Increased Competition from the Digitalization of Healthcare Delivery

Today, EMRs generated from connected ultrasound machines can give instant visibility into customer demand for expert review and enable a physician in San Francisco to be sourced as-needed to review New York City patients.  [2] In the age of a digitalized healthcare supply chain, Cardio Edge will need to be mindful that competitors can emerge more quickly due to the advent of electronic healthcare data and smart, connected products. 

Organization Selected:

Cardio Edge (pseudonym) is a rapidly-growing healthcare company which offers cardiovascular risk screening through specialized echocardiogram, EKG, and carotid scan tests.  This screening reveals risk levels for serious medical events such as sudden cardiac death (SCD).  A medical treatment paradigm is analogous to a supply chain which delivers good healthcare outcomes to consumers by involving a range of steps.  In the context of professional and elite collegiate sports, this paradigm can span from a casual team athletic director to diagnostic equipment technicians, cardiology specialists, surgeons, and personal primary care physicians.

Essay:

The delivery of healthcare in the United States is undergoing rapid changes because of the digitalization of treatment paradigms.  This digitalization is in part due to The Health Information Technology for Economic and Clinical Health Act (HITECH), which authorized up to $27 billion in incentive payments to spur the adoption and “meaningful use” of electronic medical records (EMRs).  [1] Before this movement, the traditional supply chain of healthcare delivery has relied on physical medical files, in the context of a highly-fragmented US system.  This “clunky” and haphazard legacy resulted in long lead times between steps as information was manually located and carefully transferred in compliance with HIPAA guidelines.  This all but necessitated a limited, localized supply chain with high barriers to entry.  Today, EMRs generated from connected ultrasound machines can give instant visibility into customer demand for expert review and enable a physician in San Francisco to be sourced as-needed to review New York City patients.  [2] In the age of a digitalized healthcare supply chain, Cardio Edge will need to be mindful that competitors can emerge more quickly due to the advent of electronic healthcare data and smart, connected products.

To build competitive insulation in an age of digitalized healthcare delivery, Cardio Edge is focusing on growing the scale of its proprietary database, building more specialization into its portion of the value chain, and reinforcing its complementary technology stack.  The larger the database, the more Cardio Edge can put individual results in context and discover associations and understand patterns and trends to ultimately improve care, save lives, and lower costs.  [3] Thus the same supply chain can now produce two very valuable outputs, the screening services and the accompanying data aggregation.  While the value of the primary services is linear, the value of the data can be described more as an exponential function, and a near-term, volume-based business development strategy will maintain this advantage away from potential competitors.  Equipment technicians are just one part of the cardiovascular health supply chain, but errors there can drive issues in downstream evaluation by cardiologists.  And downstream errors are tougher to rectify when physician / technician interaction is 500 miles away rather than 50 steps away.  Hence, in an age of portable medical records, quality is increasingly important and Cardio Edge is solidifying its competitive position by improving technician selection and training systems.  The connected mobile-wireless ultrasound machines used for echocardiograms merit an entirely new technology infrastructure.  [4] For example, identity and security software is a longer-term measure needed to add security given a shift to technicians entering data rather than patients.  One of these identity and security systems is work modality list software, which reduces risk and liability tied to typographical errors on medical records [5].  In an increased competitive environment, profits and switching costs are planned to be increased through offering tangential services such as EMR storage – essentially owning more of the technology stack.  All these actions will help to solidify Cardio Edge’s position by galvanizing and better leveraging its own role in the digitalized supply chain.

My recommendations are geared towards making the data as much of a focus as the direct screening itself.  Digitalization of the healthcare supply chain has coincided with a trend towards very valuable data sets, and healthcare M&A has confirmed how much value exactly there is to be had.  On paper, Quintiles and IMS Health would seem an odd match for their $9 billion 2016 merger, but that was an opportunity for a contract research organization to capitalize on real-world data provided by IMS Health, plugging an external information source into Quintiles’ budding application platforms.  [6] By acquiring Truven Health for $2.6 billion in 2016, IBM was able to fuel its Watson analytic engine with one of the world’s largest repositories of health-related data, bolstering its own technology stack.  [4] [7] To develop Cardio Edge’s own data set to the fullest, a fairly logical recommendation would be to ensure new customer contracts give Cardio Edge full rights to aggregated, blinded data.  Another mechanical opportunity would be to incentivize long-term contracting to keep out competitors while building progressively larger data sets.  Longer term, it may make sense to develop an application platform to build out the technology stack and provide external users an improved service and enable athletic benchmarking and business use.

Open questions for further examination include whether the digitalization of patient data will ultimately influence risk regarding patient privacy, and if so, what might be the best opportunities to mitigate that risk?  [8] 100,000 patient EMRs can be carried away more easily than 100,000 physical dossiers strewn across 1,000 hospitals.  Also, in this supply chain, EMRs may ultimately lower costs in the long-term by systems like IBM Watson to replace physician judgement through automated sensors.  [9] How realistic is this given legal considerations, and how long could it take?

(795 words, excluding footnotes)

Sources:

[1] David Blumenthal, M.D., M.P.P., and Marilyn Tavenner, R.N., M.H.A.  New England Journal of Medicine 2010; 363:501-504August 5, 2010DOI: 10.1056/NEJMp1006114

[2] Schrauf, S. and P. Berttram, Industry 4.0: How Digitization Makes the Supply Chain More Efficient, Agile, and Customer Focused, PWC Strategy& (2016)

[3] Raghupathi and Raghupathi Health Information Science and Systems 2014, 2:3 http://www.hissjournal.com/content/2/1/3

[4] Porter, M. and J. Heppelmann, “How smart, connected products are transforming competition,” Harvard Business Review (Nov. 2014)

[5] Kuzmak, P.M. & Dayhoff, R.E. Journal of Digital Imaging (2001) 14(Suppl 1): 153. https://doi.org/10.1007/BF03190323

[6] Hallam, Kristen and Tracer, Zachary.  “IMS Health to Buy Quintiles in $9 Billion Pharma Data Deal.”  May 3, 2016, 6:18 AM EDT.  https://www.bloomberg.com/news/articles/2016-05-03/ims-health-to-merge-with-quintiles-in-deal-valued-at-23-billion

[7] “IBM Watson Health Announces Plans to Acquire Truven Health Analytics for $2.6B, Extending Its Leadership in Value-Based Care Solutions.”  Press release.   https://truvenhealth.com/media-room/press-releases/detail/prid/185/ibm-watson-health-announces-plans-to-acquire-truven-health-analytics

[8] Patil, Harsh Kupwade, and Ravi Seshadri. “Big data security and privacy issues in healthcare.” Big Data (BigData Congress), 2014 IEEE International Congress on. IEEE, 2014.

[9] Dilsizian, S.E. & Siegel, E.L. Curr Cardiol Rep (2014) 16: 441. https://doi.org/10.1007/s11886-013-0441-8

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5 thoughts on “Increased Competition from the Digitalization of Healthcare Delivery

  1. As you pointed out, patient data are considered very sensitive and there are legal guardrails on how the data should be protected. In my view, digitization of patient data poses a risk of the data being hacked and hence, compromised. However, I believe that by providing legal barriers on how the data should be managed and also, posing consequences for data being compromised, the healthcare delivery companies will be encouraged to implement top data security systems. The banking industry also holds sensitive data, but this did not stop digitization of banking. In my opinion, the risk of data privacy in healthcare should also not be a limitation for implementing digital solutions. However, processes and standards should be set to manage the risk.

    As far as I am concerned, in the short- and mid-term, full automation of patient diagnosis would not occur, due to patients and doctors being used to the judgment happening by a real person. The question of who would be responsible for a potentially deadly mistake by AI solutions, would be a significant barrier. However, I would expect that doctors judgment could be supported by AI e.g., AI could provide preliminary results that would need to be approved by a licensed physician.

  2. While I believe that Monika has a point regarding automation of diagnosis, I do believe that there will be many short- and medium-term solutions in this area that will augment the way that physicians diagnose. While Watson is already an efficient diagnostician, there is still a long way to go to encourage patients and doctors alike to accept diagnoses from Watson and other AI.[1] Instead, I believe a step-wise approach is in order involving electronic medical records and big data mining.

    As you mentioned, there are many patterns and trends in patient data that can be used to not only diagnose, but to predict possible outcomes. Imagine the EMR with built-in differential diagnosis, allowing physicians to see not just the common diseases that often come to the top of mind, but also the rarer diseases that a simple test, additional question, or physical exam maneuver may be able to rule out. Building AI into EMR, and accumulating significant EMR data, is the key to bringing Watson and others to their full potential as diagnosticians and even predictors of potential disease.

    1. https://www.newyorker.com/magazine/2017/04/03/ai-versus-md

  3. Digitalization of patient data certainly poses risks to patient privacy, however, it also presents more opportunities to protect patient information. Physical records, for example, can be lost or transmitted insecurely (e.g. imagine medical records sitting on a fax or printer machine for several minutes before they are picked up by the respective medical professional). In this regard and others, digital records, if the proper controls are put in place, have the potential to better protect patient privacy. Kaiser Permanente is an example of an organization that has largely moved to EHRs. Patients can access information about past visits and email their doctor through one simple online interface. Similarly, physicians, log in to a computer in the patient room to see the latest data on their patient, record notes from the visit, and send prescriptions to the pharmacy for pick-up.

    As for replacing physician judgement with AI, I believe this is still far off. Medicine is much an art as it is a science, and the human component of medicine should not be underestimated. AI may be able to provide a highly accurate diagnosis, however, a robot will always lack the empathy and bedside manner which gives even the sickest patients hope in their darkest hours.

  4. Great read, Justin. I agree with your privacy concerns and the comments made above, but I do also think there are potential (near-term) solutions currently under investigation that could potentially address some of your concerns. One “hot” area in the digital health space at the moment is the advent of blockchain and its use cases for healthcare data storage. There are over 20 companies, some blockchain-as-a-service (e.g. Bitmark, PokitDok) and some data repository services (e.g. Guardtime, Mint Health), tackling this very issue by applying various forms of blockchain technology to healthcare EMR data with the hope of creating a decentralized, anonymous, and safe data storage platform. I think the digitization of healthcare and clinical data is inevitable, and solutions addressing privacy concerns are just around the corner (I hope).

  5. I’m curious to dig into the potential benefits of data mining in a healthcare setting, but also the limitations that the strict regulatory environment around healthcare impose. Also, how will the regulatory environment need to change to address advances in technology, and not limit future innovation in the space?

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