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A ‘7-1-7’ timeliness metric, developed for hastening the response to infectious disease outbreaks/pandemics, was adapted to improve screening and managing household contacts (HHCs) of pulmonary tuberculosis (TB) patients. The feasibility, enablers, challenges and utility of implementing this modified metric through TB Champions (TB survivors) for HHC management were assessed.
This was an explanatory mixed-methods study with a cohort design (quantitative) followed by a descriptive design with focus group discussions (qualitative).
The study was conducted within routine programmatic settings in public health facilities in six districts from three states of India.
In total, 595 drug-susceptible index pulmonary TB patients registered for treatment in the selected health facilities, and their listed 2108 HHCs were included in the study between December 2022 and August 2023. All 17 TB Champions involved in implementation participated in the focus group discussions.
The primary outcome measures were the percentage of eligible participants receiving the desired service within the ‘7-1-7’ timeliness metric and challenges in achieving the timeliness metrics.
In 89% of 595 index patients, their HHCs were line-listed within 7 days of initiating anti-TB treatment (‘First-7’). In 90% of 2108 HHCs, screening outcomes were ascertained within 1 day of line-listing (‘Next-1’). In 42% of 2073 HHCs eligible for further evaluation, anti-TB treatment, TB preventive treatment (TPT) or a decision to not receive medication were made within 7 days of screening (‘Second-7’). Barriers to TPT uptake included lack of money and daily wage losses for travelling to clinics, reluctance of asymptomatic contacts to take medication and fear of adverse events. TB Champions felt timeliness metrics improved performance in the systematic and timely management of HHCs.
TB Champions found ‘7-1-7’ timeliness metrics were feasible and useful, and national TB programmes should consider their operationalisation.
Acknowledging equality, diversity and inclusion (EDI) in research is not only a moral imperative but also an important step in avoiding bias and ensuring generalisability of results. This protocol describes the development of STAndards for ReporTing EDI (START-EDI) in research, which will provide a set of minimum standards to help researchers improve their consistency, completeness and transparency in EDI reporting. We anticipate that these guidelines will benefit authors, reviewers, editors, funding organisations, healthcare providers, patients and the public.
To create START-EDI reporting guidelines, the following five stages are proposed: (i) establish a diverse, multidisciplinary Steering Committee that will lead and coordinate guideline development; (ii) a systematic review to identify the essential principles and methodological approaches for EDI to generate preliminary checklist items; (iii) conduct an international Delphi process to reach a consensus on the checklist items; (iv) finalise the reporting guidelines and create a separate explanation and elaboration document; and (v) broad dissemination and implementation of START-EDI guidelines. We will work with patient and public involvement representatives and under-served groups in research throughout the project stages.
The study has received ethical approval from the Imperial College London Research Ethics Committee (study ID: 7592283). The reporting guidelines will be published in open access peer-reviewed publications and presented in international conferences, and disseminated through community networks and forums.
The project is pre-registered within the Open Science Framework (https://osf.io/8udbq/) and the Enhancing the Quality and Transparency of Health Research Network.
The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, the review aimed to explore whether implementing these interventions as bundles reduced practice pattern variance and positively impacted patient care outcomes for inpatients with T2DM. Six databases were searched for articles published from January 1, 2000, to January 1, 2024. Nine studies met criteria and were summarized by aims, outcome measures, clinical or practice implications, AI/ML model types, study variables, and AI/ML model outcomes. A variety of AI/ML models were used. Multiple data sources were leveraged to train the models, resulting in varying impacts on practice patterns and outcomes. Studies included aims across 4 thematic areas to address: therapeutic patterns of care, analysis of treatment pathways and their constraints, dashboard development for clinical decision support, and medication optimization and prescription pattern mining. Multiple disparate data sources (i.e., prescription payment data) were leveraged outside of those traditionally available within EHR databases. Notably missing was the use of holistic multidisciplinary data (i.e., nursing and ancillary) to train AI/ML models. AI/ML can assist in identifying the appropriateness of specific interventions to manage diabetic care and support adherence to efficacious treatment pathways if the appropriate data are incorporated into AI/ML design. Additional data sources beyond the EHR are needed to provide more complete data to develop AI/ML models that effectively discern meaningful clinical patterns. Further study is needed to better address nursing care using AI/ML to support effective inpatient diabetes management. 
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