PURPOSE: To simulate 24-2 visual field (VF) using optical coherence tomographic angiography (OCTA) for glaucoma evaluation. DESIGN: Cross-sectional study. METHODS: One eye each of 39 glaucoma and 31 age-matched normal participants was scanned using 4.5-mm OCTA scans centered on the disc. The peripapillary retinal nerve fiber layer plexus capillary density (NFLPCD, %area) was measured. The NFLP-CD and 24-2 VF maps were divided into 8 corresponding sectors using an extension of Garway-Heath scheme. RESULTS: Sector NFLP-CD was transformed to a logarithmic dB scale and converted to sector simulated VF deviation maps. Comparing simulated and actual 24-2 VF maps, the worst sector was in the same or adjacent location in the same hemisphere 97% of the time. VF mean deviation (VF-MD) was simulated by NFLP mean deviation (NFLP-MD). The differences between NFLP-MD and VF-MD in early, moderate, and severe glaucoma stages were - 0.9 +/- 2.0, 0.9 +/- 2.9, and 5.8 +/- 3.2 dB. NFLP-MD had better (P = .015) between-visit reproducibility (0.63 dB pooled standard deviation) than VFMD (1.03 dB). NFLP-MD had a significantly higher sensitivity than VF-MD (P < .001) and overall NFL thickness (P = .031). CONCLUSIONS: OCTA-based simulated VF agreed well with actual 24-2 VF in terms of both the location and severity of glaucoma damage, with the exception of severe glaucoma in which the simulation tended to underestimate severity. The NFLP-MD had better reproducibility than actual VF-MD and holds promise for improving glaucoma monitoring. The NFLP-MD had better diagnostic accuracy than both VF-MD and overall NFL thickness and may be useful for early glaucoma diagnosis. (C) 2019 Elsevier Inc. All rights reserved.

Purpose: To measure low perfusion areas (LPA) and focal perfusion loss (FPL) in the peripapillary retina using optical coherence tomographic angiography (OCTA) in glaucoma. Design: Prospective observation study. Participants: Forty-seven primary open angle glaucoma (POAG) patients and 36 normal subjects were analyzed. Methods: One eye of each subject was scanned using AngioVue 4.5-mm OCTA scan centered on the disc. En face nerve fiber layer plexus angiogram was generated. With the use of custom software, a capillary density map was obtained by computing the fraction of area occupied by flow pixels after low-pass filtering by local averaging 21×21 pixels. The low-perfusion map is defined by local capillary density below 0.5 percentile over a contiguous area above 98.5 percentile of the normal reference population. The LPA parameter is the cumulative area and FPL is the percent capillary density loss (relative to normal mean) integrated over the LPA. The retinal nerve fiber layer (NFL) thickness was measured from ONH scan on AngioVue. Main Outcome Measures: Peripapillary retinal LPA, FPL and NFL thickness. Results: Among POAG patients, 3 had pre-perimetric glaucoma and 44 had perimetric glaucoma, with visual field (VF) mean deviation (MD) of −5.14 ± 4.25 dB. LPA was 3.40 ± 2.29 mm 2 in POAG and 0.11 ± 0.18 mm 2 among normal subjects (P < 0.001). FPL was 21.8 ± 17.0 % in POAG and 0.3 ± 0.7% in normal subjects (P < 0.001). The diagnostic accuracy measured the area under the receiver operating curve was 0.965 for both LPA and FPL, with sensitivity of 93.7% at 95% specificity. The repeatability as measured by intraclass correlation coefficient was 0.977 for LPA and 0.958 for FPL. FPL had an excellent correlation with VF MD (Spearman’s rho= −0.843) which was significantly (P = 0.008) better than the correlation between NFL thickness and VF MD (rho = 0.760). The hemispheric difference correlation between FPL and VF (Spearman’s rho=0.770) was significantly (P < 0.001) higher than the hemispheric difference correlation between LPA and VF (rho = 0.595). Conclusions: The low-perfusion map and the LPA and FPL parameters are able to assess the location and severity of focal glaucoma damage with good agreement with VF. the contiguous low-perfusion areas. The distribution of perfusion areas normal and it 98.5 percentile cutoff was 0.053 mm 2 The binary low-perfusion map highlighted areas of normal perfusion in green and abnormally low perfusion area in red. The low-perfusion area (LPA) in each eye was defined by the cumulative area of pixels that met both low perfusion and contiguity requirements. The FPL is the capillary density loss (relative to the reference map from a normal population) integrated over the LPA, then expressed as a percentage of the capillary density integral of the normal reference map. The LPA and FPL were measured from the 4×4-mm low-perfusion map, which were global OCTA parameters. To perfusion loss field loss correspondence, LPA and FPL were divided into and values by a horizontal line crossing the center of the

Purpose: To measure low perfusion areas (LPAs) and focal perfusion loss (FPL) in the peripapillary retina using OCT angiography (OCTA) in glaucoma. Design: Prospective, observational study. Participants: A total of 47 patients with primary open-angle glaucoma (POAG) and 36 normal participants were analyzed. Methods: One eye of each subject was scanned using an AngioVue (Optovue, Fremont, CA) 4.5-mm OCTA scan centered on the disc. En face nerve fiber layer (NFL) plexus angiogram was generated. With the use of custom software, a capillary density map was obtained by computing the fraction of area occupied by flow pixels after low-pass filtering by local averaging 21 x21 pixels. The low-perfusion map is defined by local capillary density below 0.5 percentile over a contiguous area above 98.5 percentile of the normal reference population. The LPA parameter is the cumulative area, and the FPL is the percent capillary density loss (relative to normal mean) integrated over the LPA. Main Outcome Measures: Peripapillary retinal LPA and FPL. Results: Among patients with POAG, 3 had preperimetric glaucoma and 44 had perimetric glaucoma, with visual field (VF) mean deviation (MD) of -5.14 +/- 4.25 decibels (dB). The LPA was 3.40 +/- 2.29 mm(2) in those with POAG and 0.11 +/- 0.18 mm(2) in normal subjects (P < 0.001). The FPL was 21.8%+/- 17.0% in those with POAG and 0.3%+/- 0.7% in normal subjects (P < 0.001). The diagnostic accuracy as measured by the area under the receiver operating curve was 0.965 for both LPA and FPL, with a sensitivity of 93.7% at 95% specificity. The repeatability as measured by intraclass correlation coefficient was 0.977 for LPA and 0.958 for FPL. The FPL had excellent correlation with VF MD (Spearman's rho = -0.843), which was significantly (P = 0.008) better than the correlation between NFL thickness and VF MD (rho = 0.760). The hemispheric difference correlation between FPL and VF (Spearman's rho = 0.770) was significantly (P < 0.001) higher than the hemispheric difference correlation between LPA and VF (rho = 0.595). Conclusions: The low-perfusion map and LPA and FPL parameters are able to assess the location and severity of focal glaucoma damage with good agreement with VF. (C) 2019 by the American Academy of Ophthalmology

Objective:Outpatient clinics lack guidance for tackling modern efficiency and productivity demands. Workflow studies require large amounts of timing data that are prohibitively expensive to collect through observation or tracking devices. Electronic health records (EHRs) contain a vast amount of timing data - timestamps collected during regular use - that can be mapped to workflow steps. This study validates using EHR timestamp data to predict outpatient ophthalmology clinic workflow timings at Oregon Health and Science University and demonstrates their usefulness in 3 different studies.Materials and Methods:Four outpatient ophthalmology clinics were observed to determine their workflows and to time each workflow step. EHR timestamps were mapped to the workflow steps and validated against the observed timings.Results:The EHR timestamp analysis produced times that were within 3 min of the observed times for >80% of the appointments. EHR use patterns affected the accuracy of using EHR timestamps to predict workflow times.Discussion:EHR timestamps provided a reasonable approximation of workflow and can be used for workflow studies. They can be used to create simulation models, analyze EHR use, and quantify the impact of trainees on workflow.Conclusion:The secondary use of EHR timestamp data is a valuable resource for clinical workflow studies. Sample timestamp data files and algorithms for processing them are provided and can be used as a template for more studies in other clinical specialties and settings.

Importance Electronic health record (EHR) systems have transformed the practice of medicine. However, physicians have raised concerns that EHR time requirements have negatively affected their productivity. Meanwhile, evolving approaches toward physician reimbursement will require additional documentation to measure quality and cost of care. To date, little quantitative analysis has rigorously studied these topics. Objective To examine ophthalmologist time requirements for EHR use. Design, Setting, and Participants A single-center cohort study was conducted between September 1, 2013, and December 31, 2016, among 27 stable departmental ophthalmologists (defined as attending ophthalmologists who worked at the study institution for ≥6 months before and after the study period). Ophthalmologists who did not have a standard clinical practice or who did not use the EHR were excluded. Exposures Time stamps from the medical record and EHR audit log were analyzed to measure the length of time required by ophthalmologists for EHR use. Ophthalmologists underwent manual time-motion observation to measure the length of time spent directly with patients on the following 3 activities: EHR use, conversation, and examination. Main Outcomes and Measures The study outcomes were time spent by ophthalmologists directly with patients on EHR use, conversation, and examination as well as total time required by ophthalmologists for EHR use. Results Among the 27 ophthalmologists in this study (10 women and 17 men; mean [SD] age, 47.3 [10.7] years [median, 44; range, 34-73 years]) the mean (SD) total ophthalmologist examination time was 11.2 (6.3) minutes per patient, of which 3.0 (1.8) minutes (27% of the examination time) were spent on EHR use, 4.7 (4.2) minutes (42%) on conversation, and 3.5 (2.3) minutes (31%) on examination. Mean (SD) total ophthalmologist time spent using the EHR was 10.8 (5.0) minutes per encounter (range, 5.8-28.6 minutes). The typical ophthalmologist spent 3.7 hours using the EHR for a full day of clinic: 2.1 hours during examinations and 1.6 hours outside the clinic session. Linear mixed effects models showed a positive association between EHR use and billing level and a negative association between EHR use per encounter and clinic volume. Each additional encounter per clinic was associated with a decrease of 1.7 minutes (95% CI, -4.3 to 1.0) of EHR use time per encounter for ophthalmologists with high mean billing levels (adjusted R2 = 0.42; P = .01). Conclusions and Relevance Ophthalmologists have limited time with patients during office visits, and EHR use requires a substantial portion of that time. There is variability in EHR use patterns among ophthalmologists.

Clinicians today face increased patient loads, decreased reimbursements and potential negative productivity impacts of using electronic health records (EHR), but have little guidance on how to improve clinic efficiency. Discrete event simulation models are powerful tools for evaluating clinical workflow and improving efficiency, particularly when they are built from secondary EHR timing data. The purpose of this study is to demonstrate that these simulation models can be used for resource allocation decision making as well as for evaluating novel scheduling strategies in outpatient ophthalmology clinics. Key findings from this study are that: 1) secondary use of EHR timestamp data in simulation models represents clinic workflow, 2) simulations provide insight into the best allocation of resources in a clinic, 3) simulations provide critical information for schedule creation and decision making by clinic managers, and 4) simulation models built from EHR data are potentially generalizable.

Electronic health records (EHRs) have potential to improve the quality, efficiency, and cost of health care.1–6 The transition from traditional paper-based care to EHRs within both hospitals and ambulatory practices has been aggressively promoted by federal initiatives7,8 and is rapidly transforming the process of health care delivery throughout the United States.9–11 However, clinicians have raised concerns that EHR implementation has negatively impacted their real-world clinical productivity.12–16 For example, at Oregon Health & Science University (OHSU), we have one of the leading biomedical informatics departments in the world and completed a successful EHR implementation in 2006 that received national publicity. Yet we have published studies showing that OHSU ophthalmologists currently see 3–5% fewer patients than before EHR implementation and require >40% additional time for each patient encounter.17 Approaches toward improving the efficiency of clinical workflow using EHRs would have significant real-world impact. Clinicians are pressured to see more patients in less time for less reimbursement due to persistent concerns about the accessibility and cost of health care.18,19 Providers today are facing increased patient loads along with increased encounter times due to EHR use, but do not have guidance or information about how to meet these demands. For example, ophthalmologists typically see 15–30 patients or more in a half-day session, utilize multiple exam rooms simultaneously, work with ancillary staff (e.g., technicians, ophthalmic photographers), and examine patients in multiple stages (e.g., before and after dilation of eyes, before and after ophthalmic imaging studies). This creates enormous challenges in workflow and scheduling, and large variability in operational approaches.20 Patient wait time is a result of pressure on provider time as well as clinic inefficiency; wait time has been shown to affect patient satisfaction as well as create barriers to health care.21,22. Mathematics, specifically queueing theory, explains waiting by the mismatch of arrival times and service times (time with a physician).23 This mismatch can be increased by ad-hoc scheduling protocols that artificially increase patient wait time.24,25 Addressing this mismatch using smarter scheduling strategies has potential for improving patient wait time.26 Studying and evaluating appointment scheduling strategies in clinical settings is impractical, however, since patient and provider time is too valuable for experimentation. Empirical models of clinical processes using discrete event simulation (DES) can evaluate potential scheduling strategies effectively before implementing them in clinical settings. DES requires large amounts of workflow timing data—much more than can reasonably be collected using traditional time-motion studies. We believe that data to address these problems is currently available within EHR. One major benefit of EHR systems is that clinical data can be applied for “secondary use” beyond direct provision of clinical care; current efforts have focused on areas such as clinical research, public health, adverse event reporting, and quality assurance.27–29 Data mining the EHR data has been used to determine patient no-shows with success30, grouping patients in emergency departments (ED)31, and for quality assurance in the ED.32,33 DES has been used for quality improvement in healthcare, but not for evaluating scheduling strategies based on secondary use EHR data and detailed workflow data.30,31,34,35 In this paper, we present the results of using secondary EHR data for modeling clinical workflow in 3 outpatient ophthalmology clinics at OHSU. Ophthalmology is an ideal domain for these studies because it is a high-volume, high-complexity field that combines both medical and surgical practice. Our results show that the secondary use of EHR data for workflow data shows promise; it matches the trends of observed clinic workflows and is available for thousands of patient encounters. Further, workflow data can be used to build simulation models for evaluating scheduling strategies based on patient classification.

IMPORTANCE Vascular factors may have important roles in the pathophysiology of glaucoma. A practical method for the clinical evaluation of ocular perfusion is needed to improve glaucoma management.OBJECTIVE To detect peripapillary retinal perfusion in glaucomatous eyes compared with normal eyes using optical coherence tomography (OCT) angiography.DESIGN, SETTING, AND PARTICIPANTS Prospective observational study performed from July 24, 2013, to April 17, 2014. Participants were recruited and tested at Casey Eye Institute, Oregon Health & Science University. In total, 12 glaucomatous eyes and 12 age-matched normal eyes were analyzed. The optic disc region was imaged twice using a 3 x 3-mm scan by a 70-kHz, 840-nm-wavelength spectral OCT system. The split-spectrum amplitude-decorrelation angiography algorithm was used. Peripapillary flow index was calculated as the mean decorrelation value in the peripapillary region, defined as a 700-mu m-wide elliptical annulus around the disc. Peripapillary vessel density was the percentage area occupied by vessels. The data statistical analysis was performed from October 30, 2013, to May 30, 2014.MAIN OUTCOMES AND MEASURES Variability was assessed by the coefficient of variation. The Mann-Whitney test was used to compare the 2 groups of eyes. Correlations between vascular and visual field variables were assessed by linear regression analysis.RESULTS In 12 normal eyes, a dense microvascular network around the disc was visible on OCT angiography. In 12 glaucomatous eyes, this network was visibly attenuated globally and focally. In normal eyes, between-visit reproducibilities of peripapillary flow index and peripapillary vessel density were 4.3% and 2.7% of the coefficient of variation, respectively, while the population variabilities of peripapillary flow index and peripapillary vessel density were 8.2% and 3.0% of the coefficient of variation, respectively. Peripapillary flow index and peripapillary vessel density in glaucomatous eyes were lower than those in normal eyes (P < .001 for both). Peripapillary flow index (Pearson r = -0.808) and peripapillary vessel density (Pearson r = -0.835) were highly correlated with visual field pattern standard deviation in glaucomatous eyes (P = .001 for both). The areas under the receiver operating characteristic curve for normal vs glaucomatous eyes were 0.892 for peripapillary flow index and 0.938 for peripapillary vessel density.CONCLUSIONS AND RELEVANCE Using OCT angiography, reduced peripapillary retinal perfusion in glaucomatous eyes can be visualized as focal defects and quantified as peripapillary flow index and peripapillary vessel density, with high repeatability and reproducibility. Quantitative OCT angiography may have value in future studies to determine its potential usefulness in glaucoma evaluation.

IMPORTANCE Vascular factors may have important roles in the pathophysiology of glaucoma. A practical method for the clinical evaluation of ocular perfusion is needed to improve glaucoma management. OBJECTIVE To detect peripapillary retinal perfusion in glaucomatous eyes compared with normal eyes using optical coherence tomography (OCT) angiography. DESIGN, SETTING, AND PARTICIPANTS Prospective observational study performed from July 24, 2013, to April 17, 2014. Participants were recruited and tested at Casey Eye Institute, Oregon Health & Science University. In total, 12 glaucomatous eyes and 12 age-matched normal eyes were analyzed. The optic disc region was imaged twice using a 3 × 3-mm scan by a 70-kHz, 840-nm-wavelength spectral OCT system. The split-spectrum amplitude-decorrelation angiography algorithm was used. Peripapillary flow index was calculated as the mean decorrelation value in the peripapillary region, defined as a 700-µm-wide elliptical annulus around the disc. Peripapillary vessel density was the percentage area occupied by vessels. The data statistical analysis was performed from October 30, 2013, to May 30, 2014. MAIN OUTCOMES AND MEASURES Variability was assessed by the coefficient of variation. The Mann-Whitney test was used to compare the 2 groups of eyes. Correlations between vascular and visual field variables were assessed by linear regression analysis. RESULTS In 12 normal eyes, a dense microvascular network around the disc was visible on OCT angiography. In 12 glaucomatous eyes, this network was visibly attenuated globally and focally. In normal eyes, between-visit reproducibilities of peripapillary flow index and peripapillary vessel density were 4.3% and 2.7% of the coefficient of variation, respectively, while the population variabilities of peripapillary flow index and peripapillary vessel density were 8.2% and 3.0% of the coefficient of variation, respectively. Peripapillary flow index and peripapillary vessel density in glaucomatous eyes were lower than those in normal eyes (P < .001 for both). Peripapillary flow index (Pearson r = -0.808) and peripapillary vessel density (Pearson r = -0.835) were highly correlated with visual field pattern standard deviation in glaucomatous eyes (P = .001 for both). The areas under the receiver operating characteristic curve for normal vs glaucomatous eyes were 0.892 for peripapillary flow index and 0.938 for peripapillary vessel density. CONCLUSIONS AND RELEVANCE Using OCT angiography, reduced peripapillary retinal perfusion in glaucomatous eyes can be visualized as focal defects and quantified as peripapillary flow index and peripapillary vessel density, with high repeatability and reproducibility. Quantitative OCT angiography may have value in future studies to determine its potential usefulness in glaucoma evaluation.

Purpose: To use optical coherence tomography (OCT) to measure corneal power and improve the selection of intraocular lens (IOL) power in cataract surgeries after laser vision correction. Methods: Patients with previous myopic laser vision corrections were enrolled in this prospective study from two eye centers. Corneal thickness and power were measured by Fourier-domain OCT. Axial length, anterior chamber depth, and automated keratometry were measured by a partial coherence interferometer. An OCT-based IOL formula was developed. The mean absolute error of the OCTbased formula in predicting postoperative refraction was compared to two regression-based IOL formulae for eyes with previous laser vision correction. Results: Forty-six eyes of 46 patients all had uncomplicated cataract surgery with monofocal IOL implantation. The mean arithmetic prediction error of postoperative refraction was 0.05 ± 0.65 diopter (D) for the OCT formula, 0.14 ± 0.83 D for the Haigis-L formula, and 0.24 ± 0.82 D for the no-history Shammas-PL formula. The mean absolute error was 0.50 D for OCT compared to a mean absolute error of 0.67 D for Haigis-L and 0.67 D for Shammas-PL. The adjusted mean absolute error (average prediction error removed) was 0.49 D for OCT, 0.65 D for Haigis-L (P=.031), and 0.62 D for Shammas-PL (P=.044). For OCT, 61% of the eyes were within 0.5 D of prediction error, whereas 46% were within 0.5 D for both Haigis-L and Shammas-PL (P=.034). Conclusions: The predictive accuracy of OCT-based IOL power calculation was better than Haigis-L and Shammas-PL formulas in eyes after laser vision correction. Trans Am Ophthalmol Soc 2013;111:34-45

PURPOSE:To use optical coherence tomography (OCT) to measure corneal power and improve the selection of intraocular lens (IOL) power in cataract surgeries after laser vision correction.METHODS:Patients with previous myopic laser vision corrections were enrolled in this prospective study from two eye centers. Corneal thickness and power were measured by Fourier-domain OCT. Axial length, anterior chamber depth, and automated keratometry were measured by a partial coherence interferometer. An OCT-based IOL formula was developed. The mean absolute error of the OCT-based formula in predicting postoperative refraction was compared to two regression-based IOL formulae for eyes with previous laser vision correction.RESULTS:Forty-six eyes of 46 patients all had uncomplicated cataract surgery with monofocal IOL implantation. The mean arithmetic prediction error of postoperative refraction was 0.05 ± 0.65 diopter (D) for the OCT formula, 0.14 ± 0.83 D for the Haigis-L formula, and 0.24 ± 0.82 D for the no-history Shammas-PL formula. The mean absolute error was 0.50 D for OCT compared to a mean absolute error of 0.67 D for Haigis-L and 0.67 D for Shammas-PL. The adjusted mean absolute error (average prediction error removed) was 0.49 D for OCT, 0.65 D for Haigis-L (P=.031), and 0.62 D for Shammas-PL (P=.044). For OCT, 61% of the eyes were within 0.5 D of prediction error, whereas 46% were within 0.5 D for both Haigis-L and Shammas-PL (P=.034).CONCLUSIONS:The predictive accuracy of OCT-based IOL power calculation was better than Haigis-L and Shammas-PL formulas in eyes after laser vision correction.

Optic nerve head (ONH) blood flow may be associated with glaucoma development. A reliable method to quantify ONH blood flow could provide insight into the vascular component of glaucoma pathophysiology. Using ultrahigh-speed optical coherence tomography (OCT), we developed a new 3D angiography algorithm called split-spectrum amplitude-decorrelation angiography (SSADA) for imaging ONH microcirculation. In this study, a method to quantify SSADA results was developed and used to detect ONH perfusion changes in early glaucoma. En face maximum projection was used to obtain 2D disc angiograms, from which the average decorrelation values (flow index) and the percentage area occupied by vessels (vessel density) were computed from the optic disc and a selected region within it. Preperimetric glaucoma patients had significant reductions of ONH perfusion compared to normals. This pilot study indicates OCT angiography can detect the abnormalities of ONH perfusion and has the potential to reveal the ONH blood flow mechanism related to glaucoma.