What are the best practices for analyzing data from wearable technology in case management?

1 Define the purpose and scope of data analysis

Before collecting and analyzing data from wearable technology, case managers need to define the purpose and scope of their data analysis. What are the specific questions or objectives that the data analysis will address? How will the data analysis support the case management process and the client's needs? How much data is needed and for how long? Defining the purpose and scope of data analysis will help case managers select the most appropriate and relevant data sources, methods, and tools.

2 Obtain informed consent and protect privacy

Data from wearable technology can be sensitive and personal, so case managers need to obtain informed consent and protect privacy when using it. Informed consent means that the client understands the benefits, risks, and alternatives of sharing their data, and agrees to do so voluntarily. Privacy means that the client's data is stored securely, accessed only by authorized personnel, and used only for the agreed purposes. Case managers need to follow the ethical and legal standards of their profession and organization when obtaining informed consent and protecting privacy.

3 Choose reliable and valid data sources and methods

Not all wearable devices and data methods are equally reliable and valid. Reliability means that the device or method produces consistent and accurate results over time. Validity means that the device or method measures what it claims to measure. Case managers need to choose reliable and valid data sources and methods that match their purpose and scope of data analysis. For example, if the purpose is to monitor the client's physical activity, a device that measures steps, heart rate, and calories burned may be more reliable and valid than one that measures only steps.

4 Use appropriate data analysis tools and techniques

Data from wearable technology can be complex and voluminous, so case managers need to use appropriate data analysis tools and techniques to make sense of it. Data analysis tools and techniques can help case managers organize, visualize, summarize, and interpret the data. For example, case managers can use spreadsheets, databases, or software applications to store and manage the data. They can use charts, graphs, or dashboards to display and compare the data. They can use statistics, algorithms, or models to analyze and explain the data.

5 Interpret and communicate data findings and recommendations

Data analysis is not complete until case managers interpret and communicate their data findings and recommendations. Data interpretation means that case managers draw conclusions and insights from the data that relate to their purpose and scope of data analysis. Data communication means that case managers present and share their data findings and recommendations in a clear, concise, and relevant way. For example, case managers can use reports, presentations, or case notes to communicate their data findings and recommendations to clients, colleagues, or stakeholders.

6 Evaluate and improve data analysis practices

Data analysis is not a one-time activity, but a continuous process that requires evaluation and improvement. Evaluation means that case managers assess the quality, usefulness, and impact of their data analysis practices. Improvement means that case managers identify and implement changes or enhancements to their data analysis practices. For example, case managers can use feedback, reviews, or audits to evaluate their data analysis practices. They can use training, research, or innovation to improve their data analysis practices.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?