Telemedicine Program Halves Time to Treatment

When struggling to conceive, every second that ticks by feels precious. That makes it easy to get discouraged: 65 percent of those who seek fertility care eventually discontinue treatment, the majority due to stress. That's why Penn Medicine recently instituted a telemedicine-driven program aimed at seeing patients more quickly and starting treatments sooner. The program, Fast Track to Fertility, cut the time between when patients initially reached out for help to when they received their first treatment by half - getting them on the path to parenthood roughly a month and a half sooner, according to research published in NEJM Catalyst by researchers at the Perelman School of Medicine at the University of Pennsylvania.

On top of cutting the average time it took for new patients to get their first treatment (from 97 to 41 days), the program also allowed for more new patients to access fertility care, increasing the number by 24 percent in the year it was implemented as a standard of care at Penn Medicine. At a time when one in eight couples in the United States are experiencing infertility, Fast Track to Fertility allowed more than 1,000 new patients to begin treatments to help them become pregnant.

"Most of the people who seek fertility care have been trying to get pregnant for at least a year, so the emotional stakes are high and they really want to get started as soon as possible," said the study's senior author and Fast Track to Fertility co-founder, Anuja Dokras, MD, PhD, a professor of Obstetrics and Gynecology and chair of Gynecology for the Women’s Health Service Line. "Our findings show that this program can significantly speed up the time to treatment and, in so doing, opens the door for so many more people. These findings show this way of doings things can make real differences in people's lives."

Fertility care has steadily increased in demand since it was introduced, reaching a point where fertility clinics often have long waits for new patients. Fast Track to Fertility, started through the Innovation Accelerator in Penn Medicine's Center for Health Care Innovation, seeks to speed things up by deploying a relatively small team of advanced practice providers for efficient telemedicine-based initial visits with new patients as quickly as possible. That visit also gives patients the opportunity to enroll in an artificial intelligence-guided text messaging program that helps guide them through the complex fertility "workup" swiftly and with as few hiccups as possible.

"This system has made it so that as soon as a patient contacts us, their journey begins," said the study’s lead author and Fast Track to Fertility co-founder, Suneeta Senapati, MD, an assistant professor of Obstetrics and Gynecology. "Both partners in a couple need to complete a workup, which can include blood work, ultrasounds, X-rays, semen analysis, and more. Some parts of this are dependent on the menstrual cycle, making it a time-sensitive process, so making it as easy as possible to quickly work through - with minimal confusion - is invaluable."

Initial pilots (which used human texters instead of artificial intelligence to test the system) reduced the wait time to new patient visits from initial contact with the practice by 88 percent, making the average wait time just four days. And no patients during those first pilots had to call the office to figure out next steps, compared to a quarter of patients who weren't in the program.

In 2021, when the latest analysis was performed, Fast Track to Fertility was expanded to become the standard of care across Penn Medicine's department of Obstetrics and Gynecology. In addition to halving the time to treatment and increasing new patients, they also saw appointment "no-shows" - which includes those who unexpectedly don't go to their appointment or who need to cancel late - drop from 40 to 20 percent, a particularly important measure.

"Any time there is a no-show, we can’t backfill that appointment because of the precise timing that goes into this type of care," Dokras said. "So any time we can reduce no-shows, that means more people can get the care they’re looking for to start their family."

Satisfaction, both from the patients and the advanced practice providers running the system, was also found to be high. And the researchers hope that the system can be expanded to support patients throughout their fertility journey.

"These care models do not replace our clinical work force, as human interaction remains imperative to the doctor-patient relationship and care delivery. Rather, they improve efficiency - while maintaining personalized care - for both patients and their care teams to accommodate the growing demands for fertility services," Senapati said. "In the end, this enables my colleagues and I to do more of what we got into this field for: Helping people get pregnant and bring home their babies."

Suneeta Senapati, David A Asch, Raina M Merchant, Roy Rosin, Emily Seltzer, Christina Mancheno, Anuja Dokras.
The Fast Track to Fertility Program: Rapid Cycle Innovation to Redesign Fertility Care.
NEJM Catalyst Innovations in Care Delivery 2022. doi: 10.1056/CAT.22.0065

Most Popular Now

AI Catches One-Third of Interval Breast …

An AI algorithm for breast cancer screening has potential to enhance the performance of digital breast tomosynthesis (DBT), reducing interval cancers by up to one-third, according to a study published...

Great plan: Now We need to Get Real abou…

The government's big plan for the 10 Year Health Plan for the NHS laid out a big role for delivery. However, the Highland Marketing advisory board felt the missing implementation...

Researchers Create 'Virtual Scienti…

There may be a new artificial intelligence-driven tool to turbocharge scientific discovery: virtual labs. Modeled after a well-established Stanford School of Medicine research group, the virtual lab is complete with an...

From WebMD to AI Chatbots: How Innovatio…

A new research article published in the Journal of Participatory Medicine unveils how successive waves of digital technology innovation have empowered patients, fostering a more collaborative and responsive health care...

New AI Tool Accelerates mRNA-Based Treat…

A new artificial intelligence (AI) model can improve the process of drug and vaccine discovery by predicting how efficiently specific mRNA sequences will produce proteins, both generally and in various...

AI also Assesses Dutch Mammograms Better…

AI is detecting tumors more often and earlier in the Dutch breast cancer screening program. Those tumors can then be treated at an earlier stage. This has been demonstrated by...

RSNA AI Challenge Models can Independent…

Algorithms submitted for an AI Challenge hosted by the Radiological Society of North America (RSNA) have shown excellent performance for detecting breast cancers on mammography images, increasing screening sensitivity while...

AI could Help Emergency Rooms Predict Ad…

Artificial intelligence (AI) can help emergency department (ED) teams better anticipate which patients will need hospital admission, hours earlier than is currently possible, according to a multi-hospital study by the...

Head-to-Head Against AI, Pharmacy Studen…

Students pursuing a Doctor of Pharmacy degree routinely take - and pass - rigorous exams to prove competency in several areas. Can ChatGPT accurately answer the same questions? A new...

NHS Active 10 Walking Tracker Users are …

Users of the NHS Active 10 app, designed to encourage people to become more active, immediately increased their amount of brisk and non-brisk walking upon using the app, according to...

New AI Tool Illuminates "Dark Side…

Proteins sustain life as we know it, serving many important structural and functional roles throughout the body. But these large molecules have cast a long shadow over a smaller subclass...

Deep Learning-Based Model Enables Fast a…

Stroke is the second leading cause of death globally. Ischemic stroke, strongly linked to atherosclerotic plaques, requires accurate plaque and vessel wall segmentation and quantification for definitive diagnosis. However, conventional...