Month in Review

Before I could access patient files, I had to undergo HIPAA (Health Insurance Portability and Accountability Act) training, which explains the laws surrounding patient confidentiality and privacy. I learned how to properly deal with online and paper records as well as the importance of avoiding talking about patients where others can hear.

Prior to this summer, most of the patient information had already been organized. This included address, date of birth, race, medication, as well as other pertinent information. I also wanted to find the patient gender and BMI to see how this related to control of diabetes. This involved pulling each chart from an online database that practitioners use to communicate between each other and to look back on for future visits.

BMI, measured as someone’s weight in kg divided by height squared in meters) is often maligned as not representative of health because people vary in body composition widely. Therefore, I am very interested in seeing how much of a predictor BMI is for diabetes control.

After completing 200 patient files with the gender and BMI in two separate visits, I logged off for the day and went home. After returning the next day, the computer crashed and I lost the file.

Back to square one, I set off redoing all of the BMI and gender logs. This time, I made sure to save an internet copying as well (after getting privacy clearance from my supervisor).

With that finished, I integrated the new information into the pre-existing patient information. Olde Towne Medical Center has hired a statistician to run through the data and determine how where you live, your race, your gender, your income level, or the other information gathered affects your chance of having out of control diabetes.

Next week, I will begin to track patients through their visit to determine average waiting room time, wait time inside the patient room, time spent with the doctor, and time spent getting to/from the facility. From this, I hope to paint a picture of how effective Olde Towne is at making a doctor’s visit manageable for a busy person’s schedule. I would also like to see if long wait times affect patient noncompliance and out of control diabetes.



  1. So cool that you are doing this research, Jay! It’s interesting that your examining variables such as patient wait time and how much time a patient gets with a doctor and how those things correlate with patients’ health and diabetes. My perception is that sometimes we underestimate the factors outside of the strict science that affect how people recover: how they are feeling inside the waiting room or in the medical center in general may have a significant impact on their progress or lack thereof. I am excited to see how you analyze this data you are compiling and I look forward to seeing your results, whatever they may be!