Evaluating the Effectiveness of AI Solutions in Optimizing Perioperative and Inpatient Care Coordination

To understand how AI helps, it is important to look at the problems hospitals face in perioperative and inpatient care. Studies show that in the U.S., doctors and nurses spend about half of their time doing paperwork instead of caring for patients. This causes staff to get tired, raises costs, and makes hospitals less efficient. The extra work from paperwork alone may cost the U.S. healthcare system around 13 billion dollars each year.

In perioperative care, scheduling surgeries can be disorganized. This leads to unused operating rooms, late patient discharges, and fewer surgeries done than possible. Inpatient care often faces delays in planning discharges and moving patients through the hospital. These delays can make stays longer and reduce the number of available beds. Hospital managers need ways to cut down on paperwork and make clinical work smoother.

AI Solutions in Healthcare: An Emerging Approach

AI-based systems now offer ways to solve these problems. For example, Qventus makes AI tools that connect with Electronic Health Records (EHR) to help with perioperative and inpatient workflows. They use machine learning and ideas from behavioral science.

The Qventus system looks for issues in hospital operations as they happen. It predicts what patients and staff will need and helps take action. This lets healthcare workers spend more time with patients, while AI manages routine work.

Impact on Perioperative Care Coordination

In perioperative care, AI tools like Qventus’ Perioperative Solution have shown real improvements. They help with scheduling surgeries and using operating rooms more efficiently. This is important because surgeries bring in a lot of revenue for hospitals.

Data shows that Qventus’ software has helped add over three more surgeries per operating room every month. This is a big help since hospitals face tight budgets and surgeon availability issues. The system also uses early released OR times 80% of the time, so surgical teams can better fill open slots with new cases.

Hospitals like Banner Health have noticed these benefits. They saw more surgeries and higher income without needing extra staff. This helps patients get to surgery faster and improves how the hospital runs.

Improvements in Inpatient Care Coordination

For inpatient care, Qventus’ AI tools focus on planning discharges and managing patient flow. Hospitals like HonorHealth and OhioHealth have reported strong results after using these tools.

At HonorHealth, about 86% of patients got early discharge plans. This helped save over 50,000 extra hospital bed days and 62 million dollars over three years. Early planning stops delays that make patients stay too long, freeing beds for new patients and improving hospital capacity.

At OhioHealth, the AI solution cut almost 1,400 extra bed days in just the first month, saving nearly half a million dollars. These improvements make patient care better because shorter stays lower the risk of hospital infections and increase patient satisfaction.

AI and Workflow Automation in Healthcare Operations

AI does more than speed up tasks. It changes how healthcare teams manage and coordinate care.

Hospital leaders know that care workflows include many connected parts, such as scheduling surgeries, working with care teams, and planning discharges. AI systems using machine learning can study large amounts of data from EHRs and other hospital tools. They can find delays in lab results, staff shortages, or readiness of patients for discharge.

By automating repetitive paperwork, AI frees staff to do more clinical work, like making decisions and talking to patients. For example, predicting delays in OR prep or patient readiness can help fix problems quickly and keep schedules on track.

• Less manual work and fewer mistakes in scheduling and reports.
• Better coordination between surgeons, anesthesiologists, and nurses.
• Faster and more accurate discharge planning.
• Improved use of hospital resources like beds and OR time slots.
• Reduced staff burnout and higher job satisfaction.

Also, because these AI systems work with existing EHR platforms, hospital IT staff can introduce them without upsetting current workflows.

Benefits Realized by Partnered Health Systems

Many health systems have shared their experiences with AI like Qventus. These include Ardent Health Services, HonorHealth, Banner Health, Sharp HealthCare, University of Miami Health System, Allina Health, Boston Medical Center, Saint Luke’s Health System, and ThedaCare. This shows that AI is helpful across many types of hospitals.

Leaders from these systems say:

• Kim Post, Chief Operations Officer at HonorHealth, said AI assistants greatly reduce paperwork for frontline staff so clinicians can focus more on patients.
• Mudit Garg, CEO of Qventus, said he feels good about helping hospitals improve efficiency, revenue, and care.
• David Atashroo, Chief Medical Officer at Qventus, highlighted how AI supports real-time decisions in surgery scheduling.

These views match results from KLAS Research. Qventus got a 96.9 score for performance in their 2023 Capacity Optimization report. The software scored high in culture, loyalty, operations, relationships, and satisfaction.

Financial and Operational Outcomes

The financial effects for U.S. hospitals can be large. Key numbers include:

• Productivity in care roles rose by 50% because AI handles administrative tasks.
• 62 million dollars saved from fewer extra hospital bed days.
• Almost 1,400 fewer extra bed days avoided in one major hospital group within a month.
• 3 to 6 more surgical cases per operating room each month.
• Average return on investment (ROI) is 6 times, with some reaching 27 times.

Hospital managers and IT leaders see these as strong reasons to invest in AI systems that fit well with current hospital technology.

Considerations for Medical Practice Administrators and IT Managers

When looking at AI for perioperative and inpatient care, administrators and IT directors should think about these points:

• Integration Capability: The AI should work smoothly with current EHR systems without forcing staff to learn all new software.
• Data Security and Compliance: It must follow HIPAA and other rules to keep patient information safe.
• Customization and Flexibility: Because hospitals have different needs, AI should be adjustable to each one.
• User Acceptance: The system has to be easy to use and reduce paperwork, so staff will accept it.
• Demonstrable ROI: Decisions often depend on clear proof of cost savings, better efficiency, or improved patient care.
• Vendor Support and Reputation: Working with trusted AI providers who have experience in healthcare is important for success.

Summary

In the United States, AI-based operational assistants have shown clear success in improving perioperative and inpatient care coordination. By automating paperwork, these tools let healthcare teams spend more time on clinical work, increase surgery throughput, support timely discharges, and cut costs. Hospitals using these systems have seen meaningful clinical and financial results.

For medical practice administrators, hospital owners, and IT managers who want to improve healthcare delivery and efficiency, AI systems that fit well into existing workflows offer a useful resource. These technologies can help reduce long-standing inefficiencies and support better patient care in perioperative and inpatient settings across the country.