2019 Predictions: M&A, Big Tech, and the Fate of ACOs

The Meaningful Use gravy train finally came to an end in 2018. As the strongest EHR vendors struggle to define new revenue streams, weaker ones faded from view through acquisitions or leveraged buy-out. Meanwhile, funding for ‘digital health’ start-ups continued to increase, though it likely hit the high water mark in 2018. And lest we forget, Amazon, Apple and Google continue their forays into the healthcare sector as the market is simply too big to ignore.

So what’s in store for 2019?

We brought together our analysts’ brain trust and came up with the following baker’s dozen of 2019 predictions. Over the near decade of making these annual predictions, our batting average has consistently been well above .500, so don’t ever say we didn’t give you an advanced warning on the following:  

 

Revenue cycle management M&A picks up; Optum acquires Conifer

Revenue cycle management M&A activity will continue to pick up with the most notable acquisition by Optum as it doubles down on its Optum 360 managed revenue cycle business and acquires Conifer Health Solutions from Tenet.

Alternative primary care clinics remain a side-show

Despite the hype and media attention around alternative primary care clinics (e.g. Oak Street Health, Chen Med, One Medical), the actual number of physical locations serving patients will remain paltry at less than ten percent of the number of retail health clinic locations. 

Humana finds a life partner with Walmart 

Walgreens will likely make the first move to acquire Humana in 2019, but Walmart will outbid Walgreens to win Humana over.

Regulatory approvals for artificial intelligence-based (AI) algorithms accelerate, tripling the number approved in 2019

The number of FDA approvals for algorithms in 2018 was impressive and shows no signs of abating. Additionally, 2020 will see a further tripling of regulatory approvals for AI.

Choose wisely: 2019 sees the first major shake-out of DTC telehealth vendors

Consumers’ use of telehealth will continue to see rapid growth and rising competition leading to significant consolidation among the plethora of vendors. By year-end, a major non-healthcare-specific consumer brand will join the mix, and the market will be down to five direct-to-consumer (DTC) nationwide brands.

Data science services see extraordinary growth, nearly doubling in 2019

By the end of 2019, every major healthcare analytics vendor will provide a cloud-hosted offering with optional data science and report development services.

In 2019, healthcare organizations (HCOs) adopt a cloud-first strategy

Cloud offerings have become far more robust, concurrent with HCOs’ struggles to recruit IT talent and control costs. Amazon’s AWS and Microsoft’s Azure will be clear winners while Google’s own cloud infrastructure services will remain a distant third in 2019.

New rules from ONC about data blocking have little effect because the business case does not change

Laws and regulations to-date have not compelled providers to freely share data with patients. ONC’s information blocking rule, which will be released before the end of 2018, will make it easier to transfer data to other organizations but will do little to open the data floodgates for patients, clinicians, and developers.

Big tech companies’ intentions in healthcare do little to disrupt the delivery of care

  1. Despite high-profile hires, the Amazon/Berkshire/JPM initiative will make no substantive progress.
  2. Amazon will focus only on the DTC supply chain, payer, and employer—staying away from anything substantive in the provider space.
  3. Apple’s Healthkit and sensor-laden smartwatch will remain sideshows in 2019 awaiting a more actively engaged healthcare consumer.
  4. Google [Deepmind] will never break out of clinical research and drug discovery.

Majority of MSSP ACOs stay and take on risk; hospital-led ACOs lead exits

Despite loud protests, the vast majority of provider-led MSSP ACOs will take on downside-risk as CMS shows flexibility in waivers. However, hospital-led ACOs, who continue to struggle with standing up a profitable MSSP ACO, will exit the program in 2019.

Closure of post-acute facilities shows no signs of slowing

Continued changes in post-acute care reimbursement, especially from CMS, combined with the migration to home-based services, puts further economic strain on these facilities. Nearly twenty percent of post-acute care facilities will shutter or merge in 2019.

2019 Health IT IPO market fails to materialize

The warning signs are there over the last couple of months that the stock market has become skittish. This will extend well into 2019 (if not lead to a mild recession). It will hardly be an ideal time to do an IPO, and those planned by Change Healthcare, Health Catalyst and others will wait another year.

Elon Musk reinvents healthcare

Elon Musk will have a nervous breakdown leading him to reinvent the healthcare system from his bed during his two-week recovery at Cedars-Sinai.

Stay up to the minute.

Did You Know?

FDA Guidance on Clinical Decision Support: Peering Inside the Black Box of Algorithmic Intelligence

Key Takeaways:

  • The FDA has released long-anticipated draft guidance on how they intend to regulate clinical decision support products.
  • For applications with data originating from medical devices, the FDA will continue its oversight, AI or not (e.g., medical image processing).
  • Medical applications that rely on “black box” algorithms unable to be fully understood by the end-user (basically all AI) will be regulated, posing challenges for AI adoption.

Last week, the FDA finally released its long-awaited Draft Guidance on Clinical Decision Support. Following the release, STAT News mentioned experts were disappointed because the agency gave no insight into how it views artificial intelligence. Indeed, a “Command+F” search for “Artificial Intelligence” returns zero results. However, it is unnecessary for the agency to use the term “AI” to provide guidance on how it will consider associated technologies and use cases. The FDA does use the word “algorithm” in its guidance, and although algorithms can vary in sophistication, much of today’s AI technology is based on algorithmic intelligence. The suggestion that the FDA did not address the topic becasue it failed to explicitly mention AI within the document shows the challenges for those unfamiliar with understanding this complex subject.

Nearly all AI will remain under FDA oversight. However…It would be useful for the agency to offer meaningful reference to machine learning or deep learning among the examples of potential use cases.

In fact, the FDA has been reviewing technology with AI components (e.g., rule-based systems, machine learning) for more than a decade. RADLogics received FDA approval for their machine learning application in 2012, widely considered the first AI for clinical use approved by the agency. HealthMyne received FDA clearance for its imaging informatics platform in early 2016. In 2017 at least half a dozen companies received FDA clearance for machine learning applications, including Arterys, the first company to receive approval for a deep learning application, and Butterfly Network, which had 13 different applications approved along with its “ultrasound on a chip” device in late October. Others to receive clearance in 2017 include Quantitative Insights, Zebra Medical Vision, EnsoData and iCAD.

The first indirect reference to products using AI comes in the first paragraph of Section III, in which the agency begins addressing specific examples of companies that will not be exempted from review. Note that the first bolded sentence below is inclusive of nearly every application.

“Under section 520(o)(1)(E), software functions that are intended to acquire, process, or analyze a medical image, a signal from an in vitro diagnostic device, or a pattern or signal from a signal acquisition system remain devices and therefore continue to be subject to FDA oversight. Products that acquire an image or physiological signal, process or analyze this information, or both, have been regulated for many years as devices. Technologies that analyze those physiological signals and that are intended to provide diagnostic, prognostic and predictive functionalities are devices. These include, but are not limited to, in vitro diagnostic tests, technologies that measure and assess electrical activity in the body (e.g., electrocardiograph (ECG) machines and electroencephalograph (EEG) machines), and medical imaging technologies. Additional examples include algorithms that process physiologic data to generate new data points (such as ST-segment measurements from ECG signals), analyze information within the original data (such as feature identification in image analysis), or analyze and interpret genomic data (such as genetic variations to determine a patient’s risk for a particular disease).”

The word “algorithm” is used four times in the document and in each instance the use provides significant insight into the agency’s thinking. The word is first used in the second highlighted sentence above, which provides general examples of algorithms which will continue to be reviewed as medical devices. The guidance goes on in a later section to provide the following more specific examples of algorithms that continue to require premarket approval:

“Software intended for health care professionals that uses an algorithm undisclosed to the user to analyze patient information (including noninvasive blood pressure (NIBP) monitoring systems) to determine which anti-hypertensive drug class is likely to be most effective in lowering the patient’s blood pressure.

“Software that analyzes a patient’s laboratory results using a proprietary algorithm to recommend a specific radiation treatment, for which the basis of the recommendation unavailable for the HCP to review.”

The agency continues to describe the underlying features that must be present for an algorithmically-driven CDS recommendation to be exempted from review, specifically a company must clearly state and make available:

  1. The purpose or intended use of the software function;
  2. The intended user (e.g., ultrasound technicians, vascular surgeons);
  3. The inputs used to generate the recommendation (e.g., patient age and gender); and
  4. The rationale or support for the recommendation.

The first three would seem to be reasonable enough for developers of AI products to provide users, but the fourth is basically impossible. The “black box” nature of most AI systems built using machine learning methods means even leading AI experts cannot unpack an algorithm and fully understand the rationale for a given recommendation, even with full transparency and access to the training data (which is no trivial matter in and of itself).

This is especially clear when taking into consideration additional guidance provided elsewhere in the document regarding software functions that will require oversight:

A practitioner would be unable to independently evaluate the basis of a recommendation if the recommendation were based on non-public information or information whose meaning could not be expected to be independently understood by the intended health care professional user.

Frankly, the agency provided great insight and clarity if you are reading the document to be inclusive of all known AI technologies today. The conclusion is clear that nearly all AI will remain under FDA oversight. However, there are terms that could be used in the final guidance that aren’t buzzwords, such as machine learning, supervised learning and unsupervised learning, among others. It would be useful for the agency to offer meaningful reference to machine learning and/or deep learning among the examples of potential use cases that remain under oversight as medical devices.

In Chilmark’s annual predictions for 2018, we forecast that two dozen companies will receive FDA clearance for products using AI, machine learning, deep learning and computer vision, which would mark a 400-percent increase from 2017. It would be helpful if the agency would create a dedicated channel for engaging companies developing AI products and perhaps even provide guidance on how they evaluate training data sets.

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