Harnessing Big Data to Track Patient Reported Outcomes (Part 2)

A Real-World Perspective on Analyzing Patient Reported Outcomes (Part II)


As discussed in a recent article (Harnessing Big Data to Track Patient Reported Outcomes), healthcare systems are looking to consistent data capture to track patient outcomes and develop benchmarks of care quality. This ‘big data approach’ is increasingly used to measure the value of clinical services and measure care delivered against national standards. In ophthalmology, the ability to capture and analyze patient reported outcomes may have an important impact on our ability to assess the impact of our care as patients often have a subjective experience of improvements in sight depending on personal expectations and daily activities that may not be communicated in the brief follow-up after surgery. For this reason, Rayner has developed the RayPRO platform that invites patients to report their outcomes over three years after receiving a Rayner intraocular lens (IOL). This tool allows me and the other staff in my practice to track measures of patient satisfaction over time and identify opportunities to improve the care that we provide.

For a brief overview of the potential data analysis capabilities with the tool, I have compiled a snapshot of recently reported data that is reported to us from the platform, as shown in Table 1. These data represent outcomes from procedures involving 94 Rayner IOLs that I implanted (75 RayOne Aspheric, 19 RayOne EMV), with the first implant occurring in November 2020. I was encouraged that these data are consistent with IOL outcomes expectations identified by multiple sources, including the American Academy of Ophthalmology (AAO) Cataract in the Adult Eye Preferred Practice Pattern®.1 Citing well designed observational studies, the PPP states, “up to 90% of patients undergoing first-eye cataract surgery note improvement in functional status and satisfaction with vision.”1 This improved visual function also translates to improved health related quality of life, according to the AAO. These outcomes also compare favorably with those from the UK’s Royal College of Ophthalmologists’ Cataract Commissioning Guide, which cited a national mean of 51% of patients achieving a best measured visual acuity of 6/6 (no pre-existing ocular comorbidities).2 A retrospective analysis by Dr. Shira Simon and colleagues of 1275 eye surgeries that included IOL implantation reported target refraction within 1.0 diopter at rates of 94%,3 which likewise compared favorably with our patient outcomes that were tracked by RayPRO.

Patient satisfaction is important when using a relatively new product like the RayOne EMV IOL, and I am encouraged with the reported outcomes, including distance, intermediate, and near spectacle independence rates of 70%, as well as zero incidence of halo, starbursts, or haze at one month after surgery.


Other Benefits of Tracking Patient Reported Outcomes

In this and my previous article, I have highlighted the concept of big data, or the ability to analyze a robust and long-term data set, to track patient outcomes and benchmark practices to local or national quality standards. The ability to analyze my patient’s reported outcomes helps me continue comparing my care quality with that published by respected organizations, such as the AAO Cataract in the Adult Eye Preferred Practice Patternand the UK’s Royal College of Ophthalmologists’ Cataract Commissioning Guide.2 Ultimately, these approaches to data capture reflect national quality standards such as the Getting It Right First Time (GIRFT) campaign through the UK National Health Service (NHS)4 and the USA Centers for Medicare and Medicaid Services (CMS) Physician Quality Reporting System.5

While the platform is new and my experience limited, I have found myself contemplating additional opportunities to analyze this valuable patient feedback. The IOL data I am now able to capture is not only used to ensure that my patients’ outcomes are consistent with those described by respected sources of quality reporting. For instance, my ability to capture this patient feedback has also enabled me to illustrate to my entire practice staff that their care makes an important difference to patient satisfaction and perceived outcomes over time. This feedback could otherwise be difficult to capture as many patients are not regularly seen in my office after surgery. The ability to capture patient feedback over three years also helps me to better describe anticipated outcomes and set expectations with new patients. Moreover, this data tool may also help me identify patient factors that predict the long-term success of specific IOL types, which could help my practice better inform patients about best IOL choice. I am also able to generally compare my practice outcomes with those of my peers delivering care in a similar geographic area to a similar patient population, which could potentially identify areas for further quality improvements. Finally, while my ability to consistently track patient outcomes has not yet resulted in evident changes in my daily practice due to my recent adoption this management tool, I can clearly see this as a potential result of its use over time, especially if I am able to identify any gaps in patient satisfaction and adjust my practice.




  1. Cataract in the Adult Eye Preferred Practice Pattern®. American Academy of Ophthalmology. https://www.aaojournal.org/article/S0161-6420(16)31418-X/pdf. Accessed September 8, 2021.
  2. Commissioning Guide: Adult Cataract Surgery. Royal College of Ophthalmologists Clinical Council for Eye Health Commissioning. https://www.rcophth.ac.uk/wp-content/uploads/2015/12/Cataract-Commissioning-Guide-January-2018.pdf. Updated January 2018. Accessed September 8, 2021.
  3. Simon SS, Chee YE, Haddadin RI, et al. Achieving target refraction after cataract surgery. Ophthalmology. 2014;121(2):440-444.
  4. Getting It Right First Time (GIRFT). https://www.gettingitrightfirsttime.co.uk/. Accessed September 16, 2021.

Physician Quality Reporting System (PQRS) Overview. Centers for Medicare and Medicaid Services (CMS). https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/pqrs/downloads/pqrs_overviewfactsheet_2013_08_06.pdf. Accessed September 16, 2021.


Table 1. Aggregate summary of patient satisfaction and other outcomes by Rayner IOL type.


Labor Aggregated results from rest of users
Satisfaction with surgeon 98% 97%
Satisfaction with hospital 98% 95%
RayOne Aspheric IOL
Labor Aggregated results from rest of users
Satisfaction with surgeon 86% 93%
Satisfaction with hospital 90% 90%
Satisfaction with outcomes 86% 84%
Spectacle independence 66% 61%
Target refraction 100% 91%


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