An improvement in the appointment scheduling in primary health care centers using data mining
An optimal resource management in health care centers implies the use of an appropriate timetabling scheme to schedule appointments. Timetables of health centers are usually divided into time slots whose duration is equal to time required for clinical attendance. However doctors perform a series of tasks that are not always clinical in nature: issuing prescriptions or prescribing sick leave certificates. In this sense the time spent in attending a clinical or an administrative matter is different. This last required less time to attend the patient. This study is focused in the administrative task. A predictive model is generated to provide daily information on how many patients will go to the health center for an administrative issue. The accuracy of the model is less than 4,6 % absolute error and the improvement in scheduling appointments is a time saving of 21,73 %.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2014 |
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Erschienen: |
2014 |
Enthalten in: |
Zur Gesamtaufnahme - volume:38 |
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Enthalten in: |
Journal of medical systems - 38(2014), 8 vom: 14. Aug., Seite 89 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Cubillas, Juan José [VerfasserIn] |
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Links: |
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Themen: |
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Anmerkungen: |
Date Completed 20.04.2015 Date Revised 21.10.2021 published: Print-Electronic Citation Status MEDLINE |
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doi: |
10.1007/s10916-014-0089-y |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM239522850 |
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520 | |a An optimal resource management in health care centers implies the use of an appropriate timetabling scheme to schedule appointments. Timetables of health centers are usually divided into time slots whose duration is equal to time required for clinical attendance. However doctors perform a series of tasks that are not always clinical in nature: issuing prescriptions or prescribing sick leave certificates. In this sense the time spent in attending a clinical or an administrative matter is different. This last required less time to attend the patient. This study is focused in the administrative task. A predictive model is generated to provide daily information on how many patients will go to the health center for an administrative issue. The accuracy of the model is less than 4,6 % absolute error and the improvement in scheduling appointments is a time saving of 21,73 % | ||
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700 | 1 | |a Ureña, Tomás |e verfasserin |4 aut | |
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