I started to treat -I mean REALLY treat dry eyes around 2011- 10 years into my Optometric career. Prior to that, dry eye was kind of that needy friend that you truly wanted to help but didn’t know how.
I was involved with implementing one of the first dry eye clinics in our area, with the procurement of a shiny new Lipiflow machine. Much excitement could be felt that FINALLY we had something more than artificial tears and warm compresses to offer our patients. It was agreed that a dry eye exam would become a separate exam. WOW, I thought, we were cutting edge. We had Lipiview, Schirmer’s test, Osmolarity, MMP9, and various staining methods at our disposal. We were rich in testing options and were ready to tackle dry eyes like SUPERHEROS.
Suddenly, long-suffering people desperate for relief from their dry eyes came flooding into our clinic. Our waiting room filled with patients whose stories of painful irritated red eyes, and loss of productivity, income, and self worth weighed on my conscience long after I left clinic for the day. What’s worse, I quickly realized the plethora of point-of-care testing we enthusiastically collected weren’t making sense. The numbers contradicted each other. They weren’t repeatable. They confounded the diagnosis. What point-of-care test is more important? If mixed type of dry eye, which to treat first? And most importantly, how can I know if the patients were getting any better?
Shiny object syndrome created by the explosion of options
As time went on, more diagnostics and treatments became available. Each company ready to sell you the next shiny new treatment option made claims that theirs was the best. All of them were the best. All cited studies of reduced TBUTs, staining, and survey scores. There became too many tests, too many medication options, and too many treatment instruments that one could lease/buy.
I struggled with time management. Patients would pile up, the neediest ones spending copious chair time to provide their life stories of suffering, seeking care, and bags and bags of eyedrops they have purchased and didn’t work.
Then, in 2017, it was time to seek other opportunities. I joined a cataract, refractive surgical practice was involved in the beta testing of a first of its kind AI software using algorithms, machine learning and data science. . It was early in its development, and I was skeptical. It looked to me like just another place to put my data. How could a dry eye software make me any faster? How could it make the data make sense? How could it make me a better clinician?
AI shifted me from a symptom-driven to data-driven clinician
After using it for a while:
1) My exam decreased to less than 3 minutes, yet I was collecting more data than ever
2) I was able to quickly scan data and see patterns, so I stopped running thought circles in my head, agonizing on the diagnosis
3) I cut my counselling time down drastically, because everything was right there in front of the patient, AND I was able to give them a report of the findings.
4) The software interpretation allowed me to quickly identify dry eye masqueraders, so I stopped wasting time ineffectively doing dry eye treatments on patients that didn’t really have dry eyes.
5) Customized pre-determined treatment protocols allowed me to document multi-pronged treatment plans in just a few clicks.
My overall chair time went from an average of 45 minutes to 15 – including patient education. I felt more confident in my diagnosis, and patients were impressed that AI was used which helped develop credibility in our treatment recommendations.
An invested patient means willingness to invest in their care
The most rewarding part was the patients’ feeling of VALIDATION. “I’m not crazy”, “I am so glad you believe me”, “Finally I feel heard” were common comments when I handed them their dry eye report. Patients clung to that report like a life-raft, because it was a tangible representation that their concerns were valid and true. That validation meant an incredible increase in treatment conversion rates. Patients became invested in their care, because they understood we were using data driven techniques.
The AI software was the key ingredient to decrease my chair time and increase the health of the dry eye clinic and my patients. By using this software, I am also contributing to furthering the greater good of the dry eye population, as the incredible volume of data that is now within the system promises to provide future benefits of independent outcome analysis which will help optimize treatment pathways. And the more who use it, the better it will get.
To find out more about CSI Dry Eye Software visit: www.csidryeye.com.
