True to what we forecasted around the midpoint of 2021, healthcare transaction activity last year surpassed even 2020’s record level with nearly $40 billion in deals, up 32% from the previous year. Much of what we saw—and continue to see—is continued acquisitive growth driven by deal economics for providers, life sciences, and other healthcare IT areas. Although we saw a dip in the first quarter of this year, this growth continues across the healthcare industry despite macro impacts like the Russia-Ukraine War, rising inflation, and valuation changes due to economic uncertainty.
But we’re coming at that story a little differently this time around. Instead of looking down industry towers (providers, payers, and life sciences) at key trends and drivers of M&A and investment activity, we’re looking across them at digitally enabled platforms, products, data sets, and high value analytics through the lens of asset differentiation and the trends shaping portfolio value.
This approach allows us to illustrate how investment activity is being pushed and changed by all things digital and to illuminate the ways in which all four of these areas are seeing continued market interest and high valuation due to the material advantages they afford. Read on to learn more.
A continued surge in platform rollups is bringing providers economic benefits—but it’s also raising complex questions about how buyers can achieve their investment goals while combining different business architectures. To help break these architectures down, healthcare organizations would do well to orient around three focal points: people, processes, and technology. With technology in particular, successful companies are evaluating scalability, extensibility, supportability, cost, data and administration, and timing associated with necessary transitions.
When strategic and financial buyers anticipate these complexities that come with provider-related transactions, they can plan accordingly to optimize realizable value post-close.
Building the right provider platform means smart expansion, requiring dealmakers to strike the right balance between de novo building—a process that refers to the internal, organic expansion of a company, like opening new physical locations—and acquisitive building, which is based on the acquisition of preexisting businesses. Both come with their own set of pros and cons.
Acquisitive building, for instance, allows platforms to achieve growth faster—and in turn drive higher returns in the short term. But platform leaders will eventually have to come to terms with the inherited operating model of the acquired business.
When done right, such expansions can drive real results: National Veterinary Associates, a global pet platform acquired by JB Holding Co. in 2019 for $5 billion, has since more than doubled in revenue, in part through recent acquisition of $2 billion specialty animal hospital and emergency medicine providers. This degree of rapid growth is only possible through the inheritance of preexisting company infrastructure, but with such speed comes questions about reconciliation with the parent company’s overlapping processes and tools.
By contrast, de novo expansions deliver greater operational oversight and have the advantage of less “integration debt” for net-new platforms. Still, they require investment in deepening market penetration over time. They also take more time to establish, since companies must find suitable locations, obtain licenses, and attract talent for optimal patient care.
Operating model impacts
While transaction activity in the vet space has grown in recent years, other sectors like dental care have seen movement by large consolidation vehicles for some time. A great example of significant platform growth is Heartland Dental, the world’s largest dental service organization (DSO). Owned by leading global investment firm KKR, Heartland Dental grew to 1500 total locations in 2021 through the addition of 54 de novo sites, and also inherited 278 locations via acquisition of American Dental Partners in a single transaction. Platforms like Heartland are likely already capitalizing on operational synergies (e.g., using centralized shared services teams), and cost synergies (e.g., achieving economies of scale on their tech licenses) to streamline the acquisitive building process.
However, even straightforward acquisitions can come with hang-ups—DSOs are often hamstrung by technology limitations across the dental practice management (DPMS) market, which is characterized by a large portion of on-premises solutions optimized for single-site use. This means that each practice location is managing its own siloed datasets and processes (e.g., referral management, patient intake, service documentation). As a result, operational leaders will rely on an external data consolidation to achieve performance visibility, with less control over how information is collected and calculated across sites.
To improve transparency and monitor multi-site operations, some DSOs are linking data and workflows through a custom data hub or enterprise data warehouse. But as cloud-based platforms continue to expand and develop, they’re also mindful of whether transitioning from on-premises solutions would significantly disrupt their operations—and if that disruption is worth it.
Operating model impacts
Many provider platforms—particularly in-home care, primary care, and behavioral health—have started expanding into specialties and niche services to build an integrated care model that addresses a broader scope of the patient continuum. In some ways, this was accelerated by oscillating volumes of elective and in-person services through the pandemic, and alternative care delivery models have proliferated. When extending into scaled virtual care offerings or wraparound therapeutics, these platforms must decide whether their existing people, processes, and tools sufficiently support evolving service needs.
Organizations are also pursuing expansion opportunities that capitalize on their existing operating models. Matrix Medical Network, for instance, expanded into decentralized clinical trials in partnership with biopharmaceutical giant AstraZeneca. They used their historical model for in-home and mobile, near-site comprehensive health assessments to facilitate patient sourcing and management across a wider geography with more diverse populations.
Operating model impacts
Focusing on change management and provider satisfaction is the key to successful growth through operating model evolution. Sectors such as home care and human services—with commonly high attrition even prior to the pandemic—must pay special attention to the impact of technology transformation or acquisition on their large, decentralized workforces.
These historically paper-based industries are facing a significant shift in culture and processes. Many in-home providers have only recently begun adopting technologies to comply with evolving state requirements like electronic visit verification (EVV) mandates. Providers like Help at Home, a home health agency that spans multiple states, saw this moment as not only an opportunity to be EVV-compliant but also to streamline their agency and workforce management processes. Such transitions may lead to increased efficiency and operational transparency in the long run but will also require large investments in comprehensive planning and new training programs to manage near-term change impacts.
Operating model impacts
Climbing to $175.6 billion in 2021, the global digital healthcare market is now the fastest growing healthcare market segment—and it’s projected to see a compound annual growth rate of 27.7% over the next 8 years. New entrants have increasingly flooded the market thanks to movement of care from inpatient to outpatient, increasingly sophisticated and accessible technology, billions of dollars in venture capital, and huge leaps forward in consumer acceptance and adoption of digital healthcare options. COVID-19 forced even entrenched legacy organizations to jettison old beliefs and outdated routines in favor of a hyper-focus on getting viable solutions to users as fast as possible. The same nimble mindset driving both startup and enterprise innovation is at the heart of digital product development.
To stay relevant in the rush of innovation and momentum, healthcare providers and—increasingly—life sciences organizations will have to embrace principles of product development and understand rising trends and drivers in customer centricity, customer experience, and internal product operations.
A sector-wide emphasis on customer centricity may have preceded the pandemic, but it evolved rapidly during it—a result of shifting consumer and patient expectations. Virtual consultation rates quadrupled in 2021, effectively experiencing 10 years’ worth of growth in less than 18 months.
Non-traditional healthcare companies, which have long invested in omnichannel retail experiences, naturally leapt ahead on the strength of their customer service models. And because patients often have a stronger opinion about their experience than they do a deep understanding of the effectiveness of the actual care they receive, patient-centric models continue to deliver impactful results.
For example, Apple’s smartwatch has become a leader in remote patient monitoring, providing patients and their providers information about heart health during televisits and even warning wearers of impending heart attacks.
Regulatory compliance and government funding associated with the Affordable Care Act, No Surprises Act, and CARES Act—among other laws—have also driven the adoption of electronic health records, price transparency, and telehealth expansion—all key pillars of customer centricity.
Consumers have come to expect the same seamless, simple-to-navigate, and engaging experience in healthcare as they do in other industries. And the depth, breadth, and availability of consumer health and wellness options has forced organizations more than ever to differentiate their products and experiences in order to capture and keep user attention however they can—whether by offering affordability, accuracy, speed, availability, or some combination thereof. But when teams delivering the patient journey are disconnected, they often don’t have the autonomy needed to quickly iterate when a new need or opportunity arises.
To address this issue, providers of all kinds are increasingly leaning into an owned experience as a way to enable freedom and comprehensive reach of ownership and allow teams to quickly enable maximum customer focus.
One pathway to eliminating silos is through the acquisition of self-contained, adjacent companies. Another is to identify an alternate model that creates more comprehensive ownership—for example, several different providers trying out a subscription model. In guaranteeing a stable revenue stream for providers and enabling no-surprises pricing for patients, there is at least hypothetical potential to completely remove payers and the perceived obstacles to care they create.
Providers have realized that one-time platform upgrades or implementations are not enough to keep pace with expectations for improved patient experiences or outcomes. Staying relevant requires relentless agility from the human beings who are imagining and creating those experiences, including having comfort with ambiguity, testing minimum viable solutions to learn quickly and calibrate course where needed, and constantly sensing, responding, and iterating as user needs and available capabilities evolve.
Those skillsets are not a replacement for medical expertise or deep understanding of and compliance with regulatory requirements—they are complimentary, and we’re seeing more and more engagements with clients focused on helping their teams learn how to work differently and increase their ability to be nimble, proactive, and pivot quickly when needed. Another increasingly popular solution for healthcare organizations looking to close skillset gaps as fast as possible is bringing in digital-first talent through acquisitive hires: finding a company with the right talent and acquiring it for that talent—along with the company’s service or product line.
Additionally, technology companies lacking healthcare industry expertise are using a similar tactic to quickly gain credibility and expertise. Several of the largest acquisitions by dollar value last year were of this nature: In December 2021, Oracle acquired Cerner, a leading provider of digital information systems for hospitals and health systems for $28 billion; in April, Microsoft acquired Nuance, a leading provider of conversational AI and cloud-based ambient clinical intelligence for healthcare providers for $20 billion.
As life sciences organizations, providers, payers, and other healthcare organizations pursue both de novo and acquisition investment strategies for growth, parts of their organizations may end up cordoned off into separate data environments—despite their use of the same reporting tools.
High-growth organizations have been focusing on three main areas—setting a foundation, building for scale and flexibility, and finding value in their data—as a means to address this issue. By following a phased approach to the implementation of their data strategy based on these areas, they can also set the stage for the successful deployment of advanced analytics.
Growing data volumes, new tools and platforms, and recent M&A activity have led to data silos and scalability concerns. To complicate this matter further, only 53% of organizations report having developed a corporate data strategy, and only a quarter called themselves data-driven organizations. Organizational culture is often cited as the greatest obstacle to achieving measurable results with data.
To meet these challenges head-on, many providers have initiated measures to ensure data fluency and democratized data across their business. An increase in full or partial risk reimbursement models has also created opportunities for organizations to invest in their foundational capabilities. Pursuing these opportunities has empowered analysts to accelerate platform growth using a data-driven perspective.
Burdened with disorganized and siloed data, many providers are struggling to pursue advanced analytics and patient-centric care. Adopting modern, cloud-based data platforms—such as Azure, Snowflake, and Amazon Web Services—can help break down these data silos, enhance scalability, and provide a better patient experience.
More than 90% of leading organizations are reporting measurable results from investments in data and artificial intelligence (AI)—a huge increase from five years ago. Even so, many providers express difficulty in attributing specific business outcomes to data, and an increasing number of reports and raised expectations have led to adoption challenges.
That’s why many organizations who recognize the importance of data literacy and data strategy are turning to custom development platforms, like Power BI and Tableau. Most importantly, they have committed to specific, business-led outcomes that enhance collaboration and lead to value realization.
As healthcare providers continue to implement standardized reports, dashboards, and key performance indicators, they have evolved in their capabilities to provide predictive analytics, and in some cases, prescriptive analytics—the application of mathematical and computational sciences to model scenarios suggest decisions. In fact, machine learning (ML) and AI have become a key part of this advanced analytics strategy, offering companies new opportunities for revenue growth.
Much of the research in AI and ML has been transformational for the healthcare industry. But the tremendous growth in EMRs, including appointment confirmations, physician notes, messages, and lab results, means that the need for advanced analytics and processing is at an all-time high.
The simultaneous rise in other data sources (e.g., RWD/RWE, molecular, biomarker, patient provided, pharmacovigilant, and SDOH) is also fueling this surge, along with operational constraints, such as the number of staff, availability, skills, multiple vendor solutions, and required equipment.
Adopting AI and ML applications creates the opportunity for smarter, more personalized care that can be delivered more efficiently and with greater accuracy. For example, digital medical imaging company Nanox secured a new FDA clearance for its AI program in May 2022 that spots vertebral compression fractures. Not only has this development changed the standard of care, but it has access to care and early diagnosis of conditions that often go undetected. Meanwhile, Nuance has also made advances in conversational AI and speech recognition, allowing them to transcribe a physician’s voice and develop a framework that can be imported as EMR—a time savings of 45% when it comes to physician documentation.
Experienced dealmakers in the healthcare industry may recall IBM Watson’s claims of an incoming revolution in cancer care, a vision that ended abruptly after its $1 billion acquisition by Francisco Partners in early 2022. The situation is a clear bellwether for the healthcare industry: In other words, ML and AI are unlikely to usher in the next revolution and more likely to streamline existing processes or allow organizations to scale through automation. They may also foster the creation of new revenue streams—increasing end-user adoption as a result.
Certain fields like radiology have reaped benefits in three major areas of AI/ML: computer vision, speech recognition, and natural language processing. Physicians can now identify hard-to-see areas in X-rays, use their voice to take notes, and use radiology reports to extract data for clinical trial matching. Although these innovations are small, they support and enhance evidence-based medicine standards among physicians who may have ethical concerns about automation.
Due to the constant influx of small AI/ML innovations, however, it’s become impossible for healthcare companies to compete on every front. Many organizations have begun to partner with smaller, fast-iterating startups in order to keep up, offering them key components of their software platforms via API services and creating unique opportunities for partnership and growth.
Crucial bottlenecks have dogged healthcare and life sciences organizations in their attempt to push the industry toward AI and ML adoption. Experience and education remain in short supply, and finding a top data scientist or computational biologist is difficult given increasingly high demand for such talent.
Some companies have found a way around this problem by focusing on making existing talent more efficient. Snorkel, a data-centric AI company, realized that more than four-fifths of data analysts’ time was spent finding, cleaning, and organizing data in preparation for analysis and modeling. To reduce their time spent on low-level data classification, they introduced a code-based platform that relies on thousands of data points to make annotations.
Unfortunately, some of these headwinds are slowly becoming tailwinds. Over the last decade, federal regulatory bodies have expressed trepidation about AI/ML-based models making decisions on behalf of humans. On the other hand, the FDA is starting to put guidance in place for clinical decision support software—as in the case in Nanox’s recent AI program approval––that could bring clarity to investors who remain uncertain about monetization and long-term growth in digital health.
Recent innovations have reshaped how healthcare organizations approach their products, platforms, data, and analytics—and it looks as though those changes are here to stay. Over the next five years, four-fifths of US providers are planning to invest in technologies like digital health, AI/ML, and tools to support clinical staff and caregivers. As more organizations begin to recognize the benefits of adopting these new technologies, M&A activity should track closely with their implementation, too.
At the heart of this new wave of healthcare investment is the belief that digitally enabled care can not only lower costs and broaden the spectrum of care but also empower leading healthcare and life sciences organizations to make a difference in patients’ lives. As the industry’s digital transformation continues to unfold, dealmakers and business leaders need to anticipate the next opportunity on the horizon for the growth and development of sophisticated products, platforms, and analytics.