Electronic medical records (EMRs) have a great impact in clinical practice. They give greater organizational efficiency; voluminous paperwork to find the patient information is not needed anymore. Ineligible handwriting and misinterpretations or errors in critical areas, such as diagnosis and treatment, can be avoided. Computerized information is accessed faster and more efficiently within a matter of minutes.Demographic details such as age, gender, the place from which the patient is coming are routinely documented. Hence demographic details can be studied with ease at any time. As in this paper, “LVPEI-EyeSmart EMR based analytics of big data: LEAD-Uveitis Report 1: Demographics and clinical features of uveitis in a multi-tier hospital based network in Southern India,”[1] the demographics are well discussed.[1] Age-wise distribution, gender analysis, and rural–urban classifications are perfect.An EMR creates reports faster; however, we have to analyze the data more thoroughly and more effectively to derive a meaningful conclusion. The investigator has to bear with this large data and needs to spend more time to make sense of it. The authors initially say, “[I] n a three-tier eye care model which includes 176 vision centers that provide primary care in the districts and villages of Andhra Pradesh, Telangana, Odisha, and Karnataka. These are linked to 18 secondary eye care centers, which are, in turn, linked to tertiary centers in Visakhapatnam, Vijayawada, Bhubaneswar, and Hyderabad”. However, in materials and in results they have reviewed only the medical records of secondary-center and the tertiary-center patients, probably because they assumed that all uveitis patients of the 176 vision centers were referred to tertiary centers. Nevertheless, we cannot be really sure whether all were identified, and all were referred and received in tertiary centers. Even if we assume so, if all of them have not come, their statistics of uveitis constituting to 1.12% of all cases needs to be calculated again. Similarly, it may cause some changes in age, gender proportion, and geographic distribution as well.In addition, differences exist in presentations between secondary centers that provide services to rural and semi-urban areas as compared to the tertiary centers. But it misses the vision center patients; hence we cannot evidently say that the data from all rural areas are included.EMRs are defined as “a longitudinal electronic record of patient Medical information generated by one or more encounters in any care delivery setting”. However in this paper, authors have mentioned that, “all the cases that were included had their first diagnosis of uveitis during the study period”. It is not clear whether they have seen all the visits or at least the final visit after reasonable follow ups. As we all know, uveitis diagnosis is so complicated that many of the etiological diagnoses cannot be achieved at the first time. That is the reason why they do not have numbers for important etiology such as Behcet’s disease which need aggressive treatment to avoid blindness. Behcet’s disease is chronic and insidious, and the diagnosis is delayed sometimes by years. In uveitis, it is likely to give misleading data, as in this paper they have given only anatomical diagnosis, even age- and gender-wise. Results do not show even proportion of idiopathic cases and non-infectious cases which are as important as infectious uveitis.The authors state, “while cross-sectional population-based studies provide a snapshot of the prevalence, demographics, and risk factors of any disease, they are unable to give detailed clinical information or longitudinal trends. In contrast, clinical studies that describe in detail the presentation or progression of the disease are limited by their sample sizes. Electronic medical recordsdriven big data analytics can help in bridging this gap between population-based and clinicbased studies by analyzing large data sets of clinical information, which is not possible with conventional methods of manual data collection.” In this paper, unless we have a detailed analysis of all of the visits and adequate follow-up details, with this simple analysis, we cannot bridge this gap between population-based and clinic-based data.Despite all of the above said benefits, several studies have identified many potential drawbacks to EMRs including long learning curve among various cadres of staffs.[2] Authors accept that big data analysis will depend on impeccable and uniform documentation. Here, such a big data is entered by hundreds of doctors. Unless all of the visits of each patient are seen by doctors with minimum inter-observer variability and more importantly, following the same diagnostic criteria, data is not expressive.It is very difficult in case of uveitis, as the diseases mimic each other. While the first diagnosis was tuberculosis, one will always be faced with possible differential diagnosis of sarcoidosis, syphilis, masquerade syndromes, among others. This is clearly reflected in Table 10.[1] Among the three important classifications of uveitis, the authors talk about only infectious uveitis, and do not give details of idiopathic uveitis and non infectious uveitis. All other cited papers address etiological diagnosis.[345]If an EMR is not updated immediately, as soon as new information is gathered—such as test results—anyone viewing that EMR could be receiving incorrect or incomplete information. It is great to know that at the time of presentation, 56.65% of all the eyes in our series had mild or no visual impairment. It is surprising that despite it being an EMR-based study, the data on the vision of 1,002 eyes could not be retrieved.Ultimately, when weighing the advantages of EMRs against their disadvantages, once EMRs are adopted and data are meaningfully analyzed in depth on a widespread basis, they can benefit patients, providers, and society as a whole.