Eficiencia al digitalizar un archivo fotográfico antiguo de oftalmología / Efficiency when scanning old ophthalmologic slides

Objetivo Digitalizar nuestro archivo antiguo y evaluar la eficiencia de esta tarea, tanto en términos médicos como económicos. Material y métodos Se recogieron todas las diapositivas y negativos (8254) archivados en nuestro servicio, digitalizándose con un escáner de diapositivas de 5 megapíxeles. La antigüedad de las imágenes comprendía de 1972 a 1999. Se valoró la calidad y utilidad de las imágenes frente al coste del proceso, que ascendió a 2100euros, siendo realizado por el mismo oftalmólogo. esultados De aquellas cuyo paciente era identificable, un 62% ya habían fallecido. Solo un 1,5% del total de diapositivas fueron archivadas para su uso; 70 imágenes por razones docentes y 60 por razones médicas, siendo incorporadas al historial del paciente. Se calcularon unas 210horas invertidas en el escaneo, identificación, comprobación y subida de imágenes. El 84% correspondían a patología retiniana, 4% a patología glaucomatosa, 3% a patología de segmento anterior y el resto a material docente. La calidad de la mayoría de imágenes es buena, y, en algunos casos, fueron cruciales para el diagnóstico correcto de la patología. Si atendemos únicamente a razones asistenciales, la cantidad de diapositivas incorporables a la historia clínica es muy baja en archivos de más de 50 años. Conclusiones Aunque el porcentaje de imágenes escaneadas es bajo, consideramos la tarea eficiente puesto que el coste es bajo. Los archivos de más de 50 años de antigüedad deben ser evaluados antes de su escaneo por su baja utilidad (AU)

Objective To digitise our old archive and evaluate the efficiency of this task, both in medical and economic terms. Material and methods All slides and negatives (8,254) archived in our clinic were collected, digitised with a 5-megapixel slide scanner. The images were taken from 1972 to 1999. Quality and utility of images were taken into account, as far as costs of the task (up to 2,100 euros), all the work done by the same ophthalmologist. Results Of those identifiable, 62% of the patients had already died. Only 1.5% were archived for use; 70 images for teaching reasons and 60 for medical reasons, being incorporated into the patient's history. About 210hours were spent on scanning, identifying, checking and uploading images. 84% corresponded to retinal pathology, 4% to glaucomatous pathology, 3% to anterior segment pathology and the last 9% to learning material. The quality of most images is good, and, in some cases, were important for the correct diagnosis of the pathology. If only medical reasons are taken into account, the number of images uploaded is very low when working with archives older than 50 years. Conclusions Although there was a low percentage of scanned images, the task was efficient because of a low cost. Images older than 50 years must be evaluated before scanning because of their low utility (AU)

Feasibility of Embedding a Scalable, Virtually Enabled Biorepository in the Electronic Health Record for Precision Medicine.

Importance: A cornerstone of precision medicine is the identification and use of biomarkers that help subtype patients for targeted treatment. Such an approach requires the development and subsequent interrogation of large-scale biobanks linked to well-annotated clinical data. Traditional means of creating these data-linked biobanks are costly and lengthy, especially in acute conditions that require time-sensitive clinical data and biospecimens. Objectives: To develop a virtually enabled biorepository and electronic health record (EHR)-embedded, scalable cohort for precision medicine (VESPRE) and compare the feasibility, enrollment, and costs of VESPRE with those of a traditional study design in acute care. Design, Setting, and Participants: In a prospective cohort study, the EHR-embedded screening alert was generated for 3428 patients, and 2199 patients (64%) were eligible and screened. Of these, 1027 patients (30%) were enrolled. VESPRE was developed for regulatory compliance, feasibility, internal validity, and cost in a prospective cohort of 1027 patients (aged ≥18 years) with sepsis-3 within 6 hours of presentation to the emergency department. The VESPRE infrastructure included (1) automated EHR screening, (2) remnant blood collection for creation of a virtually enabled biorepository, and (3) automated clinical data abstraction. The study was conducted at an academic institution in southwestern Pennsylvania from October 17, 2017, to June 6, 2019. Main Outcomes and Measures: Regulatory compliance, enrollment, internal validity of automated screening, biorepository acquisition, and costs. Results: Of the 1027 patients enrolled in the study, 549 were included in the proof-of-concept analysis (305 [56%] men); median (SD) age was 59 (17) years. VESPRE collected 12 963 remnant blood and urine samples and demonstrated adequate feasibility for clinical, biomarker, and microbiome analyses. Over the 20-month test, the total cost beyond the existing operations infrastructure was $39 417.50 ($14 880.00 project management, $22 717.50 laboratory supplies/staff, and $1820.00 data management)-approximately $39 per enrolled patient vs $239 per patient for a traditional cohort study. Conclusions and Relevance: Results of this study suggest that, in a large US health system that collects data using a common EHR platform and centralized laboratory system, VESPRE, a large-scale, inexpensive EHR-embedded infrastructure for precision medicine can be used. Tested in the sepsis setting, VESPRE appeared to capture a high proportion of eligible patients at low incremental cost.

Increasing the Impact of JMIR Journals in the Attention Economy.

The Journal of Medical Internet Research (JMIR) has attained remarkable achievements in the past twenty years. By depth, JMIR has published the most impactful research in medical informatics and is top ranked in the field. By width, JMIR has spun off to about thirty sister journals to cover topics such as serious games, mobile health, public health, surveillance, and other medical areas. With ever-increasing data and research findings, academic publishers need to be competitive to win readers' attention. While JMIR is well-positioned in the field, the journal will need more creative strategies to increase its attention base and maintain its leading position. Viable strategies include the creation of online collaborative spaces, the engagement of more diverse audience from less traditional channels, and partnerships with other publishers and academic institutes. Doing so could also enable JMIR researchers to turn research insights into practical strategies to improve personal health and medical services.

Differences between coverage of yellow fever vaccine and the first dose of measles-containing vaccine: A desk review of global data sources.

INTRODUCTION: The strategy to Eliminate Yellow Fever Epidemics (EYE) is a global initiative that includes all countries with risk of yellow fever (YF) virus transmission. Of these, 40 countries (27 in Africa and 13 in the Americas) are considered high-risk and targeted for interventions to increase coverage of YF vaccine. Even though the World Health Organization (WHO) recommends that YF vaccine be given concurrently with the first dose of measles-containing vaccine (MCV1) in YF-endemic settings, estimated coverage for MCV1 and YF vaccine have varied widely. The objective of this study was to review global data sources to assess discrepancies in YF vaccine and MCV1 coverage and identify plausible reasons for these discrepancies. METHODS: We conducted a desk review of data from 34 countries (22 in Africa, 12 in Latin America), from 2006 to 2016, with national introduction of YF vaccine and listed as high-risk by the EYE strategy. Data reviewed included procured and administered doses, immunization schedules, routine coverage estimates and reported vaccine stock-outs. In the 30 countries included in the comparitive analysis, differences greater than 3 percentage points between YF vaccine and MCV1 coverage were considered meaningful. RESULTS: In America, there were meaningful differences (7-45%) in coverage of the two vaccines in 6 (67%) of the 9 countries. In Africa, there were meaningful differences (4-27%) in coverage of the two vaccines in 9 (43%) of the 21 countries. Nine countries (26%) reported MVC1 stock-outs while sixteen countries (47%) reported YF vaccine stock-outs for three or more years during 2006-2016. CONCLUSION: In countries reporting significant differences in coverage of the two vaccines, differences may be driven by different target populations and vaccine availability. However,these were not sufficient to completely explain observed differences. Further follow-up is needed to identify possible reasons for differences in coverage rates in several countries where these could not fully be explained.

Cost-aware active learning for named entity recognition in clinical text.

OBJECTIVE: Active Learning (AL) attempts to reduce annotation cost (ie, time) by selecting the most informative examples for annotation. Most approaches tacitly (and unrealistically) assume that the cost for annotating each sample is identical. This study introduces a cost-aware AL method, which simultaneously models both the annotation cost and the informativeness of the samples and evaluates both via simulation and user studies. MATERIALS AND METHODS: We designed a novel, cost-aware AL algorithm (Cost-CAUSE) for annotating clinical named entities; we first utilized lexical and syntactic features to estimate annotation cost, then we incorporated this cost measure into an existing AL algorithm. Using the 2010 i2b2/VA data set, we then conducted a simulation study comparing Cost-CAUSE with noncost-aware AL methods, and a user study comparing Cost-CAUSE with passive learning. RESULTS: Our cost model fit empirical annotation data well, and Cost-CAUSE increased the simulation area under the learning curve (ALC) scores by up to 5.6% and 4.9%, compared with random sampling and alternate AL methods. Moreover, in a user annotation task, Cost-CAUSE outperformed passive learning on the ALC score and reduced annotation time by 20.5%-30.2%. DISCUSSION: Although AL has proven effective in simulations, our user study shows that a real-world environment is far more complex. Other factors have a noticeable effect on the AL method, such as the annotation accuracy of users, the tiredness of users, and even the physical and mental condition of users. CONCLUSION: Cost-CAUSE saves significant annotation cost compared to random sampling.

Retrieval of individual patient data depended on study characteristics: a randomized controlled trial.

OBJECTIVES: The aim of the study was to examine the effect of providing a financial incentive to authors of randomized clinical trials (RCTs) to obtain individual patient data (IPD). STUDY DESIGN AND SETTING: Parallel-group RCT with authors identified in the RCTs eligible for two systematic reviews. The authors were randomly allocated to the intervention (financial incentive with several contact approaches) or control group (using the same contact approaches). Studied outcomes are proportion of authors who provided IPD, time to obtain IPD, and completeness of IPD received. RESULTS: Of the 129 authors contacted, 37 authors suggested or contacted a person or funder providing relevant details or showed interest to collaborate, whereas 45 authors directed us to contact a person or funder, lacked resources or time, did not have ownership or approval to share the IPD, or claimed IPD was too old. None of the authors shared their IPD. We contacted 17 sponsors and received two complete IPD datasets from one sponsor. The time to obtain IPD was >1 year after a sponsor's positive response. Common barriers included study identification, data ownership, limited data access, and required IPD licenses. CONCLUSION: IPD sharing may depend on study characteristics, including funding type, study size, study risk of bias, and treatment effect, but not on providing a financial incentive.

rEHR: An R package for manipulating and analysing Electronic Health Record data.

Research with structured Electronic Health Records (EHRs) is expanding as data becomes more accessible; analytic methods advance; and the scientific validity of such studies is increasingly accepted. However, data science methodology to enable the rapid searching/extraction, cleaning and analysis of these large, often complex, datasets is less well developed. In addition, commonly used software is inadequate, resulting in bottlenecks in research workflows and in obstacles to increased transparency and reproducibility of the research. Preparing a research-ready dataset from EHRs is a complex and time consuming task requiring substantial data science skills, even for simple designs. In addition, certain aspects of the workflow are computationally intensive, for example extraction of longitudinal data and matching controls to a large cohort, which may take days or even weeks to run using standard software. The rEHR package simplifies and accelerates the process of extracting ready-for-analysis datasets from EHR databases. It has a simple import function to a database backend that greatly accelerates data access times. A set of generic query functions allow users to extract data efficiently without needing detailed knowledge of SQL queries. Longitudinal data extractions can also be made in a single command, making use of parallel processing. The package also contains functions for cutting data by time-varying covariates, matching controls to cases, unit conversion and construction of clinical code lists. There are also functions to synthesise dummy EHR. The package has been tested with one for the largest primary care EHRs, the Clinical Practice Research Datalink (CPRD), but allows for a common interface to other EHRs. This simplified and accelerated work flow for EHR data extraction results in simpler, cleaner scripts that are more easily debugged, shared and reproduced.

The collective impact of rare diseases in Western Australia: an estimate using a population-based cohort.

PURPOSE: It has been argued that rare diseases should be recognized as a public health priority. However, there is a shortage of epidemiological data describing the true burden of rare diseases. This study investigated hospital service use to provide a better understanding of the collective health and economic impacts of rare diseases. METHODS: Novel methodology was developed using a carefully constructed set of diagnostic codes, a selection of rare disease cohorts from hospital administrative data, and advanced data-linkage technologies. Outcomes included health-service use and hospital admission costs. RESULTS: In 2010, cohort members who were alive represented approximately 2.0% of the Western Australian population. The cohort accounted for 4.6% of people discharged from hospital and 9.9% of hospital discharges, and it had a greater average length of stay than the general population. The total cost of hospital discharges for the cohort represented 10.5% of 2010 state inpatient hospital costs. CONCLUSIONS: This population-based cohort study provides strong new evidence of a marked disparity between the proportion of the population with rare diseases and their combined health-system costs. The methodology will inform future rare-disease studies, and the evidence will guide government strategies for managing the service needs of people living with rare diseases.Genet Med advance online publication 22 September 2016.

Outcomes of an investment in administrative data infrastructure: An example of capacity building at the Manitoba Centre for Health Policy.

Using the Manitoba Centre for Health Policy as an example, this commentary discusses how even small investments in population health data can create a multitude of research benefits. The authors highlight that through infrastructure development such as acquiring databases, facilitating access to data and developing data management practices, new, innovative research can be achieved at relatively low cost.

Feasibility of extracting data from electronic medical records for research: an international comparative study.

BACKGROUND: Electronic medical records (EMR) offer a major potential for secondary use of data for research which can improve the safety, quality and efficiency of healthcare. They also enable the measurement of disease burden at the population level. However, the extent to which this is feasible in different countries is not well known. This study aimed to: 1) assess information governance procedures for extracting data from EMR in 16 countries; and 2) explore the extent of EMR adoption and the quality and consistency of EMR data in 7 countries, using management of diabetes type 2 patients as an exemplar. METHODS: We included 16 countries from Australia, Asia, the Middle East, and Europe to the Americas. We undertook a multi-method approach including both an online literature review and structured interviews with 59 stakeholders, including 25 physicians, 23 academics, 7 EMR providers, and 4 information commissioners. Data were analysed and synthesised thematically considering the most relevant issues. RESULTS: We found that procedures for information governance, levels of adoption and data quality varied across the countries studied. The required time and ease of obtaining approval also varies widely. While some countries seem ready for secondary uses of data from EMR, in other countries several barriers were found, including limited experience with using EMR data for research, lack of standard policies and procedures, bureaucracy, confidentiality, data security concerns, technical issues and costs. CONCLUSIONS: This is the first international comparative study to shed light on the feasibility of extracting EMR data across a number of countries. The study will inform future discussions and development of policies that aim to accelerate the adoption of EMR systems in high and middle income countries and seize the rich potential for secondary use of data arising from the use of EMR solutions.

A Multi-Objective Compounded Local Mobile Cloud Architecture Using Priority Queues to Process Multiple Jobs.

As a result of the greatly increased use of mobile devices, the disadvantages of portable devices have gradually begun to emerge. To solve these problems, the use of mobile cloud computing assisted by cloud data centers has been proposed. However, cloud data centers are always very far from the mobile requesters. In this paper, we propose an improved multi-objective local mobile cloud model: Compounded Local Mobile Cloud Architecture with Dynamic Priority Queues (LMCpri). This new architecture could briefly store jobs that arrive simultaneously at the cloudlet in different priority positions according to the result of auction processing, and then execute partitioning tasks on capable helpers. In the Scheduling Module, NSGA-II is employed as the scheduling algorithm to shorten processing time and decrease requester cost relative to PSO and sequential scheduling. The simulation results show that the number of iteration times that is defined to 30 is the best choice of the system. In addition, comparing with LMCque, LMCpri is able to effectively accommodate a requester who would like his job to be executed in advance and shorten execution time. Finally, we make a comparing experiment between LMCpri and cloud assisting architecture, and the results reveal that LMCpri presents a better performance advantage than cloud assisting architecture.

Gauging the Purported Costs of Public Data Archiving for Long-Term Population Studies.

It was recently proposed that long-term population studies be exempted from the expectation that authors publicly archive the primary data underlying published articles. Such studies are valuable to many areas of ecological and evolutionary biological research, and multiple risks to their viability were anticipated as a result of public data archiving (PDA), ultimately all stemming from independent reuse of archived data. However, empirical assessment was missing, making it difficult to determine whether such fears are realistic. I addressed this by surveying data packages from long-term population studies archived in the Dryad Digital Repository. I found no evidence that PDA results in reuse of data by independent parties, suggesting the purported costs of PDA for long-term population studies have been overstated.

The chloroplast genome hidden in plain sight, open access publishing and anti-fragile distributed data sources.

We sequenced several cannabis genomes in 2011 of June and the first and the longest contigs to emerge were the chloroplast and mitochondrial genomes. Having been a contributor to the Human Genome Project and an eye-witness to the real benefits of immediate data release, I have first hand experience with the potential mal-investment of millions of dollars of tax payer money narrowly averted due to the adopted global rapid data release policy. The policy was vital in reducing duplication of effort and economic waste. As a result, we felt obligated to publish the Cannabis genome data in a similar spirit and placed them immediately on a cloud based Amazon server in August of 2011. While these rapid data release practices were heralded by many in the media, we still find some authors fail to find or reference said work and hope to compel the readership that this omission has more pervasive repercussions than bruised egos and is a regression for our community.