Wednesday, January 22, 2020

Advice for Aspiring CMIOs

Recently, my colleague Steve Peters, chief medical information officer, Mayo Clinic, and I discussed our life experiences as CMIOs.  We talked about how the role has evolved along with changing technology and the transition to nearly universal adoption of electronic health records by large health centers.

 I asked Steve to share his insights on 'what makes a great CMIO in 2020' and captured his thoughts for you.

"I have thought about the 'post-EHR' CMIO as most large centers and hospitals have moved on from the initial implementation.    Here are a few thoughts:

1.       Serves as the primary physician champion for all major clinical information technology projects, including EHR implementation and information security.

2.       Assists in development of IT strategy and planning.

3.       Acts as an advocate for protection of patient privacy and the security of protected health information.

4.       Supports various regulatory compliance activities including HIPAA, Meaningful Use, and billing compliance.

5.       Represents the medical community and serves as an advocate in the promoting the use of information technology in the clinical setting.

6.       Partners with leaders in the effective adoption of information technology solutions in support of clinical care, research and education.

7.       Serves as a physician champion in the development of the electronic medical record and practice management tools.

8.       Maintains an awareness of existing and emerging technology, regulatory, and market factors that have an impact on healthcare information management.

9.       Accountable for the identification, development and execution of education, training programs, and services in the area of computer skills and competencies in the use of electronic tools for patient care, quality, resource management, and performance improvement.

10.  Going back to the 'grandfather' informatics board preparation, I found some broad texts like Biomedical Informatics (Shortliffe and Cimino) very valuable, especially for those areas in which I have less experience or expertise.   The major meetings including HIMSS (from the industry standpoint) and AMIA (more academic) are valuable.    Many areas have regional CMIO meetings to share ideas.   And read blogs."

Tuesday, January 21, 2020

A New Model for Sharing Insights While Protecting Privacy

Last week at JP Morgan, Mayo Clinic announced a new collaboration with nference that I would describe as "Cloud-hosted, de-identified, federated learning in which the tools are brought to the data instead of sending data to the tools"

 This Healthleaders article describes it well.

 Here's a broad overview.  Let's start with 3 containers.

 The first container is controlled by Mayo Clinic, holds identified data, and has one purpose - the development and optimization of de-identification algorithms. Selected data scientists, who are accountable to Mayo Clinic, are asked to help with algorithms via time limited, audited access to the container.   They are either Mayo staff or collaborators from outside who are trained in Mayo policies and held accountable to the same requirements as Mayo employees.    No data ever leaves this container.

 The second container is a controlled by Mayo Clinic, holds identified data, and has one purpose - running the perfected de-identification algorithms and producing a de-identified data set. That de-identified data set is moved to the third container.

 The third container is for running innovative applications brought to Mayo by partners offering unique analytics on the de-identified data. No data leaves this container, the applications are brought into it. A joint tenancy model enables the container to be run by Mayo Clinic but others to be given limited, audited use of the container to run their applications. The only thing that ever exits the container are data insights or knowledge. For example, if nference is asked a question about drug discovery, its machine learning/natural language processing software in the container can pose the question. The answer is shared but not the data used to generate the answer.

To me, this is the perfect balance of agility, innovation, and privacy protection. I've worked in many organizations and not experienced a design that has so many safeguards against  data leakage. 

We'll populate the third container with our first wave of de-identified data later this year.  I'll continue to report on our progress.

Thursday, January 16, 2020

Dispatch from JP Morgan


 Although I've been attending healthcare and technology conferences for more than 40 years (yes, I attended Comdex in 1979), but until this week had never attended the JP Morgan Healthcare Conference.  The conference gathers 60,000 investors, innovators, and providers for four days to plan the next year’s path for healthcare and building relationships that will foster future innovation. I have extensive experience attending HIMSS , which has similar numbers, but only a small fraction actually attend the main the JP Morgan Healthcare Conference.  

My Mayo colleagues and I presented formally in the non-profit track but the remainder of the meetings was dedicated to side conferences in numerous hotels, restaurants, galleries, and office towers.  I'm a practical traveler who walks to most meetings but there was no way to attend 40+ meetings in 30+ locations without help. I had an amazing driver who took me to a different meeting every 30 minutes, adeptly navigating a sea of activity.

Was it worthwhile? What it consistent with my role and mission?

Yes, it was one of the most productive conferences I've attended.

In one city in one sprint of activity, I was able to meet with strategic partners, collaborators, innovators, the press, and stakeholders. I was able to have discussions about complex, technological, challenging, and controversial ideas while reading the emotion of others in the room. I was able to create new friendships and rekindle old acquaintances.  

I've often said that digital health progress depends upon technology, policy and psychology. People ask how I think about contracts and legal agreements for partnerships and collaborations. Although 100+ page documents with appropriate protections are essential, my great hope is that trust and friendships are built so that contracts are signed and never referenced again. Events like JP Morgan are foundational to that kind of relationship building.

Press communications at such events are convenient and productive. If we're going to change the world of health, we need to broadly explain what we're doing, why we're doing it, and how we're putting patients first. All major press outlets are well represented at the conference and were able to talk. I was pleased by the press coverage of the Mayo presentations

Finally, JP Morgan enables internal teams to bond and as we divide up the stakeholder meetings, each using our skills and expertise to greatest effect. From 6am to midnight each day we did our best to move our innovation agenda forward.

I remain energized by the optimism of the conference.

Friday, January 10, 2020

How Does a Platform Reduce Barriers to Innovation?

One of our Mayo Clinic Platform team, Emily Wampfler, recently forwarded me an overview of MIT's Platform conference

Read the Barclay's piece.   It notes that 55,000 people changed focus from product support to a platform service line orientation, remarkably enhancing business opportunities.

Few organizations in healthcare have created a Platform which connects data producers and consumers, standardizing security, enhancing reliability and accelerating agility.     What is the urgency to embrace a Platform approach to healthcare?   

 I believe a Platform strategy is the best way to reduce barriers to innovation.   What specific barriers?    I have my own opinions but also asked colleagues like Dr. Craig Monsen, CMIO of Atrius.   Our examples are not related to any single institution we've worked at (Atrius, BIDMC or Mayo) but are drawn from multiple decades of experience in healthcare.

Operational
Innovation efforts compete with staff time required for day to day activities
Innovation efforts may disrupt efforts to standardize work in an organization
Innovation efforts may not be aligned with the immediate goals and priorities of leadership

Technical
Innovation efforts may require data standards that are emerging or not yet implemented
Innovation efforts may require clarification of data use rights 
Innovation efforts may require novel IT infrastructure

Business
Innovation efforts may require significant time investment from IT, Information Security, Compliance and Legal
Innovation efforts may require new policymaking about intellectual property ownership
Innovation efforts may require clarity about unrelated business income for a 501c(3) public charity
Innovation efforts may require finalization of slowly evolving regulations (ONC information blocking rule, CMS interoperability rule)

 Legal
Innovation efforts may require re-evaluating security, privacy, conflict of interest and consent policy
Innovation efforts may require clarification about what projects are operations(as in HIPAA Treatment, Payment, Operations) versus research

What can a platform do to mitigate/eliminate these barriers?

 If senior management (Executives, Board) broadly communicates that a Platform effort will require short term additional work but the resulting standardization of technology, process, and people will simplify future work, then the organization will tolerate the extra effort and disruption.    I think of it like building a house.    Anyone who has ever done a major construction project knows about the dust, delays, and inconvenience of building.   However, when the project is done, there are decades of enjoyment to follow.

 Although data standards constantly evolve, a Platform creates a single place to get/put data using whatever technologies exist today while permitting transition to what's next when it's available.    Although EHR data extraction today may require third party tools or proprietary work arounds, once the data is flowing to a Platform then collaborators can access data via a single point of authorization and authentication  without any dependency on the EHR itself.   I've used this technique to make data available to innovators using FHIR long before FHIR was part of EHRs.   The Platform can embrace emerging standards like FHIR R4 long before such standards are native to the EHR.

 Data Use rights are a key issue and require a consensus of internal/external stakeholders.   I will detail some of the issues about Ethical Uses of data in my next post.   A Platform which serves as a single point of data input/output enables the consistent enforcement of data use policy.

 I recently worked on a project that required a novel business relationship between a 501c(3) Public Charity and an existing EHR vendor.   The business people agreed it would be unrelated business income and would be taxed.    Once a template was developed for unrelated business income arrangements, it was no longer a barrier.  Similarly, intellectual property rights covering developments made using data from a Platform require a standardized policy.   For example, is de-identified data made available for innovation at low cost with the notion that derivative intellectual property creates an ongoing revenue stream OR is the data cost initally high with the notion that derivative intellectual property is unencumbered?  A platform enables easy monitoring and execution of such arrangements

 In sum, a Platform can be an organizing framework for operational, technical, business, and legal stakeholders to create a set of standardized, templated use case variants without having to re-negotiate every new innovation project or collaboration.   In my experience, without a single enterprise approach (call it a single front door) to connecting data producers and consumers, there is unlikely to be innovation agility in healthcare.

In the first 100 days at Mayo, many stakeholders will weigh in on these issues.     I'll report on lessons learned along the way.

Saturday, January 4, 2020

Week One

My new role as president of the Mayo Clinic Platform began officially on January 1, 2020.    I was selected for the role in late November and  volunteered time in December to better understand the technology, people and processes of Mayo Clinic.     What will I do in my first weeks at Mayo? 
 
Listen to my colleagues, customers, and staff.
 
I learned a valuable lesson in 1998 when I first became a CIO.   I was seeing patients on a 2pm-2am Emergency Department shift when my Motorola flip phone rang at midnight.    The conversation went something like this
 
    Caller: "Hi, this is Jim"
    Me: "Jim who"
    Caller: "Jim the CEO of the hospitals"
    Me: "Of course, how can I help"
    Caller:  "I've selected you as the next CIO and you start at 8am tomorrow.   We'll figure the rest out later"
 
At 8am I met with three advisors/mentors who agreed to guide me on my CIO journey.   Professor F. Warren McFarlan  of Harvard Business School, John F. Keane the CEO of Keane Inc , and Samuel Fleming of Decision Resources Inc.
 
I explained to them that I'd thought about the IT path forward (for 1998) overnight and we should immediately devote 100% of IT resources to embracing the web for all applications and operations.
 
They looked at me and advised that if I simply told colleagues, customers and staff what I thought they needed, I would have failed change management 101.    Instead, I needed to follow the wisdom of John Kotter and build a guiding coalition empowered by a sense of urgency to change.
 
For the next few weeks, I held listening sessions - over 300 of them.   My mentors were right.  Listening, communicating, and serving the organization based on convening/informal authority was much more potent than using formal authority to command and control.
 
At Mayo Clinic, I had over 50 meetings before I started.    I met with key Mayo partners in industry.  I've had days that started at 6am and ended at 10pm.    And I've just scratched the surface in my understanding of possible futures.
 
In my upcoming meetings I will try to answer 5 questions
 
1.  What unique assets (intellectual property, technology, people, etc.) does Mayo Clinic have?
2.  If the Mayo Clinic Platform were to offer service lines of capabilities, what should they be and who are the intended users?
3.  What economic models are most appropriate to ensure these service lines are sustainable - subscription, licensing, equity growth?
4.  What are the barriers and enablers to creating these service lines?
5.  Are there existing projects that should be halted or de-prioritized?
 
It's becoming clear to me in my conversations thus far that Mayo Clinic has an extraordinary foundation upon which the Mayo Clinic Platform can be built.
 
    30 Petabytes of clinical data
    A large collection of genomes, biological samples and pathology slides
    Numerous state of the art machine learning algorithms
    Faculty expertise
    Access to capital
    A strategic partnership with Google
    Co-development relationships with startups
    A network of affiliates that provide diverse data sources and can serve as pilot sites
    Research in collaboration with established tech companies
    A very strong business development/licensing group
    The reputation of Mayo Clinic
    Connections to innovators worldwide (Mayo opens many doors)
 
After the next several weeks of listening, we'll widely communicate a small number of initial service lines that build on this foundation and projects already in progress.    Remarkable pre-work has been done by Dr. Clark Otley, chief medical officer, who is my partner and who served as interim president of Mayo Clinic Platform and our Business Development colleagues James Rogers, Emily Wampfler, Maneesh Goyal, Andrew Danielsen and Eric Harnisch.    At the upcoming JP Morgan conference January 12-15, we'll be able to announce some of our first partnerships and strategies. 
 
The next year will be a great journey, collaboratively defining the mission, vision and values of the Platform effort, ensuring our products and services are well aligned with the goals of Mayo Clinic and the needs of many internal/external stakeholders, all while keeping the patients first.





Tuesday, December 31, 2019

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!

Monday, December 30, 2019

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