Structure and Content

MII-Academy | Online Learning Platform for Clinician Scientists

Structure of the MII-Academy

The MII-Academy supports you in using the services and processes created by the Medical Informatics Initiative. Video tutorials provide you with information and practical instructions on how to gain access to routine medical data for your research projects, how to use it effectively and how to analyze it optimally. The teaching and learning units of the MII-Academy are divided into modules that are grouped by topic. Four subject areas are currently covered

The subject areas gradually build on each other and the complexity of the teaching and learning units increases. You are free to decide at which level you would like to start – depending on how you assess your own prior knowledge. The order in which the topics and modules are covered is not predetermined. You are free to choose the topics according to your interests

There are also no restrictions on how often a module can be used. You can listen to the tutorials as often as you like. This should give you the opportunity to use the modules as a short-term refresher.

Below you will find information on each of the four topics:

Info Level

The info level introduces you to the world of sharing medical care data. Basic questions will be clarified, such as “What is a Data Integration Center and where can I find it?” You will also gain an insight into the organizational framework of data sharing and get a rough overview of the processes to be run through.

Topics include:

Overview of the Medical Informatics Initiative (MII)

The Network of University Medicine (NUM) at a glance

Introduction to Biobanking

Basic Level

In this subject area, you will be given the tools to advance your own research questions and work in terms of your own research. In accordance with the provisions of state legislation, medical staff are permitted to use data collected in their own hospital as part of patient care for research projects. The MII-Academy is designed to encourage you to use the organizational and technical infrastructure created by the MII at the local Data Integration Centers for this purpose. The content at the basic level relates primarily to the application process for the required research data, the legal and ethical issues to be considered, the relevant documents to be submitted and the support services potentially available at the medical faculties that can help you carry out your planned data analysis project.

Topics include:

From Data Request to Data Delivery - The Services of the Data Integration Centers

Legal and Ethical Requirements for Data Use

Broad Consent, Alternatives and Consequences

Biometric Support Services

Advanced Level

After conducting research on and with the data from your own clinic, it is usually time to expand the database to other clinics. In order to underpin the diversity and variability as well as the statistical significance of the results, the patient cohort to be included should be enlarged. The German Portal for Medical Research Data (FDPG) offers the possibility of creating a virtual cohort of patient data from different clinics and using it for various evaluations. The Advanced Level of the MII-Academy provides you with the necessary overview of medical terminologies. You will learn how these terminologies are used in clinical routine and how you can use them for evaluations. You will also learn how to record additional data on patients, e.g. image or analog text findings.

Topics include:

Feasibility Studies with the German Portal for Medical Research Data (FDPG)

Standard Terminologies in Medical Informatics

Mapping Laboratory Parameters in LOINC: Basics and Best Practices

Capturing Structured Data with REDCap

Specialization Level

Building on the knowledge repertoire already acquired in the previous subject areas, you will find topics in the specialization level that represent useful alternatives for advanced analyses and research projects. These include distributed data analysis, natural language processing methods for extracting information from (diagnostic) texts and methods for analyzing MRI and CT data.

Topics include:

fhircrackr - Handling HL7 FHIR Resources in R

Personal Health Train - Perform Decentralized Analyses


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