Evidence map›Paper›PMID 27222127›Full record

ArticleDer Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft2016

[Smart eye data : Development of a foundation for medical research using Smart Data applications].

K Kortüm, M Müller, C Hirneiß, A Babenko, D Nasseh, C Kern, A Kampik, S Priglinger, T C Kreutzer

Abstract read
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In one paragraph

Article in Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. A Systematic Review of Digital Ophthalmoscopes in Medicine.Clinical ophthalmology (Auckland, N.Z.) · 2023
    Review
  2. Big data simulations for capacity improvement in a general ophthalmology clinic.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2021
    Article
  3. Article
  4. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

K KortümAugenklinik, Universität München, Mathildenstr. 8, 80336, München, Deutschland. karsten.kortuem@med.uni-muenchen.de.
M MüllerAugenklinik, Universität München, Mathildenstr. 8, 80336, München, Deutschland.
C HirneißAugenklinik, Universität München, Mathildenstr. 8, 80336, München, Deutschland.
A BabenkoAugenklinik, Universität München, Mathildenstr. 8, 80336, München, Deutschland.
D NassehInstitut für Biometrie und Epidemiologie in der Medizin, Universität München, München, Deutschland.
C KernAugenklinik, Universität München, Mathildenstr. 8, 80336, München, Deutschland.
A KampikAugenklinik, Universität München, Mathildenstr. 8, 80336, München, Deutschland.
S PriglingerAugenklinik, Universität München, Mathildenstr. 8, 80336, München, Deutschland.
T C KreutzerAugenklinik, Universität München, Mathildenstr. 8, 80336, München, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSmart Data means intelligent data accumulation and the evaluation of large data sets. This is particularly important in ophthalmology as more and more data are being created. Increasing knowledge and personalized therapies are expected by combining clinical data from electronic health records (EHR) with measurement data.

objectiveIn this study we investigated the possibilities to consolidate data from measurement devices and clinical data in a data warehouse (DW). MATERIAL AND

methodsAn EHR was adjusted to the needs of ophthalmology and the contents of referral letters were extracted. The data were imported into a DW overnight. Measuring devices were connected to the EHR by an HL7 standard interface and the use of a picture archiving and communications system (PACS). Data were exported from the review software using a self-developed software. For data analysis the software was modified to the specific requirements of ophthalmology.

resultsIn the EHR 12 graphical user interfaces were created and the data from 32,234 referral letters were extracted. A total of 23 diagnostic devices could be linked to the PACS and 85,114 optical coherence tomography (OCT) scans, 19,098 measurements from IOLMaster as well as 5,425 pentacam examinations were imported into the DW including over 300,000 patients. Data discovery software was modified providing filtering methods.

conclusionBy building a DW a foundation for clinical and epidemiological studies could be implemented. In the future, decision support systems and strategies for personalized therapies can be based on such a database.

Indexed as

Datasets as TopicElectronic Health RecordsEye DiseasesGermanyHumansInformation Storage and RetrievalMedical Record LinkagePrevalenceRadiology Information SystemsRegistriesRisk FactorsDataDigitalImage libraryMeasurement dataOphthalmology

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.