Expectations Going Into the Internship
Going into this experience, I expected to gain practical experience applying data science and AI-related skills to real healthcare and Medicaid problems. I was especially interested in understanding how real- world healthcare data is collected, cleaned, stored, analyzed, and ultimately used to support analytics and AI applications.
I also hoped to strengthen my Python and SQL skills, gain experience working with databases and data pipelines, and develop a better understanding of healthcare data beyond what I had learned in an academic environment.
Were My Expectations Met?
Overall, my expectations were met, and the experience provided valuable practical exposure that was different from classroom-based projects.
A major part of my work involved Medicaid-related data, including Provider Exclusion datasets from multiple states. Because these datasets came from different sources and often had different formats, field names, date structures, and data-quality issues, I worked on cleaning, standardizing, and validating the data before loading it into PostgreSQL.
Through this work, I gained more experience using Python for data cleaning and ETL tasks and SQL for database validation, record-count checks, missing-value checks, and other data-quality tasks. I also gained experience working with Linux servers, SSH-based file transfers, and PostgreSQL database workflows.
Another valuable part of the experience was gaining exposure to healthcare claims and transaction data, including CMS-1500 workflows and ANSI X12 837P files. This helped me better understand how healthcare data moves through real systems and how data science and AI fit into a larger operational environment.
The regular meetings with the team were also very helpful. They gave me opportunities to share progress and results, discuss challenges I encountered, and receive guidance on how to move forward. These discussions helped me stay aligned with project goals and resolve issues efficiently.
One of the most important lessons I learned was that successful AI and analytics applications depend heavily on the quality and reliability of the underlying data. Data cleaning and preparation are not simply preliminary steps; they are essential parts of building reliable models and applications.
Skills and Professional Growth
This internship strengthened my technical skills in Elixir, Phoenix, PostgreSQL, and working with JSON data. More importantly, it helped me improve how I approach problem-solving. I learned to break problems into smaller steps, test ideas incrementally, and remain patient when something did not work immediately.
Communication was also a key part of my growth. I became more confident explaining my thought process, asking clarifying questions, and discussing technical decisions. The calm and supportive guidance from Mr. Counts played an important role in this growth, as his approach helped create an environment focused on learning, clarity, and steady progress rather than pressure.
How the Experience Could Be Improved
One possible improvement would be to provide a clearer overall project roadmap at the beginning, with defined milestones, expected deliverables, and approximate timelines. This would make it easier to understand how individual tasks connect to the broader goals of the company.
It would also be valuable to have more opportunities to follow a project through multiple stages of its lifecycle, from data collection and preparation to modeling, deployment, and integration into an application. This would provide an even broader understanding of how an AI product is developed and used in a real-world environment.
Other Comments
I am very grateful for the opportunity to work with MMIS.AI and EMR Technical Solutions. The experience gave me meaningful hands-on exposure to healthcare data, data engineering, databases, and claims-related workflows while also helping me better understand the challenges of applying AI and analytics to real-world healthcare problems.
The experience strengthened my skills as an AI Data Scientist and deepened my understanding of how data engineering, healthcare domain knowledge, and analytics work together to support reliable AI applications. It also gave me practical knowledge that I will be able to apply during my graduate studies and in future professional work.
Thank you to Verbus and the team for the opportunity, guidance, and support throughout this experience. I greatly appreciated it and would be very happy to stay in touch and reconnect after graduation.
