|
 |
| ORIGINAL ARTICLE |
|
| Year : 2020 | Volume
: 68
| Issue : 3 | Page : 427-432 |
|
Big data and the eyeSmart electronic medical record system - An 8-year experience from a three-tier eye care network in India
Anthony Vipin Das, Priyanka Kammari, Ranganath Vadapalli, Sayan Basu
Department of eyeSmart EMR and AEye, L.V. Prasad Eye Institute, Hyderabad, Telangana, India
| Date of Submission | 12-Apr-2019 |
| Date of Acceptance | 06-Aug-2019 |
| Date of Web Publication | 14-Feb-2020 |
Correspondence Address:
Dr. Anthony Vipin Das
Department of eyeSmart EMR and AEye, L.V. Prasad Eye Institute, Road No 2, Banjara Hills, Hyderabad - 500 034, Telangana
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijo.IJO_710_19
Purpose: To assess the demographic details and distribution of ocular disorders in patients presenting to a three-tier eye care network in India using electronic medical record (EMR) systems across an 8-year period using big data analytics. Methods: An 8-year retrospective review of all the patients who presented across the three-tier eye care network of L.V. Prasad Eye Institute was performed from August 2010 to August 2018. Data were retrieved using an in-house eyeSmart EMR system. The demographic details and clinical presentation and ocular disease profile of all the patients were analyzed in detail. Results: In an 8-year period, a total of 2,270,584 patients were captured on the EMR system with 4,730,221 consultations. More than half of the patients presented at tertiary centers (n = 1,174,643, 51.73%), a quarter at the secondary centers (n = 564,251, 24.85%) followed by the vision centers (n = 531,690, 23.42%). The ratio of males and females was 1.18:1. Most common states of presentation were Andhra Pradesh (n = 1,103,733, 48.61%) and Telangana (n = 661,969, 29.15%). In total, 3,721,051 ocular diagnosis instances were documented in the patients. Most common ocular disorders were related to cornea and anterior segment (n = 1,347,754, 36.22%) followed by refractive error (n = 1,133,078, 30.45%). Conclusion: This study depicts the demographic details and distribution of various ocular disorders in a very large cohort of patients. There is a need to adopt digitization in geographies that cater to large populations to enable insightful research. The implementation of EMR systems enables structured data for research purposes and the development of real-time analytics for the same.
Keywords: Analytics, big data, electronic medical records, ocular diseases
How to cite this article:
Das AV, Kammari P, Vadapalli R, Basu S. Big data and the eyeSmart electronic medical record system - An 8-year experience from a three-tier eye care network in India. Indian J Ophthalmol 2020;68:427-32 |
The earliest mention of a medical record dates back to 1600 BC of an Egyptian case report from a papyrus text on surgery.[1] Case records of Hippocrates from the 5th BC were instrumental in describing the natural causes and the clinical course of illness.[2] The progress of science and understanding of the human body through the centuries further reinforced the need to document new knowledge to be passed down from generation to generation. A precursor to modern medical records first appeared by early 19th century in the form of loose paper files in major centers, such as Berlin and Paris.[3] The medical record continued to evolve over the 19th century to include patient history, clinical examination, treatment instructions, and investigations. A major innovation in 1907 was the introduction of the medical record number to patients at St Mary's Hospital and the Mayo Clinic.[4] Electronic medical record (EMR) systems are increasingly replacing paper-based records with benefits in increasing efficiency and standardizing quality while reducing costs of health care.[5] Today with the rapid adoption of different technologies impacting people's lives, there is an exciting potential for clinical research to embrace the same. However, the use of digital systems differs between the western and eastern hemispheres of the world. There is a lack of adequate data from the eastern part of the world detailing the use of EMR systems to describe the distribution of ocular disorders and its effect on population health. Research done by reviewing paper records is not only cumbersome but also prone to human errors. The amount of time taken to retrieve and analyze the large volumes of data from the EMR is minimal. The EMR system can collect large datasets (“big data”) that are characterized by the four 'V's - volume, variety, velocity, and veracity.[6] Given the challenges of connectivity, power and volume, digitization of hospitals in India is limited and evolving. The aim of this study was to evaluate the demographic details and distribution of ocular disorders from an indigenously developed EMR system (eyeSmart™) of a large three-tier eye care network in India and to describe the possibility of real-time analytics from the structured datasets.
| Methods | |  |
An 8-year retrospective review of all the patients who presented across the three-tier eye care network of L.V. Prasad Eye Institute (LVPEI) was performed from August 2010 to August 2018. The patient data were retrieved using the information captured through the in-house EMR system eyeSmart™. The study was approved by LVPEI's Institutional Review Board on 11.9.2018 with reference number of LEC 09-18-150 and adhered to the tenets of Declaration of Helsinki. A standard consent form for electronic data privacy was filled by the patient or their parents or guardians at the time of registration.
The three-tier eye care model of LVPEI 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 LVPEI Tertiary Centers in Visakhapatnam, Vijayawada, and Bhubaneswar. LVPEI's Center of Excellence at Hyderabad is at the apex of the Eye Care Pyramid. The medical records of all patients who presented to any of these Centers during August 2010 to August 2018 were reviewed retrospectively using the eyeSmart EMR database.
In total, 2,270,584 patients were captured on the EMR system and their total consultations were 4,730,221 in this 8-year period. All the patients who were registered onto the EMR system were included in the study. The variables in the collected data include age, gender, geographical location, laterality of eye affected, and ocular diagnosis. The geographical location and country as reported by the patients at the time of registration were documented in the EMR system and were included in the study.
Each eye of the patients was diagnosed separately, and each individual diagnosis was considered cumulatively for the analysis. The LVPEI coding diagnosis developed in-house was used for the patients, which includes a comprehensive list of ocular disorders, and the ICD-11 coding was automatically mapped to the relevant diagnosis. The ocular diagnosis made were categorized into different ocular disorders, such as amblyopia, cataract, cornea, and anterior segment disorders, glaucoma, neuro-ophthalmology, ocular trauma, refractive error, retina, uvea, and strabismus.
The age, gender distribution, demographic details, and proportion of ocular disorders were calculated through an SQL query written to extract information from all the databases of the centers across the network during the 8-year period. The individual numbers and percentages of the parameters to be studied were calculated through the query and exported to an excel sheet for further analysis. A detailed representation of the process is provided in the supplementary material. No identifiable information of the patient was used for analytical purposes. The de-identified information was replicated into another database from where analytics were visualized using tools for the same in real time. “eyeSmart EMR” is an indigenously built EMR system at the LVPEI, India. This system was developed in-house by using open source tools such as PHP (Zend Technologies, Cupertino, CA, USA) for programming and MySQL (Oracle Corporation, Redwood City, CA, USA) for database management. The eyeSmart App was developed on the Android platform (Google LLC, Menlo Park, CA, USA). The system allows the documentation of clinical information of patients significantly in a structured format that allows analysis for research purposes, and unstructured information is also captured. The information from the database was analyzed to provide a real-time overview. All tables for age, gender, location, and diagnosis category were drawn by using Microsoft Excel.
| Results | | |
In total, 2,270,584 patients were captured on the EMR system and their total consultations were 4,730,221 in the 8-year period.
Age
The age of the patients ranged from 0 to > 100 years. Based on the age category, pediatric population (≤16 years) presented were N = 304,100 (13.39%) and the adult population (>16 years) were N = 1,966,484 (86.61%). The most common age group of the patients who presented were between 51 and 60 years (n = 372,571, 16.41%) and followed by 41 and 50 years (n = 364,298, 16.04%). The detailed distribution of the age category is shown in [Table 1].
Gender
The ratio of males (n = 1,228,538, 54.11%) and females (n = 1,042,046, 45.89%) presenting to the network was 1.18:1. [Table 2] details the distribution of patients as per gender on EMR across various levels of the LVPEI eye care network.
Patient distribution according to level of care
More than half of the patients presented at tertiary centers (n = 1,174,643, 51.73%), a quarter at the secondary centers (n = 564,251, 24.85%) followed by the vision centers (n = 531,690, 23.42%).
Ocular diagnosis
In total, 3,721,051 ocular diagnosis instances were documented in the patients. The two most common ocular disorders were from the following categories of cornea and anterior segment (n = 1,347,754, 36.22%) followed by refractive error (n = 1,133,078, 30.45%), respectively. [Table 3] details the ocular disorder distribution captured through EMR. A significant proportion of diagnosis was made in both eyes (n = 1,985,373, 53.36%) followed by right eye (n = 810,132, 21.77%) and left eye (n = 784,725, 21.09%).
Geographical distribution
Patients presented from 109 countries to the LVPEI eye care network in the 8-year period. The highest number of patients presented from India (n = 2,264,230, 99.72%) followed by Bangladesh (n = 1608, 0.07%) and Oman (n = 1189, 0.05%). [Table 4] provides details of geographical distribution of patients from around the world.
The patients presented from 33 different states of India and the most common states of presentation were Andhra Pradesh (n = 1,103,733, 48.61%) followed by Telangana (n = 661,969, 29.15%). The least number of patients presented from the union territory of Daman and Diu (n = 3; 0.00%). [Table 5] provides details of the geographical distribution of patients from India. | Table 4: Distribution of the gender and age categories based on the geographical location (country)
Click here to view |
 | Table 5: Distribution of gender and age categories based on the geographical locations of India
Click here to view |
Further a real-time dash-board of the demographic details and ocular disorders of patient presenting to the LVPEI network from August 2010 on the EMR system was developed using the data and can now be accessed at the following link – http://www.lvpei.org/aeye/eyesmart.html.
| Discussion | | |
This study has demonstrated the demographic and ocular disorders' distribution in a large cohort of patients presenting to a three-tier eye care network in India. Gender predisposition was not noted in the presentation of patients with an equitable distribution accessing eye care services. A significant proportion of ocular disorders were in both eyes and there was no predisposition to laterality in either of them. It is of utmost importance to digitize clinical information to uniformly capture the data and assess the burden of ocular disease. In our study, we found that the cornea and anterior segment disorders and refractive error constituted about two-thirds of the ocular disorders seen in the network. The scope of this study was to provide an overview of the ocular disorders and other similar studies from the eyeSmart EMR system have reported them in detail as in dacryology and dry eye.[7],[8]
Ophthalmology is particularly conducive for data science in medicine due to structured quantifiable outcome measures that are significantly numeric and image based. This information allows us to perform big data analytics that have now evolved from the hundreds and thousands to millions and billions of data points. eyeSmart™ EMR is an indigenously developed EMR system at the LVPEI. The project that began in August 2010 has now completed the digitization of the 198 centers of the LVPEI network, which comprises of 1 Center of Excellence, 3 Tertiary Centers, 18 Secondary Centers, and 176 Vision Centers across the states of Telangana, Andhra Pradesh, Odisha, and Karnataka. It has facilitated about 4.7 million consultations since its inception. The system allows the documentation of clinical information in structured forms and images, which are stored in the database of the respective centers. All information from various centers is synced to a central database that allows the real-time analysis of the entire network.
The process of digitization poses different challenges in any large organization. Scholl et al. described the experience of the implementation of EMR in a large hospital in India.[9] The successful adoption of digital systems in complex organizations requires an alignment between the working protocols and needs of the organization and the functionality of the system. The various reasons that effect successful implementation include dynamic design strategies, user-friendly work flows, and demonstration of benefit for easy reporting of statistics. In our experience, demonstration of successful pilots at each level of the LVPEI pyramid was the most crucial step before expansion of eyeSmart™ in 198 centers across different geographies. Replication of the system across each level of Tertiary, Secondary, and Vision Center level was then achieved in a phase wise manner. Sharing of best practice patterns of utilization of EMR by different groups across the network provided the motivation to adopt the system. Time is a crucial component in the implementation strategy and the 176 rural vision centers were digitized in 90 days. Rapid implementation also provides rapid feedback that can be utilized positively to refine the application for the users.
The use of EMRs in population health management holds promise. Cavallo P et al. conducted a retrospective study of 14,958 patients and 1,728,736 prescriptions obtained from family doctors to understand the associations of comorbidities in the general population.[10] The network analysis extracted information from the prescriptions generating insights impacting both clinical practice and health system policy making. The various applications of EMR assisting population health management include quantifying treatment outcomes,[11] quantify and stratify the severity of disease,[12],[13] collect patient-reported outcomes,[14] document lifestyle patterns,[15] and potential to guide medicines regulation.[16] The use of large datasets helps to understand factors influencing health such as geographical location, nutrition, lifestyle, and their temporal evolution. The application of artificial intelligence in public health is also increasing.[17]
The population of India is 1.3 billion people. Access to health care is a challenge and nonavailability of information at scale in real time across geographies can limit policy planning. Big data analytics are a key to understanding distribution of ocular diseases in India. The ability to understand the burden of disease is very crucial to plan strategies to combat avoidable blindness. A real-time dash-board of the demographic details and ocular disorders of patients presenting to the LVPEI network from August 2010 on the EMR system can be accessed at the following link – http://www.lvpei.org/aeye/eyesmart.html.
The limitations of this study include the lack of population data, patient referral bias to a tertiary care in emerging economies, and reflection solely based of the distribution of ocular disorders and not their management. Patient duplication was also assessed as a limitation in the respective tertiary centers and was found to be negligible (0.28%) across the network. However, the strengths of the study include a very large cohort of patients and focused study of demographics and distribution of ocular disorders in patients seeking eye care in a large three-tier hospital network in India across 8 years.
| Conclusion | | |
To the best of our knowledge, this is the first description of a large cohort of patients using EMRs in a large multi-tier ophthalmology network in India. In conclusion, this study lists out the detailed demographic distribution and distribution of ocular disorders in patient seeking eye care and demonstrates the potential for real-time analytics using EMR systems.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Acknowledgements
The authors wish to acknowledge the support of our Department of eyeSmart EMR & AEye team specially Mr. Mohammad Pasha and Mr. Yasaswi Leela Ram and all the programmers who have helped develop the EMR system over the years.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Al-Awqati Q. How to write a case report: Lessons from 1600 B.C. Kidney Int 2006;69:2113-4.  |
| 2. | Reiser SJ. The clinical record in medicine. Part 1: Learning from cases. Ann Intern Med 1991;114:902-7. |
| 3. | Hess V. [Formalizing observation: The emergence of the modern patient record exemplified by Berlin and Paris medicine, 1725-1830]. Medizinhist J 2010;45:293-340. |
| 4. | Camp CL, Smoot RL, Kolettis TN, Groenewald CB, Greenlee SM, Farley DR. Patient records at Mayo Clinic: Lessons learned from the first 100 patients in Dr Henry S. Plummer's dossier model. Mayo Clin Proc 2008;83:1396-9. |
| 5. | Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, et al. Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006;144:742-52. |
| 6. | |
| 7. | Donthineni PR, Kammari P, Shanbhag SS, Singh V, Das AV, Basu S. Incidence, demographics, types and risk factors of dry eye disease in India: Electronic medical records driven big data analytics report I. Ocul Surf 2019;17:250-6. |
| 8. | Das AV, Rath S, Naik MN, Ali MJ. The incidence of lacrimal drainage disorders across a tertiary eye care network: Customization of an indigenously developed electronic medical record system-eyeSmart. Ophthalmic Plast Reconstr Surg 2019;35:354-6. |
| 9. | Scholl J, Syed-Abdul S, Ahmed LA. A case study of an EMR system at a large hospital in India: Challenges and strategies for successful adoption. J Biomed Inform 2011;44:958-67. |
| 10. | Cavallo P, Pagano S, De Santis M, Capobianco E. General practitioners records are epidemiological predictors of comorbidities: An analytical cross-sectional 10-year retrospective study. J Clin Med 2018;7. doi: 10.3390/jcm7080184. |
| 11. | Armstrong AW, Foster SA, Comer BS, Lin CY, Malatestinic W, Burge R, et al. Real-world health outcomes in adults with moderate-to-severe psoriasis in the United States: A population study using electronic health records to examine patient-perceived treatment effectiveness, medication use, and healthcare resource utilization. BMC Dermatol 2018;18:4. |
| 12. | Eggleston EM, Klompas M. Rational use of electronic health records for diabetes population management. Curr Diab Rep 2014;14:479. |
| 13. | Zghebi SS, Rutter MK, Ashcroft DM, Salisbury C, Mallen C, Chew-Graham CA, et al. Using electronic health records to quantify and stratify the severity of type 2 diabetes in primary care in England: Rationale and cohort study design. BMJ Open 2018;8:e020926. |
| 14. | Franklin P, Chenok K, Lavalee D, Love R, Paxton L, Segal C, et al. Framework to guide the collection and use of patient-reported outcome measures in the learning healthcare system. EGEMS (Wash DC) 2017;5:17. |
| 15. | Lewis KH, Skelton JA, Hsu FC, Ezouah P, Taveras EM, Block JP. Implementing a novel electronic health record approach to track child sugar-sweetened beverage consumption. Prev Med Rep 2018;11:169-75. |
| 16. | Pacurariu A, Plueschke K, McGettigan P, Morales DR, Slattery J, Vogl D, et al. Electronic healthcare databases in Europe: Descriptive analysis of characteristics and potential for use in medicines regulation. BMJ Open 2018;8:e023090. |
| 17. | Thiebaut R, Thiessard F. Artificial intelligence in public health and epidemiology. Yearb Med Inform 2018;27:207-10. |
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]
| This article has been cited by | | 1 |
Electronic medical records driven big data analytics in retinal diseases, report number 1: non-oncological retinal diseases in children and adolescents in India |
|
| Komal Agarwal, Anthony Vipin Das, Tapas Ranjan Padhi, Sushma Jayanna, Bhavik Panchal, Sameera Nayak, Taraprasad Das, Subhadra Jalali | | International Ophthalmology. 2023; | | [Pubmed] | [DOI] | | | 2 |
Clinical demographic characteristics and ocular Co-morbidities associated with Secondary lipid keratopathy at a tertiary care center |
|
| Anthony Vipin Das, Sunita Chaurasia | | Contact Lens and Anterior Eye. 2023; : 101826 | | [Pubmed] | [DOI] | | | 3 |
Implementation and scalability of shared care models for chronic eye disease: a realist assessment informed by health system stakeholders in Finland, the United Kingdom, and Australia |
|
| Belinda Ford, Blake Angell, Hueiming Liu, Andrew White, Lisa Keay | | Eye. 2023; | | [Pubmed] | [DOI] | | | 4 |
Open Globe Injuries with Concurrent Orbital Fractures – Clinical Settings and Factors Predicting Outcomes |
|
| Tarjani Vivek Dave, Prapti Praful Chheda, Anthony Vipin Das, Vivek Pravin Dave | | Seminars in Ophthalmology. 2023; : 1 | | [Pubmed] | [DOI] | | | 5 |
Incurable Blindness: The Final Frontier in Vision Restoration |
|
| Anthony Vipin Das, Sayan Basu | | Seminars in Ophthalmology. 2023; : 1 | | [Pubmed] | [DOI] | | | 6 |
The microbiological landscape and epidemiology of ocular infections in a multi-tier ophthalmology network in India: an electronic medical record driven analytics report |
|
| Anthony Vipin Das, Joveeta Joseph | | Eye. 2022; | | [Pubmed] | [DOI] | | | 7 |
Demography, Clinical Settings and Outcomes in Evisceration with Implant: An Electronic Medical Records Driven Analytics of 2071 Cases |
|
| Tarjani Vivek Dave, Anthony Vipin Das, Samir Mohapatra, Oshin Bansal, Anasua Ganguly | | Seminars in Ophthalmology. 2022; : 1 | | [Pubmed] | [DOI] | | | 8 |
Coats disease in India: clinical presentation and outcome in 675 patients (690 Eyes) |
|
| Phuntsho Dorji, Vishal Raval, Subhadra Jalali, Niroj Sahoo, Tapas Ranjan Padhi, Swathi Kaliki, Anthony Vipin Das | | International Ophthalmology. 2022; | | [Pubmed] | [DOI] | | | 9 |
Clinical and Demographic Profile of Uveal Coloboma: A Hospital-Based Study of 14,371 Eyes of 9557 Indian Patients |
|
| Anthony Vipin Das, Divya Rauniyar, Sunita Chaurasia, Subhadra Jalali, Tapas Ranjan Padhi | | American Journal of Ophthalmology. 2022; 242: 1 | | [Pubmed] | [DOI] | | | 10 |
Impact of Implementing Teleophthalmology Referral Guidelines Using the eyeSmart EMR App in 63,703 Patients from India |
|
| Anthony Vipin Das, Rohit C. Khanna, Niranjan Kumar, Padmaja Kumari Rani, Joel Colloc | | International Journal of Telemedicine and Applications. 2022; 2022: 1 | | [Pubmed] | [DOI] | | | 11 |
Vasoproliferative retinal tumors: Clinical presentation and treatment outcome |
|
| Hardik Kiri, Vishal Raval, Hasnat Ali, Anthony Vipin Das, Swathi Kaliki | | European Journal of Ophthalmology. 2022; : 1120672122 | | [Pubmed] | [DOI] | | | 12 |
The landscape of bacterial antibiotic susceptibility in a multi-tier ophthalmology network in India: an electronic medical record driven analytics report |
|
| Anthony Vipin Das, Joveeta Joseph | |
Journal of Medical Microbiology
. 2022; 71(11) | | [Pubmed] | [DOI] | | | 13 |
Realizing the potential of routinely collected data for monitoring eye health services to help achieve universal health coverage |
|
| Jacqueline Ramke, Nyawira Mwangi, Helen Burn, Esmael Habtamu, Clare E Gilbert | | IHOPE Journal of Ophthalmology. 2022; 1: 5 | | [Pubmed] | [DOI] | | | 14 |
Sample size and its evolution in research |
|
| Sai Prashanti Gumpili, Anthony Vipin Das | | IHOPE Journal of Ophthalmology. 2022; 1: 9 | | [Pubmed] | [DOI] | | | 15 |
Year One of COVID-19 Pandemic: Effect on Presentation of Patients With Glaucoma in a Multi-Tier Ophthalmology Network in India |
|
| Anthony Vipin Das, Sirisha Senthil | | Frontiers in Ophthalmology. 2022; 2 | | [Pubmed] | [DOI] | | | 16 |
LV Prasad Eye Institute EyeSmart electronic medical record-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 |
|
| Mudit Tyagi, AnthonyVipin Das, Hrishikesh Kaza, Soumyava Basu, RajeevR Pappuru, Avinash Pathengay, Somasheila Murthy, Hitesh Agrawal | | Indian Journal of Ophthalmology. 2022; 70(4): 1260 | | [Pubmed] | [DOI] | | | 17 |
Retinitis pigmentosa in Laurence-Moon-Bardet-Biedl syndrome in India: Electronic medical records driven big data analytics: Report II |
|
| DeepikaC Parameswarappa, AnthonyV Das, PratimaS Thakur, Brijesh Takkar, PrabhjotK Multani, SrikantK Padhy, MariyaB Doctor, Komal Agarwal, Subhadra Jalali | | Indian Journal of Ophthalmology. 2022; 70(7): 2533 | | [Pubmed] | [DOI] | | | 18 |
Retinitis pigmentosa in Usher syndrome in India: Electronic medical records driven big data analytics: Report III |
|
| DeepikaC Parameswarappa, AnthonyVipin Das, MariyaBashir Doctor, Ramya Natarajan, Komal Agarwal, Subhadra Jalali | | Indian Journal of Ophthalmology. 2022; 70(7): 2540 | | [Pubmed] | [DOI] | | | 19 |
Clinical profile and demographic distribution of ophthalmia nodosa: An electronic medical record-driven big data analytics from a multitier eye care network |
|
| AnthonyV Das, Ragukumar Venugopal, JagadeshC Reddy | | Indian Journal of Ophthalmology. 2022; 70(9): 3266 | | [Pubmed] | [DOI] | | | 20 |
Clinical profile and demographic distribution of Fuchs' endothelial dystrophy: An electronic medical record–driven big data analytics from an eye care network in India |
|
| AnthonyVipin Das, Sunita Chaurasia | | Indian Journal of Ophthalmology. 2022; 70(7): 2415 | | [Pubmed] | [DOI] | | | 21 |
Clinical Profile and Demographic Distribution of Synchysis Scintillans: An Electronic Medical Record-Driven Big Data Analytics From an Eye Care Network in India |
|
| Anthony Vipin Das, Yogita Kadam, Mudit Tyagi | | Cureus. 2022; | | [Pubmed] | [DOI] | | | 22 |
Temporal trend of microsporidial keratoconjunctivitis and correlation with environmental and air pollution factors in India |
|
| AnthonyVipin Das, Sayan Basu | | Indian Journal of Ophthalmology. 2021; 69(5): 1089 | | [Pubmed] | [DOI] | | | 23 |
The impact of COVID-19 “Unlock-I” on L V Prasad Eye Institute Network in Southern India |
|
| VarshaM Rathi, RajeevPappuru Reddy, Merle Fernandes, Suryasnata Rath, Sameera Nayak, JojiPrasad Satya Vemuri, NiranjanKumar Yanamala, Rajashekar Varda, Srinivas Marmamula, AnthonyVipin Das, RohitC Khanna | | Indian Journal of Ophthalmology. 2021; 69(3): 695 | | [Pubmed] | [DOI] | | | 24 |
Effect of COVID-19 Pandemic on Presentation of Patients With Diabetic Retinopathy in a Multitier Ophthalmology Network in India |
|
| Anthony V Das, Raja Narayanan, Padmaja K Rani | | Cureus. 2021; | | [Pubmed] | [DOI] | | | 25 |
Effect of Lockdown and Unlock Following COVID-19 on the Presentation of Patients With Endophthalmitis at a Tertiary Eye Center Over One Year |
|
| Anthony Vipin Das, Vivek Pravin Dave | | Cureus. 2021; | | [Pubmed] | [DOI] | | | 26 |
Waves of COVID-19 Pandemic: Effect on Ocular Surface Services at a Tertiary Eye Center in India |
|
| Anthony Vipin Das, Pragnya Rao, Swapna Shanbhag, Swati Singh, Sayan Basu | | Cureus. 2021; | | [Pubmed] | [DOI] | | | 27 |
Leveraging big data for pattern recognition of socio-demographic and climatic factors in correlation with eye disorders in Telangana State, India |
|
| Amna Alalawi, Les Sztandera, Parth Lalakia, AnthonyVipin Das, SaiPrashanthi Gumpili, Richard Derman | | Indian Journal of Ophthalmology. 2021; 69(7): 1894 | | [Pubmed] | [DOI] | | | 28 |
Demographics and clinical profile of patients with ocular Calotropis poisoning in India |
|
| VarshaM Rathi, AnthonyVipin Das, Mayank Mahajan, RohitC Khanna | | Indian Journal of Ophthalmology. 2021; 69(9): 2417 | | [Pubmed] | [DOI] | | | 29 |
Clinical profile and demographic distribution of pellucid marginal corneal degeneration in India: A study of 559 patients |
|
| AnthonyV Das, LalithaN Pillutla, Sunita Chaurasia | | Indian Journal of Ophthalmology. 2021; 69(12): 3488 | | [Pubmed] | [DOI] | | | 30 |
Clinical profile and demographic distribution of Terrien's marginal degeneration in a multitier ophthalmology network in India |
|
| AnthonyV Das, NimeshikaL Pillutla, Sunita Chaurasia | | Indian Journal of Ophthalmology. 2021; 69(12): 3482 | | [Pubmed] | [DOI] | | | 31 |
Prevalence of chronic disease in older adults in multitier eye-care facilities in South India: Electronic medical records-driven big data analytics report |
|
| UmeshChandra Behera, Brooke Salzman, AnthonyVipin Das, GumpiliSai Prashanthi, Parth Lalakia, Richard Derman, Bharat Panigrahy | | Indian Journal of Ophthalmology. 2021; 69(12): 3618 | | [Pubmed] | [DOI] | | | 32 |
Year one of COVID-19 pandemic in India: Effect of lockdown and unlock on the presentation of patients with infective keratitis at a tertiary eye center |
|
| AnthonyV Das, Sunita Chaurasia, Joveeta Joseph, SomasheilaI Murthy | | Indian Journal of Ophthalmology. 2021; 69(12): 3779 | | [Pubmed] | [DOI] | | | 33 |
Clinical profile and magnitude of diabetic retinopathy: An electronic medical record–driven big data analytics from an eye care network in India |
|
| AnthonyVipin Das, GumpiliSai Prashanthi, Taraprasad Das, Raja Narayanan, PadmajaKumari Rani | | Indian Journal of Ophthalmology. 2021; 69(11): 3110 | | [Pubmed] | [DOI] | | | 34 |
Environmental and Air Pollution Factors Affecting Allergic Eye Disease in Children and Adolescents in India |
|
| Anthony Vipin Das, Sayan Basu | | International Journal of Environmental Research and Public Health. 2021; 18(11): 5611 | | [Pubmed] | [DOI] | | | 35 |
People to policy: The promise and challenges of big data for India |
|
| AnthonyVipin Das | | Indian Journal of Ophthalmology. 2021; 69(11): 3052 | | [Pubmed] | [DOI] | | | 36 |
Ocular Involvement in Sjögren Syndrome: Risk Factors for Severe Visual Impairment and Vision-Threatening Corneal Complications |
|
| Swati Singh, Anthony Vipin Das, Sayan Basu | | American Journal of Ophthalmology. 2021; 225: 11 | | [Pubmed] | [DOI] | | | 37 |
Recent indications of endothelial keratoplasty at a tertiary eye care center in South India |
|
| Anthony Vipin Das, Ashik Mohamed, Sunita Chaurasia | | International Ophthalmology. 2021; 41(10): 3277 | | [Pubmed] | [DOI] | | | 38 |
Clinical profile and microbiological trends of therapeutic keratoplasty at a network of tertiary care ophthalmology centers in India |
|
| Anthony Vipin Das, Sunita Chaurasia, Joveeta Joseph, Aravind Roy, Sujata Das, Merle Fernandes | | International Ophthalmology. 2021; | | [Pubmed] | [DOI] | | | 39 |
Outcomes and complications of evisceration with primary implant: an electronic medical record driven analytics of 1800 cases |
|
| Tarjani Vivek Dave, Anthony Vipin Das, Sameer Mohapatra, Oshin Bansal, Anasua Ganguly | | Orbit. 2021; : 1 | | [Pubmed] | [DOI] | | | 40 |
Clinical Presentation and Demographic Distribution of Retinitis Pigmentosa in India and Implications for Potential Treatments: Electronic Medical Records Driven Big Data Analytics: Report I |
|
| Deepika C Parameswarappa, Anthony Vipin Das, Vivek Pravin Dave, Komal Agarwal, Ramya Natarajan, Subhadra Jalali | | Seminars in Ophthalmology. 2021; : 1 | | [Pubmed] | [DOI] | | | 41 |
Patterns of Non-Infectious Scleritis across a Tertiary Eye Care Network Using the Indigenously Developed Electronic Medical Record System-eyeSmart |
|
| Somasheila I Murthy, Anthony Vipin Das, Priyanka Kammari, Aravind Roy, Soumyava Basu, Merle Fernandes, Varsha M Rathi, Mudit Tyagi | | Ocular Immunology and Inflammation. 2021; : 1 | | [Pubmed] | [DOI] | | | 42 |
Biogeographical and Altitudinal Distribution of Cataract: A Nine-Year Experience Using Electronic Medical Record-Driven Big Data Analytics in India |
|
| Hannah Garrigan, Cristos Ifantides, Gumpili Sai Prashanthi, Anthony Vipin Das | | Ophthalmic Epidemiology. 2021; 28(5): 392 | | [Pubmed] | [DOI] | | | 43 |
Clinical Profile and Demographic Distribution of Corneal Dystrophies in India: A Study of 4198 Patients |
|
| Anthony Vipin Das, Sunita Chaurasia | | Cornea. 2021; 40(5): 548 | | [Pubmed] | [DOI] | | | 44 |
Dry eye disease in children and adolescents in India |
|
| Pragnya Rao Donthineni, Anthony Vipin Das, Sayan Basu | | The Ocular Surface. 2020; 18(4): 777 | | [Pubmed] | [DOI] | | | 45 |
Tele-consultations and electronic medical records driven remote patient care: Responding to the COVID-19 lockdown in India |
|
| AnthonyV Das, PadmajaK Rani, PravinK Vaddavalli | | Indian Journal of Ophthalmology. 2020; 68(6): 1007 | | [Pubmed] | [DOI] | | | 46 |
Pivoting to teleconsultation for paediatric ophthalmology and strabismus: Our experience during COVID-19 times |
|
| AjinkyaV Deshmukh, Akshay Badakere, Jenil Sheth, Manjushree Bhate, Sampada Kulkarni, Ramesh Kekunnaya | | Indian Journal of Ophthalmology. 2020; 68(7): 1387 | | [Pubmed] | [DOI] | | | 47 |
Demographics and clinical presentation of patients with ocular disorders during the COVID-19 lockdown in India: A report |
|
| AnthonyVipin Das, Raja Narayanan | | Indian Journal of Ophthalmology. 2020; 68(7): 1393 | | [Pubmed] | [DOI] | | | 48 |
Commentary: Electronic medical record system – should complement but not replace traditional health care |
|
| Atul Kumar, Dheepak Sundar, Divya Agarwal | | Indian Journal of Ophthalmology. 2020; 68(3): 432 | | [Pubmed] | [DOI] | | | 49 |
Cataract Surgery in Dry Eye Disease: Visual Outcomes and Complications |
|
| Pragnya R. Donthineni, Anthony V. Das, Swapna S. Shanbhag, Sayan Basu | | Frontiers in Medicine. 2020; 7 | | [Pubmed] | [DOI] | | | 50 |
Epidemic keratoconjunctivitis in India: electronic medical records-driven big data analytics report IV |
|
| Anthony Vipin Das, Sayan Basu | | British Journal of Ophthalmology. 2020; : bjophthalm | | [Pubmed] | [DOI] | | | 51 |
Ophthatome™: an integrated knowledgebase of ophthalmic diseases for translating vision research into the clinic |
|
| Praveen Raj, Sushma Tejwani, Dandayudhapani Sudha, B. Muthu Narayanan, Chandrasekar Thangapandi, Sankar Das, J. Somasekar, Susmithasane Mangalapudi, Durgesh Kumar, Narendra Pindipappanahalli, Rohit Shetty, Arkasubhra Ghosh, Govindasamy Kumaramanickavel, Amitabha Chaudhuri, Nagasamy Soumittra | | BMC Ophthalmology. 2020; 20(1) | | [Pubmed] | [DOI] | | | 52 |
Demography and Clinical Features of Chalazion Among Patients Seen at a Multi-Tier Eye Care Network in India: An Electronic Medical Records Driven Big Data Analysis Report |
|
| Anthony Vipin Das, Tarjani Vivek Dave | | Clinical Ophthalmology. 2020; Volume 14: 2163 | | [Pubmed] | [DOI] | | | 53 |
Automated Categorization of Systemic Disease and Duration From Electronic Medical Record System Data Using Finite-State Machine Modeling: Prospective Validation Study |
|
| Gumpili Sai Prashanthi, Ayush Deva, Ranganath Vadapalli, Anthony Vipin Das | | JMIR Formative Research. 2020; 4(12): e24490 | | [Pubmed] | [DOI] | | | 54 |
Electronic medical records – The good, the bad and the ugly |
|
| SantoshG Honavar | | Indian Journal of Ophthalmology. 2020; 68(3): 417 | | [Pubmed] | [DOI] | | | 55 |
Impact of COVID-19-related lockdown-I on a network of rural eye centres in Southern India |
|
| VarshaM Rathi, AnthonyVipin Das, RohitC Khanna | | Indian Journal of Ophthalmology. 2020; 68(11): 2396 | | [Pubmed] | [DOI] | | | 56 |
Epidemic Keratoconjunctivitis in India: Trend Analysis and Implications for Viral Outbreaks |
|
| AnthonyV Das, Sayan Basu | | Indian Journal of Ophthalmology. 2020; 68(5): 732 | | [Pubmed] | [DOI] | |
|
 |
|
|
|
.png "Santen")
Search Pubmed for