(I can’t get no) Satisfaction! Or maybe I can?

Week 4 at Aravind is drawing to a close (cue the perfunctory cliché about how fast time is flying), and there is much to report on from Madurai. This past weekend we devoted our Sunday off from work to travel. Busra, Olivia, and I went to the neighboring state of Kerala for the weekend to visit the beautiful town of Thekkady, which is next to the Periyar National Park and Tiger Reserve. The trip was full of many adventures, not the least of which included us being attacked by literally hundreds (the number is not hyperbole) of leeches on a nature walk into the Tiger Reserve. But I am going to leave the grim details of that story and some of the more uplifting parts of the trip to Olivia and Busra to share in future blog posts.  Here are a couple photos as a teaser trailer:

Tea plantation in Thekkady

Tea plantation in Thekkady

Periyar Tiger Reserve: wild boar in the distance

Periyar Tiger Reserve: wild boar in the distance


I realized that I have neglected over my first few posts to describe my project for the summer at Aravind. The project that I am working on is tied to patient satisfaction. Before describing my project, though, let me give some context.

Aravind is an extremely data-driven place. I think that when I tell people from the US and even other parts of India that I am working at a hospital in south India, they have a vision of a small, noble clinic that is caring for needy patients through an admirable combination of determination and good intentions. As I briefly alluded to in one of my earlier blog posts, this is not exactly the case. Aravind has the most noble of intentions, but it is gigantic in terms of numbers. It serves more patients per annum than any eye care institution in the world, and it has all of the bells and whistles and capabilities of America’s best hospitals for eye care. What is more, the hospital is not just built on the heroic dedication of a super-doctor like Paul Farmer; instead as the founder of the hospital, Dr. Venkataswamy (Dr. V), intended, it is built fundamentally on process. In Dr. V’s eyes, the hospital was meant to be the McDonald’s of eye care, where you could deliver a product at a really cheap price, and not only that, you can do it by having a standardized process, in which you can train anyone and stick them into the system.

This morning, I shadowed Dr. Aravind, one of the directors of the hospital, in the Operating Theatre (not spelt wrong, just staying true to the British spellings in India!) as he conducted nearly 20 cataract surgeries in the span of about an hour and a half. The feel in the Operating Theatre is like an assembly line in Henry Ford’s Model T factory because there are four beds in a single spotlessly clean, state of the art room. As one patient is being operated on nurses are already readying the next for the procedure, so that the doctor can simply pivot his chair and begin the next surgery moments after finishing with inserting the new intra-ocular lens in the previous patient. Part of the reason that Aravind has been able to set up this kind of cataract factory is that they collect data on everything. They know how long patients are waiting in the waiting room; they know what the utilization rates are across the hospital; they are tracking outcomes so that they know how each department is performing along various measures of quality. Data is key to Aravind’s ability to continuously evaluate and improve the process to maximize efficiency. That’s how you get a process like Henry Ford’s factory, and that’s how you deliver cataracts at prices that (in the health care world) are tantamount to something off of the dollar menu.

A new realm of data analysis and assessment for Aravind is patient satisfaction. For the past three years, Aravind has been conducting surveys of patients through its newly developed patient feedback team: a determined group of six women from Madurai, who have to this point conducted thousands of patient satisfaction surveys. The goal is to get surveys from a random sample of roughly 1% of all Aravind patients, and as the team has grown over the past year, they have begun to systematically hit that mark. The upshot of all of this is that to date, Aravind has survey data on patient satisfaction for more than 15,000 patients.

Insert me. And my project is to take those 15,000 survey responses and explain what they tell us. There are around 20 questions in most of the surveys (there are a 6 different surveys for the different forms of care delivered at the hospital: out patient, in-patient, etc), covering patient experiences with reception staff, doctors, nurses, and facilities. So that’s well over 300,000 data points, and I have to admit that at first, I wasn’t sure where to begin. I have a little experience with using the statistical program R, though, so I started coding. From this work, I think that I have been able to come by some pretty interesting results.

It turns out that “free” (or highly subsidized) and (fully) “paying” patients have some pretty different levels of satisfaction. Free patients are consistently giving lower overall ratings for virtually all forms of care (out patient, in-patient, and day care; Day care describes when a procedure is done and patients can go home the same day without being admitted).   Why is this? It is not that free and paying patients are getting different quality in terms of outcomes; parameters measuring readmission rates and health after procedures all suggest that outcomes are the same across both free and paying.

Some more analysis eventually revealed that free patients consistently rate their doctors and their facilities lower. The facilities part makes sense at some level (though my supervisor said it was still not excusable) because the free hospital is separate from the paying hospital and does have worse amenities (that’s generally why patients opt to go to the paying hospital). But why are the doctors rated lower? Not entirely sure why that is, but we are trying to figure that one out now so that it can be dealt with…

Other interesting insights to explain the paying/free differential: it turns out that it looks like paying and free patients in out-patient settings differ in terms of what variables drive overall perceptions of Aravind. Free patients are really affected by how they perceive doctors’ treatment, while it turns out that paying patients don’t really seem to be affected much by how they perceive the doctors. Ratings for paying patients tend to come down to how clean they think the hospital is and how good the nursing care is (nursing care doesn’t look that significant for free patients).

The survey data has also been useful for pinpointing how specific clinics can improve in delivering care for patients. For example, from the data, I can tell you that Glaucoma patients feel as though they are receiving less direction than any other patient from reception staff. Reports on these sorts of things will hopefully prove useful to each individual clinic.

The next step in my project is to integrate this dataset with the existing medical records database, which has more information on the demographics of individual patients. This is going to be helpful in trying to figure out whether certain patients tend to have worse experiences at Aravind.  We will be addressing questions like: do patients coming from farther distances tend to struggle more?  Or, is it possible that women don’t feel as comfortable as men?.

Additionally, one of the big goals is to be able to analyze patient satisfaction along patient volumes. At Aravind, the prevailing notion is that high patient volumes are actually helping in a lot of ways because they bring down unit costs, and interestingly enough, they may help surgeons like Dr. Aravind become really good at their craft because they get so much practice. But it may in fact be that patient satisfaction dips off when volumes get too high; it may be that having long waiting times and having too many people around eventually begins to cause patients to feel worse about the care. I am hoping to be able to use the data to find some thresholds for patient volume where satisfaction dips off. Hopefully, Aravind can then use that information to adjust their capacity to deal with when patient volumes are really high.

One final element of the project will be to design a new IT architecture so that patient satisfaction can be reported on a consistent basis to departments and staff. But don’t worry, I am going to close the post here because this post has probably once again been way too long, and I don’t even want to know where my reader satisfaction is.

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About Vivek Nimgaonkar

Rising Senior in the Roy and Diana Vagelos Life Sciences and management Dual Degree program, majoring in Biology and concentrating in BEPP and HCMG at Wharton. Intern at Aravind Eye Care Systems in Madurai Tamil Nadu for summer 2015.