it is forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs.

Any attempt to reinvent CDS also needs to tackle the outdated paradigm that still serves as the underpinning for most patient care. This reductionistic mindset insists that most diseases have a single cause. The latest developments in systems biology indicate otherwise and point to an ensemble of interacting contributing causes for most degenerative disorders. The new paradigm, which is being assisted by advances in AI, has spawned a new specialty called network medicine, which is poised to transform patient care at its roots.

Similarly, the current medical model relies too heavily on a population-based approach to medicine. This one-size-fits-all model is being replaced by a precision medicine approach that takes into account a long list of risk factors. And once again, this new paradigm is being supported by new technologies that help clinicians combine a patient’s genomic data, including pharmacogenomic test results, with the more traditional markers available in their electronic health record (EHR).

All these new developments would be useless if they could not be implemented in the real world. The final chapter outlines many of the use cases that have been put in place at Beth Israel Deaconess Medical Center (Boston) and elsewhere. These new programs are helping to improve the scheduling of 41 operating rooms, streamline the processing of patient consent forms before surgery, and much more.

Despite all these positive developments, it is important to emphasize that AI and machine learning will not solve all of healthcare’s problems. That will require an artful blend of artificial and human intelligence, as well as a healthy dose of emotional intelligence.

Finally, our enthusiastic take on digital innovation should not give readers the impression that AI will ever replace a competent physician. That said, there is little doubt that a competent physician who uses all the tools that AI has to offer will soon replace the competent physician who ignores these tools.


Paul Cerrato, MA
John Halamka, MD, MS

References
 1. Cerrato, P. and Halamka, J. (2019). Th e Transformative Power of Mobile Medicine.
Cambridge (MA): Academic Press/Elsevier.
 2. Balogh, E., Miller, B. T., and Ball, J. R. (Eds.). (2015). Improving Diagnosis in
Health Care. Institute of Medicine, National Academies Press.
 3. Obermeyer, Z. and Lee, T. H. (2017). Lost in Th ought: Th e Limits of the Human
Mind and the Future of Medicine. New England Journal of Medicine, vol. 377,
pp. 1209–1211.
 4. Garba, S., Ahmed, A., Mai, A., Makama, G., and Odigie, V. (2010). Proliferations of Scientifi c Medical Journals: A Burden or a Blessing. Oman Medical Journal, vol. 25, pp. 311–314.
 5. Paruk, F. (2018, December 4). HIT Th ink 4 Keys to Success with AI and Machine
Learning. HealthData Management. Accessed on December 18, 2018, from https://
www.healthdatamanagement.com/opinion/4-keys-to-success-with-ai-andmachine-learning

An Engineering Eye for the Tie Buying Guy

At the Mayo Clinic, patients always come first.  In my few days of volunteering, I picked up on some subtle ways that the culture supports patient-centric values.   Office areas are very utilitarian while patient care areas are well furnished and decorated.   Everyone is professionally dressed, regardless of their role.   For me, that means wearing a tie every day (and retiring my Dr. Martens).   Over the past 20 years, I've worn engineered black clothing in my travels around the world.   I've not worn a tie and long ago donated all the ties from my youth.   Admittedly, I do have a Harvard bowtie that I wore once for a meeting in a members only club that required a tie.   

So how does a person buy an appropriate tie in 2020?  Thick or thin, solid or textured, bright or subtle colors?   It turns out there is an engineering answer.   Here's a great overview  that helped me.

The width of a tie to buy in 2020 is a function of body size, lapel width and shirt collars. 

I'm 6'2" with a body mass index of 22.    I'm approaching 60 years old with measurements of 42" chest, 33" waist, 34" inseam.    That places me in the slim/tall category but not the athletic build category.

In 2008, I wrote this post about designing clothing based on the human three dimensional shape and the basics of materials science.   The lapels of all my current suits are 2 inches wide. 

My collars are thin and I don't want the tie fabric to protrude.

So for me, the acceptable tie width range based on my body type, my lapels, and my collars is 2.25-2.75 inches wide.

So what did I buy for my professional wardrobe?  A selection of 2.5 inch wide ties in charcoal, grey, and navy blue.

Each of them has some texture to stand out against my black and peat colored shirts.    It's likely that I will need a few white shirts as I transition to a full Mayo look.

Who knew that buying a tie needed a decision support system?

Mayo Clinic's First Platform

In my first few days volunteering at Mayo Clinic to meet colleagues, establish collaborations, and better understand the amazing patient services Mayo provides throughout world, I had the opportunity to tour the historic Plummer Building with Douglas Holtan,  chair of the Department of Facilities and Support Services.  Immediately, I realized the building is Mayo's first platform.

In my last post, I described a platform as a way to use knowledge and technology to facilitate connections, creating value in the process.

How could a building be a platform?

To understand that, you need to understand the past, provided to me by Matthew Dacy, creative and administrative director of the museum of Mayo Clinic.

Henry Stanley Plummer, M.D. (March 3, 1874-December 31, 1936) was a prominent internist and endocrinologist in the founding generation of Mayo Clinic. With his interests in medicine, engineering and the arts, colleagues considered him a "diversified genius" whose example remains an inspiration for innovators at Mayo Clinic.

He recognized the limits of the previous heterogeneous system in the Mayo practice. Each physician had his or her own ledger, limiting continuity of care when a patient had a return visit.  It was extremely difficult for multiple physicians to collaborate in the care of a single patient.

What did he do?

Dr. Plummer designed a  unified record and numeric registration system to benefit each individual patient while also accommodating a virtually limitless capacity of total patients in the system.

He created the record within the construct of the Mayo brothers’ philosophy that the needs of the patient come first and that a union of forces (diverse skills applied to shared purpose) would best serve patients.

He collaborated with allied health colleague Mabel Root and business manager Harry Harwick,  working with the  full blessing and support of Mayo brothers, especially Dr. William J. Mayo.

He sought technology best practices from other industries (pneumatic tubes from department stores, etc.) and installed them in the Plummer Building, which opened in 1928 and became the model for subsequent Mayo Clinic facilities, creating universal connectivity between those who produced information and those who needed it.  The system provided “just in time” access to information – as patients moved about the clinic between departments, the record would move via conveyor belts and drops, following the patient.

Here's a schematic illustration of functions and processes in the Plummer Building. Equally important, these systems are enveloped in stunning architecture with marble and other features from throughout the world.

Dr. Plummer standardized and optimized critical components, insisting on paper with high cotton content for durability, demanding precision paper thickness since large volumes would need to be stored and selecting official long-lasting “Mayo Clinic ink” that could be used in a wide range of fountain pens.  The end result was that information had durability, integrity, and availability.

He established strictly maintained institutional process and policies for record creation, dissemination and storage. As a result, very few  records were ever lost or misplaced.   The end result was that security and privacy were maintained despite the broader sharing of information.

He expanded the system as needed with additional cards and documents , using color coding and other techniques for ease of recognition and access.   If effect, he added new  "apps" to the healthcare information ecosystem.

This unified record platform with its universal connectivity, availability, integrity, security, and scalability had a symbiotic relationship with the comprehensive Mayo Clinic general exam, advancing the standards of diagnosis, treatment and prevention of illness.   It supported research, quality measurement, and population health.

Fast forward from 1928 to 2019 and you can see that first Mayo Clinic platform in the Plummer Building inspired the next Mayo platform which seeks to connect information producers with information consumers while insuring data integrity, security, and innovation agility.   It's an update for the digital age of the foundation laid by Dr. Plummer.

In my upcoming posts, I'll provide comprehensive detail about all the novel components of the new platform and the services that will cure, connect and transform healthcare throughout the world.

For me, the Plummer Building is the inspiration for the work ahead.

What is a Platform?

This month I'll deliver several keynote addresses.  In my presentations, I'll use terms such as platform enterprises, platform thinking, and platform strategy.  But what is a platform?

Is it just a collection of standards?   If so,  is a USB flash drive a platform, since I can transfer a file from my Chromebook to someone else's Macbook using it, in a low effort, low cost fashion?

Not exactly—in the USB example, there is no agreement about what file types are preferred, what data those files may contain and what security controls will be used to protect the integrity and privacy of the data.

In my view, a platform is a combination of technology (data standards, APIs, security controls), policy (who can do what for what purpose with what privacy controls), and process (what workflow is supported by what people and what automation).   In short, it is a way to use knowledge and technology to facilitate connections, and create value in the process.

For example, Unity Farm Sanctuary is entirely controlled with the Google Home platform. My locks, lights, thermostats, cameras, and mobile devices are linked via a set of APIs and security controls (OAuth). I can delegate rights to use selected devices for selected functions to authorized collaborators but the general public cannot gain access to my heat, light and power controls.     I use a combination of approaches to support device and workflow integration - Google Assistant routines, proprietary apps, and secure websites.     The end result is that I can monitor and manage the well being of 250 animals from my phone.

How does this apply to healthcare?

We know that the CMS Interoperability Proposed Rule and the ONC Information Blocking Proposed Rule  are likely to be finalized into active regulations. This means that in 2020 or 2021, hospitals and clinician offices will be required to exchange data via APIs to patient controlled apps. Increasingly healthcare must be a competent data business as well as an empathetic care delivery business. Supporting new regulations with point solutions will create a chaotic collection of heterogeneous user experiences and security vulnerabilities. Why not create a single, managed and well supported front door which enables quality, safety and efficiency solutions to be deployed more quickly?  That's Platform thinking.

The transition from a collection of products to a platform strategy is a journey.  How will technology services be delivered - via Google Cloud platform, Amazon Web Services, Azure, or other provider? How will access be granted, managed, and monitored? What workflows for what use cases will be supported and when? How will it be paid for? How will the effort be communicated so that all stakeholders understand privacy protections, ethical use of data, and possible participants in a platform ecosystem?

There are many questions to be answered while on the road to becoming a platform enterprise.  Over the past ten years, I've written nearly 2000 posts about Life as a CIO.   I feel the next 2000 posts about the platform transformation of healthcare, my dispatches from the digital health frontier,  will be even more important.

What's Next?

After nearly 25 years in Boston, I'm beginning a new journey at Mayo Clinic in the role of president, Mayo Clinic Platform.  Many colleagues have asked me about the transition.   

First, I have profound thanks for my mentors and collaborators in Boston.   I could easily fill an entire blog post with the names of hundreds of people who worked with me since 1996 on cloud services, mobile applications, machine learning, connected devices, and data standards.

Those innovations  made a positive impact on many people.    At Mayo, I believe I can scale the lessons learned in Boston to stakeholders around the world.  How?

As an adviser to many startups, incubators, and accelerators around the world, I've experienced the barriers and enablers to innovation.    Challenges include lack of standardized technology (APIs with sufficient data granularity and workflow integration),   policies (templates for security, privacy, risk analysis, ethical use of data, and communication),   and people (sufficient staffing to run pilots and focus on collaborators).  Launching a pilot can take 6 months just to work through approval processes.     Sometimes academic medical centers can take as long as 18 months to formalize a proof of concept project.   What if a Platform of technology, policies and people were able to radically shorten the time to evaluate emerging companies and created an "innovation factory"  for  collaboration?    That's how I think about the Mayo Clinic Platform opportunity.

Although I've visited Mayo many times, I've just scratched the surface in my understanding of the culture, capabilities, and colleagues.  A good way for anyone to understand what makes Mayo uniquely Mayo is to watch the Ken Burns documentary

How will I spend my first 100 days at Mayo?   Although I do not begin the role until January 1, 2020, I'm taking personal time off to volunteer at Mayo during December, meeting dozens of stakeholders at every level of the organization.   I need to listen to their hopes and needs for Mayo Clinic Platform projects.    There is a remarkable interim team leading the Platform today and they will guide me through the work to date, the critical decisions ahead, and the refinement of the strategic plan.    Together, we'll advance the strategy, structure, and staffing for the Platform.   Outstanding support teams in legal, development, compliance, IT, and public affairs will help.    I'll speak about our early decisions at JP Morgan and HIMSS.     Mayo CEO, Gianrico Farrugia,  will keynote the HIMSS conference .

Personally, I will live in an apartment in Rochester, Minnesota from Monday-Thursday, then return to Unity Farm Sanctuary for weekend animal care and farm maintenance.     The flights are easy (2.5-3 hours, 3 times per day on Jet Blue), and even with weekly commuting I may actually travel less in 2020 than in 2019 (400,000 miles in 40 countries).

During times of great challenge and change, I've blogged on a daily basis, sharing my successes and failures transparently with government, academia and industry colleagues.  Recently I've focused on writing articles and books.   The pace of the Mayo Clinic Platform effort necessitates frequent blog posts.  I'm renaming my blog from "Life as a CIO"  to "Dispatches from the Digital Health Frontier".  My hope is those dispatches will help others with the path forward, following the best trails and avoiding pitfalls. 

Am I excited by the work ahead?  Most definitely.    Am I daunted by the responsibility and accountability of shaping the future of Mayo's digital businesses?   Of course.   I call this, excited anxiety.   During those stages of life when there is a perfect storm for innovation , I find that a little adrenaline really maximizes focus, especially when you're not sure where the path ahead will lead.   As my colleague Prof. Yitshak Kreiss, M.D., Director General, Sheba Medical Center  told me "innovation is when you have an urgency to change but don't know exactly how to get there.   If you know where you're going, it's just implementation not innovation."

With today's Mayo announcement, I believe the next perfect storm is beginning and I'm ready.   And I'll make sure my readers have a front row seat.