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.

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.

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.

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.

A method for using real world data in breast cancer modeling.

OBJECTIVES: Today, hospitals and other health care-related institutions are accumulating a growing bulk of real world clinical data. Such data offer new possibilities for the generation of disease models for the health economic evaluation. In this article, we propose a new approach to leverage cancer registry data for the development of Markov models. Records of breast cancer patients from a clinical cancer registry were used to construct a real world data driven disease model. METHODS: We describe a model generation process which maps database structures to disease state definitions based on medical expert knowledge. Software was programmed in Java to automatically derive a model structure and transition probabilities. We illustrate our method with the reconstruction of a published breast cancer reference model derived primarily from clinical study data. In doing so, we exported longitudinal patient data from a clinical cancer registry covering eight years. The patient cohort (n=892) comprised HER2-positive and HER2-negative women treated with or without Trastuzumab. RESULTS: The models generated with this method for the respective patient cohorts were comparable to the reference model in their structure and treatment effects. However, our computed disease models reflect a more detailed picture of the transition probabilities, especially for disease free survival and recurrence. CONCLUSIONS: Our work presents an approach to extract Markov models semi-automatically using real world data from a clinical cancer registry. Health care decision makers may benefit from more realistic disease models to improve health care-related planning and actions based on their own data.

How Google Glass could support patients with diabetes mellitus in daily life.

Even long before it was published, many people regarded Google Glass as a Swiss army knife for nearly every task. There are some fields of application in which the best known wearable device could simplify daily life, such as car navigation or reading recipes. But does this also apply for medicine and health care? This paper will at first explain what Google Glass is and how it works. Afterwards, diabetes mellitus (DM), is discussed. Moreover, we try to answer the question whether a Glass-like device could support and improve people with DM. Therefore, several use cases for Glass-enabled chronic disease care are outlined.

Leveraging Markov models to compute prevalence estimates for a regional breast cancer registry.

Hospital cancer registries can be a reliable source for comparative effectiveness research. We used an expanded Markov model to estimate breast cancer prevalence for a distinct region in Germany. Thereby, transition probabilities were computed with patient information gained directly from the dataset. A first validation was executed by comparing the results with numbers obtained by another prevalence estimation technique.