The healthcare industry is experiencing a digital revolution, with professionals handling up to 19 terabytes of clinical data every year. While this trend has the potential to fuel a remarkable transformation, it presents some challenges, too, especially when it comes to storage and management. For instance, this data is often stored across a variety of legacy systems that don’t communicate with each other seamlessly. To fend off healthcare data disparities, medical organizations have long been turning to data management and data analytics service providers. The aim? Bring siloed data together into single, consolidated storage—a healthcare data warehouse—and use it to draw insights. This article takes an in-depth look into enterprise healthcare data warehousing, market opportunities, architecture, benefits, and implementation challenges. Keep reading to stay updated. Healthcare Data Warehouse Market Opportunity Source The global healthcare data warehousing market is expanding at an impressive annual growth rate of 10.7%, and experts project it to reach $6.12 billion by the end of 2027. Some of the factors that will drive this steady growth rate include: The need for healthcare organizations around the world to upgrade their storage IT infrastructure to meet the needs of a bulging industry The rising volume of digital data generated in healthcare institutions The popularity of innovative cloud data storage solutions that integrate seamlessly with electronic health records (EHR) and computerized provider order entries (CPOE) The gradual acceptance of hybrid data storage solutions in the healthcare industry The implementation of disruptive technologies, such as artificial intelligence (AI), big data, and the Internet of Things (IoT) Healthcare Data Warehouse Solution Architecture Before looking into the architecture of a typical data warehouse for healthcare, it’s worth noting that ideal solutions for organizations vary depending on several factors. This includes the size of the organization, specialization, or even specific business goals. Nonetheless, organizations often opt for enterprise-wide solutions with the following data warehouse architecture: Source Data Source Layer The layer that handles incoming information from multiple internal and external data sources. This might include clinical, research, admin, or even patient-generated information from EHR, content management systems (CMS), claim management systems, or pharmacy management systems, among other sources. Staging Area The staging area of a healthcare warehousing solution offers intermediate temporary storage for incoming data sets from multiple sources before they undergo the ETL (extract, transform, load) or ELT (extract, load, transform) processes. The ETL or ELT process then combines the information into a single, consistent data set. Data Storage Layer This layer of a healthcare DWH solution serves as a centralized storage for structured data. Structured data includes information relating to multiple subject matters or a set of departmental subsets known as data marts. A data mart is a stand-alone repository of information dedicated to a single healthcare domain or department. Small-scale healthcare facilities that want to improve certain operations over a short duration can also employ data marts. For instance, the model can help healthcare professionals feed and analyze specific chronic diseases or insurance claims when targeting critical cases. Analytics and BI The data analytics and business intelligence functions come with a host of intuitive features, such as reporting, dashboarding, and visualization, that drive predictive, prescriptive, or descriptive analytics. The Main Features of a Healthcare Data Warehouse Solution Healthcare information is sensitive by nature, calling for proper handling at all stages, whether gathering, viewing, or processing for analytics by data engineers. For this primary reason, any solution for data warehousing in healthcare should come with certain core features, including: Data Integrity Any data set, whether stored in a warehouse or any other solution, is only valuable to an organization if it's correct, clear, unambiguous, and transformed under established healthcare data modeling (tech). Healthcare data warehouse solutions foster data integrity through ELT or ETL processes. An organization chooses to implement ELT or ETL, depending on the type of healthcare solutions they run on top of a data warehouse. In the ELT approach, data sets are transformed after reaching the DWH. On the other hand, the ETL process transforms a data set before it reaches a target system. However, it’s worth noting that ETL processes are more time-intensive, and the processing speed might decline with increasing data volume, as opposed to ELT. Data Security & Compliance State, federal, and industry-specific regulations require healthcare facilities to take certain measures in a bid to safeguard personally identifiable medical data from unauthorized access or use. An innovative healthcare enterprise data warehouse can help foster data security and compliance in many ways, including: Implementing raw-level permissions by account or user clearance to ensure that specific data entries are only accessible to certain levels of users Setting up permissions at the business intelligence and data analytics level to ensure that sensitive medical information isn't displayed on the dashboard carelessly Implementing all-around data management strategies and governance policies, such as pre-defined access rights, deter unauthorized viewing or use of sensitive patient information Data Storage Healthcare data warehouse solutions offer storage for historical, integrated, or summarized medical information. Besides offering on-premise, cloud, or hybrid storage environment options, a DWH also features metadata and Protected Health Information (PHI) storage. Database Performance and Reliability Healthcare information requires glitch-free manipulation processes, especially if it’s coming from linked medical devices, such as wearables. An innovative healthcare DWH solution comes with a host of performance and reliability features that facilitates seamless data querying, transmission, and retrieval. They include: Bitmap indexing for reducing the response time of ad hoc queries and enhancing performance Elastic cloud resources for scaling storage and computation power dynamically, depending on the foregoing workload demands Automated data backups to facilitate seamless recovery in the event of unforeseen calamities The Benefits of Healthcare Data Warehouse Now that you understand what is a data warehouse, how beneficial is this solution when it comes to healthcare services provision? What is the ultimate outcome of a data warehouse? Especially when implemented the right way? Well, for starters, having this solution in your healthcare facility will drive the following: Data-Driven Labor Management If there is one hard lesson that the healthcare industry learned from the Covid-19 pandemic is the importance of preparing for a foreseen calamity. Cloud data warehouse solutions enable predictive analytics for data-driven decision-making when it comes to current and future labor management. For instance, you can get insights into historical labor patterns within your organization or area of specialization to understand the patterns that are likely to remain steady or change in the near future. With this approach, you can enhance hiring efficiency. Decreased Healthcare Operation Cost A recent study estimates that about one-third of the US population can hardly meet their healthcare costs, not to mention out-of-pocket expenses. Fortunately, an innovative data repository such as a data warehouse solution can facilitate seamless information sharing across the board, enabling institutions to provide accurate care, which can help patients minimize hospital visits. Similarly, DWH solutions for healthcare enhance the use of disruptive concepts, such as machine learning models that can help practitioners provide preventive care and ultimately mitigate unnecessary admissions. Improved Patient Experience and Health Outcome One of the key benefits of enterprise data warehouse in healthcare is that the solution can help improve patient experiences and health outcomes. For instance, doctors and nurses can access historical and real-time information simultaneously thanks to the solution’s prompt and accurate reporting. Quick access to relevant patient information via the BI dashboarding tool, such as missed medication or re-admission, can help providers enhance patient experience and improve long-term outcomes. Improved Healthcare Resource Management Actionable data insights from a data warehouse solution reveal individual departments or programs with the highest business impact in your organization. With this information, healthcare facility managers can accurately discern where to allocate sizeable capital or human resources. Important Data Warehouse Integrations to Implement It’s imperative to consider and implement various integrations for data warehousing in healthcare, especially if you’re going to maximize the solution’s value and cost-efficiency. That said, it will help if you integrate the following: Data Lake Source A data lake is a relatively affordable repository that provides storage for unstructured and semi-structured data sets before they are queried in the data warehouse. Moreover, data lakes can also provide raw data for training multiple machine learning models. Typical information stored in a data lake might include video recordings, images, or real-time data from medical wearables. Machine Learning The data lake gives users raw data for training machine learning (ML) models. To complement this, you'll need to integrate ML software with your medical data warehousing solution for clinical information. Training ML models for real-time data analytics can facilitate the delivery of personalized healthcare, in-depth analysis of medical images, or even the prediction of clinical outcomes. BI Software As noted earlier, data is more valuable when it provides actionable insights. Integrating a self-service business intelligence (BI) software helps healthcare organizations to perform descriptive analytics on clean and unstructured data stored in the DWH for prudent decision-making. BI software also enables visualization, automated reporting, and interactive dashboarding to power various healthcare information functions. Challenges of Implementing Data Warehouse in Hospitals Now that you're accustomed to the benefits of various data warehouse healthcare examples, it’s ideal to understand the challenges that come with implementation as well. Here are some of the concerns that you need to pay attention to. Data Storage and Quality in Hospitals Traditional storage solutions, such as relational databases, can hardly facilitate the storage of massive healthcare information unless you employ other storage and calculation technologies, such as supercomputers. For instance, a digital medical image or omics data set can fulfill the criterion of volumetry but not that of variability. Structure and Interoperability of Hospital Health Data The concept of data science has proven to be instrumental in helping the industry structure and standardize healthcare information. Unfortunately, the concept isn’t enough to attain uniform heterogeneity, structure, and interoperability, given that it requires wide-scale mobilization of data producers to analyze the same and draw actionable insights. In other words, transforming data from multiple sources or producers to meet a specific standard is incredibly taxing. With that in mind, it will help if you build a reliable ELT or ETL pipeline that seamlessly integrates with third-party tools. Alternatively, you can partner with healthcare data warehouse vendors who support HL7 compatibility when migrating data. Regulatory and Ethical Requirements for Hospital DWH Although the exploitation of actionable and relevant health data plays a key role in driving industry progress and medical innovation, it raises legitimate ethical and regulatory concerns. Like other examples of data warehouse in healthcare, your solution must comply with stringent rules that regulate the processing of patients' personal health information. For instance, the General Data Protection Regulation (GDPR) specifies the following legal framework for hospital data warehouse solutions: Ensure governance Describe the nature of the data contained in the DWH Assumes the obligation to inform patients about the gathering and use of their personal information Provide arrangements for patients to exercise their rights of access and opposition In the US, organizations must comply with the Health Insurance Portability and Accountability Act (HIPAA when implementing a data warehouse solution, especially when their business models necessitate sharing patient information with third parties and other stakeholders. Nonetheless, the risks of non-compliance can be minimized by working with a technology partner who leverages the right tech stack alongside best practices to deliver a fully-functional data warehouse solution. Wrapping It Up All over the world, healthcare organizations and research institutions are aiming to build a big data exchange ecosystem that links all players in the care continuum with reliable, real-time, and actionable information. Implementing a data warehouse solution at the organization or facility level eases the journey to achieving this overarching vision. The rising popularity of innovative tools like Fast Healthcare Interoperability Resource (FHIR) and public Application Programming Interfaces (APIs) also make it easier for technology partners like Symphony Solutions to share data seamlessly and securely. Contact us today for a free consultation on cloud data warehouse implementation.