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The Fast Track to Fertility Program: Rapid Cycle Innovation to Redesign Fertility Care

Published September 21, 2022
NEJM Catal Innov Care Deliv 2022;3(10)
DOI: 10.1056/CAT.22.0065


Couples with infertility experience psychological distress associated with the prolonged period of recognizing a need for fertility assistance and the long journey of evaluation and management. In August 2019, Penn Medicine began developing Fast Track to Fertility, a novel model of care delivery to improve access to fertility care and decrease time to completion of fertility workup and, by June 2021, deployed division-wide implementation of the new care model. Advanced practice providers conducted new-patient visits via telehealth, and patients were offered enrollment in a texting platform to assist with completion of a complex workup for both partners. Penn Medicine initially used a fake back-end texting platform (to test and authenticate various iterations) and then transitioned to an artificial intelligence–augmented semi-automation approach to facilitate timely completion of the workup. The redesigned model increased annualized new patient access by 23.8% (to 5,570 from 4,500) and decreased the time to initiation of treatment by approximately 50% (to 41 days from 97 days). The addition of educational materials and anticipatory guidance via text contributed to increased patient engagement (to 80% from 60%) and high satisfaction scores (measured by Net Promoter Scores greater than 70).
Key Takeaways
This care delivery model is novel, as we created a digital front door to improve access to fertility care including: (1) a telehealth new-patient visit with an advanced practice provider (APP), and (2) an artificial intelligence–augmented text-messaging platform to shorten the time to completion of a complex diagnostic workup.
Pilot phases and iterative processes (using a fake back end to test mockups and configurations and developing comprehensive content for the texting platform) were crucial to building a robust framework prior to scaling the pathway.
The Fast Track to Fertility care delivery model was grounded in patient needs related to diagnostic workup that could be served by semi-automation.
Patient engagement via a semi-automated platform is not a replacement for human contact but is an efficient extender of certain approaches and activities that are personnel-intensive.
This innovative care delivery model optimizes both personalization (which is essential for identifying and engaging with diverse patients) and standardization (which facilitates an effective intake process using the APP-physician model).

The Challenge

Approximately 15% of reproductive-aged couples worldwide experience infertility, which is classified as a disease by the World Health Organization.1 In the United States, access to care is limited in part due to the wide variation in insurance coverage and considerable out-of-pocket costs associated with fertility care.2 Also, in the United States, fertility treatment discontinuation rates are up to 65%. In addition to financial and psychological factors,3,4 organizational and clinic-related factors also contribute to the reasons for discontinuation.5 Compared to other developed countries, the unmet demand for fertility services in the United States is high and projected to increase with expansion of insurance coverage and delayed childbearing; such increased demand is expected to outpace the current workforce.6-9 Both male and female factors contribute equally to infertility, and complex and time-consuming diagnostic workup in both partners often adds to the distress associated with this process.10
Patients with infertility have typically waited months to years before seeking medical care.11 At that point, additional delays add to the anxiety and stress associated with this diagnosis.12-14 Among patients who discontinue in vitro fertilization treatment, stress is cited as the most common reason (39%), driven by the impact on the couple’s relationship and the anxiety or depression associated with the experience.3 Fertility treatments are time-sensitive because each missed ovulatory cycle results in a setback by 1 month. At our academic fertility center, Penn Fertility Care, which accommodates more than 3,000 new-patient visits (NPVs) annually, we observed two challenges to initiating treatment: (1) delayed access due to high demand, and (2) difficulties related to completing multiple diagnostic tests for both partners. These factors can result in as many as 3 or 4 months of missed ovulatory cycles prior to initiation of treatment and may contribute to discontinuation of care.

The Goal

Our goal was to expedite initiating treatments using novel models of care delivery without simply hiring personnel or increasing the workload for the existing team. We developed Fast Track to Fertility, an integrated approach deploying existing resources and novel communication methods to shorten the time for patients to initiate care and to improve their experience with navigating complex care algorithms. We hypothesized that by using hybrid models (telehealth, advanced practice providers [APPs], and physicians) and artificial intelligence (AI)–augmented text-messaging systems, we could expedite the time to treatment. Based on prior experience within our health care organization,15,16 we proposed to use text-based reminders to allow couples to complete fertility workup expeditiously.

The Execution

There were three components to the execution: the discovery, pilot, and semi-automation phases. Overall, the process, which launched in August 2019, took about a year and a half before it was scaled and integrated as the standard care delivery model in June 2021. Throughout, there were Covid-19–related delays, and there was overlap of the components. The discovery phase was about 3–4 months; the pilot, about 3–6 months; and the semi-automation, about 4–6 months.
New patients were offered the option to schedule a telehealth visit with an APP, thereby beginning care before the availability of a visit with an attending physician.

Discovery Phase

First, we sought to investigate patients’ experiences during their fertility journeys. We conducted 10 informal patient surveys by phone and used secret shopper techniques to experience our own program, and local alternatives, from our patients’ perspectives.17 This contextual inquiry revealed that in some competing practices, patients obtained an appointment within a few days. In our practice, however, patients waited 4–6 weeks for an appointment and then took another 6–8 weeks to complete their diagnostic evaluations (Figure 1, Panel A).
Figure 1
Flow Chart of Fertility Journey from First Phone Call to Initiating Treatment
APP = advanced practice provider. Source: The authors
Delays in access reflected the high demand, while delays in the early stages of care reflected the complexity of coordinating patient and partner visits for diagnostic workups, particularly those timed to the menstrual cycle.18 Despite being provided written instructions, many patients struggled to comprehend the complex workup schedule, which resulted in increased phone calls to the practice.
Based on these observations, we designed Fast Track to Fertility (Figure 1, Panel B). To shorten the time from the first patient phone call to initiation of treatment, patients were offered an initial telehealth appointment with an APP. To accelerate the workup, they were enrolled in a text-message program. (Customs and practices may vary by organization or region, but generally, the obstetricians/gynecologists who refer to Penn Fertility Care prefer to leave the workup scheduling and interpretation to our staff, which does facilitate efficiency of the Fast Track to Fertility model.)

Pilot Phase

The APP Telehealth Pilot

New patients were offered the option to schedule a telehealth visit with an APP, thereby beginning care before the availability of a visit with an attending physician. The APP was an existing provider in our practice who was transitioned from nonbilling care coordination activities and trained to use standardized electronic health record (EHR) templates to procure detailed medical history from both the patient and the patient’s partner and also provided instructions and educational materials related to fertility testing. We initiated this pilot with one APP (we use nurse practitioners) and scheduled one new-patient appointment each weekday.

Fake Back-End Texting Pilot

We initiated this pilot with two physicians who offered their new infertility patients the option to enroll. A project coordinator secured patient consent and then texted reminders regarding their workup, serving as a fake back end for what would eventually become a semi-automated process.19 The fake back end is a well-tested innovation tool that uses a team member to initially respond to patient messages. We used branching logic based on our standard workup protocol (Appendix, Exhibit 1), texted patients to schedule blood tests and an ultrasound timed to their menstrual cycle, and reminded male partners to schedule blood and semen analyses. Based on patient responses, we iterated on these algorithms to include information on preprocedure medications for hysterosalpingogram, anticipatory guidance about the diagnostic tests, and developed a resource for frequently asked questions (FAQs). We selected a text-based approach, as it does not require downloading an app, supports real-time two-way engagement in a private setting,20 and can reduce racial disparities in care for reproductive age women.21 We will be considering future analyses to explore implications related to health access and equity. A summary of our patient demographics is included in the Appendix, Exhibit 2.

Semi-automation Phase

At the end of the pilot phase, we evaluated patient engagement and completion of workup and coded the messages to assess the feasibility for semi-automation, which we defined as any encounter that has the potential to be fully automated with added features to the platform (e.g., sending appointment reminders can be automated if the AI platform has the capability of integrating with an EMR system). Fully automated was defined as any encounter in which an AI texting bot can communicate with the patient without a human stepping in (Figure 2).
Figure 2
Coding of Text Message Conversations During the Fake Back-End Texting Pilot
Source: The authors
We discovered that more than 80% of the messages could be automated. We, therefore, used a technology platform (Memora Health, San Francisco, CA) to send automated reminders and added instructions and questionnaires based on the couple’s recommended workup (Figure 3). The platform interpreted patient responses using natural language processing (NLP).22
Figure 3
Memora Health Platform: Sample Intake Page for Patient Enrollment and Sample Texting Algorithm
Source: The authors
The NLP system, which was developed by Memora Health, uses keyword detection to pair keywords to a preset response. This is a deterministic model, as opposed to a probabilistic model, which enables a high degree of control and transparency in the NLP process. If the patient prompts questions with varied structures around the same keywords, the NLP system can detect and answer accordingly as opposed to relying on structured algorithms. The responses may include automated follow-up questions with branching logic for dynamic guidance. Patients are not dependent on structured messages to progress through their journey; they can ask relevant questions in real time, and they are supported with fewer reminders. Response categories of NLP-based responses include clinical guidance and FAQs (e.g., how to determine cycle start or uterine cavity evaluation preappointment instructions), administrative guidance and FAQs (when to schedule test/appointment or clinic locations or phone numbers), and conversational pleasantries.
Patients are told at their consult that they can always call our office with questions; the text platform is meant to supplement the care experience by addressing most frequently asked questions, but it does not substitute for human care.
We kept human eyes on the communications while the NLP program was operating and evaluated responses for accuracy, adding language when no content was available and refining incorrect response mappings. We progressively broadened the message content delivered to substitute for care that might otherwise have required personnel (Figure 4 and Appendix, Exhibit 3).
Figure 4
Development of Semi-automated Text-Based Communication Decreased the Number of Human Interventions
Source: The authors
The semi-automation phase took approximately 6 months of daily review by a coordinator, with weekly team meetings to review recurrent themes/issues. The care team was able to track each interaction and make timely interventions when patients did not engage. We gradually scaled the project and allowed more clinicians to enroll their new patients to receive the text-based reminders. Patients are told at their consult that they can always call our office with questions; the text platform is meant to supplement the care experience by addressing most FAQs, but it does not substitute for human care.


Acceptability of APP Telehealth Visit

There was some initial skepticism regarding the NPVs with an APP, which was rooted in entrenched conventions that a patient’s first visit should be with a physician. But we reached out directly to our colleagues and noted that this model created a bottleneck for new appointments; all were willing to try the Fast Track to Fertility model. In response, schedulers were trained to introduce the team approach to care and schedule two visits: an APP telehealth visit and a follow-up physician visit after 4–6 weeks. Rethinking this NPV resulted in completed workup in advance of the first patient–physician encounter, resulting in a more productive consultation. Further, use of EHR templates by APPs provided standardization of the complex workup10 and continuity during the subsequent physician visit.

NPV Triage

Based on high acceptability of telehealth visits with APPs, we expanded the pilot to include more APPs and added two NPVs per weekday to their templates. We recognized that some patients were not suited for this care model. The scheduling algorithm was, therefore, updated to triage patients who were seeking a second opinion, had prior fertility treatments, or were seeking an appointment specifically related to being evaluated for surgery. Review of the algorithm occurs regularly to ensure patient satisfaction with the APP visit and appropriate triage.

Enrollment and Consent for Text Messaging

We had a high consent rate (more than 90%) during the pilot phase, in part because the project coordinator was used to obtain consent during the in-person physician visit. As we expanded the pilots to include more clinical providers and physician visits switched to telehealth due to Covid-19, we used a text-based method to obtain consent that was approved by our health system.19 However, in October 2020, we observed a decrease in text messaging consent rates to 50%, as patients were required to respond to the initial text message. To address this, we added information about the texting service to the after-visit summary provided to all patients and tracked consent rates per provider to encourage them to review the consenting process with patients (Table 1 and Figure 5, Panel A). These changes led to increases in the average monthly consent rates of two-thirds or more. Incorporating the postpilot period, with the scaling/full implementation data, the consent rate average is 78.96%.
Table 1
Month and YearConsent PercentageEnrolledConsented
October 202050.0021
November 202050.0063
December 202062.501610
January 202166.702114
February 202181.502722
March 202170.704129
April 202182.602319
May 202165.202315
June 202181.502722
July 202180.003024
August 202172.402921
September 202166.7096
October 202190.603229
November 202173.503425
December 202185.007160
January 202283.003630
February 202282.004436
March 202285.006757
April 202287.003833
May 202278.004233
Patient Rate of Enrollment to Fast Track to Fertility Program and Consent to Use the Automated Texting Platform, by Month
In October 2020, patients were invited to consent to automated text messages through an initial text, which produced suboptimal consent rates of 50%. After that was resolved, consent rates have been strong, with an average of nearly 80% between October 2020 and May 2022. Source: The authors
Figure 5
Enrollment and Consent to Use the Texting Platform by Month and Provider
Source: The authors

Standardization Versus Personalization

Although we initially focused on standardization of the infertility workup in heterosexual couples, fertility care is offered in different patient settings, such as same-sex female couples using a sperm donor or single females presenting for oocyte cryopreservation or donor-sperm inseminations. Sending a prompt related to the correct workup is essential to promote trust and personalized care and increase retention. As such, during the semi-automation phase, we moved from an algorithm with discrete pathways to a modularized option (Figure 3) that allowed for selection of diagnostic tests and, therefore, personalization of messaging to ensure inclusivity.
Rethinking this NPV resulted in completed workup in advance of the first patient–physician encounter, resulting in a more productive consultation.

The Team

The team that developed and implemented this care mode included physicians, nurses, APPs, schedulers, innovation managers, and research coordinators in our health system and the Memora Health team.


Overall, we aimed to reduce the time from the patient’s first contact with our practice to initiating fertility treatment. We also developed intermediary metrics to evaluate different phases of the project. We specifically chose not to track any birth/conception data as part of this model, as there are multiple variables that impact success rate (reason for infertility, age, prior treatments, etc.) that are significant effect modifiers; we plan to investigate these subgroups as we accumulate more data.
The APP telehealth pilot (n = 17) reduced time from the scheduling phone call to first appointment by 50% (to 17.5 days from approximately 36 days) with an average Net Promoter Score (NPS) of 68 (on a scale of −100 to 100). During this pilot, the combined no-show and cancellation rates for the NPV decreased to 6% from a historical rate of 30%, indicating greater retention in our practice (Figure 6, Panel A).
Figure 6
Fast Track to Fertility Outcomes for Advanced Practice Provider (APP) Telehealth and Fake Back-End Texting Pilot
Source: The authors
Anecdotally, each of the three APPs commented that they enjoyed the new patient-facing role; previously, they only evaluated return patients for problem visits and engaged in nonbillable patient care coordination. As of August 2022, we have scaled to four APPs, all nurse practioners.
The fake back-end texting pilot (n = 10) decreased the time it took couples to complete their workups by more than 50% (to 41 days from approximately 97 days), with most patients completing their workups within one menstrual cycle (Figure 6, Panel B). We also saw improved engagement as measured by reduced no-shows: 8 of 10 (80%) patients attended the physician visit to discuss the workup and to initiate treatment versus 60% attending the physician visit in the prepilot period.
Accuracy of the semi-automated texting platform increased over the course of the pilot phase and through implementation, with improvement in correct responses and a decrease in no content (Table 2 and Figure 7).
Table 2
MonthWrong MappingNo ContentCorrectAccuracy
October 202001375.00%
November 202010266.67%
December 2020402083.33%
January 202111193352.38%
February 20211264671.88%
March 202126166560.75%
April 202115176466.67%
May 2021653375.00%
June 20211395471.05%
July 2021474981.67%
August 20213203862.30%
September 2021032288.00%
October 2021253683.72%
November 2021293576.09%
December 20216811288.89%
January 20228814690.12%
February 20224109987.61%
March 202281110184.17%
April 2022149494.95%
May 20220054100.00%
Accuracy of the Artificial Intelligence (AI)–Augmented Text Platform
During the pilot phase, and after division-wide implementation of the new care model in June 2021, the accuracy of the texting platform remained effective. Despite some month-to-month variation, the overall trend has remained positive. Source: The authors
Figure 7
Accuracy of the Semi-automated Texting Platform
Source: The authors
Overall, 73% of patient messages were managed solely by the chatbot, and only 6% (86 of 1,372) required escalation to the clinical team. For the remaining messages, we continue to add information when there is no content and make changes for incorrect mapping. By May 2021, 8 months after program onset, patient engagement with the chatbot, including proactive texting, was high. The average number of patient-initiated messages was 9, and the average number of outgoing messages to patients was 18.
The diagnostic workup, which had taken as much as 2 or 3 months from the patient’s initial call to the practice to complete under the traditional model, is now completed in less than 30 days.
Patient acceptability, completion, and satisfaction of the ongoing program. As of May 2022, 627 patients have been enrolled with an overall consent rate of 78% (Table 1), and the accuracy of the automated text responses has increased to nearly 80% (Table 2). After some ups and downs early in the program, in the 7 months from November 2021 through May 2022, the NPS among 155 patients was consistently higher than 50 (Figure 8, Panel A), which is considered a strong score.23,24 In addition, the diagnostic workup, which had taken as much as 2 or 3 months from the patient’s initial call to the practice to complete under the traditional model, is now completed in less than 30 days (Figure 8, Panel B). We continue weekly team meetings and monitor monthly metrics.
Figure 8
Metrics for Ongoing Fast Track to Fertility Program
Source: The authors
The Fast Track to Fertility program was well received by physicians, based on direct informal conversations. A positive element was that 84% of patients came to the physician visit with completed workups, expediting treatment planning.
Capacity was increased. In our practice of 8 physicians and 3 APPs located at 3 sites, we were able to increase our annualized NPV capacity to 5,570 from 4,500 (23.8%) by altering the APP templates to accommodate 2 NPVs per day for 5 days each week without changing the physician templates. By improving patient engagement and initiating treatments faster, we will continue to increase the annualized revenues generated through fertility treatments.25 Importantly, implementation has paralleled an increase in patient volume in our practice: by expediting the workup, we are facilitating patient access to treatment sooner, which has spurred engagement and access to our program overall.

Where to Start

Regarding costs, this project was developed as part of the accelerator program awarded by the Center for Health Care Innovation at Penn Medicine. Given our academic affiliation and the Penn Medicine contractual agreements with Memora in a shared development model, our team had no direct costs, other than time invested by clinical and nonclinical team members for development and maintenance of the platform.
For others looking to develop and implement such an effort, we suggest that you begin with a contextual inquiry to identify pain points for access to care and treatment pathways. We started down our path thinking that the largest delays came from patients not meeting the demanding schedule of evaluation; but we embraced an open-minded approach and sought patient perspectives that ultimately revealed that our own care delivery scheduling protocol created impediments to patient access. Second, we thought digital solutions might be sufficient to speed patient evaluation, but a wider perspective helped us recognize that most effective solutions require a blend of human and digital approaches. Our hybrid technology-enabled model can be applied in outpatient practices in which the initial assessment of health conditions both can be effectively performed via telehealth and requires complex diagnostic testing prior to treatment initiation. For example, in Obstetrics and Gynecology, we plan to extend this model to the workup of abnormal uterine bleeding and diagnosis of polycystic ovary syndrome, both chronic conditions with multiple touchpoints.


We acknowledge the efforts of Lara Sissman, Lisa Mills, Nicole Houser, Matt Van der Tuyn, Abigail Mandel, Ryan Shumacher, Caleb Johnston, Todd Joseph, Kyle White, and the Memora Health Team.
Suneeta Senapati, David A. Asch, Raina M. Merchant, Roy Rosin, Emily Seltzer, Christina Mancheno, and Anuja Dokras have nothing to disclose.



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Information & Authors


Published In

NEJM Catalyst Innovations in Care Delivery


Published online: September 21, 2022
Published in issue: September 21, 2022




Suneeta Senapati, MD, MSCE
Assistant Professor, Obstetrics & Gynecology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
David A. Asch, MD, MBA
Professor of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
Executive Director, Center for Health Care Innovation, Penn Medicine, Philadelphia, Pennsylvania, USA
Raina M. Merchant, MD, MSHP
Professor of Emergency Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
Director, Center for Digital Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
Roy Rosin, MBA
Chief Innovation Officer, Penn Medicine, Philadelphia, Pennsylvania, USA
Emily Seltzer, MPH
Senior Innovation Manager, Penn Medicine Center for Health Care Innovation, Philadelphia, Pennsylvania, USA
Christina Mancheno, MPH
Project Manager, Penn Medicine Center for Healthcare Innovation, Philadelphia, Pennsylvania, USA
Anuja Dokras, MD, MHCI, PhD
Professor of Obstetrics & Gynecology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
Gynecology Team Chair, Women’s Health Service Line, Penn Medicine, Philadelphia, Pennsylvania, USA

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