Introduction
Assisted reproduction technology (ART), more recently referred to across Europe as medically assisted reproduction (MAR), has had a significant societal impact, resulting in over 10 million live births worldwide [1,2]. Although modern fertility case management practice varies widely across roles and regions, fertility nurses and midwives are typically the first point of contact for patients navigating the complexities of ART [3] and have the most frequent and intimate involvement in patient care [4]; therefore, they have a crucial role to play in its success. Key performance indicators (KPIs) are an objective measure that can be used to evaluate critical healthcare domains, which aim to improve patient safety and overall delivery of quality care [5,6]. Although consensus reports exist on optimising ART laboratory performance [7] and clinical practice [8], the unique and vital contributions of nurses and midwives within fertility services appear to be overlooked: there are currently no established KPIs in fertility nursing and midwifery care. Nursing KPIs are important for organisational performance: apart from improving patient care, they enable processes of transparent accountability [9]. The lack of widely accepted best practice standards and measures to evaluate the performance of nurses and midwifes in fertility care [10] makes it difficult for clinics to assess quality of care and benchmark against national standards. Integrating KPIs into fertility care, to ensure well-informed and appropriately trained fertility nurses and midwives, enhances the patient experience: integrating continuity and patient-centred care [10,11]. A national survey of fertility nurses identified a significant gap in knowledge and familiarity related to KPIs in nursing practice [10], concluding that most nurses considered it beneficial to establish a KPI consensus with recommended KPIs within their clinic [12].
Board Coordinators
Board Members
Eleanor L. Stevenson
Durham, North Carolina, USA
Helen Kendrew
Bath, UK
Hilde Cotton
Oslo, Norway
Julia Gonnot
Paris, France
Kriston Ward
Rochester, NewYork, USA
Louise Dias
Augustinus, Belgium
Nicole Callahan
Mayo Clinic
Rochester, Minnesota, USA
Patti Roberts
San Ramon, CA
USA
Rodriq E. Stubbs
Boston, Massachusetts, USA
Sara Somers
Ghent, Belgium
Expert Panel
Violet Augustus | Reproductive Medicine Unit, Groote Schuur Hospital, Cape Town, South Africa |
Morgan Baker | Utah Fertility Center, UT, USA |
Remi Béranger | Rennes University Hospital, Rennes, France |
Madeleine Bernet | Bern University of Applied Sciences, Bern, Switzerland |
Louise Boyle | NHS Forth Valley, Falkirk, Scotland, UK |
Roxanne Buckmaster | Spring Fertility, Beaverton, OR, USA |
Isabel Bustamante | Northwell Health, New York, NY, USA |
Eline Dancet | Faculty of Medicine, KU Leuven, Belgium |
Jonalyn De Vera Edades | ART Fertility Clinics, Abu Dhabi, United Arab Emirates |
Melanie Dinis | Eugin Coimbra Fertility Clinic, Coimbra, Portugal |
Jamie Feingold | Strong Fertility Center, Rochester, NY, USA |
Ilkay Güngör Satılmış | Istanbul University – Cerrahpaşa, Turkey |
Marta Hajdarevic | Pacific Centre for Reproductive Medicine, Edmonton, AB, Canada |
Karin Hammarberg | Monash University, Melbourne, VIC, Australia |
Anne Howard | Royal College of Nursing, UK |
Lisa Hughes-Thompson | Ghana College of Nurses and Midwives, Accra, Ghana |
Amber Kiddie | Fertility Centers of Illinois, IL, USA |
Karen Henriette Kirchheiner Jensen | Rigshospitalet, Copenhagen, Denmark |
Linda Kluge | NMCC, Q-IVF, The University of Gothenburg, Sweden |
Vicky Loney* | Aberdeen Fertility Centre, NHS Grampian, Scotland, UK |
Jessica Medvedich | Reproductive Medicine Associates, Annandale, NJ, USA |
Danielle Miller | San Diego Fertility Center, San Diego, CA, USA |
Darshana Naik | Shady Grove Fertility, Walter Reed Medical Center, Bethesda, MD, USA |
Hetty Ockhuijsen | Utrecht University Medical Centre, Utrecht, The Netherlands |
Malgorzata Piatkowska | Invimed Clinic, Warsaw Mokotów, Poland |
Melissa Pichi | Reproductive Medicine Associates, New York, NY, USA |
Katherine Pleace | School of Nursing, Psychotherapy and Community Health, University of Essex, UK |
Shana Puetz | Tennessee Fertility Institute, TN, USA |
Leigh Anne Reid* | Aberdeen Fertility Centre, NHS Scotland, UK |
Elisa Rinta-Lusa | HUS, Helsinki, Finland |
Fernanda Robin | Ser Fertilidade Ltda, Brazil |
Courtney Schmidt | Shady Grove Fertility, Richmond, VA, USA |
Britt Schoonjans | Brussels IVF, University Hospital Brussels, Belgium |
Carole Tilbrook | Repromed, Adelaide, SA, Australia |
Jen Ulyatt | NHS Tayside, Dundee, Scotland, UK |
Chloë Van Loock | UZ Leuven, Leuven, Belgium |
Maureen Wood | NHS Tayside, Dundee, Scotland, UK |
Jennifer Wood | Shady Grove Fertility, Washington, DC, USA |
world map showing the geographic distribution of respondents
The concept of “advanced practice” in nursing and midwifery
The term “midwifery” in this study is used broadly to describe the role of advanced nursing practice. Globally, providers hold different educational backgrounds and professional titles; nevertheless, their role is similar: they are trained to provide autonomy of practice, including leadership, medical diagnosis, treatment, research and education within their area of specialist practice.
- In the United States, common titles include Nurse Practitioner (NP), Physician Assistant (PA/Physician Associate), and Certified Nurse-Midwife (CNM). Collectively, these roles are often referred to as Advanced Practice Providers (APPs).
- In the United Kingdom, the pathway to becoming a registered midwife previously required completion of 3 years of college-based training and registration in general nursing, followed by 18 months of college-based midwifery training, but now allows for direct entry, and requires completion of a midwifery degree (3 years). All qualifications must be approved by the Nursing and Midwifery Council (NMC).
- For a midwifery degree obtained within EU/EEA states, students must complete a 3-year university-based course.
“Advanced practice” in the UK is defined by the level of practice, rather than reflecting a job title. Although several definitions of advanced practice exist globally, in the UK it is underpinned by a master’s level of study and four pillars of practice, characterised by clinical management, leadership, education and research. Advanced practice also encompasses complex clinical reasoning and decision-making to manage high levels of complexity [13]. Irrespective of terminology, the underlying concept of highly trained professionals delivering advanced, specialist practice remains consistent.
What do KPIs mean for patients?
Key performance indicators are essential tools for evaluating the quality, safety, and effectiveness of healthcare services. Although KPIs have been developed for IVF laboratories and clinical decision-making processes, none currently exist for fertility nurses and nurse midwives – despite their pivotal role in shaping the patient experience, ensuring safety, and influencing outcomes. Therefore, this report explores measurable outcomes linked to the visible aspects of nursing and nurse midwifery practice, which we regard as fundamental to optimising outcomes and advancing patient-centred care. The aim was to use a modified Delphi consensus to identify, define, and establish a core set of KPIs relevant to fertility nursing and midwifery practice. The current report recommends 10 KPIs for evaluating fertility nursing and midwifery practice: (1) dignity, empathy, and respect; (2) positive, therapeutic and collaborative fertility nursing and midwifery care; (3) patient communication preferences; (4) equitable access to patient information; (5) medicines management; (6) complication follow-up rates; (7) minimum educational requirements; (8) knowledge and competency of professional practice; (9) continuing education; and (10) transition from fertility to antenatal care. The Expert Panel concluded that these 10 suggested KPIs represent an important first step towards benchmarking and enhancing the quality of care delivered by fertility nurses and midwives. Such KPIs would establish a foundation for assessing relevant metrics and conducting audits against defined standards. Clinics may select specific or supplementary indicators tailored to their patient populations or workflows. Once implemented, further research and/or service evaluation will be required to assess the impact of these KPIs on patient safety, outcomes, and satisfaction.
Materials and methods
The Medical Education Academy (MedEA) was commissioned to facilitate the project and enlisted the expertise of two Coordinators (one affiliated to the European Society of Human Reproduction and Embryology [ESHRE], the other to the American Society for Reproductive Medicine [ASRM]). An online platform was created to facilitate the exchange of ideas and provide access to a curated collection of relevant publications. Given the limited evidence relating to fertility nursing and midwifery KPIs, a modified Delphi technique was implemented to elicit consensus. The Delphi method, widely applied in healthcare, is a structured approach to development of consensus where research is scarce [14]. Important elements of the Delphi technique include anonymity, iteration, controlled feedback, and statistical stability of agreement [14]. Traditionally, the Delphi process involves several rounds of open-ended questions and ends with closed questions; however, approved modifications incorporate meetings facilitated by skilled moderators [15]. For this project, modifications included proposed pre-selected topics (statements) discussed in virtual meetings, with feedback submitted via email to the Coordinators. This was followed by three rounds of voting, with participant responses discussed and refined (where appropriate) before subsequent voting rounds, until consensus of ≥66% was reached for each statement. The key modification from the traditional Delphi method was the inclusion of this interactive element, which enabled direct discussion, clarification, and debate among experts to foster deeper understanding. The Delphi technique guaranteed anonymity during all voting stages, with controlled feedback provided to panellists via email and web conferences (iteration). No significant changes were observed across successive rounds, indicating stability of consensus. Anonymity of responses was maintained for all Working Group members and the extended Expert Panel (Appendix A) throughout the process; however, Coordinators were not blinded, as they retained password-protected access to responses and received feedback correspondence via email.
The Medical Education Academy (MedEA) was commissioned to facilitate the project and enlisted the expertise of two Coordinators (one affiliated to the European Society of Human Reproduction and Embryology [ESHRE], the other to the American Society for Reproductive Medicine [ASRM]). An online platform was created to facilitate the exchange of ideas and provide access to a curated collection of relevant publications. Given the limited evidence relating to fertility nursing and midwifery KPIs, a modified Delphi technique was implemented to elicit consensus. The Delphi method, widely applied in healthcare, is a structured approach to development of consensus where research is scarce [14]. Important elements of the Delphi technique include anonymity, iteration, controlled feedback, and statistical stability of agreement [14]. Traditionally, the Delphi process involves several rounds of open-ended questions and ends with closed questions; however, approved modifications incorporate meetings facilitated by skilled moderators [15]. For this project, modifications included proposed pre-selected topics (statements) discussed in virtual meetings, with feedback submitted via email to the Coordinators. This was followed by three rounds of voting, with participant responses discussed and refined (where appropriate) before subsequent voting rounds, until consensus of ≥66% was reached for each statement. The key modification from the traditional Delphi method was the inclusion of this interactive element, which enabled direct discussion, clarification, and debate among experts to foster deeper understanding. The Delphi technique guaranteed anonymity during all voting stages, with controlled feedback provided to panellists via email and web conferences (iteration). No significant changes were observed across successive rounds, indicating stability of consensus. Anonymity of responses was maintained for all Working Group members and the extended Expert Panel (Appendix A) throughout the process; however, Coordinators were not blinded, as they retained password-protected access to responses and received feedback correspondence via email.
The modified Delphi approach was selected in accordance with the Maribor consensus [8] and consisted of three phases:
Figure 1. Study design.
Delphi Consensus 2026
Fertility Nurses & Midwives
Results – key concepts
Irrespective of ethnicity, cultural values and beliefs, patients should be treated with dignity, empathy and respect and experience a positive, therapeutic and collaborative fertility nursing and midwifery relationship. Relevant KPIs suggest that patient-centred relationships improve patient outcomes [16].
Effective therapeutic communication underpins high-quality nursing care [17] and may reduce the risk of patient treatment “drop-out” [18]. Fertility nurses and midwives play a central role in ensuring that patients have access to health-related information that is both clear and compassionate [19]. This approach supports patients in understanding, retaining, and applying health information, thereby empowering them to make better health decisions [20] that ultimately improve treatment outcomes.
Nurses and midwives are frequently the primary patient educators in relation to self-administration of medication. In fertility treatment, strict adherence to prescribed regimens is essential to minimise errors or treatment cancellations and optimise clinical outcomes. Patients therefore rely on the knowledge and competency of fertility nurses and midwives to provide comprehensive advice that can help to minimise error and negative patient experiences [21].
In line with safe and effective medicines management, patient education on self-administration requires an in-depth knowledge of patient history, prescribed medication, dosage, route(s) of administration, and known side effects [22]. Given that such expertise enhances compliance and alleviates patient anxiety, it is therefore considered a relevant KPI.
Quantifying a patient’s understanding of self-administration can be challenging. After their initial education session, a useful benchmark for this KPI would be to track any additional follow-up required – such as number of call-backs to the clinic for additional information or clarification on medications (excluding those related to reassurance where patient perception is correct). However, this metric alone is insufficient, as some patients might not request further information despite uncertainty, potentially resulting in medication errors.
Therefore, a second KPI rate will track the number of medication errors during a treatment cycle relative to the number of patients in treatment. The minimum expected competence threshold for medication errors was set at <15% of medicated treatment cycles. This benchmark was deemed achievable, despite the existing literature reporting self-administration error rates between 19% and 59% [23]. In contrast, Huisman et al. (2009) [24] 7
highlighted patient reluctance to report mistakes to their healthcare provider, which reinforces the rationale for adopting the <15% benchmark as a pragmatic starting point.
ART procedures are generally safe [25]; however, significant complications can occur as consequence of controlled ovarian hyperstimulation, oocyte retrieval, or early pregnancy, and are frequently identified through nurse/midwife–patient communication. While complication rates following oocyte pick-up (OPU) are already established as a clinical KPI, the Working Group proposed that timely follow-up communication and/or care for complications (e.g. moderate or severe ovarian hyperstimulation syndrome, ectopic pregnancy, pregnancy of unknown location, ovarian torsion) should be recognised as a measure of competency in the context of fertility nursing and midwifery practice.
Patient safety depends on early recognition and reporting of complications by nurses and midwives [26], underscoring their fundamental role in this process [27]. Accordingly, the complication/follow-up rate is considered an important KPI. It should be calculated as the number of patients who received timely follow-up communication/care after documented complications (numerator) divided by the total number of patients who experienced complications from an ART procedure (denominator).
A 6-monthly data collection period was considered sufficient to generate meaningful data, but smaller clinics may adjust this interval as appropriate. The competence threshold (minimum expected value) was set at 70% to allow for circumstances beyond the control of the fertility nurse/midwife, while reflecting competency for most of the patients in their care. The benchmark value (aspiration value) was set at 90%, representing sustained excellence and encouraging continuous improvement beyond foundational competence, while accounting for uncontrollable incidental factors.
Nursing and midwifery fertility practice requires a strong educational foundation, supported by the development of specialist knowledge and skills in endocrinology and reproductive health. Evidence suggests that establishing minimum education requirements, ensuring professional competency, and maintaining programmes of continuing education all helps to improve patient safety, outcomes, and quality of care [28-32]. These elements are therefore considered relevant KPIs, to be evaluated annually as part of appraisal and/or professional development processes.
KPI 9 provides a framework for clinics to define both core and advanced practice competencies based on the specific needs of their country, state, and/or patient population. Once competencies are established, this KPI enables clinics to monitor the proportion of nurses and midwives actively working towards completion or practising within the defined competency framework.
Pregnancy following fertility treatment can be both exciting and challenging; it is often an anxious time for patients, because of the disconnect between fertility and obstetric services [33]. A gap exists in the psychological support of women conceiving (often after a lengthy fertility journey), as most fertility-related pregnancies are confirmed at around 6–7 weeks, followed by a potentially lengthy wait – up to 7 weeks – before the obstetric consultation and confirmatory ultrasound.
Awareness and understanding within obstetric teams is therefore critical to appreciating the patient journey and ensuring appropriate planning and delivery of care. For this reason, the transition from fertility to antenatal care was identified as a relevant KPI, reflecting the importance of continuity, timely support, and patient‑centred care during this vulnerable stage.
Table 1. Statements evaluated
Discussion
Evaluation of survey-based KPIs
KPIs 1–4 and 10 would be monitored using patient satisfaction questionnaires, structured around key defined themes and assessed through psychometrically tested, standardised methods (Likert scale) to evaluate patient perceptions and experiences.
Minimum expected values (competence thresholds), regardless of clinic size or patient profile, and aspirational or benchmark values (best practice goals) were determined for each KPI based on available data and standards from other disciplines or nursing professional bodies. Baseline satisfaction scores reported in healthcare, and specifically in ART care, generally exceed 60% [34-36]. Accordingly, a competency threshold of 60% was set, with a higher threshold of 70% applied to KPI 1 (Dignity, Empathy, and Respect) to account for factors not directly attributable to nursing performance but known to impact patient survey responses [37].
The setting of the competence value received lower levels of agreement in several instances. Figure 2 illustrates the degree of consensus for each KPI, colour graded from dark green (strong agreement) to red (consensus not reached).
Although consensus (≥66%) was achieved, experts acknowledged that the agreed value may be insufficient; further research may be warranted to evaluate its appropriateness. The defined KPIs should be calculated every 3 months or per 100 responses, depending on clinic size. This frequency would enable for early identification of issues and timely intervention to prevent negative patient experience and/or outcomes, while ensuring a sufficiently robust dataset for accurate evaluation.
Figure 2. Distribution of agreement levels (heat map)
COLOUR KEY
Numbered statements correspond with those in Table 1. Agreement levels are represented using a red–amber–green gradient:
- <66% agreement is shown in amber to red, indicating areas that did not achieve consensus
- ≥66% agreement (consensus) is shown in green, with colour intensity increasing as agreement approaches 100%
Limitations
Where available, consensus values were informed by the existing literature and expert discussion across several rounds of KPI review. However, as noted earlier, the evidence base in this area is limited. Competence and benchmark values should therefore be interpreted in the context of clinic size, scope of practice, and patient population.
Participation in the final voting stage was lower than in the second stage. Attrition across rounds is a recognised limitation of the Delphi technique, particularly with larger panels, where declining participation rates are common [38]. While this might suggest reduced agreement with modifications to the original statements, it could simply reflect the methodological characteristics of the Delphi technique. Notably, Nasa et al. (2021) [14] recommend that a double-digit number (30–50 participants) is considered optimal for concluding rounds in a homogenous Delphi study population.
Further considerations for implementation of the proposed KPIs
The roles and scope of fertility nursing and midwifery clinical practice vary considerably across regions, states, and countries. Although laws, policies, and guidelines often dictate levels of clinical (and specialist) practice, the creation of KPIs specifically relevant to fertility nurses and midwives offers the potential for more consistent implementation. The following factors will need to be considered:
- Clinic size. The KPIs will inevitably need to be tailored to clinic size and patient population.
- Limited evidence base. A paucity of data on the direct influence of fertility nursing and midwifery care made it challenging to set benchmarks and aspirational values.
- Data collection challenges. Effective KPI frameworks may be hindered by inconsistent documentation and data collection practices. Depending on implementation methods, some clinics may experience an increased workload due to additional data entry or paperwork requirements.
- Survey limitations. Low patient survey response rates would pose a risk of sampling bias, leading to unreliable or non-generalisable conclusions.
- Leadership dependency. Effective KPI implementation relies on strong support and positive leadership; without this, compliance and progress are likely to be constrained.
Wider implications of the findings
These proposed nursing and midwifery KPIs complement the previously defined clinical and laboratory indicators for ART. Their development completes the framework of direct and indirect clinical indicators in reproductive medicine, with the overall aim of delivering safe, patient-centred, timely, and equitable fertility care throughout the entire fertility journey, in line with quality management requirements. By leveraging measurable outcomes, KPIs enable fertility services to demonstrate quality and efficiency, inform patient decision-making, and ultimately enhance patient satisfaction with care received.
The introduction of fertility nursing and midwifery KPIs represents an important first step, allowing clinics to begin tracking these metrics to evaluate whether they accurately reflect safety, quality of care, and improved outcomes. Further research will be required to assess the impact of KPI implementation on patient compliance and satisfaction.
Following publication, our intention is to disseminate the project aims, objectives, process and findings to colleagues at local and annual meetings (ESHRE/ASRM). A pilot project will then be implemented, with a clearly defined plan and objectives, recruiting from a small, representative sample of clinics (both large and small). Data and feedback from participants will be collated to assess feasibility and identify potential issues or refinements prior to wider implementation.
Some metrics may require subtle changes to workflow (e.g. identifying staff educational status and needs, or collecting information on current continuing education practices), while others may require evaluation of current processes. For example, clinics already conducting patient satisfaction surveys could incorporate additional questions to capture the new metrics, rather than creating a new survey.
IT software programs have the capacity to monitor and analyse real-time quantitative and qualitative data in accordance with KPI compliance, enabling identification of any deviations, informing clinical decision-making, and facilitating necessary changes. Ideally, maximum automation should be implemented to reduce staff burden in distributing patient questionnaires and collecting data. With the increasing role of AI, KPI data can now be captured directly from electronic medical records without altering documentation workflows, thus permitting large-volume data analysis and automated extraction from relevant fields.
Regardless of how data is captured, it is paramount that fertility nurses and midwives recognise the value of metrics to their own specific patient population and clinic, so that they are motivated to participate. Focussed training may be required to ensure appropriate data input for meaningful KPI outputs.
The proposed benchmarks provide a starting point for clinics, especially those with limited or no historical data. Following implementation of the proposed KPIs, clinics can use their data to track trends – both positive and negative – within specific performance indicators. Such ongoing monitoring is essential to retain key stakeholder engagement and ensure that metrics are tracked at regular intervals.
References
1. Anderson C, Wardell AC, Deal AM, Mersereau JE, Cameron K, Spandorfer SD, et al. Fertility preservation and in vitro fertilization (IVF) success rates after cancer. JNCI Cancer Spectr. 2025; 9.
2. Adamson GD, Creighton P, de Mouzon J, Zegers-Hochschild F, Dyer S, Chambers GM. How many infants have been born with the help of assisted reproductive technology? Fertil Steril. 2025; 124:40–50.
3. Alanazi ZBH, Alanazi AAF, Al-Ruwaili SM, Alnawazi NA, Alshammari MHA, Althahabi EF, et al. The role of nurses in fertility treatment support. Lett High Energy Phys 2023;2:192-207. 2023. https://lettersinhighenergyphysics.com/index.php/LHEP/article/download/905/579/.
4. Hariton E, Alvero R, Hill MJ, Mersereau JE, Perman S, Sable D, et al. Meeting the demand for fertility services: the present and future of reproductive endocrinology and infertility in the United States. Fertil Steril. 2023; 120:755–766.
5. Institute of Medicine Committee on Quality of Health Care in America. To err is human: building a safer health system. National Academies Press (USA); 2000.
6. Wirka KA, Maldonado Rosas I, Anagnostopoulou C, Singh N, Manoharan M, Barros B, et al. Taking a closer look at the key performance indicators in an assisted reproductive technology laboratory: a guide for reproductive professionals. Panminerva Med. 2022; 64:200–207.
7. ESHRE Special Interest Group of Embryology; Alpha Scientists in Reproductive Medicine. The Vienna consensus: report of an expert meeting on the development of art laboratory performance indicators. Hum Reprod Open. 2017; 2017:hox011.
8. ESHRE Clinic PI Working Group, Vlaisavljevic V, Apter S, Capalbo A, D’Angelo A, Gianaroli L, et al. The Maribor consensus: report of an expert meeting on the development of performance indicators for clinical practice in ART. Hum Reprod Open. 2021; 2021:hoab022.
9. Gormley E, Connolly M, Ryder M. The development of nursing-sensitive indicators: a critical discussion. Int J Nurs Stud Adv. 2024; 7:100227.
10. Dancet EA, D’Hooghe TM, Spiessens C, Sermeus W, De Neubourg D, Karel N, et al. Quality indicators for all dimensions of infertility care quality: consensus between professionals and patients. Hum Reprod. 2013; 28:1584–1597.
11. van Empel IW, Hermens RP, Akkermans RP, Hollander KW, Nelen WL, Kremer JA. Organizational determinants of patient-centered fertility care: a multilevel analysis. Fertil Steril. 2011; 95:513–519.
12. Patrick T, Hebert C, Roberts P, Ribeiro F, Collares F, Catherino AB, et al. Key performance indicators in the ART nursing practice – a survey study. Fertil Steril. 2023; 120:e41–e42.
13. Nursing & Midwifery Council (NMC). Principles for advanced practice. June 2025. https://www.nmc.org.uk/globalassets/sitedocuments/advanced-practice-review/principles-for-advanced-practice-english.pdf5
14. Nasa P, Jain R, Juneja D. Delphi methodology in healthcare research: how to decide its appropriateness. World J Methodol. 2021; 11:116–129.
15. Hohmann E, Beaufils P, Beiderbeck D, Chahla J, Geeslin A, Hasan S, et al. Guidelines for designing and conducting Delphi consensus studies: an expert consensus Delphi study. Arthroscopy. 2025; 41:4208–4224.e4210.
16. Allande-Cussó R, Fernández-García E, Porcel-Gálvez AM. Defining and characterising the nurse–patient relationship: a concept analysis. Nursing Ethics. 2021; 29:462–484.
17. Kwame A, Petrucka PM. A literature-based study of patient-centered care and communication in nurse-patient interactions: barriers, facilitators, and the way forward. BMC Nurs. 2021; 20:158.
18. Palmer-Wackerly AL, Voorhees HL, D’Souza S, Weeks E. Infertility patient-provider communication and (dis)continuity of care: an exploration of illness identity transitions. Patient Educ Couns. 2019; 102:804–809.
19. Barrière P, Avril C, Benmahmoud-Zoubir A, Bénard N, Dejager S. Patient perceptions and understanding of treatment instructions for ovarian stimulation during infertility treatment. Reprod Biomed Soc Online. 2019; 9:37–47.
20. Wilandika A, Pandin MGR, Yusuf A. The roles of nurses in supporting health literacy: a scoping review. Front Public Health. 2023; 11:1022803.
21. Aremu TO, Oluwole OE, Adeyinka KO, Schommer JC. Medication adherence and compliance: recipe for improving patient outcomes. Pharmacy (Basel). 2022; 10.
22. Bashir H, Barkatullah M, Raza A, Mushtaq M, Khan KS, Saber A, et al. Practices used to improve patient safety culture among healthcare professionals in a tertiary care hospital. Glob J Qual Saf Healthc. 2024; 7:9–14.
23. Mira JJ, Lorenzo S, Guilabert M, Navarro I, Pérez-Jover V. A systematic review of patient medication error on self-administering medication at home. Expert Opinion on Drug Safety. 2015; 14:815–838.
24. Huisman D, Raymakers X, Hoomans EH. Understanding the burden of ovarian stimulation: fertility expert and patient perceptions. Reprod Biomed Online. 2009; 19 Suppl 2:5–10.
25. Wang J, Sauer MV. In vitro fertilization (IVF): a review of 3 decades of clinical innovation and technological advancement. Ther Clin Risk Manag. 2006; 2:355–364.
26. Langkjaer CS, Bundgaard K, Bunkenborg G, Nielsen PB, Iversen KK, Bestle MH, et al. How nurses use National Early Warning Score and Individual Early Warning Score to support their patient risk assessment practice: a fieldwork study. J Adv Nurs. 2023; 79:789–797.
27. Glarcher M, Vaismoradi M. A systematic integrative review of specialized nurses’ role to establish a culture of patient safety: a modelling perspective. J Adv Nurs. 2025; 81:5248–5263.
28. Azad A, Min J-G, Syed S, Anderson S. Continued nursing education in low-income and middle-income countries: a narrative synthesis. BMJ Global Health. 2020; 5:e001981.
29. de Silva D, Crossman S, Rogers G. Feasibility of adopting Minimum Education Standards for general practice nursing. Practice Nursing, 2 May 2022. 2022. https://www.practicenursing.com/content/research/feasibility-of-adopting-minimum-education-standards-for-general-practice-nursing.
30. Main PAE, Anderson S. Evidence for continuing professional development standards for regulated health practitioners in Australia: a systematic review. Hum Resour Health. 2023; 21:23.
31. Pouresmail Z, Nabavi FH, Rassouli M. The development of practice standards for patient education in nurse-led clinics: a mixed-method study. BMC Nurs. 2023; 22:277.
32. Tober D, Garibaldi C, Blair A, Baltzell K. Alignment between expectations and experiences of egg donors: what does it mean to be informed? Reprod Biomed Soc Online. 2021; 12:1–13.
33. Royal College of Nursing NPA. Transition from fertility to maternity care, 2nd ed. Royal College of Nursing; 2025.
34. Leslie HH, Lee H-Y, Blouin B, Kruk ME, García PJ. Evaluating patient-reported outcome measures in Peru: a cross-sectional study of satisfaction and net promoter score using the 2016 EnSuSalud survey. BMJ Quality &amp; Safety. 2022; 31:599.
35. Sacks GD, Lawson EH, Dawes AJ, Russell MM, Maggard-Gibbons M, Zingmond DS, et al. Relationship between hospital performance on a patient satisfaction survey and surgical quality. JAMA Surgery. 2015; 150:858–864.
36. Shandley LM, Hipp HS, Anderson-Bialis J, Anderson-Bialis D, Boulet SL, McKenzie LJ, et al. Patient-centered care: factors associated with reporting a positive experience at United States fertility clinics. Fertility and Sterility. 2020; 113:797–810.
37. Alharbi HF, Alzahrani NS, Almarwani AM, Asiri SA, Alhowaymel FM. Patients’ satisfaction with nursing care quality and associated factors: a cross-section study. Nurs Open. 2023; 10:3253–3262.
38. Gargon E, Crew R, Burnside G, Williamson PR. Higher number of items associated with significantly lower response rates in COS Delphi surveys. J Clin Epidemiol. 2019; 108:110–120.
Acknowledgements
This report was developed by nurses and midwives representing ASRM and ESHRE. The authors thank the Working Group members and international experts who contributed to the stakeholder review for their helpful comments and suggestions. The meetings and technical assistance for this project were facilitated by MedEA Medical Education Academy, with editorial support from Alison Terry, and funded by EMD Serono Inc.
Funding
This work was supported by EMD Serono Inc., covering time associated with meetings, literature searches, and dissemination.
Competing interests
VP reports previous honoraria from MedEA, Merck, Ferring, IBSA and Gedeon-Richter outside the submitted work. CH reports honoraria from EMD Serono Inc., Ferring, and Medscape outside the submitted work.
Disclaimer
This report represents the views of the contributing authors and expert panellists, including individuals affiliated with the American Society for Reproductive Medicine (ASRM) and the European Society of Human Reproduction and Embryology (ESHRE). The consensus, where applicable, was informed by the scientific evidence available at the time of writing. These recommendations are intended to support clinical practice and professional discussion and should not be interpreted as establishing a standard of care. Clinical judgement should always be exercised in the application of these recommendations to individual patients.
Appendix A. Working Group and Expert Panel members
The nurses and midwives who participated in the voting are listed below, together with a world map showing the geographic distribution of respondents.
Working Group
Nicole Callahan | Mayo Clinic, Rochester, MN, USA | ASRM |
Hilde Cotton | Klinikk Hausken, Bergen, Norway | ESHRE |
Louise Dias | Fertility Clinic of ZAS Augustinus, Antwerp, Belgium | ESHRE |
Julia Gonnot | Cochin Hospital, Paris, France | ESHRE |
Helen Kendrew | Independent Nurse Specialist (retired) | ESHRE |
Sarah Lyndon | ESHRE Nurses and Midwives Certification Committee, Dublin, Ireland | ESHRE |
Tonie Patrick | Colorado Center for Reproductive Medicine, Littleton, CO, USA | ASRM |
Patti Roberts | Reproductive Science Center of the Bay Area (a USF affiliate), San Ramon, CA, USA | ASRM |
Sara Somers | Ghent University Hospital, Ghent, Belgium | ESHRE |
Eleanor L. Stevenson | Duke University School of Nursing, Durham, NC, USA | ASRM |
Rodriq E. Stubbs | Weill Cornell Medicine, New York, NY, USA | ASRM |
Kriston Ward | Strong Fertility Center, University of Rochester, Rochester, NY, USA | ASRM |
Helen Kendrew reports consultancy for IBSA Pharmaceuticals; no other Working Group members have any relevant conflicts of interest to declare.
Expert Panel
|
Violet Augustus |
Reproductive Medicine Unit, Groote Schuur Hospital, Cape Town, South Africa |
|
Morgan Baker |
Utah Fertility Center, UT, USA |
|
Remi Béranger |
Rennes University Hospital, Rennes, France |
|
Madeleine Bernet |
Bern University of Applied Sciences, Bern, Switzerland |
|
Louise Boyle |
NHS Forth Valley, Falkirk, Scotland, UK |
|
Roxanne Buckmaster |
Spring Fertility, Beaverton, OR, USA |
|
Isabel Bustamante |
Northwell Health, New York, NY, USA |
|
Eline Dancet |
Faculty of Medicine, KU Leuven, Belgium |
|
Jonalyn De Vera Edades |
ART Fertility Clinics, Abu Dhabi, United Arab Emirates |
|
Melanie Dinis |
Eugin Coimbra Fertility Clinic, Coimbra, Portugal |
|
Jamie Feingold |
Strong Fertility Center, Rochester, NY, USA |
|
Ilkay Güngör Satılmış |
Istanbul University – Cerrahpaşa, Turkey |
|
Marta Hajdarevic |
Pacific Centre for Reproductive Medicine, Edmonton, AB, Canada |
|
Karin Hammarberg |
Monash University, Melbourne, VIC, Australia |
|
Anne Howard |
Royal College of Nursing, UK |
|
Lisa Hughes-Thompson |
Ghana College of Nurses and Midwives, Accra, Ghana |
|
Amber Kiddie |
Fertility Centers of Illinois, IL, USA |
|
Karen Henriette Kirchheiner Jensen |
Rigshospitalet, Copenhagen, Denmark |
|
Linda Kluge |
NMCC, Q-IVF, The University of Gothenburg, Sweden |
|
Vicky Loney* |
Aberdeen Fertility Centre, NHS Grampian, Scotland, UK |
|
Jessica Medvedich |
Reproductive Medicine Associates, Annandale, NJ, USA |
|
Danielle Miller |
San Diego Fertility Center, San Diego, CA, USA |
|
Darshana Naik |
Shady Grove Fertility, Walter Reed Medical Center, Bethesda, MD, USA |
|
Hetty Ockhuijsen |
Utrecht University Medical Centre, Utrecht, The Netherlands |
|
Malgorzata Piatkowska |
Invimed Clinic, Warsaw Mokotów, Poland |
|
Melissa Pichi |
Reproductive Medicine Associates, New York, NY, USA |
|
Katherine Pleace |
School of Nursing, Psychotherapy and Community Health, University of Essex, UK |
|
Shana Puetz |
Tennessee Fertility Institute, TN, USA |
|
Leigh Anne Reid* |
Aberdeen Fertility Centre, NHS Scotland, UK |
|
Elisa Rinta-Lusa |
HUS, Helsinki, Finland |
|
Fernanda Robin |
Ser Fertilidade Ltda, Brazil |
|
Courtney Schmidt |
Shady Grove Fertility, Richmond, VA, USA |
|
Britt Schoonjans |
Brussels IVF, University Hospital Brussels, Belgium |
|
Carole Tilbrook |
Repromed, Adelaide, SA, Australia |
|
Jen Ulyatt |
NHS Tayside, Dundee, Scotland, UK |
|
Chloë Van Loock |
UZ Leuven, Leuven, Belgium |
|
Maureen Wood |
NHS Tayside, Dundee, Scotland, UK |
|
Jennifer Wood |
Shady Grove Fertility, Washington, DC, USA |
*Combined vote
Appendix B. Timeline
• November 2024: First web conference to review the project aims and objectives and elicit feedback; content was shared with the two Coordinators via email.
• December 2024: During a second web conference, the Coordinators, together with the Working Group, proposed a list of potential KPIs and associated statements based on the existing literature.
• January 2025: Third web conference to refine the proposed statements with anonymised voting by the 12 members of the Working Group (first voting stage).
• April/May 2025: The Working Group members and extended Expert Panel were invited to complete an online questionnaire to systematically develop a consensus on specific statements of competency and associated benchmarks (second voting stage). Altogether, 49 votes were completed, representing 20 countries.
• June 2025: The two Coordinators collected and analysed the results, which were then presented as calculated percentiles to the Working Group during a web conference. Statements (including indicators, definitions, benchmarks, and competency thresholds) were considered approved when ≥66% of the responders agreed.
• June 2025: The final voting stage was communicated via email and sought to determine the level of agreement regarding the proposed competency level of a single statement that did not reach consensus of ≥66 (third voting stage).
• June 2025: All 10 indicators were accepted by the Working Group and Expert Panel and included in the final list.
Note: Anonymity of responses was maintained throughout the study for all members of the Working Group and Expert Panel; however, Coordinators had password-protected access to the responses and received feedback correspondence via email; therefore, they were not blinded.