{"id":1066,"date":"2020-01-26T13:30:05","date_gmt":"2020-01-26T12:30:05","guid":{"rendered":"https:\/\/www.sw-stein.de\/sw\/?page_id=1066"},"modified":"2023-11-01T11:01:53","modified_gmt":"2023-11-01T10:01:53","slug":"forschendes-lernen-und-scientific-speed-dating","status":"publish","type":"page","link":"https:\/\/www.sw-stein.de\/sw\/dr-stefan-wagner\/forschendes-lernen\/forschendes-lernen-und-scientific-speed-dating\/","title":{"rendered":"Forschendes Lernen und Scientific Speed Dating"},"content":{"rendered":"<div class=\"shariff shariff-align-left shariff-widget-align-left\" style=\"display:none\" data-services=\"facebook\" data-url=\"https:\/\/www.sw-stein.de\/sw\/dr-stefan-wagner\/forschendes-lernen\/forschendes-lernen-und-scientific-speed-dating\/\" data-timestamp=\"1698836513\" data-hidezero=\"1\" data-backendurl=\"https:\/\/www.sw-stein.de\/sw\/wp-json\/shariff\/v1\/share_counts?\"><ul class=\"shariff-buttons theme-round orientation-horizontal buttonsize-large\"><li class=\"shariff-button mailto shariff-nocustomcolor\" style=\"background-color:#a8a8a8;border-radius:43%\"><a href=\"mailto:?body=https%3A%2F%2Fwww.sw-stein.de%2Fsw%2Fdr-stefan-wagner%2Fforschendes-lernen%2Fforschendes-lernen-und-scientific-speed-dating%2F&subject=Forschendes%20Lernen%20und%20Scientific%20Speed%20Dating\" title=\"Per E-Mail versenden\" aria-label=\"Per E-Mail versenden\" role=\"button\" rel=\"noopener nofollow\" class=\"shariff-link\" style=\";border-radius:43%; 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background-color:#000; color:#fff\" target=\"_blank\"><span class=\"shariff-icon\" style=\"\"><svg width=\"32px\" height=\"20px\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\"><path fill=\"#000\" d=\"M14.258 10.152L23.176 0h-2.113l-7.747 8.813L7.133 0H0l9.352 13.328L0 23.973h2.113l8.176-9.309 6.531 9.309h7.133zm-2.895 3.293l-.949-1.328L2.875 1.56h3.246l6.086 8.523.945 1.328 7.91 11.078h-3.246zm0 0\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"700\" height=\"722\" src=\"https:\/\/www.sw-stein.de\/sw\/wp-content\/uploads\/2020\/01\/Speeddating2-700x722.jpg\" alt=\"Scientific Speed Dating\" class=\"wp-image-1062\" srcset=\"https:\/\/www.sw-stein.de\/sw\/wp-content\/uploads\/2020\/01\/Speeddating2-700x722.jpg 700w, https:\/\/www.sw-stein.de\/sw\/wp-content\/uploads\/2020\/01\/Speeddating2-291x300.jpg 291w, https:\/\/www.sw-stein.de\/sw\/wp-content\/uploads\/2020\/01\/Speeddating2-768x792.jpg 768w, https:\/\/www.sw-stein.de\/sw\/wp-content\/uploads\/2020\/01\/Speeddating2-1490x1536.jpg 1490w, https:\/\/www.sw-stein.de\/sw\/wp-content\/uploads\/2020\/01\/Speeddating2.jpg 1940w\" sizes=\"auto, (max-width: 700px) 100vw, 700px\" \/><figcaption class=\"wp-element-caption\">Scientific Speed Dating<\/figcaption><\/figure>\n<\/div>\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Eigenes innovatives Lehrkonzept: Forschendes Lernen und Scientific Speed Dating (2016-2017)<\/h2>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.sw-stein.de\/sw\/dr-stefan-wagner\/forschendes-lernen\/\">Wie wurde das Konzept methodisch in der Praxis umgesetzt?<\/a><\/div>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>At the anaesthesiological outpatient department of Erlangen University Hospital (EUH) processes were analysed in 2016 for two weeks to identify weak points. The <strong>goals <\/strong>were to <strong>optimize patient and staff satisfaction with preoperative risk evaluation and <\/strong>to<strong> reduce waiting time<\/strong>. 114  patients had been observed. <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Patients had to wait for at least 48 minutes on an average before talking to the doctor. <\/li>\n\n\n\n<li>Examination of paper files  took 11 minutes on average, talking with the doctor about 16 minutes. <\/li>\n\n\n\n<li>34 % of all viewed patients rated waiting time as not acceptable in their point of view, compared to 60 % as acceptable.<\/li>\n\n\n\n<li>90 % agreed that  perioperative anaesthesiological evaluation was very important.<\/li>\n<\/ul>\n\n\n\n<p><strong>391.000 assigned complete preoperative datasets<\/strong> are available in the  <strong>hospital-wide data warehouse<\/strong>. Moreover, an important approach in form of  a prototype for prefilling paper based questionnaires was developed in a  project as part of the interdisciplinary teaching event \u201c<a href=\"https:\/\/www.sw-stein.de\/sw\/dr-stefan-wagner\/lehrportfolio\/scientific-speed-dating\/\"><strong>Scientific Speed Dating<\/strong><\/a>\u201d (at University of Erlangen-Nuremberg, faculties of medicine and engineering) last semester (WS 2016\/2017). Two students of  medical engineering helped with the programming of the web application.  Project leader and supervisor was <strong><a href=\"https:\/\/www.sw-stein.de\/sw\/dr-stefan-wagner\/\">Dr. med. Stefan Wagner, MHBA<\/a><\/strong>. After completion of the event the code base was improved by him to generate the <strong><a href=\"https:\/\/www.sw-stein.de\/sw\/medizinische-informatik\/application-of-big-data-analysis-and-data-mining-for-medical-risk-evaluation\/\">prototype<\/a><\/strong>. Information of completed anaesthesia protocols is  extracted and imported into a relational Oracle database in the  background without any user interaction.<\/p>\n\n\n\n<p>However, the <strong>data structure<\/strong> is <strong>very complex for secondary data reuse<\/strong>.  The data warehouse helps solving this problem. It includes data from  nearly all different health information systems such as operation  protocols, laboratory, radiology and instrument-based diagnostics. The  data warehouse imports a huge amount of information from a great number  of health information systems every night, also datasets from the AIMS.  Advantage of the data warehouse is the logical data structure. For our  preliminary data analysis we exported datasets of former evaluation  visits for the categories preoperative status, findings and visit as  well as corresponding accounting data including diagnoses and  procedures. Free text information and structured entries were included.  We selected more than <strong>64.000 patients<\/strong> (more than <strong>82.000 treated cases<\/strong>),  starting at the age of 18 and with operations during the years 2013 to 2015. <\/p>\n\n\n\n<p>All datasets were pseudonymised to ensure privacy and to involve two  students studying medical engineering at FAU. 5 ECTS credits were  granted to each of them for their support. The CSV export files have  been imported into a MySQL open source project database (MariaDB  10.1.13). Furthermore, we installed an Apache webserver (Apache 2.4.17, 8  GB RAM, Windows Server operating system) with PHP (Version 5.6.23 and  as a follow-up step 7.1.2), for development and testing purposes. Paper  based patient questionnaires consist of 24 main questions including  sub-categories. We selected the most <strong>important items for our prototype development. Doctors in every evaluation visit asked for: <\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>height<\/li>\n\n\n\n<li>weight<\/li>\n\n\n\n<li>age<\/li>\n\n\n\n<li>gender<\/li>\n\n\n\n<li>former operations<\/li>\n\n\n\n<li>current medications<\/li>\n\n\n\n<li>allergies or oversensitivities<\/li>\n\n\n\n<li>heart<\/li>\n\n\n\n<li>circulatory or blood vessel diseases, diseases of the respiratory tract (breathing passages) or lungs<\/li>\n\n\n\n<li>thyroid diseases<\/li>\n\n\n\n<li>kidney diseases<\/li>\n\n\n\n<li>nervous system<\/li>\n\n\n\n<li>smoking and drinking.<\/li>\n<\/ul>\n\n\n\n<p><strong><a href=\"https:\/\/www.sw-stein.de\/sw\/medizinische-informatik\/elektronische-narkoseprotokolle\/\">NarkoData<\/a><\/strong> offers doctors preselection of items for different diseases  and input of further free text. Apart from this style of documentation,  including shortcuts for diseases, differs significantly between staff  members. Data analysis, possibilities of filtering and evaluation become  even more difficult. <strong><a href=\"https:\/\/www.sw-stein.de\/sw\/dr-stefan-wagner\/\">Dr. Wagner<\/a><\/strong>, not only active at all main operation facilities of our hospital, but also performing many visits, took a deep  look at structure and contents of the datasets. Corresponding items of  the questionnaire and of NarkoData were successfully identified. The  paper form exists in two versions: a printed document and a  PDF-document, which cannot be edited. <\/p>\n\n\n\n<p>Therefore, a <strong><a href=\"https:\/\/www.sw-stein.de\/sw\/medizinische-informatik\/elektronische-perioperative-risikoevaluation\/\">web application prototype<\/a><\/strong> was developed, including all  paper version questions and items. Now easy data entry was achieved,  using a web browser, with possibilities of item pre-selection.  Relational database queries were implemented using PHP and matching items. These queries took into account not only structured items but  also free text comments. The fields \u201cyes\u201d and \u201cno\u201d, e.g. for existing  diseases, allergies and long-term medication use, were also pre-checked.  As a result the questionnaire could be prefilled automatically by  entering a patient identification number. Only the last hospital stay  was recognized in cases of more than one visit. Thus, the use of only  the latest information could be ensured.<\/p>\n\n\n\n<p><strong>Free text fields <\/strong>about height, weight, former operations, current drugs,  allergies, smoking and drinking were prefilled as well. Data updates  might be necessary. All automatic evaluated information was loaded into an <strong>editable browser form<\/strong>. Finally, it could be exported to PDF-format  and stored using the free fpdf library. At the moment the execution of  the relational database queries takes 2 minutes on average for one run, without using any table indexes or other database performance optimization. <\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><strong>Weiterf\u00fchrende Informationen zum <a href=\"https:\/\/www.sw-stein.de\/sw\/dr-stefan-wagner\/forschendes-lernen\/\">Forschenden Lehren und Lernen<\/a>. <\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.sw-stein.de\/sw\/sws\/sws.php\" alt=\"\" width=\"0\" height=\"0\">\n","protected":false},"excerpt":{"rendered":"<p>Eigenes innovatives Lehrkonzept: Forschendes Lernen und Scientific Speed Dating (2016-2017) At the anaesthesiological outpatient department of Erlangen University Hospital (EUH) processes were analysed in 2016 for two weeks to identify weak points. The goals were to optimize patient and staff satisfaction with preoperative risk evaluation and to reduce waiting time. 114 patients had been observed. 391.000 assigned complete preoperative datasets are available in the hospital-wide data warehouse. Moreover, an important approach in form of a prototype for prefilling paper based questionnaires was developed in a project as part of the interdisciplinary teaching event \u201cScientific Speed Dating\u201d (at University of Erlangen-Nuremberg, faculties of medicine and engineering) last semester (WS 2016\/2017). Two students of medical engineering helped with the programming of the web application. Project leader and supervisor was Dr. med. Stefan Wagner, MHBA. After completion of the event the code base was improved by him to generate the prototype. Information of completed anaesthesia protocols is extracted and imported into a relational Oracle database in the background without any user interaction. However, the data structure is very complex for secondary data reuse. The data warehouse helps solving this problem. It includes data from nearly all different health information systems such as operation protocols, laboratory, radiology and instrument-based diagnostics. The data warehouse imports a huge amount of information from a great number of health information systems every night, also datasets from the AIMS. Advantage of the data warehouse is the logical data structure. For our preliminary data analysis we exported datasets of former &hellip;<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":449,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_metis_text_type":"standard","_metis_text_length":5498,"_post_count":0,"footnotes":""},"class_list":["post-1066","page","type-page","status-publish","hentry"],"public_identification_id":"80094a199a014b4b947b49c72194f5f2","private_identification_id":"7ea59a13789041a88684ee008d31b7a0","_links":{"self":[{"href":"https:\/\/www.sw-stein.de\/sw\/wp-json\/wp\/v2\/pages\/1066","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sw-stein.de\/sw\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.sw-stein.de\/sw\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.sw-stein.de\/sw\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sw-stein.de\/sw\/wp-json\/wp\/v2\/comments?post=1066"}],"version-history":[{"count":5,"href":"https:\/\/www.sw-stein.de\/sw\/wp-json\/wp\/v2\/pages\/1066\/revisions"}],"predecessor-version":[{"id":2218,"href":"https:\/\/www.sw-stein.de\/sw\/wp-json\/wp\/v2\/pages\/1066\/revisions\/2218"}],"up":[{"embeddable":true,"href":"https:\/\/www.sw-stein.de\/sw\/wp-json\/wp\/v2\/pages\/449"}],"wp:attachment":[{"href":"https:\/\/www.sw-stein.de\/sw\/wp-json\/wp\/v2\/media?parent=1066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}