Healthcare companies are racing against the clock to improve the efficiency of their electronic health data (EHR). As a consequence, healthcare providers need to create new analytical models in order to spot at-risk patients, avoid adverse events, and practice evidence-based medicine. The emergence of cutting-edge research and forecasting models has led healthcare organizations to use data to verify theories and improve them using these techniques.
Data-informed Healthcare Organisations
The boom in AI and machine learning capabilities, and the accessibility of high-performance, storage-efficient hardware via the Commercial Cloud have all contributed to the emergence of the data-informed healthcare organization. The entrance hurdle for data-informed intelligence has decreased as a result of the market’s recent influx of technology and talent. This has ushered in a decrease in expense and improved the learning curve propelled by market and innovation.
Having a solid data infrastructure is the first step toward becoming a data-informed healthcare company. The information must be safe while still being easily accessible to those who require it. Systems must be able to search through the data in a matter of seconds or less, also the data must be inexpensive to keep in extremely large quantities. Complex data such as JS Object Notation or pictures. must be available through common query languages like SQL.
Enter the Healthcare Data Lake, a compilation of datasets that includes clinical data from electronic health record systems, societal factors of health data, analytical output from quality assessment and risk adjustment programs, and patient claims history.
Understanding Data Lakes
When compared to the clean, processed data kept in conventional data warehouse systems, a data lake’s ad hoc character is indicated by the word “data lake.”
A data lake is a gathering of different data assets that are kept within a Hadoop ecosystem with little alteration to the original format or substance of the source data. It is not just Big Data. As a result, the data lake does not have an explicit schema-on-write. Several programs that use “schema-on-read” are used to access the data lake’s information.
Big data from numerous sources are kept in a raw, granular version in a data lake, which is a primary storage location. Data can be retained in a more flexible shape for future use because it can be stored in an organized, semi-structured, or unstructured manner. A data lake identifies the data it stores with IDs and metadata markers to speed up retrieval.
Leveraging Data Lake for Healthcare
The data lake produces one complete, consolidated source of data for healthcare companies by removing the barriers posed by siloed data sources in various forms. This must be accessible on-demand to aid a motley of clinical and business use cases.
Healthcare organizations and health plans are considering whether their general data design needs a corporate data repository, a data lake, or both.
Some businesses seek to address complicated problems with a combination of alternative and conventional implementations, and there is a market change towards mixed approaches to data management.
The challenge facing healthcare leaders is to drive member and patient involvement, enhance patient medical results, and reduce the cost curve. These organizations must have the capacity to quickly ingest and evaluate sizable quantities of data in batch or in real-time from a wide range of sources in a variety of forms in order to accomplish this.
Benefits of Data Lake for Healthcare
Healthcare companies can enhance their data with data lakes to obtain more complete, useful insights to drive therapeutic and business efforts. For example, it can assist healthcare companies to leverage clinical data in order to find populations or diagnoses that may be under-reported for risk and quality initiatives. Additionally, it can provide access to real-time clinical data to care managers, allowing them to effectively prevent unnecessary emergency room visits, hospitalizations, and so on.
Data lakes can also aid in the integration of useful clinical results into provider report cards, as well as the tracking of opiate prescription trends to spot potential patient safety concerns and discover instances of fraud, waste, and abuse. Benefit design, network, and quality efforts can all gain from evaluating member care-seeking trends using data lakes.
We have innovators working to expedite data-centric techniques which enable biopharma and medical technology firms to provide cost-effective treatments to patients more quickly. Please contact us at open-innovator@quotients.com if you want to learn more about such solutions.
