Patient-Centred Gestational Diabetes Care: Preference Elicitation Methods and Machine Learning Innovations

Shaikha Alharmoodi; Mariam Al Nweran; Imad El-Kebbi; Dean Everett; Saleh Mohamed Ibrahim; Joviana Farhat; Basem Al-Omari

doi:10.2147/PPA.S567113

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Review

Patient-Centred Gestational Diabetes Care: Preference Elicitation Methods and Machine Learning Innovations

Authors Alharmoodi S, Al Nweran M, El-Kebbi I, Everett D, Ibrahim SM, Farhat J, Al-Omari B

Received 12 September 2025

Accepted for publication 11 November 2025

Published 24 December 2025 Volume 2025:19 Pages 4219—4231

DOI https://doi.org/10.2147/PPA.S567113

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Johnny Chen

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Shaikha Alharmoodi,^1,^* Mariam Al Nweran,^1,^* Imad El-Kebbi,² Dean Everett,^1,³ Saleh Mohamed Ibrahim,^4,⁵ Joviana Farhat,¹ Basem Al-Omari¹

¹Department of Public Health and Epidemiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; ²Division of Endocrinology, Sheikh Shakhbout Medical City (SSMC), Abu Dhabi, United Arab Emirates; ³Infection Research Unit, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; ⁴College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; ⁵Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

*These authors contributed equally to this work

Correspondence: Basem Al-Omari, Department of Public Health and Epidemiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates, Tel +971 2 312 4452, Email [email protected] Joviana Farhat, Department of Public Health and Epidemiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates, Email [email protected]

Abstract: Gestational Diabetes Mellitus (GDM) requires long-term management, frequent visits, and additional financial costs compared to normal pregnancies. Patients often express preferences for services that save time, reduce expenses, and simplify screening. Virtual and telehealth services are valued as they shorten travel and waiting times, lower costs, and improve satisfaction. Screening preferences emphasize accuracy, affordability, and convenience, while recent machine learning (ML) models have enhanced prediction and early detection, supporting more personalized strategies. Patients’ preferences have been explored through qualitative, quantitative, and mixed methods, which capture lived experiences, quantify trade-offs, and contextualize results. This review aims to examine GDM patients’ experiences with time, costs, and screening, highlight the role of machine learning in screening, and synthesize evidence from preference-elicitation methods to inform patient-centred care. By linking patient preferences with technological advances in ML, this review provides a broader and more integrated perspective than previous reviews, helping to guide future GDM research and service design.

Keywords: gestational diabetes mellitus, patient preferences, quantitative, qualitative, machine learning

Introduction

In recent years, the evaluation of healthcare services has been considered a crucial component for achieving high-quality care and limiting illnesses and deaths across individuals.¹ This is highly relevant to clinical conditions that require long-term management, regular hospital visits, and follow-ups, such as Gestational Diabetes Mellitus (GDM).² Women with GDM may experience multiple care needs that can make healthcare delivery complex and expensive.³ Recent GDM literature focuses on assessing the quality, accessibility, and utilization of healthcare services among patients.⁴ Some studies evaluated the link between service quality, patient satisfaction, hospital utilization, and financial performance.⁵ For example, the lack of available and accessible maternal healthcare services has been associated with limited health outcomes and adverse events in low to middle-income countries.¹ In other cases, postpartum screening and follow-up could be affected by social and demographic factors, as well as patient and healthcare providers’ awareness and attitude.⁶ This justifies the direction of some studies to evaluate the possible variation in healthcare experiences and GDM patients’ preferences based on the model of care applied alongside patients’ inter-individual factors.⁷ Thus, medical services should be regularly improved based on patients’ needs to ensure adequate health care.⁸

Multiple qualitative methods, including semi-structured interviews and focus groups, have been widely used to explore GDM patients’ needs, perceptions, and preferences toward healthcare services.^9,10 Studies across different regions have shown that women often describe GDM as emotionally demanding and time-consuming, emphasizing challenges in balancing dietary control, self-monitoring, and family responsibilities.^11,12 These approaches have helped uncover barriers to self-management, such as limited time, emotional burden, and challenges in adhering to treatment, while also highlighting how cultural beliefs, family support, and communication with healthcare providers influence women’s care experiences and decisions.¹³ Quantitative methods, primarily Discrete Choice Experiment (DCE), have been used to quantify how patients value different aspects of care, such as visit frequency, screening type, and cost, thereby generating real-world preference data.¹⁴ In the past few years, advanced analytical strategies, including Machine Learning (ML) models, have enhanced prediction accuracy and disease classification in GDM.¹⁵ Clinically, these models support early diagnosis, guide individualized interventions, and improve patient experience by optimizing healthcare delivery and outcomes.¹⁶

This review aims to examine GDM patients’ experiences with time, financial costs, and screening, while highlighting the emerging role of ML in improving screening and diagnosis. It also synthesizes evidence from preference-elicitation methods, including qualitative, quantitative, and mixed approaches, to better understand how women perceive and value different aspects of GDM care.

Materials and Methods

Literature searches utilizing PubMed (MEDLINE), EMBASE, and Web of Science were conducted using terms related to GDM, Patient Preferences, Quantitative, Qualitative, and Machine Learning concepts. No language, age, or date restrictions or filters were applied.

LibreOffice software was used to develop the tables in this manuscript to support high-quality data presentation and differentiation. Figures were designed and structured using PowerPoint.

Patients’ Time

Patients’ time is an important factor to consider when examining patients’ preferences for healthcare services.¹⁷ The increase in patient numbers and treatment options requiring services from multiple healthcare professionals (HCPs) may cause long waiting times for consultation.¹⁸ This can impact patients’ satisfaction and their responsiveness to the medical information and treatment instructions given by HCPs.¹⁹ Some GDM patients correlate the long waiting times in the clinic to their inability to control their glucose levels.²⁰ Another factor is travel time, as some studies reported that 44.5% of GDM patients travelled for more than 30 minutes each way to the hospital.²¹ Moreover, around 65.1% of pregnant women with diabetes spent more than 2 hours in the clinic, and this percentage increased to 78% when considering travel times that exceeded 15 minutes.²¹ A travel time of over 30 minutes, with appointment durations varying from 5 to 60 minutes, was also reported.²² Thus, it is understandable that GDM patients prefer to manage their condition remotely through virtual platforms.²¹

GDM patients require more frequent doctor visits compared to those with normal pregnancies due to a higher risk for complications.²³ Therefore, it is recommended that regular clinic visits be made to ensure better glycemic levels, lower risk of premature infant birth, and neonatal intensive care unit (NICU) admissions.²⁴ In many low- and middle-income countries, GDM patients are still non-adherent to the essential services.²⁵ In the northern area of Tanzania, 23% of GDM patients who were screened and scheduled for a next-day appointment and one month for fasting plasma glucose (FPG) and Oral Glucose Tolerance Test (OGTT) did not return.²⁶ Around 35.7% of pregnant South African women designated for GDM testing did not undergo it because of inaccessible telephone lines and relocation from the area.²⁷ This non-adherence may be correlated to obstetric history, financial commitments, transportation limitations, and sociocultural practices.²⁸ A recent Chinese study reported that the majority of pregnant women expressed a preference for fewer hospital visits, especially those with poor socioeconomic backgrounds and residing in remote locations.²⁹

In recent years, advanced technologies have enabled either in-person or remote consultations between clinicians and patients.³⁰ Current evidence shows a high level of patient satisfaction with remote consultations.³¹ It was reported that 83% of GDM patients had a positive experience with virtual care, particularly due to the benefits of saving both money and time.³² Women residing in rural areas preferred virtual care to avoid long travel times and time off work.²² Women with a history of GDM, who have experienced multiple hospital visits and long wait times at clinics, strongly preferred remote care.²¹ These patients expressed a willingness to self-manage their condition at home while being monitored remotely by their medical team.²¹ In developed countries like Singapore, smartphone applications have become a more preferred alternative to in-person visits for pregnant women with GDM.³³ However, limited use of digital devices, language obstacles, and older age can affect patient preferences for in-person appointments,³⁴ especially among those with a history of GDM who received limited education.³⁵ Despite the benefits of remote care, some GDM patients continue to prefer the occasional face-to-face appointments.²²

However, the effectiveness of telehealth and virtual care is not uniform across all populations. Limited digital literacy, poor internet connectivity, and unequal access to smart devices create a “digital divide” that may exclude women from low-income, rural, or older age groups.³⁶ These barriers can lead to frustration, disengagement, and lower adherence, particularly in regions where health systems lack technical support or user training. Therefore, while telehealth enhances efficiency and accessibility for many, future models must incorporate strategies to improve digital literacy, provide affordable technologies, and ensure equitable access to virtual services for all GDM patients.³⁷

Patients’ Expenses

The financial costs associated with GDM care services are an important factor that impacts patients’ preferences and adherence. The cost of health care associated with GDM is 25.1% higher than the cost for healthy pregnant women.³⁸ The financial costs of hospital visits and neonatal ICU use are 44% and 49% higher in GDM cases, respectively.³⁸ During the pregnancy and delivery stages, maternity insurance does not always include,³⁹ and often does not cover the full costs.⁴⁰ For instance, in China, women and their families are responsible for covering a large amount of financial expenses associated with GDM healthcare,⁴¹ leading to approximately 60% of the delivery costs being paid by patients even after the introduction of the insurance system.⁴² It is also suggested that the limited number of experienced HCPs may compel some GDM patients to choose private hospital care, where considerably higher costs are paid, often through out-of-pocket (OOP) payments that add further financial burden.^43,44 Another added financial burden is the need to purchase healthy food options, which is an added cost during GDM management.⁴⁵

Addressing these financial challenges requires accessible and flexible healthcare appointments, along with physician-led lifestyle counselling, to help reduce overall costs by preventing complications and unnecessary hospital visits.³⁸ This emphasizes the need to follow a standardized model in diabetes centres that includes scheduling appointments, activating reminders, structuring communication sheets, and reallocating work responsibilities.⁴⁶ Recent studies further highlight that redesigning care pathways into integrated maternity units can generate cost savings of nearly 10% while maintaining patient satisfaction and clinical outcomes.⁴⁷ Early diagnosis and timely treatment of GDM have also been shown to be more cost-effective than delayed intervention, preventing escalation into more resource-intensive care.⁴⁸ In addition, lifestyle programs—particularly those delivered in group settings or healthcare facilities—significantly reduce the incidence of GDM and related expenses.⁴⁹ Telehealth-based dietitian counselling and virtual lifestyle coaching offer additional scalable solutions, proving both cost-saving and effective in reducing complications by providing patients with accessible support outside traditional care settings.^50,51 Together, these insights underscore that screening models aligned with patient preferences—combining diagnostic accuracy, reduced costs, minimal inconvenience, and supportive counselling—can improve participation, satisfaction, and overall acceptance of GDM screening.

Patients’ Screening

The screening for GDM plays a vital role in identifying asymptomatic pregnant women and preventing adverse outcomes associated with the disease.⁵² A recent study reported that GDM patients prefer a screening strategy associated with a high diagnostic rate, reduced out-of-pocket expenses, a low number of blood draws, limited screening waiting times, and minimal hospital visits.²⁹ These findings highlight that women value not only the accuracy of screening but also its affordability and convenience, particularly when balancing pregnancy with work and family responsibilities. For instance, qualitative studies have shown that women often perceive repeated blood tests and extended hospital visits as stressful and disruptive, which can reduce willingness to attend screening.^33,53 Patients also emphasized the importance of supportive elements during the screening process, such as clear communication of results and counselling aimed at safeguarding the baby’s health. These elements increase women’s willingness to undergo and complete screening. Together, these insights underscore that screening models aligned with patient preferences—combining diagnostic accuracy, reduced costs, minimal inconvenience, and supportive counselling—can improve participation, satisfaction, and continuity of care.

Recent advances highlight the potential of machine learning (ML) models to strengthen GDM screening by moving beyond traditional risk-factor–based approaches. These models integrate multiple maternal characteristics—such as age, BMI, family history, ethnicity, and early pregnancy biomarkers—to predict GDM risk with greater accuracy than conventional clinical criteria.⁵⁴ For example, a large multicenter study showed that ML-based prediction models achieved higher sensitivity and specificity compared to current screening guidelines, allowing for earlier identification of high-risk women.⁵⁵ Such tools can help design personalized screening strategies, reducing unnecessary tests for low-risk women while ensuring timely and efficient testing for those at high risk.⁵⁶ In addition, ML-driven digital platforms can be linked with electronic health records to automate risk alerts and support clinicians in making screening decisions.⁵⁷ By combining predictive analytics with patient preferences for fewer visits and reduced costs, these solutions have the potential to make GDM screening more accurate, affordable, and patient-centered. Table 1 presents a summary of ML models applied across different aspects of GDM management, outlining their role in screening, diagnosis, glycemic control, postpartum follow-up, and cost evaluation.

Table 1 Application of Machine Learning Models in GDM Care

Despite these advances, several challenges limit clinical translation of ML-based screening tools. Data quality and completeness vary widely across hospitals and populations, often introducing bias and limiting model reliability.⁷⁵ Many algorithms are developed on small or single-center datasets, reducing their generalizability to diverse ethnic or socioeconomic groups. Model interpretability also remains a significant barrier, as complex algorithms can act as “black boxes”, making it difficult for clinicians to understand or trust the decision process.⁷⁶ In addition, real-world adoption is slowed by integration issues with electronic health systems, lack of clinician training, and regulatory uncertainties.⁷⁷ Addressing these limitations through standardized data collection, transparent model reporting, and validation in multicultural settings is essential for ML to become a trusted component of GDM screening.

In practice, the evidence across time, expenses, and screening dimensions highlights that GDM care remains shaped by structural and socioeconomic barriers that limit accessibility and adherence.⁷⁸ Long travel distances, high out-of-pocket costs, and time-intensive screening procedures create overlapping challenges that directly influence women’s satisfaction and participation in care.⁷⁹ While digital health solutions and ML models have introduced promising ways to reduce these burdens, their success depends on ensuring equity, cultural sensitivity, and patient engagement. Future work should focus on integrating these technological and organizational innovations within patient-centered care models that recognize women’s lived realities and preferences, rather than relying solely on efficiency-driven solutions.

Beyond time, cost, and screening factors, substantial heterogeneity exists among GDM patients in terms of socioeconomic status, education, and cultural background. Women with higher education or income levels may engage more readily with self-management and digital tools, while those from disadvantaged or rural settings often face barriers related to health literacy, financial limitations, and access to care.⁸⁰ Cultural beliefs and language differences further shape women’s perceptions of risk, diet adherence, and communication with healthcare providers.⁸¹ These disparities highlight that patient preferences are not uniform but context-dependent, limiting the generalizability of findings across populations. Future studies should stratify analyses by socioeconomic and cultural variables and include more diverse populations to develop inclusive, globally relevant patient-centered care strategies.

Another limitation concerns the geographic distribution of existing evidence. Most available studies on GDM preferences, telehealth adoption, and screening models originate from high-income countries such as Australia, Singapore, the United Kingdom, and the United States.⁸² In contrast, research from low- and middle-income countries (LMICs) remains scarce, despite differing healthcare infrastructures, insurance coverage, and sociocultural contexts that may strongly influence patient experiences and preferences.⁸³ This imbalance restricts the applicability of current findings to resource-limited settings, where barriers such as transportation costs, healthcare accessibility, and digital connectivity are often more pronounced. Expanding research efforts and data collection in LMICs is therefore essential to capture a more representative and equitable understanding of GDM care preferences worldwide.

Methods Used in Eliciting Patients’ Preferences

Several qualitative and quantitative methods have been adopted to determine patients’ relative desirability or acceptability for these services.⁸⁴

Qualitative Methods

Qualitative methods are used to collect descriptive data about patients’ observations, their subjective experiences, and decisions made by them.⁸⁵ Semi-structured interviews are a qualitative method that uses open-ended questions and enables researchers to explore participants’ beliefs about a particular topic, and delve into sensitive health-related issues.⁸⁶ This method was used to investigate women’s beliefs about GDM during and post pregnancy, and assess their impact on the progression.⁸⁷ These interviews were also conducted to explore patients’ attitudes toward lifestyle changes and to enable them to provide advice to those newly diagnosed with GDM.¹¹ In the follow-up stage, interviews were conducted to identify the barriers and facilitators of diabetes screening based on HCPs’ experience.^88,89 For example, Jane et al evaluated the impact of patients’ experience with GDM management on the degree of participation in the follow-up phase.⁹⁰ In the high-risk GDM population, women were interviewed to understand their views on the development of advanced technologies such as mobile health applications.⁹¹

Another qualitative method used to elicit GDM patients’ preferences is focus groups. In comparison to individual interviews, the process of focus groups can help people better clarify their views.⁹² Focus groups allow participants to respond in their own words and to choose discussion topics.⁹³ This method also helps in reflecting the experiences of certain social groups, empowering and motivating patients toward behavioral changes.⁹⁴ In some instances, arranging focus groups may require additional time and effort compared to individual interviews.⁹⁵ For GDM, focus groups have been organized to evaluate the self-management experiences of newly diagnosed women⁹⁶ and assess patients’ experience with insulin and anti-diabetic agents.⁹⁷ The implementation of focus groups helped in determining the concerns, needs, and knowledge of GDM patients.⁹⁸ In the postpartum stage, focus groups were used to identify the key factors that may improve GDM patients’ participation in the TDM2 prevention program.⁹⁹

In the low-income population, semi-structured interviews and focus groups were used to evaluate the preferences of women in self-managing GDM.¹⁰⁰ These methods were also used to assess the factors that may affect the health behaviors of GDM patients and their preferences for lifestyle support,¹⁰¹ especially in women of culturally and linguistically diverse (CALD) backgrounds.¹⁰² In the postpartum stage, the combination of these qualitative methods revealed factors that may affect healthy lifestyle changes and facilitate participation in a lifestyle intervention program.¹⁰³

Quantitative Methods

Quantitative or stated preference (SP) methods are mainly implemented for collecting quantifiable data that can be assessed through statistical inferences or analysis.¹⁰⁴ One of the most commonly employed SP methods for eliciting patients’ preferences is DCE.¹⁰⁵ This method is used for quantifying preferences, evaluating alternative options, and understanding preference variations among subgroups.¹⁰⁶ DCE has been contributing to the generation of data used for clinical and policy decision-making.¹⁰⁷ It has also been implemented to improve treatment adherence, identify patients’ preferences for complex health choices, and develop health programs and technologies that match patients’ demands.¹⁰⁸ Overall, this methodology was applied to elicit pregnant women’s views for screening strategies in terms of frequency of blood draws and hospital visits, extra pocket costs, screening waiting time, and positive diagnosis rate.²⁹ DCE has also been implemented to assess the relative importance of healthcare services specific to pregnant women searching for a health facility for delivery.¹⁰⁹ Therefore, DCE is a promising tool for investigating understudied patients’ care experiences and preferences¹¹⁰ in multiple illnesses such as GDM.

Mixed Methods

In the context of stated preference methods such as DCE, qualitative approaches are mainly used to identify attributes and levels, and to pilot surveys.¹¹¹ Focus groups and semi-structured interviews are instrumental in the early stages of DCE development, as they allow researchers to explore women’s lived experiences and perceptions of GDM care.²⁹ These discussions highlight which aspects of care—such as waiting times, number of blood draws, dietary counselling, or costs— are most meaningful to patients, ensuring that attributes reflect real concerns rather than researcher assumptions.¹¹²

Once attributes and levels are defined, specialized software is used to construct choice sets that present different combinations of these attributes.¹¹³ This allows the DCE to measure quantifiable preferences by observing trade-offs that women are willing to make across attributes.¹¹⁴ For example, qualitative pre-studies in GDM settings have shown that women often highlight the inconvenience of repeated hospital visits, long waiting times, and high out-of-pocket costs during focus groups.²⁹ These insights informed the inclusion of attributes such as the number of clinic visits and the cost of screening in DCE surveys on GDM.^25,115,116

Therefore, mixed methods add value not only to survey design but also to the interpretation of results. Qualitative findings explain why women prioritize specific attributes, while the DCE quantifies how much those attributes matter. Hewage et al showed that combining qualitative interviews with DCE helped contextualize women’s preferences for GDM services.³³ Follow-up focus groups have also clarified unexpected results; for instance, some women valued convenience—such as fewer hospital visits and shorter waiting times—more highly than clinical accuracy, and qualitative discussions helped explain these choices.²⁹

Overall, integrating qualitative and quantitative methods ensures that GDM preference studies are grounded in patient experience while also producing robust, generalizable estimates. This strengthens the ability of DCEs to inform patient-centered healthcare design for GDM. Table 2 presents a comparative overview of preference elicitation methods (qualitative, quantitative, mixed) used in practice, while Figure 1 summarizes the manuscript framework, showing how mixed methods and ML approaches align with key GDM services to support patient-centered care.

Table 2 Comparison of Qualitative, Quantitative, and Mixed Methods for Eliciting Patient Preferences in GDM

Figure 1 Methods for Eliciting Patient Preferences and Enhancing GDM Services in Literature.

Together, these insights indicate that while mixed methods have improved understanding of patient preferences, they are still rarely integrated into real-world healthcare design or policy. Most studies remain descriptive and limited to specific cultural or clinical settings. Future research should aim to bridge the gap between preference elicitation and implementation by combining patient-centered evidence with technological innovations—such as ML-driven decision support—to develop more inclusive and responsive GDM care models.

Conclusion

GDM care involves complex needs shaped by patients’ time, financial costs, and access to screening. Evidence shows that women prioritize convenient, affordable, and supportive services, with strong preferences for fewer hospital visits, shorter waiting times, and clear counselling. Addressing these needs requires flexible appointments, cost-effective care pathways, lifestyle programs, and digital solutions such as telehealth. Recent advances in ML further enhance screening accuracy and support personalized strategies. Qualitative, quantitative, and mixed methods together provide robust insights into these preferences. Consequently, integrating patient perspectives into service design is essential to ensure GDM care that is effective, affordable, and patient-centered.

Acknowledgments

The authors would like to thank Khalifa University for their support.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The authors would like to thank Khalifa University for Science and Technology for funding this project (Grant No. 8474000490, Project Reference: ESIG-2023-012). The authors would like to thank Khalifa University for Science and Technology for funding this project (Grant FSU-2023-017).

Disclosure

The authors declare that they have no competing interests.

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