New York City-based Urban Health Plan used artificial intelligence to improve operational efficiency and patient care, and gained control over high patient no-show rates each year with cost-effective patient interventions.
Above average missed appointments
No-shows are due to multiple factors, including social determinants of health such as transportation.
But they also represent a long-standing challenge for healthcare organizations that can impact the quality of patient care, increase healthcare organization costs, and present unnecessary reductions in overall patient access to healthcare appointments.
In March, Urban Health Plan had 42,000 health care visits, the most in its history, according to a presentation at the eClinicalWorks Health Summit in Boston last week.
“We had a multifaceted approach to addressing just patient access and engagement in general,” said Alison Connelly-Flores, chief medical information officer at Urban Health Plan.Health informatics news.
This result is significant because UHP experiences a high number of missed appointments each year, and appointments for waiting patients are sometimes “too far booked.” Vendors are typically overbooked to deal with no-shows, Connelly-Flores said.
The federally qualified community health center organization, one of the largest in New York State, provides primary care, 18 specialty, diagnostic and other services to approximately 86,000 patients. The no-show rate was very high across UHP’s 12 locations in the South Bronx, Corona Queens and Central Harlem neighborhoods, its 12 school health centers, and behavioral health services.
While UHP scheduled 794,322 visits in 2022, only 57.6% were completed compared to the national average in eClinicalWorks EHR data of 71%.
With 336,600 no-shows and overbookings to compensate, the results can range from long wait times, patient dissatisfaction and supplier stress, all of which can exacerbate burnout.
The organization needed to change things to keep health centers open.
Management wanted to know if their no-show rate was above or below the national average and which patients kept missing appointments.
Through the pilot, UHP learned that its no-show rate was 16.52% higher than its EHR peers.
He also learned that despite the friction between the low-, moderate-, and high-probability groups, UHP exhibit rates were consistent for each group from 2019 to 2022 even during the pandemic.
“It was just interesting that those groups behave the same way. I didn’t expect that it was definitely a pattern.” Connelly-Flores said after the session.
Network layer data machine learning analysis
The no-show algorithm is a screening analysis that allows systematic and consistent sorting of data that allows analysts and data users to view data at the network level.
“I can essentially have a conversation with this data,” said Sameer Bhat, eClinicalWorks co-founder and vice president of sales, before discussing the pilot test of UHP’s algorithm, which is part of a larger eClinicalWorks study with numerous clients.
Bhat demonstrated how to look at demographics like ethnicity and poverty level on a helow dashboard and noted that the platform is EHR independent.
The platform can also aggregate discrete EHR data to identify gaps in care, according to the company’s website.
When he introduced Connelly-Flores and the pilot team, he said, “we’re blown away by some of the results.”
They say the algorithm can find needles in haystacks and can help identify patients with a high probability of no-show with 85%-90% accuracy.
Missed appointment surgeries that worked
In addition to working with the no-show prediction model, the UHP team adapted their outreach process using eClinicalMessenger.
UHP already handles more than one million voice messages a year, secure text messages and email reminders, according to eClinicalWorks’ announcement of the pilot project.
After the model identified patients with the highest and moderate risks of missing appointments, UHP tested new targeted interventions to ensure patients kept to scheduled appointments.
Would a phone call help? With 3,000 appointments a day, UHP can’t call all of its scheduled patients, Connelly-Flores said.
eClinicalWorks retained 38,431 with a high probability of no-show in a control group. The company shared 18,061 with a high probability of no-show, as well as 908 with a medium no-showprobability with UHP to test targeted interventions.
UHP distributed a high-risk no-show report to designated associates and provided a script to make calls and messages as consistent as possible. The designated associates documented the results of the call.
Patients who missed appointments that day were given the opportunity to transition to a virtual same-day visit by initiating telehealth visits or rescheduling appointments. UHP doctors will call patients who have missed appointments themselves.
Connelly-Flores said that if a patient is reached by a doctor, nearly 100% will accept the same-day virtual reschedule option. They use the video app to call those patients and transition to a telehealth visit once a patient accepts.
“If you get a last-minute cancellation or reschedule, you may still be able to recover your appointment,” says an eClinicalWorks blog dated March 2023.
“If the last minute change is due to a transport or travel issue, perhaps a televised visit will suffice. This can save the appointment and even encourage more frequent visits for your convenience.”
For telehealth visits scheduled with patients who have a high or moderate probability of missing their appointment and have not been seen for 15 months or more, UHP has sent additional text messages.
UHP has also increased access to virtual support by extending hours to 89 per week.
To better support providers, the healthcare organization revised its workload analysis templates to account for each provider’s no-show rate and added same-day slots to allow for the transition to virtual care.
While these adjustments may require monitoring, overall, UHP found that the strategies could further reduce the no-show rate.
The algorithm was implemented in January 2023 and the intervention resulted in 4,432 additional visits during the three pilot months.
The low likelihood of no-show rate for 2023 so far is more than 5% higher than in the previous four years.
The result between the two pilot patient groups also showed a 24.14% increase in the likelihood of scheduling appointments for those patients at high risk of no-show and an 8.08% improvement for those at moderate risk.
While the presentation rate for patients most likely to miss appointments increased by 154%, UHP interventions also increased the presentation rate by 19.17% for moderate risk no-show patients.
UHP has added a specific full-time role and adapted an existing role to provide part-time support to call only those who the algorithm has identified as being at the highest risk of no-show.
During the pilot, there were about 400 targeted phone calls a day, Connelly-Flores said.
“It wasn’t a huge switch.”
He said next steps for UHP include integrating the algorithm into its EHR, engaging case management, addressing barriers to care, sending more personalized appointment reminders to patients at high risk of missed appointments, and making a splash. deeper into the data to find out more.
“By harnessing the power of data and machine learning, we can help providers like Urban Health Plan deliver more effective care to their patients and reduce the burden of missed appointments,” said Girish Navani, CEO and co-founder of eClinicalWorks, in the statement. of the company.
“This ultimately helps reduce health care costs and helps achieve better patient outcomes.”
“When patients receive timely care, they see better health outcomes.” Connelly-Flores said.
Andrea Fox is managing editor of Healthcare IT News.
Healthcare IT News is a HIMSS Media publication.
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