Child welfare technology is constantly evolving: trends ebb and flow, policies and mandates change, and new tools become available seemingly every day. Beyond changing how work gets done, it’s also changing the way you talk about it—both about your challenges and how technology can be used to help solve them.
We’ve compiled a list of terms you’ve likely heard or read about related to technology, along with their definitions, how they relate to each other, and what they mean for child welfare.
Terms are listed in alphabetical order, or you can use the table below to jump to a specific word. And, if you want to dive deeper into a certain topic, most entries include links to additional resources to learn more.
Editor’s note: this post was originally publish in 2018 but was recently updated to include emerging terms and current resources.
Artificial Intelligence (AI)
The ability of a machine to imitate human intelligence.
Digital assistants like Siri, Alexa, and Cortana, or cleaning “robots” like a Roomba, are a couple of examples of AI you might already be interacting with every day.
AI can be applied in child welfare to help automate processes and eliminate redundant work. AI can also help surface meaningful data that otherwise might have gone unnoticed (see dark data) and put critical information right at workers’ fingertips with no need to re-collect, re-interpret, or dig for data (see case discovery), which means they have more time to focus on clinical interactions with children and families.
Today, AI is advancing so rapidly that it’s hard to find the same definition (or definitive list of types of AI) twice. Potential use cases for AI change almost as often as the terminology does too! That’s why you’ll come across several different terms related to AI throughout this glossary, each with additional examples of its potential applications in child welfare.
Read our blog “Don’t Get Spooked by Artificial Intelligence for Child Welfare” or check out Gartner’s article “6 AI Myths Debunked” to learn about common misperceptions surrounding AI in child welfare.
(Note: no machine can ever replace a human’s ability to understand the complexities of each child welfare case. Technology can empower workers to discover elements of a case that might not have otherwise been found, but only a human can know what’s best for each child and family. This is a critical point to keep in mind as you read through any AI-related terms that follow.)
Case Discovery
The ability to read and analyze an entire case file, like a human would, through a child welfare lens.
The case discovery module of Northwoods’ software Traverse® uses natural language processing to uncover dark data (see below) and automatically extract major life events, people mentions and connections, and critical concepts mentioned in case content (for example, drugs, risk factors, and protective factors). It presents a complete picture of the child or family’s past and present to safeguard their future.
We’ve publish several resources to further explain case discovery and its impact on child welfare agencies:
Infographic: Artificial Intelligence in Social Work Explained
Blog: What Would You Ask if Your Child Welfare Case File Could Talk?
Video: Child Welfare Software: Finding a Forever Home
Article: The Key to Kinship: Technology Helps Keep Kids Close to Home
Since case discovery is a module of our software, you may not hear the term often outside of your conversations with Northwoods; however, you may hear about other similar ideas, like natural language processing, machine learning, or artificial intelligence (see above).
Dark Data
Gartner’s IT glossary defines dark data as “the information assets organizations collect, process, and store during regular business activities, but generally fail to use for other purposes. Like dark matter in physics, dark data often comprises most organizations’ universe of information assets. Thus, organizations often retain dark data for compliance purposes only.”
In the context of child welfare, dark data is typically defined as information collected and compiled from numerous sources over a long period of time that become hidden or virtually impossible to retrieve when making decisions to protect children and strengthen families.
Dark data is some of the most valuable information your agency has within its case files, but due to its sheer volume and complex nature, it is often the most difficult for social workers to manage and discover.
If the case files are not electronic, accessibility becomes an additional issue—for example, information can easily get lost in a paper case file or on sticky notes, hand-written notes, and other places workers might jot it down. Plus, the large paper files that contain this dark data are often not even in the same physical location as the worker making critical, time-sensitive decisions.
We’ve heard from several agencies, “well, we don’t have any dark data, so that’s not a problem for us!” But, as we continue talking and exploring the concept, they realize there’s a lot more hidden information than anyone ever thought (think psychological reports, court records, emails, medical reports, case notes, etc.).
We’ve created a few resources and visuals to help you better understand what dark data is, how it gets created, and why it’s so important:
- What Causes Dark Data in Child Welfare? (Infographic and blog)
- Shining Light on Dark Data in Child Welfare (eBook)
- Dark Data is Hiding in Your Child Welfare Case Files (Blog)
Data Exchange
The automated sharing of data or files between two systems or organizations.
In child welfare, you’ll often hear this term described as “bi-directional data exchange,” as that’s how it was named in Comprehensive Child Welfare Information System (CCWIS) regulations.
Think about when you sync your banking account with a budgeting or financial app that can also help manage your money—that’s an everyday example of the type of exchange we’re describing.
Here’s another example using child welfare information systems: Our software Traverse autofills case, client, and service provider data provided by CCWIS into state and county electronic forms. Social workers can also complete additional information as needed to be made available to CCWIS. Because of the data exchange, workers spend less time filling out basic information (names, addresses, dates, etc.) on forms and more time engaging families, which increases the potential for a positive case outcome. Reducing these administrative obstacles can also help minimize social worker burnout and turnover.
Despite these benefits, sharing data can feel scary for a system that has always had privacy top of mind for such sensitive information. However, the Administration of Children & Families (ACF) encourages data sharing where appropriate for the benefits of children and families.
The Capacity Building Center for States, part of ACF’s Children’s Bureau, further explores the impact of data sharing in their article “Inventory of Innovations: Data and Child Welfare.” You can also view Casey Family Programs’ article “How Can Data Sharing Across Child- And Family-Serving Systems Be Implemented Effectively?” for practical advice to get started.
Deep Learning
A form of machine learning and artificial intelligence that is inspired by the human brain and is particularly effective in feature detection.
If you’ve ever uploaded a photo to Facebook and been prompted to tag someone in it, you’ve seen deep learning in action. Facebook has analyzed hundreds or thousands of photos of that person to determine what set of features make up his or her face, so it can now identify that person.
Think back to middle school science class: the human brain functions through a network of neurons, or nerve cells, that interact with each other to communicate and process information. This information determines how we operate and make decisions.
At its simplest, deep learning functions the same way: neural networks send signals to each other that help a machine process and understand very large amounts information. The more times this happens (referred to as “layers”), the more complex a conclusion the machine can make. As a result, it can teach itself to identify the features (think shapes, colors, patterns, or textures) that make up images and objects that humans recognize.
There’s an excellent article on Medium called “Neural Networks: Is Your Brain Like a Computer?” that goes into detail on this comparison. Our partners at Amazon Web Services (AWS) also have a comprehensive page “What is Deep Learning?” that answers common questions on the topic.
Here’s one way deep learning could be applied in child welfare: imagine if a machine could learn to identify a specific object in a photo in a case file, plus understand the context of that object based on other things surrounding it and then make assumptions based on all of this information (e.g., a needle is on a coffee table in someone’s home, where there are also children’s toys in the background, which means this is an unsafe living environment for a child).
In this sense, deep learning could help turn dark data and other unstructured data into insight that workers could immediately find and use to make more informed decisions.
Descriptive Analytics
Examining data or content to answer the question “What happened?” or “What is happening?”
While descriptive analytics is performed manually in many industries, tools like Traverse are now available for child welfare that can analyze large sets of data in a matter of minutes. Traverse applies natural language processing and machine learning to automatically read case files like a social worker would and highlight the most important information in a case. That way, a social worker is very quickly presented with a child’s whole story and can use it to make more informed decisions to ensure safety.
Descriptive analytics and predictive analytics are sometimes used interchangeably, but they are in fact different, and it is important for a social worker to know this distinction. Descriptive analytics surfaces information within the case, presenting it to a social worker to help guide them in making case planning decisions. However, the social work makes the final decision. Predictive analytics also surfaces information, but then translates the information into a decision that the social worker did not have to make for themselves—for example, using case data to make safety decisions and predictions.
Document Management
A system or process used to capture, store, retrieve, and manage documents and files.
While some agencies unfortunately lack funding, resources, or justification to move away from paper-based systems, many have realized that electronic document solutions are critical for keeping up with increasing and complex caseloads and changing work expectations (like the ability to work remotely). An electronic document management system (EDMS) is also foundational for leveraging other emerging technologies like automation and artificial intelligence.
When evaluating document management solutions through a child welfare lens, consider the following features:
- Scan, upload, and capture documents and other case content (audio, video, or photos) to the electronic case file using agency defined taxonomy.
- Provide agency staff with immediate and simultaneous access to documents from anywhere.
- Quickly find and filter case content by date or content type, or by using full text search.
- Documents can be routed within the agency to automate child welfare processes.
- Keeps an audit trail of where documents have been routed and how they’ve been managed.
View our customer story “Wilson County Supports Workers with Modern Software for Social Services” to see the benefit of these features in action for a child welfare agency. Our blog “Every Case File, One Solution: The Future of Human Services Software” also details how a modern EDMS can help child welfare workers improve collaboration and information/data exchange with other programs.
Forms Management
A system or process used to manage, complete, and process forms.
Like electronic documents, many child welfare agencies have recognized the importance of digitizing their forms as a first step toward increasing their efficiency, timeliness, and service delivery capacity.
Here are some features of electronic forms that can have the greatest impact on your workers and clients in child welfare:
- Allow staff to complete forms from anywhere they conduct their work, regardless of connectivity.
- Autofill client, case, and provider information to minimize duplicate work.
- Digitally and securely share forms for review or signature, both internally and externally.
- Make all form data searchable.
- Allow for form data to be utilized in reports to gain insights on a macro level.
Read our blog “A Fresh Approach to Electronic Forms in Human Services” to learn more.
Generative AI
A type of artificial intelligence that can generate new content, often in the form of text, images, or other media, based on the data it was trained on.
Generative AI has recently become a key topic of discussion in human services, thanks in large part to public tools like ChatGPT that anyone with an email address can access. They offer promising capabilities, from enhancing client communication to managing case details and streamlining manual tasks, but also pose unique challenges related to data privacy and trust in their outputs.
Our blog “Artificial Intelligence and ChatGPT for Human Services and Social Work: Dos and Don’ts” provides emerging use cases and best practices to help child welfare agencies use generative AI tools responsibly and effectively, plus links to 10 industry resources to learn more. AWS has another resource page “What is Generative AI?” that answers additional questions and shares common applications of the model.
Human-Centered Design
An approach to designing a product or service in which the people who actually use it are placed in the center of the process. It seems like common sense that a product or service should be designed with its prospective user in mind, but there are many instances where that’s not the case—especially when it comes to child welfare technology.
Human-centered design assures that people, processes, and products are connected. It requires a deep understanding of not just how someone is going to use a tool, but also what they expect it to help them accomplish, how they need to be trained and supported, or what potential challenges they’ll face while learning it. Human-centered systems are intrinsically linked to a child welfare agency’s ability to improve outcomes and do meaningful work. If a system isn’t user-friendly, social workers and their clients just won’t use it, which means the agency won’t experience its benefits.
Child Trends has a good post explaining how human-centered design can be applied in child welfare: “Human-Centered Design Can Create More Efficient and Effective Social Service Programs.”
We’ve also created several of our own resources to explain how Northwoods approaches this concept:
- Human-Centered Design: How Northwoods Builds Solutions Around You
- Designing Technology to Enhance Engagement & Meet Users Where They Are
- 5 Must-Haves for Mobile Technology in Child Welfare
- 7 Ways to See if Your Technology is Meeting Caseworkers’ Expectations
Interoperability
The ability of different IT systems and software applications to communicate, exchange data, and use the information that has been exchanged.
The concept of interoperability, initially brought to the forefront for its impact on CCWIS) data exchange requirements, has sparked an industry-wide shift in thinking about how case data and information can and should be stored and shared. Here are a couple of examples of how this can positively impact child welfare:
- Reduce duplicate data entry: the less time workers have to spend copying and pasting data from one system to another, the more time they have to focus on engaging children and families.
- Access to information: when data flows freely from one system to another, workers can piece together a more complete and holistic view of each family’s story, ensuring better quality within the case.
- “No Wrong Door” approach: child welfare workers need to collaborate and exchange information with their counterparts in programs like economic assistance, child care, and child support to create holistic support plans for families.
Our blog “How CCWIS Federal Requirements Should Spark Systemic Change” shares action steps for agencies looking to capitalize on the possibilities of interoperable systems and meaningful data exchange. AWS discusses direct cost savings associated with interoperability (spoiler: $3.5 million!) in their article “Forrester Study Commissioned by AWS Estimates an ROI of 33% from Data Integration.”
Machine Learning
A form of artificial intelligence that teaches machines to act in specific ways without explicitly programming them to do so.
If you’ve ever ordered something on Amazon and then received a follow-up email on additional items you might like, you’ve seen machine learning in action. Amazon is using a machine-learning model to analyze what you’ve browsed, what you’ve bought, and what other people who have similar interests to you have browsed and bought, and then build recommendations based off that data.
Here’s how it works: at its core, machine learning is all about gaining insight from large amounts of data. Let’s say you have a spreadsheet with 10,000 rows of data that you split into two groups: training data and test data. A machine can learn a series of algorithms that teach it how to read and find patterns in the training data, and then use those patterns to make predictions or assumptions about the remaining test data.
Google has a video “The 7 Steps of Machine Learning” that goes into more detail if you want to check it out. AWS’ resource page also answers common questions: “What is Machine Learning?”
Case discovery is an example of how machine learning can provide guidance to help workers understand all the information that exists, so they can apply it toward decision-making. When we talk about how Traverse can read a case file “like a social worker would,” we’re talking about machine learning: case discovery relies on machine learning to read, analyze, and extract the key concepts, events, and connections in a case.
Natural Language Processing (NLP)
A form of artificial intelligence that allows computers to understand human language.
In child welfare, using NLP to read and analyze case content can surface incredible amounts of information.
Traverse, for example, can read the entire case file—everything from documents and case notes to photos, audio, and video files. However, to make the technology even more impactful, Traverse uses NLP in conjunction with machine learning (see above) to not only read the case, but also understand it the same way a social worker would because the models have been trained specifically for child welfare. Take the phrase “Bobby beat Jenny at the race” as an example. A machine that hasn’t been trained through a child welfare lens may interpret that Bobby is very fast and won the race. However, from a child welfare perspective, this same phrase could indicate physical abuse.
Coursera’s article “What is Natural Language Processing? Definitions and Examples” breaks down how NLP works and the benefits it provides. AWS has a helpful resource page as well: “What is NLP?”
Predictive Analytics
Examining data or content to predict “What is going to ha
