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Digital health enters a recalibration phase as ROI pressure reshapes procurement and AI workflows

Digital health is in a recalibration phase driven by increased pressure on ROI in procurement and AI workflows. A report by Holland & Knight highlights key trends in the healthcare sector, focusing on measurable outcomes and enhanced AI governance. The shift also emphasizes better management of chronic diseases.

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By MarketScale Newsroom · Digital HealthAi WorkflowsHealthcare TechnologyChronic Disease Management
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Digital health enters a recalibration phase as ROI pressure reshapes procurement and AI workflows

Key takeaways

01

Digital health must demonstrate ROI due to increased pressure.

02

Measurable outcomes and tighter AI governance are trending in healthcare.

03

Chronic disease management is a critical focus area.

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Digital health tops the list of forces reshaping the healthcare sector in 2026, and the signal from Holland & Knight's mid-year Healthcare Trend Report is clear: the sector is no longer rewarding growth for growth's sake. The law firm, which advises health systems, payers, and digital health companies on transactions and regulatory matters, describes the current moment as a recalibration phase, one in which capital and attention are concentrating on platforms that can prove measurable outcomes and return on investment.

That framing carries real weight for health system operators and the procurement and IT leaders evaluating digital tools. A vendor that cannot quantify clinical impact or cost efficiency faces a harder sales cycle and a shorter runway in 2026 than it did two years ago. The bar for enterprise adoption has shifted from feature richness to evidence.

AI workflow integration moves from pilot to accountability

Holland & Knight's report singles out AI workflow integration as one of the two operational areas drawing the most transactional and regulatory scrutiny. Health systems that have spent the past two years running pilots are now facing pressure to formalize governance structures around those tools. That means defining who is accountable when an AI-assisted clinical or administrative decision goes wrong, and building documentation trails that satisfy both internal compliance teams and external regulators.

For CIOs and CMIOs, the practical implication is that AI procurement can no longer sit entirely with a single department. Contracts for AI workflow tools increasingly require cross-functional sign-off from legal, compliance, and clinical operations, not just IT. The report's emphasis on litigation trends alongside transactional ones suggests that post-deployment liability is already a live concern, not a future risk.

The governance question is especially acute for tools embedded in care delivery workflows. A coding assistant or prior authorization bot that operates at scale creates a different compliance surface than a standalone analytics dashboard. Operators evaluating AI vendors should ask specifically how the vendor handles model updates, audit logging, and clinical override documentation.

Chronic disease management draws deal activity

The second major operational focus in the report is chronic disease management, a category that spans remote patient monitoring, condition-specific digital therapeutics, and population health platforms. This area is attracting deal attention because it represents one of the clearest paths to demonstrating the ROI that recalibration-phase investors and health system CFOs now demand. Programs that reduce hospital readmissions or emergency department utilization for high-cost chronic conditions produce numbers that are relatively straightforward to audit.

For supply chain and vendor management teams, that concentration of deal activity means more vendor consolidation is likely. Smaller point solutions targeting single chronic conditions face acquisition or partnership pressure from larger platforms trying to offer integrated chronic care suites. Procurement leaders evaluating chronic care tools should factor platform longevity and integration roadmaps into their selection criteria, not just current functionality.

Regulatory and litigation exposure adds a new layer to vendor evaluation

Holland & Knight's report covers regulatory and litigation trends alongside the transactional picture, a pairing that reflects how intertwined compliance exposure and technology decisions have become. Digital health tools that touch billing, prior authorization, or clinical documentation carry fraud and abuse risk that did not exist when those processes were purely manual. As federal and state regulators increase scrutiny of AI-assisted billing and algorithmic decision support, health systems bear responsibility for the tools they deploy, not just the outcomes they produce.

Efficiency is the third theme the report emphasizes. The drive to reduce administrative burden through digital tools is real, but operators should approach efficiency claims with the same evidence standard now applied to clinical outcomes. A platform that promises to cut prior authorization time by a specific percentage should be able to show that figure from a comparable deployment, not just a modeled projection.

What this means for your team

  • Require outcome data, not projections: ask digital health vendors for audited results from comparable health system deployments before advancing any contract, particularly for AI workflow and chronic care tools.
  • Build cross-functional AI governance now: if your organization runs AI-assisted clinical or administrative workflows without a documented accountability framework, the regulatory and litigation environment described in Holland & Knight's report makes that a near-term priority, not a future one.
  • Stress-test chronic care vendors on platform longevity: given consolidation pressure in this category, evaluate whether a point solution vendor has the scale or partnership structure to remain a viable long-term partner.
  • Align procurement criteria with the recalibration standard: update RFP scorecards to weight measurable ROI evidence at least as heavily as feature sets and implementation timelines.

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