Literature DB >> 10512266

Design of appointment systems for preanesthesia evaluation clinics to minimize patient waiting times: a review of computer simulation and patient survey studies.

F Dexter1.   

Abstract

Anesthesiologists can use the science of clinic scheduling to design appointment systems for preanesthesia evaluation clinics. The principal reasons reported for inappropriately [or arguably unethically] long patient waiting times are provider tardiness, lack of patient punctuality, patient no-shows, and improperly designed appointment systems. However, the fundamental reason why anesthesia clinics have such long patient waiting times is because of their relatively long mean (and consequently standard deviation) of consultation times. If commonly applied valuations of provider idle time to patient waiting time are used in anesthesia clinics, appointment intervals will be sufficiently brief that the mean patient waiting time will be at least the mean consultation time or half an hour. Patients will be dissatisfied with this level of service. Therefore, efforts to decrease the mean patient waiting time in anesthesia clinics should focus foremost on minimizing the mean consultation time and its variability, which can most likely be achieved by assuring that providers have rapid access to relevant clinical information, including external medical records, surgical dictations, etc. Anesthesiologists managing anesthesia clinics may find it valuable to apply other interventions to decrease patient waiting times. Scheduling of preanesthesia evaluation and surgical clinics should be coordinated to assure patient punctuality. Providers should be on time for the start of their sessions. If an add-on patient cannot be seen during a scheduled clinic session, because all appointment times have been assigned to other patients, the add-on patient should be seen by a different provider or at the end of the regularly scheduled clinic session. Mean consultation times should be measured accurately for each provider. Substantial provider idle time should be expected. Appropriate values for breaks, appointment intervals, and percentage no-shows should be determined by computer simulation, using parameters appropriate for each provider and anesthesia clinic. Finally, traditional efforts at making waiting for a consultation tolerable should be made.

Entities:  

Mesh:

Year:  1999        PMID: 10512266     DOI: 10.1097/00000539-199910000-00020

Source DB:  PubMed          Journal:  Anesth Analg        ISSN: 0003-2999            Impact factor:   5.108


  12 in total

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2.  [Analysis and options for optimization of preoperative assessment for anesthesia at a university hospital].

Authors:  M Kieninger; C Eissnert; M Seitz; K Judemann; T Seyfried; B Graf; B Sinner
Journal:  Anaesthesist       Date:  2017-12-11       Impact factor: 1.041

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Authors:  Xiuli Qu; Jing Shi
Journal:  Health Care Manag Sci       Date:  2009-03

Review 4.  FASStR: a framework for ensuring high-quality operational metrics in health care.

Authors:  Elham Torabi; Tugba Cayirli; Craig M Froehle; Kenneth J Klassen; Michael Magazine; Denise L White; Michael J Ward
Journal:  Am J Manag Care       Date:  2020-06-01       Impact factor: 2.229

5.  Hourly-block and standard patient scheduling systems at two private hospitals in Alexandria.

Authors:  Ashraf Ahmad Zaher Zaghloul; Nagwa Younes Abou El Enein
Journal:  J Multidiscip Healthc       Date:  2010-12-07

6.  Assessment of the Correlation between Appointment Scheduling and Patient Satisfaction in a Pediatric Dental Setup.

Authors:  Amar N Katre
Journal:  Int J Dent       Date:  2014-12-29

7.  Using diffusion of innovation theory to understand the factors impacting patient acceptance and use of consumer e-health innovations: a case study in a primary care clinic.

Authors:  Xiaojun Zhang; Ping Yu; Jun Yan; Ir Ton A M Spil
Journal:  BMC Health Serv Res       Date:  2015-02-21       Impact factor: 2.655

8.  Changes to physician processing times in response to clinic congestion and patient punctuality: a retrospective study.

Authors:  Chester G Chambers; Maqbool Dada; Shereef Elnahal; Stephanie Terezakis; Theodore DeWeese; Joseph Herman; Kayode A Williams
Journal:  BMJ Open       Date:  2016-10-18       Impact factor: 2.692

9.  Patient satisfaction with anaesthesia services and associated factors at the University of Gondar Hospital, 2013: a cross-sectional study.

Authors:  Endale Gebreegziabher Gebremedhn; Wubie Birlie Chekol; Wubet Dessie Amberbir; Tesera Dereje Flatie
Journal:  BMC Res Notes       Date:  2015-08-26

10.  Time spent by patients in a pre-anaesthetic clinic and the factors affecting it: An audit from a tertiary care teaching hospital.

Authors:  Justin P James; Suma Mary Thampi
Journal:  Indian J Anaesth       Date:  2018-01
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