A Paradigm Shift in Digital Health

Innovation is best scaled when pipelines are replaced with platforms.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

The Digital Health Frontier includes cutting edge predictive analytics, machine learning enhanced algorithms and big data analytics. But for these innovations to have their full impact on patient care requires the right strategic and operational foundation. In the past, many technology-focused organizations have relied on a pipeline approach as the foundation to construct innovations and promote growth. But history suggests this approach is less sustainable than a platform approach. A recent article in Harvard Business Review (HBR) sums up the difference: “Platform businesses bring together producers and consumers in high-value exchanges. Their chief assets are information and interactions, which together are also the source of the value they create and their competitive advantage…. Pipeline businesses create value by controlling a linear series of activities — the classic value-chain model. Inputs at one end of the chain (say, materials from suppliers) undergo a series of steps that transform them into an output that’s worth more: the finished product.” While this explanation gets the point across, it’s rather abstract. To really appreciate the advantages of one approach over the other, we need an example or two.

Apple’s handset business follows the pipeline model, making sure there are adequate supplies available to build the device and then overseeing the various other steps to create a finished iPhone, as well as its distribution, sales, and servicing. But when Apple linked the phone to its App store, the situation changed dramatically, turning the operation into a sustainable platform that connected app developers with iPhone owners. In their HBR article, Geoffrey Parke and Sangeet Paul Choudary explain: “The resource-based view of competition holds that firms gain advantage by controlling scarce and valuable — ideally, inimitable — assets. In a pipeline world, those include tangible assets such as mines and real estate and intangible assets like intellectual property. With platforms, the assets that are hard to copy are the community and the resources its members own and contribute, be they rooms or cars or ideas and information.” Apple’s success and the loss of market share by pipeline-oriented companies like Nokia can be explained by such differences.

Like Apple, John Deere has successfully employed a platform approach. They own not just the physical assets — e.g. tractors and combines — but a vast collection of intellectual property — including APIs and apps to help farmers manage what is now being called precision agriculture. The company links third party providers and producers to their farming customers and reaps the benefits. With all these technological tools in place, farmers now have the ability to more efficiently monitor their equipment with data on fuel consumption, location, machine hours, and engine RPMs; and they can improve crop management with weather prediction data, community pricing and the like. Some of John Deere’s more advanced combines incorporate a grain quality camera, grain loss sensor, a Gen4 display monitor, and remote access to an operations center from inside the cab. For every new connected tractor sold, more data flows into the John Deere Platform, enhancing the value of the platform to partners creating new apps and analytics.

Mayo Clinic Platform (MCP) is taking a similar approach. Instead of creating dozens of pipeline businesses or building an organization chart to support pipeline businesses, we are leveraging external collaborators, network effects, and data flowing back to the Platform, which increases its value for producers of products and consumers of services. Mayo Clinic and Commure, a General Catalyst portfolio health care technology company, have launched Lucem Health to connect data from remote medical devices with AI-enabled algorithms. External collaborators who have partners with MCP include nference, Medically Home, Kaiser Permanente, and K Health. The strategic approach allows the Mayo Clinic Platform to offer products and services that fall into 4 broad categories of functionality: Gather, Discover, Validate, and Deliver. For example, in the Deliver “bucket” is the combined ECG/algorithm system that was recently validated and published in Nature Medicine. The digital tool was able to detect low ejection fraction, thereby improving the diagnosis of left ventricular systolic dysfunction. While the ECG/algorithm is improving direct patient care at Mayo Clinic, it can also be offered to external partners and embedded in their ECG waive form viewer, which in turn will improve the relationship between a community hospital and its patient population.  Similarly, the clinical data analytics tools developed by MCP are being made available to outside partners like K Health, which provides symptom checking, access to virtual visits with a clinician. The data analytics function is helping K Health improve its services to their clientele.

As the HBR article emphasized, the chief assets of a platform are information and interactions, which together are also the source of the value they create and their competitive advantage. Such value and advantages are what will sustain healthcare innovators through the next several decades.

When AI Meets SDOH

Artificial intelligence can help identify and address the social determinants of health.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Machine learning is getting better at predicting things. There are now algorithms that improve the detection of diabetic retinopathy, predict the onset of sepsis, and help determine a critically ill patient’s risk of dying.  But a piece of wisdom from Warren Buffet comes to mind: “Predicting rain doesn’t matter. Building arks does.” Even the most impressive algorithm is relatively useless if it doesn’t allow us to build better “arks” to address the medical  disorder or  complications that the digital tool identifies.  And building the best healthcare interventions requires that clinicians not just identify the right signs, symptoms, and biomarkers, whether they be high cholesterol levels, elevated A1c, or a lump in a woman’s breast.  It requires we understand what’s happening in patients’ everyday lives outside  the clinic, the so-called social determinants of health (SDOH), and then using that data to inform treatment.

A great deal has been written recently about SDOH. Health professionals are slowly beginning to realize that we cannot “remove health and illness from the social contexts in which they are produced,” according to Simukai Chigudu, Oxford Department of International Development, University of Oxford.¹  That begs the questions: What social issues are mostly likely to influence our patients’ clinical course and what do we do about them? How can AI help alleviate the impact of these issues?

The Centers for Disease Control and Prevention (CDC) has numerous data sources to help incorporate SDOH into public health initiatives and medical practice. But as the agency points out, moving from data to action is the hard part. CDC has several programs designed to focus clinicians’ attention on key social issues, including socioeconomic status, educational level, and work history. One initiative, for instance, zeros in on the role of EHRs. Its purpose is to support the incorporation and use of structured work information into health IT systems. How might this SDOH element inform a physician’s different diagnosis? Consider a patient with hypertension who doesn’t respond to a low sodium diet or anti-hypertension medication. Awareness of his 10 year history as a house painter might point the clinician in the direction of lead poisoning, a possible cause of hypertension. Similarly, a nurse practitioner may be at a loss to figure out why a patient with type 2 diabetes has recently seen a spike in her A1c levels.  If at EHR system is linked to work history, when the NP enters the reason for the clinic visit into the EHR field for chief complaint, this might trigger a pop up box that states that the patient works the night shift and that shift work can affect diabetes control. The system would then provide recommendations on diabetes management among shift workers. The same CDC program is also working on a work information data model, as well as national standards for vocabulary, system interoperability, and instructions for health IT system developers. 

At Mayo Clinic, we are also studying the impact of SDOH on health and disease. Young Juhn, MD, MPH, Director of the AI Program and Precision Population Science Lab of Department of Pediatric & Adolescent Medicine at the Clinic, has studied  the effects of socioeconomic status on health since in 2006 when his research work was supported by the NIH. With the support from the NIH, he developed and validated a housing-based socioeconomic measure called the Housing Based Index of Socioeconomic Status or HOUSES index, which is being used in epidemiologic research to help understand health disparities and differences in a variety of health outcomes in both adults and children. The index has enabled researchers to overcome the absence of socioeconomic measures in commonly used data sources (e.g., medical records or administrative data), conduct geospatial analysis in health disparities research, and apply a life course approach.

The HOUSES index is an objective way to measure the individual-level socioeconomic status of  patients because it is based on real property data for individual (not aggregated) housing units and is derived from public records; it uses 4 data points: the number of bedrooms in a person’s residence, as well as the number of bathrooms, square footage of the unit, and estimated building value of the unit. The index can help target patients who are most at risk of poor health outcomes and inadequate access to health care , demonstrating the real value of adding SDOH into the mix by addressing the limitations of the existing SDOH. For example, Stevens et al have shown that patients with a higher HOUSES score (quartiles 2-4) had 53% lower risk of  kidney transplant rejection (adjusted hazard ratio 0.47), when compared to those with the lowest score (quartile 1).² Dr Juhn and his colleagues have found that HOUSES can  predict 44 different health outcomes and behavioral risk factors in both adults and children.

Of course, clinicians still have to be reasonable in their expectations. Even if an algorithm were outfitted with every conceivable SDOH, it still may not reduce disparities in healthcare. Patients and providers may choose to ignore the recommendations of the improved algorithm because they believe the recommended diagnostic test is too expensive or unjustified, for example, because it is too difficult for patients to get to the testing facility, or because a patient’s lack of health literacy prevents them from seeing the value of said test.

Despite these shortcomings, SDOH-enhanced algorithms have the potential to improve patient care. While physicians and nurses have gained tremendous insights into health and disease by measuring countless clinical parameters during office visits, it’s clear now that’s not enough.  The clinical picture generated with these metrics is too often hazy and needs to be supplemented by a long list of social metrics that can influence a patient’s access to care and their long-term outcomes.

References

 1. Chigudu S. Book: An ironic guide to colonialism in global health. Lancet. 2021. 397:1874-1975.

 2. Stevens M, Beebe TJ, Wi Chung-II et al. HOUSES index as an innovative socioeconomic measure predicts graft failure among kidney transplant recipients. Transplantation 2020; 104:2383-2392.

The Digital Reconstruction of Healthcare is Upon Us

The transition from brick and mortar to digital medicine will profoundly impact the way clinicians and patients interact—and will likely improve clinical outcomes.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Paul Cerrato and I are excited to finally announce the publication of our 5th book together: The Digital Reconstruction of  Healthcare: Transitioning from Brick and Mortar to Virtual Care. In March, we posted the table of contents of the new book. Now that it’s reached the “newsstand,” we wanted to whet readers’ appetite by sharing some additional excerpts.

The logical place to start any discussion on this topic is to explain why  digital reconstruction is necessary, which we address in Chapter 1:

Episodic Medical Care Often Falls Short

White coat hypertension, the tendency for patients to only present with elevated blood pressure during a doctor visit, illustrates a problem that permeates the entire healthcare ecosystem. Any sign or symptom that a patient exhibits during an office or clinic visit may not be a true presentation of their underlying condition. Unfortunately, this phenomenon not only affects a person’s blood pressure but other common parameters. White coat hyperglycemia has also been documented. And since psychosocial stress is likely a contributing cause of such white coat reactions, white coat hypercholesterolemia, asthma attacks, and numerous other conditions probably exist as well, all triggered by stress hormones. Conversely, any normal readings during a physical examination or laboratory test do not necessarily mean a patient is in good health.

The common denominator in all these scenarios is episodic care. In such situations, clinicians are making a judgement about a patient’s health status based on cross-sectional data, which can be misleading. But given the financial restraints and incentives that exist in healthcare today, it has been the only viable option—until now. With the emergence of virtual care and remote patient monitoring (RPM), gathering long-term data for many clinical parameters is no longer out of reach. That steady stream of online data can be inserted into predictive analytics algorithms to help locate patients at high risk. Some thought leaders refer to this shift in priorities as the movement from episodic to life-based care.

Such digitally enhanced patient engagement is the future of healthcare. No responsible practitioner would conclude a diabetic patient is in good metabolic control based on a single blood glucose reading, and yet that is often the same reasoning we use when a routine metabolic panel comes back stating LDL cholesterol, serum calcium, white blood count, blood pressure, and numerous other parameters are all “within reference range.” We now have the technology to move beyond this outdated mindset. That technology enables us to detect longitudinal patterns of change in patients’ health status. By way of example: Longitudinal data on systolic blood pressure has been linked to patients’ risk of cardiovascular disease.

The Power of Remote Patient Monitoring

Many patients and healthcare professionals have yet to appreciate the power of remote patient monitoring. When executed correctly, it can be truly transformative, combining medical self-care, objective physiological data, and expert advice to improve both preventive and therapeutic care. And as RPM continues to mature, it has the potential to completely reinvent healthcare, especially among those motivated patients who see it as a source of self-empowerment. The power of RPM in the hands of motivated asthmatic patients was well illustrated in an experiment conducted by University of Wisconsin and Centers for Disease Control and Prevention researchers. Using an electronic medication sensor that was attached to inhalers of 30 patients, Van Sickle et al. tracked patients’ use of inhaled short-acting bronchodilators for 4 months. To evaluate patients’ health status, investigators asked them to fill out surveys, including the Asthma Control Test (ACT). One month into the study, they also received weekly emails that summed up their medication usage for the preceding week and offered suggestions on how to comply with the National Asthma Education and Prevention Program guidelines. No changes were observed in ACT scores after the first month, but they increased by 1.40 points each month after that. Patients also reported significant decreases in daytime and nighttime symptoms. They also noted “increased awareness and understanding of asthma patterns, level of control, bronchodilator use (timing, location) and triggers, and improved preventive practices.” That last statement is worth closer inspection.

Very often, patients do not understand the triggers that cause symptoms, unless they are actually attuned to subtle changes in their physiology. Providing graphic displays of their symptoms paired with the medication usage can be eye opening for many patients who never noticed patterns of use before. These newfound revelations were summed up by several patients participating in the study:

“I learned that I used my inhaler more than I remember. I was able to see and relate to my doctor that my asthma is not under control.’’ Participants also reported that the receipt of information about the time and location where they used their inhaler helped to highlight locations and exposures to triggers that led to symptoms. ‘‘I’ve been more keen to note surroundings when I feel shortness of breath,’’ one participant said. ‘‘It opened my eyes to triggers I wasn’t aware of in the past.’’

The results of this experiment highlight 2 important lessons for patients and clinicians, summed up in a few choice words from Kamal Jethwani, MD, MPH, from Partners HealthCare: “The future of health is proactive, self-managed wellness. We want to put the onus back on the person. We’re saying: It’s your health, and I’m no longer your babysitter.”