Development of a patient-oriented navigation model for patients with lung cancer and stroke in Germany

Background: The concept of patient navigation was first established in the USA to support vulnerable patient groups in receiving timely and comprehensive access to cancer care. It has recently gained increasing interest in Germany to support patients with chronic diseases in a fragmented healthcare system. The aim of this paper is to present the development of such a model adapted to the German context based on the results of mixed-methods studies investigating the need for and barriers to patient-oriented care. Methods: In a process adapted from Delphi rounds, we conducted regular structured workshops with investigators of the project to discuss results of their studies and identify content and structure of the model based on the data. Workshop discussions were structured along seven core components of a navigation model including target patient groups, navigator tasks, occupational background and education of navigators, and patient-navigator interaction mode. Results: Using an approach based on empirical data of current care practices with special focus on patients' perspectives, we developed a patient-oriented navigation model for patients who have experienced stroke and lung cancer in the German healthcare context. Patients without personal social support were viewed as struggling most with the healthcare system, as well as multimorbid and elderly patients. Navigators should serve as a longer-term contact person with a flexible contact mode and timing based on the individual situation and preferences of patients. Navigator tasks include the provision of administrative and organizational support as well as referral and guidance to available resources and beneficial health programs with special forms of knowledge. Implementation of the navigator should be flexibly located to ensure a reliable outreach to vulnerable patients for first contact in settings like specialized in-patient and out-patient settings, while navigation itself focuses on care coordination in the out-patient setting. Conclusion: Flexibility of navigator tasks needed to be a core characteristic of a navigation model to be perceived as supportive from patients' perspectives. In a subsequent feasibility study, an intervention based on the model will be evaluated according to its acceptance, demand, and practicality

Using decision tree analysis to identify population groups at risk of subjective unmet need for assistance with activities of daily living

Abstract Background Identifying predictors of subjective unmet need for assistance with activities of daily living (ADL) is necessary to allocate resources in social care effectively to the most vulnerable populations. In this study, we aimed at identifying population groups at risk of subjective unmet need for assistance with ADL and instrumental ADL (IADL) taking complex interaction patterns between multiple predictors into account. Methods We included participants aged 55 or older from the cross-sectional German Health Update Study (GEDA 2019/2020-EHIS). Subjective unmet need for assistance was defined as needing any help or more help with ADL (analysis 1) and IADL (analysis 2). Analysis 1 was restricted to participants indicating at least one limitation in ADL (N = 1,957). Similarly, analysis 2 was restricted to participants indicating at least one limitation in IADL (N = 3,801). Conditional inference trees with a Bonferroni-corrected type 1 error rate were used to build classification models of subjective unmet need for assistance with ADL and IADL, respectively. A total of 36 variables representing sociodemographics and impairments of body function were used as covariates for both analyses. In addition, the area under the receiver operating characteristics curve (AUC) was calculated for each decision tree. Results Depressive symptoms according to the PHQ-8 was the most important predictor of subjective unmet need for assistance with ADL. Further classifiers that were selected from the 36 independent variables were gender identity, employment status, severity of pain, marital status, and educational level according to ISCED-11. The AUC of this decision tree was 0.66. Similarly, depressive symptoms was the most important predictor of subjective unmet need for assistance with IADL. In this analysis, further classifiers were severity of pain, social support according to the Oslo-3 scale, self-reported prevalent asthma, and gender identity (AUC = 0.63). Conclusions Reporting depressive symptoms was the most important predictor of subjective unmet need for assistance among participants with limitations in ADL or IADL. Our findings do not allow conclusions on causal relationships. Predictive performance of the decision trees should be further investigated before conclusions for practice can be drawn