Steps Involved in Health Data Management

Data management can be defined as the development and execution of policies, practices, architectures, and procedures to properly manage full data lifecycle needs of an enterprise. Health Data Management is essentially important in the healthcare industry because of the magnitude of data that it produces and needs daily. Every area of the healthcare industry relies on an endless stream of data flowing in order for the system to function. Data management evolved with the advent of technology which ushered in electronic records. Data previously was stored in paper records, files, and boxes which made it almost impossible to manage. Issues like missing document, misfiling of a document or illegible handwritings served as a hindrance to data scientists who could use data from patient history to predict the future of patient care. The true value of the data warehouse is to organize data, provide links to disparate data sources (so the analysts don’t have to), and provide access so analysts and clinicians can sort through data themselves and find what they need. Aligning the analysts and developing clear clinical data governance and management policies will strengthen the entire analytics environment and make for a better Health Data Management.

Health Data Management which can interchangeably be referred to as Clinical Data Management also referred to as CDM is the generation of high-quality, reliable, and statistically sound data from clinical trials. It’s a critical stage of research that that ensures the collection, integration, and availability of data at appropriate quality and cost. Health Data Management plays a key role in the setup, arrangement, and conduct of clinical trials. During clinical trials, data is collected and this data forms the basis of further safety and efficacy analysis which drives decision making on product development in the pharmaceutical industry. Clinical trials lay an influential role in the development and manufacturing of new drugs and medication in the pharmaceutical industry. Clinical data management is crucial and leaves little space for error. Hence, Health Catalyst constantly seeks ways to improve clinical data management. Read more here:

Steps of Health Data Management

There are various steps involved in Health data management, the data gotten from clinical trials are very important as mentioned above and so protocol in handling this data must be followed. These are the steps involved in clinical/healthcare data management:

  • Source data is generated: Source data is the raw data gotten from the trials, it could include, lab results, patient medical records etc
  • Case Report Forms (CRFs): If paper Case Report Forms are being used as opposed to electronic report forms, the clinical site records are transcribed onto the paper case report forms. Using paper case forms are not as efficient as using electronic case forms.
  • Clinical Trial Database: Data from the CRFs, as well as other source data, are entered into the clinical trial database. Electronic CRFs (eCRFs) allow data to be entered directly into the database from source documents. Data from paper CRFs are often entered twice and reconciled in order to reduce the error rate.
  • Checking for Accuracy: The data is checked for accuracy, quality, and completeness, and any problems found are resolved. This often involves queries to the clinical site.
  • Database Lock or Database Freeze: The database is locked when the data is considered correct and final.
  • Reforming the Data: The data is formatted for reporting and analysis purposes. The database manager generates tools for analysis such as tables, listings, graphs, and figures.
  • Analysis: The data is analyzed, and the analysis results are reported. When significant results are found, this step may result in the generation of additional tables, listings, graphs, or figures.
  • Integration: The results are integrated into high-level documentation such as Investigator’s Brochures (IBs) and Clinical Study Reports (CSRs).
  • Archived: The database and other study data generated are archived for future use and referrals.