Infographic: The Data Acquisition Guide for Quality Analytics

Infographic: The Data Acquisition Guide for Quality Analytics

Data Ingestion header

It might surprise you to learn that one of the main obstacles to building quality analytics is data access. This is why being able to ingest various formats is so important. Regardless of the format, if we can get our hands on the data, we can help you do something valuable with it.

Below is a list of data formats we ingest to help our clients with their quality analytics and population health initiatives.


Designed to be a clinical messaging format to standardize how data is exchanged between systems. Common format types are ORM, ORU, ADT, VXU, MSM and more. As its primary purpose is interoperability, the data lends itself some to utilization measures but does not contain the data elements needed for more traditional measures like diabetes or blood pressure control.

Consolidated Clinical Document Architecture (CCDA)

HL7 standard that provides a snapshot of a patient’s health history. Electronic Health Records that are 2015 Edition CHERT can create CCDAs. Despite its popularity, there are multiple versions (C32, R1, R2), variation in what information is contained in the files, not all the required data for measure build is available, and while a single patient’s CCDA may be available, obtaining bulk extracts for an entire population on a routine basis is sometimes an additional obstacle.

Quality Reporting Document Architecture (QRDA)

A data submission format for the exchange of electronic clinical quality measure (eCQM) data that is used in numerous value-based care programs. QRDA III is an aggregate report that contains quality data for a set of patients across one or more measures. QRDA I contain quality data for a single patient, for one or more quality measures. A great format for many eCQM-centric use cases, but if you want patient level data at a provider or organization level, or need more non-traditional measures, you will need a vendor that has these additional capabilities.

Fast Interoperability Resources (FHIR)

Emerging standard (currently R4) that describes resources and APIs for exchanging health information electronically. Most promising, it exposes discrete data elements which makes the data open and extensible.  Will greatly advance interoperability, clinical decision support, reports and analytics, but does not specify data content standards so will continue to witness variation in the way data is captured and represented.

Administrative Claims

A health insurance document that is generated at every health care encounter to collect and store billing information related to eligibility, encounters and claims. While great for retrieving codes that are used in quality measurement, does not always provide all the information needed to calculate clinical measures (i.e. vitals data), is not standard across organizations and has a three month or more lag time.

Direct Database Integration

Access to the system’s back-end data. A popular method for quality due to the ability to retrieve data that live outside other file formats and at the frequency you desire. Examples include MySql, MSSql and more.

Flat Files

A non-relational file, meaning there is no structured interrelationships between elements. The two most common types are CSV and delimited files. As there are no standards, inherently it has great variation. If you can find a vendor that can work at-the-elbow with you to understand your internal standards and needs, sometimes a great method for access to data that is not available otherwise.

Application Programming Interface (API)

An interface that allows systems to talk to each other to exchange data in real-time. Comes in JSON and XML format, but because there are no standards, every integration is unique and not all Electronic Health Records have the capability to expose an API.

Infographic: The Data Acquisition Guide for Quality Analytics

Renee Towne

About the Author
Renee Towne
VP of Population Health at KPI Ninja, Inc.
Renee provides operational leadership of quality initiatives at KPI Ninja. Towne has a background in occupational therapy, education and experience in operational excellence across a variety of healthcare domains. Based on prior experience as a clinician that drove outcomes patient by patient, she is leaving a larger footprint by improving health care more comprehensively, population by population.

About KPI Ninja
KPI Ninja is a data analytics company that helps healthcare organizations accelerate their quality, safety, and financial goals with a unique combination of software and service. We are differentiated by our signature mix of technology, performance management consulting and healthcare expertise. We don’t merely offer software solutions but work shoulder to shoulder with clients to help them draw on the power of analytics and continuous improvement methodologies to become more efficient. In harmony with our data-centered ethos, we truly believe that our success is strongly co-related with yours.

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