Healthcare data and healthcare analytics have increased in popularity in the past decade and more and more healthcare organizations have tried to adapt to their advantage. One of the ways healthcare organizations has tried to adopt data analytics it by encouraging their healthcare professionals and Doctors to attend a Healthcare Analytics Summit where they can gain new knowledge about data, big data, and healthcare analytics. In there health analytics summits and conferences, there are many things that can be learned. Below we provide a summary of information that would be gained extensively when a healthcare professional attends the Health Catalysts Healthcare Analytics Summit.
Health Professionals will learn:
Tips to Improve Clinical Data Management
You will find it challenging to get work done with a desk full of scattered papers, sticky notes on your monitors and missed call messages everywhere. In fact, this type of chaos can be demotivating. The same premise holds true when it comes to your health data management system. If you have no structured system, how can you provide patient-centered care and reduce costs simultaneously? You can’t. The following are some tips to help to improve clinical data management within your system.
How to Create an effective team
Many analysts are dispersed throughout the organization working on collecting and analyzing data for different processes. If you create a team of analysts that work together to complete these tasks, you can significantly improve clinical data management within your organization. Analysts’ teams will help your organization:
- Achieve higher quality outcomes
- Resolve problems quicker
- Increase productivity
- Create an environment for skills development
How to Examine Risks within Your Organization
Risks are the unknowns that have the potential to derail your efforts to manage your health data. You can delegate managing risks to specific analysts to ensure that you have a plan for avoiding or reducing the impact of each risk. You should consider some of the following risks to get your team started:
- Challenges with core competencies
- Eliminate data silos
- Examine reporting procedures
- Assess current data governance to ensure strategic alignment
Although the list isn’t exclusive, it is a great starting point for your analysts to safeguard your data management system. Where your team observes gaps, have them develop a strategy to remedy the problems and submit proposals to C-suite executives, including developing a business case.
How to Build Enterprise Data Warehouse
Every Clinical Data Management System Needs an Enterprise Data Warehouse. Medical facilities collect, store and transmit a lot of data throughout the day. Without a central repository, analysts have a hard time performing their jobs, which reduces productivity. An enterprise data warehouse (EDW) is an internal system that helps your organization report and analyze information. Your EDW is the foundation of your data management system and your healthcare analytics. With a central repository, your analysts can base their decisions on uniform data, which improves data quality overall.
The Steps involved in Clinical Data Management
There are various steps involved in clinical data management, the data gotten from clinical trials are very important as mentioned above and so protocol in handling this data must be followed to the tee. These are the steps:
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.