Digital technologies for health financing in low-income and middle-income countries: a scoping review protocol.

INTRODUCTION: Universal health coverage (UHC) is a global priority, ensuring equitable access to quality healthcare services without financial hardship. Many countries face challenges in progressing towards UHC. Health financing is pivotal for advancing UHC by raising revenues, enabling risk-sharing through pooling of funds and allocating resources. Digital technologies in the healthcare sector offer promising opportunities for health systems. In low-income and middle-income countries (LMICs), digital technologies for health financing (DTHF) have gained traction, supporting these three main functions of health financing for UHC. As existing information on DTHF in LMICs is limited, our scoping review aims to provide a comprehensive overview of DTHF in LMICs. Our objectives include identifying and describing existing DTHF, exploring evaluation approaches, examining their positive and negative effects, and investigating facilitating factors and barriers to implementation at the national level. METHODS AND ANALYSIS: Our scoping review follows the six stages proposed by Arksey and O'Malley, further developed by Levac et al and the Joanna Briggs Institute. The reporting adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews framework. Eligibility criteria for studies reflect the three core elements of the search: (1) health financing, (2) digital technologies and (3) LMICs. We search multiple databases, including Medline via PubMed, EMBASE via Ovid, the Web of Science Core Collection, CENTRAL via Cochrane and the Global Index Medicus by the WHO. The extracted information is synthesised from both quantitative and qualitative studies. ETHICS AND DISSEMINATION: As our scoping review is based solely on information gathered from previously published studies, documents and publicly available scientific literature, ethical clearance is not required for its conduct. The findings are presented and discussed in a peer-reviewed article, as well as shared at conferences relevant to the topic.

Applying a data-driven population segmentation approach in German claims data.

BACKGROUND: Segmenting the population into homogenous groups according to their healthcare needs may help to understand the population's demand for healthcare services and thus support health systems to properly allocate healthcare resources and plan interventions. It may also help to reduce the fragmented provision of healthcare services. The aim of this study was to apply a data-driven utilisation-based cluster analysis to segment a defined population in the south of Germany. METHODS: Based on claims data of one big German health insurance a two-stage clustering approach was applied to group the population into segments. A hierarchical method (Ward's linkage) was performed to determine the optimal number of clusters, followed by a k-means cluster analysis using age and healthcare utilisation data in 2019. The resulting segments were described in terms of their morbidity, costs and demographic characteristics. RESULTS: The 126,046 patients were divided into six distinct population segments. Healthcare utilisation, morbidity and demographic characteristics differed significantly across the segments. The segment "High overall care use" comprised the smallest share of patients (2.03%) but accounted for 24.04% of total cost. The overall utilisation of services was higher than the population average. In contrast, the segment "Low overall care use" included 42.89% of the study population, accounting for 9.94% of total cost. Utilisation of services by patients in this segment was lower than population average. CONCLUSION: Population segmentation offers the opportunity to identify patient groups with similar healthcare utilisation patterns, patient demographics and morbidity. Thereby, healthcare services could be tailored for groups of patients with similar healthcare needs.