Programme d’évaluation de l’administration des chimiothérapies en hôpital de jour en Champagne-Ardenne (PEACH) : satisfaction et délais d’attente des patients

Debreuve‐Theresette, Adeline; Stona, Anne-Claire; Kraïem-Leleu, M.; Burde, F.; Parent, D.; Hettler, D.; Rey, Jean‐Baptiste

doi:10.1016/j.respe.2015.08.006

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Of note, the item "waiting time" in the OUT-PATSAT35 questionnaire deals especially with the delay in obtaining an appointment for a consultation. However, waiting time has been considerably reduced between steps 1 and 2, a factor known to impact patients' satisfaction (Debreuve-Theresette et al, 2015;Gjolaj, Campos, Olier-Pino, & Fernandez, 2016;Gourdji, McVey, & Loiselle, 2003;Kallen, Terrell, Lewis-Patterson, & Hwang, 2012).…”

Section: Discussionmentioning

confidence: 99%

Optimisation of chemotherapy prescription and preparation in an ambulatory unit: Validation of the OPTIMA program

et al. 2019

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Objective We implemented the two‐step OPTIMA program to anticipate chemotherapy prescription which aims to assess the discrepancy rate between anticipated and real prescription and its impact on waiting time and quality of care. Methods This prospective study included cancer patients receiving any intravenous chemotherapy. The OPTIMA program consists in a nurse phone call and a blood sample two days before the planned treatment. Collected information and biological results were used by a physician to issue a non‐effective (step 1) or effective (step 2) anticipated prescription the day before the consultation. The real prescription was given as usual by another physician on the day of the consultation. Waiting time was collected, and patients’ satisfaction with care was assessed with the OUT‐PATSAT35 questionnaire. Results Respectively, 540 and 979 consultations (283 and 294 patients) were analysed in both steps. The discrepancy rate was 8.7% (step 1). In routine practice, the OPTIMA program (step 2) reduced patients’ waiting time (median time 55 vs. 95 min, p < 0.001). A high general care satisfaction score was observed in both steps (80.7% and 80.2%). Conclusions This anticipation program demonstrated the accuracy of chemotherapy prescription, whatever the regimen and cancer site, and its impact on waiting time optimisation.

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Section: Discussionmentioning

confidence: 99%

Optimisation of chemotherapy prescription and preparation in an ambulatory unit: Validation of the OPTIMA program

et al. 2019

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“…Despite this activity, outpatient chemotherapy clinics are often far from optimal efficiency. For instance, studies have reported that patients wait 72.7 minutes from check-in to treatment at the University of Texas MD Anderson Cancer Center (Kallen et al 2012), 97 minutes in 11 French clinics (Debreuve-Theresette et al 2015). For these patients, waiting time at the outpatient clinic is an important factor of dissatisfaction Lamé, Jouini, Stal-Le Cardinal, Carvalho, Tournigand, and Wolkenstein (Lis, Rodeghier, and Gupta 2009).…”

Section: Introductionmentioning

confidence: 99%

Patient-hospital communication: A platform to improve outpatient chemotherapy

Lamé

Jouini

Cardinal

et al. 2016

2016 Winter Simulation Conference (WSC)

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We apply Discrete Event Simulation (DES) to a system of two strongly interconnected departments, an outpatient oncology clinic for chemotherapy delivery, and the pharmacy unit that prepares the chemotherapy drugs. The model is developed in close collaboration with the French hospital Henri Mondor, and is validated using real data. The objective is to identify sources of patient waiting times in the outpatient oncology clinic and to identify relevant corrective actions. We show that the coordination between the two departments is the key barrier to higher performance. Solutions are proposed based on increased information sharing and obtaining advanced information on patient status, to allow advanced drug preparation. A two-phase project for improvement is proposed. This paper contributes to the literature on multi-department simulation, which is still rare in healthcare OR compared to onedepartment studies.

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