The effects of the Veterans Health Administration's Referral Coordination Initiative on referral patterns and waiting times for specialty care.

OBJECTIVE: To investigate whether the Veterans Health Administration's (VA) 2019 Referral Coordination Initiative (RCI) was associated with changes in the proportion of VA specialty referrals completed by community-based care (CC) providers and mean appointment waiting times for VA and CC providers. DATA SOURCES/STUDY SETTINGS: Monthly facility level VA data for 3,097,366 specialty care referrals for eight high-volume specialties (cardiology, dermatology, gastroenterology, neurology, ophthalmology, orthopedics, physical therapy, and podiatry) from October 1, 2019 to May 30, 2022. STUDY DESIGN: We employed a staggered difference-in-differences approach to evaluate RCI's effects on referral patterns and wait times. Our unit of analysis was facility-month. We dichotomized facilities into high and low RCI use based on the proportion of total referrals for a specialty. We stratified our analysis by specialty and the staffing model that high RCI users adopted: centralized, decentralized, and hybrid. DATA COLLECTION/EXTRACTION METHODS: Administrative data on referrals and waiting times were extracted from the VA's corporate data warehouse. Data on staffing models were provided by the VA's Office of Integrated Veteran Care. PRINCIPAL FINDINGS: We did not reject the null hypotheses that high RCI use do not change CC referral rates or waiting times in any of the care settings for most specialties. For example, high RCI use for physical therapy-the highest volume specialty studied-was associated with -0.054 (95% confidence interval [CI]: -0.114 to 0.006) and 2.0 days (95% CI: -4.8 to 8.8) change in CC referral rate and waiting time at CC providers, respectively, among centralized staffing model adopters. CONCLUSIONS: In the initial years of the RCI program, RCI does not have a measurable effect on waiting times or CC referral rates. Our findings do not support concerns that RCI might be impeding Veterans' access to CC providers. Future evaluations should examine whether RCI facilitates Veterans' ability to receive care in their preferred setting.

Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data.

Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed to improve detection of abnormal traces. This study aims to classify CTGs with and without severe compromise at birth using routinely collected CTGs from 51,449 births at term from the first 20 min of FHR recordings. Three 1D-CNN and LSTM based architectures are compared. We also transform the FHR signal into 2D images using time-frequency representation with a spectrogram and scalogram analysis, and subsequently, the 2D images are analysed using a 2D-CNNs. In the proposed multi-modal architecture, the 2D-CNN and the 1D-CNN-LSTM are connected in parallel. The models are evaluated in terms of partial area under the curve (PAUC) between 0-10% false-positive rate; and sensitivity at 95% specificity. The 1D-CNN-LSTM parallel architecture outperformed the other models, achieving a PAUC of 0.20 and sensitivity of 20% at 95% specificity. Our future work will focus on improving the classification performance by employing a larger dataset, analysing longer FHR traces, and incorporating clinical risk factors.

Data on eye movements of glaucoma patients with asymmetrical visual field loss during free viewing.

This paper describes data from Asfaw at al. [1], which examined the eye movements of glaucoma patients (n=15) with pronounced asymmetrical vision loss (visual field loss worse in one eye). This allows for within-subject comparisons between the better and worse eye, thereby controlling for the effects of individual differences between patients. All patients had a clinical diagnosis of open angle glaucoma (OAG). Participants were asked to look at images of nature monocularly (free viewing; fellow eye patched) while gaze was recorded at 1000 Hz using a remote eye tracker (EyeLink 1000). Raw and processed eye tracking data are provided. In addition, clinical (visual acuity, contrast sensitivity and visual field) and demographic information (age, sex) are provided.

Geographic Variation in Appointment Wait Times for US Military Veterans.

Importance: Timely access to medical care is an important determinant of health and well-being. The US Congress passed the Veterans Access, Choice, and Accountability Act in 2014 and the VA MISSION (Maintaining Systems and Strengthening Integrated Outside Networks) Act in 2018, both of which allow veterans to access care from community-based clinicians, but geographic variation in appointment wait times after the passage of these acts have not been studied. Objective: To describe geographic variation in wait times experienced by veterans for primary care, mental health, and other specialties. Design, Setting, and Participants: This is a cross-sectional study using data from the Veterans Health Administration (VHA) Corporate Data Warehouse. Participants include veterans who sought medical care from January 1, 2018, to June 30, 2021. Data analysis was performed from February to June 2022. Exposures: Referral to either VHA or community-based clinicians. Main Outcomes and Measures: Total appointment wait times (in days) for 3 care categories: primary care, mental health, and all other specialties. VHA medical centers are organized into regions called Veterans Integrated Services Networks (VISNs); wait times were aggregated to the VISN level. Results: The final sample included 22 632 918 million appointments for 4 846 892 unique veterans (77.3% male; mean [SD] age, 61.6 [15.5] years). Among non-VHA appointments, mean (SD) VISN-level appointment wait times were 38.9 (8.2) days for primary care, 43.9 (9.0) days for mental health, and 41.9 (5.9) days for all other specialties. Among VHA appointments, mean (SD) VISN-level appointment wait times were 29.0 (5.5) days for primary care, 33.6 (4.6) days for mental health, and 35.4 (2.7) days for all other specialties. There was substantial geographic variation in appointment wait times. Among non-VHA appointments, VISN-level appointment wait times ranged from 25.4 to 52.4 days for primary care, from 29.3 to 65.7 days for mental health, and from 34.7 to 54.8 days for all other specialties. Among VHA appointments, wait times ranged from 22.4 to 43.4 days for primary care, from 24.7 to 42.0 days for mental health, and from 30.3 to 41.9 days for all other specialties. There was a correlation between wait times across care categories and setting (VHA vs community care). Conclusions and Relevance: This cross-sectional study found substantial variation in wait times across care type and geography, and VHA wait times in a majority of VISNs were lower than those for community-based clinicians, even after controlling for differences in specialty mix. These findings suggest that liberalized access to community care under the Veterans Access, Choice, and Accountability Act and the VA MISSION Act may not result in lower wait times within these regions.

Acceptability of a home-based visual field test (Eyecatcher) for glaucoma home monitoring: a qualitative study of patients' views and experiences.

OBJECTIVES: To explore the acceptability of home visual field (VF) testing using Eyecatcher among people with glaucoma participating in a 6-month home monitoring pilot study. DESIGN: Qualitative study using face-to-face semistructured interviews. Transcripts were analysed using thematic analysis. SETTING: Participants were recruited in the UK through an advertisement in the International Glaucoma Association (now Glaucoma UK) newsletter. PARTICIPANTS: Twenty adults (10 women; median age: 71 years) with a diagnosis of glaucoma were recruited (including open angle and normal tension glaucoma; mean deviation=2.5 to -29.9 dB). RESULTS: All participants could successfully perform VF testing at home. Interview data were coded into four overarching themes regarding experiences of undertaking VF home monitoring and attitudes towards its wider implementation in healthcare: (1) comparisons between Eyecatcher and Humphrey Field Analyser (HFA); (2) capability using Eyecatcher; (3) practicalities for effective wider scale implementation; (4) motivations for home monitoring. CONCLUSIONS: Participants identified a broad range of benefits to VF home monitoring and discussed areas for service improvement. Eyecatcher was compared positively with conventional VF testing using HFA. Home monitoring may be acceptable to at least a subset of people with glaucoma.

Glaucoma Home Monitoring Using a Tablet-Based Visual Field Test (Eyecatcher): An Assessment of Accuracy and Adherence Over 6 Months.

PURPOSE: To assess accuracy and adherence of visual field (VF) home monitoring in a pilot sample of patients with glaucoma. DESIGN: Prospective longitudinal feasibility and reliability study. METHODS: Twenty adults (median 71 years) with an established diagnosis of glaucoma were issued a tablet perimeter (Eyecatcher) and were asked to perform 1 VF home assessment per eye, per month, for 6 months (12 tests total). Before and after home monitoring, 2 VF assessments were performed in clinic using standard automated perimetry (4 tests total, per eye). RESULTS: All 20 participants could perform monthly home monitoring, though 1 participant stopped after 4 months (adherence: 98% of tests). There was good concordance between VFs measured at home and in the clinic (r = 0.94, P < .001). In 21 of 236 tests (9%), mean deviation deviated by more than ±3 dB from the median. Many of these anomalous tests could be identified by applying machine learning techniques to recordings from the tablets' front-facing camera (area under the receiver operating characteristic curve = 0.78). Adding home-monitoring data to 2 standard automated perimetry tests made 6 months apart reduced measurement error (between-test measurement variability) in 97% of eyes, with mean absolute error more than halving in 90% of eyes. Median test duration was 4.5 minutes (quartiles: 3.9-5.2 minutes). Substantial variations in ambient illumination had no observable effect on VF measurements (r = 0.07, P = .320). CONCLUSIONS: Home monitoring of VFs is viable for some patients and may provide clinically useful data.

The Human Touch: Using a Webcam to Autonomously Monitor Compliance During Visual Field Assessments.

Purpose: To explore the feasibility of using various easy-to-obtain biomarkers to monitor non-compliance (measurement error) during visual field assessments. Methods: Forty-two healthy adults (42 eyes) and seven glaucoma patients (14 eyes) underwent two same-day visual field assessments. An ordinary webcam was used to compute seven potential biomarkers of task compliance, based primarily on eye gaze, head pose, and facial expression. We quantified the association between each biomarker and measurement error, as defined by (1) test-retest differences in overall test scores (mean sensitivity), and (2) failures to respond to visible stimuli on individual trials (stimuli -3 dB or more brighter than threshold). Results: In healthy eyes, three of the seven biomarkers were significantly associated with overall (test-retest) measurement error (P = 0.003-0.007), and at least two others exhibited possible trends (P = 0.052-0.060). The weighted linear sum of all seven biomarkers was associated with overall measurement error, in both healthy eyes (r = 0.51, P < 0.001) and patients (r = 0.65, P < 0.001). Five biomarkers were each associated with failures to respond to visible stimuli on individual trials (all P < 0.001). Conclusions: Inexpensive, autonomous measures of task compliance are associated with measurement error in visual field assessments, in terms of both the overall reliability of a test and failures to respond on particular trials ("lapses"). This could be helpful for identifying low-quality assessments and for improving assessment techniques (e.g., by discounting suspect responses or by automatically triggering comfort breaks or encouragement). Translational Relevance: This study explores a potential way of improving the reliability of visual field assessments, a crucial but notoriously unreliable clinical measure.

Using eye movements to detect visual field loss: a pragmatic assessment using simulated scotoma.

Glaucoma is a leading cause of irreversible sight-loss and has been shown to affect natural eye-movements. These changes may provide a cheap and easy-to-obtain biomarker for improving disease detection. Here, we investigated whether these changes are large enough to be clinically useful. We used a gaze-contingent simulated visual field (VF) loss paradigm, in which participants experienced a variable magnitude of simulated VF loss based on longitudinal data from a real glaucoma patient (thereby controlling for other variables, such as age and general health). Fifty-five young participants with healthy vision were asked to view two short videos and three pictures, either with: (1) no VF loss, (2) moderate VF loss, or (3) advanced VF loss. Eye-movements were recorded using a remote eye tracker. Key eye-movement parameters were computed, including saccade amplitude, the spread of saccade endpoints (bivariate contour ellipse area), location of saccade landing positions, and similarity of fixations locations among participants (quantified using kernel density estimation). The simulated VF loss caused some statistically significant effects in the eye movement parameters. Yet, these effects were not capable of consistently identifying simulated VF loss, despite it being of a magnitude likely easily detectable by standard automated perimetry.

Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review.

In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.

Does Glaucoma Alter Eye Movements When Viewing Images of Natural Scenes? A Between-Eye Study.

Purpose: To investigate whether glaucoma produces measurable changes in eye movements. Methods: Fifteen glaucoma patients with asymmetric vision loss (difference in mean deviation [MD] > 6 dB between eyes) were asked to monocularly view 120 images of natural scenes, presented sequentially on a computer monitor. Each image was viewed twice-once each with the better and worse eye. Patients' eye movements were recorded with an Eyelink 1000 eye-tracker. Eye-movement parameters were computed and compared within participants (better eye versus worse eye). These parameters included a novel measure: saccadic reversal rate (SRR), as well as more traditional metrics such as saccade amplitude, fixation counts, fixation duration, and spread of fixation locations (bivariate contour ellipse area [BCEA]). In addition, the associations of these parameters with clinical measures of vision were investigated. Results: In the worse eye, saccade amplitude\(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\unicode[Times]{x1D6C2}}\)\(\def\bupbeta{\unicode[Times]{x1D6C3}}\)\(\def\bupgamma{\unicode[Times]{x1D6C4}}\)\(\def\bupdelta{\unicode[Times]{x1D6C5}}\)\(\def\bupepsilon{\unicode[Times]{x1D6C6}}\)\(\def\bupvarepsilon{\unicode[Times]{x1D6DC}}\)\(\def\bupzeta{\unicode[Times]{x1D6C7}}\)\(\def\bupeta{\unicode[Times]{x1D6C8}}\)\(\def\buptheta{\unicode[Times]{x1D6C9}}\)\(\def\bupiota{\unicode[Times]{x1D6CA}}\)\(\def\bupkappa{\unicode[Times]{x1D6CB}}\)\(\def\buplambda{\unicode[Times]{x1D6CC}}\)\(\def\bupmu{\unicode[Times]{x1D6CD}}\)\(\def\bupnu{\unicode[Times]{x1D6CE}}\)\(\def\bupxi{\unicode[Times]{x1D6CF}}\)\(\def\bupomicron{\unicode[Times]{x1D6D0}}\)\(\def\buppi{\unicode[Times]{x1D6D1}}\)\(\def\buprho{\unicode[Times]{x1D6D2}}\)\(\def\bupsigma{\unicode[Times]{x1D6D4}}\)\(\def\buptau{\unicode[Times]{x1D6D5}}\)\(\def\bupupsilon{\unicode[Times]{x1D6D6}}\)\(\def\bupphi{\unicode[Times]{x1D6D7}}\)\(\def\bupchi{\unicode[Times]{x1D6D8}}\)\(\def\buppsy{\unicode[Times]{x1D6D9}}\)\(\def\bupomega{\unicode[Times]{x1D6DA}}\)\(\def\bupvartheta{\unicode[Times]{x1D6DD}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bUpsilon{\bf{\Upsilon}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\(\def\iGamma{\unicode[Times]{x1D6E4}}\)\(\def\iDelta{\unicode[Times]{x1D6E5}}\)\(\def\iTheta{\unicode[Times]{x1D6E9}}\)\(\def\iLambda{\unicode[Times]{x1D6EC}}\)\(\def\iXi{\unicode[Times]{x1D6EF}}\)\(\def\iPi{\unicode[Times]{x1D6F1}}\)\(\def\iSigma{\unicode[Times]{x1D6F4}}\)\(\def\iUpsilon{\unicode[Times]{x1D6F6}}\)\(\def\iPhi{\unicode[Times]{x1D6F7}}\)\(\def\iPsi{\unicode[Times]{x1D6F9}}\)\(\def\iOmega{\unicode[Times]{x1D6FA}}\)\(\def\biGamma{\unicode[Times]{x1D71E}}\)\(\def\biDelta{\unicode[Times]{x1D71F}}\)\(\def\biTheta{\unicode[Times]{x1D723}}\)\(\def\biLambda{\unicode[Times]{x1D726}}\)\(\def\biXi{\unicode[Times]{x1D729}}\)\(\def\biPi{\unicode[Times]{x1D72B}}\)\(\def\biSigma{\unicode[Times]{x1D72E}}\)\(\def\biUpsilon{\unicode[Times]{x1D730}}\)\(\def\biPhi{\unicode[Times]{x1D731}}\)\(\def\biPsi{\unicode[Times]{x1D733}}\)\(\def\biOmega{\unicode[Times]{x1D734}}\)\((P = 0.012; - 13\% \)) and BCEA \((P = 0.005; - 16\% )\) were smaller, while SRR was greater (\(P = 0.018; + 16\% \)). There was a significant correlation between the intereye difference in BCEA, and differences in MD values (\({\rm{Spearman^{\prime} s}}\ r = 0.65;P = 0.01\)), while differences in SRR were associated with differences in visual acuity (\({\rm{Spearman^{\prime} s}}\ r = 0.64;P = 0.01\)). Furthermore, between-eye differences in BCEA were a significant predictor of between-eye differences in MD: for every 1-dB difference in MD, BCEA reduced by 6.2% (95% confidence interval, 1.6%-10.3%). Conclusions: Eye movements are altered by visual field loss, and these changes are related to changes in clinical measures. Eye movements recorded while passively viewing images could potentially be used as biomarkers for visual field damage.

Data on eye movements in people with glaucoma and peers with normal vision.

Eye movements of glaucoma patients have been shown to differ from age-similar control groups when performing everyday tasks, such as reading (Burton et al., 2012; Smith et al., 2014) [1], [2], visual search (Smith et al., 2012) [3], face recognition (Glen et al., 2013) [4], driving, and viewing static images (Smith et al., 2012) [5]. Described here is the dataset from a recent publication in which we compared the eye-movements of 44 glaucoma patients and 32 age-similar controls, while they watched a series of short video clips taken from television programs (Crabb et al., 2018) [6]. Gaze was recorded at 1000 Hz using a remote eye-tracker. We also provide demographic information and results from a clinical examination of vision for each participant.

Patients'/Clients' Expectation Toward and Satisfaction from Pharmacy Services.

OBJECTIVE: Satisfaction is becoming a popular health-care quality indicator as it reflects the reality of service or care provided. The aim of this study was to assess the level of patients' expectation toward and satisfaction from pharmacy service provided and to identify associated factor that might affect their expectation and satisfaction. METHODS: A cross-sectional study was conducted on 287 patients, who were served in five pharmacies of Gondar University Hospital in May 2015. Data regarding socio-demographic characteristics and parameters that measure patients' expectation and satisfaction were collected through interview using the Amharic version of the questionnaire. Data were entered into SPSS version 21, and descriptive statistics, cross-tabs, and binary logistic regressions were utilized. P < 0.05 was used to declare association. FINDINGS: Among 287 respondents involved in the study, 149 (51.9%) claimed to be satisfied with the pharmacy service and setting. Two hundred and twenty-nine (79.4%) respondents have high expectation toward gaining good services. Even though significant association was observed between the pharmacy type and patients level of satisfaction, sociodemographic characteristics of a patient were not found to predict the level of satisfaction. There is a higher level of expectation among study participants who earn higher income per month (>(2000 Ethiopian birr [ETB]) than those who get less income (<1000 ETB). CONCLUSION: Although patients have a higher level of expectation toward pharmacy services, their satisfaction from the service was found to be low.