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Klinisches_Datenmanagement

Clinical data management

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Our clinical data management services include

  • eCRF design and creation of annotated CRFs
  • Database design, programming of data entry masks and edit checks
  • EDC and double data entry
  • Transfer and integration of external data (e.g. laboratory)
  • Coding (adverse events, diagnosis and concomitant medication)
  • Data validation and query management
  • Quality control and database lock
  • (Blind) Data Review
  • Reconciliation of SAE data
  • Creation of status reports and statistics
  • Preparation of final statistical reports

Recording, understanding and communicating clinical data

Clinical data management refers to processes that serve to manage, use and record data and is a general term for a wide variety of tasks.
As a branch of medical data management, clinical data management deals with the review and coding of data collected in research studies.

Data protection and transparency are paramount

Due to the continuous change in legal regulations and the increasing awareness of the protection of sensitive data, quality assurance and quality control, data management is becoming increasingly important and is an essential component of clinical trials.

Transparency of all work steps and high-quality processed data are crucial for reproducible and comprehensible results and statistical evaluations.

From collection to analysis - we ensure that your data is of the highest quality, reliable and trustworthy.

During a clinical trial, a lot of information is collected that needs to be structured in order to enable precise, scientifically sound analysis. Using state-of-the-art software and online technology, we achieve the ultimate goal of data management: data quality.

Based on our experience and tried-and-tested procedures as well as our commitment as a member of the Society of Clinical Data Management (SCDM), we guarantee fast and secure handling of sensitive data.

Standort

Locations

Maps

Email

contact@mediconomics.com

Email

Telefon

+49 (0) 511 560 998-0

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And what can we do for you?

Uwe_Albrecht

Dr. (MD) Uwe Albrecht,
FIBMS, FFPM (Dis.), FICR, CSci

CEO

Hauptsitz_Mediconomics

Headquarters

Hanover

FAQ – frequently asked questions

What is clinical data management?
Clinical data management is a branch of medical data management that deals with the coding and review of data collected in medical or clinical studies in which new drugs and medical technologies are tested. Data management includes data collection using the case report form, which is nowadays mainly provided in electronic form, the creation of study databases in accordance with regulatory requirements, query management to clarify anomalies from plausibility checks or missing data, and the preparation and transfer of raw data to biometrics. It is a highly structured area of work that makes a major contribution to quality assurance in clinical research.
What is clinical data management needed for?
Clinical data management is required to ensure that the data used as source material for results reports is of high quality, reliable and trustworthy. Clinical data managers check the data against various criteria before releasing it for further processing. As this study data contributes significantly to the success of the subsequent evaluation, data management is an essential component of clinical studies. Due to the increased awareness of quality assurance and the constantly increasing requirements of authorities, study registries or scientific journals, the structured handling of study data in clinical research and for the pharmaceutical industry is becoming more and more important.
What standards must clinical data management comply with?
Numerous guidelines and quality assurance measures must be observed when carrying out data management, including GCP, AMG and GCDMP. Common standards such as CDASH and SDTM must be used when developing databases. For electronic signatures, the requirements of 21 CFR Part 11 are usually mandatory. Suitable standards such as GAMP5 must be observed for validation. Compliance with these guidelines not only promotes data quality, but also helps to make the subsequent processing of data more efficient and to harmonize it.
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