The functional resonance analysis method (FRAM) is increasingly used to analyse healthcare processes. FRAM uses four steps to analyse a process and its potential variability. We systematically reviewed studies using FRAM in healthcare on how the four steps in FRAM are reported, defined and supported by data.

Systematic review following the preferred reporting items for systematic reviews and meta-analyses 2020 guidelines.

Web of Science, PubMed, Embase, Scopus, PsycINFO, Dimensions and Lens were searched up to December 2025.

All peer-reviewed studies using FRAM in a healthcare context that presented a FRAM visualisation were included. The papers had to be written in English.

Two independent reviewers screened titles and abstracts, and subsequently the full text of selected papers. Data was extracted reporting on the steps of FRAM, how functions were supported by data, and the functions and couplings of the visualisations.

Sixty-eight papers were included, of which 20 (29%) reported at least one aspect of all four steps in FRAM. While most studies (85%) described how functions were supported by data, the methods used varied widely. Terminology was interpreted differently concerning variability, the output of variability and the effect of combined variability.

Most FRAM studies in healthcare do not report all steps of FRAM, and interpretations of key terms differ. FRAM studies should more clearly describe which steps of the method are conducted, and how data is collected and analysed. Refinement of FRAM guidelines, particularly on data use and terminology, would enhance consistency and comparability across studies.

CRD42024592858.

Maternal and child mortality has markedly decreased worldwide over the past few decades. Despite this success, the decline remains unequal across countries and is overall insufficient to meet the Sustainable Development Goals. South Asia and sub-Saharan Africa bear most of the burden of maternal and child morbidity and mortality. Major gaps persist in our understanding of the causes, timing, diagnostic thresholds and risk factors for adverse outcomes in these regions. Addressing these gaps requires new ways to prevent and treat disease, from novel diagnostics to precision public health strategies, all of which rely on high-quality clinical data from diverse populations. The Pregnancy Risk, Infant Surveillance, and Measurement Alliance (PRISMA) Maternal and Newborn Health Study aims to estimate population-level prevalence of morbidities and mortality and to assess biological, clinical and sociodemographic risk among mother–infant pairs in India, Pakistan, Kenya, Ghana and Zambia.

This study is a prospective, open cohort study with a planned recruitment of about 6000 women annually across six research sites in five countries. Participants are pregnant women enrolled less than 20 weeks gestation, as determined by ultrasound, identified through active house-to-house and facility-based surveillance. Robust clinical data will be collected at 12 scheduled study visits during antenatal care, labour and delivery, and through 1 year postpartum. A total of 34 outcomes will be captured. The primary analysis will estimate the burden of adverse outcomes and examine associated risk factors to inform future intervention strategies. Data will also be used to develop normative values for pregnant and postpartum women, as well as predictive models to assess pregnancy risk.

PRISMA received institutional and national ethical approvals. Findings will be published in peer-reviewed open-access journals and disseminated at national and international forums to inform clinical guidelines and public health practice.

NCT05904145.

Values and preferences are key determinants of optimal care, and variability in patient values and preferences often dictates differences in patient management. Clinicians’ views of patients’ values and preferences may differ across cultural aspects and stage of training, but the extent to which this is the case remains uncertain. One key value and preference issue is the trade-off between quantity and quality of life, and this issue is particularly prominent among patients with dementia. We therefore propose to investigate the extent to which physicians’ perceptions of optimal management for patients living with advanced dementia may differ due to cross-cultural factors and stage of medical training.

We will conduct a sequential explanatory mixed-methods study (QUAN -> qual). First, we will administer paper-based or electronic surveys during educational sessions, conferences and rounds to medical students, residents and physicians in ten countries, either in person or online. Following that, a qualitative inquiry, guided by the findings of the quantitative study and the principles of the interpretive description design, will inform an in-depth exploration of the predictive factors identified in the quantitative data analysis.

The Hamilton Integrated Research Ethics Board at McMaster University has approved this study (approval number 2024-17651). We will disseminate our findings in peer-reviewed publications and present results at conferences as oral and poster presentations.

Diagnosing pulmonary tuberculosis (PTB) in children is challenging owing to paucibacillary disease, non-specific symptoms and signs and challenges in microbiological confirmation. Chest X-ray (CXR) interpretation is fundamental for diagnosis and classifying disease as severe or non-severe. In adults with PTB, there is substantial evidence showing the usefulness of artificial intelligence (AI) in CXR interpretation, but very limited data exist in children.

A prospective two-stage study of children with presumed PTB in three sites (one in South Africa and two in Pakistan) will be conducted. In stage I, eligible children will be enrolled and comprehensively investigated for PTB. A CXR radiological reference standard (RRS) will be established by an expert panel of blinded radiologists. CXRs will be classified into those with findings consistent with PTB or not based on RRS. Cases will be classified as confirmed, unconfirmed or unlikely PTB according to National Institutes of Health definitions. Data from 300 confirmed and unconfirmed PTB cases and 250 unlikely PTB cases will be collected. An AI-CXR algorithm (qXR) will be used to process CXRs. The primary endpoint will be sensitivity and specificity of AI to detect confirmed and unconfirmed PTB cases (composite reference standard); a secondary endpoint will be evaluated for confirmed PTB cases (microbiological reference standard). In stage II, a multi-reader multi-case study using a cross-over design will be conducted with 16 readers and 350 CXRs to assess the usefulness of AI-assisted CXR interpretation for readers (clinicians and radiologists). The primary endpoint will be the difference in the area under the receiver operating characteristic curve of readers with and without AI assistance in correctly classifying CXRs as per RRS.

The study has been approved by a local institutional ethics committee at each site. Results will be published in academic journals and presented at conferences. Data will be made available as an open-source database.

PACTR202502517486411