Know Your Internal Data Sources

By Julie A. Dooling, MSHI, RHIA, CHDA and Shauna M. Overgaard, MHI

We can picture it now. Sitting at the desk, gazing at the computer screen as our minds are drawn into that special kind of bliss that can only be achieved through data of the highest integrity, relational consistency, and internal and external harmony. Yes, in those sweet moments all of the data you could ever need and desire are right at your fingertips, ready to be creatively sliced and diced into a treasured reporting masterpiece.

…and cue the alarm clock to wake us up, because that time has not arrived.

Right now, the reality is that locating and extracting the correct set of data for analysis often feels more like a small miracle than another day at the office. And it’s no wonder with the mirage of disparate systems still in existence in our healthcare facilities. Although we are making strides with the implementation of electronic health records and data warehouses, and we can trust in the hope and promise that new technology will produce efficient sources, we have not yet reached our promised data utopia. Until then, where must we go to find data?

Knowing your internal as well as external data sources is critical to your success in data analysis. Internal data sources can include information systems such as a radiology information system, a cancer registry, or the patient financial system. Hospital Compare, The Joint Commission, and Centers for Medicare and Medicaid Services are examples of external data sources.

Here’s a self-check list. For each of your internal data sources, do you know:

• Associated data definitions and the structure of the data in the database?
• How and where the data are collected?
• If a classification or terminology is required?
• How time and quality relate?
• How it interfaces or integrates with other internal or external systems?

In the case of a pharmacy information system, data collected could be specific to ordering or dispensing, include date, time and duration, drug form, dosage, route, frequency, and any special instructions. To facilitate the electronic sharing of information, terminologies such as the National Drug Code (NDC) and RxNorm are utilized and should be defined in the data dictionary. The NDC contains information on the manufacturer, the size of package, the dosage formulation and if it is generic versus brand. RxNORM is maintained by the National Library of Medicine and provides names and unique identifiers for clinical drugs.

Indeed, each data source is unique in its own way. Being confident in the location of the data, understanding its processes of collection, management, and relation to each of your contributing sources will allow you to construct an environment of trust, worth, and efficiency.

You must imagine such scenarios and you must continue to dream and plan for the day when it will become a reality.

Reference

2015 AHIMA CHDA Exam Preparation Workshop; Data Structures

Julie A. Dooling, RHIA, CHDA is a Director with the American Health Information Management Association (AHIMA). With many years of healthcare experience, Dooling has served in various roles including transcription service owner, HIM manager for a large integrated delivery network, and senior sales support for leading document management vendors in the US and Canada. Dooling serves as an instructor for the Certified Health Data Analyst exam preparation workshops and has authored many articles, briefs and toolkits related to data in today’s healthcare.

Shauna Overgaard, MHI, (sovergaard@css.edu) is an adjunct professor of healthcare data analytics in the Department of Health Informatics and Information Management, for The College of St. Scholastica’s CAHIIM accredited MS program. She is a PhD student of Biomedical Health Informatics and Biostatistics at the University of Minnesota.