• 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • NASA TLX was used to measure


    NASA-TLX was used to measure workload. It is based on six dimensions; mental demand, physical demand, effort, frustration level, and performance [27]. This tool has been used in several situ-ations to assess operator workload in complex environments [28]. NASA-TLX uses a weighting procedure to combine the six indi-vidual scale ratings into a global score; this LY 379268 procedure required a paired comparison task to be performed prior to the workload assessments [29,30]. The overall workload score is calculated by
    Table 1
    Example of data collection form.
    Time Time slot NASA-TXL HR (bpm) BR (rpm) Skin temperature (IR Thermometer) ECG BR (rpm) Nurse ID Day Location No. of patients
    multiplying each raw rating by the weight given to that factor for each participant. The sum of the weighted ratings is then divided by 15 (the sum of the weights) to give an absolute workload score, which takes values between zero and 100 [31].
    Since NASA-TLX is a subjective method that measures the par-ticipant’s perception on the task load, some studies have doubted the ability of people to report on their own cognitive processes. This implies a possible dissonance in self-perception of the cog-nitive process [32]. To reduce the uncertainty generated at the moment that nurses report the perception of the workload, we measured the physiological responses of participants during the performance of the tasks with the objective to validate the re-sponses obtained by the subjective tool (NASA-TLX). The physio-logical responses were measured using the Equivital system which consists of a belt and electrodes that participants used during their workday.
    EquivitalTM TnR is an FDA approved equipment which has been validated in previous research studies [33–37]. The chest belt and EQ02 device was placed in participants. The belts were adjusted to the size of each participant, and data was sent to the computer using a Bluetooth connection which gives the participants free mobility and the capacity to perform any regular task without interruptions. Data was used only with the purpose of measuring workload. All recorded data was kept confidential, only the re-searchers had access to the collected data. The data was processed by two types of software, the Equivital Manager (version 1.2) and the Equivital Professional (version 1.2) which permits data collec-tion in real time and also data exportation to Excel for subsequent analysis.
    Table 1 displays an example of the data collection form. We used the data collected on further analysis LY 379268 which will be explained in section 3.6. In addition to collecting data on nurses’ workload, a deterministic mathematical model was developed to determine the optimal scheduling policies oriented to increase the utilization of the resources and increase the capacity of the infusion area while balancing the workload of human resources.
    The scheduling model for outpatient clinics faces several chal-lenges which makes the system design a complex problem. It involves multiple stakeholders, a sequential booking process, ran-dom arrivals, no-shows, varying degrees of urgency of different patients’ needs, service time variability, and patient and provider preferences.
    We included scheduling concepts which have been addressed independently by other studies such as operations management and human resources workload. Operations management concepts were used with the purpose of improving processes in the system and to help balance the usage of the clinic resources. Also, we added constraints related to human resources workload. The new model considered the human resources in the nursing department. The mathematical model was solved using a General Algebraic Modeling System (GAMS) software version 24.1.2. GAMS is a high-level modeling system which consists of a language compiler and a stable of integrated high-performance solvers. GAMS is tailored for complex, large scale modeling applications, and allows the user to build large maintainable models that can be adapted quickly to new situations [38]. For the purpose of predatory release research study, CPLEX was used as the optimizer solver behind GAMS.