The concept of plug and play analytics easily applies to most, if not all, industries and maybe most importantly to healthcare. A plug and play solution for healthcare outcomes reporting typically connects to an EHR database (or subnet) through an interface or direct data connection and then ingests data for the purpose of measuring various healthcare outcomes and tracking patient care. There are many benefits to setting up a plug and play analytics solution, which easily pulls (read-only) data through from an Electronic Health Record (EHR) system Practice Management (PM) system on whatever time interval the user or customer requests.
Perhaps the most significant reason to choose a direct data connection type of plug and play analytics system is the ease and accuracy experienced when pulling down new data and ingesting it for the purpose of outcomes reporting and analytics. The pull request process is standard and can be set to run according to the processor’s schedule or whenever they choose. The failure rate of data ingestion is minimal and typically only occurs when a change has happened to the database that breaks the data connection. Outside of those instances, the data pull is standard and pulls the same data in the same format with each interval.
The concept of standardized data pulled through a plug and play solution with an interface connection, typically without error, is especially not lost on organizations that utilize disparate data systems. Whether a separate PM system and EHR or a separate lab system, or just multiple disparate EHRs across multiple practice locations, disparate data systems most often mean autonomous datasets with unmatchable data serving little to no purpose for managing patient care. In these circumstances, the ability to directly connect to all different systems, add a plug and play matching system, and then continue pulling down standardized data sets is crucial if practices and clinicians want to truly understand their patient populations in order to improve their care and reduce their costs.
The alternative to plug and play solutions for outcomes reporting and analytics is often the use of “standardized” extract files, which are created at regular intervals and then ingested through an Extract, Transfer, Load (ETL) process that ingests the data and then runs various engines off the data. The difficulty with this type of production is that it is frequently burdened by data fields or naming conventions that differ from a prior version and which cause a breakdown in the ETL process when the information doesn’t match what is expected.
Plug and play solutions that are truly plug and play in that they utilize direct data connection(s) to support easy and regular data ingestion for the purpose of managing outcomes, is critical if organizations really want to move the dial forward to more quality driven and cost effective healthcare. Until our entire health history is viewable, downloadable, and accessible through a chip in our arm that updates every known database in the world with any change to the state of our individual and global health, we have to create standards, solutions, and processes that stimulate healthcare in the right direction now. The quicker we leave behind paper charts and extract files, the quicker we’ll be on the right road to optimal healthcare and health data management.