- Our innovative data-driven healthcare client wanted the ability to quickly analyze healthcare provider free-text task notes to spot new opportunities for improving patient care.
- Aptitive used custom built text processing algorithms to group over 150,000 free-text healthcare tasks into 11 task groups to create this advanced healthcare analytics solution.
- The client is now able to identify and analyze these task groups according to their category without any additional efforts from the provider, patient, or the data user.
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Our data-driven healthcare client wanted to easily analyze the over 150,000 task notes that their healthcare workers took when visiting patients. The challenge our client faced was that the descriptions of these tasks were all free-text so analysis was nearly impossible.
Their hope was to find a way to automatically identify and categorize free-text notes into task groups to enable the ability to spot trends, undercover issues, and ultimately provide better care.
Aptitive had helped this client build out a modern data platform and enable internal and external reporting in the past. Combined with Aptitive’s deep healthcare and data science expertise, partnering with Aptitive for this advanced healthcare analytics project was a no brainer.
Aptitive created a Latent Dirichlet Allocation model in Python in order to analyze all the free-form task notes from our client. This model goes through each task and assigns it a group based on patterns and key words found within the text for the task. Thus, similar tasks are expected to be assigned to the same group, while different tasks will be in different groups. Aptitive then went through each of the groups with our client in order to assign a group name to each of the 11 groups created.
Aptitive ran the model in Python through Apache Airflow and was able to leverage the existing data warehouse in Snowflake in order to assign new tasks as they came in.
With this new advanced healthcare analytics and data science solution, the client is now able to analyze the data and metrics from these free-form text notes through these newly formed task groups. The client can now spot trends and find opportunities to improve patient care quickly and easily, without having to manually read through or categorize notes. The model also automatically categorizes new notes as they come in so the client always has the most up to date information.