Leah G. Reznick

Retinal morphology of patients with achromatopsia during early childhood: Implications for gene therapy

IMPORTANCEnWhile older children and adults with achromatopsia have been studied, less is known of young children with achromatopsia.nnnOBJECTIVESnTo characterize the macular and foveal architecture of patients with achromatopsia during early childhood with handheld spectral-domain optical coherence tomographic imaging and to make phenotype-genotype correlations.nnnDESIGN, SETTING, AND PARTICIPANTSnComparative case series of 9 patients with achromatopsia and 9 age-matched control participants at a tertiary ophthalmology referral center.nnnMAIN OUTCOMES AND MEASURESnPatients underwent complete ocular examination, full-field electroretinography, handheld spectral-domain optical coherence tomographic imaging, and screening for genetic mutations.nnnRESULTSnThe mean (SD) age of the patients with achromatopsia was 4.2 (2.4) years, and the mean (SD) age of the control participants was 4.0 (2.1) years. Cone-driven responses to photopic single-flash or 30-Hz stimuli were nonrecordable in 7 patients and severely attenuated in 2. Rod-driven responses to dim scotopic single-flash stimuli were normal in 7 patients and mildly subnormal in 2. Six patients (67%) had foveal ellipsoid zone disruption, of which 1 had a hyporeflective zone. Four patients (44%) had foveal hypoplasia. The average total retinal thicknesses of the macula and fovea in the patients with achromatopsia were 14% and 17% thinner than in the control participants (Pu2009<u2009.001 and Pu2009=u2009.001), which was mostly due to the outer retina that was 18% and 26% thinner than in control participants (both Pu2009<u2009.001), respectively. Genetic testing revealed a common homozygous mutation in CNGB3 in 5 patients with complete achromatopsia and heterozygous mutations in CNGA3 in 2 patients with incomplete achromatopsia. The youngest and worst-affected patient harbored compound heterozygous mutations in CNGB3 and a single mutation in CNGA3.nnnCONCLUSIONS AND RELEVANCEnIn early childhood, there is a spectrum of foveal pathology that is milder than reported in older individuals with achromatopsia, which suggests the need for early therapeutic intervention. Neither age alone nor genotype alone predicts the degree of photoreceptor loss or preservation. Thus, in anticipation of future gene therapy trials in humans, we propose that handheld spectral-domain optical coherence tomography is an important tool for the early assessment and stratification of macular architecture in young children with achromatopsia.

Secondary use of electronic health record data for clinical workflow analysis

ObjectivenOutpatient 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.nnnMaterials and MethodsnFour 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.nnnResultsnThe EHR timestamp analysis produced times that were within 3u2009min of the observed times foru2009>80% of the appointments. EHR use patterns affected the accuracy of using EHR timestamps to predict workflow times.nnnDiscussionnEHR 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.nnnConclusionnThe 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.

Spectrum of ocular manifestations in cobalamin C and cobalamin A types of methylmalonic acidemia

ABSTRACT Background: Cobalamin C disease (cblC), which leads to methylmalonic acidemia with homocystinuria, is the most common inherited disorder of vitamin B12 metabolism. Reported ocular findings associated with cblC have been maculopathy, pigmentary retinopathy, and optic nerve atrophy. Cobalamin A disease (cblA) which causes an isolated methylmalonic acidemia without homocystinuria is rarer than cblC. This is the first detailed report of the ocular findings associated with cblA. We also describe the spectrum of ocular findings in our cblC patients. Materials and methods: A case series describing the ophthalmologic clinical course of six patients with a diagnosis of cobalamin C type and one patient with cobalamin A type of methylmalonic acidemia. Patients were diagnosed through biochemical laboratory testing and genetic analysis was conducted on most patients. Longitudinal fundus findings, optical coherence tomography (OCT), autofluorescence, and electrophysiology were followed in the patients. Results: The cblA patient demonstrated a relatively mild ocular phenotype with late-onset and slowly progressing temporal disc pallor and peripapillary atrophy in the second decade of life. The patient maintained good visual acuity and central vision, without evidence of maculopathy. The six cblC patients demonstrated a range of ocular findings from unremarkable and mild phenotypes to significant retinopathy, including bull’s eye maculopathy, severe maculopathy with punched out chorioretinal atrophy, peripheral bone spicules, and optic nerve atrophy. Conclusions: The spectrum of ocular manifestations seen with inherited disorders of cobalamin metabolism is wide, ranging from mild optic nerve atrophy to severe macular or retinal degeneration. This heterogeneity may in part reflect the associated biochemical phenotype, such as that observed between our cblA and cblC patients. We also observed heterogeneity within the cblC type in agreement with previous reports.

Time requirements for electronic health record use in an academic ophthalmology center

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 R2u2009=u20090.42; Pu2009=u2009.01). 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.

Data Driven Scheduling for Improving Patient Efficiency in Ophthalmology Clinics

PURPOSEnTo improve clinic efficiency through development of an ophthalmology scheduling template developed using simulation models and electronic health record (EHR) data.nnnDESIGNnWe created a computer simulation model of 1 pediatric ophthalmologists clinic using EHR timestamp data, which was used to develop a scheduling template based on appointment length (short, medium, or long). We assessed its impact on clinic efficiency after implementation in the practices of 5 different pediatric ophthalmologists.nnnPARTICIPANTSnWe observed and timed patient appointments in person (nxa0= 120) and collected EHR timestamps for 2 years of appointments (nxa0= 650). We calculated efficiency measures for 172 clinic sessions before implementation vs. 119 clinic sessions after implementation.nnnMETHODSnWe validated clinic workflow timings calculated from EHR timestamps and the simulation models based on them with observed timings. From simulation tests, we developed a new scheduling template and evaluated it with efficiency metrics before vs. after implementation.nnnMAIN OUTCOME MEASURESnMeasurements of clinical efficiency (mean clinic volume, patient wait time, examination time, and clinic length).nnnRESULTSnMean physician examination time calculated from EHR timestamps was 13.8±8.2 minutes and was not statistically different from mean physician examination time from in-person observation (13.3±7.3 minutes; Pxa0= 0.7), suggesting that EHR timestamps are accurate. Mean patient wait time for the simulation model (31.2±10.9 minutes) was not statistically different from the observed mean patient wait times (32.6±25.3 minutes; Pxa0= 0.9), suggesting that simulation models are accurate. After implementation of the new scheduling template, all 5 pediatric ophthalmologists showed statistically significant improvements in clinic volume (mean increase of 1-3 patients/session; P ≤ 0.05 for 2 providers; P ≤ 0.008 for 3 providers), whereas 4 of 5 had improvements in mean patient wait time (average improvements of 3-4 minutes/patient; statistically significant for 2 providers, P ≤ 0.008). All of the ophthalmologists examination times remained the same before and after implementation.nnnCONCLUSIONSnSimulation models based on big data from EHRs can test clinic changes before real-life implementation. A scheduling template using predicted appointment length improves clinic efficiency and may generalize to other clinics. Electronic health records have potential to become tools for supporting clinic operations improvement.

Ophthalmic imaging in children: current practice patterns and perceived barriers

Pediatric ophthalmologists were surveyed to determine current practice patterns regarding ophthalmic imaging for children and to identify perceived barriers to the adoption of imaging technologies in their practices. Some form of imaging was available in the majority of practices (94%), but its use varied widely among different clinical scenarios. The two most frequently perceived barriers to performing imaging in children were cooperation and lack of sufficient data supporting ophthalmic imaging in clinical practice.