A bundled nurse-physician follow-up team improves treatment initiation in infertility care: a quality improvement study

A bundled nurse-physician follow-up team improves treatment initiation in infertility care: a quality improvement study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A bundled nurse-physician follow-up team improves treatment initiation in infertility care: a quality improvement study Liuyan Wei, Zhouhong Wei, Bingling Zhao, Jia Wei, Caiping Luo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9240198/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The period between first infertility consultation and treatment initiation is a critical point of attrition in assisted reproductive technology (ART) care, particularly in resource-constrained settings. We evaluated a bundled nurse–physician follow-up intervention designed to improve treatment initiation among first-visit infertility patients. Methods We conducted a before–after quality improvement study at a tertiary public reproductive medicine center in Guangxi, China, from March to December 2023, with baseline data collected from January to February 2023. The intervention included: (1) "1 physician + 2 nurses" teams assigned to new patients; (2) digital tracking through QR-code-linked questionnaires; and (3) triage-based alerts for patients at high risk of disengagement. Primary outcome was treatment initiation within 6 months of first visit. Secondary outcomes included loss to follow-up, time to treatment initiation, follow-up completion, and patient complaints. A total of 4,336 first-visit patients were included (baseline n = 712; intervention n = 3624). Results Treatment initiation increased from 19.43% at baseline to 25.83% during the intervention, corresponding to an absolute increase of 6.4 percentage points and a relative increase of 32.94%. Lost-to-follow-up declined from 98.0% to 0.0%, and median time to initiation decreased from 112 to 78 days. QR code registration reached 99.7%, and 95.2% of patients completed first follow-up within 72 hours. Complaints related to care pathway confusion decreased from 12 cases to zero. Conclusions A low-cost, team-based follow-up model improved treatment initiation and continuity of care among first-visit infertility patients without requiring additional staffing or new IT infrastructure. This scalable intervention provides a pragmatic approach to reducing early attrition and strengthening the transition from diagnosis to treatment in resource-constrained reproductive health. Treatment initiation Infertility care Follow-up Team-based care Quality improvement Figures Figure 1 Background Infertility affects an estimated 17.5% of the global adult population, with prevalence rising in low- and middle-income countries (LMICs) due to delayed childbearing, limited access to timely diagnosis, and structural barriers within health systems[ 1 , 2 ]. While assisted reproductive technology (ART) offers effective treatment, its impact is severely constrained by low uptake—particularly during the critical transition from initial consultation to treatment commencement[ 3 , 4 ]. This phase—commonly termed the “diagnostic-to-treatment gap”—represents one of the most vulnerable points in the global infertility care pathway. As corroborated by empirical studies across diverse health systems, newly diagnosed individuals frequently disengage from care before initiating treatment due to insufficient health information, ambiguous next-step guidance, and absence of structured follow-up support—leading to substantial delays or complete discontinuation of care[ 5 – 7 ]. In China, where public maternal and child health hospitals serve as primary gateways to ART services, this loss to follow-up is especially pronounced among first-visit patients—driven by complex interplay of financial concerns, fragmented referral pathways, insufficient health literacy, and lack of coordinated follow-up support[ 8 , 9 ] . Current strategies to improve treatment initiation remain fragmented and poorly scaled. Provider-led reminder systems are often inconsistent and time-intensive; digital health tools (e.g., SMS or app-based alerts) show promise but face low adoption in settings with limited digital infrastructure or older patient demographics[ 10 , 11 ]. Meanwhile, task-sharing models—such as nurse-led counseling or community health worker outreach—have demonstrated efficacy in chronic disease management[ 12 , 13 ], yet their application to reproductive health service delivery remains underexplored, particularly within public hospital settings where workforce constraints and hierarchical workflows pose implementation challenges[ 14 , 15 ]. Crucially, most existing interventions focus on individual-level adherence (e.g., medication taking) rather than addressing system-level failures in patient flow coordination, continuity of care, and relational trust-building between patients and providers[ 16 ]. This implementation gap highlights the need for pragmatic and scalable quality improvement (QI) strategies that can be integrated into routine clinical workflows, leverage existing human resources, and strengthen patient-centered communication. In response, we developed and implemented a bundled nurse–physician follow-up intervention at a tertiary public maternal and child health hospital in Guangxi, China, and evaluated its impact on treatment initiation among first-visit infertility patients. Grounded in principles of relational coordination and proactive care navigation, this model aims not only to reduce lost-to-follow-up but also to strengthen continuity between diagnosis and treatment. Improving treatment initiation may serve as a measurable indicator of accessibility, responsiveness, and equity in reproductive health services. Methods Study design and setting This study was a prospective, single-center, before–after QI study conducted at the Reproductive Medicine Center of Liuzhou Maternal and Child Health Hospital—a tertiary public maternal and child health hospital in Guangxi Zhuang Autonomous Region, China. The center is accredited to perform all generations of assisted reproductive technology (ART), including preimplantation genetic testing (PGT), and serves as the regional training base for ART standardization in Guangxi. The intervention was implemented from 1 March 2023 to 31 December 2023. Baseline data were collected from 1 January to 28 February 2023. The study followed the SQUIRE 2.0 guidelines[ 17 ] and was registered as an internal QI project with the hospital’s Quality Management Office (No. LZFY-ART-QI-2023-001). Study population All adult patients (≥ 18 years) presenting for their first consultation at the Reproductive Medicine Center during the study period were eligible for inclusion. Patients were excluded if they: (1) Prior receipt of assisted reproductive technology (ART) treatment at this hospital; (2) Presentation for non-ART services only (e.g., isolated gynecologic surgery, routine prenatal care, or contraceptive counseling); (3) Explicit refusal to participate in telephone follow-up (i.e., inability to provide informed consent for the follow-up component). As this was a pragmatic, system-level QI intervention targeting all eligible patients during routine clinical operations, no formal sample size calculation was performed. A total of 4336 first-visit patients were included in the study (baseline period: n = 712; intervention period: n = 3624). Intervention The intervention consisted of a bundled nurse–physician follow-up model designed to improve patient continuity between the first consultation and treatment initiation. The intervention included three components: multidisciplinary follow-up teams, a digital patient tracking system, and standardized follow-up communication protocols. Multidisciplinary follow-up teams Twelve multidisciplinary teams were established across the two hospital campuses. Each team consisted of one reproductive physician and two nurses and was responsible for a defined group of first-visit patients. Physicians remained responsible for clinical assessment, treatment planning, and direct communication with patients identified as high risk for treatment delay. One nurse conducted the first telephone follow-up within 72 hours of the initial consultation using a standardized communication script. The second nurse maintained a digital tracking sheet documenting follow-up contacts and monitoring patient progression along the treatment pathway. Digital patient tracking and triage alerts At the first visit, patients registered by scanning a QR code linked to a brief intake questionnaire developed using a WeChat-integrated survey platform. The questionnaire captured demographic information, contact details, and initial treatment intentions. Patient progress was monitored using a shared digital spreadsheet that recorded visit dates, follow-up contacts, completion of diagnostic testing, and treatment initiation. A colour-coded triage system was used to prioritise follow-up actions: 1) Green status: follow-up plan confirmed within 7 days; 2) Yellow status: no treatment decision within 14 days; 3) Red status: treatment not initiated within 30 days or patient expressed uncertainty about continuing care. Patients classified as yellow or red status were escalated to nurse managers or physicians for additional contact. Standardized follow-up communication A follow-up communication guide was developed to standardise interactions with patients. The guide included structured scripts for the initial 72-hour follow-up call and subsequent follow-up contacts, focusing on confirming patient understanding, clarifying next steps, and addressing barriers to treatment initiation. Quality monitoring Monthly quality review meetings were conducted involving physicians, nurses, and administrative staff participating in the intervention. During these meetings, aggregated data from the tracking system were reviewed, including follow-up completion rates, diagnostic testing completion, and treatment initiation rates. The meetings also provided opportunities to discuss complex cases and refine workflow processes. Outcomes The primary outcome was the proportion of first-visit patients who initiated an ART treatment cycle within six months of the initial consultation. Secondary outcomes included: loss to follow-up, time from initial consultation to treatment initiation, completion of follow-up contact within 72 hours, and selected indicators of patient experience (including complaints related to follow-up). Data collection Patient demographic and follow-up data were recorded in the digital tracking system during routine clinical operations. Information collected included patient registration data, follow-up contact dates, diagnostic testing completion, and treatment initiation status. Data were extracted from the tracking system and the center’s routine clinical database for analysis. Ethical considerations This quality improvement study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Medical Research Ethics Committee of Guangzhou Women and Children's Medical Center Liuzhou Hospital (Approval No.: Expedited-Research-2026-050). As this study involved the analysis of routinely collected anonymized data from a quality improvement initiative, the requirement for informed consent was waived by the ethics committee. All patient information was handled in accordance with hospital confidentiality policies. Patients were informed during their initial visit that follow-up contact might occur as part of routine care and could opt out at any time. Statistical analysis Descriptive statistics were used to summarize patient characteristics and outcomes, with continuous variables reported as medians with interquartile ranges and categorical variables as frequencies with percentages. Baseline characteristics were compared between the pre-intervention and intervention periods using the χ² test for categorical variables and the Mann–Whitney U test for continuous variables. The primary outcome—treatment initiation within six months—was calculated as a proportion with 95% confidence intervals estimated using the Wilson score method; absolute and relative changes were derived accordingly. Secondary outcomes, including lost-to-follow-up, time to treatment initiation, and follow-up completion rates, were summarized descriptively, with patient complaints compared across quarters. All analyses were conducted using R software (version 4.2.0), and statistical significance was defined as p < 0.05. Results Patient population and baseline characteristics From 1 January to 31 December 2023, a total of 4336 first-visit infertility patients were included in the study, comprising 712 patients in the pre-intervention period (January to February 2023) and 3624 patients during the intervention period (March to December 2023). Baseline demographic and clinical characteristics were similar across the two periods (Table 1 ). The mean age was 32.4 years (SD 5.2) in the pre-intervention period and 32.7 years (SD 5.4) during the intervention period. The proportion of patients who had received infertility treatment elsewhere was 23.1% before the intervention and 24.3% during the intervention.The distribution of infertility diagnoses, including female factor, male factor, combined, unexplained infertility, did not differ significantly between periods (all p > 0.05). Table 1 Baseline characteristics of patients Characteristic Pre-intervention (Jan–Feb 2023) Intervention period (Mar–Dec 2023) Statistical value P value Number of patients 712 3624 Age (years), median (IQR) 34.00 (8.00) 34.00 (8.00) -0.870 a 0.384 Prior infertility treatment elsewhere, n (%) 164 (23.03%) 880 (24.28%) 0.508 b 0.476 Duration of infertility (years), median (IQR) 3.00(3.00) 3.00(4.00) -0.531 a 0.595 Infertility diagnosis category, n (%) Female factor 581(81.60%) 3006(82.95%) 4.809 b 0.186 Male factor 48(6.74%) 229(6.32%) Combined factor 59(8.29%) 314(8.66%) Unexplained 24(3.37%) 75(2.07%) Note: a Z value, b χ 2 value。 Primary outcome: Treatment initiation rate Primary outcome, treatment initiation within 6 months of the first visit, increased after implementation of the bundled nurse-physician follow-up model. During the pre-intervention period, 19.43% of first-visit patients initiated treatment within 6 months (95% CI 16.7–22.4%), compared with 25.83% during the intervention period (95% CI 24.5–27.2%), corresponding to 6.4 percentage points and a relative increase of 32.94%. This exceeded the pre-specified improvement target of 6 percentage points. The monthly run chart showed a progressive increase in treatment initiation across the intervention period, from 20.1% in March 2023 to 26.8% in December 2023, with the steepest gains observed after full implementation in July to September 2023 (Fig. 1 ). Lost-to-follow-up rate Lost to follow-up decreased markedly after the intervention. Before the intervention, 98.0% of first-visit patients did not receive any active follow-up after leaving the clinic. During the intervention period, all 4,336 patients were entered into the digital tracking system and received at least one follow-up attempt, such that the proportion with no active follow-up decreased to 0.0%. Among intervention-period patients, 2.8% could not be reached after three contact attempts; however, these patients remained in the tracking system and were therefore not classified as lost to follow-up under the study definition. Time-to-treatment initiation The median time from initial visit to treatment initiation decreased from 112 days (interquartile range [IQR] 78–156) before the intervention to 78 days (IQR 52 to 114) during the intervention, a reduction of 34 days (30.4%). This reduction was observed across age groups and diagnostic categories, and was greatest among patients aged 35 years or older, in whom the median time decreased from 134 to 93 days. Process measures Clinical volume outcomes Process indicators showed high uptake and implementation fidelity. During the intervention period, QR-code registration was completed by 4323/4336 (99.7%); the remaining 13 patients (0.3%) were entered manually because of technical difficulties or refusal to provide digital information. First follow-up within 72 hours was completed for 4128/4336 (95.2%). Among the 208 patients not reached within 72 hours, the most common reasons were incorrect telephone numbers (89/208, 42.8%), no answer after three attempts (76/208, 36.5%), and patient request not to be contacted (43/208, 20.7%). Second follow-up, scheduled 14 to 21 days after the first visit, was completed for 3785/4336 patients (87.3%). At 30 days after the initial visit, 61.4% of patients (2662/4336) were classified as green status, indicating that a follow-up plan had been confirmed and the patient was progressing through the treatment pathway; 26.8% (1162/4336) were yellow status and were escalated to nurse managers; and 11.8% (512/4336) were red status and were referred back to physicians for direct communication (Table 2 ). Among red-status patients, 47.2% (242/512) initiated treatment within an additional 30 days after physician contact. The remaining patients either continued to delay decision-making (158/512, 30.9%), had treatment contraindications identified (67/512, 13.1%), or declined further contact (45/512, 8.8%). Table 2 Primary and secondary outcome Outcome measure Pre-intervention (Jan–Feb 2023) Intervention period (Mar–Dec 2023) Absolute change Relative change Primary outcome Treatment initiation rate (within 6 months), % 19.43 25.83 + 6.40 pp + 32.94% Secondary outcomes Lost-to-follow-up rate, % 98.0 0 -98.0 pp -100% Median time-to-initiation (days) 112 78 -34 days -30.4% Process measures QR code registration rate, % 0 99.7 + 99.7 pp - First follow-up completion within 72 hours, % 0 95.2 + 95.2 pp - Patient complaints (quarterly), n 12 (Q1) 0 (Q4) -12 -100% As a supplementary indicator of service activity, we examined oocyte retrieval cycles. Compared with the corresponding months in 2022, retrieval cycles in 2023 were lower during the early implementation period but increased in later months. In December 2023, 405 retrieval cycles were recorded, compared with 328 in December 2022, an increase of 23.5%. Across the full calendar year, the total number of retrieval cycles was 4,576 in 2023 and 4,553 in 2022. For the intervention period specifically (March to December), the cumulative number increased from 3,927 in 2022 to 3,976 in 2023. Patient experience and complaints Patient complaints related to care pathway confusion or lack of follow-up decreased over time. According to institutional records, 12 such complaints were documented in the first quarter of 2023. In addition, follow-up records suggested improved patient acceptance of the proactive follow-up approach. Sustainability of improvements In the first two months after the formal intervention period, improvements were sustained. The treatment initiation rate was 26.1% in January 2024 and 26.4% in February 2024, while follow-up completion remained above 94%, indicating continued routine use of the tracking and follow-up model. Discussion Following implementation of the bundled nurse–physician follow-up intervention, the proportion of first-visit infertility patients initiating ART treatment within 6 months increased from 19.43% to 25.83%, an absolute increase of 6.4 percentage points and a relative increase of 32.94%. The proportion of patients receiving no active follow-up decreased from 98.0% to 0.0% under the study definition, and the median time from first consultation to treatment initiation decreased from 112 to 78 days. These improvements were achieved without additional staffing or new health information technology infrastructure, relying instead on reallocation of existing roles, a simple digital tracking system, and structured follow-up processes. Comparison with existing literature Our findings extend the evidence supporting task-sharing and team-based care in reproductive health. Research from sub-Saharan Africa demonstrates that involving nurses and community health workers in contraceptive service delivery substantially improves family planning indicators, including threefold increases in injectable contraceptive use in Burkina Faso and doubling of prevalence rates in Ethiopia [ 18 , 19 ]. Similarly, a multidisciplinary oncofertility team intervention improved fertility preservation counseling from 70% to 95.5% and referrals from 57.1% to 89.3% [ 20 ]. However, application of such approaches to infertility care—particularly the critical diagnostic-to-treatment transition—has been limited. Our study addresses this gap by demonstrating that structured nurse–physician partnership can mitigate the uncertainty and information deficit patients experience following initial diagnosis. The 32.94% relative improvement in treatment initiation compares favorably with other quality improvement interventions. A colorectal cancer screening initiative using provider education and electronic outreach achieved a 35% relative improvement in Cologuard completion (from 7.38% to 10.00%) [ 21 ]. Our larger absolute effect size (from 19.43% to 25.83%) likely reflects exceptionally low baseline performance and our multi-component approach addressing multiple barriers simultaneously. Our lightweight digital tracking system's success is noteworthy given documented challenges with digital health adoption in resource-constrained settings. Poor digital literacy is a primary barrier in lower- and middle-income country hospitals [ 22 ]; research from Egypt found limited digital literacy (43%) and lack of awareness (58%) significantly impeded mobile health uptake, with educational level as a critical predictor[ 23 ]. Studies of older adults with chronic conditions confirm that while technology barriers diminish with familiarity, perceived irrelevance and competing health concerns persist[ 24 ].Critically, hybrid care models combining digital tools with human interaction facilitate adoption[ 24 ], and addressing the digital divide requires attention to infrastructure, governance, and human-centered approaches that maintain patient-provider connection[ 25 – 27 ]. By using a platform already integrated into daily life (WeChat via QR codes) and embedding it within ongoing nurse–patient interaction, we achieved near-universal registration (99.7%) without requiring patients to navigate unfamiliar interfaces—offering a pragmatic alternative to more sophisticated digital solutions in settings with limited digital literacy or infrastructural barriers. Interpretation of findings Several mechanisms likely contributed to the observed improvements. First, role reconfiguration addressed a fundamental coordination failure: physicians lacked time for ongoing support, while nurses were not empowered to initiate proactive contact. By assigning follow-up responsibility to dedicated nurses and creating pathways for physician re-engagement with high-risk patients, we bridged the gap between diagnosis and treatment. This aligns with evidence that nurse-led interventions combining face-to-face and telephone follow-up significantly improve adherence in chronic disease management[ 12 , 13 ], and that nurses play a pivotal role in infertility prevention and health promotion through public health initiatives, including health education, screening, and community-based interventions[ 28 ]. Second, the triage-based alert system enabled risk-stratified care management, allowing teams to prioritize patients most at risk of disengagement—an approach proven effective for allocating limited resources in chronic disease populations[ 16 ]. Recent evidence confirms that population risk stratification tools can effectively tailor interventions and prioritize resources for high-risk individuals, with studies demonstrating significant reductions in emergency department visits and hospitalizations when targeted interventions are applied to risk-stratified populations[ 29 ]. Third, monthly quality control meetings created feedback loops that transformed individual experience into organizational learning. Evidence from low- and middle-income countries demonstrates that team-based goals with performance feedback can significantly improve health worker coordination and quality of patient interactions[ 15 ]. Furthermore, multifaceted physician-led interventions incorporating peer-to-peer feedback, longitudinal education and coaching, and standardized QI project templates have been shown to significantly improve the quality and content of departmental quality improvement plans, enhancing physician knowledge of QI methodology and focus on improvement priorities[ 30 ]. Implications for policy and practice Our findings carry several implications for reproductive health services in China and other resource-constrained settings. A key implication is that meaningful improvements in patient continuity can be achieved without expensive technology or additional personnel, making the intervention potentially scalable across China's 500 + ART centers operating under budget constraints[ 8 , 9 ]. This is particularly relevant given that nurses are well-positioned to lead public health campaigns, conduct reproductive health counseling, and advocate for policy reforms to improve infertility prevention and care[ 28 ]. Beyond scalability, the intervention addresses the "first-mile" problem—ensuring patients transition from initial consultation to treatment—which has received insufficient policy attention compared to expanding access through clinic construction and insurance coverage. Expert consensus identifies structured follow-up as critical alongside legislative review, education, and psychosocial support[ 3 , 4 ]. Policymakers and hospital administrators should consider incorporating similar follow-up protocols into standard infertility care pathways, recognizing that access alone does not guarantee treatment initiation. The intervention also offers a template for other chronic conditions requiring sustained patient engagement. A systematic review of task-sharing for non-communicable diseases in low- and middle-income countries found 81% of studies reported positive outcomes, with economic analyses showing reduced healthcare costs[ 12 , 13 ]. The success of our hybrid care model—combining digital tools with human interaction—is particularly relevant given that hybrid approaches have been identified as critical facilitators of digital health adoption, especially among older adults and populations with limited digital literacy[ 22 ]. Health systems seeking to improve continuity of care for conditions such as diabetes, hypertension, or mental health disorders could adapt this model to their specific contexts. Furthermore, proactive structured follow-up may be particularly valuable for marginalized populations facing systemic barriers to care continuity, addressing documented disparities in infertility care access[ 1 , 2 ]. Our approach of using widely accessible digital platforms (WeChat via QR codes) aligns with recommendations that addressing the digital divide requires going beyond access to include availability, adequacy, acceptability, and affordability of digital health solutions[ 26 ]. As noted in recent literature, integrating digital health innovations to achieve universal health coverage requires attention to infrastructure, governance, and human-centered approaches that maintain patient-provider connection[ 31 ]. Policymakers should therefore prioritize investments in hybrid care models that combine technological solutions with human support, particularly in settings where patients may have limited digital literacy. Strengths and limitations The main strength of this quality improvement study is its real-world implementation in a high-volume public reproductive medicine center, where the intervention was embedded into routine practice and evaluated across a large cohort of first-visit patients. The study included 4336 patients and used routinely collected clinical and operational data, which enhances its practical relevance for similar public hospital settings. Another strength is that the intervention was implemented using existing personnel and widely available digital tools, which increases the feasibility of replication in resource-constrained settings. In addition, the use of a structured QI framework and fidelity monitoring strengthened the consistency of implementation[ 17 ]. Several questions require further investigation. Multi-center studies are needed to assess whether similar effects can be achieved in different organisational and regional contexts [ 14 , 15 ]. Longer follow-up is required to determine whether improved treatment initiation translates into higher treatment completion, pregnancy, and live birth rates. Future research should also examine which patient groups benefit most, and whether socioeconomic position, distance from care, health literacy, or digital literacy modify intervention effects[ 22 – 24 ]. Formal economic evaluation would be valuable, although the present intervention required no additional staffing or complex technological investment[ 3 , 4 , 32 ]. Comparative studies could also help identify which intervention components—team restructuring, digital tracking, triage escalation, or performance feedback—are most critical for success and therefore most essential for scale-up[ 10 , 11 , 31 , 33 , 34 ]. Conclusions In conclusion, this quality improvement study suggests that a bundled nurse–physician follow-up model can improve treatment initiation and continuity of care among first-visit infertility patients in a public reproductive medicine center. By introducing explicit follow-up responsibility, simple digital tracking, and risk-stratified escalation within existing staffing structures, the intervention addressed a critical gap between initial consultation and treatment initiation. This low-cost and potentially scalable approach may offer a practical strategy for reducing early attrition in infertility care pathways in resource-constrained settings. Declarations Ethical approval and consent to participate This quality improvement study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Medical Research Ethics Committee of Guangzhou Women and Children's Medical Center Liuzhou Hospital (Approval No.: Expedited-Research-2026-050). As this study involved the analysis of routinely collected anonymized data from a quality improvement initiative, the requirement for informed consent was waived by the ethics committee. All patient information was handled in accordance with hospital confidentiality policies. Patients were informed during their initial visit that follow-up contact might occur as part of routine care and could opt out at any time. Consent for publication Not applicable. This manuscript does not contain any individual person's data in any form (including individual details, images, or videos). Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to institutional confidentiality policies but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article. Authors' contributions LW and ZW contributed to the study conception and design. BZ and JW supervised the intervention implementation and data collection. CL and FZ performed the statistical analysis. WH contributed to data interpretation. All authors read and approved the final manuscript. Acknowledgements We are grateful for the clinical support provided by all medical staff at Guangzhou Women and Children's Medical Center and Liuzhou Maternal and Child Healthcare Hospital. 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Ahmed MM, Okesanya OJ, Olaleke NO, Adigun OA, Adebayo UO, Oso TA, Eshun G, Lucero-Prisno DE 3. rd: Integrating Digital Health Innovations to Achieve Universal Health Coverage: Promoting Health Outcomes and Quality Through Global Public Health Equity. Healthcare (Basel, Switzerland) 2025, 13(9). Hollimon LA, Taylor KV, Fiegenbaum R, Carrasco M, Garchitorena Gomez L, Chung D, Seixas AA. Redefining and solving the digital divide and exclusion to improve healthcare: going beyond access to include availability, adequacy, acceptability, and affordability. Front Digit health. 2025;7:1508686. Sousa L, Käppler C, Faleiros F, Alburquerque G, José H. Management of Chronic Health Situations. Healthc (Basel Switzerland) 2026, 14(2). Sagheb Ray Shirazi M, Salarkarimi F, Moghadasi F, Mahmoudikohani F, Tajik F, Bastani Nejad Z. Infertility Prevention and Health Promotion: The Role of Nurses in Public Health Initiatives. Galen Med J. 2024;13:e3534. Golinelli D, Pecoraro V, Tedesco D, Negro A, Berti E, Camerlingo MD, Alberghini L, Lippi Bruni M, Rolli M, Grilli R. Population risk stratification tools and interventions for chronic disease management in primary care: a systematic literature review. BMC Health Serv Res. 2025;25(1):526. Hobbs H, Calder-Sprackman S, Wilkinson A, Digby GC. Improving departmental Quality Improvement Plans through standardisation, structured peer-to-peer feedback and building improvement capacity and culture. BMJ open Qual 2025, 14(4). Nguyen-Tien T, Unger F, Yano T, Mutua F, Cook EAJ, Lee HS, Bett B, Nguyen-Viet H. Challenges and recommendations for embracing digital tools for one health surveillance in LMICs: Viewpoints from a special session of 17th ISVEE. One health (Amsterdam Netherlands). 2025;21:101242. Walker N, Heuer A, Sanders R, Tong H. The costs and benefits of scaling up interventions to prevent poor birth outcomes in low-income and middle-income countries: a modelling study. Lancet Global health. 2024;12(9):e1526–33. Landis-Lewis Z, Boisvert P, Seifi F, Renji AD, Cao Y, Chung H, Janda A, Shah N, Flynn A. Scalable Coaching and Appreciation Feedback for Optimal Learning and Decision-Making (SCAFFOLD). Studies in health technology and informatics 2025, 329:431–435. İnam Ö, Satılmış İG. The effect of mindfulness-based nursing support on the psychosocial status of women receiving infertility treatment: a randomized controlled trial. BMC Womens Health. 2025;25(1):127. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 03 May, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 23 Apr, 2026 Editor invited by journal 01 Apr, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 31 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9240198","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634120959,"identity":"b1041b9a-c860-4255-a0dd-97fd33af825a","order_by":0,"name":"Liuyan Wei","email":"","orcid":"","institution":"Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center Liuzhou Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liuyan","middleName":"","lastName":"Wei","suffix":""},{"id":634120966,"identity":"02677ca1-c946-4f02-b9a3-516a30c894db","order_by":1,"name":"Zhouhong Wei","email":"","orcid":"","institution":"Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center Liuzhou Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhouhong","middleName":"","lastName":"Wei","suffix":""},{"id":634120967,"identity":"bfe7f457-2c3f-4e1e-aefc-1e81991bc80e","order_by":2,"name":"Bingling Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACfv72A4f/VPyXk2dvIFKL5IwziQ94zjAbG/YcIFKLwYEEYwPeFubEhhsJxLrswIE0CckGNmPGmY833mCosYkmqIOxufGYhOEOHjl26bRiC4ZjabkNhLQwMwBtSTwjYcw4O8dMgrHhMGEtbAwJZhIH2wwSG26eIVILD0OCsWFjWwLQ+zxEapGQOJP4mOHMAWAgA/2SQIxf7M8Do5Kh4gAwKg9vvPGhxoawFmRgIJFAinKIFlJ1jIJRMApGwcgAAMi0Qspd2qMsAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center Liuzhou Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bingling","middleName":"","lastName":"Zhao","suffix":""},{"id":634120969,"identity":"f38a1ad4-c89c-416f-8839-57486abdbe6c","order_by":3,"name":"Jia Wei","email":"","orcid":"","institution":"Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center Liuzhou Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Wei","suffix":""},{"id":634120970,"identity":"be7f1694-874f-4f9c-afe5-e67c9120e442","order_by":4,"name":"Caiping Luo","email":"","orcid":"","institution":"Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center Liuzhou Hospital","correspondingAuthor":false,"prefix":"","firstName":"Caiping","middleName":"","lastName":"Luo","suffix":""},{"id":634120971,"identity":"5bc987af-b0de-4bba-821b-af857cc9e763","order_by":5,"name":"Fengying Zhang","email":"","orcid":"","institution":"Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center Liuzhou Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fengying","middleName":"","lastName":"Zhang","suffix":""},{"id":634120974,"identity":"be6d1bcc-ef6c-4350-aa66-022acc9181e8","order_by":6,"name":"Wenjie Huang","email":"","orcid":"","institution":"Department of Reproductive Medicine, Guangzhou Women and Children’s Medical Center Liuzhou Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-03-27 05:08:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9240198/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9240198/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108837790,"identity":"acd6c987-38a6-4d61-8c3c-be04135d05d0","added_by":"auto","created_at":"2026-05-09 00:17:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43700,"visible":true,"origin":"","legend":"\u003cp\u003eTreatment initiation rate\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9240198/v1/490668a5786c956f4ea6631d.png"},{"id":108977643,"identity":"140d45dd-0927-4dfd-a4c5-3d50f499f588","added_by":"auto","created_at":"2026-05-11 11:32:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":360068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9240198/v1/552f0b1f-26fb-4bd1-a98e-592290af6c98.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A bundled nurse-physician follow-up team improves treatment initiation in infertility care: a quality improvement study","fulltext":[{"header":"Background","content":"\u003cp\u003eInfertility affects an estimated 17.5% of the global adult population, with prevalence rising in low- and middle-income countries (LMICs) due to delayed childbearing, limited access to timely diagnosis, and structural barriers within health systems[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While assisted reproductive technology (ART) offers effective treatment, its impact is severely constrained by low uptake\u0026mdash;particularly during the critical transition from initial consultation to treatment commencement[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This phase\u0026mdash;commonly termed the \u0026ldquo;diagnostic-to-treatment gap\u0026rdquo;\u0026mdash;represents one of the most vulnerable points in the global infertility care pathway. As corroborated by empirical studies across diverse health systems, newly diagnosed individuals frequently disengage from care before initiating treatment due to insufficient health information, ambiguous next-step guidance, and absence of structured follow-up support\u0026mdash;leading to substantial delays or complete discontinuation of care[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In China, where public maternal and child health hospitals serve as primary gateways to ART services, this loss to follow-up is especially pronounced among first-visit patients\u0026mdash;driven by complex interplay of financial concerns, fragmented referral pathways, insufficient health literacy, and lack of coordinated follow-up support[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eCurrent strategies to improve treatment initiation remain fragmented and poorly scaled. Provider-led reminder systems are often inconsistent and time-intensive; digital health tools (e.g., SMS or app-based alerts) show promise but face low adoption in settings with limited digital infrastructure or older patient demographics[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Meanwhile, task-sharing models\u0026mdash;such as nurse-led counseling or community health worker outreach\u0026mdash;have demonstrated efficacy in chronic disease management[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], yet their application to reproductive health service delivery remains underexplored, particularly within public hospital settings where workforce constraints and hierarchical workflows pose implementation challenges[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Crucially, most existing interventions focus on individual-level adherence (e.g., medication taking) rather than addressing system-level failures in patient flow coordination, continuity of care, and relational trust-building between patients and providers[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis implementation gap highlights the need for pragmatic and scalable quality improvement (QI) strategies that can be integrated into routine clinical workflows, leverage existing human resources, and strengthen patient-centered communication. In response, we developed and implemented a bundled nurse\u0026ndash;physician follow-up intervention at a tertiary public maternal and child health hospital in Guangxi, China, and evaluated its impact on treatment initiation among first-visit infertility patients. Grounded in principles of relational coordination and proactive care navigation, this model aims not only to reduce lost-to-follow-up but also to strengthen continuity between diagnosis and treatment. Improving treatment initiation may serve as a measurable indicator of accessibility, responsiveness, and equity in reproductive health services.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis study was a prospective, single-center, before\u0026ndash;after QI study conducted at the Reproductive Medicine Center of Liuzhou Maternal and Child Health Hospital\u0026mdash;a tertiary public maternal and child health hospital in Guangxi Zhuang Autonomous Region, China. The center is accredited to perform all generations of assisted reproductive technology (ART), including preimplantation genetic testing (PGT), and serves as the regional training base for ART standardization in Guangxi. The intervention was implemented from 1 March 2023 to 31 December 2023. Baseline data were collected from 1 January to 28 February 2023. The study followed the SQUIRE 2.0 guidelines[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and was registered as an internal QI project with the hospital\u0026rsquo;s Quality Management Office (No. LZFY-ART-QI-2023-001).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eAll adult patients (\u0026ge;\u0026thinsp;18 years) presenting for their first consultation at the Reproductive Medicine Center during the study period were eligible for inclusion. Patients were excluded if they: (1) Prior receipt of assisted reproductive technology (ART) treatment at this hospital; (2) Presentation for non-ART services only (e.g., isolated gynecologic surgery, routine prenatal care, or contraceptive counseling); (3) Explicit refusal to participate in telephone follow-up (i.e., inability to provide informed consent for the follow-up component). As this was a pragmatic, system-level QI intervention targeting all eligible patients during routine clinical operations, no formal sample size calculation was performed. A total of 4336 first-visit patients were included in the study (baseline period: n\u0026thinsp;=\u0026thinsp;712; intervention period: n\u0026thinsp;=\u0026thinsp;3624).\u003c/p\u003e\n\u003ch3\u003eIntervention\u003c/h3\u003e\n\u003cp\u003eThe intervention consisted of a bundled nurse\u0026ndash;physician follow-up model designed to improve patient continuity between the first consultation and treatment initiation. The intervention included three components: multidisciplinary follow-up teams, a digital patient tracking system, and standardized follow-up communication protocols.\u003c/p\u003e\n\u003ch3\u003eMultidisciplinary follow-up teams\u003c/h3\u003e\n\u003cp\u003eTwelve multidisciplinary teams were established across the two hospital campuses. Each team consisted of one reproductive physician and two nurses and was responsible for a defined group of first-visit patients.\u003c/p\u003e \u003cp\u003ePhysicians remained responsible for clinical assessment, treatment planning, and direct communication with patients identified as high risk for treatment delay. One nurse conducted the first telephone follow-up within 72 hours of the initial consultation using a standardized communication script. The second nurse maintained a digital tracking sheet documenting follow-up contacts and monitoring patient progression along the treatment pathway.\u003c/p\u003e\n\u003ch3\u003eDigital patient tracking and triage alerts\u003c/h3\u003e\n\u003cp\u003eAt the first visit, patients registered by scanning a QR code linked to a brief intake questionnaire developed using a WeChat-integrated survey platform. The questionnaire captured demographic information, contact details, and initial treatment intentions.\u003c/p\u003e \u003cp\u003ePatient progress was monitored using a shared digital spreadsheet that recorded visit dates, follow-up contacts, completion of diagnostic testing, and treatment initiation. A colour-coded triage system was used to prioritise follow-up actions: 1) Green status: follow-up plan confirmed within 7 days; 2) Yellow status: no treatment decision within 14 days; 3) Red status: treatment not initiated within 30 days or patient expressed uncertainty about continuing care. Patients classified as yellow or red status were escalated to nurse managers or physicians for additional contact.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStandardized follow-up communication\u003c/h2\u003e \u003cp\u003eA follow-up communication guide was developed to standardise interactions with patients. The guide included structured scripts for the initial 72-hour follow-up call and subsequent follow-up contacts, focusing on confirming patient understanding, clarifying next steps, and addressing barriers to treatment initiation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuality monitoring\u003c/h3\u003e\n\u003cp\u003eMonthly quality review meetings were conducted involving physicians, nurses, and administrative staff participating in the intervention. During these meetings, aggregated data from the tracking system were reviewed, including follow-up completion rates, diagnostic testing completion, and treatment initiation rates. The meetings also provided opportunities to discuss complex cases and refine workflow processes.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the proportion of first-visit patients who initiated an ART treatment cycle within six months of the initial consultation. Secondary outcomes included: loss to follow-up, time from initial consultation to treatment initiation, completion of follow-up contact within 72 hours, and selected indicators of patient experience (including complaints related to follow-up).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003ePatient demographic and follow-up data were recorded in the digital tracking system during routine clinical operations. Information collected included patient registration data, follow-up contact dates, diagnostic testing completion, and treatment initiation status. Data were extracted from the tracking system and the center\u0026rsquo;s routine clinical database for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e This quality improvement study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Medical Research Ethics Committee of Guangzhou Women and Children's Medical Center Liuzhou Hospital (Approval No.: Expedited-Research-2026-050). As this study involved the analysis of routinely collected anonymized data from a quality improvement initiative, the requirement for informed consent was waived by the ethics committee. All patient information was handled in accordance with hospital confidentiality policies. Patients were informed during their initial visit that follow-up contact might occur as part of routine care and could opt out at any time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize patient characteristics and outcomes, with continuous variables reported as medians with interquartile ranges and categorical variables as frequencies with percentages. Baseline characteristics were compared between the pre-intervention and intervention periods using the χ\u0026sup2; test for categorical variables and the Mann\u0026ndash;Whitney U test for continuous variables. The primary outcome\u0026mdash;treatment initiation within six months\u0026mdash;was calculated as a proportion with 95% confidence intervals estimated using the Wilson score method; absolute and relative changes were derived accordingly. Secondary outcomes, including lost-to-follow-up, time to treatment initiation, and follow-up completion rates, were summarized descriptively, with patient complaints compared across quarters. All analyses were conducted using R software (version 4.2.0), and statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePatient population and baseline characteristics\u003c/h2\u003e \u003cp\u003eFrom 1 January to 31 December 2023, a total of 4336 first-visit infertility patients were included in the study, comprising 712 patients in the pre-intervention period (January to February 2023) and 3624 patients during the intervention period (March to December 2023). Baseline demographic and clinical characteristics were similar across the two periods (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age was 32.4 years (SD 5.2) in the pre-intervention period and 32.7 years (SD 5.4) during the intervention period. The proportion of patients who had received infertility treatment elsewhere was 23.1% before the intervention and 24.3% during the intervention.The distribution of infertility diagnoses, including female factor, male factor, combined, unexplained infertility, did not differ significantly between periods (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-intervention (Jan\u0026ndash;Feb 2023)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntervention period (Mar\u0026ndash;Dec 2023)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026nbsp;value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of patients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.00 (8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.00 (8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.870\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior infertility treatment elsewhere, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 (23.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e880 (24.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.508\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of infertility (years), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00(3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00(4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.531\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfertility diagnosis category, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e581(81.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3006(82.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4.809\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(6.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229(6.32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(8.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314(8.66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnexplained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(3.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(2.07%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNote: \u003csup\u003ea\u003c/sup\u003e Z value, \u003csup\u003eb\u003c/sup\u003eχ\u003csup\u003e2\u003c/sup\u003evalue。\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003ePrimary outcome: Treatment initiation rate\u003c/h2\u003e \u003cp\u003ePrimary outcome, treatment initiation within 6 months of the first visit, increased after implementation of the bundled nurse-physician follow-up model. During the pre-intervention period, 19.43% of first-visit patients initiated treatment within 6 months (95% CI 16.7\u0026ndash;22.4%), compared with 25.83% during the intervention period (95% CI 24.5\u0026ndash;27.2%), corresponding to 6.4 percentage points and a relative increase of 32.94%. This exceeded the pre-specified improvement target of 6 percentage points. The monthly run chart showed a progressive increase in treatment initiation across the intervention period, from 20.1% in March 2023 to 26.8% in December 2023, with the steepest gains observed after full implementation in July to September 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLost-to-follow-up rate\u003c/h2\u003e \u003cp\u003eLost to follow-up decreased markedly after the intervention. Before the intervention, 98.0% of first-visit patients did not receive any active follow-up after leaving the clinic. During the intervention period, all 4,336 patients were entered into the digital tracking system and received at least one follow-up attempt, such that the proportion with no active follow-up decreased to 0.0%. Among intervention-period patients, 2.8% could not be reached after three contact attempts; however, these patients remained in the tracking system and were therefore not classified as lost to follow-up under the study definition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTime-to-treatment initiation\u003c/h2\u003e \u003cp\u003eThe median time from initial visit to treatment initiation decreased from 112 days (interquartile range [IQR] 78\u0026ndash;156) before the intervention to 78 days (IQR 52 to 114) during the intervention, a reduction of 34 days (30.4%). This reduction was observed across age groups and diagnostic categories, and was greatest among patients aged 35 years or older, in whom the median time decreased from 134 to 93 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProcess measures\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003eClinical volume outcomes\u003c/h2\u003e \u003cp\u003eProcess indicators showed high uptake and implementation fidelity. During the intervention period, QR-code registration was completed by 4323/4336 (99.7%); the remaining 13 patients (0.3%) were entered manually because of technical difficulties or refusal to provide digital information. First follow-up within 72 hours was completed for 4128/4336 (95.2%). Among the 208 patients not reached within 72 hours, the most common reasons were incorrect telephone numbers (89/208, 42.8%), no answer after three attempts (76/208, 36.5%), and patient request not to be contacted (43/208, 20.7%). Second follow-up, scheduled 14 to 21 days after the first visit, was completed for 3785/4336 patients (87.3%).\u003c/p\u003e \u003cp\u003eAt 30 days after the initial visit, 61.4% of patients (2662/4336) were classified as green status, indicating that a follow-up plan had been confirmed and the patient was progressing through the treatment pathway; 26.8% (1162/4336) were yellow status and were escalated to nurse managers; and 11.8% (512/4336) were red status and were referred back to physicians for direct communication (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among red-status patients, 47.2% (242/512) initiated treatment within an additional 30 days after physician contact. The remaining patients either continued to delay decision-making (158/512, 30.9%), had treatment contraindications identified (67/512, 13.1%), or declined further contact (45/512, 8.8%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimary and secondary outcome\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-intervention (Jan\u0026ndash;Feb 2023)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntervention period (Mar\u0026ndash;Dec 2023)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsolute change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelative change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary outcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment initiation rate (within 6 months), %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;6.40 pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;32.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecondary outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLost-to-follow-up rate, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-98.0 pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian time-to-initiation (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-34 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcess measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQR code registration rate, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;99.7 pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst follow-up completion within 72 hours, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;95.2 pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient complaints (quarterly), n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (Q1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (Q4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs a supplementary indicator of service activity, we examined oocyte retrieval cycles. Compared with the corresponding months in 2022, retrieval cycles in 2023 were lower during the early implementation period but increased in later months. In December 2023, 405 retrieval cycles were recorded, compared with 328 in December 2022, an increase of 23.5%. Across the full calendar year, the total number of retrieval cycles was 4,576 in 2023 and 4,553 in 2022. For the intervention period specifically (March to December), the cumulative number increased from 3,927 in 2022 to 3,976 in 2023.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePatient experience and complaints\u003c/h2\u003e \u003cp\u003ePatient complaints related to care pathway confusion or lack of follow-up decreased over time. According to institutional records, 12 such complaints were documented in the first quarter of 2023. In addition, follow-up records suggested improved patient acceptance of the proactive follow-up approach.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSustainability of improvements\u003c/h2\u003e \u003cp\u003eIn the first two months after the formal intervention period, improvements were sustained. The treatment initiation rate was 26.1% in January 2024 and 26.4% in February 2024, while follow-up completion remained above 94%, indicating continued routine use of the tracking and follow-up model.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e Following implementation of the bundled nurse\u0026ndash;physician follow-up intervention, the proportion of first-visit infertility patients initiating ART treatment within 6 months increased from 19.43% to 25.83%, an absolute increase of 6.4 percentage points and a relative increase of 32.94%. The proportion of patients receiving no active follow-up decreased from 98.0% to 0.0% under the study definition, and the median time from first consultation to treatment initiation decreased from 112 to 78 days. These improvements were achieved without additional staffing or new health information technology infrastructure, relying instead on reallocation of existing roles, a simple digital tracking system, and structured follow-up processes.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eComparison with existing literature\u003c/h2\u003e \u003cp\u003eOur findings extend the evidence supporting task-sharing and team-based care in reproductive health. Research from sub-Saharan Africa demonstrates that involving nurses and community health workers in contraceptive service delivery substantially improves family planning indicators, including threefold increases in injectable contraceptive use in Burkina Faso and doubling of prevalence rates in Ethiopia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, a multidisciplinary oncofertility team intervention improved fertility preservation counseling from 70% to 95.5% and referrals from 57.1% to 89.3% [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, application of such approaches to infertility care\u0026mdash;particularly the critical diagnostic-to-treatment transition\u0026mdash;has been limited. Our study addresses this gap by demonstrating that structured nurse\u0026ndash;physician partnership can mitigate the uncertainty and information deficit patients experience following initial diagnosis.\u003c/p\u003e \u003cp\u003eThe 32.94% relative improvement in treatment initiation compares favorably with other quality improvement interventions. A colorectal cancer screening initiative using provider education and electronic outreach achieved a 35% relative improvement in Cologuard completion (from 7.38% to 10.00%) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our larger absolute effect size (from 19.43% to 25.83%) likely reflects exceptionally low baseline performance and our multi-component approach addressing multiple barriers simultaneously.\u003c/p\u003e \u003cp\u003eOur lightweight digital tracking system's success is noteworthy given documented challenges with digital health adoption in resource-constrained settings. Poor digital literacy is a primary barrier in lower- and middle-income country hospitals [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; research from Egypt found limited digital literacy (43%) and lack of awareness (58%) significantly impeded mobile health uptake, with educational level as a critical predictor[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Studies of older adults with chronic conditions confirm that while technology barriers diminish with familiarity, perceived irrelevance and competing health concerns persist[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].Critically, hybrid care models combining digital tools with human interaction facilitate adoption[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and addressing the digital divide requires attention to infrastructure, governance, and human-centered approaches that maintain patient-provider connection[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. By using a platform already integrated into daily life (WeChat via QR codes) and embedding it within ongoing nurse\u0026ndash;patient interaction, we achieved near-universal registration (99.7%) without requiring patients to navigate unfamiliar interfaces\u0026mdash;offering a pragmatic alternative to more sophisticated digital solutions in settings with limited digital literacy or infrastructural barriers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of findings\u003c/h2\u003e \u003cp\u003eSeveral mechanisms likely contributed to the observed improvements. First, role reconfiguration addressed a fundamental coordination failure: physicians lacked time for ongoing support, while nurses were not empowered to initiate proactive contact. By assigning follow-up responsibility to dedicated nurses and creating pathways for physician re-engagement with high-risk patients, we bridged the gap between diagnosis and treatment. This aligns with evidence that nurse-led interventions combining face-to-face and telephone follow-up significantly improve adherence in chronic disease management[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and that nurses play a pivotal role in infertility prevention and health promotion through public health initiatives, including health education, screening, and community-based interventions[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, the triage-based alert system enabled risk-stratified care management, allowing teams to prioritize patients most at risk of disengagement\u0026mdash;an approach proven effective for allocating limited resources in chronic disease populations[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recent evidence confirms that population risk stratification tools can effectively tailor interventions and prioritize resources for high-risk individuals, with studies demonstrating significant reductions in emergency department visits and hospitalizations when targeted interventions are applied to risk-stratified populations[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThird, monthly quality control meetings created feedback loops that transformed individual experience into organizational learning. Evidence from low- and middle-income countries demonstrates that team-based goals with performance feedback can significantly improve health worker coordination and quality of patient interactions[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, multifaceted physician-led interventions incorporating peer-to-peer feedback, longitudinal education and coaching, and standardized QI project templates have been shown to significantly improve the quality and content of departmental quality improvement plans, enhancing physician knowledge of QI methodology and focus on improvement priorities[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eImplications for policy and practice\u003c/h2\u003e \u003cp\u003eOur findings carry several implications for reproductive health services in China and other resource-constrained settings. A key implication is that meaningful improvements in patient continuity can be achieved without expensive technology or additional personnel, making the intervention potentially scalable across China's 500\u0026thinsp;+\u0026thinsp;ART centers operating under budget constraints[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This is particularly relevant given that nurses are well-positioned to lead public health campaigns, conduct reproductive health counseling, and advocate for policy reforms to improve infertility prevention and care[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond scalability, the intervention addresses the \"first-mile\" problem\u0026mdash;ensuring patients transition from initial consultation to treatment\u0026mdash;which has received insufficient policy attention compared to expanding access through clinic construction and insurance coverage. Expert consensus identifies structured follow-up as critical alongside legislative review, education, and psychosocial support[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Policymakers and hospital administrators should consider incorporating similar follow-up protocols into standard infertility care pathways, recognizing that access alone does not guarantee treatment initiation.\u003c/p\u003e \u003cp\u003eThe intervention also offers a template for other chronic conditions requiring sustained patient engagement. A systematic review of task-sharing for non-communicable diseases in low- and middle-income countries found 81% of studies reported positive outcomes, with economic analyses showing reduced healthcare costs[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The success of our hybrid care model\u0026mdash;combining digital tools with human interaction\u0026mdash;is particularly relevant given that hybrid approaches have been identified as critical facilitators of digital health adoption, especially among older adults and populations with limited digital literacy[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Health systems seeking to improve continuity of care for conditions such as diabetes, hypertension, or mental health disorders could adapt this model to their specific contexts.\u003c/p\u003e \u003cp\u003eFurthermore, proactive structured follow-up may be particularly valuable for marginalized populations facing systemic barriers to care continuity, addressing documented disparities in infertility care access[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Our approach of using widely accessible digital platforms (WeChat via QR codes) aligns with recommendations that addressing the digital divide requires going beyond access to include availability, adequacy, acceptability, and affordability of digital health solutions[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As noted in recent literature, integrating digital health innovations to achieve universal health coverage requires attention to infrastructure, governance, and human-centered approaches that maintain patient-provider connection[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Policymakers should therefore prioritize investments in hybrid care models that combine technological solutions with human support, particularly in settings where patients may have limited digital literacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe main strength of this quality improvement study is its real-world implementation in a high-volume public reproductive medicine center, where the intervention was embedded into routine practice and evaluated across a large cohort of first-visit patients. The study included 4336 patients and used routinely collected clinical and operational data, which enhances its practical relevance for similar public hospital settings. Another strength is that the intervention was implemented using existing personnel and widely available digital tools, which increases the feasibility of replication in resource-constrained settings. In addition, the use of a structured QI framework and fidelity monitoring strengthened the consistency of implementation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Several questions require further investigation. Multi-center studies are needed to assess whether similar effects can be achieved in different organisational and regional contexts [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Longer follow-up is required to determine whether improved treatment initiation translates into higher treatment completion, pregnancy, and live birth rates. Future research should also examine which patient groups benefit most, and whether socioeconomic position, distance from care, health literacy, or digital literacy modify intervention effects[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Formal economic evaluation would be valuable, although the present intervention required no additional staffing or complex technological investment[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Comparative studies could also help identify which intervention components\u0026mdash;team restructuring, digital tracking, triage escalation, or performance feedback\u0026mdash;are most critical for success and therefore most essential for scale-up[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this quality improvement study suggests that a bundled nurse\u0026ndash;physician follow-up model can improve treatment initiation and continuity of care among first-visit infertility patients in a public reproductive medicine center. By introducing explicit follow-up responsibility, simple digital tracking, and risk-stratified escalation within existing staffing structures, the intervention addressed a critical gap between initial consultation and treatment initiation. This low-cost and potentially scalable approach may offer a practical strategy for reducing early attrition in infertility care pathways in resource-constrained settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis quality improvement study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Medical Research Ethics Committee of Guangzhou Women and Children's Medical Center Liuzhou Hospital (Approval No.: Expedited-Research-2026-050). As this study involved the analysis of routinely collected anonymized data from a quality improvement initiative, the requirement for informed consent was waived by the ethics committee. All patient information was handled in accordance with hospital confidentiality policies. Patients were informed during their initial visit that follow-up contact might occur as part of routine care and could opt out at any time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person's data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to institutional confidentiality policies but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no financial support for the research, authorship, and/or publication of this article.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLW and ZW contributed to the study conception and design. BZ and JW supervised the intervention implementation and data collection. CL and FZ performed the statistical analysis. WH contributed to data interpretation. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the clinical support provided by all medical staff at Guangzhou Women and Children's Medical Center and Liuzhou Maternal and Child Healthcare Hospital. We also sincerely appreciate the trust and dedication of the patient families participating in this study toward the scientific research endeavor. \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCox CM, Thoma ME, Tchangalova N, Mburu G, Bornstein MJ, Johnson CL, Kiarie J. 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BMJ open Qual 2025, 14(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen-Tien T, Unger F, Yano T, Mutua F, Cook EAJ, Lee HS, Bett B, Nguyen-Viet H. Challenges and recommendations for embracing digital tools for one health surveillance in LMICs: Viewpoints from a special session of 17th ISVEE. One health (Amsterdam Netherlands). 2025;21:101242.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalker N, Heuer A, Sanders R, Tong H. The costs and benefits of scaling up interventions to prevent poor birth outcomes in low-income and middle-income countries: a modelling study. Lancet Global health. 2024;12(9):e1526\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandis-Lewis Z, Boisvert P, Seifi F, Renji AD, Cao Y, Chung H, Janda A, Shah N, Flynn A. Scalable Coaching and Appreciation Feedback for Optimal Learning and Decision-Making (SCAFFOLD). \u003cem\u003eStudies in health technology and informatics\u003c/em\u003e 2025, 329:431\u0026ndash;435.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eİnam \u0026Ouml;, Satılmış İG. The effect of mindfulness-based nursing support on the psychosocial status of women receiving infertility treatment: a randomized controlled trial. BMC Womens Health. 2025;25(1):127.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Treatment initiation, Infertility care, Follow-up, Team-based care, Quality improvement","lastPublishedDoi":"10.21203/rs.3.rs-9240198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9240198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe period between first infertility consultation and treatment initiation is a critical point of attrition in assisted reproductive technology (ART) care, particularly in resource-constrained settings. We evaluated a bundled nurse\u0026ndash;physician follow-up intervention designed to improve treatment initiation among first-visit infertility patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a before\u0026ndash;after quality improvement study at a tertiary public reproductive medicine center in Guangxi, China, from March to December 2023, with baseline data collected from January to February 2023. The intervention included: (1) \"1 physician\u0026thinsp;+\u0026thinsp;2 nurses\" teams assigned to new patients; (2) digital tracking through QR-code-linked questionnaires; and (3) triage-based alerts for patients at high risk of disengagement. Primary outcome was treatment initiation within 6 months of first visit. Secondary outcomes included loss to follow-up, time to treatment initiation, follow-up completion, and patient complaints. A total of 4,336 first-visit patients were included (baseline n\u0026thinsp;=\u0026thinsp;712; intervention n\u0026thinsp;=\u0026thinsp;3624).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTreatment initiation increased from 19.43% at baseline to 25.83% during the intervention, corresponding to an absolute increase of 6.4 percentage points and a relative increase of 32.94%. Lost-to-follow-up declined from 98.0% to 0.0%, and median time to initiation decreased from 112 to 78 days. QR code registration reached 99.7%, and 95.2% of patients completed first follow-up within 72 hours. Complaints related to care pathway confusion decreased from 12 cases to zero.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA low-cost, team-based follow-up model improved treatment initiation and continuity of care among first-visit infertility patients without requiring additional staffing or new IT infrastructure. This scalable intervention provides a pragmatic approach to reducing early attrition and strengthening the transition from diagnosis to treatment in resource-constrained reproductive health.\u003c/p\u003e","manuscriptTitle":"A bundled nurse-physician follow-up team improves treatment initiation in infertility care: a quality improvement study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:17:47","doi":"10.21203/rs.3.rs-9240198/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-03T11:00:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254869540728837663610173120760519710884","date":"2026-04-26T21:53:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T11:57:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-23T08:03:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-01T06:50:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T01:20:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-04-01T01:16:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7403b7fb-c838-4506-a65c-4526779850ba","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-03T11:00:28+00:00","index":45,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T00:17:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:17:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9240198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9240198","identity":"rs-9240198","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}