There are substantial barriers to initiate advance care planning (ACP) for persons with chronic-progressive disease in primary care settings. Some challenges may be disease-specific, such as communicating in case of cognitive impairment. This study assessed and compared the initiation of ACP in primary care with persons with dementia, Parkinson’s disease, cancer, organ failure and stroke.

Longitudinal study linking data from a database of Dutch general practices’ electronic health records with national administrative databases managed by Statistics Netherlands.

Data from general practice records of 199 034 community-dwelling persons with chronic-progressive disease diagnosed between 2008 and 2016.

Incidence rate ratio (IRR) of recorded ACP planning conversations per 1000 person-years in persons with a diagnosis of dementia, Parkinson’s disease, organ failure, cancer or stroke, compared with persons without the particular diagnosis. Poisson regression and competing risk analysis were performed, adjusted for age, gender, migration background, living situation, frailty index and income, also for disease subsamples.

In adjusted analyses, the rate of first ACP conversation for persons with organ failure was the lowest (IRR 0.70 (95% CI 0.68 to 0.73)). Persons with cancer had the highest rate (IRR 1.75 (95% CI 1.68 to 1.83)). Within the subsample of persons with organ failure, the subsample of persons with dementia and the subsample of stroke, a comorbid diagnosis of cancer increased the probability of ACP. Further, for those with organ failure or cancer, comorbid dementia decreased the probability of ACP.

Considering the complexity of initiating ACP for persons with organ failure or dementia, general practitioners should prioritise offering it to them and their family caregivers. Policy initiatives should stimulate the implementation of ACP for people with chronic-progressive disease.

Care pathways are crucial for patients with mental health disorders and should be designed to support integrated rehabilitation while reducing the burden of these disorders. The contemporary shift toward an outpatient follow-up model of care presents an opportunity to improve mental health care beyond the stagnation in advancements in pharmacological treatments. Various pharmacist-led interventions exist and can serve as levers to address ongoing challenges in mental health care pathways: they could help manage difficult transitions, ensure continuity between inpatient and outpatient care, and reduce high rehospitalisation rates. However, the contexts in which these solutions benefit patients and improve care outcomes remain unclear. Thus, the primary objective of this study will be to identify how pharmaceutical solutions contribute to improving mental health care pathways, what works, for whom and in what context. The secondary objective will be to identify the key outcomes currently used to evaluate the impact of pharmaceutical solutions on care pathways.

A systematic realist review will be conducted, following 5 iterative steps to synthesise heterogeneous evidence: (1) Scope definition with a general review of the literature and experts’ discussions, (2) Initial programme theory development based on the preliminary searches, (3) Systematic review for evidence, to refine and test initial programme theory across PubMed, Embase and Web Of Science, (4) Data extraction, including context-mechanism-outcome configurations, and evidence appraisal and (5) Data analysis, synthesis and refined programme theory construction with the realist logic. This process will involve consensus among expert researchers, incorporating insights from individuals with lived experience.

The final programme theory modelling will result in a new framework for pharmaceutical solutions applied in diverse mental health contexts. The findings of this systematic realist review could serve as a guide for implementing pharmaceutical solutions across healthcare settings, ensuring that interventions are evidence-based, contextually relevant and grounded in real-world needs.

As this realist review will collect previously published data and will not involve human or animal participants, no ethical approval is required. Since this manuscript is a review protocol, no datasets were generated or analysed. All data extraction forms will be made available as part of the publication of the realist review.

Systematic review registration PROSPERO 2025 CRD420251011954.

Dates of the study: September 2025 to September 2026.

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