Needs and Demands for e-Health Interventions in Patients Attending a Psychosomatic Outpatient Clinic: A Baseline Assessment for a User-Centered Design Approach.

Objective: Increased utilization of e-health services can help to meet shortages of psychotherapeutic treatment. e-Health interventions can be effective if tailored according to the individual needs and demands of the target group. To gather comprehensive data for the development of a user-centered e-health intervention, a cross-sectional study was conducted among a heterogeneous cohort of 309 patients seeking treatment or consultation at psychosomatic university hospital in a densely populated region of Germany. Methods: Sociodemographic data, psychometric dimensions of mental burden, as well as needs and demands regarding an e-health intervention were assessed. A descriptive statistical analysis and a cluster analysis were performed to examine distribution of preferences and differences based on level of burden regarding needs and demands for e-health interventions. Results: Two hundred thirty-nine (N = 239) participants were included in the final data analysis. Among this primarily urban target group smartphone availability was favored by 77.8% of the participants. The cluster analysis revealed significant differences dependent on mental burden. 75.2% of participants with a high mental burden preferred longer interventions of 1-4 months compared with 49% in the low burden group, which also considered short interventions of up to 1 month (46%). Differences were also identified for content preferences and daily-life integration and were consistent irrespective of the initial reason for consultation. Conclusion: The findings of this study can provide a foundational framework for developing user-centered psychosomatic interventions. The potential relationship between individual burden and individual needs and demands highlights the crucial role of preliminary research to tailor interventions to effectively address diverse needs and preferences.

Falling off the screening grid-Predictors for postponed utilization of psycho-oncological support in cancer patients and its implications for distress assessment and management.

OBJECTIVE: Distress assessment of cancer patients is considered state-of-the-art. In addition to distress scores, individual care needs are an important factor for the initiation of psycho-oncological interventions. In a mono-centric, observational study, we aimed for characterization of patients indicating a subjective need but declining to utilize support services immediately to facilitate implementation of adapted screenings. METHODS: This study analyzed retrospective data from routine distress screening and associated data from hospital records. Descriptive, variance and regression analyses were used to assess characteristics of postponed support utilization in patients with mixed cancer diagnoses in different treatment settings. RESULTS: Of the total sample (N = 1863), 13% indicated a subjective need but postponed support utilization. This subgroup presented as being as burdened by symptoms of depression (p < 0.001), anxiety (p < 0.001) and distress (p < 0.001) as subjectively distressed patients with intent to directly utilize support. Time periods since diagnosis were shorter (p = 0.007) and patients were more often inpatients (p = 0.045). CONCLUSIONS: Despite high heterogeneity among the subgroups, this study identified distress-related factors and time since diagnosis as possible predictors for postponed utilization of psycho-oncological interventions. Results suggest the necessity for time-individualized support which may improve utilization by distressed patients.

An e-mental health intervention to support burdened people in times of the COVID-19 pandemic: CoPE It.

The outbreak of the novel SARS CoV-2-virus (COVID-19) is pushing national and international healthcare systems to their limits. The aspect of mental health issues, which has been neglected (so far) in times of social isolation and governmental restrictions, now demands innovative and situation-based approaches to support psychological burdened people. The developed e-mental health intervention 'CoPE It' offers manualized, evidence-based psychotherapeutic/psychological support to overcome psychological distress in times of COVID-19. E-mental health approaches offer great possibilities to support burdened people during the SARS-CoV-2 pandemic effectively.

Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models.

Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topological changes in different sleep stages. However, the number of network links can quickly grow above the number of parameters that are typically analyzed with standard statistical methods. Artificial Neural Networks (ANNs) are a promising approach as they are successful in large parameter spaces, such as in digital imaging. On the other hand, ANN models do not provide an intrinsic approach to interpret their predictions, and they typically require large training data sets. Both aspects are critical in biomedical research. Medical decisions need to be explainable, and large data sets of quality assured patient and control data are rare. In this paper, different models for the classification of insomnia-a common sleep disorder-have been trained with 59 patients and age and gender matched controls, based on their physiological networks. Feature relevance evaluation is employed for all methods. For ANNs, the extrinsic interpretation method DeepLift is applied. The results are not identical across methods, but certain network links have been rated as relevant by all or most of the models. While ANNs show less classification accuracy (0.89) than advanced tree-based models (0.92 and 0.93), DeepLift provides an in-depth ANN interpretation with feature relevance scores for individual data samples. The analysis revealed modifications in the pulmonar, ocular, and cerebral subnetworks that have not been described before but are consistent with known findings on the physiological impact of insomnia.

A systematic review and meta analysis on digital mental health interventions in inpatient settings.

E-mental health (EMH) interventions gain increasing importance in the treatment of mental health disorders. Their outpatient efficacy is well-established. However, research on EMH in inpatient settings remains sparse and lacks a meta-analytic synthesis. This paper presents a meta-analysis on the efficacy of EMH in inpatient settings. Searching multiple databases (PubMed, ScienceGov, PsycInfo, CENTRAL, references), 26 randomized controlled trial (RCT) EMH inpatient studies (n = 6112) with low or medium assessed risk of bias were included. A small significant total effect of EMH treatment was found (g = 0.3). The effect was significant both for blended interventions (g = 0.42) and post-treatment EMH-based aftercare (g = 0.29). EMH treatment yielded significant effects across different patient groups and types of therapy, and the effects remained stable post-treatment. The results show the efficacy of EMH treatment in inpatient settings. The meta-analysis is limited by the small number of included studies.

An e-mental health intervention to reduce depression symptoms in individuals with obesity: study protocol for the randomized, controlled, two-armed, confirmatory LightMood trial.

BACKGROUND: Patients with obesity often experience psychological distress, specifically depression symptoms. Due to various barriers, such as limitations of healthcare offers, digital interventions, for example medical apps, can provide a suitable approach to support affected people. In the envisaged prospective randomized controlled trial, we aim to examine the efficacy of the LightMood intervention. The LightMood intervention is a manualized and user-centered, digital intervention for patients with obesity, with a duration of 4 months, which contains elements of cognitive behavioral therapy and mindfulness-based and skills-based exercises. We expect the LightMood intervention to be superior to treatment as usual (TAU) in terms of reducing depression symptoms. METHODS: The trial incorporates four distinct measurement time points: the baseline assessment, the post-treatment assessment, and 1- and 3-month follow-up assessments. Furthermore, we implemented in-treatment assessments for both groups. Participants will be randomized into two groups (LightMood intervention vs TAU). The aim is to include 128 participants (64 per group) in the study. Inclusion criteria are patients who are obese, at least 18 years old, with a private Internet access, and with adequate digital literacy and show depression symptoms (PHQ ≥ 10). Exclusion criteria are weekly outpatient individual psychotherapy, obesity surgery within the last year or planned within the next 7 months, no private Internet access, and the prescription of a new psychotropic drug within the last 2 weeks. The primary outcome is the post-assessment reduction in depression symptoms. Secondary outcomes will include the improvement in self-efficacy, quality of life, mindfulness, reduction in eating disorder symptoms, and body mass index (BMI). Furthermore, we expect a positive development of depression symptoms throughout the different time points (T1, T2, and T3) in patients with obesity. DISCUSSION: LightMood is an evidence-based, efficient, low-threshold online intervention that aims to reduce depression symptoms in people with obesity. Online interventions could offer a promising alternative to conventional face-to-face therapy. The primary objective of the current study is to add essential insight into the feasibility, efficacy, effectiveness, and acceptance of e-mental health interventions for people with obesity and depression symptoms. TRIAL REGISTRATION: German Clinical Trial Register (DRKS), DRKS00029219. Registered on May 19, 2023.

Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative.

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA Platform and successfully integrated 14 AI-based image analysis apps from eight different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.

Anonymization of whole slide images in histopathology for research and education.

Objective: The exchange of health-related data is subject to regional laws and regulations, such as the General Data Protection Regulation (GDPR) in the EU or the Health Insurance Portability and Accountability Act (HIPAA) in the United States, resulting in non-trivial challenges for researchers and educators when working with these data. In pathology, the digitization of diagnostic tissue samples inevitably generates identifying data that can consist of sensitive but also acquisition-related information stored in vendor-specific file formats. Distribution and off-clinical use of these Whole Slide Images (WSIs) are usually done in these formats, as an industry-wide standardization such as DICOM is yet only tentatively adopted and slide scanner vendors currently do not provide anonymization functionality. Methods: We developed a guideline for the proper handling of histopathological image data particularly for research and education with regard to the GDPR. In this context, we evaluated existing anonymization methods and examined proprietary format specifications to identify all sensitive information for the most common WSI formats. This work results in a software library that enables GDPR-compliant anonymization of WSIs while preserving the native formats. Results: Based on the analysis of proprietary formats, all occurrences of sensitive information were identified for file formats frequently used in clinical routine, and finally, an open-source programming library with an executable CLI tool and wrappers for different programming languages was developed. Conclusions: Our analysis showed that there is no straightforward software solution to anonymize WSIs in a GDPR-compliant way while maintaining the data format. We closed this gap with our extensible open-source library that works instantaneously and offline.

Needs and Demands for e-Mental Health Interventions in Individuals with Overweight and Obesity: User-Centred Design Approach.

INTRODUCTION: Several studies indicate an association between mental disorders and overweight or obesity. E-Mental health interventions offer an effective way to overcome barriers to health care access for individuals with overweight and obesity. The objective of this study was to examine the needs and demands for e-mental health interventions among individuals with overweight and obesity. METHODS: A cross-sectional study was conducted from 2020 to 2021 in Germany. A total of 643 participants were recruited through specialized social media platforms and the Alfried-Krupp hospital in Essen, Germany. Sociodemographic and medical data were analysed, as well as data on depressive symptoms and on the needs and demands for e-mental health interventions. RESULTS: Contact with and recommendation by experts appear to be key aspects in the acceptance and use of e-mental health interventions. In summary, most participants preferred a 20-30-min weekly session via smartphone over a 4-month period. The highest preference in terms of features included practicing coping skills and being provided with information; in regard to desired topics, nutrition consultation, quality of life, and adapting to new life situations were considered most important. DISCUSSION: e-Mental health interventions can be highly beneficial for individuals, especially when developed through a user-centred design approach. The results of the study indicate which content and design are preferred and, thereby, provide valuable information for consideration when developing a tailored e-mental health intervention.

A mHealth intervention to reduce perceived stress in patients with ischemic heart disease: study protocol of the randomized, controlled confirmatory intervention "mStress-IHD" trial.

BACKGROUND: Stress is highly prevalent in patients with ischemic heart disease (IHD) and is associated with lower health-related quality of life and impaired cardiovascular outcome. The importance of stress management is now recognized in recent guidelines for the management of cardiovascular disease. However, effective stress management interventions are not implemented in clinical routine yet. The development of easily disseminated eHealth interventions, particularly mHealth, may offer a cost-effective and scalable solution to this problem. The aim of the proposed trial is to assess the efficiency and cost-effectiveness of the mHealth intervention "mindfulHeart" in terms of reducing stress in patients with IHD. METHODS AND ANALYSIS: This randomized controlled confirmatory interventional trial with two parallel arms has assessments at six measurement time points: baseline (T0, prior randomization), post-treatment (T1), and four follow-ups at months 1, 3, 6, and 12 after intervention (T2, T3, T4, and T5). We will include patients with confirmed diagnosis of IHD, high-perceived stress, and use of an internet-enabled smartphone. Patients will be randomized into two groups (intervention vs. control). The proposed sample size calculation allocates 128 participants in total. The primary analysis will be performed in the intention-to-treat population, with missing data imputed. An ANCOVA with the outcome at T1, a between-subject factor (intervention vs. control), and the participants' pre-intervention baseline values as a covariate will be used. Different ANOVAs, regression, and descriptive approaches will be performed for secondary analyses. ETHICS: The Ethics Committee of the Medical Faculty of the University of Duisburg-Essen approved the study (22-11,015-BO). TRIAL REGISTRATION: ClinicalTrials NCT05846334. Release 26.04.2023.

EMPAIA App Interface: An open and vendor-neutral interface for AI applications in pathology.

BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) apps hold great potential to make pathological diagnoses more accurate and time efficient. Widespread use of AI in pathology is hampered by interface incompatibilities between pathology software. We studied the existing interfaces in order to develop the EMPAIA App Interface, an open standard for the integration of pathology AI apps. METHODS: The EMPAIA App Interface relies on widely-used web communication protocols and containerization. It consists of three parts: A standardized format to describe the semantics of an app, a mechanism to deploy and execute apps in computing environments, and a web API through which apps can exchange data with a host application. RESULTS: Five commercial AI app manufacturers successfully adapted their products to the EMPAIA App Interface and helped improve it with their feedback. Open source tools facilitate the adoption of the interface by providing reusable data access and scheduling functionality and enabling automatic validation of app compliance. CONCLUSIONS: Existing AI apps and pathology software can be adapted to the EMPAIA App Interface with little effort. It is a viable alternative to the proprietary interfaces of current software. If enough vendors join in, the EMPAIA App Interface can help to advance the use of AI in pathology.

Mindfulness and skills-based eHealth intervention to reduce distress in cancer-affected patients in the Reduct trial: Intervention protocol of the make it training optimized.

Introduction: Cancer-affected patients experience high distress due to various burdens. One way to expand psycho-oncological support is through digital interventions. This protocol describes the development and structure of a web-based psycho-oncological intervention, the Make It Training optimized. This intervention is currently evaluated in the Reduct trial, a multicenter randomized controlled trial. Methods: The Make It Training optimized was developed in six steps: A patient need and demand assessment, development and acceptability analysis of a prototype, the formation of a patient advisory council, the revision of the training, implementation into a web app, and the development of a motivation and evaluation plan. Results: Through a process of establishing cancer-affected patients' needs, prototype testing, and patient involvement, the Make It Training optimized was developed by a multidisciplinary team and implemented in a web app. It consists of 16 interactive self-guided modules which can be completed within 16 weeks. Discussion: Intervention protocols can increase transparency and increase the likelihood of developing effective web-based interventions. This protocol describes the process and results of developing a patient-oriented intervention. Future research should focus on the further personalization of web-based psycho-oncological interventions and the potential benefits of combining multiple psychotherapeutic approaches.

Evaluation of the E-Mental Health Mindfulness-Based and Skills-Based "CoPE It" Intervention to Reduce Psychological Distress in Times of COVID-19: Results of a Bicentre Longitudinal Study.

Background: The SARS-CoV-2 pandemic poses immense challenges for health care systems and population-wide mental health. The e-mental health intervention "CoPE It" has been developed to offer standardized and manualized support to overcome psychological distress caused by the pandemic. The aim of this study was to assess the effectiveness of "CoPE It" in terms of reducing distress (primary outcome), depression and anxiety symptoms, and improving self-efficacy, and mindfulness (secondary outcomes). Furthermore, the intervention's usability, feasibility, and participants' satisfaction with "CoPE It" was evaluated (tertiary outcome). The study protocol has been published previously. Methods: A bicentre longitudinal study was conducted from April 27th 2020 to May 3rd 2021. N = 110 participants were included in the analyses. The intervention consisted of four modules featuring different media promoting evidence-based methods of cognitive behavioral therapy and mindfulness-based stress reduction. Difference in psychological distress between baseline (T0) and post-intervention (T1) were analyzed by repeated measure analysis of covariance. Mixed linear models were applied to assess moderating effects. Depressive symptoms, generalized anxiety symptoms, self-efficacy, and mindfulness were compared between baseline (T0) and post-intervention (T1) via t-tests. Usability of the "CoPE It" intervention and participants' satisfaction was evaluated by calculation means and frequencies. Results: Primary outcome: A significant effect of time on psychological distress at post-intervention (T1) after controlling for age, gender, education, mental illness and attitudes toward online interventions was found. Depressive and anxiety symptoms, and mindfulness were a significant moderators of the relationship between time and psychological distress for consistent wording. Secondary outcomes: There was a significant decrease in depressive symptoms and generalized anxiety, and a significant increase in self-efficacy and mindfulness between baseline (T0) and post-intervention (T1). Tertiary outcomes: 95.83% of the participants thought the "CoPE It" intervention was easy to use and 87.50% were satisfied with the "CoPE It" intervention in an overall, general sense. Conclusion: The e-mental health "CoPE It" intervention seems to be an effective approach in reducing psychological distress, anxiety and depressive symptoms, and in enhancing self-efficacy and mindfulness during the COVID-19 pandemic. Participants' satisfaction and the program's feasibility, and usability were proven to be high. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: DRKS00021301.

Techniques, Methods, and Dissemination of Community-Based Psychological Support Strategies in Times of the COVID-19 Pandemic.

In times of the coronavirus pandemic caused by SARS-CoV-2 psychological support needs to meet certain requirements. Due to the lockdown in many countries of the world, the every-day activities of millions of people are reduced to a minimum. This may cause increased psychosomatic symptoms in persons with pre-existing mental illnesses, and additionally raises new challenges for the general population. As a result of the current contact restrictions, access to psychotherapy is further complicated. To guarantee the best possible care under the given conditions, we developed the CoPE (Coping with Corona: Extended Psychosomatic care in Essen) concept. CoPE is delivered by telephone or video calls as well as online contents. The materials presented at our webpage www.cope-corona.de aim to easily reach citizens affected by symptoms such as worries, depression or anger and let them receive readily understandable expert knowledge and training in basic self-help methods.

E-mental health mindfulness-based and skills-based 'CoPE It' intervention to reduce psychological distress in times of COVID-19: study protocol for a bicentre longitudinal study.

INTRODUCTION: The SARS-CoV-2 (COVID-19) pandemic poses immense challenges for national and international healthcare systems. Especially in times of social isolation and governmental restrictions, mental health should not be neglected. Innovative approaches are required to support psychologically burdened people. The e-mental health intervention 'CoPE It' has been developed to offer manualised and evidence-based psychotherapeutic support adapted to COVID-19-related issues in order to overcome psychological distress. In our study, we aim to assess the efficacy of the e-mental health intervention 'CoPE It' in terms of reducing distress (primary outcome), depression and anxiety symptoms as well as improving self-efficacy, quality of life and mindfulness (secondary outcomes). Furthermore, we want to evaluate the programme's usability, feasibility and participants' satisfaction with 'CoPE It' (tertiary outcome). METHODS AND ANALYSIS: The e-mental health intervention 'CoPE It' consists of four 30 min modules, conducted every other day, involving psychotherapeutic techniques of mindfulness-based stress reduction and cognitive-behavioural therapy. The widely applied and previously established content has been adapted to the context of the COVID-19 pandemic by experts in psychosomatic medicine and stress prevention. In our longitudinal study, adult participants-with adequate German language and computer skills, and who have provided informed consent-will be recruited via emergency support hotlines in Germany. Flyers will be distributed, and online channels will be used. Participants will complete a baseline assessment (T0), a postintervention assessment (T1) and assessments 1 and 3 months later (T2 and T3, respectively). We will perform repeated measures analysis of covariance, mixed linear models, standard analyses of variance and regression, and correlation coefficients. In case of binary outcome variables, either mixed logistic regression or χ² tests will be used. ETHICS AND DISSEMINATION: The Ethics Committees of the University of Duisburg-Essen (20-9243-BO) and University of Tübingen (469/2020BO) approved the study. Results will be published in peer-reviewed journals and conference presentations. TRIAL REGISTRATION NUMBER: DRKS00021301.

Feature relevance in physiological networks for classification of obstructive sleep apnea.

OBJECTIVE: Physiological networks (PN) model couplings between organs in a high-dimensional parameter space. Machine learning methods, in particular artifical neural networks (ANNs), are powerful on high-dimensional classification tasks. However, lack of interpretability of the resulting models has been a drawback in research. We assess relevant PN topology changes in obstructive sleep apnea (OSA) by novel ANN interpretation techniques. APPROACH: ANNs are trained to classify OSA based on the PNs of 48 patients and 48 age and gender matched healthy controls. The PNs consisting of 2812 links are derived from overnight biosignal recordings. The interpretation technique DeepLift is applied to the resulting ANN models, enabling the determination of the relevant features for classification decisions on individual subjects. The mean relevance scores of the features are compared to other machine learning methods (decision tree and random forests) and statistical tests on group differences. MAIN RESULTS: The ANN interpretation results show good agreement with the compared methods and 87% of the samples could be correctly classified. OSA patients show a significantly higher coupling (p [Formula: see text] 0.001) in light sleep (N2) between breathing rate and EEG [Formula: see text] power in all electrode locations and to chin and leg muscular tone. In deep sleep (N3), OSA leads to significantly lower coupling (p [Formula: see text] 0.01) in lateral connections of EEG [Formula: see text] and [Formula: see text] power in central and frontal positions. Misclassified OSA patients had all mild/moderate AHIs and did not show PN topology changes. Both nights of these patients have been consistently misclassified as healthy. This may indicate, that the impact of respiratory events differs in subjects, thus forming different phenotypes. SIGNIFICANCE: The proposed PN analysis provides a powerful and robust method to quantify a broad range of physiological interactions. Interpretability of the ANN make them a promising tool to identify new diagnostic markers in data-driven approaches.

Age and gender dependency of physiological networks in sleep.

Recently, time delay stability analysis of biosignals has been successfully applied as a multivariate time series analysis method to assess the human physiological network in young adults. The degree of connectivity between different network nodes is described by the so-called link strength. Based on polysomnographic recordings (PSGs), it could be shown that the network changes with the sleep stage. Here, we apply the method to a large set of healthy controls spanning six decades of age. As it is well known, that the overall sleep architecture is dependent both on age and on gender, we particularly address the question, if these changes are also found in the network dynamics. We find moderate dependencies of the network on gender. Significantly higher link strengths up to 13% are found in women for some links in different frequency bands of central and occipital regions in REM and light sleep (N2). Higher link strengths are found in men consistently in cardio-cerebral links in N2, but not significant. Age dependency is more pronounced. In particular a significant overall weakening of the network with age is found for wakefulness and non-REM sleep stages. The largest overall decrease is observed in N2 with 0.017 per decade. For individual links decrease rates up to 0.08 per decade are found, in particular for intra-brain links in non-REM sleep. Many of them show a significant decrease with age. Non-linear regression employing an artificial neural network can predict the age with a mean absolute error (MAE) of about five years, suggesting that an age-resolution of about a decade would be appropriate in normative data for physiological networks.

[Ambulatory healthcare centres from a legal point of view]. / Medizinische Versorgungszentren aus juristischer Sicht.

The concept of ambulatory healthcare centre (MVZ) has been introduced by the German Health System Modernisation Act (GMG) which came into effect on January 01, 2004. For the first time, non-doctors also in the legal form of a corporation are entitled to become a contracted provider of ambulatory health services within the statutory health insurance system. A so-called ambulatory healthcare centre can be established by all the contractible care providers in the healthcare system. The MVZ can avail itself of whatever kind of legal form. It has to be headed by a physician and must be a multidisciplinary institution, which means it has to offer at least two special disciplines. Doctors working at an MVZ may be employed or statutory health insurance (SHI) physicians. Special fields with demand management require the availability of a vacant SHI physician position.

[Aesthetic medicine and aspects related to liability, medical professional and social law]. / Haftungs-, Berufs- und Sozialrechtliche Aspekte der ästhetischen Medizin.

There are no special legal arrangements for the field of aesthetic medicine; rather, the general medico-legal regulations apply although they raise specific questions as far as aesthetic medicine is concerned. Legally, a contract exists between physician and patient which is also applicable to aesthetic medicine. This means that the physician owes the patient only the provision of a proper, non-defective service, but does not need to guarantee that it actually leads to the desired outcome. Before performing a medically non-indicated procedure the physician is obliged to provide the patient with particularly thorough information about this procedure. Various problems and issues are raised by the advertising limitations for medical professionals and the maintenance of the boundaries confining the special field of aesthetic medicine. Medically indicated procedures are suitable for statutory reimbursement if the patient suffers from "physical disfigurement" or somatic complaints that lead to considerable impairment and if there are no other, cheaper treatment options available.

[Legal forms of cooperation among physicians]. / Kooperationsformen unter Arzten.

Medical occupational rights introduced in the sixties have entitled physicians to cooperate with one another. This form of professional cooperation is subject to the general corporate law, the medical occupational law, and the panel-doctor's right. The legal framework for the cooperation of physicians is provided by the federal law of the General Medical Council, the medical law in each German federal state, and the individual occupational ordinances of the single medical councils. These ordinances are essentially based on the pattern occupation ordinance approved during the Annual General Meeting of German Physicians (Deutsche Arztetag). This is regulated in the Federal German Code of Social Law Chapter V and in the panel-doctor's right (with approval of the physicians), and contains further ordinances regulating the legal cooperation of physicians. A novelty concerning the cooperation of physicians is the so-called "Medical Care Center", which has been introduced by law to reform the German health system. The Medical Care Center is a multidisciplinary institution that enables physicians to work as employees or as contracted physicians. For the set-up of a Medical Care Center all legal forms (including all kinds of legal entities) can be chosen. Some details of this recent change of law are in dispute.