Menstrual Cycle Tracking Apps: An Applied Combined Medical and Data Privacy Scoring.

Today, many menstruating individuals track their cycles with mobile apps. These cycle apps use a lot of highly sensitive personal data. The goal of this study is to evaluate current cycle apps based on data privacy and medical criteria. First, a market analysis of currently available apps was conducted. Second, a scoring system was developed based on Digital Health application (Digitale Gesundheitsanwendungen, DiGA in German) guidelines, Mobile App Rating Scale (MARS), and other resources. A total of 18 apps were evaluated. The final scores (range from 0 to 1) ranged from 0.12 (worst result) to 0.64 (best result). The average "data privacy score" was 0.4, and the average "medical score" was 0.11. Only six apps received any points in the medical part of the scoring. A clear weakness of many tested apps was the issue of data minimization. 89% of the apps had permissions that were not necessary for this type of health app.

Two Routes to Alzheimer's Disease Based on Differential Structural Changes in Key Brain Regions.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification. OBJECTIVE: The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages. METHODS: Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages. RESULTS: The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments. CONCLUSION: Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.

A German Smartphone-Based Self-management Tool for Psoriasis: Community-Driven Development and Evaluation of Quality-of-Life Effects.

BACKGROUND: Psoriasis is a chronic disease characterized by inflammation, increased scaling, itching, and other symptoms. Psoriasis is not contagious, but patients have often felt shunned. Therefore, in addition to psoriasis symptoms, stress, anxiety, and depression can also affect quality of life (QoL). Surveys show that only a quarter of patients are satisfied with the success of their therapy. However, in addition to medical therapy, self-management can also make it easier to deal with chronic diseases like psoriasis. OBJECTIVE: The aim of this project was to develop a smartphone-based self-management tool (SMT) specifically for patients with psoriasis using a community-driven process. The impact of the SMT on QoL as well as its acceptance and usability were evaluated. METHODS: In collaboration with an internet-based self-help community, 2 user surveys were conducted to determine the requirements for a smartphone-based SMT. The surveys consisted of semistructured questionnaires asking for desired features in an SMT for psoriasis. A pilot study was conducted to evaluate QoL, acceptance, and usability. Community users were recruited to use the app for 21 days and complete the Dermatology Life Quality Index (DLQI) questionnaire at the beginning (T0) and end (T1). Afterward, participants were asked to complete another questionnaire on usability and ease of use. RESULTS: SMT requirements were collected from 97 members of an internet-based community. The SMT was built as a progressive web app that communicates with a server back end and an Angular web app for content management. The app was used by 15 participants who also provided qualitative feedback, and 10 participants answered all questionnaires. The average DLQI score was 7.1 (SD 6.2) at T0 and 6.9 (SD 6.6) at T1. The minimal required sample size of 27 was not reached. CONCLUSIONS: The high degree of community participation in the development process and the responses during the requirement engineering process indicated that there is a general need for an independently developed SMT for patients with psoriasis. However, the feedback received after app use shows that the SMT does not meet the needs of the community. It can be concluded that a more customizable app is needed. The focus and needs of the users were very heterogeneous. Similar developments and research could benefit from the findings of this project.

Readability of English, German, and Russian Disease-Related Wikipedia Pages: Automated Computational Analysis.

BACKGROUND: Wikipedia is a popular encyclopedia for health- and disease-related information in which patients seek advice and guidance on the web. Yet, Wikipedia articles can be unsuitable as patient education materials, as investigated in previous studies that analyzed specific diseases or medical topics with a comparatively small sample size. Currently, no data are available on the average readability levels of all disease-related Wikipedia pages for the different localizations of this particular encyclopedia. OBJECTIVE: This study aimed to analyze disease-related Wikipedia pages written in English, German, and Russian using well-established readability metrics for each language. METHODS: Wikipedia database snapshots and Wikidata metadata were chosen as resources for data collection. Disease-related articles were retrieved separately for English, German, and Russian starting with the main concept of Human Diseases and Disorders (German: Krankheit; Russian: Заболевания человека). In the case of existence, the corresponding International Classification of Diseases, Tenth Revision (ICD-10), codes were retrieved for each article. Next, the raw texts were extracted and readability metrics were computed. RESULTS: The number of articles included in this study for English, German, and Russian Wikipedia was n=6127, n=6024, and n=3314, respectively. Most disease-related articles had a Flesch Reading Ease (FRE) score <50.00, signaling difficult or very difficult educational material (English: 5937/6125, 96.93%; German: 6004/6022, 99.7%; Russian: 2647/3313, 79.9%). In total, 70% (7/10) of the analyzed articles could be assigned an ICD-10 code with certainty (English: 4235/6127, 69.12%; German: 4625/6024, 76.78%; Russian: 2316/3314, 69.89%). For articles with ICD-10 codes, the mean FRE scores were 28.69 (SD 11.00), 20.33 (SD 9.98), and 38.54 (SD 13.51) for English, German, and Russian, respectively. A total of 9 English ICD-10 chapters (11 German and 10 Russian) showed significant differences: chapter F (FRE 23.88, SD 9.95; P<.001), chapter E (FRE 25.14, SD 9.88; P<.001), chapter H (FRE 30.04, SD 10.57; P=.049), chapter I (FRE 30.05, SD 9.07; P=.04), chapter M (FRE 31.17, 11.94; P<.001), chapter T (FRE 32.06, SD 10.51; P=.001), chapter A (FRE 32.63, SD 9.25; P<.001), chapter B (FRE 33.24, SD 9.07; P<.001), and chapter S (FRE 39.02, SD 8.22; P<.001). CONCLUSIONS: Disease-related English, German, and Russian Wikipedia articles cannot be recommended as patient education materials because a major fraction is difficult or very difficult to read. The authors of Wikipedia pages should carefully revise existing text materials for readers with a specific interest in a disease or its associated symptoms. Special attention should be given to articles on mental, behavioral, and neurodevelopmental disorders (ICD-10 chapter F) because these articles were most difficult to read in comparison with other ICD-10 chapters. Wikipedia readers should be supported by editors providing a short and easy-to-read summary for each article.

Assessment of SARS-CoV-2 Infection among Healthcare Workers of a German COVID-19 Treatment Center.

To date, more than 160 million people have been infected with COVID-19 worldwide. In the present study, we investigated the history of SARS-CoV-2 infection among 3067 healthcare workers (HCW) in a German COVID-19 treatment center during the early phase of the pandemic (July 2020) based on the seroprevalence of SARS-CoV-2 antibodies and self-reported previous PCR results. The results demonstrate a low prevalence of SARS-CoV-2 infection (n = 107 [3.5%]) with no increased risk for employees with a high level of patient exposure in general or working in COVID-19-confined areas in particular. This suggests that the local hygiene standards implemented in our hospital during the first wave of COVID-19 pandemic were effective in preventing patient-to-HCW transmission. No evidence for highly mobile staff serving as a vector for SARS-CoV-2 transmission could be found. In addition, impairment of smell and/or taste was strongly associated with SARS-CoV-2 history.

Merging Genomics Public Datasets with Clinical Cancer Registry Data - Lessons Learned.

Publicly available datasets - for example via cBioPortal for Cancer Genomics - could be a valuable source for benchmarks and comparisons with local patient records. However, such an approach is only valid if patient cohorts are comparable to each other and if the documentation is complete and sufficient. In this paper, records from exocrine pancreatic cancer patients documented in a local cancer registry are compared with two public datasets to calculate overall survival. Several data preprocessing steps were necessary to ensure comparability of the different datasets and a common database schema was created. Our assumption that the public datasets could be used to augment the data of the local cancer registry could not be validated, since the analysis on overall survival showed a significant difference. We discuss several reasons and explanations for this finding. So far, comparing different datasets with each other and drawing medical conclusions on such comparisons should be conducted with great caution.

Temporal and Location Variations, and Link Categories for the Dissemination of COVID-19-Related Information on Twitter During the SARS-CoV-2 Outbreak in Europe: Infoveillance Study.

BACKGROUND: The spread of the 2019 novel coronavirus disease, COVID-19, across Asia and Europe sparked a significant increase in public interest and media coverage, including on social media platforms such as Twitter. In this context, the origin of information plays a central role in the dissemination of evidence-based information about the SARS-CoV-2 virus and COVID-19. On February 2, 2020, the World Health Organization (WHO) constituted a "massive infodemic" and argued that this situation "makes it hard for people to find trustworthy sources and reliable guidance when they need it." OBJECTIVE: This infoveillance study, conducted during the early phase of the COVID-19 pandemic, focuses on the social media platform Twitter. It allows monitoring of the dynamic pandemic situation on a global scale for different aspects and topics, languages, as well as regions and even whole countries. Of particular interest are temporal and geographical variations of COVID-19-related tweets, the situation in Europe, and the categories and origin of shared external resources. METHODS: Twitter's Streaming application programming interface was used to filter tweets based on 16 prevalent hashtags related to the COVID-19 outbreak. Each tweet's text and corresponding metadata as well as the user's profile information were extracted and stored into a database. Metadata included links to external resources. A link categorization scheme-introduced in a study by Chew and Eysenbach in 2009-was applied onto the top 250 shared resources to analyze the relative proportion for each category. Moreover, temporal variations of global tweet volumes were analyzed and a specific analysis was conducted for the European region. RESULTS: Between February 9 and April 11, 2020, a total of 21,755,802 distinct tweets were collected, posted by 4,809,842 distinct Twitter accounts. The volume of #covid19-related tweets increased after the WHO announced the name of the new disease on February 11, 2020, and stabilized at the end of March at a high level. For the regional analysis, a higher tweet volume was observed in the vicinity of major European capitals or in densely populated areas. The most frequently shared resources originated from various social media platforms (ranks 1-7). The most prevalent category in the top 50 was "Mainstream or Local News." For the category "Government or Public Health," only two information sources were found in the top 50: US Centers for Disease Control and Prevention at rank 25 and the WHO at rank 27. The first occurrence of a prevalent scientific source was Nature (rank 116). CONCLUSIONS: The naming of the disease by the WHO was a major signal to address the public audience with public health response via social media platforms such as Twitter. Future studies should focus on the origin and trustworthiness of shared resources, as monitoring the spread of fake news during a pandemic situation is of particular importance. In addition, it would be beneficial to analyze and uncover bot networks spreading COVID-19-related misinformation.

PrositNG - A Machine Learning Supported Disease Model Generation Software.

Decision models (DM), especially Markov Models, play an essential role in the economic evaluation of new medical interventions. The process of DM generation requires expert knowledge of the medical domain and is a time-consuming task. Therefore, the authors propose a new model generation software PrositNG that is connectable to database systems of real-world routine care data. The structure of the model is derived from the entries in a database system by the help of Machine Learning algorithms. The software was implemented with the programming language Java. Two data sources were successfully utilized to demonstrate the value of PrositNG. However, a good understanding of the local documentation routine and software is paramount to use real-world data for model generation.

[Abdominal Sonography in Medical Emergencies: do Complete Abdominal Scans offer Advantages over a Focused Strategy?] / Internistische Sonografie des Abdomens für Notaufnahmepatienten: Bietet eine vollständige Abdominalsonografie Vorteile?

PURPOSE: To determine, whether in case of abdominal emergencies, complete abdominal ultrasound scans offer advantages over a goal-directed strategy in regard to problem-solving and detection of relevant pathologies. MATERIAL AND METHODS: Retrospective analysis of consecutive emergency exams that were ordered by the ER in our gastroenterological sonography unit from 06/2012 to 06/2013. All patients got an ultrasound of the complete abdomen.We compared the findings of the complete abdomen study with the findings of a hypothetical focused sonography. The scope of the focused sonography depended on the presenting problem. An ultrasound finding was classified as relevant, if it triggered diagnostic or therapeutic consequences.Sensitivity and negative predictive value of the goal-directed strategy were calculated with the full abdominal scan as reference. RESULTS: 629 patients (53 % female), with an average age of 59 years (18-97) had emergency sonographies.Focused sonography detected relevant pathologies in 63 % (396/629) of patients. In 17 % of patients (106/629) complete abdominal ultrasound led to additional relevant findings. This translates into a number needed to scan for relevant pathologies of 6. Focused sonography had a sensitivity of 76 % and a negative predictive value of 64 % for relevant pathologies.The problem could be solved by 57 % of focused sonographies, whereas complete abdominal scans solved the problem in 63 % for a number needed to scan of 18. Sensitivity and negative predictive value of focused sonography were 87 % and 63 % respectively.The rate of problem-solving-scans depended on the indication (with jaundice having the highest (90 %) and left-upper-quadrant pain having the lowest (45 %) rate) and increased with age (37 % for the second decade up to 85 % for the 10th decade). CONCLUSION: In medical abdominal emergencies, ultrasound scans of the complete abdomen detect more relevant pathologies and solve more problems than focused sonography does.

Barriers and Facilitators to the Implementation of eHealth Services: Systematic Literature Analysis.

BACKGROUND: The field of eHealth has a history of more than 20 years. During that time, many different eHealth services were developed. However, factors influencing the adoption of such services were seldom the main focus of analyses. For this reason, organizations adopting and implementing eHealth services seem not to be fully aware of the barriers and facilitators influencing the integration of eHealth services into routine care. OBJECTIVE: The objective of this work is to provide (1) a comprehensive list of relevant barriers to be considered and (2) a list of facilitators or success factors to help in planning and implementing successful eHealth services. METHODS: For this study, a twofold approach was applied. First, we gathered experts' current opinions on facilitators and barriers in implementing eHealth services via expert discussions at two health informatics conferences held in Europe. Second, we conducted a systematic literature analysis concerning the barriers and facilitators for the implementation of eHealth services. Finally, we merged the results of the expert discussions with those of the systematic literature analysis. RESULTS: Both expert discussions (23 and 10 experts, respectively) identified 15 barriers and 31 facilitators, whereas 76 barriers and 268 facilitators were found in 38 of the initial 56 articles published from 12 different countries. For the analyzed publications, the count of distinct barriers reported ranged from 0 to 40 (mean 10.24, SD 8.87, median 8). Likewise, between 0 and 48 facilitators were mentioned in the literature (mean 9.18, SD 9.33, median 6). The combination of both sources resulted in 77 barriers and 292 facilitators for the adoption and implementation of eHealth services. CONCLUSIONS: This work contributes a comprehensive list of barriers and facilitators for the implementation and adoption of eHealth services. Addressing barriers early, and leveraging facilitators during the implementation, can help create eHealth services that better meet the needs of users and provide higher benefits for patients and caregivers.

[DNVF-Memorandum - Health and Medical Apps]. / DNVF-Memorandum ­ Gesundheits- und Medizin-Apps (GuMAs).

The memorandum outlines core questions that should be addressed by future health services research in order to evaluate the impact of health and medical apps on quality of processes and patient outcomes and to take advantage of their potential as new data sources for scientific research.

[DNVF-Memorandum - Health and Medical Apps]. / DNVF-Memorandum ­ Gesundheits- und Medizin-Apps (GuMAs).

There are huge expectations to improve quality and efficiency of prevention and healthcare by using digital health applications. In contrast to the dynamically growing supply and a high affinity of large parts of the population to use health and medical apps, there is a lack of data and methods to assess quality, benefit, and patient safety with health apps, most of them are not yet regulated .This memorandum outlines core questions that should be addressed by future health services research in order to evaluate the impact of health and medical apps on quality of processes and patient outcomes and to take advantage of their potential as new data sources for scientific research.

Computer-Based Readability Testing of Information Booklets for German Cancer Patients.

Understandable health information is essential for treatment adherence and improved health outcomes. For readability testing, several instruments analyze the complexity of sentence structures, e.g., Flesch-Reading Ease (FRE) or Vienna-Formula (WSTF). Moreover, the vocabulary is of high relevance for readers. The aim of this study is to investigate the agreement of sentence structure and vocabulary-based (SVM) instruments. A total of 52 freely available German patient information booklets on cancer were collected from the Internet. The mean understandability level L was computed for 51 booklets. The resulting values of FRE, WSTF, and SVM were assessed pairwise for agreement with Bland-Altman plots and two-sided, paired t tests. For the pairwise comparison, the mean L values are LFRE = 6.81, LWSTF = 7.39, LSVM = 5.09. The sentence structure-based metrics gave significantly different scores (P < 0.001) for all assessed booklets, confirmed by the Bland-Altman analysis. The study findings suggest that vocabulary-based instruments cannot be interchanged with FRE/WSTF. However, both analytical aspects should be considered and checked by authors to linguistically refine texts with respect to the individual target group. Authors of health information can be supported by automated readability analysis. Health professionals can benefit by direct booklet comparisons allowing for time-effective selection of suitable booklets for patients.

Technology Adoption, Motivational Aspects, and Privacy Concerns of Wearables in the German Running Community: Field Study.

BACKGROUND: Despite the availability of a great variety of consumer-oriented wearable devices, perceived usefulness, user satisfaction, and privacy concerns have not been fully investigated in the field of wearable applications. It is not clear why healthy, active citizens equip themselves with wearable technology for running activities, and what privacy and data sharing features might influence their individual decisions. OBJECTIVE: The primary aim of the study was to shed light on motivational and privacy aspects of wearable technology used by healthy, active citizens. A secondary aim was to reevaluate smart technology adoption within the running community in Germany in 2017 and to compare it with the results of other studies and our own study from 2016. METHODS: A questionnaire was designed to assess what wearable technology is used by runners of different ages and sex. Data on motivational factors were also collected. The survey was conducted at a regional road race event in May 2017, paperless via a self-implemented app. The demographic parameters of the sample cohort were compared with the event's official starter list. In addition, the validation included comparison with demographic parameters of the largest German running events in Berlin, Hamburg, and Frankfurt/Main. Binary logistic regression analysis was used to investigate whether age, sex, or course distance were associated with device use. The same method was applied to analyze whether a runner's age was predictive of privacy concerns, openness to voluntary data sharing, and level of trust in one's own body for runners not using wearables (ie, technological assistance considered unnecessary in this group). RESULTS: A total of 845 questionnaires were collected. Use of technology for activity monitoring during events or training was prevalent (73.0%, 617/845) in this group. Male long-distance runners and runners in younger age groups (30-39 years: odds ratio [OR] 2.357, 95% CI 1.378-4.115; 40-49 years: OR 1.485, 95% CI 0.920-2.403) were more likely to use tracking devices, with ages 16 to 29 years as the reference group (OR 1). Where wearable technology was used, 42.0% (259/617) stated that they were not concerned if data might be shared by a device vendor without their consent. By contrast, 35.0% (216/617) of the participants would not accept this. In the case of voluntary sharing, runners preferred to exchange tracked data with friends (51.7%, 319/617), family members (43.4%, 268/617), or a physician (32.3%, 199/617). A large proportion (68.0%, 155/228) of runners not using technology stated that they preferred to trust what their own body was telling them rather than trust a device or an app (50-59 years: P<.001; 60-69 years: P=.008). CONCLUSIONS: A total of 136 distinct devices by 23 vendors or manufacturers and 17 running apps were identified. Out of 4, 3 runners (76.8%, 474/617) always trusted in the data tracked by their personal device. Data privacy concerns do, however, exist in the German running community, especially for older age groups (30-39 years: OR 1.041, 95% CI 0.371-0.905; 40-49 years: OR 1.421, 95% CI 0.813-2.506; 50-59 years: OR 2.076, 95% CI 1.813-3.686; 60-69 years: OR 2.394, 95% CI 0.957-6.183).

Analyzing the Readability of Health Information Booklets on Cardiovascular Diseases.

A low level of patient health literacy represents a major reason for worse prognosis or reduced therapy adherence. Health information booklets are a major tool for improving patient's health literacy. This paper presents a computer-based readability analysis of patient information booklets from the cardiovascular domain. The study relies on 34 English booklets mostly on heart disease, prevention and procedures. It compares five different, well-established readability instruments. On average, readers of the assessed booklets have to visit school at least until the 9th U.S. school grade when applying the Flesch-Kincaid formula. According to the Gunning-Fog metric, readers would have to attend school until the 11th grade. The presented study demonstrates the feasibility of a fully automated text processing tool-chain for patient information booklets. The results reveal that readability metrics should be carefully interpreted and only be interchanged with caution.

Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study.

BACKGROUND: Today, runners use wearable technology such as global positioning system (GPS)-enabled sport watches to track and optimize their training activities, for example, when participating in a road race event. For this purpose, an increasing amount of low-priced, consumer-oriented wearable devices are available. However, the variety of such devices is overwhelming. It is unclear which devices are used by active, healthy citizens and whether they can provide accurate tracking results in a diverse study population. No published literature has yet assessed the dissemination of wearable technology in such a cohort and related influencing factors. OBJECTIVE: The aim of this study was 2-fold: (1) to determine the adoption of wearable technology by runners, especially "smart" devices and (2) to investigate on the accuracy of tracked distances as recorded by such devices. METHODS: A pre-race survey was applied to assess which wearable technology was predominantly used by runners of different age, sex, and fitness level. A post-race survey was conducted to determine the accuracy of the devices that tracked the running course. Logistic regression analysis was used to investigate whether age, sex, fitness level, or track distance were influencing factors. Recorded distances of different device categories were tested with a 2-sample t test against each other. RESULTS: A total of 898 pre-race and 262 post-race surveys were completed. Most of the participants (approximately 75%) used wearable technology for training optimization and distance recording. Females (P=.02) and runners in higher age groups (50-59 years: P=.03; 60-69 years: P<.001; 70-79 year: P=.004) were less likely to use wearables. The mean of the track distances recorded by mobile phones with combined app (mean absolute error, MAE=0.35 km) and GPS-enabled sport watches (MAE=0.12 km) was significantly different (P=.002) for the half-marathon event. CONCLUSIONS: A great variety of vendors (n=36) and devices (n=156) were identified. Under real-world conditions, GPS-enabled devices, especially sport watches and mobile phones, were found to be accurate in terms of recorded course distances.

A Smartwatch-Driven Medication Management System Compliant to the German Medication Plan.

Medication adherence is an important factor for the outcome of medical therapies. To support high adherence levels, smartwatches can be used to assist the patient. However, a successful integration of such devices into clinicians' or general practitioners' information systems requires the use of standards. In this paper, a medication management system supplied with smartwatch generated feedback events is presented. It allows physicians to manage their patients' medications and track their adherence in real time. Moreover, it fosters interoperability via a ISO/IEC 16022 data matrix which encodes related medication data in compliance with the German Medication Plan specification.

Clinical Cancer Registries - Are They Up for Health Services Research?

Clinical cancer registries are a valuable data source for health services research (HSR). HSR is in need of high quality routine care data for its evaluations. However, the secondary use of routine data - such as documented cancer cases in a disease registry - poses new challenges in terms of data quality, IT-management, documentation processes and data privacy. In the clinical cancer registry Heilbronn-Franken, real-world data from the Giessen Tumor Documentation System (GTDS) was utilized for analyses of patients' disease processes and guideline adherence in follow-up care. A process was developed to map disease state definitions to fields of the GTDS database and extract patients' disease progress information. Thus, the disease process of sub-cohorts could be compared to each other, e.g., comparison of disease free survival of HER2 (human epidermal growth factor receptor 2)-positive and -negative women who were treated with Trastuzumab, a targeted therapy applied in breast cancer. In principle, such comparisons are feasible and of great value for HSR as they depict a routine care setting of a diverse patient cohort. Yet, local documentation practice, missing flow of information from external health care providers or small sub-cohorts impede the analyses of clinical cancer registries data bases and usage for HSR.

Consumer Health Informatics in the Context of Engaged Citizens and eHealth Services - A New CHI Meta Model.

Consumer Health Informatics (CHI) is a relatively new and interdisciplinary field in Medical Informatics. It focuses on consumer- rather than professional-centered services. However, the definitions and understanding of a) what is a "consumer"? or b) what is health technology in the context of CHI? and c) what factors and actors influence the usage of eHealth services? vary widely. The CHI special interest group (SIG) - associated with the German Association for Medical Informatics, Biometry and Epidemiology - conducted two workshops in 2015 to improve the common understanding on these topics. The workshop outcomes, the derived CHI-specific meta model and examples how to apply this model are presented in this paper. The model supports the definition of multi-actor contexts, as it not solely reflects the conventional patient-physician relationship but also allows for the description of second health market providers.

PROSIT Open Source Disease Models for Diabetes Mellitus.

UNLABELLED: There has been legitimate criticism with regard to the quality and the transparency of health economic modelling studies. For that reason, the aim of the PROSIT Disease Modelling Community is to develop transparent open source health economic disease models for diabetes mellitus. RESULTS: Markov type models were developed in the open source spread sheet software OpenOffice Calc for myocardial infarction, stroke, retinopathy, nephropathy, diabetic foot syndrome, and hypoglycemia. The basic concept is to describe a disease as a cascade of disease states with transitions between them. The transition probability is based on time, gender, age, disease related risks and medical interventions. An internet platform hosts the models and the documentation for public download. Incidence rates of complications were derived from population data and clinical studies. The models have to be adapted according to the specific needs and type of health economic analysis. The software is prepared to allow validation and model testing. The PROSIT Disease Modelling Community with its Markov models for diabetes mellitus suggests a new approach and methodology for developing health economic disease models in a transparent and sustainable manner. Going open source with disease models could overcome the lack in credibility that hampers modelling based health economic studies.