Preference Signaling: Does It Work? A MultispecialtyReview of the Data

Abstract

Background:

Preference signaling was introduced in 2021 to address application inflation and improve applicant-program alignment in residency selection. This study examined its impact on residency application data across several specialties.

Methods:

Data from the Electronic Residency Application Service and the National Resident Matching Program for presignaling (2020–2022) and postsignaling (2022–2024) eras were analyzed. Metrics, including number of applicants, applications per applicant, signals offered, and match rates, were compared using mean differences.

Results:

Among specialties offering 10 or more signals, ear, nose, and throat and neurosurgery had the highest reductions in the mean number of applications per applicant (−23.8% and −12.8%, respectively), whereas orthopedics and dermatology had the lowest reduction (−7.8% and −0.1%, respectively). Anesthesiology and obstetrics and gynecology both saw increases in the number of applications per applicant (+5.8% and +2.2%, respectively). Ear, nose, and throat; anesthesiology; orthopedic surgery; and neurosurgery all had a concurrent increase in match rates over the same period. Among specialties offering fewer than 10 signals, plastic surgery showed the largest reduction (−18.2%) in applications per applicant, followed by emergency medicine (−14.7%), family medicine (−7.6%), and pediatrics (−0.4%). Internal medicine and general surgery saw increases in applications per applicant (+7.2% and +9.7%, respectively). Plastic surgery, pediatrics, and family medicine all had concurrent increases in match rates over the same period.

Conclusions:

Preference signaling may have reduced application inflation and improved match rates, however, shifts in application trends following the COVID-19 pandemic may have confounded these. Ultimately, time and how programs choose to use preference signaling will determine how preference signaling reshapes these application data.

Takeaways

Questions: What is the impact of preference signaling on application inflation across different specialties?

Findings: Using data from the National Resident Matching Program and Electronic Residency Application Service between 2020 and 2024, we found that preference signaling reduced the average number of applications per applicant in plastic surgery; ear, nose, and throat; neurosurgery; orthopedic surgery; emergency medicine; and family medicine.

Meaning: The data trend currently shows that the current impact of preference signaling is in line with one of its core intended goals, which is to reduce application inflation.

INTRODUCTION

The residency application process is a daunting task for both applicants and residency programs.^1,2 Preference signaling was introduced as a solution to combat these issues by allowing applicants to “signal” a limited number of residency programs they are most interested in, thus aiming to potentially reduce the total number of applications submitted, decrease costs for applicants, and assist programs in identifying, and ultimately matching, genuinely interested candidates.^3–5 It was first introduced as a pilot concept in the 2021 otorhinolaryngology residency match following a successful computer-based trial in 2019, and since then, it has seen integration into the residency selection processes of other specialties.^3,6–8 This approach gives applicants more visibility, including those who are less competitive and might otherwise have been potentially screened out before a holistic review.⁹

Programs are more likely to prioritize and closely review applications from applicants who signal them, thereby increasing those applicants' chances of receiving an interview invitation.^10–14 Traditionally, the residency application process typically offers multiple informal opportunities for applicants to signal their interest in a program: away rotations, research fellowships, advocacy from faculty mentors, self-advocacy, and participation in departmental events.^15,16 However, these methods can introduce bias and disparity into the application process, as candidates with strong home programs and faculty mentors are more likely to receive support and sponsorship than those without any of these.^9,17,18 Further disparity is introduced because only a few candidates can afford the cost of pursuing a research fellowship.^19,20 Preference signaling helps mitigate these inequities by offering all applicants an equal number of signals, which they can use as they see fit to send to their program(s) of interest.³

However, whether the process has achieved its aim remains unknown. This study aimed to examine the impact of preference signaling on the existing application inflation across several specialties and to provide data-driven recommendations for its refinement.

METHOD

To evaluate the impact of preference signaling, data were queried from the Electronic Residency Application Service (ERAS) and the National Resident Matching Program (NRMP) databases. Application data were extracted from the ERAS database, and match data were extracted from the NRMP database. We evaluated match cycles from 2 years before and 2 years after the adoption of preference signaling for each included specialty, defining these periods as the “presignaling” and “postsignaling” eras, respectively. For example, if a specialty adopted preference signaling in the 2022/2023 cycle, the 2020/2021 and 2021/2022 cycles were designated as the “presignaling era,” and the 2022/2023 and 2023/2024 cycles as the “postsignaling era.” All specialties included in this study adopted preference signaling at the same time, except ear, nose, and throat (ENT), which piloted it. Key metrics included the number of applicants, the average number of applications per applicant, and match rates.^21–43 Applicants’ data from the presignaling era were compared with those from the postsignaling era to delineate the changes over the study period. Differences over these periods were summarized using mean differences, and changes were quantified as percentages of the mean differences.

Finally, specialties offering 10 or more signals were defined as the “big signal” group, while those offering fewer than 10 signals were defined as the “small signal group.”

RESULTS

Mean Number of Applications per Applicant

In the big signal group (10+ signals), the average number of applications per applicant decreased by −23.8% in ENT, −12.8% in neurosurgery, +7.8% in orthopedics, and −0.1% in dermatology. Anesthesiology and obstetrics and gynecology both experienced an increase in the average number of applications per applicant by +5.8% and +2.2%, respectively (Fig. 1). Table 1 also shows the changes in the number of applicants over the study period.

Fig. 1.

Mean number of applications per applicant trend among the big signaling specialties (star indicates the year specialties adopted preference signaling [star does not apply to ENT]). OBGYN, obstetrics and gynecology.

Table 1.

Data From the Big Signaling Specialties

Specialty	Percentage Difference in Average No. Applications per Applicant (Pre- vs Post-signaling Era)	Percentage Difference in Total No. Applicants (Pre- vs Post-signaling Era)	Match Rate Mean Difference (Pre- and Post-signaling Era)
Orthopedics	−7.8	−0.1	0.6
Dermatology	−0.1	11.8	−10.2
Neurosurgery	−12.8	1.7	0.9
Otorhinolaryngology	−23.8	16.4	12.0
Obstetrics and gynecology	2.2	−4.0	−3.2
Anesthesiology	5.8	3.0	0.2

In the small signal group (<10 signals), integrated plastic surgery had the highest drop in the average number of applications per applicant (−18.2%), which incidentally ranked second overall among all specialties (big and small). Internal medicine, family medicine, emergency medicine, pediatrics, psychiatry, and general surgery received the highest number of applications. Among these, only emergency medicine (−14.7%), family medicine (−7.6%), thoracic surgery (−6.5%), and pediatrics (−0.4%) had a reduction in the number of applications per applicant since the adoption of preference signaling. Despite a reduction in the number of applicants, internal medicine and general surgery showed an increase in the number of applications per applicant (+6.6% and +7.2%, respectively) (Table 2) (Fig. 2). Figure 3 shows the mean number of applications per applicant trend among programs historically known to have relatively few programs in the match.^38,39 (See table, Supplemental Digital Content 1, which displays data for specialties in the big signal group [A] and in the other signal group [B], https://links.lww.com/PRSGO/E203.)

Table 2.

Data From Specialties Offering Fewer Than 10 Signals

Specialty	Percentage Difference in Average No. Applications per Applicant (Pre- vs Postsignaling Era)	Percentage Difference in Total No. Applicants Pre- vs Postsignaling Era)	Match Rate Mean Difference (Pre- and PostSignaling Era)
Internal medicine	6.6	1.0	−0.6
General surgery	7.2	−7.8	0.7
Psychiatry	5.0	−11.4	3.5
Emergency medicine	−14.7	19.5	3.2
Family medicine	−7.6	−11.1	5.1
Pathology	7.1	−20.9	3.0
Pediatrics	−0.4	−11.1	5.6
Plastic surgery	−18.2	−3.0	4.5
Neurology	5.5	−8.2	2.3
Thoracic surgery	−6.5	−60.8	0.2

Fig. 2.

Mean number of applications per applicant trend among specialties offering fewer than 10 signals (star indicates the year specialties adopted preference signaling).

Fig. 3.

Mean number of applications per applicant trend among the top 6 most competitive specialties (star indicates the year specialties adopted preference signaling [star does not apply to ENT]).

Match Rates

Among the big signaling group, ENT has had the greatest increase in match rate after adopting preference signaling (+12.0%). Anesthesiology, orthopedic surgery, and neurosurgery had slight increases in match rates (+0.2%, +0.6%, and +0.9%, respectively). Dermatology and obstetrics and gynecology both had a reduction in match rates by −10.2% and −3.2%, respectively, after adopting preference signaling (Table 1).

Among specialties that offered fewer than 10 signals during the study periods, the highest improvements in match rates were observed in pediatrics (+5.6%), family medicine (+5.1%), and plastic surgery (+4.5%), whereas general surgery only had a slight rise (+0.7%). Internal medicine saw a drop in match rates by −0.6%. Other specialties data are shown in Table 2.

DISCUSSION

This study sought to determine the impact of preference signaling on the residency application numbers, including the number of applications per applicant and match rate across multiple specialties. Preference signaling was introduced in 2021 as a structured approach to formalize applicants’ interactions with their programs of interest and potentially reduce the application burden for both applicants and program directors. By comparing pre- and postsignaling data, we found that preference signaling had a differential impact. Neurosurgery and ENT saw the highest reduction in applications per applicant, consistent with the intended goal of preference signaling to reduce application inflation. This was despite increases in the number of applicants per year. The higher number of signals allowed in these specialties may have provided applicants with greater flexibility to express genuine interest, leading to more efficient application strategies.⁴⁴ Most other specialties in the big signal group also had reductions in the number of applications per applicant, with notable exceptions being obstetrics and gynecology and anesthesiology. Cai et al⁴⁵ studied 1500 obstetrics and gynecology applicants and found that this could be due to a “learning curve,” whereby applicants needed time to learn and adjust to the signaling system. This finding may also hold true for other specialties, implying that time (and greater familiarity and experience with signaling) may be important cofactors in achieving the intended goal of decreasing the overall quantity of applications while increasing match rates.

Integrated plastic surgery has decreased the number of applications per applicant by more than 18% since adopting preference signaling while improving the match rate by 4.5%. This observation is particularly significant, especially for a highly competitive specialty with historically high numbers of applications per applicant and low match rates.^46–49 Recently, Elemosho et al⁴⁷ determined that match probability in integrated plastic surgery does not further increase beyond 15 interviews and contiguous ranks. With preference signaling, candidates can select their top choices to send their signals, potentially reducing costs.^47,50–52 The Plastic Surgery Central Application was recently developed to reduce application-related costs and improve holistic review,^53–56 and it not only included preference signaling into its platform, but introduced a unique “signal statement,” which allowed the applicant to briefly communicate why they were signaling a specific program. This feature was crafted as an open text field with word limits to permit concise freeform, customized applicant responses, allowing strict regulation of how signals are used and received.⁵³ These advances may further reduce the number of applications per applicant for plastic surgery. Regarding the potential impact of an increased number of signals in plastic surgery residency,⁵⁷ only time can tell the true impact of this change. We have shown that both high and low numbers of signals led to a reduction in the number of applications per applicant; and offering a higher number of signals (10–30) does not necessarily translate to a reduction in the number of applications per applicant.

Emergency medicine and family medicine are the other 2 specialties with a consistent drop in the mean number of applications/applicants since the adoption of preference signaling. This observation in emergency medicine may, however, be significantly confounded by the COVID-19 pandemic and may not be a direct impact of preference signaling. The COVID-19 pandemic impacted several specialties, but emergency medicine saw the most dramatic drop in the number of applicants. Although this has started to rebound more recently, it has still not returned to pre-COVID levels.⁵⁸ Pelletier-Bui et al⁵⁹ found that some emergency medicine program directors did not consider candidate signals as an important factor for interview selections in the first year of adoption in emergency medicine, again implying a “time effect” that may be necessary to socialize and adopt signaling within specialties.

Internal medicine, family medicine, emergency medicine, pediatrics, and general surgery receive the greatest number of applications.^24–26,28 The implication of being allowed only 5 signals is significant, as candidates must judiciously select their top programs to signal.^10,60 Further implications are that programs may prioritize interviewing candidates who signal them by using filters that specifically identify those applicants.^10–13 A low number of signals may prevent a candidate from signaling all of their preferred programs, potentially causing unsignaled programs to miss out on candidates who could have been an ideal fit.^9,61 Conversely, too many signals put candidates at risk of overapplication, essentially diluting the signal, which may devalue its importance. This is especially true for the “big signaling” group, where, despite a high number of signals, application numbers continue to rise.^22,29,30

The discussion of the ideal number of signal tokens still remains a hot topic.⁶¹ One proposed solution is to offer “weighted signals,” whereby applicants have different tiered signal strengths (eg, “gold, silver, bronze”). In this system, applicants would send their most important signals (“gold”) to their most highly valued programs.⁶² Programs receiving more highly weighted signals would prioritize those over lesser or unweighted ones. Conceptually, this could largely abolish the need to overapply, because candidates are unlikely to be given an interview if a program is unsignaled or receives a lesser weighted signal. Dermatology, for example, used this system in the 2023/2024 cycle and saw a reduction in the number of applications per applicant following the designation of 3 of its 28 signals as “gold signals.” Following this, they had a 14.8% drop in applications per applicant compared with a 19% rise in applications from the previous year.

It should be explicitly acknowledged that preference signaling is only one of the major factors influencing match rates. Highly competitive specialties typically have an inherently lower match rate, with most or all positions filled compared with others. Therefore, preference signaling may help streamline the selection process without ultimately impacting the match rate. This is seen in our analysis of the data, as well, with some specialties experiencing only marginal changes in match rates when comparing presignaling and postsignaling periods.

Overall, the data trend shows that time (and familiarity through a “learning curve”) for programs and applicants alike is a significant factor in determining the full impact of preference signaling on application data. With greater experience and modification, revisions to both the number and weight of signals, along with the wider incorporation of personalized signal statements (as in the Plastic Surgery Central Application example earlier) may lead to a greater impact across each specialty.

This study is not without limitations. First, the COVID-19 pandemic was a major confounder in the rise or drop in application numbers observed in certain specialties, and the apparent limited impact of preference signaling in some specialties. Concerns about going unmatched and the shift to virtual interviews also drove an increase in applications across many specialties.^2,63–66 Postpandemic, these effects persist and continue to influence the impact of preference signaling.

This review used original applicant data from ERAS and NRMP due to a paucity of high-quality specialty-specific studies on preference signaling. This calls for more original studies documenting the progress of preference signaling across different specialties that will allow for a broader review and understanding of the sustained impact of preference signaling. The lack of complete data for specialties that have moved from ERAS to other forms of application limited our ability to report actual data for these specialties.

CONCLUSIONS

Since the adoption of preference signaling, there have been mixed effects on the number of applications per applicant and match rates across several specialties. For the most part, it has reduced the average number of applications per applicant and improved match rates. The COVID-19 pandemic is likely to have confounded the true effects of preference signaling across specialties. Growing familiarity with the concept and implementation of preference signaling strategies will help reshape the application process for years to come.

DISCLOSURES

Dr. Janis receives royalties from Thieme Medical Publishers and Springer Publishing and is a cofounder of the Plastic Surgery Central Application. The other authors have no financial interest to declare in relation to the content of this article.