15 Healthcare Technology Trends and Innovations to Watch in 2027

In this article, you’ll learn about 15 healthcare technology trends for 2027, from clinical AI and connected medical records to hospital-at-home care, advanced diagnostics, and emerging treatments. We explore the future of healthcare technology through real-world projects and recent research, explain which innovations are already in use, and highlight practical considerations for healthcare providers and healthtech teams.

A consultation ends, but the work continues: someone must finish the note, arrange follow-up, obtain authorization, and make sure the next clinician receives the right records. New technology becomes useful when it helps complete those tasks or enables a better way to deliver care.

The main healthcare technology trends for 2027 include clinical AI, connected health records, care at home, targeted diagnostics, and emerging treatments. The Centers for Medicare & Medicaid Services (CMS) requires certain payers to implement electronic prior authorization application programming interfaces (APIs) from January 2027. Personalized gene editing and autonomous surgical tasks remain emerging fields.

The American Medical Association’s 2026 physician survey found that 72% of the 1,342 physicians answering its AI-use question reported incorporating at least one AI use case into their practice. The examples below examine where these applications, and other medical innovations, are heading.

[IMAGE PLACEHOLDER 01: Healthcare Technology in 2027: Connected Care and Clinical Innovation]

Caption: Healthcare innovation connects clinical teams, patient information, and care beyond the hospital.

ALT text: Clinician and patient in a connected healthcare setting, with home monitoring and medical research represented in the background.

Healthcare Technology Trends for 2027 at a Glance

Technology	Practical Use	Maturity
Ambient AI scribes	Draft clinical notes	Deployed; results vary
Administrative AI agents	Complete scheduling and related tasks	Early deployments
Multimodal clinical AI	Combine images and patient information	AMIE: simulated consultations
Electronic prior authorization	Submit medical-service requests, excluding drugs	Specified payers; generally from January 2027
Connected health records	Support care across organizations	Expanding networks
AI monitoring and governance	Evaluate tools throughout their lifecycle	Operating programs
Connected-device cybersecurity	Protect distributed care systems	Established practices; updated guidance
Hospital at home	Deliver acute care outside hospital buildings	Operating programs
Continuous medical monitoring	Track selected conditions between visits	Authorized, indication-specific uses
Prescription digital therapies	Deliver structured behavioral treatment	Authorized products
Liquid biopsy	Support cancer screening and treatment decisions	Specific authorized uses
Personalized gene editing	Address selected rare genetic conditions	Patient-specific approach: early clinical evidence
Surgical task automation	Assist with complex procedural steps	SRT-H: preclinical experiments
AI-assisted drug discovery	Develop and test new drug candidates	Rentosertib: phase 3 trial
Federated learning	Build models across institutions	Multi-institutional research

Maturity refers to the named applications discussed below, based on evidence available before 2027. Broader capabilities may remain experimental. The prior authorization provisions discussed here concern medical items and services, excluding drugs.

1. Ambient AI Scribes Become Part of Clinical Workflows

Ambient AI scribes turn a consultation into a draft clinical note for clinician review. Their usefulness depends on how well the draft fits the specialty, visit type, and electronic health record (EHR).

Documentation is already a practical entry point for AI. The AMA’s 2026 survey found that respondents reported using AI for:

• Discharge instructions, care plans, or progress notes: 30%, compared with 20% in 2024.
• Billing codes, medical charts, or visit notes: 28%, compared with 21% in 2024.
• Chart summaries: 28%, compared with 12% in 2024.

Respondents could select multiple uses, so these percentages overlap. Reported use of chart summaries more than doubled between the two surveys.

A randomized UCLA trial compared Microsoft DAX Copilot, Nabla, and usual care among 238 outpatient physicians. Nabla reduced note-writing time by 9.5% relative to the control group; DAX did not produce a statistically significant reduction on that measure. Clinicians reported occasional inaccuracies.

For 2027, a useful pilot measures total effort through note completion, including corrections and review. Consent, recording retention, and specialty-specific accuracy also matter.

An approved note can then support post-visit workflow automation, such as patient instructions and follow-up tasks, provided each additional action has an appropriate review process.

2. AI Agents Handle Scheduling and Administrative Work

AI agents can complete administrative tasks across connected systems, including appointment booking and exception handoffs. Hospital for Special Surgery (HSS) provides a concrete example. In its 2026 Ema deployment, HSS described an agent that supports patient scheduling and triage through phone, text, and online channels. The system can book appointments, account for provider availability, and pass control to staff.

That capability makes integration depth central to the business case. An accurate conversation has limited value if employees must repeat the information in another system afterward. Conversely, incorrect bookings can create additional work for both patients and staff.

Organizations evaluating healthcare AI development should start with a bounded workflow and explicit permissions. Measure completed tasks, booking errors, and successful handoffs. HSS’s announcement establishes a deployment example; it does not provide independent evidence that every similar agent will improve access or reduce costs.

3. Multimodal AI Connects Images With Patient Information

Clinical information arrives in several forms. A skin photograph, an electrocardiogram (ECG), and a patient’s description each contribute something different. Multimodal AI attempts to interpret these inputs together instead of treating every encounter as a text conversation.

Google researchers’ 2026 AMIE study explored this approach in 105 simulated consultations involving dermatology photographs, ECGs, and clinical documents. Specialist reviewers rated the multimodal system above the participating primary care physicians on 29 of 32 evaluation dimensions, including measures of diagnostic dialogue and image handling.

These results establish a research direction, not a substitute for trials involving actual care. Patient-actor consultations do not reproduce every difficulty of incomplete records, urgent illness, poor-quality uploads, or local treatment pathways.

The opportunity for 2027 is more context-aware clinical support. A useful application must identify which information belongs to the patient, preserve dates and units, and show clinicians what informed its output. Those requirements matter as much as the model’s ability to interpret an image. Local validation should test the complete workflow, including how the tool handles missing or conflicting information.

[IMAGE PLACEHOLDER 02: How Clinical AI Supports a Visit]

Caption: Clinical AI can draft information for review; clinical approval remains a separate step before a note or action is released.

ALT text: Clinical AI workflow showing patient information feeding a draft, clinician review, and an approved note or action, with a revision loop.

4. Electronic Prior Authorization Moves Into EHR Workflows

Among healthcare IT trends, electronic prior authorization has an unusually concrete 2027 milestone. Under CMS-0057-F, specified payer categories, including Medicare Advantage plans and Medicaid programs, face API requirements generally beginning January 1, 2027. The rule’s relevant authorization provisions cover medical items and services, excluding drugs; exact compliance dates vary by payer type.

The workload helps explain the push for change. In its May 2026 release, the AMA reported findings from its 2025 survey of 1,000 physicians:

• An average of 40 prior authorizations per physician each week.
• 13 hours of physician and staff time weekly spent on that work.
• 40% of physicians employing staff dedicated exclusively to prior authorization.

These figures cover prior authorization broadly, including work beyond the CMS API requirements. Connected software can help teams check requirements, supply documentation, and receive payer responses within the clinical workflow.

An earlier implementation shows what that can look like. In a 2024 Da Vinci evaluation, MultiCare Connected Care and Regence BlueCross BlueShield of Oregon, a Cambia health plan, used Fast Healthcare Interoperability Resources (FHIR) exchanges for authorization work. MultiCare reported an increase from three to five submissions per staff hour to 10 to 12. The findings covered one provider-payer implementation.

For healthcare organizations, preparation means testing actual payer connections and identifying where required clinical data resides. Missing information in a separate specialty system can still interrupt the process. An available API does not guarantee an immediate decision, approval, or a fully automated workflow.

[IMAGE PLACEHOLDER 03: Electronic Prior Authorization: Requests, Responses, and Exceptions]

Caption: Electronic prior authorization connects requirements, documentation, and payer responses, with separate paths for exceptions and additional information.

ALT text: Prior authorization workflow branching into no authorization required or submission to a payer, followed by approval, denial, or a request for information.

5. EHR Interoperability Connects Care Across Providers

A specialist receiving a referral needs the relevant history, medications, and previous results. The challenge is making information from another organization available in a form the receiving team can use.

The Trusted Exchange Framework and Common Agreement (TEFCA) provides a national framework connecting participating health information networks. In November 2026, the Office of the National Coordinator for Health Information Technology (ONC) announced VA’s TEFCA participation. ONC also reported nearly 25,000 organizations live on TEFCA and more than 2 billion records exchanged since its December 2023 launch. Those figures demonstrate the network’s scale, while individual care teams still need to assess the completeness and usefulness of incoming records.

This is a practical example of expanding connectivity across organizations. TEFCA does not create one universal patient database. FHIR, meanwhile, provides standards for exchanging structured information through software interfaces.

For 2027, the next step is turning access into usable clinical context. Incoming records need reliable patient matching, source identification, and reconciliation of duplicate or conflicting information. A large exchange volume alone does not establish that a medication list is current. Providers should assess how often retrieved records answer the receiving clinician’s question and how much manual review remains.

[IMAGE PLACEHOLDER 04: Connected Records Without One Universal Database]

Caption: TEFCA connects participating networks; healthcare organizations retain their own records, while FHIR supports structured data exchange.

ALT text: Two connected health information networks link clinics, hospitals, and specialists that each retain local records, with separate explanations of TEFCA and FHIR.

6. Healthcare AI Governance Extends Beyond Deployment

By September 2026, the Food and Drug Administration (FDA) had authorized more than 1,600 AI-enabled medical devices for US marketing. As the available product range expands, oversight needs to continue after selection: a tool can become less useful when patient populations, documentation, or hospital systems change.

Duke Health’s ABCDS Oversight program provides an operating example of algorithm-based clinical decision support (ABCDS) oversight. The program maintains a registry of algorithmic solutions, assigns review pathways according to risk, and evaluates tools at checkpoints extending through deployment, monitoring, and maintenance.

That approach makes governance part of running the service. Responsibility continues after procurement and initial validation.

A practical monitoring plan identifies the clinical owner, the technical owner, and the evidence needed to continue using the tool. Depending on its purpose, measures might include important errors, performance across patient groups, clinician overrides, and unexpected changes in output.

As organizations plan their 2027 AI portfolios, these requirements should influence purchasing and development decisions. A vendor’s aggregate accuracy figure cannot answer whether a model remains appropriate for a particular clinic. Teams also need a process for pausing a tool, reviewing a software update, and communicating changes to its users.

7. Healthcare Cybersecurity Protects Connected Care

Connected care extends across hospitals, vendors, cloud platforms, and patients’ homes. Security planning must account for the whole route through which information and clinical actions travel.

The National Institute of Standards and Technology’s RPM security project built a representative remote patient monitoring (RPM) ecosystem in a laboratory. Healthcare and technology partners helped assess risks across the provider, platform, and home-device environment. The project produced an example approach to securing those connections.

Its relevance grows as organizations connect more devices and services. FDA’s February 2026 guidance also updates recommendations for medical-device cybersecurity design and premarket documentation.

For a provider, useful questions include who maintains each component, how vendor access is controlled, and what happens when a connection fails. Device inventories, network segmentation, incident response, and recovery exercises support that work.

The NIST demonstration is a reference implementation, not proof that any particular hospital is secure. Device-manufacturer recommendations also serve a different purpose from an organization’s broader security and HIPAA responsibilities. Clear ownership across those boundaries supports continuity of care.

[IMAGE PLACEHOLDER 05: AI Oversight Continues After Deployment]

Caption: AI oversight combines validation, assigned responsibility, and monitoring throughout the system’s use.

ALT text: Clinical AI lifecycle from registration and validation to authorized deployment, monitoring, and reassessment, with a route to review or pause a tool.

8. Hospital-at-Home Programs Extend Acute Care

Hospital at home combines acute clinical services with the coordination needed to deliver them outside a hospital building. It includes much more than video visits or collecting vital signs.

Penn Medicine’s Hospital at Home launched in April 2026 and enrolled its 100th patient within five months, with home stays averaging 4.6 days. That early activity illustrates the need to coordinate several days of care in each patient’s home.

Mayo Clinic’s Advanced Care at Home illustrates the delivery model: a virtual command center coordinates with in-person services, which can include laboratory work, intravenous treatment, imaging, and medication management.

The model also has a longer planning horizon. CMS states that Congress extended its Acute Hospital Care at Home program waivers through September 30, 2030.

For organizations considering expansion in 2027, the software must coordinate visits, supplies, observations, and escalation. Someone must remain responsible for responding when a measurement changes or a patient feels worse.

Eligibility is equally important. Clinical condition, home support, distance from emergency care, and available services all affect suitability. Evaluating the complete program means considering safety, caregiver experience, staffing, and transfers back to hospital, alongside the technology’s reliability.

[IMAGE PLACEHOLDER 06: Hospital-Level Care at Home]

Caption: Hospital-at-home programs combine virtual clinical support, in-person services, and a plan for escalation.

ALT text: Nurse checking an older adult’s blood pressure at home while a tablet connects the patient with a remote clinical team.

9. Medical Wearables Support Continuous Monitoring

Continuous monitoring can reveal patterns that a measurement taken during an appointment may miss. Its usefulness depends on a clinically appropriate device and a clear plan for interpreting the readings.

One recent expansion is Dexcom’s Stelo glucose biosensor. In June 2026, FDA cleared over-the-counter use for people aged two and older who do not use insulin, extending the earlier adult indication. The system pairs a wearable sensor with an app to display glucose measurements and trends.

Expanded availability creates product and workflow questions. Who helps a patient interpret the information? What appears in the clinical record? Which changes require attention, and who responds?

These questions make wearable EHR integration an important implementation task. Sending every reading into a clinician’s inbox can add burden. Appropriate summaries, patient identification, and agreed follow-up rules make the information more usable.

Device scope remains essential: Stelo is not intended for insulin users or people with problematic hypoglycemia. Broader access should support suitable care pathways, with success measured through useful follow-up and condition-specific outcomes.

[IMAGE PLACEHOLDER 07: From Wearable Readings to Clinical Follow-Up]

Caption: A monitoring workflow needs reliable data, an assigned reviewer, and defined follow-up responsibilities.

ALT text: Wearable readings pass through patient matching and data checks to a trend summary and clinical review before follow-up or escalation.

10. Prescription Digital Therapies Expand Behavioral Health Care

Some software delivers a defined therapeutic intervention under clinical supervision. These products have specific intended uses and evidence requirements, which distinguish them from general wellness apps.

Otsuka’s Rejoyn prescription app illustrates the category. FDA cleared it as an adjunct to clinician-managed outpatient care for major depressive disorder in adults aged 22 and older who take antidepressant medication. It combines cognitive-emotional exercises, behavioral lessons, and supporting messages.

The practical question for providers is how prescribed software fits into ongoing treatment. Clinicians need to know whether patients use it, how progress is reviewed, and when additional support is required.

Payment pathways also influence adoption. CMS expanded selected payment policies in 2026 to include qualifying digital treatment devices for attention deficit hyperactivity disorder (ADHD) alongside ongoing behavioral healthcare. That change does not establish coverage for every product.

For 2027, digital therapies deserve evaluation as part of a clinical service: prescription, onboarding, engagement, follow-up, and outcome assessment. Purchasing access to an app addresses only one part of that delivery model.

11. Liquid Biopsy Supports Cancer Detection and Treatment Decisions

Liquid biopsies analyze cancer-related signals in blood. Their uses differ: screening looks for possible disease in eligible people, while residual-disease testing looks for molecular evidence after treatment.

Freenome’s SimpleScreen CRC test, approved in July 2026, supports colorectal cancer screening in average-risk adults aged 45 and older. Positive results require colonoscopy. The test does not replace diagnostic colonoscopy or surveillance for high-risk patients.

A different application reached a milestone in May 2026. FDA approved Natera’s Signatera CDx to identify circulating tumor DNA residual disease after bladder removal surgery in a defined bladder-cancer treatment pathway.

In the supporting 250-patient randomized trial, median disease-free survival was 9.9 months with atezolizumab versus 4.8 months with placebo. This measure tracks time without recurrence or death. The result applies to the selected, residual-disease-positive trial population and supports a specific treatment decision, rather than a general claim that a blood test alone improves survival.

These developments make laboratory integration and follow-up increasingly consequential. Software must preserve the test’s purpose, timing, result, and intended clinical action.

For 2027 planning, a new test should come with a complete pathway for eligible patients. Teams need to explain results, arrange confirmatory procedures where indicated, and track completion. The value lies in an appropriate clinical decision supported by the test.

[IMAGE PLACEHOLDER 08: Liquid Biopsy Answers Different Clinical Questions]

Caption: Screening and residual-disease testing have different purposes, eligible populations, and follow-up pathways.

ALT text: Comparison of liquid-biopsy pathways for colorectal screening and residual-disease testing after bladder-cancer surgery, with different follow-up steps.

12. Personalized Gene Editing Advances for Rare Diseases

Some rare genetic diseases arise from a specific DNA change that disrupts an essential biological function. Personalized gene editing aims to address that change with a treatment designed for the individual case.

A team at Children’s Hospital of Philadelphia and the University of Pennsylvania developed a patient-specific base-editing therapy for an infant with CPS1 deficiency. In this condition, the body cannot adequately process ammonia. The 2025 report described improved protein tolerance and reduced medication needs after treatment, with longer follow-up still necessary.

The development challenge now includes repeatability. FDA’s October 2026 draft addresses how individualized genome-editing product variants could be handled in a biologics application and managed after approval. It remains draft guidance.

Among emerging medical technology trends, this field has substantial implications for specialist centers, laboratories, and treatment developers. Reliable genetic interpretation, manufacturing records, and long-term outcome tracking are central to delivery.

For 2027, the meaningful milestones will include additional clinical evidence and reproducible development approaches. One successful individual case does not establish broad efficacy, affordability, or routine availability.

13. Surgical Robotics Moves Toward Greater Task Assistance

Surgical robots can give clinicians precise control of instruments. Research into greater autonomy asks whether a system can also perform a sequence of tasks and respond appropriately when the situation changes.

Johns Hopkins researchers’ SRT-H system explored that question through a hierarchical approach: one component plans tasks while another controls instrument movement. The team validated the system during a phase of gallbladder surgery using eight ex vivo gallbladders. These were experiments outside a living patient.

The distinction matters when assessing recent advances in healthcare technology. Successful preclinical task automation does not establish that a system is ready to operate autonomously on people. Remote operation also differs from autonomy because a clinician may still control each action from another location.

For 2027, procedure-specific evidence, training, and integration with surgical teams should guide expectations. Hospitals evaluating robotics need to consider instrument compatibility, technical support, failure recovery, and clinical outcomes.

Developers face a related task: proving that assistance remains reliable when anatomy, tissue appearance, and workflow differ from the conditions used during development.

14. AI Drug Discovery Moves Into Larger Clinical Trials

AI can help researchers identify biological targets and design compounds that might affect them. Clinical trials must then establish whether those candidates are safe and useful in patients.

Insilico Medicine’s rentosertib provides a concrete example. A 2025 phase 2a trial tested the AI-discovered and AI-designed candidate in 71 patients with idiopathic pulmonary fibrosis, a progressive lung disease. The study examined safety and early treatment signals over 12 weeks.

In September 2026, Insilico announced the first patient dosed in GENESIS-IPF-3. The sponsor’s phase 3 plan calls for 320 participants across 47 centers in China, with treatment evaluated over 52 weeks. That is a larger and longer test than the earlier 71-patient, 12-week study. These are planned trial parameters, not completed results.

For 2027, this shifts attention toward the quality of human evidence. A useful assessment asks what the trial measures, whether the benefit persists, and what safety findings emerge.

Biopharma software teams also need reproducible links between candidate selection, experimental data, and clinical evidence. Faster molecule generation alone cannot establish a successful medicine, and one candidate’s progress cannot prove industry-wide reductions in development cost.

[IMAGE PLACEHOLDER 09: Medical Innovations at Different Evidence Stages]

Caption: Authorization, an individual clinical case, preclinical experiments, and an ongoing drug trial represent different evidence stages.

ALT text: Four medical innovation examples labeled by evidence stage: FDA-cleared software, an early clinical case, preclinical experiments, and a phase 3 trial.

15. Federated Learning Connects Hospitals Without Pooling Raw Records

Many useful medical datasets are spread across institutions. Combining them can be difficult because of governance requirements, incompatible systems, and restrictions on moving patient information.

Federated learning offers another approach. Participating institutions contribute to a shared modeling process while keeping raw records locally. They exchange the information needed to develop the model rather than first building one central patient database.

The international atomCAT collaboration applied this approach to anal-cancer prognosis. Its study, published in 2026, separated model development from an independent check:

• Development: 1,428 patients across 14 centers.
• External validation: 277 patients from two additional centers.

The additional sites tested how well the models performed beyond the development settings. This was multi-institutional research, not proof of widespread clinical deployment.

The approach is particularly relevant when one organization has too few suitable cases to support a reliable analysis. It can make collaboration feasible, but institutions still need compatible definitions, quality controls, and agreed responsibilities.

As future trends in healthcare technology develop, collaboration methods deserve attention alongside model capability. Keeping raw records local does not automatically prevent information leakage. Security controls and validation across sites remain necessary to assess whether the resulting model is useful beyond its development settings.

[IMAGE PLACEHOLDER 10: Training Across Hospitals Without Pooling Raw Records]

Caption: Federated learning enables coordinated modeling across institutions while raw records remain at each site; security and validation still matter.

ALT text: Three hospitals retain local patient records and exchange model information with a shared modeling process instead of sending records to a central database.

Which Healthcare Technology Trends Should Your Organization Prioritize?

The future of healthcare technology will look different for an independent clinic, a hospital, and a therapy developer. A practical starting point is the problem that already consumes staff time, delays care, or prevents a service from working reliably.

Organization	Useful Starting Priorities	First Evaluation
Independent clinic	Documentation, scheduling, authorization	Measure manual work and errors in one workflow
Hospital or health system	Record exchange, AI oversight, cybersecurity, home care	Assess clinical ownership, integration, and service continuity
Specialty care program	Relevant diagnostics, monitoring, treatment pathways	Check eligibility, evidence, access, and follow-up capacity
Healthtech or biopharma team	Product-specific AI, data collaboration, clinical evidence	Validate the intended use and requirements for deployment

Begin with a baseline. For an authorization workflow, record staff effort and requests returned for missing information. For monitoring, examine actionable alerts and completed follow-up. For clinical AI, evaluate important errors and performance in the population you serve.

Then test the complete workflow with the people who will use it. Include integration work, training, review time, support, and recurring costs in the evaluation. Define what would justify expansion and what would require a change.

The latest trends in healthcare technology provide options. A useful investment connects one of those options to a documented need, a responsible team, and an outcome the organization can assess.

How TATEEDA GLOBAL Can Help Implement Healthcare Technology

TATEEDA GLOBAL develops software for healthcare organizations and healthtech companies, including connected-device applications, patient portals, EHR-related integrations, and AI-enabled workflows.

Our VentriLink monitoring application illustrates that work: the team built tablet software that presents ECG data to medical professionals through an existing backend and biosensor system.

Through healthcare software development, we can help assess integration requirements, build an appropriate application, and support testing and modernization. Share the workflow you want to improve, the systems involved, and the outcome you need to measure so we can discuss a practical development scope.

FAQs About the Future of Healthcare Technology

What Are the Main Healthcare Technology Trends for 2027?

Important digital health trends include ambient documentation, administrative agents, connected records, electronic authorization, AI oversight, and care supported by home monitoring. Medical innovations also include indication-specific blood tests, personalized gene editing, surgical task automation, and AI-assisted drug development. Their maturity and relevance vary by application.

Which Healthcare Innovations Are Already in Clinical Use?

AI scribes, selected medical sensors, prescription digital therapies, and defined liquid-biopsy applications are in use or authorized for specific purposes. Hospital-at-home programs also operate today. Broad autonomous clinical reasoning and individualized gene editing require a different assessment because much of their evidence remains at a research or early clinical stage.

What Makes 2027 an Important Year for Healthcare IT?

CMS schedules API requirements for specified payer categories generally from January 2027. These support electronic prior authorization and data exchange. Providers should distinguish those payer deadlines from their own reporting obligations, then check which connections their EHR and payer partners can support.

How Can Healthcare Organizations Use AI Safely?

Choose a defined task, validate performance in the intended setting, and assign responsibility for reviewing outputs and responding to errors. Monitor the tool after deployment, including changes in data and software. The appropriate level of human oversight depends on the consequences of an incorrect action.

Which Technologies Should Smaller Healthcare Providers Prioritize?

Start with a measurable operational need, such as documentation workload, scheduling errors, or authorization delays. Compare existing system capabilities with the proposed addition before commissioning development. Integration, staff training, data protection, ongoing costs, and review requirements should all inform the decision.