We conducted a longitudinal study about the length of waiting times for consultations between 1 October 2014 and 28 February 2017, and for filling prescriptions between 1 March 2015 and 28 February 2017 in the studied hospital with a quasi-experimental design of before and after assessments.
Population and setting
The study populations related to waiting times for consultations consisted of all visiting outpatients of the studied hospital during 1 October 2014–28 February 2017 (excluding the easily accessible outpatients who only requested repeat prescriptions), while the study populations related to filling prescriptions included all patients who filled prescriptions from the outpatient pharmacy during 1 March 2015–28 February 2017. The studied hospital was a public provincial tertiary general hospital located in the capital city of Fujian Province, where there are approximately 2500 daily outpatient visits and 2800 prescriptions filled.
Routinely collected data extracted from the hospital information system
We obtained all relevant information (including process management, personal, and clinical information) from different information systems. Each visiting outpatient is given an electronic patient card with a unique identity code at the registration desk. The electronic patient card can be used at any service point within the hospital, including the appointment system, the registration system, working computers of doctors, nurses, labs, pharmacists, and payment. All process management, diagnosis, and treatment information are recorded in the electronic patient card. Waiting times for consultations and filling prescriptions were calculated based on the time points of registration, consultation, prescription bill payment, and dispensing. These times are recorded in different management models as soon as the electronic patient card is swiped at respective computer working stations. The sequence numbers and exact times for consultation and dispensing are automatically allocated by the hospital information system to each visiting outpatient with a printed receipt upon registration and completing payment of prescription bill.
Data obtained from the patient satisfaction survey
Upon approval of the Hospital Ethics Committee, starting from 1 January 2016, the taskforce started to conduct daily patient satisfaction survey, who invited outpatients to fill approximately 50 questionnaires every day. The structured Likert five-point scale questionnaire was pre-installed into i-pad. An Informed Consent statement was read by the taskforce staff before filling the questionnaire, and only the patients who have no objections responded to the survey. One specific indicator of the questionnaire used by the studied hospital is about pharmacy services (“Are you satisfied with pharmacy services?”), and another is about the consulting doctor (“Are you satisfied with the consulting doctor?”). Patients were asked to rate their satisfaction to each indicator. The outpatient respondents were identified from the waiting area outside the outpatient pharmacy during working hours every day using convenient sampling method. A total of around 1000 responded outpatients constitute the sample size of monthly average outpatient satisfaction score calculations for respective healthcare service after excluding the non-experienced patients. Patient satisfaction results were used as a tool to evaluate the performance of on-duty doctors and pharmacists, and financial penalties were given to poor performance. All these were integrated into the routine management.
We defined the length of waiting time for consultation as the time period between the moment when the consultation is automatically allocated to each visiting outpatient by the hospital information system upon their registration, and the moment when the electronic patient card is recorded by the doctor in the computer system and the patient is attended by the on-duty doctor. We also defined the length of waiting time for filling prescriptions as the time period between the moment when the prescription bill is paid (the electronic patient card is swiped at the payment ATM machine or the payment window and a receipt is printed out) and the exact time when the patient’s name and sequence number are shown on the LED screen outside the outpatient pharmacy. The following indicators were employed to measure waiting times and patient satisfactions:
The monthly average length of waiting time for consultations (the studied hospital data)
Measured by having the exact time when the electronic patient card is recorded by the doctor in the computer system, minus the exact time of appointment time. The averages of the above results of all visiting outpatients in each month during 1 October 2014–28 February 2017 were calculated.
The monthly average length of waiting time for filling prescriptions (the studied hospital data)
Measured by having the exact time when the sequence number and the name of the prescribed outpatient are shown on the LED screen outside the outpatient pharmacy, minus the exact time when the payment of the prescription bill is completed. The averages of the results of all prescribed outpatients in each month during 1 March 2015–28 February 2017 were calculated.
The monthly average outpatient satisfaction scores towards consulting doctors and pharmacy services (the survey conducted by the studied hospital)
Measured by having 5, 4, 3, 2, and 1 assigned to each Likert scale respectively, having the sum of scores of “very satisfied” and “satisfied” divided by the sum of the scores of all five scales (“very satisfied”, “satisfied”, “neither satisfied nor unsatisfied”, “unsatisfied” and “very unsatisfied”), and multiplying by 20 for each respective indicator to obtain the centesimal satisfaction score.
We used monthly average length of waiting time data and corresponding outpatient satisfaction data to assess trajectories in waiting times for consultation and filling prescriptions, as well as outpatient satisfactions towards consulting doctors and pharmacy services over time. We analyzed the time series data using a segmented linear regression model with statistical software (SPSS 21.0) to assess changes in levels and trends of waiting times for consultations and for filling prescriptions before and after the introduction of respective waiting time reduction interventions. Interrupted time series analysis statistical software can control for auto-correlated errors, and can also adjust for potential serial correlation of the data [23, 24]. We regarded September 2015 and February 2016 as the intervention time points for reducing waiting times for consultations and for filling prescriptions, respectively. Segmented linear regression divides the time series into pre- and post-September 2015 and pre- and post-February 2016 segments. We also compared the changes in trends and levels of waiting times before and after implementation of the respective waiting time reduction interventions. Regression analysis was also conducted for the outpatient satisfaction scores towards the consulting doctor and towards pharmacy services. Pearson correlation analysis was conducted to indicate the strength of association between waiting times and respective patient satisfactions. The statistical significance level was set at 0.05.