Healthcare Data Warehouse: Architecture, Benefits, Implementation 

A data warehouse for healthcare (healthcare DWH) is a digital entity that complies with industry standards and is responsible for storing data and preparing it for further processing. Typically, such instances store the medical records of patients, their medical insurance, test results, prescriptions for drugs, etc. 

Currently, since the vast majority of industries strive to digitize their business processes, it becomes obvious that sooner or later healthcare DWHs will be implemented in any organization providing services and/or goods in the field of medicine. Below we will tell you about the benefits of data warehouses in healthcare and also talk about the nuances of their practical implementation.

You can also learn more about data lake engineering services here.

Unlike conventional databases, the healthcare data warehouse model implies data consolidation and storing historical data (not just current). Thus, traditional databases are an auxiliary tool for DWHs, since information often comes to them from such databases.

From a business perspective, a data warehouse for healthcare addresses three key issues that are common with traditional databases.

Availability of Prompt Access to the Necessary Information

The larger the healthcare organization is, the more departments have their own databases tailored to specific needs. Thus, to create a coherent picture and analyze it, before finding the necessary information, analytics departments have to do a lot of manipulations (identify the department that operates the necessary data, get access rights, extract the data, convert it to the appropriate format, etc.). In turn, DWHs speed up this procedure and make it as simple as possible.

The Need to Ensure the Preservation of Historical Data

Often, traditional databases get rid of irrelevant information by deleting it. As for data warehouses in healthcare, such repositories can store data that is ten years old and even older.

Exclusion of the Impact of Manipulations with DWH on Other Processes and Systems

Regardless of the manipulations performed at a particular moment with the DWH, their workload won’t affect the rest of the business processes in which the DWH takes part and which take place in the organization.

Thus, incorporating clinical data warehouses into the workflows of healthcare organizations is almost a critical factor in maintaining their efficiency as the volume of data they deal with increases.

To learn about the differences between a data lake and a data warehouse and consider specific examples of their use, please check out this article.

How a DWH Addresses Urgent Problems of Healthcare Organizations

Given the above properties that are specific to DWHs and not found in traditional databases, we have compiled a list of the main benefits for the healthcare industry.

Reporting Optimization

In a nutshell, a DWH is a unified, centralized repository with simplified data interaction capabilities for reporting and analytics. For example, with the help of a DWH, in a matter of minutes, your employees will be able to analyze case histories for a specific sample of patients (it may currently be needed for tracking the dynamics of the epidemic), identify the most frequently sold medicines, determine the quality of service for your medical staff, etc. Usually, for these purposes, a DWH is integrated with analytical data visualization solutions, so that the generated reports become understandable to people who don’t have experience interacting with raw data.

Eliminating the Issues of Disparate Databases

The main problem of traditional disparate databases is the inefficiency of processing the information they contain. Usually, this happens quite slowly for a number of reasons, ranging from the difficulty of bringing them to a uniform format to obtaining access rights. Also, conventional databases often lack built-in analytics tools. All these factors make them unsuitable for achieving the global goals of the healthcare industry. As for DWHs, they can provide your staff with simple tools to process this data and make informed decisions, which can be useful both in clinical diagnostics and in planning business strategies for attracting new and retaining old customers (patients).

Boosting the Processing of Insured Events

Medical insurance requires a constructive approach and implies the need for a global review of all information available about the patient. With DWHs, medical organizations can significantly optimize their insurance policies, make them more accurate, and protect themselves from fraud.

Smarter Resource Planning

The introduction of a data warehouse in healthcare plays an important role in business planning, which is relevant for both commercial and public medical institutions. In particular, such organizations get the opportunity to implement an integrated approach to the distribution of available resources and plan their further optimization. That is why many medical centers often use clinical data warehouses not on their own, but in combination with systems for inventory management, CRM, and other software that collects data in the company departments for which it is intended.

Better Customer Loyalty

When staff in a specific medical organization has a complete understanding of what is happening with all its patients from the moment they first contact to the present day, this definitely inspires great confidence on the part of the latter. Thus, a DWH eliminates the disadvantages associated with the existing model of customer service and inaccurate or delayed clinical diagnoses.

Increasing the Value of Services for Customers

Healthcare data warehouses integrated with solutions based on smart technologies (machine learning, natural language processing, artificial intelligence, etc.) allow medical institutions to achieve more accurate diagnostic results for their patients and, thereby, increase the value of their services. From the patients’ point of view, they receive more effective treatment and spend less on medicine and procedures that are ineffective for them (compared to medical institutions using outdated diagnostic models without the application of digital technologies).

How to Choose the Best Model of Healthcare Data Warehouse Implementation?

Depending on the complexity of your company structure, business objectives, and budget, you can choose from two healthcare data warehouse implementation models. They will be discussed in more detail below. 

Individual Data Mart Model

This is the most viable healthcare data warehouse model for small organizations that aim to optimize only one or a few areas of their business. It can work with insured events, customer service optimization, or other use cases.

From a practical point of view, this model implies the consolidation of several data sources from different domains. This approach provides excellent opportunities for further scaling and at the same time does not initially require huge financial investments and a global reorganization of the existing digital infrastructure.

Enterprise Data Model

The model of a healthcare enterprise data warehouse is more suitable for medium and large companies. In addition to bringing together disparate data sources, it includes advanced analytics, reporting, and processing tools. Often, such DWH implementations are completely tailored to the business processes of a particular organization and therefore require custom development. 

To find out about healthcare enterprise data warehouse and, in particular, its concepts and mechanisms, please read this article.

How to Implement DWH in Practice

From an implementation point of view, a data warehouse for healthcare contains four basic layers:

• Primary data collection layer. This layer is responsible for the flow of information from various company departments and databases.
• Staging zone. This layer stores disparate information that comes to the repository. Then, this data is structured and brought to the needed keys (the desired format). This layer is responsible for the integrity and completeness of the data.
• Data storage layer. At this level, data is converted into structures that will be more convenient to work with when solving specific problems. In some cases, it is enough to implement a layer with primitive functionality to solve fairly simple tasks, while in others it is required to integrate tools for solving non-standard tasks, compiling complex analytical reports, etc.
• Analytics and business intelligence layer. This layer manages the three previous layers. It doesn’t have any input information, but it manages and audits it. With its help, the DWH monitors data, as well as promptly eliminates errors.

Depending on the computing resources of your organization, as well as security and scalability requirements, the above four layers can be partially or completely hosted on-premises, in the cloud, or in a hybrid format (when some software is hosted in the cloud, while the rest is located on the company’s internal network infrastructure). Let’s look at these deployment options in more detail.

On-premise Deployment

Large companies, taking into account the volumes of consumption of computing power and IT services, are much more profitable to maintain their own park of servers and their own staff of IT specialists. 

Typically, such companies have to implement intra-corporate security policies and compliance standards, which significantly reduces the risks associated with maintaining their own IT infrastructure. Also, the option with local servers is suitable for public healthcare organizations that don’t have the ability to share data of their clients (patients) with third parties, as well as institutions that cannot effectively integrate their existing software services with those provided by cloud vendors.

On the other hand, you should understand that such a deployment model involves huge start-up costs, developing your own security and data integrity standards, as well as implementing fault-tolerant solutions.

Cloud Hosting

As your healthcare organization grows, it becomes more and more critical to keep your digital infrastructure up and running, regularly back up data, and update hardware. All these problems are much more efficiently solved by transferring the warehouse data to a cloud server.

In addition to savings, a cloud server provides higher mobility compared to a local server. Also, thanks to this deployment model, companies increase the reliability of their IT infrastructure, because virtual servers, unlike physical ones, break down extremely rarely and the consequences of such breakdowns are usually much less serious (at least because they are the responsibility of the cloud provider, not yours).

In general, for small and medium businesses, cloud infrastructure is much cheaper than on-premises infrastructure, since you only have to pay for the resources you use. It’s also about safety: after all, as data accumulates, IT managers of your organization will have to not only ensure the continuity of the local server but also take care of the safety of the data and prevent third parties from accessing it. As for certified cloud providers, their hosting usually has advanced security and fault tolerance mechanisms, which eliminates the vast majority of network threats.

Hybrid Model

To get the most out of the above two models, you can take a hybrid approach, combining the power of local and cloud hosting. In particular, innovative digital solutions are best deployed initially in the cloud to provide a good foundation for their further scaling. As for well-established digital business processes, they can be left on local equipment for reasons of intra-corporate security and saving time and money. Cloud services can also be used as a backup tool. In this case, you don’t have to buy additional server hardware to maintain the fault tolerance of your system.

4 Stages of Data Warehouse Deployment in Your Healthcare Organization 

Formally, the process of implementation of the data warehouse for healthcare can be divided into four main stages.

Planning

At this stage, you will have to decide which business processes you want to optimize with the implementation of DWH and choose the optimal model for its deployment, taking into account the advantages and disadvantages that we described in the previous paragraph. In particular, you will need to understand what data challenges your employees currently face and how they can be improved through DWH implementation. 

After that, you will need to analyze the strengths and weaknesses in your existing IT infrastructure and create a description for a new, better data warehouse. Finally, you should clarify what security standards and policies must be followed after migrating to a new data repository.

Design

At this stage, you will have to involve specialists who have experience in designing and deploying DWHs. They will help you build the architecture of the future data warehouse and develop the processes of extracting, transforming, and loading data.

Development and Deployment

At this stage, specialists will use the necessary technologies and software tools to develop the needed components of an updated digital infrastructure and deploy them, taking into account integrations with your other digital services.

Testing

After the initial deployment, the updated data warehouse and services interacting with it will be end-to-end tested. Thus, specialists will exclude the possibility of incorrect functioning of business processes in your organization.

Final Thoughts

As you can see, a data warehouse in healthcare can significantly improve existing business processes by automating and simplifying the processes of interaction with the information contained in it. Note that all these advantages are guaranteed only in case of careful planning of the implementation procedure and its support by experienced specialists. If you are looking for such specialists, you are in the right place. Contact us right now to discuss in detail the implementation of a DWH for your organization.