A comprehensive overview of data modeling in healthcare

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Significant amounts of information have forged a new era of facts-based innovation in organizations by supporting ideas with solid evidence.

In the past decade, business leaders continuously gathered data, invested in technologies, and spent generously on strategies that ensure better customer experiences.

And who could blame them? Recent data shows that data-driven decision-making improves patient retention by 23 times.

The way the world works is changing as a result of data. It is used for everything, including studies on disease treatments, business revenue strategies, the development of efficient buildings, and the tailored adverts you see on social media.

This data is machine-readable information, as opposed to human-readable. If consumer data cannot be tied to individual product transactions, it is useless to a product team. The same data won’t be helpful to a marketing team either if the IDs don’t correspond to particular price points during the buying process.

This is where data modeling is functional. It is the procedure that gives logical data rules. A data model simplifies data into valuable information businesses can utilize for planning and decision-making.

Through proper data modeling, leaders can garner information that gives them an edge over their competitors. High-level business processes can be integrated with data rules, data structures, and the technological execution of physical data thanks to data modeling.

Data models give a company’s operations and data utilization a streamlined, universally understandable approach. Other reasons why healthcare organizations may want to use data modeling are:

  • It ensures that every data object that the database requires is correctly represented since the absence of information will result in inaccurate reporting and results.
  • A data model aids in conceptual, physical, and logical database architecture.
  • Relational tables, primary and foreign keys, and stored procedures are defined using the data model structure.
  • Database developers can use it to build a physical database since it gives a clear image of the fundamental data. It can also help in finding duplicated and missing data.
  • It makes IT infrastructure upgrades and maintenance less expensive and quicker.

While data modeling is not new, it often remains a mystery to stakeholders, particularly healthcare workers. This is because most healthcare organizations do not use the value in their data and lack the technology to extract insights for decision-making.

This article discusses the different techniques, types, examples, and importance of data modeling in healthcare. So, let’s get started.

What is data modeling?

Data modeling uses formal methodologies to streamline a software system’s diagram or data model. It involves communicating information and data using text and symbols. The data model provides the blueprint for creating a new database or redesigning legacy applications.

It is the first crucial step in determining the structure of the information already available. The process of developing data models, through which data linkages and limitations are documented and ultimately written for reuse, is known as data modeling. It uses diagrams, symbols, or text to represent data conceptually and show how it relates to one another.

Thus, data modeling aids in improving naming, rules, semantics, and security consistency. This ultimately enhances data analytics. The focus is on the requirement for data organization and accessibility, regardless of how it will be used.

A good data model can answer the following questions:

  • What are our business processes?
  • How do we format our business data?
  • What kinds of data do we apply within these processes?

Because it creates a realistic picture of how users interact with your organization, including specifics like which fields they access and how frequently they use them, many businesses employ a data modeling strategy.

This level of understanding offers crucial details about where issues are and the most effective ways to address them. By doing routine data model audits, you can ensure that your data model is continually optimized for your users and their objectives.

Implementing data modeling in healthcare

Now that we’ve established what data modeling is, you might wonder how to implement it in healthcare.

Reliable and reproducible research is challenging because most healthcare data sources store information using their particular schemas. Transforming and loading information into these models can sometimes alter the semantics of the original data.

More often than not, healthcare organizations design a data model with a hierarchical structure to streamline the transformation process and reduce data tampering.

Building models that concentrate on pertinent material and specific use cases is crucial since healthcare data models, without which many new technologies may never be realized, are the foundation of innovation in the industry.

The following factors contribute to making a healthcare data model robust and adaptive.

Relevant content

As healthcare data is often plenty and disparate, you must first determine the most relevant content and focus entirely on that. This means understanding what people are looking for regularly to select all the data points you need for analysis.

A rule of thumb is to include elements with in-depth persistent agreement about their definitions. You may also want to provide support access to the raw source data to enable analyses that are not limited to the data models.

External validation

Several data models authorized by Fast Healthcare Interoperability Resources have resulted from much research and experience, even though there isn’t a single industry-defined data model that is practical to use in business-critical analytics and outcomes improvement.

Long-term planning

Each data model has a product owner who is accountable for the product’s long-term performance. The owners and teams of each data model are integrated within the organizations that use it and are specialists in the field covered by the data model.

Healthcare organizations create feature backlogs and roadmaps for their services, oversee standard data model releases, and collaborate with deployment teams to determine what is working and what is not. These teams use powerful development tools like source control and integrated development environments to treat data models like products.

Through this process, you can concentrate on the long-term success of the data model rather than only addressing immediate problems as they occur. In doing so, you ensure that the data model stays healthy and relevant.

Different types of healthcare data models

There are four different types of healthcare data models. This section explores the pros and cons of each model to give you a comprehensive idea of when you should use each type to model your healthcare data.

Hierarchical model

This data model arranges the data into a structure. In this approach, the primary hierarchy starts at the top and expands as you deal with more data.

Since the data in this data model grows, you need multiple permissions to access it. The hierarchical data model has a variety of relationships between different kinds of information. The information is kept as records and is related through links.

The hierarchical model is adequate for healthcare because it can simplify information by breaking down complex data into manageable pieces. As such, you can efficiently work with large amounts of information.

Network model

A specific database model called a “network model” is based on a flexible approach to describing objects and the connections between patients and providers. The network data model, which can be depicted as a graph where edges and nodes represent relationships that define things, depends on the architecture.

The hierarchical and network data models differ fundamentally in that the former represents data as a hierarchy and the latter defines information as a graph. One advantage of a network model is that the underlying relationships are also depicted.

ER model

Another valuable method for data modeling in healthcare is the ER model. The database structure is described by the ER model using an entity-relationship graph.

The database design, or in this instance, the patient-provider connection, utilized to create the database is analogous to the ER model. The relationship that exists in the entity set can also be shown. The entity set consists of entities with attributes that share common types.

Relational model

Data tables are used in this data model to group a collection of objects into relations. In this method, the relationships and data are expressed via interlinked tables.

The table also has multiple data rows representing an entity’s characteristics. The table’s rows represent records.

In this data model, unique keys separate each form in the table. If you can categorize your data with ease and define the links between various data pieces, use a relational data model.

If you were to represent information regarding patient examination results, a relational model would make sense because the data would be consistent, easy to order, and stable over time.

How to build effective healthcare data models

Building an effective healthcare data model takes time. You must follow a reproducible framework to ensure the best results. Here are specific steps to make the most effective healthcare data model.

Determine business requirements

The first step is to investigate how your healthcare firm will process the data. You don’t need to worry about precise variables since needs can be general.

Acquiring requirements entails determining the goals of your healthcare business and a brief idea of the data required to accomplish these goals.

This entails interacting with various business stakeholders, such as end users, decision-makers, clients, and technical coworkers, to build an overview of the necessary adjustments for the healthcare organization.

Define healthcare processes

You may then begin developing your needs into more detailed processes. Define what the data model should do in response to various events and triggers. This covers both system operations and user interaction responses.

Once more, it helps if you approach this without considering any particular variables. This procedure is sometimes referred to as building a logical data model. You’ll later employ this knowledge to create a more detailed data structure. But for now, you can just state in plain business-speak what procedures you want to have.

Identify data sources

Finding out where values will come from and how they should be stored for everything to work correctly is a significant component of your data model. This is where you identify your data sources.

These data sources can include internal and external databases, APIs and web services, and other healthcare information. By identifying these data sources, you can subsequently define entities and attributes, allowing you to create a conceptual model.

Creating a conceptual model lets you determine how you will structure different kinds of data to meet your goals. Furthermore, this makes it easier to create a physical model that includes more specific details on how you’ll structure databases and connect them to external sources.

Get an expert to build a data model

Data modeling is a trial-and-error process so you’ll need someone with the right skills and expertise to ensure that your data models are accurate and relevant to your industry.

Hiring someone with experience in advanced courses such as the Aston MBA programmes at Aston University is crucial to ensuring that your data models can turn complex data into easy-to-digest information.

This is because MBA graduates have often analyzed cases and practiced using data to make better and more educated decisions.

They evaluate and analyze information using comprehensive data exploration, data management, and integration. As such, they can generate information that is clear and easy to understand.

This way, businesses can evaluate particular data sets and determine whether their projections and the answer are accurate. Remember that the successful deployment of healthcare models usually requires a complete understanding of issues and previous models.

Experts overcome any difficulties and prevent dependency-related problems. This is only possible if they have comprehensive data modeling and architecture skills and a solid experience in programming languages.

Become the data modeling expert a healthcare institution needs

The data modeling industry is growing exponentially. Become the expert every healthcare institution needs by enrolling today.

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