Nurses feel “empowered” with an integrated patient discharge analytics technology that leverages artificial intelligence to analyze administrative tasks, such as discharge coordination, test ordering and prescriptions, says Jean Halpin, chief operating officer at Grant Medical Center.
It’s been shown recently that many nurses are distrustful of AI. But at the Central Ohio-based health system, nurses are embracing discharge automation – thanks in large part to the discharge planning efficiencies, reduced manual workloads and a better experience on rounds the technology enables, according to Halpin.
By shrinking the discharge process and opening bed capacity, the health system is not only increasing care access, but also achieving cost savings with analytics that detect gaps in care plans, prompt orders and manage milestones, says Mudit Garg, CEO of Qventus, which develops the software.
Garg says OhioHealth is forecast to care for an additional 3,500 patients in the first year of using the early discharge planning and prioritization capabilities in the electronic health record workflows.
“The result is a substantial impact on both cost savings and operational improvements for OhioHealth,” he explained – equating to approximately $500,000.
In a joint interview with Healthcare IT News, Halpin and Garg described the key factors that are leading to improvements in patient experiences and operational efficiencies through AI-driven inpatient coordination technology.
Q. Has OhioHealth’s staff efficiency and overall quality of care delivery helped to address burnout and any workforce shortages?
Halpin: Absolutely. The burden of administrative work reduces the amount of face time our healthcare teams have with their patients, which is crucial for establishing positive relationships between nurses, doctors and patients.
AI tools have empowered our staff to work at the top of their license versus spending so much of their days completing paperwork and digging for answers in research. Not only are our staff able to see more patients, but they are able to better care for them too, by dedicating additional time they didn’t necessarily have before.
I think we’re seeing a lot of our staff reinvigorated by this technology.
Q. Have the nurses embraced early discharge planning tools?
Halpin: Yes, our nurses are fully embracing the AI tools we are bringing in to better support them in their day-to-day administrative work.
Many of them have felt the burnout and burden of coordination when it comes to discharging patients, and with the seamless integration into our EHRs, our nursing staff feels empowered to prioritize clinical interactions and care with our patients, while Qventus can handle the more time-consuming admin asks.
Q. What factors were addressed in care coordination or other operational or clinical aspects that reduced stays?
Halpin: The key factor was determining our gaps in patient flow.
By analyzing our data, Qventus was able to spot areas of improvement for our operations day-to-day. Tasks like discharge coordination to rehab facilities, the ordering of tests, prescription of medication and more consume much of our healthcare teams’ time, and Qventus alleviated much of that administrative burden.
Should there be a delay in coordinating a discharge, for example, that can extend a patient’s stay unnecessarily, which is an industry-wide problem. By tackling all of these gaps in patient flow, we were able to expedite the speed of care, getting patients admitted earlier to be seen and out the door once they were ready to go home; accounting for that reduction in excess stays for patients by nearly 1,400 days.
Garg: By embedding EDP Intelligence insights and flow prioritization capability into OhioHealth’s existing EHR workflows, we predicted achievable discharge dates and patient dispositions, which enabled their care teams to review and adjust based on their clinical expertise – and reduced manual tasks overall.
Integration into the EHR streamlines workflows, reducing healthcare team’s cognitive load and enhancing care efficiency.
There are hundreds of different tests and procedures that have to happen in a timely way to execute a timely discharge for a patient. For example, assessing if a patient is ready to go home or to a skilled nursing facility may require payor insurance and coordination between family and the facility.
The technology anticipates the date the patient can be discharged, where they can go after the hospital on the first day and then continuously adapts to the patient’s clinical condition as it evolves. The clinical team reviews the recommendations [in making care decisions].
Q. How did increasing inpatient bed capacity improve access to care?
Halpin: We have all witnessed the extensive wait times that burden emergency rooms, but when you peel back the curtain, a lot of that wait time boils down to the lengthy discharge process.
While you wait in the emergency room for an open bed, those in the ER waiting to be admitted for longer-term care are being delayed due to a patient upstairs not being discharged in a timely fashion.
By optimizing our patient flows through the use of AI, we speed up the coordination process and better predict discharge days for our patients – reducing the delay of care for our patients waiting to be seen or admitted into the ER.
Garg: Our optimization of discharge planning and reduction in the length of stay has allowed beds to be made available quickly for incoming patients.
This improved turnover rate means that OhioHealth can admit and treat more patients in need without needing additional physical beds. In addition to treating thousands of more patients, the tool will simultaneously save patients 400,000 excess hours in the hospital.
Increased bed capacity has also alleviated overcrowding in emergency departments, reduced strain on healthcare staff and improved resource allocation.
Q. How is care coordination improvement measured? Why is it significant?
Halpin: Care coordination improvement for us means more time spent with patients and less time behind the screen digging for answers, which is significant when it comes to the experiences our patients have at OhioHealth.
The longer a patient waits in an ER to be seen, the worse their experience will be.
By expediting the rate of care by safely reducing roadblocks in our patient flows, we are seeing more patients, which is one way we are measuring improvement. Some of our teams most impacted at Grant Medical Center, for example, are our physical therapy, imaging and lab teams, who can reference the recommendations in patient charts to determine which patients might be a priority for testing and which ones are ready to go home and come back as an outpatient.
Garg: We measure success through key performance indicators such as reduced length of stay/excess days, decreased readmission rates, improved patient flow and timely discharge planning. Enhanced patient satisfaction scores and reduced manual workload for healthcare staff are also essential metrics.
The AI provides real-time insights and predictive analytics for OhioHealth, allowing for continuous optimization as Qventus learns by becoming increasingly integrated into the OhioHealth health system.
The impact of improved care coordination is profound – it enhances patient outcomes by ensuring timely, appropriate care and minimizes delays, resulting in a seamless experience from admission to discharge.
Q. What feedback could OhioHealth share about saving 60% of rounding time for staff?
Halpin: Much of the rounding time for our staff pertains to the discussion of the estimated date of discharge for our patients and next step for care, which fluctuates each day, depending on progress. This discussion includes looking over patient data and referencing research to come to a collective decision amongst nursing staff, physicians and ancillary teams, such as physical therapy, lab, imaging and more.
In an effort to expedite this process, the technology utilizes the data collected from our EHR for each patient to recommend the next steps and coordinate discharge by comparing similar cases and other research.
In doing so, our healthcare teams can quickly reference the recommendations during rounds for each patient – and determine whether or not they disagree, alleviating a lot of the manual labor of combing through charts and labs, which saves our teams valuable time and enables them to get back to patient care.
Andrea Fox is senior editor of Healthcare IT News.
Email: [email protected]
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Source : Healthcare IT News