[Development of an ophthalmological clinical information system for inpatient eye clinics]

[Automated data extraction into a cataract surgery registry : Automatic investigation of results in the registry on ophthalmological analysis (ROPHA)]

BACKGROUND

Medical data should be evaluated with respect to quality management, for scientific and research purposes on the basis of registries. These data are stored in the EMR in an unstructured way and are not suitable for automatic evaluation.

METHOD

The EMR tomedo (Zollsoft/Jena) and the PACS of FORUM (Zeiss/Oberkochen) manage personal, medical and image data for cataract surgery. New developed input masks gather data from diagnostic tools direct in a structured way. These data are automatically extracted by an ETL process and are stored in our data warehouse. Via Excel specific data can be selected and displayed in the dashboard. All medical data are structured and stored electronically. Few data are entered manually and most of the data are entered by pull-down menus or taken over directly from diagnostic equipment.

RESULTS

A total of 2816 cataract surgeries of 1844 patients were selected for a first analysis. Results of IOL type distribution in relation to the patients and to the surgeons are displayed as well as prediction error of refraction for the 4 different groups of IOLs.

DISCUSSION

Structured data input into the EMR (electronic medical record) makes data for automatic extraction available thus reducing input errors or confounding data. The result is a high quality of data sets. Input of epidemiological and general medical data also helps to perform health services research. These registry data represent better information for real live data than data from clinical trials. Bureaucratic hurdles in Germany prevent the use of registry data.

[Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data]

Hintergrund

Der Einsatz von künstlicher Intelligenz (KI) ist unter anderem in der automatischen Bildsegmentierung, -analyse und Klassifikation interessant und bereits für verschiedene Bereiche der Augenheilkunde beschrieben.

Fragestellung

Diese Arbeit soll einen Überblick über aktuelle Ansätze und Fortschritte bei der Anwendung von Big Data und KI bei verschiedenen Erkrankungen des Sehnervenkopfes geben.

Material und Methode

Es wurde eine PubMed-Recherche durchgeführt. Gesucht wurde nach Studien, die klinische Fragestellungen mithilfe von Big-Data-Ansätzen beantworteten oder klassische Methoden des maschinellen Lernens bei der Analyse von multimodaler Bildgebung des Sehnervenkopfes verwendeten.

Ergebnisse

Big Data kann bei Volkskrankheiten wie dem Glaukom helfen, klinische Fragestellungen zu beantworten. KI findet sowohl bei der Segmentierung von multimodaler Bildgebung des Sehnervenkopfes als auch bei der Klassifikation von Erkrankungen wie dem Glaukom oder der Stauungspapille auf diesen Bilddaten Anwendung.

Schlussfolgerung

Mithilfe von Big Data und KI können Zusammenhänge besser erkannt und die Diagnostik und Verlaufsbeurteilung von Erkrankungen des Sehnervenkopfes erleichtert oder automatisiert werden. Eine Voraussetzung für die klinische Anwendung ist in Europa die CE-Kennzeichnung als ein Medizinprodukt und in den USA die Zulassung durch die Food and Drug Administration.

[Per aspera ad astra: implementation of electronic patient records in a university eye hospital : Experience with FIDUS in the Clinic for Ophthalmology at the Saarland University Medical Center UKS]

BACKGROUND

For more than 60 years patient records of the Department of Ophthalmology at the Saarland Medical Center in Homburg were documented on handwritten paper documents and filed in archives. Increasing requirements for medical documentation, exploding volumes of diagnostic data, overcrowded archives and unavailable files when needed, were the essential rationales for the implementation of an electronic patient archive (EPA).

METHODS

The EPA has been specifically adapted to the requirements of the Department of Ophthalmology in order to guarantee a standardized unobstructed documentation and patient care across all sections of the department. Seamless integration into the clinic information system (KIS) and network-compatible diagnostic software were essential as well as a timely digitization of existing paper charts. Decisive factors for using FIDUS (Arztservice Wente GmbH, Darmstadt, Germany) as the EPA software were the visual layout of the EPA, user friendliness and reference installations of the software in other ophthalmology departments.

RESULTS

After one and a half years of meticulous preparation with specific adaptations to the requirements of our department, the EPA was finally implemented in January 2016 and since then we have been working on improvements. The EPA software retrieves basic patient data from the KIS and diagnostic data from electronic devices via various interfaces. Expenses for printers could be reduced but computer workplaces had to be expanded or newly created. For previous patients paper files are digitized externally and stored in an electronic archive directly accessible from the EPA.

DISCUSSION

Successful reorganization and implementation of electronic documentation during clinical routine is feasible with careful preparation and timely involvement of information technology experts, motivated physicians, nurses, research departments and the administration.

Modern Corneal Eye-Banking Using a Software-Based IT Management Solution

Background

Increasing government legislation and regulations in manufacturing have led to additional documentation regarding the pharmaceutical product requirements of corneal grafts in the European Union. The aim of this project was to develop a software within a hospital information system (HIS) to support the documentation process, to improve the management of the patient waiting list and to increase informational flow between the clinic and eye bank.

Materials and Methods

After an analysis of the current documentation process, a new workflow and software were implemented in our electronic health record (EHR) system.

Results

The software takes over most of the documentation and reduces the time required for record keeping. It guarantees real-time tracing of all steps during human corneal tissue processing from the start of production until allocation during surgery and includes follow-up within the HIS. Moreover, listing of the patient for surgery as well as waiting list management takes place in the same system.

Conclusion

The new software for corneal eye banking supports the whole process chain by taking over both most of the required documentation and the management of the transplant waiting list. It may provide a standardized IT-based solution for German eye banks working within the same HIS.

Comparison of Two Toric IOL Calculation Methods

PURPOSE

To compare two calculators for toric intraocular lens (IOL) calculation and to evaluate the prediction of refractive outcome.

METHODS

Sixty-four eyes of forty-five patients underwent cataract surgery followed by implantation of a toric intraocular lens (Zeiss Torbi 709 M) calculated by a standard industry calculator using front keratometry values. Prediction error, median absolute error, and refractive astigmatism error were evaluated for the standard calculator. The predicted postoperative refraction and toric lens power values were evaluated and compared after postoperative recalculation using the Barrett calculator.

RESULTS

We observed a significant undercorrection in the spherical equivalent (0.19 D) by using a standard calculator (p ≤ 0.05). According to the Baylor nomogram and the refractive influence of posterior corneal astigmatism (PCA), undercorrection of the cylinder was lower for patients with WTR astigmatism, because of the tendency of overcorrection. An advantage of less residual postoperative SE, sphere, and cylinder for the Barrett calculator was observed when retrospectively comparing the calculated predicted postoperative refraction between calculators (p ≤ 0.01).

CONCLUSION

Consideration of only corneal front keratometric values for toric lens calculation may lead to postoperative undercorrection of astigmatism. The prediction of postoperative refractive outcome can be improved by using appropriate methods of adjustment in order to take PCA into account.

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

BACKGROUND

Smart 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.

OBJECTIVE

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

MATERIAL AND METHODS

An 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.

RESULTS

In 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.

CONCLUSION

By 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.

Correlation between weather and incidence of selected ophthalmological diagnoses: a database analysis

Purpose

Our aim was to correlate the overall patient volume and the incidence of several ophthalmological diseases in our emergency department with weather data.

Patients and methods

For data analysis, we used our clinical data warehouse and weather data. We investigated the weekly overall patient volume and the average weekly incidence of all encoded diagnoses of “conjunctivitis”, “foreign body”, “acute iridocyclitis”, and “corneal abrasion”. A Spearman’s correlation was performed to link these data with the weekly average sunshine duration, temperature, and wind speed.

Results

We noticed increased patient volume in correlation with increasing sunshine duration and higher temperature. Moreover, we found a positive correlation between the weekly incidences of conjunctivitis and of foreign body and weather data.

Conclusion

The results of this data analysis reveal the possible influence of external conditions on the health of a population and can be used for weather-dependent resource allocation.

The influence of a specific ophthalmological electronic health record on ICD-10 coding

BACKGROUND

A specific Electronic Health Record (EHR) for ophthalmology was introduced in an academic center in Germany. As diagnoses coding corresponding to the International Classification of Diseases Version 10 (ICD-10) is mandatory for billing reasons in Germany, we analyzed whether a change occurred in the diversity and number of diagnoses after the EHR introduction. The number of patients was also analyzed. Proper diagnoses coding is of the utmost importance for further data analysis or billing.

METHODS

Graphical User Interfaces (GUIs) were created by using Advanced Business Application Programming language in EHR "i.s.h.med." Development of an EHR was conducted in close collaboration between physicians and software engineers. ICD-10 coding was implemented by using a "hit list" and a search engine for diagnoses. An observational analysis of a 6-month period prior to and after the introduction of an ophthalmological specific EHR was conducted by investigating the diversity and number of diagnoses in various ophthalmological disease categories and the number of patient consultations.

RESULTS

During the introduction of a specific ophthalmological EHR, we observed a significant increase in the emergency department cases (323.9 vs. 359.9 cases per week), possibly related to documentation requirements. The number of scheduled outpatients didn't change significantly (355.12 vs. 360.24 cases per week). The variety of diagnoses also changed: on average, 156.2 different diagnoses were made per week throughout our hospital before the EHR launch, compared to 186.8 different diagnoses per week thereafter (p < 0.05). Additionally, a significantly higher number of diagnoses per case and per week were observed in both emergency and subspecialty outpatient clinics (1.15 vs. 1.22 and 1.10 vs. 1.47, respectively).

CONCLUSIONS

An optimized EHR was created for ophthalmological needs and for simplified ICD-10 coding. The implementation of digital patient recording increased the diversity of the diagnoses used per case as well as the number of diagnoses coded per case. A general limitation to date is the suboptimal precision of ICD-10 coding in ophthalmology. Correct coding is of utmost importance for future data analysis.