Understanding the Language of Healthcare Data

The Healthcare Data Language Crisis

Healthcare organizations have been capturing, collecting, transforming, accessing, analyzing, securing, and storing large amounts of data for many years, and yet there is still doubt in trusting and/or understanding their data and how to utilize it. For many healthcare organizations, data is still a vast, field of information and definitions, with no common understanding and unrealized potential.

And this is why it’s necessary for healthcare organizations, and for those working on digital healthcare projects, to assure that everyone understands the language of data. Lack of common understanding regarding data terms used extensively in day-to-day business conversations results in delays, confusion, and chaos.

All healthcare models spanning payer, provider and life sciences will need to understand that success and growth is dependent on everyone in the organization understanding the language of data.

In this post, we’ll first look at what data literacy means and why it’s important for the culture of healthcare organizations working in the digital space. We’ll then define for you some of the most used data-related terms.

Let’s Define Data Literacy

Data literacy is the ability to read, understand, create, and communicate data as information. Gartner defines data literacy as, “the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied and the ability to describe the use case, application, and resulting value.”

Meanwhile, data literacy describes the ability to read, work with, analyze, and argue with data, according to Raul Bhargava and Catherine D’ignazio from MIT and Emerson College.

Successful organizations must be literate in data, specifically how they communicate the information and operationalize the meaning of their data. To get to the deeper meaning of their data, data understanding and literacy is essential.

Data Literacy is the Way Forward

“By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80 percent of data and analytics strategies and change management programs.”

Gartner Inc.

One leading data executive recently defined the need for data literacy as the need to have data as a second language throughout your organization. While data dictionaries, business glossaries, and documented definitions are the foundational beginning, data literacy represents more than that. It comes from a place of understanding, that is, not just understanding ‘data speak,’ but being able to converse with one another about the lifecycle of data in the data ecosystem.

Simply put, data literacy is a culture change with roots in better understanding of one another. Perhaps most importantly, data literacy is also the cornerstone to data-driven decision-making throughout your organization. Want a digital transformation? Then you need to start by making sure people can understand data, what that is—and know how to speak to it.

Defining the language of data within your organization is necessary for several reasons. It allows for more trust, better conversations, and less confusion around data.

Also, large volumes of data are now available, if employees cannot access, use, and interpret it the data value is not realized. Plus, poor data literacy impedes an organization’s digital transformation growth.

Meanwhile, understanding data definitions helps to communicate clear expectations and enables quicker deliverables with minimum iterations. It allows organizations to have a common understanding of a word or subject—you’re on the same page when discussing or reading business cases with others in your organization.

To connect on a personal level with customers, it is important to develop a specific linguistic style and talk the same language across the organization.

Let’s Define the Data

It begins with defining the right terms within your organization and at Emids we recommend you define the following terms within your organization. Take a look at the table below.

Term Definition
Discreet   entities that are described objectively without interpretation.   
Data   that are interpreted, organized, or structured.   
Information   that is synthesized so that relationships are identified and formalized.   
Descriptive Analytics   
The examination of data or content to   answer the question “What happened?” It is typically characterized by   traditional business intelligence (BI) and data visualization.   
Diagnostic Analytics   
A form of advanced analytics that   examines data to answer the question “Why did it happen?” You can achieve it   with the help of techniques such as data mining, statistics, and machine   learning.   
Predictive Analytics    
A form of advanced analytics that   examines data to answer the question “What is likely to happen?” You can   achieve it with the help of techniques such as machine learning and   Artificial Intelligence (AI).   
Refers to new ways of doing business (new   business models) coupled with emerging technologies (Big Data, Cloud, RPA,   IoT, Mobility, AI/ML/NLP, Blockchain, etc.), creating new consumer   experiences, values, revenue & business results.   
Digital – Consulting   
Enablement Strategy, Consumer Experience   Design, Architecture Design, Business Transformation.   
The process of using digital technologies   to create new—or modify existing —business processes, culture, and customer   experiences to meet changing business and market requirements. It deals with   reimagining of business in the digital age. (Ref: Salesforce)      
It’s the technology for discovering and   investigating data quality issues, such as duplication, lack of consistency,   and lack of accuracy and completeness.   
The process of moving a company’s digital   assets, services, databases, IT resources, and applications either partially,   or wholly, into the cloud. Cloud migration is also about moving from one   cloud to another.    
Integration Bridge   
A software component installed on the   customer system to mediate between Agile Manager and on-premises applications   located behind firewalls, such as ALM, enabling two-way communication between   the two.    
Data Science   
The discipline of applying advanced analytics   techniques to extract valuable information from data for business   decision-making and strategic planning. It brings together fields such as   data mining, statistics, mathematics, machine learning, data visualization,   and software programming.    
Data Scientist   
A person who creates or generates models   that leverage predictive or prescriptive analytics, but whose primary job   function is outside of the field of statistics and analytics.    
Data Distribution   
A data distribution is a function or a listing   which shows all the possible values (or intervals) of the data. It also (and   this is important) tells you how often each value occurs.   
The place at which independent and often   unrelated systems meet and act on or communicate with each other.    
Interface Engine   
An interface engine/integration engine is   a software program that processes data between numerous Healthcare IT   systems.   
Data mapping is the process of matching   fields from one database to another. It’s the first step to facilitate data   migration, data integration, and other data management tasks. (Ref: Talend)   
Data Management   
Consists of the practices, architectural   techniques, and tools for achieving consistent access to and delivery of data   across the spectrum of data subject areas and data structure types in the   enterprise, to meet the data consumption requirements of all applications and   business processes.    
Data Governance   
A framework and a set of practices to   help all stakeholders across an organization identify and meet their   information needs.   
Data Literacy   
The ability to read, write and   communicate data in context. It includes an understanding of data sources,   analytical techniques, business applications, and resulting value.   
Data Culture   
Refers to values, behavior, and norms   shared by most individuals within an organization regarding data-related   issues. Broadly, it refers to the ability of an organization to use data for   informed decision-making.   

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