{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:47:37Z","timestamp":1740098857641,"version":"3.37.3"},"posted":{"date-parts":[[2023,5,10]]},"group-title":"JMIR Medical Informatics","reference-count":10,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<sec>\n                    <title>BACKGROUND<\/title>\n                        <p>The digital age, with ICT, IoT, big data, has opened new opportunities for improving the delivery of healthcare services, in which data mining approach can help improve the hospital management process by providing a big picture identifying process efficiencies<\/p>\n                <\/sec>\n                                <sec>\n                    <title>OBJECTIVE<\/title>\n                        <p>This is a multidisciplinary study where a data mining approach was applied to hospital data to the improvement management process. From this data, we can provide a big picture of hospital processes and check process efficiency. Since we get data from the emergency department we check process, patients waiting time, physicians\u2019 efficiency and length of stay (LOS).<\/p>\n                <\/sec>\n                                <sec>\n                    <title>METHODS<\/title>\n                        <p>A data analytic investigation of the impact of patients\u2019 average waiting time and LOS was performed to compare the efficiency of different groups of the physicians. In this research-based on CRISP-DM method by using an emergency department\u2019s (general surgery unit) real-life data from 2015 to 2017, through the One-Way Analysis of Variance (ANOVA) method.<\/p>\n                <\/sec>\n                                <sec>\n                    <title>RESULTS<\/title>\n                        <p>From the developed approach using hospital data, it was found that the waiting time and average LOS belonging to experienced physicians (who visit more patients) are longer than the less experienced physicians (who visit less patients). Using the Two-Way ANOVA method, the emerging and very urgent patients (Red and Orange triage color) who were visited by experienced physicians have longer average LOS than the same level patients who visited by less experienced physicians. On the other hand, not urgent patients (Blue triage color) who were visited by high experienced physicians have shorter average LOS than the same patients who were visited by less experienced physicians. This study was performed using two scenarios of physicians' grouping, including Pareto grouping (80-20) and Frequency grouping (\"Very High\", \"High\", \"Medium\", \"Low\" and \"Very Low\").<\/p>\n                <\/sec>\n                                <sec>\n                    <title>CONCLUSIONS<\/title>\n                        <p>We show that data visualization tools for healthcare analysts to help them make better decisions, by the big picture identified. Big data offers a lot of potential for improving healthcare administration and taking the sector to the next level, identifying working problems, reduce costs.<\/p>\n                <\/sec>","DOI":"10.2196\/preprints.48891","type":"posted-content","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T15:44:04Z","timestamp":1684856644000},"source":"Crossref","is-referenced-by-count":0,"title":["Process Mining for Healthcare Management (Preprint)"],"prefix":"10.2196","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7489-4380","authenticated-orcid":false,"given":"Luis B.","family":"Elvas","sequence":"first","affiliation":[]},{"given":"Raeisi","family":"Saeed","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1600-7867","authenticated-orcid":false,"given":"Berit","family":"Helgheim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8533-6595","authenticated-orcid":false,"given":"Ana L\u00facia","family":"Martins","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6662-0806","authenticated-orcid":false,"given":"Jo\u00e3o C","family":"Ferreira","sequence":"additional","affiliation":[]}],"member":"1010","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9781107053779"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119205326"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2012.12.019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1186\/2047-2501-2-3"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-22348-3_9"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/confluence.2016.7508117"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-016-9641-2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/cloudtech.2015.7337020"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2016.04.007"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.103994"}],"container-title":[],"original-title":[],"deposited":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T15:44:06Z","timestamp":1684856646000},"score":1,"resource":{"primary":{"URL":"http:\/\/preprints.jmir.org\/preprint\/48891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":10,"URL":"https:\/\/doi.org\/10.2196\/preprints.48891","relation":{},"subject":[],"published":{"date-parts":[[2023,5,10]]},"subtype":"preprint"}}