- Email: [email protected]
The 2016 American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight) Database
The 2016 American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) Database Characteristics and Methods Michael F. Chiang, MD,1 Alfred Sommer, MD, MHS,2 William L. Rich, MD,3 Flora Lum, MD,3 David W. Parke II, MD3 Purpose: To describe the characteristics of the patient population included in the 2016 IRIS Registry (Intelligent Research in Sight) database for analytic aims. Design: Description of a clinical data registry. Participants: The 2016 IRIS Registry database consists of 17 363 018 unique patients from 7200 United Statesebased ophthalmologists in the United States. Methods: Electronic health record (EHR) data were extracted from the participating practices and placed into a clinical database. The approach can be used across dozens of EHR systems. Main Outcome Measures: Demographic characteristics. Results: The 2016 IRIS Registry database includes data about patient demographics, top-coded disease conditions, and visit rates. Conclusions: The IRIS Registry is a unique, large, real-world data set that is available for analytics to provide perspectives and to learn about current ophthalmic care and treatment outcomes. The IRIS Registry can be used to answer questions about practice patterns, use, disease prevalence, clinical outcomes, and the comparative effectiveness of different treatments. Limitations of the data are the same limitations associated with EHR data in terms of documentation errors or missing data and the lack of images. Currently, open access to the database is not available, but there are opportunities for researchers to submit proposals for analyses, for example through a Research to Prevent Blindness and American Academy of Ophthalmology Award for IRIS Registry Research. Ophthalmology 2017;-:1e6 ª 2017 by the American Academy of Ophthalmology
The American Academy of Ophthalmology launched the IRIS Registry (Intelligent Research in Sight) on March 24, 2014, after a 12-month pilot period, and by December 31, 2016, it included the electronic health record (EHR) integrated practices of 7200 United Statesebased ophthalmologists. This unique data set will provide the opportunity for investigation of numerous aspects of ophthalmic practice, ranging from identifying populations that receive or do not receive adequate clinical services to recognizing which treatments and method of delivery provide optimal outcomes in real-world clinical settings. Recognizing the unique opportunities this data set provides, the Academy is in the process of establishing a set of guidelines and incentives to maximize the scientific value of these data. The guidelines will ensure that those trained in relevant clinical and data analytic techniques examine the data thoroughly to learn how best to deliver ophthalmic clinical care. The basic methods of data extraction and characteristics of the database are common to these examinations. This article describes the process the IRIS Registry uses to extract data from EHRs, the rapid growth in the ª 2017 by the American Academy of Ophthalmology Published by Elsevier Inc.
number and types of practices represented in the data set, and the characteristics of the patients seen by the EHR practices that used the IRIS Registry as of December 31, 2016. Future studies using the IRIS Registry database will need only to enumerate changes in the characteristics of the practices that make up the data set at the time of the study. Future annual reports on the status of the IRIS Registry will describe relevant characteristics of the patient population. The Academy plans to freeze the data set at the start of each year so that all studies conducted on the identified data set will have identical demographics. Some studies undoubtedly will investigate changes in practice and outcomes over time, which these annual demographic updates will inform. In 1995, the Academy embarked on its first outcomes registry, the National Eye Care Outcomes Network, to improve the quality of eye care. This registry depended on manual data entry and never attained more than 20 000 patients before the advent of the broad adoption of EHR systems. The Academy remained committed to measuring quality and advancing the scientific base of knowledge for the profession to ensure optimal clinical outcomes and to https://doi.org/10.1016/j.ophtha.2017.12.001 ISSN 0161-6420/17
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Ophthalmology Volume -, Number -, Month 2017
Figure 1. Illustration showing the data pull method of the IRIS Registry.
create a learning health system for ophthalmologists, which is now possible with the IRIS Registry database. The primary purposes of the IRIS Registry are to drive quality improvement, to introduce the concept of population health, and to generate new scientific knowledge. To achieve this, metrics in the form of quality measures have been developed to evaluate the quality of care provided to a patient population and for research purposes. Quality measures also can be used to demonstrate compliance with federal government reporting requirements and to receive a value-based payment adjustment. Individual physician participants can monitor their own performance rates on quality measures and also benefit from benchmarking against the average of performance rates on quality measures of other reporting physicians in the country. Academy members rapidly joined the IRIS Registry, most likely because it eliminates the need for most practices to enter data manually, it greatly simplifies meeting the increasing complex quality reporting requirements by the federal government, and it provides tools and platforms that simplify the collection of data from the EHRs of individual practices and the analysis of big data. However, for practices without an EHR system, the IRIS Registry provides a manual web portal for entering data to submit quality measures to the federal government, but their data are not included in the analytic database.
Methods In September 2012, the Academy Board of Trustees approved the development of an Academy registry where clinical data could be pooled from the EHRs of interested members. An Academy committee sought an approach that minimized the work of participating members, and it settled on a technique that extracts data from EHRs. This technique can pull data from each participant’s EHR or data from each participant’s EHR vendor can be sent to the registry without the participant needing to engage in the process. After an ophthalmologist member agrees to participate in the IRIS Registry, the registry vendor maps the quality measures and other data to the relevant fields in the member’s EHR through a process that relies on the vendor’s software (Registry Practice Connector; FIGmd, Rockford, IL).
2
FIGmd’s data extraction can work across most client-server EHRs. This software is installed on the same server of the practice that houses the practice’s EHR database and extracts the data into the IRIS Registry on a periodic basis, generally when the practice is not engaging the server for clinical care tasks. The data then are uploaded onto a secure cloud-based site (AWS Gov Cloud; Amazon Web Services, Seattle, WA), where the protected health information is stored separately; the remaining de-identified information is stored in the IRIS Registry database. This method of data extraction into the IRIS Registry is shown in Figure 1. Cloudbased EHRs either can send files or can store files on a separate server that can be accessed by the IRIS Registry vendor. These files are transmitted in a standardized format, known as the HL7 Clinical Data Architecture standard, and the information then is stored in the IRIS Registry database. Data are stored in the IRIS Registry database in defined fields (Table 1). These fields include patient demographics, payers, results in terms of clinical findings and diagnostic test values, and codes for diagnoses and for procedures. The IRIS Registry also integrates with the practice management system if it is separate from the EHR system to obtain accurate information on billed visits (i.e., Current Procedural Terminology and International Classification of Diseases codes). To avoid double counting patients as they move from practice to practice, the IRIS Registry assigns each patient a unique identifier. The data in the IRIS Registry database are collected from the records signed off by the practices. These completed records are considered the legal record, and thus, there is no other source for comparison purposes. There are checks put in place for values that are outside the appropriate limits so that these are not included for analytic purposes. However, the IRIS Registry does not have any authority to request any changes or additions to the completed medical records. Individual patient consent is not required for the use of the aggregate, de-identified data in a clinical data registry, according to the Health Insurance Portability and Accountability Act Standards for Privacy of Individually Identifiable Health Information, because the information is no longer protected health information.
Table 1. Defined Fields in the IRIS Patient demographics (including year of birth, year of death, gender, race, ethnicity) Payers (including information about insurance plans: private, Medicare fee for service, Medicare Managed Care, Medicaid, military, other, no insurance, effective date) Allergies and adverse reactions (including immunizations, patient allergy description, and status) Social history (including smoking history and other observations) Encounters (including billed and unbilled visits, usually described by CPT codes) Results (including all ocular examination laterality and values, such as visual acuity, cup-to-disc ratio, intraocular pressure, optic nerve appearance, fundus exam observables) Problems (including the diagnoses, both ocular and systemic, usually described by ICD codes) Procedures (including the billed procedures performed, usually described by CPT or HCPCS codes and laterality) Medications (including drug name, code, dosage, route) CPT ¼ Current Procedural Terminology; HCPCS ¼ Healthcare Common Procedure Coding System; ICD ¼ International Classification of Diseases. A more detailed data dictionary is available at: https://www.aao.org/irisregistry/data-analysis/requirements.
Chiang et al
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Table 2. Characteristics of Patients in the 2016 IRIS Registry Database Unique Patients Mean age (yrs) Gender Female Male Missing Race White Black Asian American Indian/Alaskan Native Native Hawaiian/Pacific Islander Unknown/missing/other/declined/multiracial Health insurance Medicare and Medicaid dual eligible Medicare fee for service Medicare managed Private Medicaid Government/other No insurance/self-pay/unknown Geographic region Northeast New England Mid Atlantic Midwest East north central West north central South South Atlantic East south central West south central West Mountain Pacific United States territories Unknown
No. (n [ 17 363 018)
%
% (United States Census)
10 212 208 7 129 848 20 962
58.8 41.1 0.1
49.2 (2015) 50.8 (2015) 0 (2015)
12 123 421 1 211 116 494 030 61 483 32 350 3 440 618
69.8 7.0 2.8 0.4 0.2 19.8
73.6 (2015) 12.6 (2015) 5.1 (2015) 0.1 0.1 4.7 (2015)
669 193 5 801 706 1 490 418 6 485 438 940 605 241 941 1 731 617
3.9 33.4 8.6 37.4 5.4 1.4 10.0
16.7 (all Medicare; 2016) See above 67.5 (2016) 19.4 (2016) 4.6 (military; 2016) 8.8 (2016)
3 594 527 940 082 2 654 445 3 780 871 2 449 571 1 331 300 6 601 883 3 682 987 863 400 2 055 496 3 208 967 1 273 842 1 935 125 113 293 61 377
20.7 5.4 15.3 21.8 14.1 7.7 38.0 21.2 5.0 11.8 18.5 7.3 11.1 0.7 0.4
60.1
Results Growth of the Database There has been rapid growth in the number of participants and records accumulated in the IRIS Registry, from 17 million visits at the end of 2014 to more than 118 million visits at the end of 2016. As the number of users grew, many of the demographic characteristics of patients in the database changed, particularly patient age and geographic distribution. By December 2015, the demographic characteristics stabilized, which makes the data for 2016, covering 2307 ophthalmic practices in the United States, reasonably representative of the present status.
17.4 (2016)
21.0 (2016)
37.9 (2016)
23.7 (2016)
retinopathy, dry eye, or glaucoma (Table 3). A total of 1 683 829 cataract surgeries and 2 464 043 intravitreal injections (antievascular endothelial growth factor and corticosteroid injections) were performed in this period. Table 4 lists the 20 most common eye conditions in the 2016 IRIS Registry database according to International Classification of Diseases, Ninth Edition, codes. In 2016, a total of 10 120 providers, including 7200 individual ophthalmologists, 2700 optometrists (working in ophthalmologists’ practices), and 220 other eligible providers, including nurses, physician assistants, and certified registered nurse anesthetists, contributed data from their EHRs. This represents a total of 42% of active ophthalmologists (estimated at 17 000) in the United States.
Description of the Database Between January 1 and December 31, 2016, the IRIS Registry database included data on a total of 36 799 984 visits from 17 363 018 unique patients. Table 2 shows the demographic characteristics of these patients. The comparison with the United States Census1,2 shows that there are more female patients in the IRIS Registry database and that the proportion of races and health insurance plans also differs from the general United States population. During 2016, more than 1 million patients sought treatment with 1 of 4 conditions: age-related macular degeneration, diabetic
Table 3. Patients with Major Eye Conditions in the 2016 IRIS Registry Database Condition Age-related macular degeneration Diabetic retinopathy Dry-eye syndrome Glaucoma
No. of Patients 1 1 1 2
684 014 222 229
451 506 103 802
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Ophthalmology Volume -, Number -, Month 2017 Table 4. The 20 Most Common Eye Conditions in the 2016 IRIS Registry Database According to International Classification of Diseases, Ninth Edition, Code International Classification of Diseases, Ninth Edition, Code 366.16 375.15 379.21 362.51 367 367.4 365.01 362.56 367.1 250.5 365.11 379.24 366.19 362.57 367.21 373 362.52 366.53 366.15 365
Eye Condition
No. of Patients
% of Total
Senile nuclear sclerosis Tear film insufficiency, unspecified Vitreous degeneration Nonexudative macular degeneration Hypermetropia Presbyopia Open angle with borderline findings, low risk Macular puckering Myopia Diabetes with ophthalmic manifestations, type II, or unspecified type, not stated as uncontrolled Primary open-angle glaucoma Other vitreous opacities Other and combined forms of senile cataract Drusen (degenerative) Regular astigmatism Blepharitis, unspecified Exudative senile macular degeneration After-cataract, obscuring vision Cortical senile cataract Preglaucoma, unspecified
4 463 595 1 597 986 1 448 356 1 305 190 1 303 895 1 255 249 1 185 430 1 155 722 1 131 944 813 307
25.7 9.2 8.3 7.5 7.5 7.2 6.8 6.7 6.5 4.7
Also, another 4707 providers registered with the IRIS Registry participated via manual entry. These providers also might have had an EHR, but were unable to integrate it with the IRIS Registry in 2016. An estimated 70% of all active ophthalmologists in the United States (11 900 of 17 000) had adopted EHRs by the end of 2016. The average IRIS Registry practice with an integrated EHR contained 4.2 providers compared with an average of 1.8 providers
687 617 597 536 519 502 501 491 441 389
957 801 492 178 016 961 427 375 007 508
in the non-EHR practices in the IRIS Registry. The distribution of EHR practices in the IRIS Registry database as of December 31, 2016, is depicted in Figure 2. The age distribution of patients in the IRIS Registry database is shown in Figure 3, with the largest single age group being 45 to 54 years of age. Approximately 42% of visits were for patients 65 years of age and older.
Figure 2. Map showing the distribution of electronic health record integrated practices in the 2016 IRIS Registry Database.
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4.0 3.6 3.4 3.1 3.0 2.9 2.9 2.8 2.5 2.2
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2016 AAO IRIS Registry Database
Figure 3. Bar graph showing the age distribution of patients included in the 2016 IRIS database.
Discussion The IRIS Registry is a real-world database representing many ophthalmic EHR-based practices around the country. The comprehensiveness and breadth of the data varies across EHR systems, particularly for nonophthalmology EHR systems compared with ophthalmology systems, and varies across practices. The data reflect the real-world setting of ophthalmic care and are subject to the same documentation and coding errors and missing data as found in EHR data, including medication data.3e6 Another data source on eye care is the National Ambulatory Medical Care Survey, a national survey designed to meet the need for objective, reliable information about the provision and use of ambulatory medical care services in the United States.7 Findings are based on a sample of visits to nonfederally employed office-based physicians who are engaged primarily in direct patient care. The distribution of visits by age in the 2010 National Ambulatory Medical Care Survey was as follows: 28% for patients 45 to 64 years of age, 25% for patients 65 to 74 years of age, and 29% for patients 75 years of age and older. The comparison of patients in the IRIS Registry database with the National Ambulatory Medical Care Survey findings suggests that the age distribution is similar to what would be expected for patients seeing ophthalmologists, but as expected, not similar to the general United States population. The 1 group of practices greatly underrepresented in the IRIS Registry database is academic medical centers, which make up only 1% of IRIS Registryecontracted practices, but 11% of all practices in the United States. Given the lack of participation from practices without EHRs and academic medical centers using Epic software (Epic Systems Corp, Verona, WI), the 2016 IRIS Registry database is not representative of the entire spectrum of ophthalmic care in the United States. The IRIS Registry includes fewer patients from inner-city urban neighborhoods and rural areas and fewer patients with a higher severity of disease or rare diseases than those seen at academic medical centers. Several academic medical centers using Epic software have become IRIS participants during 2017, which likely will shift the future description of clinical practices. The EHRs of practices do not commonly include hospital inpatient or outpatient department operating room data, and ambulatory surgery centers often do not have EHRs. Therefore, operating room information about anesthesia, specific devices, or adjuncts used (including intraocular lenses that have been implanted), for example, are not captured routinely
in the IRIS Registry. At present, EHRs commonly do not contain actual images or their metrics, although many do allow input of image interpretation and findings. Nonophthalmic and even ophthalmic EHRs often lack structured fields for many ocular parameters, such as the grading of cells in the anterior chamber in uveitis, ocular alignment in strabismus, or reattachment of the retina after retinal detachment. The IRIS Registry database is a valuable source of data for researchers and analysts, but open access currently is not available because of high demand and the complexity of performing analytics. There are several approaches for requesting IRIS Registry database analytics. A request-forproposals process was open for research analytic teams that have the experience and expertise to access the IRIS Registry data and perform their own projects using external funding. Subspecialty societies may provide funding opportunities for individual investigators or research analytic teams. Individual investigators can apply for funding from a joint Research to Prevent Blindness and American Academy of Ophthalmology Award for IRIS Registry Research that would provide training and access to a defined data set in the database. A Hoskins Center IRIS Registry Research Fund also is available for individual investigators, and the analytics would be performed by Academy staff. These data already have been used in publications in the peer-reviewed literature that have addressed topics related to cataract surgery8 and myopic choroidal neovascularization.9,10 Information about these analytic opportunities can be found at https://www.aao.org/ iris-registry/data-analysis/requirements. The IRIS Registry already includes an estimated 42% of ophthalmologists, and it continues to grow as the number of ophthalmic practices that adopt EHRs increases and academic medical centers using Epic as their EHR join the system. During 2017, it is anticipated that the number of ophthalmologists in the IRIS Registry database will grow to approximately 9000. The IRIS Registry database includes data on clinical outcomes, such as visual acuity, intraocular pressure, cup-to-disc ratio, disease severity, and complications that can be evaluated to describe the baseline characteristics of the patient population. As a result of its size and the inclusion of both administrative and clinical data, the IRIS Registry is a resource for gaining insights about realworld practice patterns, the natural history of disease, disease prevalence, and clinical outcomes.
References 1. United States Census Bureau. ACS demographic and housing estimates: 2011e2015 American Community Survey 5-year estimates. Available at: https://factfinder.census.gov/ faces/tableservices/jsf/pages/productview.xhtml?pid¼ACS _15_5YR_DP05&src¼pt; 2015. Accessed October 10, 2017. 2. Barnett JC, Berchick ER. Health insurance coverage in the United States: 2016. Available at: https://www.census.gov/library/publications/2017/demo/p60e260.html; 2017. Accessed October 10, 2017. 3. Wagner MM, Hogan WR. The accuracy of medication data in an outpatient electronic medical record. J Am Med Inform Assoc. 1996;3:234e244.
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Ophthalmology Volume -, Number -, Month 2017 4. Peabody JW, Luck J, Jain S, et al. Assessing the accuracy of administrative data in health information systems. Med Care. 2004;42:1056e1072. 5. Biggerstaff KS, Frankfort BJ, Orengo-Nania S. Validity of code based algorithms to identify primary open angle glaucoma (POAG) in Veterans Affairs (VA) administrative databases. Ophthalmic Epidemiol. 2017:1e7. 6. Pimental MA, Browne EN, Janardhana PM, et al. Assessment of the accuracy of using ICD-9 codes to identify uveitis, herpes zoster ophthalmicus, scleritis, and episcleritis. JAMA Ophthalmol. 2016;134:1001e1006. 7. Centers for Disease Control and Prevention. 2010 National Ambulatory Medical Care Survey factsheet: ophthalmology.
Available at: https://www.cdc.gov/nchs/data/ahcd/namcs_2010_ factsheet_ophthalmology.pdf; 2010. Accessed October 10, 2017. 8. Coleman AL. How big data informs us about cataract surgery: The LXXII Edward Jackson Memorial Lecture. Am J Ophthalmol. 2015;160:1091e1103. 9. Willis JR, Vitale S, Morse L, et al. The prevalence of myopic choroidal neovascularization in the United States: analysis of the IRIS Data Registry and NHANES. Ophthalmology. 2016;123:1771e1782. 10. Willis JR, Morse L, Vitale S, et al. Treatment patterns for myopic choroidal neovascularization in the United States: analysis of the IRIS Registry. Ophthalmology. 2017;124: 935e943.
Footnotes and Financial Disclosures Originally received: August 30, 2017. Final revision: November 9, 2017. Accepted: December 1, 2017. Available online: ---. 1
HUMAN SUBJECTS: This study does not include human subjects/tissues. Author Contributions: Manuscript no. 2017-2015.
Oregon Health and Science University, Portland, Oregon.
2
Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 3 American Academy of Ophthalmology, San Francisco, California. Financial Disclosure(s): The author(s) have made the following disclosure(s): M.F.C.: Grants e National Institutes of Health; Supported by unrestricted departmental funding from the Research to Prevent Blindness (New York, NY); unpaid member of the Scientific Advisory board for Clarity Medical Systems (Pleasanton, CA); Consultant e Novartis (Basel, Switzerland). Flora Lum and David Parke are employees of the American Academy of Ophthalmology, and the IRIS registry is owned by the American Academy of Ophthalmology.
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Conception and design: Chiang, Sommer, Rich, Lum, Parke Analysis and interpretation: Chiang, Sommer, Rich, Lum, Parke Data collection: Rich, Lum, Parke Obtained funding: none Overall responsibility: Chiang, Sommer, Rich, Lum, Parke Abbreviations and Acronyms: EHR ¼ electronic health record; IRIS ¼ Intelligent Research in Sight. Correspondence: Flora Lum, MD, American Academy of Ophthalmology, 655 Beach Street, San Francisco, CA 94019. E-mail: fl[email protected].
The American Academy of Ophthalmology launched the IRIS Registry (Intelligent Research in Sight) on March 24, 2014, after a 12-month pilot period, and by December 31, 2016, it included the electronic health record (EHR) integrated practices of 7200 United Statesebased ophthalmologists. This unique data set will provide the opportunity for investigation of numerous aspects of ophthalmic practice, ranging from identifying populations that receive or do not receive adequate clinical services to recognizing which treatments and method of delivery provide optimal outcomes in real-world clinical settings. Recognizing the unique opportunities this data set provides, the Academy is in the process of establishing a set of guidelines and incentives to maximize the scientific value of these data. The guidelines will ensure that those trained in relevant clinical and data analytic techniques examine the data thoroughly to learn how best to deliver ophthalmic clinical care. The basic methods of data extraction and characteristics of the database are common to these examinations. This article describes the process the IRIS Registry uses to extract data from EHRs, the rapid growth in the ª 2017 by the American Academy of Ophthalmology Published by Elsevier Inc.
number and types of practices represented in the data set, and the characteristics of the patients seen by the EHR practices that used the IRIS Registry as of December 31, 2016. Future studies using the IRIS Registry database will need only to enumerate changes in the characteristics of the practices that make up the data set at the time of the study. Future annual reports on the status of the IRIS Registry will describe relevant characteristics of the patient population. The Academy plans to freeze the data set at the start of each year so that all studies conducted on the identified data set will have identical demographics. Some studies undoubtedly will investigate changes in practice and outcomes over time, which these annual demographic updates will inform. In 1995, the Academy embarked on its first outcomes registry, the National Eye Care Outcomes Network, to improve the quality of eye care. This registry depended on manual data entry and never attained more than 20 000 patients before the advent of the broad adoption of EHR systems. The Academy remained committed to measuring quality and advancing the scientific base of knowledge for the profession to ensure optimal clinical outcomes and to https://doi.org/10.1016/j.ophtha.2017.12.001 ISSN 0161-6420/17
1
Ophthalmology Volume -, Number -, Month 2017
Figure 1. Illustration showing the data pull method of the IRIS Registry.
create a learning health system for ophthalmologists, which is now possible with the IRIS Registry database. The primary purposes of the IRIS Registry are to drive quality improvement, to introduce the concept of population health, and to generate new scientific knowledge. To achieve this, metrics in the form of quality measures have been developed to evaluate the quality of care provided to a patient population and for research purposes. Quality measures also can be used to demonstrate compliance with federal government reporting requirements and to receive a value-based payment adjustment. Individual physician participants can monitor their own performance rates on quality measures and also benefit from benchmarking against the average of performance rates on quality measures of other reporting physicians in the country. Academy members rapidly joined the IRIS Registry, most likely because it eliminates the need for most practices to enter data manually, it greatly simplifies meeting the increasing complex quality reporting requirements by the federal government, and it provides tools and platforms that simplify the collection of data from the EHRs of individual practices and the analysis of big data. However, for practices without an EHR system, the IRIS Registry provides a manual web portal for entering data to submit quality measures to the federal government, but their data are not included in the analytic database.
Methods In September 2012, the Academy Board of Trustees approved the development of an Academy registry where clinical data could be pooled from the EHRs of interested members. An Academy committee sought an approach that minimized the work of participating members, and it settled on a technique that extracts data from EHRs. This technique can pull data from each participant’s EHR or data from each participant’s EHR vendor can be sent to the registry without the participant needing to engage in the process. After an ophthalmologist member agrees to participate in the IRIS Registry, the registry vendor maps the quality measures and other data to the relevant fields in the member’s EHR through a process that relies on the vendor’s software (Registry Practice Connector; FIGmd, Rockford, IL).
2
FIGmd’s data extraction can work across most client-server EHRs. This software is installed on the same server of the practice that houses the practice’s EHR database and extracts the data into the IRIS Registry on a periodic basis, generally when the practice is not engaging the server for clinical care tasks. The data then are uploaded onto a secure cloud-based site (AWS Gov Cloud; Amazon Web Services, Seattle, WA), where the protected health information is stored separately; the remaining de-identified information is stored in the IRIS Registry database. This method of data extraction into the IRIS Registry is shown in Figure 1. Cloudbased EHRs either can send files or can store files on a separate server that can be accessed by the IRIS Registry vendor. These files are transmitted in a standardized format, known as the HL7 Clinical Data Architecture standard, and the information then is stored in the IRIS Registry database. Data are stored in the IRIS Registry database in defined fields (Table 1). These fields include patient demographics, payers, results in terms of clinical findings and diagnostic test values, and codes for diagnoses and for procedures. The IRIS Registry also integrates with the practice management system if it is separate from the EHR system to obtain accurate information on billed visits (i.e., Current Procedural Terminology and International Classification of Diseases codes). To avoid double counting patients as they move from practice to practice, the IRIS Registry assigns each patient a unique identifier. The data in the IRIS Registry database are collected from the records signed off by the practices. These completed records are considered the legal record, and thus, there is no other source for comparison purposes. There are checks put in place for values that are outside the appropriate limits so that these are not included for analytic purposes. However, the IRIS Registry does not have any authority to request any changes or additions to the completed medical records. Individual patient consent is not required for the use of the aggregate, de-identified data in a clinical data registry, according to the Health Insurance Portability and Accountability Act Standards for Privacy of Individually Identifiable Health Information, because the information is no longer protected health information.
Table 1. Defined Fields in the IRIS Patient demographics (including year of birth, year of death, gender, race, ethnicity) Payers (including information about insurance plans: private, Medicare fee for service, Medicare Managed Care, Medicaid, military, other, no insurance, effective date) Allergies and adverse reactions (including immunizations, patient allergy description, and status) Social history (including smoking history and other observations) Encounters (including billed and unbilled visits, usually described by CPT codes) Results (including all ocular examination laterality and values, such as visual acuity, cup-to-disc ratio, intraocular pressure, optic nerve appearance, fundus exam observables) Problems (including the diagnoses, both ocular and systemic, usually described by ICD codes) Procedures (including the billed procedures performed, usually described by CPT or HCPCS codes and laterality) Medications (including drug name, code, dosage, route) CPT ¼ Current Procedural Terminology; HCPCS ¼ Healthcare Common Procedure Coding System; ICD ¼ International Classification of Diseases. A more detailed data dictionary is available at: https://www.aao.org/irisregistry/data-analysis/requirements.
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2016 AAO IRIS Registry Database
Table 2. Characteristics of Patients in the 2016 IRIS Registry Database Unique Patients Mean age (yrs) Gender Female Male Missing Race White Black Asian American Indian/Alaskan Native Native Hawaiian/Pacific Islander Unknown/missing/other/declined/multiracial Health insurance Medicare and Medicaid dual eligible Medicare fee for service Medicare managed Private Medicaid Government/other No insurance/self-pay/unknown Geographic region Northeast New England Mid Atlantic Midwest East north central West north central South South Atlantic East south central West south central West Mountain Pacific United States territories Unknown
No. (n [ 17 363 018)
%
% (United States Census)
10 212 208 7 129 848 20 962
58.8 41.1 0.1
49.2 (2015) 50.8 (2015) 0 (2015)
12 123 421 1 211 116 494 030 61 483 32 350 3 440 618
69.8 7.0 2.8 0.4 0.2 19.8
73.6 (2015) 12.6 (2015) 5.1 (2015) 0.1 0.1 4.7 (2015)
669 193 5 801 706 1 490 418 6 485 438 940 605 241 941 1 731 617
3.9 33.4 8.6 37.4 5.4 1.4 10.0
16.7 (all Medicare; 2016) See above 67.5 (2016) 19.4 (2016) 4.6 (military; 2016) 8.8 (2016)
3 594 527 940 082 2 654 445 3 780 871 2 449 571 1 331 300 6 601 883 3 682 987 863 400 2 055 496 3 208 967 1 273 842 1 935 125 113 293 61 377
20.7 5.4 15.3 21.8 14.1 7.7 38.0 21.2 5.0 11.8 18.5 7.3 11.1 0.7 0.4
60.1
Results Growth of the Database There has been rapid growth in the number of participants and records accumulated in the IRIS Registry, from 17 million visits at the end of 2014 to more than 118 million visits at the end of 2016. As the number of users grew, many of the demographic characteristics of patients in the database changed, particularly patient age and geographic distribution. By December 2015, the demographic characteristics stabilized, which makes the data for 2016, covering 2307 ophthalmic practices in the United States, reasonably representative of the present status.
17.4 (2016)
21.0 (2016)
37.9 (2016)
23.7 (2016)
retinopathy, dry eye, or glaucoma (Table 3). A total of 1 683 829 cataract surgeries and 2 464 043 intravitreal injections (antievascular endothelial growth factor and corticosteroid injections) were performed in this period. Table 4 lists the 20 most common eye conditions in the 2016 IRIS Registry database according to International Classification of Diseases, Ninth Edition, codes. In 2016, a total of 10 120 providers, including 7200 individual ophthalmologists, 2700 optometrists (working in ophthalmologists’ practices), and 220 other eligible providers, including nurses, physician assistants, and certified registered nurse anesthetists, contributed data from their EHRs. This represents a total of 42% of active ophthalmologists (estimated at 17 000) in the United States.
Description of the Database Between January 1 and December 31, 2016, the IRIS Registry database included data on a total of 36 799 984 visits from 17 363 018 unique patients. Table 2 shows the demographic characteristics of these patients. The comparison with the United States Census1,2 shows that there are more female patients in the IRIS Registry database and that the proportion of races and health insurance plans also differs from the general United States population. During 2016, more than 1 million patients sought treatment with 1 of 4 conditions: age-related macular degeneration, diabetic
Table 3. Patients with Major Eye Conditions in the 2016 IRIS Registry Database Condition Age-related macular degeneration Diabetic retinopathy Dry-eye syndrome Glaucoma
No. of Patients 1 1 1 2
684 014 222 229
451 506 103 802
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Ophthalmology Volume -, Number -, Month 2017 Table 4. The 20 Most Common Eye Conditions in the 2016 IRIS Registry Database According to International Classification of Diseases, Ninth Edition, Code International Classification of Diseases, Ninth Edition, Code 366.16 375.15 379.21 362.51 367 367.4 365.01 362.56 367.1 250.5 365.11 379.24 366.19 362.57 367.21 373 362.52 366.53 366.15 365
Eye Condition
No. of Patients
% of Total
Senile nuclear sclerosis Tear film insufficiency, unspecified Vitreous degeneration Nonexudative macular degeneration Hypermetropia Presbyopia Open angle with borderline findings, low risk Macular puckering Myopia Diabetes with ophthalmic manifestations, type II, or unspecified type, not stated as uncontrolled Primary open-angle glaucoma Other vitreous opacities Other and combined forms of senile cataract Drusen (degenerative) Regular astigmatism Blepharitis, unspecified Exudative senile macular degeneration After-cataract, obscuring vision Cortical senile cataract Preglaucoma, unspecified
4 463 595 1 597 986 1 448 356 1 305 190 1 303 895 1 255 249 1 185 430 1 155 722 1 131 944 813 307
25.7 9.2 8.3 7.5 7.5 7.2 6.8 6.7 6.5 4.7
Also, another 4707 providers registered with the IRIS Registry participated via manual entry. These providers also might have had an EHR, but were unable to integrate it with the IRIS Registry in 2016. An estimated 70% of all active ophthalmologists in the United States (11 900 of 17 000) had adopted EHRs by the end of 2016. The average IRIS Registry practice with an integrated EHR contained 4.2 providers compared with an average of 1.8 providers
687 617 597 536 519 502 501 491 441 389
957 801 492 178 016 961 427 375 007 508
in the non-EHR practices in the IRIS Registry. The distribution of EHR practices in the IRIS Registry database as of December 31, 2016, is depicted in Figure 2. The age distribution of patients in the IRIS Registry database is shown in Figure 3, with the largest single age group being 45 to 54 years of age. Approximately 42% of visits were for patients 65 years of age and older.
Figure 2. Map showing the distribution of electronic health record integrated practices in the 2016 IRIS Registry Database.
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4.0 3.6 3.4 3.1 3.0 2.9 2.9 2.8 2.5 2.2
Chiang et al
2016 AAO IRIS Registry Database
Figure 3. Bar graph showing the age distribution of patients included in the 2016 IRIS database.
Discussion The IRIS Registry is a real-world database representing many ophthalmic EHR-based practices around the country. The comprehensiveness and breadth of the data varies across EHR systems, particularly for nonophthalmology EHR systems compared with ophthalmology systems, and varies across practices. The data reflect the real-world setting of ophthalmic care and are subject to the same documentation and coding errors and missing data as found in EHR data, including medication data.3e6 Another data source on eye care is the National Ambulatory Medical Care Survey, a national survey designed to meet the need for objective, reliable information about the provision and use of ambulatory medical care services in the United States.7 Findings are based on a sample of visits to nonfederally employed office-based physicians who are engaged primarily in direct patient care. The distribution of visits by age in the 2010 National Ambulatory Medical Care Survey was as follows: 28% for patients 45 to 64 years of age, 25% for patients 65 to 74 years of age, and 29% for patients 75 years of age and older. The comparison of patients in the IRIS Registry database with the National Ambulatory Medical Care Survey findings suggests that the age distribution is similar to what would be expected for patients seeing ophthalmologists, but as expected, not similar to the general United States population. The 1 group of practices greatly underrepresented in the IRIS Registry database is academic medical centers, which make up only 1% of IRIS Registryecontracted practices, but 11% of all practices in the United States. Given the lack of participation from practices without EHRs and academic medical centers using Epic software (Epic Systems Corp, Verona, WI), the 2016 IRIS Registry database is not representative of the entire spectrum of ophthalmic care in the United States. The IRIS Registry includes fewer patients from inner-city urban neighborhoods and rural areas and fewer patients with a higher severity of disease or rare diseases than those seen at academic medical centers. Several academic medical centers using Epic software have become IRIS participants during 2017, which likely will shift the future description of clinical practices. The EHRs of practices do not commonly include hospital inpatient or outpatient department operating room data, and ambulatory surgery centers often do not have EHRs. Therefore, operating room information about anesthesia, specific devices, or adjuncts used (including intraocular lenses that have been implanted), for example, are not captured routinely
in the IRIS Registry. At present, EHRs commonly do not contain actual images or their metrics, although many do allow input of image interpretation and findings. Nonophthalmic and even ophthalmic EHRs often lack structured fields for many ocular parameters, such as the grading of cells in the anterior chamber in uveitis, ocular alignment in strabismus, or reattachment of the retina after retinal detachment. The IRIS Registry database is a valuable source of data for researchers and analysts, but open access currently is not available because of high demand and the complexity of performing analytics. There are several approaches for requesting IRIS Registry database analytics. A request-forproposals process was open for research analytic teams that have the experience and expertise to access the IRIS Registry data and perform their own projects using external funding. Subspecialty societies may provide funding opportunities for individual investigators or research analytic teams. Individual investigators can apply for funding from a joint Research to Prevent Blindness and American Academy of Ophthalmology Award for IRIS Registry Research that would provide training and access to a defined data set in the database. A Hoskins Center IRIS Registry Research Fund also is available for individual investigators, and the analytics would be performed by Academy staff. These data already have been used in publications in the peer-reviewed literature that have addressed topics related to cataract surgery8 and myopic choroidal neovascularization.9,10 Information about these analytic opportunities can be found at https://www.aao.org/ iris-registry/data-analysis/requirements. The IRIS Registry already includes an estimated 42% of ophthalmologists, and it continues to grow as the number of ophthalmic practices that adopt EHRs increases and academic medical centers using Epic as their EHR join the system. During 2017, it is anticipated that the number of ophthalmologists in the IRIS Registry database will grow to approximately 9000. The IRIS Registry database includes data on clinical outcomes, such as visual acuity, intraocular pressure, cup-to-disc ratio, disease severity, and complications that can be evaluated to describe the baseline characteristics of the patient population. As a result of its size and the inclusion of both administrative and clinical data, the IRIS Registry is a resource for gaining insights about realworld practice patterns, the natural history of disease, disease prevalence, and clinical outcomes.
References 1. United States Census Bureau. ACS demographic and housing estimates: 2011e2015 American Community Survey 5-year estimates. Available at: https://factfinder.census.gov/ faces/tableservices/jsf/pages/productview.xhtml?pid¼ACS _15_5YR_DP05&src¼pt; 2015. Accessed October 10, 2017. 2. Barnett JC, Berchick ER. Health insurance coverage in the United States: 2016. Available at: https://www.census.gov/library/publications/2017/demo/p60e260.html; 2017. Accessed October 10, 2017. 3. Wagner MM, Hogan WR. The accuracy of medication data in an outpatient electronic medical record. J Am Med Inform Assoc. 1996;3:234e244.
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Ophthalmology Volume -, Number -, Month 2017 4. Peabody JW, Luck J, Jain S, et al. Assessing the accuracy of administrative data in health information systems. Med Care. 2004;42:1056e1072. 5. Biggerstaff KS, Frankfort BJ, Orengo-Nania S. Validity of code based algorithms to identify primary open angle glaucoma (POAG) in Veterans Affairs (VA) administrative databases. Ophthalmic Epidemiol. 2017:1e7. 6. Pimental MA, Browne EN, Janardhana PM, et al. Assessment of the accuracy of using ICD-9 codes to identify uveitis, herpes zoster ophthalmicus, scleritis, and episcleritis. JAMA Ophthalmol. 2016;134:1001e1006. 7. Centers for Disease Control and Prevention. 2010 National Ambulatory Medical Care Survey factsheet: ophthalmology.
Available at: https://www.cdc.gov/nchs/data/ahcd/namcs_2010_ factsheet_ophthalmology.pdf; 2010. Accessed October 10, 2017. 8. Coleman AL. How big data informs us about cataract surgery: The LXXII Edward Jackson Memorial Lecture. Am J Ophthalmol. 2015;160:1091e1103. 9. Willis JR, Vitale S, Morse L, et al. The prevalence of myopic choroidal neovascularization in the United States: analysis of the IRIS Data Registry and NHANES. Ophthalmology. 2016;123:1771e1782. 10. Willis JR, Morse L, Vitale S, et al. Treatment patterns for myopic choroidal neovascularization in the United States: analysis of the IRIS Registry. Ophthalmology. 2017;124: 935e943.
Footnotes and Financial Disclosures Originally received: August 30, 2017. Final revision: November 9, 2017. Accepted: December 1, 2017. Available online: ---. 1
HUMAN SUBJECTS: This study does not include human subjects/tissues. Author Contributions: Manuscript no. 2017-2015.
Oregon Health and Science University, Portland, Oregon.
2
Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 3 American Academy of Ophthalmology, San Francisco, California. Financial Disclosure(s): The author(s) have made the following disclosure(s): M.F.C.: Grants e National Institutes of Health; Supported by unrestricted departmental funding from the Research to Prevent Blindness (New York, NY); unpaid member of the Scientific Advisory board for Clarity Medical Systems (Pleasanton, CA); Consultant e Novartis (Basel, Switzerland). Flora Lum and David Parke are employees of the American Academy of Ophthalmology, and the IRIS registry is owned by the American Academy of Ophthalmology.
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Conception and design: Chiang, Sommer, Rich, Lum, Parke Analysis and interpretation: Chiang, Sommer, Rich, Lum, Parke Data collection: Rich, Lum, Parke Obtained funding: none Overall responsibility: Chiang, Sommer, Rich, Lum, Parke Abbreviations and Acronyms: EHR ¼ electronic health record; IRIS ¼ Intelligent Research in Sight. Correspondence: Flora Lum, MD, American Academy of Ophthalmology, 655 Beach Street, San Francisco, CA 94019. E-mail: fl[email protected].