Using Predictive Analytics and Data Mining to Reduce the Patients’ Appointment ‘Waiting Waste’

dc.contributor.authorMalik, MMnull
dc.contributor.authorAbdallah, Salamnull
dc.contributor.authorChaudhry, UZnull
dc.date.accessioned2022-07-04T11:44:21Znull
dc.date.accessioned2023-08-20T11:14:24Z
dc.date.available2022-07-04T11:44:21Znull
dc.date.available2023-08-20T11:14:24Z
dc.date.issued2021null
dc.description.abstractEmiratesAbstractThe outpatient appointment ‘waiting waste’ is caused by healthcare capacity constraints and the variability associated with patients arrivals. Appointments overbooking minimizes the disruptive influence ofnoshows on healthcare quality but the homogenous handling of patients, despite the evidence that multiple factors contribute to noshows rates,limits the potential benefits. This study advocates the use of data mining to identify various patterns in thebig healthcare data to determine appropriate ‘overbooking levels’ by matching patients’individual characteristics to the historic arrival patterns.Predictive Analytics for overbooking is likely to reduce the outpatients’ delays substantially by augmenting the healthcare capacity.en_US
dc.identifier.citationMalik, M. M., Abdallah, S., & Chaudhry, U. Z. Using Predictive Analytics and Data Mining to Reduce the Patients’ Appointment ‘Waiting Waste’.en_US
dc.identifier.urihttps://edms.wexl.in/handle/1/3875
dc.language.isoenen_US
dc.subjectOutpatients appointment schedulingen_US
dc.subjectHealthcare overbookingen_US
dc.subjectHealthcare predictive analyticsen_US
dc.titleUsing Predictive Analytics and Data Mining to Reduce the Patients’ Appointment ‘Waiting Waste’en_US
dc.title.alternativejournal Articalen_US
dc.typeArticleen_US

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