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Emergence of Phenotypic Imaging in Modern Healthcare

Luhovy, Mark MD

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Progress in Preventive Medicine: February 2020 - Volume 5 - Issue 1 - p e0027
doi: 10.1097/pp9.0000000000000027
  • Open

As global healthcare systems enter the modern era, medical technology has the potential to play an increasingly visible role on the forefront. The U.S. healthcare system, currently transitioning from a fee-for-service model to a pay-for-value model, can benefit from transformational technology. In particular, digital health platforms, employing augmented and virtual reality capabilities, colocalized additive manufacturing partnerships between industry and health system organizations, artificial intelligence (AI), machine learning, and deep learning modalities, and personalized medicine solutions that will deliver multi-omic data sets to clinicians, each has the potential to disrupt, enhance, and forever change healthcare from its current state.

The inevitability that these products will find mainstream utilization has growing support[1–4] provided of course that AI applications deliver clinical and financial value to hospitals and patients. Some experts believe that organizations, including health systems, that fail to adapt to the implementation of AI solutions early on, for example, will fall behind those that do.[1] The adoption rates of individual health systems may, therefore, play a prominent role in their ability to change with the times and flourish, or remain archaic in thought, and fall behind in the competitive landscape.

From a systems perspective, technology may serve as the primary driver of change. Yet the true impact, both individually and collectively, that each of these 21st century tools will have on health system economics, quality, and above else, patient outcomes remains to seen. If leveraged and employed correctly, new healthcare technologies may in fact lead to significant cost savings and improvements to patient care quality within each health system, transforming medicine in the process.

The goal of health tech implementation in the modern healthcare system should also focus on improving quality of care, upholding and enhancing the physician–patient relationship, lowering costs, and democratizing access to healthcare services across all populations and geographies. This problem is as much a financial one as it is an administrative, clinical, and technical challenge, one which will require not only changes to provider group workflows but also a change in the philosophy of clinicians themselves.

The main challenges will be integration and scaling. Moving from the sphere of solutions provided by the tech industry into the sphere of implementation whereby products and services are actually put into play will require institutional change and sponsorship. Specifically, healthcare models themselves must be revised to support the full capability of these products. New systemic architecture requirements may increasingly become called into play, to shift the point of care from our current archaic system of downstream, reactive symptom management, to a future state focused on upstream, preventive, and holistic wellness promotion.

Personalized medicine serves to directly address this problem. Instead of broadly focusing on cookbook solutions to common ailments, with uniform therapeutic regimens across all patient populations, novel perspectives that integrate social determinants of health and the need for custom, individualized solutions are increasingly becoming more relevant. By factoring in and considering individuals from every segment of the population, the opportunity to improve disparities in health access, clinical outcomes, and longevity may become possible with personalized medicine.

The American Medical Association recently paired with United Healthcare to support the creation of nearly two-dozen new 10th revision of the International Statistical Classification of Diseases and Related Health Problems codes focused strictly on social determinants of health.[5] This type of institutional sponsorship is necessary to turn the gears within health systems, allowing specialized technologies to improve population health across cultural, economic, and geographic patient demographics.

Once new reimbursement structures have been endorsed and implemented, financial models can be engineered to support innovative technology. Each clinical domain within the health system has the potential to be disrupted, with progressive, cost-effective solutions driving change. These products may inherently alter the structure of care workflows themselves. Imaging modalities, for example, currently allow clinicians the ability to peer directly into a patient’s body and gain perspective on existing and emerging disease processes. By promoting enhanced imaging tools, a new care model that prioritizes prevention over reaction may soon emerge.

Our work at PhenoMx, a digital health company, aims to deliver personalized solutions to providers and patients across the globe, via a quantitative imaging platform. As such, we provide a new breed of technology in healthcare. By offering quantitative imaging as a comprehensive screening tool, we make it possible to transform magnetic resonance imaging (MRI) from its current role as a confirmatory care modality to a future where MRI serves as a central modality in patient care. This evolution of MRI from a diagnostic service to a core prognostic service may have broader implications, including the capacity to shift the central point of patient care upstream.

Deep phenotyping, or the ability to computationally derive comprehensive analysis of phenotypic irregularities, will allow physicians to integrate personalized patient factors into care.[6] Phenotyping patient disease via quantitative imaging will help clinicians gain insight into the full extent of how cumulative environmental and lifestyle factors have impacted each organ system or the patient as a whole. By promoting the adoption of phenotypic imaging, we aim to provide clinicians with a detailed fingerprint of the body’s structure, captured within a 20- to 30-minute scan. By offering imaging solutions that incorporate predictive disease biomarkers, clinician workflows are simplified; a definitive diagnosis can be reached earlier, thereby leading to specific, targeted treatment options for each patient.

Further integration of highly personalized services such as genomic reports will allow future physicians the ability to offer custom- and patient-specific treatment regimens to each individual patient.[6] This future state, where patients see their primary care physician, who is able to offer personalized therapeutics based on each patient’s unique phenotypic requirements, offers a more precise care model than the current one-size-fits-all approach.

By offering comprehensive MRI screening via an affordable annual or semiannual health examination, we may also target improvements in patient access. In the future, patients will be able to undergo prophylactic whole body digital examinations, well in advance of, or early on, in the progression of disease. By preemptively acquiring unique, patient-specific phenotypic data, focused on individual organ systems or the body as a whole, a digital image can be captured and recorded. This record may then serve as a baseline state for physicians to understand the health of each patient and to plan personalized, preventive therapeutic management regimens across the panel.

A paradigm shift is coming in modern healthcare. Unsustainable economics, the emergence of new technologies, calls for a more democratic distribution of care, physician burnout, and continuous improvement in healthcare quality are together creating the perfect storm for the emergence of a new type of systemic model. Changes will take place across the spectrum of care, but healthcare technology will undoubtedly play a central role. Maintain eyes on the quantitative imaging space for developments that aim to support this cause, specifically PhenoMx, and our vision:

“To improve lives, transforming healthcare along the way.”

Key Points

  • Modern medical technology, including digital health imaging modalities, have the power to transform healthcare by improving care quality, democratizing access to health, and lowering operational costs across the system.
  • Institutional sponsorship and endorsement will pave the way for new reimbursement models, clinical adoption of innovative products, and restructuring of clinician workflows.
  • Full adoption of digital health products will require financial, administrative, and clinical solutions, and a change in the philosophy of clinicians themselves.
  • Personalized medicine aims to improve disparities in health access, clinical outcomes, and patient longevity.
  • Phenotypic imaging modalities have the potential to shift the point of care from downstream, symptom management, to a future state focused on upstream, preventive, and holistic wellness promotion.

Disclosure

Dr. Luhovy reports personal fees from PhenoMx, Inc., outside the submitted work. The article processing charge was paid for by the author.

REFERENCES

[1]. Mahidhar V, Davenport TH. “Why companies that wait to adopt AI may never catch up.” Harvard Business Review. 2018. hbr.org/2018/12/why-companies-that-wait-to-adopt-ai-may-never-catch-up.
[2]. Arcadu F, et al. “Deep learning algorithm predicts diabetic retinopathy progression in individual patients.” Nature News. 2019. Nature Publishing Group, www.nature.com/articles/s41746-019-0172-3.
[3]. Coudray N, et al. “Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning.” Nature News. 2018. Nature Publishing Group, www.nature.com/articles/s41591-018-0177-5?sf197831152=1.
[4]. Yuan Y, Shi Y, Su X, et al. “Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks.” BMC Genomics. 2018;19:565. bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-018-4919-z.
[5]. “New ICD-10 Codes Will Help Physicians Tackle Social Barriers to Care.” 2 Apr. 2019, American Medical Association, www.ama-assn.org/practice-management/digital/new-icd-10-codes-will-help-physicians-tackle-social-barriers-care.
[6]. Yehia L, Eng C. “Largescale population genomics versus deep phenotyping: brute force or elegant pragmatism towards precision medicine.” Nature News. 2019. Nature Publishing Group, www.nature.com/articles/s41525-019-0080-0.
Keywords:

artificial intelligence; digital; digital health; digital physical examination; imaging; machine learning; magnetic resonance imaging; multi-omics; phenotype; population health; preventive; quantitative; screening; SDoH; social determinants of health; upstream; whole body magnetic resonance imaging

Copyright © 2019 The Author(s). Published by Wolters Kluwer on behalf of the European Society of Preventive Medicine.