AI and Telehealth: How Hospitals Can Cut Costs Without Cutting Care

Hospitals can run robotic surgery and gene therapy, yet still fax referral letters and hand key in billing codes. That friction costs money. A lot of it. The deeper truth is simple enough to say, harder to fix. Health care is expensive because it is inefficient.

Artificial intelligence, telehealth, and a more empathetic model of care are starting to change that. Not with flashy hype, but with quiet wins. A chart that writes itself. A discharge that happens two days sooner. A triage call that routes the right person to the right place at the right time. When you add these up across a health system, costs fall and care feels better.

Below, a practical tour of how AI and telehealth lower costs, where empathy fits, and what the evidence says.

Quick takeaways

• Broad adoption of AI reduces cost burden by automating manual tasks, cutting waste, and improving speed, accuracy, and personalisation across the care continuum. Frost & Sullivan projected more than 150 billion USD in annual savings potential by 2025, with similar figures cited for 2026 in other analyses. (Frost & Sullivan, Forbes)

• Core functions include NLP, machine learning, computer vision, and predictive analytics, applied to diagnostics, scheduling, billing, remote monitoring, and decision support.

• Predictive models forecast readmission risks, complications, staffing needs, and supply usage. That shifts spend from reactive to preventive care. Avoided admissions and shorter stays compound the savings. (PMC, ScienceDirect)

• Administrative automation accelerates prior authorisations, claims, and appointment workflows. Ambient AI documentation tools reduce note time and after hours charting. (PMC, ScienceDirect)

• AI assisted diagnostics detect disease earlier and more accurately, which avoids costly late stage interventions. Real world mammography and diabetic retinopathy data back this up. (Nature, Diabetes Journals)

• Operational AI helps cut length of stay, reduce overtime, and maximise existing capacity. Health systems report measurable LOS reductions when AI augments EHR workflows. (wellsheet.com)

• Fraud and error detection models flag anomalies at scale, recovering spend faster than manual audits. (PMC)

• In R&D, AI shortens discovery and trial matching timelines, which lowers development costs and gets effective therapies to patients sooner.

  
  

Why the money pressure is not letting up

Health spending in the United States sat at 17.3 percent of GDP in 2022, with national health expenditures rising again in 2023 to 17.6 percent. Per person spend was 14,570 USD in 2023. Budgets are tight and demand is rising. You do not fix that with more forms and more clicks. (CMS)

On top of that, there are not enough people. The AAMC projects a shortfall of up to 124,000 physicians by 2034 in the US. Globally, WHO estimates a shortfall of about 10 to 11 million health workers by 2030. AI and telehealth do not replace clinicians. They help the workforce we have go further. (AAMC, World Health Organization, WHO Apps)

The quiet wins that save real money

1) Administration that handles itself

No one trains for medicine to resubmit the same claim twice. AI can. NLP turns free text into structured data. RPA tools move prior authorisations and eligibility checks along without phone tag.

2) Fewer missed appointments and faster triage

Telehealth and AI agents reduce no shows with smart reminders and quick rescheduling.

3) Diagnostics that catch trouble earlier

Earlier detection is cheaper care. AI supported reading is now live in breast screening programmes and is associated with higher cancer detection without higher recall. Autonomous tools for diabetic retinopathy have documented sensitivity of about 87 percent and specificity around 91 percent in prospective trials. That is the kind of balance that reduces missed disease and unnecessary follow ups. (Nature, Diabetes Journals)

4) Length of stay that actually comes down

A single extra inpatient day is expensive. AI layered into EHR workflows has been associated with shorter stays.

AI triage for imaging can also shave hours off critical pathways. For incidental pulmonary embolism, AI worklist prioritisation reduced time to diagnosis in real clinical settings. Faster answers move discharges forward and avoid downstream complications. (PMC)

5) Fewer readmissions and tighter follow up

Medicare readmissions have long been a multi billion problem, with estimates commonly cited in the 26 to 35 billion USD range. If models can flag risk before discharge, teams can plan follow up, reconcile medicines, and schedule early reviews. Each avoided bounce back protects patients and budgets. (PMC, CMS)

6) Fraud and error that does not slip through

Machine learning spots billing anomalies and duplicate claims at scale. This is not just crime detection. It is catching everyday errors quickly, which protects revenue and reduces rework.

7) Research that wastes less

AI narrows the funnel in discovery and trial matching. That means fewer dead ends and faster enrolment. Shortened timelines lower development costs and bring effective therapies forward, which lowers the cost of untreated disease on the system.

Telehealth plus empathy is a cost lever too

Telehealth lowers travel and time costs for patients and opens capacity for clinicians. The trick is to pair it with an empathetic, continuous care model. Consistent follow ups, plain language plans, and fast answers between visits improve adherence and satisfaction, which in turn lowers spend from avoidable complications.

Trust matters. Surveys and reports from global bodies like the World Economic Forum show patients are more comfortable when AI is visible, governed, and paired with a clinician who explains the why. The next wave is not AI in place of people. It is AI beside people, making the relationship feel clearer and more consistent.

What leaders should do next

1. Pick the bottlenecks that bleed money

   Start with documentation burden, radiology triage, discharge planning, or readmission risk. These have immediate payoff and stable evidence.

2. Measure length of stay, after hours charting, and no show rates

   Publish the deltas internally so staff can see the wins.

3. Adopt tools with real world evidence and clear guardrails

   Favour solutions with peer reviewed studies, transparent performance metrics, and clinical oversight.

4. Invest in change support

   AI saves money when people use it. Train teams, tune workflows, and put patient experience first.

5. Mind the workforce gap

   Use AI and telehealth to extend clinicians rather than stretch them thinner. Global shortages will not magically fix themselves.

   
   

Sources

• AI and cognitive computing could save more than 150 billion USD per year by mid decade. (Frost & Sullivan, Forbes)

• Health spending share of US GDP was 17.3 percent in 2022 and 17.6 percent in 2023. Per person spend reached 14,570 USD in 2023. (CMS)

• Physician shortage projections to 2034 in the US range up to 124,000. Global health worker shortfall is about 10 to 11 million by 2030. (AAMC, World Health Organization, WHO Apps)

• AI supported double reading in breast screening increased detection without raising recall. Autonomous DR tools show sensitivity around 87 percent and specificity around 91 percent. (Nature, Diabetes Journals)

• AI worklist triage reduced time to diagnosis for incidental pulmonary embolism. (PMC)

• AI enhanced EHR workflows associated with shorter length of stay in live deployments. (wellsheet.com)

• Readmissions cost Medicare tens of billions annually, with a meaningful share avoidable. (PMC, CMS)