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Background: Palliative care comprises multiprofessional, integrated, person-centered healthcare services for patients and their families facing problems related to progressive or advanced diseases and limited life expectancy. Although non-oncology patients' needs are similar to those of tumor patients, they are often underestimated. The purpose of our study was to investigate the actual utilization of palliative care services in Germany, especially in the outpatient setting., Methods: Using the IQVIA Disease Analyzer database, a total of 14,792 outpatients from 805 primary care practices in Germany with documented palliative care and related diagnosis between 2018 and 2021 were analyzed. Proportions of different diagnoses among patients receiving outpatient palliative care were stratified by gender and different age groups., Results: The most common underlying diagnosis for outpatient palliative care was cancer (55%), followed by heart failure (16%) and dementia (8%), with age- and sex-specific differences found in the proportion of diagnoses for utilization. While the relative proportions of cancers decreased with age (87% in the 18- to 50-year-old age group versus 37% in the 80-plus age group), the proportion of palliative care related to heart failure increased in the older population (2% in the 18- to 50-year-old age group versus 25% in the 80-plus age group)., Conclusions: This study provides an overview of the situation of outpatient palliative care in Germany and shows age- and gender-specific trends regarding the underlying medical diagnoses. Based on these data, palliative care should be adapted to current demographic developments.

The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given their capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will enhance both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics., (Copyright © 2021 Elsevier Ltd. All rights reserved.)