Abstract

Background Diabetes-related foot ulcers (DFUs) are a serious complication of diabetes, often resulting in infection, amputation, or even mortality. Offloading footwear is a key intervention that promotes ulcer prevention and healing by reducing pressure on affected areas of the foot. However, patient adherence to prescribed footwear remains low. Current clinical guidelines, such as those from the International Working Group on the Diabetic Foot (IWGDF), offer general recommendations but lack a standardized and structured framework. Furthermore, they do not support personalized footwear prescription or integration into clinical decision support systems (CDSSs) and artificial intelligence (AI) applications.

Objective The primary objective of this study was to identify and systematically categorise the key factors influencing footwear prescription and design for patients with DFUs. A secondary objective was to integrate these factors into a structured framework aligned with HL7 Fast Healthcare Interoperability Resources (FHIR) standards, with the goal of informing the development of AI-powered CDSSs.

Methods A systematic scoping review was conducted in accordance with the PRISMA-ScR guidelines. Seventeen academic databases and Google Scholar were searched for relevant studies published between 2014 and 2025. Eligible studies included original research involving adults with diabetes or diabetic foot ulcers (DFUs) and focused on offloading footwear or orthotic interventions. Data were extracted and categorized using the WHO Dimensions of Adherence to Long-Term Therapies framework and mapped to FHIR resources using standardized terminologies such as SNOMED CT, and LOINC. Where existing standards were insufficient, custom FHIR extensions were proposed.

Results A total of 81 studies met the inclusion criteria, encompassing data from 5,001 participants. Key outcome measures across the studies included plantar pressure reduction (n = 55), adherence (n = 27), gait and balance (n = 20), and ulcer recurrence (n = 13). The review identified 90 unique decision factors influencing footwear prescription, classified according to the five WHO dimensions of adherence to long-term therapies: patient-related, condition-related, therapy-related, socioeconomic, and healthcare system– related factors. Of the 90 factors identified, 48 were categorized as input variables and 42 as output variables. The review found that only 16/90 of the decision factors could be mapped directly to existing FHIR standards, while the remaining 74/90 required custom extensions—particularly those related to detailed footwear attributes and socioeconomic patient data. This is because of the unique nature of the domain as the result of lack of standardized definition related to the domain.

Conclusions This study introduces the first structured and interoperable framework for prescribing offloading footwear in the management of diabetic foot ulcers (DFUs). By aligning key decision factors with HL7 FHIR standards and the WHO Dimensions of Adherence, the framework enables seamless integration into electronic health records and supports the development of AI-driven clinical decision support systems (CDSSs). Future work should prioritize expert validation of the identified factors, implementation studies, and real-world testing to evaluate the framework’s usability, clinical relevance, and impact on patient outcomes.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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Data Availability

All data is available in the manuscript.

AbbreviationsAIArtificial intelligenceCDSSClinical decision support systemCOPCentre of pressureDFUDiabetes-related foot ulcerDPNDiabetic peripheral neuropathyEHRElectronic health recordsFHIRFast Healthcare Interoperability ResourcesHCPHuman centered designHL7Health Level Seven InternationalHRQoLHealth-related quality of lifeIWGDFInternational Working Group on the DiabeticFoot MLMachine LearningPADPeripheral arterial diseasePGradPeak pressure gradientPICOPopulation intervention comparison outcomePMapPeak pressure mapPPPlantar pressurePPPPeak plantar pressurePTCPeak pressure time curvePTIpressure time integralPTMPeak pressure time mapRCTrandomized controlled trialSNOMEDSystematized nomenclature of medicineLOINCLogical Observation Identifiers Names and CodesICD-10International Classification of Diseases, 10th Revision