AI in Hospitals


How AI can help hospitals strengthen their financial performance and reduce clinician burnout

By embracing AI-driven solutions, health care systems can take a proactive stance in tackling the challenges at hand and create significant value for their patients, employees, and operations—explore how AI solutions reduce provider burnout, enhance patient care, and drive financial performance in a high-pressure health care landscape.

The hospital financial landscape

There is a myriad of financial pressures confronting hospitals today.

  • Turnover: By one estimate, 56% of hospitals’ total operating revenue goes toward labor—not even counting temporary or contract personnel.1 This is occurring even as hospitals see an uptick in patient volumes, putting remaining staff at risk due to burnout and safety concerns.
  • Supply costs: Hospitals face a record-high inflation and ongoing shortfalls of drugs, devices, and other essential medical supplies. This has contributed to a significant decline in margins.
  • Administrative expenses: In the United States, these account for more than one-third of total health care costs.2 Much of the administrative burden falls on clinicians, which detracts from the patient care they’re trained to deliver. The consequences are costly because they tend to lengthen hospital stays and increase readmissions.

Further, these challenges have been compounded with by reduced reimbursements due to payer denials and utilization management. At many hospitals, the rise of ambulatory surgery centers has had a significant impact on inpatient revenue and elective surgeries, leading to lost revenue. Competition with other health systems, telehealth companies, and emerging health players further add to the profit squeeze.

So what does artificial intelligence offer hospitals in these high-pressure situations?

How AI can help hospitals strengthen their financial performance and reduce clinician burnout

AI health care solutions: Empowering providers to help patients

AI—the branch of computer science dedicated to building machines that mimic human intelligence—includes the following technologies that can be used independently or combined to create a solution.

AI solutions automate processes and alleviate administrative burden—enabling health care clinicians to operate at the top of their licenses for to provide optimal patient care. Here are seven artificial intelligence use cases for hospitals—with improvement metrics to aim for based on our experience.

  • Maximize through put by accurately predicting patient demand and length of stay, increasing transparency into available beds, finding potential bottlenecks, automating discharge prioritization, and initiating actions to address flow barriers to enhance patient flow and reduce wait times. Aim for 4% to 10% improvement in avoidable days.
  • Optimize operating room blocks by leveraging predictive analytics to reduce operational waste, increase administrative efficiency, and enable them to achieve peak performance. Aim for 10% to 20% increase in utilization.
  • Accelerate prior authorization to improve operational efficiency, reduce denials, increase revenues, and enhance patient care based on a large language model’s understanding of medical policiesy. Aim for 4% to 6% reduction in denials due to missing or incomplete information and 60% to 80% improvement in operational efficiency.
  • Revolutionize supply management by optimizing preference cards and removing unused surgical instruments based on analytical insights. This can reduce wear and tear, drive down instrument costs, minimize surgical delays, and enhance patient satisfaction. Aim for: 2% to 8% reduction in preference card cost.
  • Automate appeal letter generation with generative AI based on an understanding of medical policies from payers and plans—detailing underlying denial issues and showing the resolutions needed to overcome the denial. Aim for appeal responses that are up to 30 times faster than before.
  • Predict staffing needs in the immediate, short, and near terms based on claims, electronic health records data, and environmental data for conditions such as asthma, which may drive up volumes in the ER.
  • Identify health equity gaps and trends by leveraging AI to combine and mine large data sets like patient data, claims data, social determinants of health, etc.

Many other use cases exist for AI, Generative AI, and automation to improve margins, boost efficiency, reduce clinician burnout, and enhance patient care. Here is a non-exhaustive list

Health care systems are reaping the benefits of AI solutions



A leading health care provider improved throughput (and therefore margin) by using machine learning models to drive down avoidable days and optimize stay length.

Result: a 10% improvement in avoidable days identified within first quarter.

4 out of 5

Another leading provider developed an AI and automation strategy to transform its talent acquisition function.

Result: a 70% increase in hiring speed and a 2,000-employee improvement in talent acquisition throughput within six months.


A prominent revenue cycle outsourcer used AI to automate more than 12 million transactions, streamline financial clearance and registration, automate authorization processes, and reduce calls and no-shows through text reminders.

Result: $35 million in annual savings.

365 to 5

A large health care provider empowered its accounts payable function with various AI solutions to process more than $2.1 billion in invoices.

Result: a 70% reduction in manual processing costs, avoidance of $385 million in duplicate payments, and $25 million in savings over 18 months.



1 Ron Southwick, “Hospitals facing ‘very difficult year,’ with some signs of hope,” Chief Healthcare Executive, April 6, 2023.
2 Ibid.

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Get in touch

Managing Director

AI & Insights

Deloitte Consulting LLP

Anubhav Rastogi

Specialist Leader

AI and Insights

Deloitte Consulting LLP

Jay Bhatt D.O. M.P.H.,

M.P.A., F.A.C.P.

Managing Director

Center for Health Solutions &

Health Equity Institute

Deloitte Services LP

Amritpal S. Bhohi, MD, MBA

Senior Manager

Healthcare Operations


Deloitte Consulting LLP