Sickle cell disease places a significant burden on health systems in sub-Saharan Africa, including Ghana, where access to high-quality, patient-centred care remains limited. This study evaluated patient-perceived quality of sickle cell disease care at a tertiary-level facility in Ghana and explored process factors influencing perceived quality of care.

Cross-sectional, questionnaire-based study.

A tertiary-level healthcare facility in Accra, Ghana.

A total of 424 individuals with sickle cell disease were recruited using convenience sampling. Data were collected between 4 September and 16 October 2023 using pretested, interviewer-administered questionnaires. First-time clinic attendees and those requiring urgent medical intervention were excluded.

Primary outcome was patient-perceived quality of care. Secondary outcome measures included socio-demographic and process-related factors influencing patient-perceived care quality.

Participants’ ages ranged from 15 to 66 years, with a median (IQR) age of 32 (27–42) years. Most were female (67.4%), had the SS genotype (51.9%) and 68.6% were on hydroxyurea. Overall, 81.8% of respondents reported receiving good-quality care. Predictors of higher perceived care quality included age (adjusted OR (AOR)=8.9, (95% CI 3.3 to 24.3), p=0.001), hydroxyurea use (AOR=2.3, (95% CI 1.2 to 4.2), p=0.008), good health worker-patient communication (AOR=3.2, (95% CI 1.7 to 6.0), p=0.001), positive provider attitudes (AOR=3.1, (95% CI 1.7 to 5.7), p=0.001), receipt of health education (AOR=2.1, (95% CI 1.1 to 3.9), p=0.030) and shorter waiting times for emergency care (AOR=0.2, (95% CI 0.1 to 0.6), p=0.001).

This study provides context-specific evidence on process-level determinants of quality of sickle cell disease care in Ghana. Interventions to improve provider communication, enhance provider attitudes, strengthen patient education and reduce waiting times may improve patient experience and contribute to progress towards Universal Health Coverage in resource-limited settings.

The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect in clinical practice, especially in outpatient settings. Speech provides a quantitative clinical marker for detecting such early warning signs. The EU Horizon project TRUSTING (A TRUSTworthy speech-based AI monitoring system for the prediction of relapse in individuals with schizophrenia) aims to develop and evaluate a speech-based monitoring system for predicting imminent psychotic relapses. The study will examine the potential for prospective relapse prediction, and feasibility and usability of the monitoring system.

In this multicentre observational study, n=240 remitted and at-risk-of-relapse adults with psychotic disorders and a comparison group with n=120 healthy participants (matched by age and sex) will be examined at six sites and in six different languages (German, French, Dutch, English, Czech and Turkish). The follow-up period is 6 months. The TRUSTING smartphone app will be used to collect weekly voice recordings through speech tasks; information on medication adherence, substance use, mood, anxiety and sleep quality; and motor data from a tapping task. Primary endpoints encompass model performance for relapse prediction, user adherence, transcription quality, usability of recordings and overall system usability. The primary analysis of user adherence, transcription quality, usability of recordings and overall system usability will be an unadjusted description of the respective proportions using 95% Wilson confidence intervals. Regarding relapse prediction, the predictive value of the risk estimates for relapse occurrence will be assessed using the area under the receiver operating characteristic curve. Exploratory analysis will be performed on potential speech-based markers associated with relapse risk.

This study has been approved by swissethics (Business Administration System for Ethics Committees number: 2025–01177). Findings from this project will be disseminated through peer-reviewed journal publications and presentations at relevant scientific conferences, as well as at public events related to mental health.

ClinicalTrials.gov ID: NCT07397975.

To explore and map the literature on referral pathway gaps and improvement strategies in primary healthcare (PHC) to secondary healthcare (SHC) referrals, and to categorise both gaps and improvements using the Quintuple Aim framework for healthcare improvement—a model encompassing patient experience, population health, costs, provider well-being and health equity.

Scoping review guided by Arksey and O’Malley’s framework, refined by the Joanna Briggs Institute and reported following the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines.

PubMed, CINAHL and Scopus were searched for publications from January 2013 to May 2024.

Peer-reviewed, English-language studies reporting on improvements to PHC-to-SHC referral processes. Studies were eligible if they examined referrals initiated by primary care physicians (population) addressed interventions or improvements targeting referral gaps (concept) in PHC settings (context). Studies focused exclusively on secondary care perspectives, non-English publications and those outside the 2013–2024 date range were excluded.

Data were charted using a standardised form based on a Population–Concept–Context framework. Data extraction was partially supported by the artificial intelligence-based platform Elicit, with systematic verification against source articles. Referral gaps and improvements were mapped to the five Quintuple Aim domains using structured analytical columns applied to each included study, then synthesised narratively.

85 studies were included. Analysis revealed recurring referral pathway gaps across five stages (patient assessment, information transfer, coordination, specialty access and feedback), negatively impacting all five Quintuple Aim domains. Eight categories of improvements were identified (eg, educational initiatives, electronic referral systems, structured templates, decision support tools). These improvements frequently enhanced referral efficiency; however, evidence of impact on provider well-being and health equity was limited.

Referral improvements can enhance efficiency, but evidence of impact on provider well-being and health equity remains sparse. Future research should prioritise equity-focused designs and address administrative burden and resource constraints to achieve full Quintuple Aim alignment.

Open Science Framework (https://osf.io/87htv/).

The soluble FMS-like tyrosine kinase-1 and placental growth factor (sFlt-1/PlGF) ratio has demonstrated impressive predictive test characteristics in women with suspected pre-eclampsia. However, it remains a matter of debate whether the introduction of this novel test can indeed translate to a reduction in pre-eclampsia-related hospital admissions, outpatient visits and can consequently lower overall healthcare costs. The PREPARE II study aims to investigate whether the sFlt-1/PlGF ratio, along with digital self-monitoring, can reduce pre-eclampsia-related healthcare utilisation in the first week following the test for women with suspected pre-eclampsia.

This is a randomised controlled trial across six centres which includes women (≥16 years old) between 20 and 37 weeks of gestation with suspected pre-eclampsia due to one or more identified symptoms. For power calculation, we assumed that the sFlt-1/PlGF ratio including a telemonitoring strategy leads to a de-escalation of care by cumulatively reducing the frequency of pre-eclampsia-related hospital admissions and/or outpatient visits in the first week from 50% in the control group to 35% in the intervention group. Considering a loss to follow-up of 5%, a sample size of approximately 470 women is required with 235 women per arm (α=5%; power=90%). The intervention is an algorithm based on the urine protein/creatinine ratio (PCr)+sFlt-1/PlGF ratio, along with a telemonitoring strategy. The algorithm incorporates a PCr cut-off of 30 (mg/mmol) and a sFlt-1/PlGF ratio cut-off of 38 for risk classification. Subsequent clinical follow-up recommendations are stratified based on this classification: low risk entails no additional follow-up, returning to routine antenatal care; intermediate risk includes telemonitoring; high risk necessitates immediate admission. The primary outcome is the occurrence of pre-eclampsia-related healthcare utilisation in the first week after testing. Secondary outcomes are maternal/perinatal adverse events, total healthcare usage, pre-eclampsia diagnosis, quality of life and productivity losses. A cost-effectiveness analysis from a societal perspective will be performed.

Ethical approval was obtained from the Medical Ethics Committee of Leiden University Medical Centre (METC LDD) on 21 July 2025 (reference NL-009295). The results will be disseminated through peer-reviewed publications and presentations at international conferences.

NL88527.058.24.

One-third of patients operated for degenerative conditions in the lumbar spine do not report substantial improvement after 12 months. Most previous outcome prediction models are classifiers. This constrains nuances in prediction and use for decision support.

To develop and test models for the prediction of continuous outcome scores and retrieval of similar patients’ outcomes, and to evaluate the models’ fairness.

Norwegian public and private specialist healthcare.

All cases recorded with an elective operation for lumbar disc herniation (LDH, n=18 377) or lumbar spinal stenosis (LSS, n=24 540) in the Norwegian Registry for Spine Surgery from 1 January 2007 to 23 May 2023.

All outcomes were patient-reported 12 months after the operation. The primary outcome was the Oswestry disability index (ODI), modelled on a scale ranging from 0 to 100. Numeric Rating Scale scores (range 0–10) for back and leg pain were secondary outcomes.

We selected 22 predictors recorded preoperatively by patients and clinicians based on Shapley Additive Explanations values. Data were split into 80%/20% training/test samples for LDH and LSS. Six machine learning methods for regression, that is, with a continuous outcome (extreme gradient boosting (XGBoost), Gaussian process regression, gradient boosting regression, artificial neural networks and linear regression), were trained for both conditions using fivefold cross-validation. We report the magnitude and distribution of errors as mean absolute error (MAE) with 95% CIs, and explanatory power as the coefficient of determination (R²). Fairness and calibration were assessed with violin and calibration plots of error. We developed a patient-similarity function that uses a K-nearest neighbour model to retrieve the individual outcomes of the 50 most similar patients and evaluated it by calculating L1 distances (Manhattan distances) across subgroups.

XGBoost regression performed best for both conditions. The models showed good calibration and predicted ODI with MAE 11.32 (95% CI 11.00 to 11.63) and R² 0.27 (95% CI 0.24 to 0.29) for LDH and MAE 12.05 (95% CI 11.76 to 12.32) and R² 0.31 (95% CI 0.28 to 0.34) for LSS. The MAEs for back and leg pain were 2.09 (95% CI 2.04 to 2.15) and 1.95 (95% CI 1.90 to 2.00) for LDH and 2.33 (95% CI 2.28 to 2.38) and 2.13 (95% CI 2.08 to 2.16) for LSS. All models were fair with differences in error between subgroups for sex, age, education level and native language. In the patient-similarity function, distances at baseline were evenly distributed across subgroups.

Our machine learning models predicted continuous outcomes with MAEs close to the SEs of measurements. The models were fair across sociodemographic subgroups. We succeeded in developing a patient-similarity function which supplements the predictions.