Patient reported outcome measures (PROMs) have been integrated in national quality registries or specific national monitoring initiatives to inform the improvement of quality of care on a national scale. However there are many unanswered questions, such as: how these systems are set up, whether they lead to improved quality of care, which stakeholders use the information once it is available. The aim of this study was to examine supporting and hindering factors relevant to integrating patient-reported outcome measures (PROMs) in selected health information systems (HIS) tailored toward improving quality of care across the entire health system. First, a systematic search and review was conducted to outline previously identified factors relevant to the integration of PROMs in the selected HIS. A social network analysis was performed to identify networks of experts in these systems. Second, expert interviews were conducted to discuss and elaborate on the identified factors. Directive content analysis was applied using a HIS Evaluation Framework as the frame of reference. This framework is structured into four components: Organization, Human, Technology, and Net benefits. The literature review revealed 37 papers for the NHS PROMs Programme and 26 papers for the SHPR and SKAR: Five networks of researchers were identified for the NHS PROMs Programme and 1 for the SHPR and SKAR. Seven experts related to the NHS PROMs Programme and 3 experts related to the SKAR and SHPR participated in the study. The main themes which revealed in relation to Organization were Governance and Capacity building; to Human: Reporting and Stakeholder Engagement; to Technology: the Selection and Collection of PROMs and Data linkage. In relation to Net benefits, system-specific considerations are presented. Both examples succeeded in integrating PROMs into HIS on a national scale. The lack of an established standard on what change PROMs should be achieved by an intervention limits their usefulness for monitoring quality of care. Whether the PROMs data collected within these systems can be used in routine clinical practice is considered a challenge in both countries.

We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD). Development and validation of a deep-learning model for feature segmentation. Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2712 B-scans. A deep neural network was trained with these data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by 4 independent observers. The main outcome measures were Dice score, intraclass correlation coefficient, and free-response receiver operating characteristic curve. On 11 of 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared with 0.61 ± 0.17 for the observers. The mean intraclass correlation coefficient for the model was 0.66 ± 0.22, compared with 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively because of a lack of data. Free-response receiver operating characteristic analysis demonstrated that the model scored similar or higher sensitivity per false positives compared with the observers. The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.

Background/aims Clinical trials suggest anti-vascular endothelial growth factor is more effective than intravitreal dexamethasone as treatment for macular oedema secondary to branch retinal vein occlusion. This study asks if ‘real world’ data from a larger and more diverse population, followed for a longer period, also support this conclusion. Methods Data collected to support routine care at 27 NHS (National Health Service) Trusts between February 2002 and September 2017 contained 5661 treatment-naive patients with a single mode of treatment for macular oedema secondary to branch retinal vein occlusion and no history of cataract surgery either during or recently preceding the treatment. Number of treatment visits and change in visual acuity from baseline was plotted for three treatment groups (anti-vascular endothelial growth factor (anti-VEGF), intravitreal dexamethasone, macular laser) for up to 3 years. Results Mean baseline visual acuity was 57.1/53.1/62.3 letters in the anti-VEGF/dexamethasone/macular laser groups, respectively. This changed to 66.72 (+9.6)/57.6 (+4.5)/63.2 (+0.9) at 12 months. Adequate numbers allowed analysis at 18 months for all groups (66.6 (+9.5)/56.1 (+3.0)/60.8 (-1.5)) and for anti-VEGF at 36 months (68.0, +10.9) Mean number of treatments were 5.1/1.5/1.2 at 12 months, 5.9/1.7/1.2 at 18 months for all three groups and 10.3 at 36 months for anti-VEGF. Conclusions Visual acuity improvements were higher and more sustained with anti-VEGF. Higher treatment burden occurred with anti-VEGF but this reduced over 36 months. Patients with better vision at baseline than those in the clinical trials maintained high levels of vision with both anti-VEGF and dexamethasone.

Background/Aims To assess the effectiveness, burden and safety of two categories of treatment for central retinal vein occlusion (CRVO): intravitreal injections of anti-vascular endothelial growth factor (anti-VEGF) and dexamethasone (Ozurdex). Methods A retrospective analysis of Medisoft electronic medical record (EMR) data from 27 National Health Service sites in the UK identified 4626 treatment-naive patients with a single mode of treatment for macular oedema secondary to CRVO. Statistics describing the overall CRVO patient cohort and individual patient subpopulations stratified by treatment type were generated. Mean age at baseline, gender, ethnicity, social deprivation and visual acuity (VA) follow-up was reported. Absolute and change in VA using ETDRS are used to describe treatment effectiveness, the number of injections and visits used to describe treatment burden and endophthalmitis rates as a marker of treatment safety. Results Mean VA was 47.9 and 45.3 EDTRS letters in the anti-VEGF and Ozurdex groups, respectively. This changed to 57.9/53.7 at 12 months, 58.3/46.9 at 18 months and 59.4/51.0 at 36 months. Mean number of injections were 5.6/1.6 at 12 months, 6.0/1.7 at 18 months and 7.0/1.8 at 36 months. Endophthalmitis rates were 0.003% (n=4) for the anti-VEGF group and 0.09% (n=1) for the Ozurdex group. Conclusions VA improvements were greater and more sustained with anti-VEGF treatment. Lower starting acuity resulted in bigger gains in both groups, while higher starting acuity resulted in higher VA at 36 months. Although treatment burden was greater with anti-VEGF, Ozurdex was associated with higher rates of endophthalmitis.

Mortality rates are increasingly used to compare the performance of hospitals and of units within hospitals. Different approaches have been taken to develop ‘standardized’ measures which adjust for the ‘expected’ mortality based on key characteristics of the population being treated. This article discusses the original motivation for looking at hospitals in this way in the UK, describes two recent incidents in which such measures were a critical element in decision making and reviews some of the strengths and weaknesses of the approaches. The current practice on the use of standardized mortality ratios is described.