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Equity in the Age of Health Care Information Technology and Innovation

Addressing the Digital Divide

Mullangi, Samyukta MD, MBA*; Kaushal, Rainu MD, MPH; Ibrahim, Said A. MD, MPH, MBA*

doi: 10.1097/MLR.0000000000001033
Editorials
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*Department of Healthcare Policy and Research, Division of Healthcare Delivery Science and Innovation

Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY

S.I. is supported in part by a K24 Mid-Career Development Award from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (K24AR055259). The views expressed in this editorial are those of the author and do not represent those of the National Institute of Arthritis and Musculoskeletal and Skin Diseases, or the National Institutes of Health.

The authors declare no conflict of interest.

Reprints: Said A. Ibrahim MD, MPH, MBA, Department of Healthcare Policy and Research, Division of Healthcare Delivery Science and Innovation, Weill Cornell Medicine; 402 E. 67th Street, LA 215, New York, NY 10065. E-mail: sai2009@med.cornell.edu.

In recent years, the health care industry has been the beneficiary of a proliferation of innovations aimed at improving efficiency, reducing diagnostic and treatment errors, and mitigating costs. Such innovation is heterogeneous—split by product (goods and services that can generate new revenue) and process (techniques that improve internal capabilities to drive efficiencies) and categorized as either disruptive (generating new lines of business) or sustaining (incrementally improving existing opportunities). The pursuit of innovation has become such a critical capability for health care systems that new divisions of innovation and design studios are cropping up all over the country. However, how these health systems define “innovation” in the context of their particular communities’ resources and needs can differ quite significantly.

A significant driver of this happy abundance has been the widespread adoption of information technology (IT), spurred initially by requirements writ by the Affordable Care Act and propagated further by network effects. IT tools that are built into electronic health records can illuminate patterns and trends within data that in turn generate practical solutions. Thus, by creating an information industry out of static data, IT has the potential to turn a cost center (the mechanisms of patient care) into a revenue center by feeding data back into research and entrepreneurial opportunities. For example, Flatiron Health, a cancer-centric startup that was recently sold to Roche for $1.9 billion, based its business model on extracting data from community oncology practices to inform both quality efforts and even further upstream pharmaceutical research.1 In this way, IT tools can mine data that would otherwise go unrecognized to generate ideas and scope new projects.

It should be noted that most of the time, technological advances represent sustaining product innovations that typically make care more expensive and niche. For example, a hospital’s purchase of a proton therapy center is likely to make overall oncologic care more expensive. The acquisition of a da Vinci surgical system will likely drive up the price of a laparoscopic surgery. Although the ability to advertise the most advanced diagnostics and outfitted patient rooms can be a differentiating factor for health systems, as Clay Christensen of the Harvard Business School and others have written, these types of sustaining product innovations do not open new markets or lines of business.2

In the case of health care, these new markets might represent historically disadvantaged populations, who for reasons of race, class, socioeconomic status, or education, may not be as plugged into the health care ecosystem as would be ideal. In fact, relatively little work has been performed to explore the transformative potential of technology in addressing prevalent and enduring health care inequities. Disparities in health care are financially significant estimated at the population level to cost over $300 billion annually and likely amenable to health care innovation/technology strategies.3 However, Christensen’s 2017 analysis of Pitchbook Data showed that >$200 billion had been poured into health care venture capital, largely in biotech, pharma, and devices, where innovation typically makes health care more sophisticated, but also more expensive. As our society simultaneously makes sense of a fifth of our GDP being directed towards health care, with some of the worst health care disparities among the cohort of developed nations, we must actively consider what types of innovation can mitigate gaps in care and promote greater care equity.

Reaching vulnerable populations traditionally not touched by existing health care solutions will require creative and disruptive innovations that are low-cost, portable, and convenient. Essentially, these will likely not be the fastest or most powerful technologies in the market, but rather, just good-enough. For example, retail clinics may only cater to uncomplicated medical complaints, such as conjunctivitis and ear infections, but by delivering care in an easy-to-access venue, and promising low prices and wait-times, retail clinics are slowly encroaching on traditional clinics’ revenue.4 Similarly, mobile apps that monitor our mental health may never be as thorough and effective as regularly seeing a psychiatrist, but the promise of constant connectivity for a good-enough product ensures their dissemination to a larger audience.5

The increasing convergence of software, sensors, devices and embedded systems in health care has started to enable a veritable Internet of Things, creating opportunities for integrating the physical world into the datasphere, and processing new data streams for little effort.6 This explosion in big data coincides with the rise of advanced computing power that can be leveraged for understanding trends and patterns, as well to localize those segments of the population that may benefit the most from an extra touch. However, in a paradox fitting for our time, the patients who are likely to benefit the most from newer technology are the least likely to use it—for reasons of affordability, tech-savviness, or health literacy. Thus, perhaps the best use case of disruptive technology is to identify those patients who would benefit not from high-tech, but high-touch services.

What this suggests is that disruptive products need to be paired with thoughtful processes. Often, this means that software cannot stand alone, but needs to be coupled with service. Successful delivery startups that are successfully raising capital while expressly directing their care toward the underserved follow this model. The recently acquired Aspire Health, which contracted out home-based palliative and hospice care from insurers, paired effective monitoring technology, and savvy data analysis with actual boots on the ground—nurses, social workers, nutritionists and chaplains—to deliver care.7 Aledade, a Bethesda-based delivery startup, lends its sophisticated data platform to primary care practices to assist with population health management, but also matches these practices with care coordinators who shepherd these new processes and workflows to (typically) change-resistant entities.8 More recently, New York City witnessed the launch of CityBlock Health, a primary care clinic model rolled out by Alphabet’s Sidewalk Labs, that hopes to serve the uninsured and Medicaid populations by using custom-built technology to enable collaboration between interdisciplinary team members in caring for their underserved patient population.9 What these 3 very different companies have in common is their ability to merge leading technologies that collect and mine data, with service providers who can apply those insights to care.

In conclusion, to improve health equity health care systems should consider leveraging the knowledge economy of the booming IT industry. Forward leaning strategies might include cultivating a keen understanding of patient characteristics and social/medical needs using large datasets and the advanced analytic power to drive unique insights for better decision-making. Health care systems could also focus their attention and initiatives on meaningful health outcomes instead of surrogate care process measures. This is particularly critical in resource-constrained settings where investments are lower at baseline. Lastly, health care system and funding agencies might consider alternative funding streams that support high-risk innovations and research that adopt longer timelines to demonstrate a return on investment.

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REFERENCES

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