Skip to content


3 steps to leverage data science in home care

How to Leverage Data Science in Home Care [3 step guide]

The potential for leveraging data in home care organizations is undeniable. However, as organizations begin to collect and interpret data, navigating this evolving landscape can be daunting.  

Launching a data science project without the right strategy can often lead to results that don’t meet expectations and leave project managers fielding some tough queries from their stakeholders. 

This guide aims to help home care organizations understand data science in home and community care, take the important first steps, and generate value at each stage of your home care data science project.   

Toolkit: Make the most out of your home care data

Access our data toolkit containing a whole host resources to help you maximize the information you have at hand and make data-backed decisions about the future of your business.


What is data science for home care?

Data science is a broad field that uses scientific methods, machine learning algorithms and systems to extract knowledge and insights from structured and unstructured data.  

While each data science project is different than the next, I have found that a specific three-step process plants the right foundation for getting underway.  

Step 1 – Gather your data requirements

Begin first by determining the goal of this home care data science project. For instance, are you looking for insights into a business problem your organization is facing? Perhaps general insights are insufficient and you’re actually seeking strategic recommendations of actionable insights? Or, do you have very specific goals, such as activating a predictive alert that flags potential risks ahead of time? (And, if so, how will this alert merge with the agency’s workflow?) 

Such questions can significantly impact every aspect of your project, from who you can involve to what level of data you collect and what specifications are sent to which team members. In truth, there can be endless questions to ask before embarking on a data science project.
To help you, we’ve identified the key topics that are ideally covered while you gather requirements for the project:

A. Business context

What is motivating this project? What problem are we trying to solve?  

It’s essential that team members have a general understanding of the key problem addressed and, as much as possible, underlying complexities that impact stakeholders.  

B. Stakeholder hypothesis

What do the Small and medium-sized enterprises (SMEs) consider the most important data points/factors? Do they have theories as to the nature of the problem and what it will take to solve it?  

Learning this helps data scientists (who are often at arm’s length to the business problem) gain context and a feel for what’s causing the trends that the data might reveal. Answers here also point to initial ideas to explore and validate when performing data analysis.  

C. Deliverables

What do stakeholders want? Insights, strategic recommendations, or predictive alerts? For any of the three, how can it be delivered to, and used by, each stakeholder?  

Understanding how people can feasibly consume your home care data is key to delivering real value to an organization. So here you will deduce the involvement of additional team members. For strategic recommendations, you’ll need various SMEs to turn insight into action. For predictive alerts, you’ll need a user experience (“UX”) designer, and a stakeholder to design and validate an enhanced workflow that benefits from alerts.  

D. Existing data

Does a dataset already exist? Are there complexities of how the data are created that should be understood before analyzing it?  

The ideal business problem on which to base a home care data science project is one that already has loads of data. Bonus points if the data has been audited and consumed by someone already.  

E. Technical validation

Can this idea work?  

Before stakeholders and project managers get too excited about the next greatest machine learning-powered product feature, technical validation is an important step to predicting any challenges that the team will face.

Speak to technical stakeholders about:   

  • The feasibility of improving existing data  
  • The feasibility of building a machine learning model for desired business goal  
  • Other potential technical solutions that could address the business problem  
  • Complexities of integrating alerts into existing software (if applicable)  

At the end of step 1, it’s a good idea for the project manager to present a summary of the above topics (PowerPoint is handy) so all stakeholders can understand the true ins and outs of the project. Problem solving early sets the stage for success.   

From insight to impact: Using home care data to drive decisions and take action 

Step 2 – Home Care data profiling and improvement

Once you possess the insights that will form the base of your research, you can begin creating a dataset from existing data systems.  

It’s time to begin looking at the data, but before attempting to glean insights about your business problem, we are going to need to give your dataset a health-check. This addresses three key questions:   

  1. How many data points do we have and are there enough to form meaningful insights? 
  2. What fields are missing in the data? 
  3. Are inferred fields accurately computed/labelled so there is no “dirty” data? (Example: caregivers still marked as active staff even when they left six months ago) 

Your answers will yield both data improvement recommendations to help get more insight into the business problem, as well as a greater understanding of the reliability of the data analysis – which happens in step 3.

Step 3 – Data analysis

Now, this is where the real fun begins. Regardless of the business goal you’ve identified, discerning the basic trends in your data is essential. There is no one exact way to analyze data, but here are some places to start: 

  • Search for simple correlations in the data. (Example: yearly turnover rates – is employee retention slipping each year?)  
  • Plot the frequency distribution of all the dimensions in the data respective to what you are aiming to predict.  
  • Try unsupervised learning to reveal hidden structures. 
  • Try to find evidence that proves or disproves any stakeholder hypothesis. 
  • Formulate new hypothesefrom the above work and prove/disprove them.  

Congratulations! Once youve performed this step, you will have a fairly clear understanding of what is happening in your dataset. Regardless of the delivery method, this kind of data analysis is the foundation of effective data science projects. 

Now, data scientists, stakeholders and subject matter experts can start to uncover the basic trends and factors affecting the data – and plan for more complex analyses in future iterations of this project. And when there are important verified findings, be sure to spread awareness throughout the organization so the new data insights can be used each day to drive real business valueFor the maximum value, consider a real-time dashboard that demonstrates insights in a self-serve environment to enable business optimization efforts to be measured for effectiveness. 

Hear how Elara Caring harnessed data for better patient outcomes

If the creative wheels are turning when it comes to data science and you’re wondering if AlayaLabs can help bring your project to life, connect with your AlayaCare Client Relationship Manager at any time, and how we can work together on your next data science project.   


If your creative wheels are turning when it comes to data science and you’re wondering if AlayaLabs can help bring your project to life, connect with your AlayaCare Account Manager, or connect with our team to request a demo, to learn how we can work together on your next data science project.

Automation guide
Never miss a new post

Get the latest blog posts straight to your inbox