Database design and management are essential skills for healthcare informaticians, who need to store, process, and analyze large amounts of health data. In this article, you will learn how to apply some basic principles of database design and management to healthcare informatics, and discover how they can help you improve data quality, security, and usability.
A data model is a representation of how data is organized, structured, and related in a database. Depending on the purpose and scope of the database, data models can vary in complexity and abstraction. In healthcare informatics, you need to consider the type, source, and format of the health data you want to store, as well as the queries and operations you want to perform on it. There are three common data models used in this field: the relational data model, the hierarchical data model, and the network data model. The relational data model uses tables, rows, and columns to store data and defines relationships between them using keys and constraints. It is effective for structured data such as patient records, lab results, and billing information. The hierarchical data model uses a tree-like structure to store data where each node has a parent-child relationship with other nodes; it is useful for storing naturally hierarchical data such as medical terminologies, classifications, and ontologies. Finally, the network data model uses a graph-like structure to store complex and dynamic relationships such as social networks, clinical pathways, and epidemiological data.
Data normalization is a process of organizing data in a database to reduce redundancy, inconsistency, and anomalies. By applying a set of rules or normal forms to the data model, you can avoid data duplication, ensure data integrity and accuracy, simplify queries and joins, and enhance data security and privacy. For healthcare informatics, data normalization can help you save storage space, prevent update, insert, and delete anomalies, facilitate data retrieval and manipulation, and separate sensitive and non-sensitive data.
Data quality is paramount in healthcare informatics, as it directly impacts the decisions and outcomes based on the data. To ensure data quality, you need to define clear and consistent data standards and policies for data collection, entry, validation, and cleaning. Additionally, you should implement data quality checks and controls at various stages of the data lifecycle, such as source, transformation, storage, and analysis. Monitoring and evaluating data quality indicators and metrics can help identify any issues or gaps in the data. These can be resolved through manual correction, automated correction, or imputation.
To continue; VistA has been running the VA 160 hospitals for nearly 50 years and was adopted for the Department of Defense 200 hospitals back in the mid 1980's, 90's and into the 2000's. This system is very dynamic and grows dendritically as data is added. The database has a logical schema that allows nearly endless extension to the multiple fields and word processing fields. Take a look at the system, it is FOIA, developed by VA personnel on Government time. See worldvista.org . The system has been adopted by other organizations and is completely extendable.
Data quality is paramount in healthcare informatics, as it directly impacts the decisions and outcomes based on the data. To ensure data quality, you need to define clear and consistent data standards and policies for data collection, entry, validation,to ensure quality control and standards or practice.
No one has a crystal ball and no amount of research before hand will cover all new admissions to the database. The people at the point of care are the best arbiters of what is needed in the database, so making the database as LAYGO (Learn as you Go) and having a data dictionary that is self extending makes the job so much easier and the flow of the hospital and patient data is much easier if the people at the point of care can add new items into the formulary and other fields that can be monitored after the fact to provide additional information as needed after the fact if the addition needs to be backed out of modified.
Data security is a critical element of healthcare informatics, as health data is often very sensitive and subject to various laws and regulations, such as HIPAA and GDPR. To ensure data security, you should encrypt data at rest and in transit using strong algorithms and protocols. Additionally, data access control mechanisms should be implemented to restrict and monitor who can access what data and how. Data anonymization or pseudonymization techniques, such as masking, hashing, or generalization, can also be used to remove or replace identifiable information from data. Furthermore, you should follow best practices such as backup, recovery, patching, and testing.
Data usability is the ease of use and usefulness of data for its intended users and purposes. In healthcare informatics, data usability is essential, as it enables users to find, understand, and apply the data effectively and efficiently. To improve data usability, you need to provide data catalogues or directories for searching and browsing the available data sources and metadata; data APIs or interfaces for accessing and interacting with the data; data standards or formats for exchanging and integrating the data across different systems and platforms; and data documentation or guidance for learning about the data origin, quality, meaning, and usage.