Data Dictionary: Healthcare Edition

Understanding the many facets of Big Data in healthcare means understanding the meaning of terms often used in the tech and healthcare industries.

Welcome to our Data Dictionary series, where we continue to explore the terms you will come across as you learn about Big Data and analytics.

For this edition, we focus on Big Data and healthcare.

Understanding the many facets of Big Data in healthcare means understanding the meaning of terms often used in the tech and healthcare industries. For those still working on learning all the terms, here are some definitions of specific phrases and concepts.

Accountable Care Organization: Health care providers who have pooled data resources to coordinate care and better manage chronic diseases. By sharing information, the Accountable Care Organization can foster better patient health outcomes while also saving on costs.

Clinical decision support: The analysis of a patient’s data as well as large datasets from others with similar conditions to create recommendations for the optimal course of patient treatment as well as identifying the signs of potential future health issues.

Data matching: The use of data by the federal government to ensure that the information entered by individuals in the Healthcare Marketplace matches data gathered from other sources, such as medical providers and insurance companies.

Health informatics: This is a catchall term that describes the collection, storing, retrieval and sharing of healthcare information in a medical operation. The goal is to keep accurate records that support collaboration among a patient’s healthcare providers, as well as insurance companies and government healthcare agencies.

Healthcare analytics: Describes the process by which data from healthcare records is analyzed and interpreted to provide insights to healthcare providers that lead to better health outcomes for patients.

Information blocking: The practice of blocking information sharing among healthcare providers. The federal Office of the National Coordinator for Health Information Technology works to prevent use of such systems and will not use its authority to certify any products that lead to information blocking.

Information governance: As the amount of data coming into medical operations grows, information governance is the term to describe a healthcare organization’s overall plan for collection, storage and analysis of massive amounts of patient data.

Population health management: The use of large datasets to identify specific health issues that are more common in certain demographics of the population, leading to better preventative healthcare and medical treatment for individual patients within those populations.

Risk adjustment: The use of electronic healthcare records data in a statistical process in which a patient’s health status and medical spending in an insurance plan are used when assessing both their projected healthcare outcomes and costs.

Telemedicine: A term that describes a patient receiving medical treatment remotely. Medical professionals can use data from a patient to give advice on needed steps to prevent or manage some health issues. In some cases, this could involve data sent from wearable devices.

Total cost estimate: The use of data, including the factors involved with risk adjustment, to project the total cost to a patient for healthcare insurance before they even begin using their coverage.

Wearable devices: This term covers a range of devices that collect a variety of data ranging from how much a person walks every day to calorie counts. Eventually, collection and storage of this data will lead to vast databases that medical professionals can use to identify potential health issues before they happen. This also can encompass devices such as heart monitors worn by the patient that send data remotely to healthcare providers.


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