From Hospitals to Homes: AI and Robots Drive a $1 Trillion Healthcare Shift by 2035

Healthcare in 2035: Proactive, automated, robot-enabled, and delivered anywhere

By 2035, analysts expect $1 trillion in annual healthcare spending to move from fragmented, infrastructure-heavy operations to digital-first care and empowered "super consumers." The center of gravity shifts from hospital-centric to home-centric. Technology doesn't sit alongside care delivery-it becomes the operating system of care.

Virtual-first delivery, AI-supported decision-making, and interoperable, consumer-centered platforms are already here. The next decade is about scale, integration, and outcomes.

What's changing now

AI is compressing timelines: drug design in months, not years. Robotic-assisted surgeries are lowering complication rates. Home programs are replacing inpatient days, supported by remote monitoring and care orchestration.

Expect broader use of robotics, AI-driven discovery and diagnosis, and interoperable APIs to streamline data flow. Routine work gets automated; clinicians focus on higher-acuity decisions.

Where the spend moves

Roughly $1 trillion will shift from administrative overhead and brick-and-mortar facilities into AI-enabled intake, home-based services, and digital platforms. That reallocation funds prevention, precision interventions, and continuous care models.

Clinical impact: anticipate, then treat

Care moves from managing disease to predicting it. AI engines flag risk before symptoms surface, enabling earlier interventions and fewer hospitalizations.

Digital twins and genomics align treatment with biology, behavior, and lifestyle-making prevention personal.

Care settings: home as default, hospital as hub

Continuous glucose monitors, connected inhalers, and wearable cardiac sensors stream real-time data. Teams can intervene sooner with less friction.

Hospitals evolve into specialized hubs for trauma, surgery, and acute care-while virtual command centers coordinate large panels of patients at home.

The rise of the "super consumer"

A growing segment of tech-enabled, higher-income patients will choose how they spend their healthcare dollars. They'll pay out of pocket for innovations first, setting the demand signal that public and commercial payers later fund and scale.

The operating model you'll need

Consumer-first, virtual by design, and data-intelligent. Interoperable APIs to plug into partners fast. AI and robotics to automate routine tasks. Algorithms and insight leading every process.

What healthcare leaders should do next

• Stand up AI governance with clinical, legal, privacy, and ops oversight; define risk tiers and approval paths.
• Re-map budgets: shift dollars from low-yield admin and physical footprint to home programs, data infrastructure, and automation.
• Build a home-first service line: remote monitoring, hospital-at-home, and virtual command centers with clear escalation protocols.
• Adopt interoperable APIs; require vendors to meet standards and publish endpoints.
• Pilot predictive models for readmission, deterioration, and chronic risk; validate locally and monitor drift.
• Redesign workflows for continuous data: alert thresholds, team roles, documentation, and closed-loop follow-up.
• Invest in data quality, cybersecurity, and consent management; bake privacy into product design.
• Upskill your workforce on AI literacy and prompt-based tools; certify super-users in each department.
• Partner with device makers and automation vendors; align on shared outcomes and integration milestones.
• Modernize revenue models to support at-home episodes, remote management, and performance-based contracts.
• Elevate patient experience: self-service intake, transparent pricing, and on-demand messaging.
• Measure what matters: time-to-intervention, complication rates, patient-reported outcomes, and total cost of care.

Maturity check: where the field stands

Most health systems report using AI internally, yet only a small portion have mature governance and a full strategy. Many have pilots in finance, revenue cycle, or clinical areas, but scale requires clear ownership, data standards, and a training plan.

For broader context, see market perspectives from PwC's health industries group here and physician views on AI from the AMA here.

Build the skills to execute

If your teams need practical, role-based AI training and certifications, explore curated programs by job function at this resource. Focus on frontline use cases, governance basics, and ROI tracking.

Bottom line

Care is moving home. Data is continuous. Algorithms run the playbook. The organizations that act now-on governance, home-first models, and workforce skills-will lead the next decade of healthcare.