Improving Outpatient Clinic Operations: An Exploratory ...



Improving Outpatient Clinic Operations: An Exploratory Case Study

Karndee Prichanont1[1], Chompoonut Visrutwong2, and Praetip Roddon2

1Thammasat Business School, Thammasat University, Bangkok, 10200 Thailand

2Sirindhorn International Institute of Technology, Thammasat University, Prathum-Thani, 12121 Thailand

ABSTRACT

This study focuses on improving outpatient clinic operations in an existing public hospital in Thailand. According to our preliminary investigation, the outpatient clinic of this hospital is currently suffering from inefficient operations which lead to a significant patient’s waiting time to receive the doctor care and other services. This eventually leads to a decline in patient’s satisfaction in overall hospital services. The primary objective is to reduce the patient waiting time in each stage of operations. On-site data collection and exploratory study are carried out in order to fully understand the current operations and to identify the root causes of problems. Process analysis is performed to identify the process bottleneck. Integrating areas of queuing theory, and facility layout, a set of improvement guidelines is proposed. Discrete-vent simulation results show that, with the proposed guidelines and operation settings, the overall patient’s waiting time can be significantly reduced.

Keywords: simulation, efficiency, hospital, case study

1. INTRODUCTION

This study explores current operations in an outpatient clinic department in an existing public hospital. The participating hospital is one of the largest public hospitals in Thailand, which is currently facing patient’s long waiting time to receive the hospital services. Alike any other major public hospitals, this hospital is subsidized by the government which allows the hospital to charge with much less fee compared to private hospitals. Therefore, public hospitals appear to be the only place affordable by the low income groups of patients. On the other hand, with high quality of professional and medical services, the hospital also serves middle to high income groups of patients as well. Besides the charged fees and the quality of health care, responding time to patients is another aspect of overall service quality that highly affects the patient’s satisfaction. The outpatient clinic department of the participating hospital is currently facing patients’ dissatisfaction in unrealistically long waiting time to receive the medical cares as well as other services within the outpatient clinic department. Based on preliminary evidence, patients are likely to experience significantly less waiting time to receive medical and other cares in the private hospitals. The threat arises in the environment where public and private hospitals co-exist. That is, majority of patients could be willing to pay more in order to reduce queue time [1].

The primary objectives of this exploratory study are therefore to investigate the current operations and to identify the causes of extensive waiting time in each stage of operations. The operations in the participating hospital are approached and investigated by defining resource and the effectiveness of the use of resource [2]. A set of quantitative and qualitative studies are carried out. Using discrete-event simulation and empirical study, this study proposes the guidelines for improving the patient’s overall waiting time without affecting the quality of professional and medical quality. Employing areas of queuing theory and facility layout, a set of recommendations is proposed.

2. ASSESSING CURRENT SCENARIO

To understand the overall processes and to identify the causes of problems, the study is initially carried out by observing the current operations and common procedures in outpatient clinic departments. The outpatient clinic department (OPD) of this hospital consists of seven clinics as follows: General Practice Clinic, Pathology Clinic, Obstetrics-Gynecology Clinic, Pediatrics Clinic, Ear Nose Throat Clinic, Surgery-Orthopedics Clinic, and Eye Clinic. Once enter the hospital, patient has to report to the registration department (see Figure 1). Then the registration department will retrieve the patient record and send it in a hard copy to the appropriate clinic. In the meantime, patient can go directly to one of seven outpatient clinics as appropriate or as suggested by the registration department staff. Patient that has appointment does not need to report at the registration department as his/her record is already transferred to the appointed clinic at the beginning of the day. Some patient may attend more than one clinic in one visit depending on their case complication. Once the treatment is completed, patient receives prescription and goes to the medicine/cashier department. As prescribed medicine is received and transaction is completed, patient may exit the hospital.

[pic]

Figure 1: Common processes for outpatients

Although each outpatient clinic has different number of doctors, nurses, and staffs, and different clinic detailed layout, it is found that all seven outpatient clinics have similar flow of operations as shown in Figure 2. As patient enters the clinic, s/he must first contact the front desk (counter 1). Nurses will check whether patient’s medical record has arrived from registration department. Patient stays in queue waiting for their medical record and the availability of pre-diagnose nurses (counter 2). It is found that the waiting time at this stage is not significant. As pre-diagnosis is completed, patient transfers to a large queue waiting to be treated by one of the doctors. According to on-site data collection, it is found that patient spend a significant time in this queue. In some clinic during peak time, some patient may have to wait as long as three hours or more to receive the doctor care in this queue. After receiving medical treatment, patient contacts counter 4 to give the medical record to nurses and wait in queue. Nurses will print out the prescription and release to patient at counter 5. Patient exits the clinic with prescription and transfers to the medicine/cashier department. In this department, it is observed that each patient also spends significant time waiting to complete the procedure.

[pic]

Figure 2: Overall patient flow within clinic

After observing the existing patient’s flows and common procedures in all outpatient clinics, the data collection sheet is designed and distributed. The primary elements required in data sheet are the average waiting time and service times in each stage of operations, the average time patient spends in the outpatient clinic department, and the number of patients entering each outpatient clinic in each time period during the day. These information are then further analyzed to identify the process bottleneck and to calculate patient interarrival time, patient arrival rate, and service rate in each operations which will be used in simulation models. The data was collected for 1-month period. It is found that patients spend more than 3 hours on average in the patient clinics waiting for doctor cares and other hospital services. Although most internal operations in each clinic are similar, some clinics are suffering from extensive patient waiting time than the others.

The problem statement and the scope for improvement can be stated into three main categories as follows:

1. Insufficient number of doctors: According to on-site observation and data collection, it is found that there are a significant large number of patients waiting in front of the doctor rooms. Comparing the service times of all operations, it can be concluded that this is the bottleneck of the process. This mainly due to insufficient number of doctors to serve as compared to the number of patients arrive the clinic. This problem seems to be common for all seven outpatient clinics. Reducing the treatment time will significantly decline the medical service quality. The simulation model is then developed to investigate the appropriate number of doctors with respect to average patient’s arrival rate that leads to satisfied waiting time. Simulation model and experimental results are illustrated in section 3.1.

2. Inappropriate appointment system: Currently, appointment patient has no priority over non-appointment patient. Two types of patients follow the same process. Therefore, appointment patients are likely to ignore their appointment time and tend to arrive the clinic very early. This causes congestion in the clinic during the beginning of the day. Moreover, there is no formal appointment system in place. There is no time slot information to guarantee the availability of doctors on the appointment day. Based on simulation results, appointment guideline is developed. The simulation model is tested with various ratios between appointment and non-appointment patients. The experiment is based on an assumption such that appointment patients will have higher priority to receive the services in all stages. The aim of this experiment is to propose the appropriate proportion of appointment patients in each time in order to reach the most satisfied overall waiting time. Simulation model and experimental results are illustrated in section 3.2.

3. Long waiting time at Medicine room: After patients receive the prescription from the clinic counter 4, patients are directed to medicine room to pay for the fee and receive the medicines. According to our observation, patients currently spend significant amount of time at the medicine room counter. Most of which is waiting for medicine. After carefully observing the operations within the medicine room, it is found that the medicine shelf within the medicine room can be rearranged in order to reduce the operation time of preparing medicine according to the prescription. Layout analysis and recommendations for medicine room are described in section 4.

3. SIMULATION MODELS AND EXPERIMENTAL RESULTS

Based on empirical data collected from seven outpatient clinics, the simulation models are developed in ProModel simulation software. The experiments are designed to investigate two primary issues; appropriate number of doctors and appointment system. Since operations in all outpatient clinics are common, only simulation result from representative clinic will be presented.

3.1 Appropriate number of doctors

Pathology clinic is the largest diagnosis clinic with the largest number of patients visiting each day. Therefore, pathology clinic is chosen to be a representative from seven clinics. From data collection, the average patient arrival rate (λ) is 0.95 patients per minute while average doctor treatment time is 9.74 minutes per patient (i.e., service rate, µ, is 0.10). Currently there are 8 doctors being served at the same time. Simulation results are shown in Figure 3. As number of doctors increase from 8 to 10, the average patient waiting time is significantly decreased from 184.8 minutes to only 25.1 minutes (Figure 3a) while average doctor utilization is not significantly changed (Figure 3b). The results imply that with marginal increase in number of doctors, the overall patient waiting time can be significantly improved.

|[pic] |[pic] |

|(a) |(b) |

Figure 3: Simulation results (a) average patient waiting time vs number of doctors;

(b) average doctor utilization vs number of doctors

In practice, the patient arrival rate can be varied during the day. The model is then further examined the relationship between patient arrival rate and the appropriate number of doctors, given that average service time remains the same. The appropriate number of doctors can then be presented as shown in Table 1. This will be useful when the hospital wishes to construct the doctor’s schedule in order to be best fit with patient arrival rate in each time period during the day.

Table 1: Simulation results on patient arrival rate and appropriate number of doctors

|Arrival Rate |Number. of Doctor|Ave. Doctor |Ave. Patient |

| | |Utilization (%) |Waiting Time (minutes) |

|1.23 |14 |85.8 |19.1 |

|0.94 |10 |91.7 |22.7 |

|0.76 |8 |92.8 |23.9 |

|0.64 |7 |89.0 |18.7 |

|0.55 |6 |89.7 |20.0 |

|0.49 |6 |94.6 |14.8 |

|0.39 |5 |76.1 |14.8 |

|0.33 |4 |79.4 |16.2 |

Further, the simulation results are then investigated whether they agree with deterministic calculations using Little’s Law. Given the arrival rate, service rate, and preferred average doctor utilization, the number of doctors can be calculated using Equation (1).

[pic] (1)

Where S : Number of doctors

µ : Service rate (assume constantly)

( : Arrival rate

( : Percent doctor utilization

The simulation results and deterministic calculation results using Little’s Law can be compared as shown in Table 2. The appropriate number of doctors are identical in most cases.

Table 2: Comparing the result between two methods

|Arrival rate |Number of Doctors |

| |Simulation |Little's Law |

|1.23 |14 |14 |

|0.94 |10 |10 |

|0.76 |8 |8 |

|0.64 |7 |7 |

|0.55 |6 |6 |

|0.49 |6 |5 |

|0.39 |5 |5 |

|0.33 |4 |4 |

3.2 Appointment system

Currently, both appointment and non-appointment patients have the same priority after entering the clinic. Psychologically, patients may feel that they should have higher priority to receive doctor care when they have an appointment. Further, no formal appointment system is currently in place in outpatient clinic department. The simulation models are therefore developed in order to provide the appointment guideline that leads to a satisfied overall patient waiting time. The simulation models assume that appointment patient will have higher priority in all stages of operations. Based on empirical data from general practice clinic, the experiment is conducted by varying the proportion of appointment patients entering the clinic while other system parameters (i.e., patient’s arrival rate and service times) remain unchanged. This experiment aims to understand how sensitive the average patient’s time in clinic to the proportions of appointment and non-appointment patients.

Simulation results in Figure 4 show that as the proportion of appointment patients increases, the average time for appointment patients are not significantly affected. On the other hand, the average time for non-appointment patients dramatically increase after a certain proportion. From the results based on empirical data from general practice clinic, appropriate range of proportion that leads to satisfied overall average patient time is 10 percent to 50 percent. For implementation purpose, the results suggest that in each hour period, hospital staffs should reserve 10 percent to 50 percent of doctor time slot for appointment patients. Detailed experiments show that this appropriate range of proportion can be varied with different system parameters.

[pic]

Figure 4: Simulation results on patient’s average time in system versus proportion of appointment patients

4. RECOMMENDATION FOR MEDICINE ROOM

Currently, medicines are shelved according to the types of medicines (i.e., liquid, tablet, and etc.). On each shelf, medicines are then sorted by their names alphabetically. In most time, pharmacists have to walk all over the medicine room to complete a prescription. The walking distance and time to get the medicines from different shelves are considered as non-value added. According to our observation, it is found that some medicines are more frequently prescribed. The dedicated storage location concept indicates that more activity stock units should be assigned in the location that close to the input/output point. Therefore, it is recommended that most frequent prescribed medicines should be placed in the location so that retrieving distance and time are minimized. ABC analysis is applied to classify medicines into three classes:

- Class A item: 80% of storage and retrieval activity is generated by 20% of item

- Class B item: 15% of storage and retrieval activity is generated by 30% of item

- Class C item: 5% of storage and retrieval activity is generated by 50% of item

Integrating the dedicated storage location and ABC analysis, it is recommended that the medicine room unit should investigate the frequency of medicines prescribed and classify medicines into classes A, B, and C. The shelf should be rearranged such that class A or most often prescribed medicines are placed near the input/output point or the pick up point which requires the least retrieving distance and time, as shown in Figure 5. Similarly, classes B and C medicines should be assigned to the next shelves, respectively.

[pic]

Figure 5: Proposed arrangement of classes A, B, and C medicines where d is shelf length

5. CONCLUSIONS

Using outpatient clinic department in an existing public hospital as a case study, this paper presents preliminary investigation of extensive patient waiting time to receive doctor care and other services. The study is initially carried out by observing the clinic floor and collecting necessary time-related information, such as patients’ interarrival time and service times at each stage of operations. From preliminary analysis, three major sources of extensive patient waiting time are identified: (1) an insufficient number of doctors as compared to patient arrival rate, (2) a lack of appropriate appointment system, and (3) a poor design of medicine storage in medicine room.

Firstly, simulation models are constructed and tested for an appropriate number of doctors that should be available according to average patient’s arrival rate. Results based on simulation and Little’s law calculation can assist the hospital staffs in planning the doctor schedules (i.e., how many doctors should be available) according to average patient’s arrival rate in each operation hour. Secondly, due to a lack of appointment system in most outpatient clinics, a set of appointment and non-appointment patient ratios are examined. Based on empirical data from an existing clinic, a range of ratios that leads to a satisfied overall patient’s time are suggested. Lastly, it is found that patients spend significant time waiting for medicine at the medicine room. It can be observed that the pharmacists spend significant time retrieving different kinds of medicines from the shelves. The concept of dedicated storage location and ABC analysis are applied to form a recommendation to the medicine room staffs, which is expected to significantly reduce medicine retrieving time, and as a consequence, to reduce patient’s waiting time at the medicine room.

ACKNOWLEGEMENT

We are indebted to our project team members who have been heavily involved in data collection procedures and preliminary process analysis; Spun Paleewong, Porames Suwannapisit, Nath Laeadon, and Vakin Lawatanatrakul.

References

[1] Leung, G. M., Yeung, R. Y. T., Wong, I. O. L., Castan-Cameo, S., and Johnston, J.M. 2005. “Time costs of waiting, doctor-shopping and private-public sector imbalance: Microdata evidence from Hong Kong,” Health Policy.

[2] Kontodimopoulos, N., Nanos, P. and Niakas, D. 2005. “Balancing efficiency of health services and equity of access in remote areas in Greece,” Health Policy.

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[1] Corresponding author: email: karndee@tu.ac.th; Tel: +66 2 613 2202; Fax: +66 2 225 2109

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Medicine type B

(80%frequency)

Medicine type A

d

0.5d

0.2d

(5%frequency)

Medicine type C

(15%frequency)

Pick up point

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