The development of the presented EHR was supported over the last 20 years by internal research funding and public funding from different institutions and foundations.

The protocol demonstrates the use of the electronic health record TBase, how to add data into the database, and how to extract them for research purposes. All steps are in accordance with the guidelines of the human research ethics committee of Charit&#233; - Universit&#228;tsmedizin Berlin.
1. Register a new patient and add basic patient data into TBase
<ol>
	<li>Upon registration, transfer the patient&#39;s basic data (name, birth date, and health insurance data) from the patient&#39;s hea...

<ol>
	<li>Halleck, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+care+of+renal+transplant+patients+-+Development+of+an+electronic+health+care+service+platform.">Integrated care of renal transplant patients - Development of an electronic health care service platform.</a> Dialyse aktuell. 20 (06), 285-290 (2016).</li><li>Sonntag, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Clinical+Data+Intelligence+Project.">The Clinical Data Intelligence Project.</a> Informatik-Spektrum. 39 (4), 290-300 (2016).</li><li>Kara, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Domain-adapted+Dependency+Parser+for+German+Clinical+Text.">A Domain-adapted Dependency Parser for German Clinical Text.</a> Proceedings of the 14th Conference on Natural Language Processing. , (2018).</li><li>Maier, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experiences+of+Transforming+a+Complex+Nephrologic+Care+and+Research+Database+into+i2b2+Using+the+IDRT+Tools.">Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools.</a> Journal of Healthcare Engineering. 2019, 5640685 (2019).</li><li>Jensen, P. B., Jensen, L. J., Brunak, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mining+electronic+health+records:+towards+better+research+applications+and+clinical+care.">Mining electronic health records: towards better research applications and clinical care.</a> Nature Reviews Genetics. 13 (6), 395-405 (2012).</li><li>Schmidt, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+tool+for+the+identification+of+correlations+in+medical+data+by+faceted+search.">A novel tool for the identification of correlations in medical data by faceted search.</a> Computers in Biology and Medicine. 85, 98-105 (2017).</li><li>Veit, K., Wessels, M., Deiters, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gesundheitsdaten+und+Digitalisierung+-+Neue+Anforderungen+an+den+Umgang+mit+Daten+im+Gesundheitswesen.">Gesundheitsdaten und Digitalisierung - Neue Anforderungen an den Umgang mit Daten im Gesundheitswesen.</a> Digitale Transformation von Dienstleistungen im Gesundheitswesen VI: Impulse f&#252;r die Forschung. , 19-33 (2019).</li><li>Ehrler, F., Geissbuhler, A., Jimeno, A., Ruch, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data-poor+categorization+and+passage+retrieval+for+gene+ontology+annotation+in+Swiss-Prot.">Data-poor categorization and passage retrieval for gene ontology annotation in Swiss-Prot.</a> BMC Bioinformatics. 6, 23 (2005).</li><li>Esteban, C., Schmidt, D., Krompa&#223;, D., Tresp, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+Sequences+of+Clinical+Events+by+Using+a+Personalized+Temporal+Latent+Embedding+Model.">Predicting Sequences of Clinical Events by Using a Personalized Temporal Latent Embedding Model.</a> 2015 International Conference on Healthcare Informatics. , 130-139 (2015).</li><li>Roller, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+Named+Entities+and+Relations+in+German+Clinical+Reports.">Detecting Named Entities and Relations in German Clinical Reports.</a> Language Technologies for the Challenges of the Digital Age. , 146-154 (2018).</li><li>Roller, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+fine-grained+corpus+annotation+schema+of+German+nephrology+records.">A fine-grained corpus annotation schema of German nephrology records.</a> Clinical Natural Language Processing Workshop (ClinicalNLP). , (2016).</li><li>Esteban, C., Staeck, O., Baier, S., Yang, Y., Tresp, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+Clinical+Events+by+Combining+Static+and+Dynamic+Information+Using+Recurrent+Neural+Networks.">Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks.</a> IEEE International Conference on Healthcare Informatics (ICHI). , 93-101 (2016).</li><li>Schmidt, D., Niemann, M., Trzebiatowski, G. L. v. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Handling+of+Missing+Values+in+Medical+Domains+with+Respect+to+Pattern+Mining+Algorithms.">The Handling of Missing Values in Medical Domains with Respect to Pattern Mining Algorithms.</a> Proceedings of the 24th International Workshop on Concurrency, Specification and Programming (CS&amp;P 2015). 1492, 147-154 (2015).</li><li>Burchardt, A., Uszkoreit, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IT+f&#252;r+soziale+Inklusion:+Digitalisierung+-+K&#252;nstliche+Intelligenz+-+Zukunft+f&#252;r+alle.">IT f&#252;r soziale Inklusion: Digitalisierung - K&#252;nstliche Intelligenz - Zukunft f&#252;r alle.</a> De Gruyter. , (2018).</li><li>Schroter, G., Lindemann, L. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TBase2+-+A+Web-Based+Electronic+Patient+Record.">TBase2 - A Web-Based Electronic Patient Record.</a> Fundamenta Informaticae. 43 (1-4), 343-353 (2000).</li><li>Duettmann, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=eHealth+in+Transplantation.">eHealth in Transplantation.</a> Transplant International. , (2020).</li><li>Durr, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Late+Conversion+to+Belatacept+After+Kidney+Transplantation:+Outcome+and+Prognostic+Factors.">Late Conversion to Belatacept After Kidney Transplantation: Outcome and Prognostic Factors.</a> Transplantation Proceedings. 49 (8), 1747-1756 (2017).</li><li>Halleck, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MHealth+and+digital+management+after+kidney+transplantation.">MHealth and digital management after kidney transplantation.</a> Nieren- und Hochdruckkrankheiten. 46, 474-480 (2017).</li><li>D&#252;ttmann-Rehnolt, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuartige+Kommunikationswege+und+Strukturen+zur+Optimierung+der+h&#228;uslichen+Versorgung+am+Beispiel+von+nierentransplantierten+Patienten.">Neuartige Kommunikationswege und Strukturen zur Optimierung der h&#228;uslichen Versorgung am Beispiel von nierentransplantierten Patienten.</a> Digitale Transformation von Dienstleistungen im Gesundheitswesen VI: Impulse f&#252;r die Forschung. , 407-421 (2019).</li><li>Hils, S., Bogatyreva, L., Hauschke, D., Pisarski, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Telemedical+Supported+Aftercare+as+an+Innovative+Project-Study+Improves+the+Quality+of+Life+After+Living+Kidney+Transplantation+-+A+Single+Center+Experience.">Telemedical Supported Aftercare as an Innovative Project-Study Improves the Quality of Life After Living Kidney Transplantation - A Single Center Experience.</a> American Journal of Transplantation. 14 (3), 843 (2014).</li><li>Schmidt, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrierte+Versorgung+chronisch+kranker+Patienten+am+Beispiel+von+MACSS:+Digitalisierung+-+K&#252;nstliche+Intelligenz+-+Zukunft+f&#252;r+alle.">Integrierte Versorgung chronisch kranker Patienten am Beispiel von MACSS: Digitalisierung - K&#252;nstliche Intelligenz - Zukunft f&#252;r alle.</a> IT f&#252;r soziale Inklusion. , 41-50 (2018).</li><li>Massie, A. B., Kucirka, L. M., Segev, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Big+data+in+organ+transplantation:+registries+and+administrative+claims.">Big data in organ transplantation: registries and administrative claims.</a> American Journal of Transplantation. 14 (8), 1723-1730 (2014).</li><li>McAdams-DeMarco, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frailty,+Length+of+Stay,+and+Mortality+in+Kidney+Transplant+Recipients:+A+National+Registry+and+Prospective+Cohort+Study.">Frailty, Length of Stay, and Mortality in Kidney Transplant Recipients: A National Registry and Prospective Cohort Study.</a> Annals of surgery. 266 (6), 1084-1090 (2017).</li><li>The Most Popular Browsers. w3schools.com Available from: <a target="_blank" href="https://www.w3schools.com/browsers/">https://www.w3schools.com/browsers/</a> (2020)</li><li>H.L.S.I. HL7 International Available from: <a target="_blank" href="https://www.hl7.org/">https://www.hl7.org/</a> (2020)</li><li>Duettmann, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+home+monitoring+of+patients+after+kidney+transplantation:+The+MACCS+platform.">Digital home monitoring of patients after kidney transplantation: The MACCS platform.</a> Journal of Visualized Experiments. , (2021).</li><li>Haas, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Banff+2017+Kidney+Meeting+Report:+Revised+diagnostic+criteria+for+chronic+active+T+cell-mediated+rejection,+antibody-mediated+rejection,+and+prospects+for+integrative+endpoints+for+next-generation+clinical+trials.">The Banff 2017 Kidney Meeting Report: Revised diagnostic criteria for chronic active T cell-mediated rejection, antibody-mediated rejection, and prospects for integrative endpoints for next-generation clinical trials.</a> American Journal of Transplantation. 18 (2), 293-307 (2018).</li><li>Roufosse, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+2018+Reference+Guide+to+the+Banff+Classification+of+Renal+Allograft+Pathology.">A 2018 Reference Guide to the Banff Classification of Renal Allograft Pathology.</a> Transplantation. 102 (11), 1795-1814 (2018).</li><li>Duerr, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increased+incidence+of+angioedema+with+ACE+inhibitors+in+combination+with+mTOR+inhibitors+in+kidney+transplant+recipients.">Increased incidence of angioedema with ACE inhibitors in combination with mTOR inhibitors in kidney transplant recipients.</a> Clinical Journal of the American Society of Nephrology. 5 (4), 703-708 (2010).</li><li>Lachmann, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Invited+letter+in+response+to+"Predicted+indirectly+recognizable+HLA+epitopes+(PIRCHE):+Only+the+tip+of+the+iceberg?".">Invited letter in response to "Predicted indirectly recognizable HLA epitopes (PIRCHE): Only the tip of the iceberg?".</a> American Journal of Transplantation. 18 (2), 523-524 (2018).</li><li>Huber, L., Naik, M., Budde, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Desensitization+of+HLA-incompatible+kidney+recipients.">Desensitization of HLA-incompatible kidney recipients.</a> The New England Journal of Medicine. 365 (17), 1644-1645 (2011).</li><li>Choi, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Low+Seroprevalence+of+SARS-CoV-2+Antibodies+during+Systematic+Antibody+Screening+and+Serum+Responses+in+Patients+after+COVID-19+in+a+German+Transplant+Center.">Low Seroprevalence of SARS-CoV-2 Antibodies during Systematic Antibody Screening and Serum Responses in Patients after COVID-19 in a German Transplant Center.</a> Journal of Clinical Medicine. 9 (11), (2020).</li><li>Lehner, L. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Risk+Factors+and+Long-Term+Outcomes+in+Kidney+Transplant+Patients+with+Identified+Lymphoceles.">Analysis of Risk Factors and Long-Term Outcomes in Kidney Transplant Patients with Identified Lymphoceles.</a> Journal of Clinical Medicine. 9 (9), (2020).</li><li>Zhang, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+relationship+between+proteinuria+and+allograft+survival+in+patients+with+transplant+glomerulopathy:+a+retrospective+single-center+cohort+study.">The relationship between proteinuria and allograft survival in patients with transplant glomerulopathy: a retrospective single-center cohort study.</a> Transplant International. , (2020).</li><li>Bissler, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Everolimus+for+angiomyolipoma+associated+with+tuberous+sclerosis+complex+or+sporadic+lymphangioleiomyomatosis+(EXIST-2):+A+multicentre,+randomised,+double-blind,+placebo-controlled+trial.">Everolimus for angiomyolipoma associated with tuberous sclerosis complex or sporadic lymphangioleiomyomatosis (EXIST-2): A multicentre, randomised, double-blind, placebo-controlled trial.</a> Lancet. 381, (2013).</li><li>Budde, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Everolimus-based,+calcineurin-inhibitor-free+regimen+in+recipients+of+de-novo+kidney+transplants:+an+open-label,+randomised,+controlled+trial.">Everolimus-based, calcineurin-inhibitor-free regimen in recipients of de-novo kidney transplants: an open-label, randomised, controlled trial.</a> Lancet. 377 (9768), 837-847 (2011).</li><li>Lindemann, G., Schr&#246;ter, K., Schlaefer, A., Budde, K., Neumayer, H. 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The corresponding authors have nothing to declare.

TBase combines a web-based EHR for specialized outpatient care of KTR with a research database, creating a comprehensive long-term database for patients with kidney disease6,11,15,37. Regarding organizational structure, this is enabled by implementing a modern software design process as an institutional agent and including over 20 years of experience as developers, clinical users and researcher...

Motivation for an integrated electronic health record and research database 
Clinical research is based on the availability of high-quality data, regardless of whether classical statistical methods or Machine Learning (ML) techniques are used for analysis1,2. In addition to routine data (e.g., demographic, laboratory, and medication data), domain-specific data (e.g., transplantation-relevant data) are required with high granularity3,4. However, routine care at many university hospitals is performed with hospital information systems (HIS) that neither allow for systematic collection of research-specific data nor for easy data extraction of routine data5,6,7. As a result, clinical researchers create specific research databases, which have a variety of problems including complex process of setting up a database, manual data entry, data protection issues, and long-term maintenance (Table 1). Limited amount of data, missing data, and inconsistencies are a major problem for clinical research in general and impede the use of ML technologies8,9,10,11,12,13. These standalone research databases are usually focused on certain disease or patient aspects, not connected to other databases, and often discontinued after a certain period, resulting in inaccessible &#34;data silos&#34;. Ultimately, high-quality, long-term data on various disease aspects are sparse. In the era of digital medicine there is an increasing need for a comprehensive electronic health record (EHR)7,14,15, which enables easy documentation of domain-specific data and automated collection of routine data from the systems of inpatient and outpatient care.
These general considerations apply to transplantation medicine as well16. Hence, a complete documentation of the patient&#39;s medical history including all inpatient and outpatient treatments, clinical routine data as well as transplantation-specific data is necessary for successful follow-up care17,18. Since ordinary HIS are static and focused on inpatient treatment, they cannot integrate transplantation-specific data, such as donor data, cold ischemia times, and human leukocyte antigens (HLA) data. However, these data are a basic prerequisite for transplantation research19,20,21,22 as well as from long-term clinical care. While the initial hospital stay usually is only 1-2 weeks and processes as well as early outcomes after kidney transplantation are comparable between many transplant centers, lifelong post-transplant care is complicated and lacks a common structured approach. This motivates an integrated EHR and research database to capture the lifelong post-transplant patient journey.23
In order to integrate these functionalities for routine care and research of KTR, an EHR named &#34;TBase&#34; was developed with the idea that the routine use for post-transplant care will create a unique research database with highest data quality (Table 2).
Design and Architecture 
TBase is based on a typical client-server architecture. For development, the components and tools of SAP High Performance Analytic Appliance extended application advanced (SAP HANA XSA) were used. Based on the latest Hypertext Markup Language 5 (HTML5) web-technologies the EHR has been developed and tested for the Google Chrome Engine. This web engine is used by Chrome and the Microsoft Edge Browser and allows to use the EHR in the most frequently used web browsers24 without the need for local installation. The applied technology enables a responsive web design and allows the web-based EHR to be used on all devices (PC, tablet, smartphone). The innovative high-performance development platform is composed of various components (Web IDE, UI5 and HANA DB) and has enabled us to rapidly implement the EHR project TBase with state-of-the-art software tools (Figure 1).
For the representation of patient data, a simple table structure was implemented for an intuitive and self-explanatory design of the EHR. For example, the patient table with the PatientID as the primary key is at the center of the table structure. Almost all tables (except individual sub-tables) are connected to this central table through PatientID (Figure 2).
Figure 3 shows part of TBase&#39;s table structure and the data types used in greater detail. The end user can access the data fields via graphical user interface (GUI), for which an example is shown in Figure 4.
This EHR contains all current patient data and is used for routine outpatient care. Important routine clinical data (e.g., laboratory data, medical results, radiology, microbiology, virology and pathology data, hospital data, etc.) are directly imported into TBase via standardized interfaces (e.g., on the basis of Health Level Seven (HL7) - a standard for digital communication in the healthcare sector25). Transplant-specific data such as cold ischemia times, donor data, HLA data as well as follow-up notes, vital signs, medical reports and the medication list are entered by the users via GUI into the EHR. Before data are transferred to the database, an automated plausibility check is performed for prompt detection of erroneous data entry providing the option to correct immediately. In addition, data validation takes part during clinical routine in which clinical users routinely write reports and letters to patients and physicians. These letters must provide correct data (e.g., on medication, lab values and clinical remarks) for further treatment and medication plans. As a consequence physicians and patients constantly validate and correct the clinical data in their daily practice, a process resulting in high data quality. If data are entered via application programming interfaces (API) or other interfaces, plausibility checks are performed in the backend similarly to the plausibility checks in the frontend.
Frontend (GUI) 
To implement the frontend, the UI5 Framework is used. This framework provides an extensive library for frontend elements as well as a variety of additional features such as multilingualism and graphical libraries for data visualization. Currently, TBase frontend elements are displayed either in English or German depending on the language setting of the browser.
A master-detail interface is used for the frontend to ensure a simple, intuitive page structure. The upper part of the viewing page consists of individual tabs for the detail pages (basic data, medical data, transplantation data, etc.). This master part remains unchanged regardless of which detail page is shown below (Figure 4). The detail view of each page enables an easy overview on the page topic.
For data manipulation, the EHR has different levels of user rights (&#34;read&#34;, &#34;write&#34;, &#34;delete&#34;, and &#34;administrator&#34;). There is an &#34;edit&#34; level in addition to the &#34;view&#34; level, which can only be activated by users with higher rights than &#34;read&#34;. If the user has the right to write, all input fields for data entry are activated and can be filled with data. Users with &#34;delete&#34; rights can delete data via a corresponding button, but only after confirmation through a pop-up window.
Database structure and interfaces 
The development of TBase is performed in the development database. Extensive and detailed testing of all software changes such as new functionalities is carried out in the quality assurance database. Software updates that pass the quality control checks are transferred to the live system. For research purposes the live system is copied into the replication database, which can be queried via standard Open Database Connectivity (ODBC) interfaces (e.g., via open-source software R Studio). As there is no direct connection between replication and live system the data in the live system are protected from corruption, loss or manipulation of data. This modular structure and the clear separation of the four databases (development, quality assurance, live system and replication database), which are tailored to the specific needs of developers, researchers, and clinicians facilitates maintenance and data protection of sensitive patient data.
The EHR is fully integrated into the Data Infrastructure of Charit&#233; and relies on different interfaces for data import from various data sources. The interface to the HIS imports all relevant data such as administrative data, examinations, medications, laboratory findings and discharge letters. This interface connects both systems via a staging area. Here, all new data (data delta) are transferred from the HIS to TBase in real time. Patients are identified via a patient number or case number and the corresponding data from the HIS is imported (if not already available in TBase).
For outpatients, our laboratory partner provides the laboratory results via HL7 messages. These are deployed to a shared area in the laboratory system and picked up via an HL7 interface and imported into the EHR. For bi-directional communication and data exchange with KTR (via smartphone apps) and home nephrologists, a HL7 Fast Healthcare Interoperability Resource (HL7 FHIR) interface was implemented26. This interface grants interoperability and flexibility for a safe data exchange with other data sources (e.g., Eurotransplant, patient apps) in the future.
User Management and Data Protection 
TBase is based on user management at the application level. Thus, the user can only access the frontend of the application, but not the database itself. As described above, a four-stage authorization concept was chosen, reserving user management for those with administrative rights. Administrators use an &#34;Identity Management Console&#34; application to add new users from the Charit&#233; user pool for the TBase application and to maintain their user rights (Figure 5). Most users can access all patients in the database. However, it is possible to restrict access for specific users such as study monitors to a group of patients.
Using the commercial in-memory database platform a secure database technology that protects data with strategies such as application-level authorization, single sign-on (SSO), MIT-Kerberos protocol and Security Assertion Mark-up Language (SAML) is used. The platform secures communication, data storage, and application services using the latest encryption and testing techniques. All developments on the database are controlled by authorizations. This ensures the security of data by design at a high level. In addition, all data are kept behind the certified Charit&#233; firewall. In compliance with the latest European Union General Data Protection Regulation (EU GDPR) a robust data protection concept was implemented, including data flow diagrams, data protection risk assessment (DSFA) and authorization concept. All documents are laid down in a procedure directory of the Charit&#233; Data Protection Office.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Developer platform SAP Web IDE</td>
			<td>SAP SE</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>GUI Toolbox SAPUI5</td>
			<td>SAP SE</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>In-memory database SAP-HANA</td>
			<td>SAP SE</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Interface Standard HL7</td>
			<td>Health Level Seven International</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Interface Standard HL7 FHIR</td>
			<td>Health Level Seven International</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>RStudio</td>
			<td>RStudio Inc.</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>TBase - Electronic Health Record</td>
			<td>Charit&#233; - Universit&#228;tsmedizin Berlin</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Webserver SAP-HANA XSA</td>
			<td>SAP SE</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

tbase - an integrated electronic health record and research database for kidney transplant recipients

TBase is an electronic health record (EHR) for kidney transplant recipients (KTR) combining automated data entry of key clinical data (e.g., laboratory values, medical reports, radiology and pathology data) via standardized interfaces with manual data entry during routine treatment (e.g., clinical notes, medication list, and transplantation data). By this means, a comprehensive database for KTR is created with benefits for routine clinical care and research. It enables both easy everyday clinical use and quick access for research questions with highest data quality. This is achieved by the concept of data validation in clinical routine in which clinical users and patients have to rely on correct data for treatment and medication plans and thereby validate and correct the clinical data in their daily practice. This EHR is tailored for the needs of transplant outpatient care and proved its clinical utility for more than 20 years at Charit&#233; - Universit&#228;tsmedizin Berlin. It facilitates efficient routine work with well-structured, comprehensive long-term data and allows their easy use for clinical research. To this point, its functionality covers automated transmission of routine data via standardized interfaces from different hospital information systems, availability of transplant-specific data, a medication list with an integrated check for drug-drug interactions, and semi-automated generation of medical reports among others. Key elements of the latest reengineering are a robust privacy-by-design concept, modularity, and hence portability into other clinical contexts as well as usability and platform independence enabled by HTML5 (Hypertext Markup Language) based responsive web design. This allows fast and easy scalability into other disease areas and other university hospitals. The comprehensive long-term datasets are the basis for the investigation of Machine Learning algorithms, and the modular structure allows to rapidly implement these into clinical care. Patient reported data and telemedicine services are integrated into TBase in order to meet future needs of the patients. These novel features aim to improve clinical care as well as to create new research options and therapeutic interventions.

TBase was first released in 1999 at Charit&#233; Campus Mitte and is in use ever since. For more than 20 years the TBase-EHR prospectively collects data from all KTR. Starting in 2001, the other transplant programs at Charit&#233; used TBase for the routine care of KTR and wait-listed patients as well. Since 2007, this EHR is in use for routine care of living donors and all patients in the department of nephrology.
By providing the TBase software with its functionalities, which has been furthe...

Watch this Scientific Journal Video about TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients at JoVE.com

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

TBase combines an electronic health record with an innovative research database for kidney transplant recipients. TBase is built upon an in-memory database platform, connected to different hospital systems, and used for regular outpatient care. It automatically integrates all relevant clinical data including transplantation-specific data creating a unique research database.

TBase is an electronic health record (EHR) for kidney transplant recipients (KTR) combining automated data entry of key clinical data (e.g., laboratory ...

We demonstrate the visibility and benefits of a database that is used as an electronic health record for post-transplant care and clinical routine. And also has a highly sophisticated modular research design for the specific requirements of transplant research. Our electronic health record enables easy everyday clinical use, and quick access for research questions while ensuring highest data quality through data validation in a clinical routine setting.Clinical users and patients rely on correct laboratory data, and medication plans for treatment thereby validating, and correcting the clinical data in daily practice. The electronic health record TBase has a modular database design based, and commonly used standards such as HL7, which allows for easy scalability into other transplant centers. This design also allows for implementation of other features that may be required for different outpatient clinics or other research areas.To begin, enter the web link for TBase into a Chrome engine-based web browser in Charite intranet. Enter the username and password assign by the TBase administrator and click log on. Next, click on the add new patient button at a bottom of the patient overview frame on the left.In the input screen that appears, enter the patient's name, date of birth, Charite hospital patient number or case number, and the information regarding patient consent for data processing. Once data entry is complete, click on the save button on the bottom right. Search for desired patient by entering the patient's name or date of birth in the search field on the top left, then click on the search button on the right of the search field or hit enter.From the results that appear, click on the correct patient's name to open the master data viewing page. To navigate back from another page, click on the master data tab on the top left. To change the master data, click on the change button on the bottom right.In the new input screen, change data such as the patient's phone number, address or identification code by typing the new information into the designated input fields. Once the changes are made, click on the save button on the bottom right. This will redirect to the master data viewing page where the changes can be seen and verified.To add a new medication, in the medication tab, click on the new button at the bottom right. Enter the name of the substance dosing scheme, and starting date, which is automatically set at the current date but can be changed if required. Additionally, add an indication or a remark into the designated input fields if required.Add the medication to the list by clicking on the save button on the bottom right. To change an existing medication, click on the appropriate item in the medication list, then click on the change button on the bottom right. Make the changes regarding dosage or application method in the designated input fields, and apply them by clicking on the save button on the bottom right.To discontinue a drug, click on designated drug, then click on the discontinue button on the top. To search for previous medication, click on the search field on the top left, then select the correct option from the historic medication list to open a chart with all previous medications. To create a standardized medication list for the patient, according to German Regulations, click on the German Standard Medication Plan button on the top right to create a PDF file, and download it automatically for printout.To view the medical course, click on the course tab on the top. To add a new entry to the medical course, click on the new button on the bottom right, and enter the information assessed into the desired input fields. Add the date and time of the next appointment into the designated input fields on the top right, and submit the data by clicking the save button on the bottom right.To change an existing entry, click on it, then click the change button on the bottom right. Enter additional data into the designated input fields or change existing data. Change or update information in the notification field, and submit the changes by clicking the save button on the bottom right.To create an automated medical report with a few clicks, click on the medical report button on the bottom right, then click on outpatient medical report. The patient's name, treating physician, last date of the laboratory values, and last date of medical course will be automatically filled out but can be changed if needed. After confirmation, click on okay to generate a properly formatted Word document containing the selected information.To actively log out of TBase, click the log button at the bottom right. To query the collected data, use the replication server, and any data processing programs that can connect to a database via ODBC or JDBC. For example, to set up an ODBC database connection, open the ODBC tool, Add a new user data source name under control panel and security management.Then enter the available connection data to the replication database. To generate a query in the open-source software RStudio, open file, click new file in the application RStudio at the top left, then click on our script. Enter the script code in the empty script window that appears.Click on the run button on the top of the script window to run the script. Data from the connected database is used to generate the bar chart defined in the script. For more than 20 years, TBase has been prospectively collecting data from all kidney transplant recipients.Over the years, a total of 6, 317 patients with 7, 595 kidney transplantation have been documented. The electronic health record TBase is based on four different databases with the live system at the core, enabling different groups of agents to work in parallel. Clinical users enter data via the graphical user interface, maintaining its high-quality.Most information is automatically imported via interfaces from hospital information systems, laboratory partners, and drug-to-drug interaction checkers. Developers can implement new functionalities which are tested on the quality database before integration into the live system. For research purposes, the live database is replicated regularly so that no interference with live system is necessary when the database queries are performed by clinical researchers.For the representation of patient data, a simple table structure was implemented. The patient table with the patient ID as the primary key is at the center of the table structure. Almost all tables except individual sub tables are connected to the central table through patient ID.Graphical results, such as the one shown here can be easily obtained by connecting to the database via a database interface in using data processing software like RStudio to send queries.For a clinician using the graphic user interface of the electronic health record, it's very easy, intuitive, and self explanatory. For the researcher, it is important to set up the ODBC database connection to enable use of the standard statistical software packages like SPSF or RStudio. Over the last 20 years, TBase has been used to address numerous research questions related to kidney transplantation.TBase can be easily used in other transplant centers due to its modularity and platform independence. Its design also allows for integration of home monitoring data, and AI-driven data analysis.

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