Within healthcare systems in all countries, vulnerable groups of patients can be identified and are characterized by the reduced utilization of available healthcare. Many different reasons can be attributed to this observation, summarized as implementation barriers involving acceptance, accessibility, affordability, acceptability and quality of care. For many patients, cancer care is specifically associated with the occurrence of vulnerability due to the complex disease, very different target groups and delivery situations (from prevention to palliative care) as well as cost-intensive care. Sociodemographic factors, such as educational level, rural/remote location and income, are known determinants for these vulnerable groups. However, different forms of financial burdens likely influence this vulnerability in cancer care delivery in a distinct manner. In a narrative review, these socioeconomic challenges are summarized regarding their occurrence and consequences to current cancer care. Overall, besides direct costs such as for treatment, many facets of indirect costs including survivorship costs for the cancer patients and their social environment need to be considered regarding the impact on vulnerability, treatment compliance and abundance. In addition, individual cancer-related financial burden might also affect the society due to the loss of productivity and workforce availability. Healthcare providers are requested to address this vulnerability during the treatment of cancer patients.

Background: Adopting Universal Health Coverage for implementation of a national health insurance system [Jaminan Kesehatan Nasional (JKN)/Badan Penyelenggara Jaminan Sosial or the Indonesian National Social Health Insurance Scheme (BPJS)] targets the 255 million population of Indonesia. The availability, accessibility, and acceptance of healthcare services are the most important challenges during implementation. Referral behavior and the utilization of primary care structures for underserved (rural/remote regions) populations are key guiding elements. In this study, we provided the first assessment of BPJS implementation and its resulting implications for healthcare delivery based on the entire insurance dataset for the initial period of implementation, specifically focusing on poor and remote populations.Methods: Demographic, economic, and healthcare infrastructure information was obtained from public resources. Data about the JKN membership structure, performance information, and reimbursement were provided by the BPJS national head office. For analysis, an ANOVA was used to compare reimbursement indexes for primary healthcare (PHC) and advanced healthcare (AHC). The usage of primary care resources was analyzed by comparing clustered provinces and utilization indices differentiating poor [Penerima Bantuan Iur (PBI) membership] and non-poor populations (non-PBI). Factorial and canonical discrimination analyses were applied to identify the determinants of PHC structures.Results: Remote regions cover 27.8% of districts/municipalities. The distribution of the poor population and PBI members were highly correlated (r2 > 0.8; p < 0.001). Three clusters of provinces [remote high-poor (N = 13), remote low-poor (N = 15), non-remote (N = 5)] were identified. A discrimination analysis enabled the >82% correct cluster classification of infrastructure and human resources of health (HRH)-related factors. Standardized HRH (nurses and general practitioners [GP]) availability showed significant differences between clusters (p < 0.01), whereas the availability of hospital beds was weakly correlated. The usage of PHC was ~2-fold of AHC, while non-PBI members utilized AHC 4- to 5-fold more frequently than PBI members. Referral indices (r2 = 0.94; p < 0.001) for PBI, non-PBI, and AHC utilization rates (r2 = 0.53; p < 0.001) were highly correlated.Conclusion: Human resources of health availability were intensively related to the extent of the remote population but not the numbers of the poor population. The access points of PHC were mainly used by the poor population and in remote regions, whereas other population groups (non-PBI and non-Remote) preferred direct access to AHC. Guiding referral and the utilization of primary care will be key success factors for the effective and efficient usage of available healthcare infrastructures and the achievement of universal health coverage in Indonesia. The short-term development of JKN was recommended, with a focus on guiding referral behavior, especially in remote regions and for non-PBI members.

The COVID-19 pandemic put healthcare systems, hospitals and medical personal under great pressure. Based on observations in Germany, we theorise a general model of rapid decision-making that makes sense of the growing complexity, risks and impact of missing evidence. While adapting decision-making algorithms, management, physicians, nurses and other healthcare professionals had to move into uncharted territory while addressing practical challenges and resolving normative (legal and ethical) conflicts. During the pandemic, this resulted in decisional uncertainties for healthcare professionals. We propose an idealised risk-based model that anticipates these shifts in decision-making procedures and underlying value frameworks. The double pyramid model visualises foreseeable procedural adaptations. This does not only help practitioners to secure operational continuity in a crisis but also contributes to improving the conceptual underpinnings of the resilience of healthcare during the next pandemic or similar future crises situations.

The early COVID-19-pandemic was characterized by changes in decision making, decision-relevant value systems and the related perception of decisional uncertainties and conflicts resulting in decisional burden and stress. The vulnerability of clinical care professionals to these decisional dilemmas has not been characterized yet. Methods: A cross-sectional questionnaire study (540 patients, 322 physicians and 369 nurses in 11 institutions throughout Germany) was carried out. The inclusion criterion was active involvement in clinical treatment or decision making in oncology or psychiatry during the first year of COVID-19. The questionnaires covered five decision dimensions (conflicts and uncertainty, resources, risk perception, perception of consequences for clinical processes, and the perception of consequences for patients). Data analysis was performed using ANOVA, Pearson rank correlations, and the Chi²-test, and for inferential analysis, nominal logistic regression and tree classification were conducted. Results: Professionals reported changes in clinical management (27.5%) and a higher workload (29.2%), resulting in decisional uncertainty (19.2%) and decisional conflicts (22.7%), with significant differences between professional groups (p < 0.005), including anxiety, depression, loneliness and stress in professional subgroups (p < 0.001). Nominal regression analysis targeting “Decisional Uncertainty” provided a highly significant prediction model (LQ p < 0.001) containing eight variables, and the analysis for “Decisional Conflicts” included six items. The classification rates were 64.4% and 92.7%, respectively. Tree analysis confirmed three levels of determinants. Conclusions: Decisional uncertainty and conflicts during the COVID-19 pandemic were independent of the actual pandemic load. Vulnerable professional groups for the perception of a high number of decisional dilemmas were characterized by individual perception and the psychological framework. Coping and management strategies should target vulnerability, enable the handling of the individual perception of decisional dilemmas and ensure information availability and specific support for younger professionals.

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