Objectives: Global warming affects health and puts a strain on the healthcare system. Prediction models can forecast healthcare demand and optimize resource allocation. The aim of this study was to identify heat-related diseases and their influencing factors necessary for the development of such a prediction model.Methods: A literature search was conducted in the PubMed, Embase and Medline databases. The focus was on reviews of heat-related diseases published within the past 30 years in the German and English languages. A qualitative synthesis of the results was conducted.Results: The literature search produced a total of 737 results. A total of 15 reviews were included in the synthesis. As a result of the synthesis, a spectrum of heat-related diseases such as electrolyte imbalances, cardiovascular disease, kidney disease, respiratory disease, mental health issues, infectious diseases and other diseases were identified. Furthermore, specific climatic and other influencing factors were determined.Conclusion: The meta-review highlighted a wide range of diseases that can occur in connection with heat, along with their influencing factors. The findings can serve as the basis for developing preventive measures such as a prediction model in order to better forecast the resource load due to acute heat.

Aim: An increase in average temperatures and acute heat events has been observed, which has led to an increase in hospital treatments. In the course of climate change, this may lead to a temporary overload of the healthcare system in the future. The aim of this review was to find out how digital health planning tools can be used to optimize the distribution of available resources and counteract overloads. In addition, the review sought to gather comprehensive insights to develop a corresponding tool as part of the project Medical Informatics Hub in Saxony (MiHUBx). Subjects and methods: Six scientists from different disciplines were involved in the study, which was carried out as part of MiHUBx. The inclusion and exclusion criteria, based on the PICOS-scheme, guided the selection of relevant studies. The search was conducted in the Medline, IEEE and Web of Science databases and limited to the last ten years. Results: We identified 21 studies published between 2013 and 2022. A connection was found between heat and use of the health system. These studies presented a conceptual framework, but none described the implementation of the instruments in standard care. However, it was shown that statistic models based on weather and health data can be suitable for predicting resource utilization and can optimize health supply. Conclusion: It was shown that statistic models are suitable for predicting the use of resources to optimize healthcare provision and can therefore be developed in the project. In addition, requirements for the development of a resource allocation and planning tool were identified.

Background: As a result of climate change, temperatures and the burdens on human health are rising. This study investigates the relationship between acute heat and hospitalizations for specific diseases at the University Hospital Carl Gustav Carus in Dresden, Germany. Methods: A statistical analysis was conducted using clinical data from 4,363 patients treated between January 1, 2018, and December 31, 2023. Data included demographic information and heat-related diagnoses categorized by codes from the International Classification of Diseases. Climatic data was sourced from the Dresden-Klotzsche weather station, focusing on days with air temperatures exceeding 23 °C (warm days) and 30 °C (heat days). Correlation analyses were performed to assess the relationship between heat events and hospital admissions on these days. Results: We found a positive correlation between hospital admissions and heat at temperatures ≥ 23 °C for all included diseases combined (p < 0.05). A positive correlation was observed between temperatures ≥ 23 °C and hospitalizations for the diseases dehydration (E86), stroke (I63) and chronic obstructive pulmonary disease (J44) (all p < 0.05). At temperatures of ≥ 30 °C, no statistically significant correlation was identified. We discovered that men, young children and older people were particularly affected by heat-related diseases. Conclusions: The findings based on routine data from one German university hospital indicate that moderate heat can impact hospitalizations for certain diseases, at temperatures above 30 °C, no evidence for a statistically significant association was found, possibly due to a lack of statistical power or behavioural adaptations. This underlines the need for low-threshold preventive measures in response to the health risks associated with rising temperatures.

Background: Climate change is leading to an increase in acute heatwaves, thereby raising risks to human health and healthcare systems. The aim of this study was to quantify the relationship between heat and hospital admissions, and to develop a predictive model for patient admissions. Methods: A retrospective secondary data analysis was conducted for the period 2018–2023, using data from Chemnitz Hospital and the Regional Climate Information Service of TU Dresden. Hospital admissions with the following diagnoses were analyzed: E86 (Volume depletion), I20 (Angina pectoris), I21 (Acute myocardial infarction), I63 (Cerebral infarction), I64 (Stroke, unspecified as hemorrhagic or ischemic), J44 (COPD), L55 (Sunburn), T67 (Effects of heat and light). Meteorological variables considered included temperature, humidity, air pressure, and daily sunshine hours. Analyses included correlations between daily maximum temperatures (≥ 23°C and ≥ 30°C) and hospital admissions, as well as linear and non-linear regression analyses., both univariate (maximum temperature) and multivariate (humidity, air pressure, sunshine hours). Results: A positive correlation between maximum temperature and hospital admissions was observed: r = 0.44 ( p > 0.05) for ≥ 23°C and r = 0.88 ( p < 0.01) for ≥ 30°C. Regression analyses suggested a non-linear relationship (U-shaped pattern with alternating curvature) between temperature and hospital admissions for days with a maximum temperature between 23°C and 29°C, and a linear effect for days above 30°C, additionally influenced by air pressure, humidity, and sunshine hours. Conclusion: The results indicate a complex association between heat and hospital admissions. Based on these findings, a predictive model was developed to forecast patient admissions. The study highlights the importance of primary preventive measures and the consideration of moderate heat stress in healthcare planning.

Introduction The nursing care sector faces significant challenges due to an ageing population and a concurrent increase in demand for care services. Digitalisation can be one way of overcoming these challenges by optimising care processes, such as streamlining documentation procedures. However, Germany remains in the early stages of digitalisation in nursing care. This systematic review aims to provide an overview of the current state of digitalisation, focusing on the level of digitalisation and potential strategies for digitalisation in the nursing care sector through an international comparison. Methods and analysis The literature databases PubMed, Embase, CINHAL and IEEE will be searched for studies published between 1 January 2010 and 31 December 2023, using search strings based on the PICOS scheme (covering Population, Intervention, Comparison, Outcome and Study Types). This systematic review will conduct a qualitative synthesis of the included studies. Ethics and dissemination As the present study is a systematic review and patients are not directly involved, an ethical review is not required. The findings of this review will be disseminated through peer-reviewed publications, conference presentations and detailed reports shared with relevant stakeholders in the nursing care sector. PROSPERO registration number CRD42024524587.

Predicting resource utilization can help to optimize the distribution of limited resources in the healthcare system. This requires different climatic and medical data from different sources, which can lead to problems with interoperability. In the paper we describe which data is needed for the prediction and how the data can be made interoperable using OMOP CDM.