A data warehouse is a centralized repository for the storage of data from one or more aggregated sources, updated immediately with real-time data yet retaining prior data for a more comprehensive dataset. The data in the data warehouse can be different types of data in various formats for disparate sources such as electronic health records and other clinical data and operational and administrative records – all can be in other formats and come from multiple sources of technologies and people. A Healthcare data warehouse often depends on integration tools to support extraction, transformation and loading (ETL) from proprietary healthcare systems such as EPIC, Cerner, and many others.
Healthcare Data Warehouse
Healthcare data can be used for analytics often categorized into three significant areas which are descriptive, predictive, and prescriptive analytics. This data can be used by many different experts in the healthcare field, ranging from clinicians to healthcare provider administrators and those on the Payer side (claims adjusters, underwriters, provider network managers, and so forth).
Examples of sources of healthcare data:
- Medical records, inpatient, outpatient
- Vital records such as claims
- Financial records such as reimbursements
- Disease and cause of mortality registries and other Population Health records such as HEDIS
- Administrative records
- Laboratory test
- Social determinants of health (SDOH)
Within a healthcare data warehouse, descriptive analytics or trend analysis can be done for a patient to determine what has happened to them over a period of time. This data then can be anonymized and aggregated over larger populations and then used for predictive and prescriptive analysis to prescribe solutions for the individual patient or to understand how medical solutions support people in general. Payers can use healthcare data to determine what rates to set for group policies and individuals as customers and what reimbursement schedules to set for in-network and out-of-network providers.
There are many healthcare data model examples that can be established using data from a healthcare data warehouse. There can be many healthcare consumers of the same data for different purposes. The value of common shared data from various sources to solve different problems cannot be underestimated using healthcare data warehouse analytics. Such as those used by drug manufacturers for new and existing drugs usage in society. Or combining data from hospital intake and release records, bed management systems, and EHR records to reduce the length of hospital stays without increasing hospital-acquired infection rates is critical to retaining favorable reimbursement rates from Medicaid and Medicare.
Besides, the service and technology providers such as drug manufacturers, payers, and providers can benefit from an enterprise healthcare data warehouse, especially with challenges such as managing cost, risk, patient experience, and overall delivering better outcomes. Payers need insights to facilitate the shift to value-based care and support payment integrity. Improved Payer-Provider alignment is now possible with data interoperability mandates between payers and provider systems. This creates an abundance of data to mine for actionable insights and decisions. Data must be extracted, transformed, and loaded (ETL) into a data warehouse and possibly into an Enterprise Data Hub for easy usage by payers and providers.
Statistical data from various populations of people or individuals can lead to research advancements, cures, improved preventive measures, and the overall health of the world’s population. Payers and providers can use data in an enterprise warehouse to deliver better-valued care while at the same time reducing cost and improving the economic value of the service offered to all.
Benefits of Healthcare Data Warehouse
The clinical use of an enterprise data warehouse cannot be underestimated. Besides the expertise of people in various medical fields, clinical data from healthcare data warehouses can be invaluable in helping with the analysis of tons of healthcare data.
The benefits of a healthcare data warehouse all begin with how the technology is used. How models are created and data is processed. Information technology, including the integration of medical devices and instrumentation into the IoT, big data, data warehouses, and other innovations, have improved the ability of all organizations, especially in healthcare, to become more efficient and effective in delivering outcomes. The ease of use of these data warehouse solutions has made them more valuable than ever in our society.
Data and information are the most efficient and effective ways of communication and creating coordination and collaboration for successful outcomes between people of different expertise and backgrounds. Many healthcare specialists can look at the same data and information and create collaborative solutions based on the expertise of each for a patient or society as a whole. Without the data, healthcare becomes very opinionated, which can lead to less collaboration between experts for the person’s benefit and reactionary non-scientific prescriptions and medical procedures and protocols during emergency scenarios such as the Covid-19 pandemic.
Now with a Healthcare Data Warehouse and the ability to use integrated collaborative data across payers, providers, members, and patients, the business of healthcare becomes more transparent while at the same time adhering to HIPAA compliance. This allows a shift from fee-for-service to a more coordinated and collaborative value-based system focused on outcomes.
The Actian Healthcare Data Analytics Hub is powered by the Actian Avalanche™ hybrid-cloud data warehouse, enables payers, providers, and others in the healthcare ecosystem to gain greater insights and drive better outcomes with data and enables an organization to shift from siloed models of business and operations to models that are forward-looking and collaborative. Down load our eBook to learn more.