To Build a Fire

In 2021, much of our work at Mayo Clinic Platform will be creating repeatable processes that achieve their intended result in a timely, repeatable, scalable fashion. To understand what it means to achieve process maturity, let me tell the story of firewood management at Unity Farm Sanctuary, a great illustration of use case definition and attention to detail.

At the Sanctuary, we heat the farmhouse in the evening with a wood fire using fallen trees from the property. The logs must be sorted into wood species — ash and black birch can be burned without aging, while maple and oak must be aged. Cedar and pine are not good firewood because their oils cause the wood to pop and sputter. Poplar is not a good firewood because it smells and doesn't generate much heat.

Once we've identified the right wood for the right purpose, it needs to be cut into logs less than 2 feet long so they can be split and stored.

How do you cut up a fallen tree? You need multiple tools, including a chain saw for bucking, a forest axe for limbing, a timber jack to lift the tree off the ground, a sawbuck to trim the logs that are too long and a felling wedge to prevent the chain saw from getting pinched as logs are cut.*

If you have all these tools and the training to use them, you can reduce a fallen tree into firewood for splitting.

Then how do you split it? An engineer in West Bridgewater, Mass., custom built the SuperSplit, which uses flywheels instead of hydraulics. With this tool, I can split a cord of wood by myself in 30 minutes.

Then how do you store it? We use firewood brackets to create whatever size and shape storage we need, and then we cover it with a tarp.

Next, we have to transport it to the fireplace. We use a Vermont cart with flat-free tires and a log carrier.

When the chain saw chains are dull and covered with sap, how do you clean and sharpen them? We use a small tank of mineral spirits to soak the chains, and then scrub them with a stainless-steel brush. Then we use a commercial chain sharpener adjusted at precise angles to bring each chain back to factory specification. Then we use another tank to soak the chain in oil before storing it.

I mention all these process steps because to achieve maturity, we needed to identify each action to turn a fallen tree into a cozy fire. We had to implement the collection of technologies and training to do it rapidly and repeatedly.

In the Mayo Clinic Platform, we're developing mature processes for bringing on new partners, ingesting new kinds of data, launching new projects with existing joint ventures, evaluating AI algorithms and accelerating pilots with startups. Like the tree-to-fire procedure at Unity Farm Sanctuary, we’ll develop detailed use cases, appropriate vendors, training/staffing, toolsets and key performance indicators to support these Platform processes.

Maneesh Goyal, COO of Platform, notes that we need People, Processes and Products to be successful at Mayo Clinic Platform. My role as President of Mayo Clinic Platform and co-founder of Unity Farm Sanctuary is to support these concepts in both my professional and home life. 

*Products mentioned are not endorsements. The Sanctuary has no relationship with any of these companies.

Unity Farm Sanctuary – A Community Benefit Startup

Recently I was speaking to Nick Dougherty, managing director of MassChallenge HealthTech, the digital health accelerator in Massachusetts. After analyzing hundreds of startups, he concluded there are three types of companies:

1. Transactional – Buy my product.  If you don't like it, I'll try someone else.
2.  Confirmatory – Do you like my product?  If you don't like it, I'll change it for you.
3.  Diagnostic – What product do you need to solve your most pressing issues?

Transactional companies limp along. Confirmatory companies succeed but not wildly. Diagnostic companies can grow exponentially.

Unity Farm Sanctuary in Sherborn, Mass., was started by my family in late 2016 as a non-profit providing a safe and loving lifelong home for farm animals.

Our sense was that such services were an unmet need in Massachusetts, where few organizations serve as sanctuaries and most are at capacity. Although we had experience raising dogs and caring for small animals, as well education in farm operations from the University of Massachusetts, every day has been a learning opportunity. We had to master large animal handling, medical care and four-season food/water supplies.

The first two years were very transactional and confirmatory with the public. We had a vision and modified that vision as we learned. As an engineer and clinician, I focused on the facilities and animal care, maturing the processes of daily operation. My wife, educated in the humanities and arts, is a people person. She immersed herself in the daily hours of training and educating every new volunteer and visitor.

With time, however, we discovered that running Unity Farm Sanctuary required a diagnostic approach. We learned that the public really wanted a place that promoted a culture of kindness and connection.

What do I mean?

In a world of too much stimulation – streaming videos, a 24x7 news cycle and high-paced video games –  there needs to be a place where you can step away from the anxiety and spend an hour grooming a goat. Or giving a 2500 pound bull a back scratch. Or feeding the chickens. Walking a woodland trail for quiet reflection is a powerful tonic. Watching alpaca pronk (bounce around) at sunset puts life into perspective.

Especially in a time of COVID and political polarization, there is a need for human-animal bonding. These interactions are diverse and spontaneous. In the days following the 2020 Presidential election, we had Boston-area visitors arrive to sit with the goats and just enjoy their company.

When we first started the sanctuary, the volunteer program was small and unstructured. Today we have 350 volunteers that earn specific credentials/badges for their knowledge and experience.

We serve volunteers and visitors with handicaps of all kinds. Some may not relate to other people, but they can easily relate to a horse. Some people develop a bond with a particular animal, and some people bond to the whole environment of the forest and paddocks. We have story after story of people from one to one hundred years old finding their bliss while serving rescued animals.

I've not found the term "community benefit startup" in common use, but that is what Unity Farm Sanctuary has become. The structure is a 501c (3) charity on 30 acres of owned land designated as a town forest/trail, and surrounded by other protected space, creating a 70 acre plateau with no immediate neighbors. There are 4 miles of trails, 5 streams, 2 ponds and 3 tree houses.  

The transformation in thinking that surprised me the most was that our service to volunteers is as important as our service to animals.  Updates about the health and activities of each rescue bring a bright spot to the day of the volunteers who give their time, energy and contributions to sustain the small city that Unity Farm Sanctuary has become. Feeds on Facebook, Instagram, Twitter, Tumblr and Pinterest require daily diligence, but the community benefit of posting them is palpable.  

At Mayo Clinic where I serve as President of Mayo Clinic Platform, we believe that innovation happens when you start small, think big, and move fast https://www.amazon.com/Think-Start-Small-Move-Fast/dp/007183866X. Unity Farm Sanctuary is a perfect example of that idea. It started with a few chickens and now, three years later, is one of the largest animal sanctuaries in New England.  Being agile enough to listen to the needs of hundreds of stakeholders led us to create a community benefit startup. I firmly believe that a new class of organization, creating positive emotional, physical and mental experiences for volunteers, will be increasingly important in a world that needs more empathy and compassion. Unity Farm Sanctuary, a community benefit startup, is an early example.

Network medicine offers new insights into susceptibility to diseases such as COVID-19

In the past year, we've become familiar with the factors that can make a person more vulnerable to COVID-19 infection. The elderly are more at risk, as are those who smoke and are already dealing with other diseases, such as cancer and Type 2 diabetes.

At a deeper level, though, there are dozens of other factors that may come into play and influence a person's susceptibility to disease. A recent analysis of hospitalized COVID-19 patients in 14 states found that among patients ages 50-64 that obesity was the most prevalent underlying medical condition. Similarly, there's growing evidence to suggest that vitamin D deficiency contributes to COVID-19 infection.

The emerging field of network medicine, powered by this type of digital analysis of large data sets, sheds light on the interplay between microbial virulence and the ability of a person's immune system to defend against diseases such as COVID-19. Network medicine allows researchers and physicians to look beyond the traditional root causes of disease and take a more holistic approach to identify agents that can influence a person's susceptibility to disease.

In an article that I co-authored with Paul Cerrato and Adam Perlman, M.D., MPH, for Mayo Clinic Proceedings: Innovations, Quality and Outcomes, we describe how the analytic power of supercomputers and the emergence of big data sets has given researchers new insights into the causal relationships that influence susceptibility to disease. This technology dramatically improves our ability to assess the relative strengths and weaknesses of factors as contributing agents.

Some of these agents are not surprising — nutritional status, for one, and environmental factors. Others may be harder to assess, like sleep habits, exercise, physical and psychosocial stressors, obesity, protein-calorie malnutrition and emotional resilience. Genetic variations such as single-nucleotide polymorphisms also are examined as possible agents affecting a person's vulnerability to disease.

With possible factors identified, deep learning algorithms can assess each's likely strengths and weaknesses as contributing factors to disease and help identify therapeutic options.

Using machine learning-enhanced algorithms to analyze risk factors and their interactions can help determine which ones can predict a person's risk of COVID-19 infection or the prognosis for someone who already has tested positive.

At a time when we're all looking for reasons for hope and encouragement — and the national rollout of a COVID-19 vaccine is a big one — it's good to remember that our capabilities to gain essential insights from AI, network medicine and deep learning algorithms are ever-growing and that we have the potential not only to resolve this pandemic more quickly but to completely redesign how we respond to pandemics in the future.

The Facilitation of Change in Health Care

Recently, the Washington Post gave an in-depth analysis from the frontlines of the pandemic in Eau Claire, Wis. The moving piece portrays the compassion and ingenuity needed from frontline providers to meet patient needs during a COVID-19 surge. It is a portrayal of excellence that reminds me why so many pursue work in health care. 

The article highlights the deployment of a hospital-at-home model to increase hospital capacity for the surge. Rita Huebner’s experience in Mayo Clinic’s advanced care at home offering provides a great exemplar of how technology facilitates patient-focused change within the health care system.  

Recently, Paul Cerrato and I published “The Digital Reconstruction of Health Care,” where we explored the digital transformation in health care that will facilitate care delivery change. Artificial intelligence and remote monitoring enable new knowledge generation, cost efficiencies and expand the care continuum. Our analysis examines the transition from brick-and-mortar to online care, providing a rationale for the shift. 

Many industries have undergone digital transformation. Nine years ago, Uber launched an app and ride-sharing service that focused on connecting users seamlessly to the ubiquitous “black” cars prevalent in the major cities. This service's facilitation through Uber’s platform grew in popularity, expanding to ordinary cars and flipping the taxicab industry on its head.

Health care is experiencing a similar digital renaissance that will change how some elements of care are delivered. COVID-19 has accelerated the adoption of telemedicine, hospital-at-home and remote patient monitoring. The capabilities offer valuable methods for scaling the health care system, achieving cost efficiencies, and expanding the care continuum. However, as we see in Wisconsin, people will remain at the heart of the strategies and care.

COVID-19 Update Part II: Collaborations & Insights to Come

Over the past seven months, COVID-19 has impacted our lives, and my writing moved from social media to Mayo internal communications, pandemic response research papers, and COVID-focused public awareness campaigns. While the challenges have been considerable, the work since April has validated that the health care system needs novel technologies, policy reform and cultural change.  It's time to return to social media posting.

COVID-19 has forced a level of focus and collaboration that is accelerating the Mayo Clinic Platform's formation. While we are still navigating the pandemic challenges, but despite the obstacles, there has been significant work accomplished on the platform and through external organizations.

The COVID-19 Healthcare Coalition and The Fight is in Us are two organizations that have emerged, and I have engaged with them to address pandemic needs. Both represent examples of how private and public-private collaborations leverage individual strengths to accomplished shared goals. It is this spirit of cooperation that platform business enables what we are creating with the Mayo Clinic Platform.

While 2020 derailed many business plans, the Mayo Clinic Platform has maintained its focus and implementation schedule. The focus, dedication and teamwork have been extraordinary. The first two business lines, the Clinical Data Analytics Platform and the Virtual Care Platform, were launched and operationalized. A third business line, the Remote Diagnostics and Management Platform, is in development with promising projects.

I look forward to chronicling this journey for you with weekly posts to this blog and additional insights on LinkedIn and Twitter.

Reinventing Clinical Decision Support

In our latest book, Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning, Paul Cerrato and I explore the promise of artificial intelligence and machine learning for improving clinicians’ ability to make more informed diagnostic and therapeutic decisions. Here’s an excerpt from Chapter 2:

“AI is a once-in-a-generation transformative technology. As such, expect its impact to be on the scale of the advent of electricity or the Internet,” says Jean-Claude Saghbini, Wolters Kluwer Health.(1)

“Artificial intelligence and machine learning are set to transform healthcare. From front line care delivery, including triage, clinical decision support and patient experience to back office operations, such as billing and revenue cycle, algorithms and emerging technologies are already proving their value,” according to a recent report from Healthcare Information Management Services Society (HIMSS). (1)

Both enthusiastic visions suggest that artificial intelligence (AI) and machine learning (ML) are poised to transform medicine and bring in an era of cost effective patient care. But these predictions have to be weighed against less optimistic views, including those that suggest AI will disrupt the workforce in healthcare and other industries, causing many to lose their jobs to soulless

algorithms and robots.

Israeli historian Yuval Noah Harari, for example, believes that: “For now, most of the skills that demand a combination between the cognitive and manual are beyond AI’s reach. Take medicine . . . ; if you compare a doctor with a nurse, it’s easier for AI to replace a doctor—who basically just analyzes data for diagnoses and suggests treatments. But replacing a nurse, who injects medications and bandages, is far more difficult. But this will change; we are really at the beginning of AI’s full potential.” (2)

There are futurists who are far more optimistic, however. They imagine a scenario in which every patient gets the same quality of care afforded presidents in affluent countries or billionaire CEOs at major technology companies. With the assistance of AI, machine learning, and massive databases, they envision a world in which we each have the electronic equivalent of a personal physician who has access to the very latest research, the best medical facilities that specialize in each individual’s health problems, access to cutting-edge data sets, predictive analytics, testing options, clinical trials currently enrolling new patients, and much more. For example, Alvin Rajkomar, MD; Jeffrey Dean, MD, of Google; and Isaac Kohane, MD, PhD, of Harvard Medical School, describe a possible future in which:

A 49-year-old patient takes a picture of a rash on his shoulder with a smartphone app that recommends an immediate appointment with a dermatologist. His insurance company automatically approves the direct referral, and the app schedules an appointment with an experienced nearby dermatologist in 2 days. This appointment is automatically cross-checked with the patient’s personal calendar. The dermatologist performs a biopsy of the lesion, and a pathologist reviews the computer-assisted diagnosis of stage I melanoma, which is then excised by the dermatologist.(3)

This scenario stands in stark contrast to the current state of affairs that often transpires in today’s broken healthcare ecosystem. As Rajkomar et al.3 point out, in today’s ecosystem, this patient is more likely to ignore his skin lesion for far too long; his primary care physician may misdiagnose the melanoma because of its atypical appearance, and the delay may result in a metastatic malignancy that requires systemic chemotherapy.

With such contrasting views, clinicians have to wonder: What precisely will the future look like? Our purpose in Chapter 2 is to explore the strengths and weaknesses of AI and ML and to help clinicians and technologists gain a realistic view of the near future—a future that promises to deliver more cost effective, more personalized care but also one that faces numerous challenges. We will explore basic terminology and concepts and discuss AI/Ml solutions in

a variety of medical specialties. In Chapter 3, we will outline the many challenges that stand in the way of the full implementation of these solutions. 

More details from Chapter 2 will appear in a subsequent blog. A full discussion of AI/ML is available in our book.

References

1. “AI and Machine Learning: What Cuts Hype from Reality.” Healthcare IT News. Retrieved on April 8, 2019.

2. Kaufman D. (2018, October 19). “Workers Beware: Algorithms Could Replace You—Someday.” The New York Times, p. F2.

3. Rajkomar, A., Dean, J., and Kohane, I. (2019). “Machine Learning in Medicine.” New England Journal of Medicine, vol. 380, pp. 1347–1354.

A COVID update

I realize that my blog post frequency has diminished during COVID.   Writing time has been redirected to the national COVID-19 coalition and its 14 workgroups:

Analytics
Modeling & Simulation
Health Systems and Clinical SME
Supply Chain
Telehealth
Testing
ICU and Mechanical Ventilation
COVID-19 Data Standardization (mCoVD = Minimal COVID-19 Viable Dataset)
Non-Pharmaceutical Interventions
Optimization of Clinical Therapeutics/Protocols
Data Storage and Source
Dis/Misinformation
Privacy Advisory
Contact Tracing

Below is a description of this week's highlights

a. Critical Shortages of Personal Protective Equipment (PPE)

Worked with international suppliers to deliver 580,000 FDA-certified respirators to New York City hospitals, working through Governor Cuomo’s procurement office.

Connected 100,000 FDA-certified respirators to Masks for America, a volunteer coalition of everyday Americans joining forces to deliver protective masks directly to frontline healthcare workers in COVID-19 hotspots.

Prepared guidelines, assisted purchases, and piloted novel solutions to decontaminate respirators so they can be reused multiple times, extending their lifecycle.

b. Implementing Social Policies

Launched a Non-Pharmaceutical Intervention  (NPI) dashboard that provides real-time tracking of state-wide NPI implementation.  As States begin relaxing NPIs, we’ll be able to see the impact. 

c. Achieving Data-Driven Clinical Care Outcomes

Coalition members, including numerous electronic health record vendors, are developing a minimal common data set for COVID-19.  This is now being used to research the outcomes of clinical outcomes on treatments, such as hydroxychloroquine, Remdesivir, and others.   We have designed a federated query approach so that any institution can participate in studies by running queries locally in their EHR. 

Supporting the rapid scale-up of telemedicine as part of a fully integrated healthcare delivery system.  With Mayo's help we are running multiple studies about the adoption of telemedicine during COVID.   See a starting set of best practices in the Resource Library

Detecting Dis/misinformation Related to COVID-19.   Dis/misinformation or outright fraud can affect patients' ability to understand and adhere to non-pharmaceutical interventions and, most importantly, their health.  A new dashboard will be published soon.

Supporting the Mayo Clinic-led national convalescent plasma trial, which is attracting thousands of health systems and patients.  We have worked closely with EHR vendors to develop order sets and a data analysis approach.

Supporting the Contact Tracing activities of Apple, Google, MIT and others with connections to the Association of State and Territory Health Officers, Bluetooth experts at Lincoln Labs, and leveraging an Amber alert like technology called Sara alert for infected patient followup. 

d. International Regulatory Standards and Comparison for N95 Respirators

Due to the shortage of N95 respirators in the U.S. during the pandemic, organizations are ordering similar respirators from international companies. We’ve listed what’s available from 7 countries and which products are FDA-authorized.  

e. Ventilator Training App

This multi-vendor library of training and product materials for medical professionals was created through a partnership of leading ventilator manufacturers and Allego, Inc. You’ll find free mobile access to video overviews, instruction manuals, and other training materials for equipment that is critical to treating patients suffering from COVID-19-related respiratory distress.   See

f. Coronavirus Scientific Literature Topic Browser

Researchers looking for COVID-19-related articles of clinical or scientific interest should try this interactive tool. The COVID-19 Open Research Dataset (CORD-19) is presented in several views, allowing users to quickly find clusters of papers on a desired topic.