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?

Revisiting Our 2018 Predictions

As is our custom here, we like to look back on our predictions for the closing year and see just how well we did. Some years we do amazingly well, others we over-reach and miss on quite a few. For 2018, we got seven of our 13 predictions spot-on, two were mixed results and four predictions failed to materialize. If we were a batter in the MLB we would have gotten the MVP award with a .538 batting average. But we are not and have to accept that some years our prediction average may hover just above the midpoint as it did this year.

Stay tuned, 2019 predictions will be released in about one week and it is our hope that they will inspire both rumination and conversation.

(Note: the bigger and plain text are the original predictions we made in 2017, while the italic text is our review of 2018). 

Merger & acquisition activity continues; Humana or Cigna acquired.

Major mergers and acquisitions that mark the end of 2017 (CVS-Aetna, Dignity Health-CHI and rumored Ascension-Providence) will spill over into 2018. Both Humana and Cigna will be in play, and one of them will be acquired or merged in 2018.

MISS – neither happened. However, Cigna did pick-up PBM service Express Scripts and rumors continue to swirl about a possible Humana-Walmart deal or more recently, even a Walgreens-Humana deal.

 

Retail health clinics grow rapidly, accounting for 5 percent of primary care encounters.

Hot on the health heels of CVS’ acquisition of Aetna, growth in retail health reignites, albeit off a low overall footprint. By end of 2018, retail health clinic locations will exceed 3,000 and account for ~5% of all primary care encounters; up from 1,800 and ~2%, respectively, in 2015.

MISS – Modest growth in 2018 for retail health clinics with an estimate of around ~2,100 by year’s end. Telehealth, which is seeing rapid growth and on-site clinics may be partially to blame.

 

Apple buys a telehealth vendor.

In a bid to one-up Samsung’s partnership with American Well, and in a bid to establish itself as the first tech giant to disrupt healthcare delivery, Apple will acquire a DTC telehealth vendor in 2018.

MISS – Apple continues to work on the periphery of care with a focus on driving adoption of its Health Records service in the near-term with a long-term goal of patient-directed and curated longitudinal health records.

 

Sixty percent of ACOs struggle to break even.

Despite investments in population health management (PHM) solutions, payers still struggle with legacy back-end systems that hinder timely delivery of actionable claims data to provider organizations. The best intentions for value-based care will flounder and 60% of ACOs will struggle to break even. ACO formation will continue to grow, albeit more slowly, to mid-single digits in 2018 to just under 1,100 nationwide (up from 923 as of March 2017).

HIT – MSSP performance data showed only 34% earned shared savings in 2017 (up from 31% in 2016) and by year’s end it is estimated there will be ~1,025 ACOs in operation.

 

Every major EHR vendor delivers some level of FHIR support, but write access has to wait until 2019.

While some of the major EHR vendors have announced support for write access sometime this year and will definitely deliver this support to their most sophisticated customers, broad-based use of write APIs will happen after 2018. HCOs will be wary about willy-nilly changes to the patient record until they see how the pioneers fare.

HIT FHIR-based read APIs are available from all of the major EHR vendors. Write APIs are still hard to find. To be fair, HCOs as a group are not loudly demanding write APIs.

 

Cloud deployment chips away at on-premises and vendor-hosted analytics.

True cloud-based deployments from name brand vendors such as AWS and Azure are in the minority today. But their price-performance advantages are undeniable to HIT vendors. Cerner will begin to incent its HealtheIntent customers to cloud host on AWS. Even Epic will dip its toes in the public cloud sometime in 2018, probably with some combination of Healthy Planet, Caboodle, and/or Kit.

HIT – adoption of cloud computing platforms is accelerating quickly across the healthcare landscape for virtually all applications. Cloud-hosted analytics is seeing particularly robust growth.

 

True condition management remains outside providers’ orbit.

Providers will continue to lag behind payers and self-insured employers in adopting condition management solutions. There are two key reasons why: In particular, CMS’s reluctance to reimburse virtual Diabetes Prevention Programs, and in general, the less than 5% uptake for the CMS chronic care management billing code. In doing so, providers risk further isolation from value-based efforts to improve outcomes while controlling costs.

HIT – Awareness of the CCM billing code (CPT code 99490) remains moderate among providers and adoption is still estimated at a paltry less than 15%.

 

Mobile-first becomes the dominant platform for over 75% of care management solutions.

Mobile accessibility is critical for dynamic care management, especially across the ambulatory sector. More than 75% of provider-focused care management vendors will have an integrated, proprietary mobile application for patients and caregivers by end of 2018. These mobile-enabled solutions will also facilitate collection of patient-reported outcome measures, with 50% of solutions offering this capability in 2018.

MIXED – While the majority of provider-focused care management vendors do have an integrated mobile application (proprietary or partnership), collecting PROMs is still a functionality that remains limited through an integrated mobile solution.

 

Solutions continue to document SDoH but don’t yet account for them.

A wide range of engagement, PHM, EHR, and care management solutions will make progress on documenting social determinants of health, but no more than 15% of solutions in 2018 will be able to automatically alter care plan interventions based on SDoH in 2018.

HIT – despite all the hoopla in the market about the need to address SDoH in care delivery, little has been done to date to directly affect dynamic care plans.

 

ONC defines enforcement rules for “data blocking,” but potential fines do little to change business dynamics that inhibit data liquidity.

The hard, iron core of this issue is uncertainty about its real impact. No one knows what percentage of patients or encounters are impacted when available data is rendered unavailable – intentionally or unintentionally. Data blocking definitely happens but most HCOs will rightly wonder about the federal government’s willingness to go after the blockers. The Office of the National Coordinator might actually make some rules, but there will be zero enforcement in 2018.

MIXED – Last December we said, “The hard iron core of this issue is uncertainty about its real impact.” Still true. Supposedly, rulemaking on information blocking is complete but held up in the OMB. The current administration does not believe in regulation. So “data blocking” may be defined but there was and will be no enforcement or fines this year.

 

PHM solution market sees modest growth of 5-7%.

Providers will pull back on aggressive plans to broadly adopt and deploy PHM solution suites, leading to lackluster growth in the PHM market of 5% to 7% in 2018. Instead, the focus will be on more narrow, specific, business-driven use cases, such as standing up an ACO. In response, provider-centric vendors will pivot to the payer market, which has a ready appetite for PHM solutions, especially those with robust clinical data management capabilities.

HIT – PHM remains a challenging market from both payment (at-risk value-based care still represents less than 5% of payments nationwide) and value (lack of clear metrics for return on investment) perspectives. All PHM vendors are now pursuing opportunities in the payer market, including EHR vendors.

 

In-workflow care gap reminders replace reports and dashboards as the primary way to help clinicians meet quality and utilization goals.

This is a case where the threat of alert fatigue is preferable to the reality of report fatigue. Gaps are important, and most clinicians want to address them, but not at the cost of voluminous dashboards or reports. A single care gap that is obvious to the clinician opening a chart is worth a thousand reports or dashboards. By the end of 2018, reports and dashboards will no longer be delivered to front-line clinicians except upon request.

MISS – Reports and dashboards are alive and well across the industry and remain the primary way to inform front-line clinicians about care gaps.

 

At least two dozen companies gain FDA-approval of products using machine learning in clinical decision support, up from half a dozen in 2017.

Arterys, Quantitative Insights, Butterfly Network, Zebra Medical Vision, EnsoData, and iCAD all received FDA approval for their AI-based solutions in 2017. This is just the start of AI’s future impact in radiology. Pioneer approvals in 2017 — such as Quantitative Insights’ QuantX Advanced breast CADx software and Arterys’s medical imaging platform — will be joined by many more in 2018 as vendors look to leverage the powerful abilities of AI/ML to reduce labor costs and improve outcomes dependent on digital image analysis.

HIT – With about a month left in 2018 the count of FDA approved algorithms year to date is approaching 30 and could potentially hit three dozen by year end. This is a significant ramp up in the regulatory pipeline, but more is needed in the way of clear guidance on how they plan to review continuously learning systems and best practices for leveraging real-world evidence in algorithm training and validation.

 

What do you think of 2018 for health IT?

Give Us Your Data – Is it Really That Easy?

HIMSS18 Review 1 of 4

By Ken Kleinberg, Brian Murphy, and Brian Edwards

What’s inside the black box of algorithims?

During Eric Schmidt’s opening keynote at HIMSS18, he asserted that, given the state of algorithms today, it’s possible to take any large data set and make strong predictions – and healthcare is no exception. There’s no need for clinical content knowledge, rules, or past experience. His statements were met with plenty of skepticism – less about the capabilities of Alphabet and its algorithms, and more about the realities of gaining access to the right healthcare data sets. This is not trivial.

So who should get data from who? What about patient consent? Who can be trusted? Historically, health systems have done their own analytics and research within the boundaries of their own systems. Vendor analytic solutions were implemented on site. Even this limited scenario presented complex challenges – in particular, just bringing data together to a point where it could be analyzed. Transferability of models was difficult, and costs were not shared. The use of analytics was therefore sparse, mostly limited to research and quality improvement.

Bulk data access will be critical for the industry to move beyond the current artisanal methods of building and maintaining data stores for analytics purposes.

Slowly but surely, the situation is changing. The cloud is becoming much more accepted, with many options possible (private, public, hybrid). Algorithms, enabled by rapidly advancing hardware/computing power, are capable of dealing with much larger and more complex data sets. Data operating system approaches can stream data in a liquid fashion from multiple locations/sources, reducing the need for centralized repositories.

A next step as data becomes more available is to fully utilize it. Advances in natural language processing (NLP) are able to extract/mine useful features from unstructured data such as text, faxes, and reports. Algorithms can increasingly use incomplete, messy, or ill-defined data and “fill in the blanks.” At a certain scale, data quality becomes less of a factor in conducting analytics.

Despite the black-box nature of AI systems, they can still be validated using objective methods, such as how and what they were trained upon, and how they perform in real-world clinical scenarios vs. human performance.

Whose Black Box?

There is also a lot of healthy discussion about how AI systems make decisions. The primary concerns are black-box algorithms and a lack of data transparency. This even reached a point where a major educational institution recently recommended that governments not rely on any AI or algorithmic systems for “high stakes” domains, such as healthcare technology, where the way a system makes a decision cannot be understood in terms of due process, auditing, testing, and accountability standards.

Despite the black-box nature of these AI systems, the fact is that they can still be validated using objective methods, such as how and what they were trained upon, and how they perform in real-world clinical scenarios vs. human performance. As long as they are not used in closed- loop systems, and as long as there is a human expert able to accept or dismiss their recommendations, they provide valuable input (before or after) for difficult cases (such as whether surgery or therapy is the best course of action). They may also serve as a last resort with the proper consent (as with a terminal cancer patient).

So that all is highly promising – but is healthcare ready to hand this data over?

Flat FHIR and Analytics

Percolating below the surface at HIMSS was disquiet about the “bulk data transfer” proposal. This proposed method would make large sets of data (think cohort-level data) more freely available for analytics purposes. It will allow a user or program in one organization to issue a broadcast query to the country at large and receive patient data from other organizations. For example, an ACO quality manager could issue a query to a community and get all of the relevant data for patients in the ACO.

This proposal, also known by the name “Flat FHIR,” is part of the TEFCA discussion (Trusted Exchange Framework and Common Agreement) insofar as such queries are a contemplated use case. But among people who are paying attention to this proposal, there are unanswered questions.

Open-ended queries arriving from anywhere in the healthcare system are not currently part of most HCO’s IT capacity plan:

  • Will organizations end up having to add more compute and network resources to satisfy such queries?
  • Does TEFCA’s requirement to provide non-discriminatory access mean that organizations will not be able to implement reasonable network traffic and quality-of-service controls?
  • If patients have different consent profiles in different organizations, how should a query recipient satisfy the request?
  • Will organizations have to establish revenue share agreements based on pro-rata data contributions?
  • Will the fact that TEFCA puts the onus on the query receiver to reconcile medications, allergies, and problem lists mean that the receiver must verify that its data is current with proximate organizations before satisfying the original query?

Such questions represent the tip of the iceberg. As a practical matter, before the bulk data transfer proposal can ever be a day-to- day reality, many technical, non-technical, and financial questions must be resolved.

Despite these questions, bulk data access will be critical for the industry to move beyond the current artisanal methods of  building and maintaining data stores for analytics purposes. After all, analytics has more to offer than the dashboards and reports that describe the recent past. HIMSS18 was less a venue to air out the challenges associated with making bulk data transfer a reality than it was an opportunity to preview some of the advanced and predictive analytics use cases it could enable.

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