Improving Cardiac Surgery Patient Flow through Computer Simulation Modeling

Transcription

1 Improving Cardiac Surgery Patient Flow through Computer Simulation Modeling Dana Khayal, Fatma Almadhoun, Lama Al-Sarraj and Farayi Musharavati Abstract In this paper, computer simulation modeling was used to improve surgical patients flow in the cardiology department of a hospital in Qatar. The aim of the analysis was to reduce the length of stay (LoS) of patients. Data was collected from a case study cardiology department. Input data modeling was carried out using the input analyzer tool in Arena simulation software. The approach to simulation was based on discrete event simulation modeling concepts. A discrete event simulation (DES) model was developed in Arena simulation software. Obtained results show that on average 5.97% reduction in LoS for home patients waiting for their surgery day can be achieved while 23% reduction in LoS for admitted patients waiting for surgery can be achieved. These potential reductions were based on implementing process improvements identified through computer simulation modeling techniques. Therefore, discrete event simulation modeling is a powerful tool that can be used to aid decision making for process improvements in cardiac surgery operation systems. Keywords Arena simulation software, Computer simulation, Discrete event simulation (DES), Length of Stay (LoS), Modeling, Process improvement. O I. INTRODUCTION NE of the leading indicators of a developed community is providing affordable, effective and efficient healthcare services to all citizens at the highest level of service. There is, therefore, an inherent need to continuously look for improvement opportunities if a community intends to meet or at least move towards leading indicators in healthcare systems. In the State of Qatar, the quality of healthcare is high, even by western standards [1]. However, the total number of visits to health centers in Qatar covering all specialties has been increasing rapidly. One study showed an increase in the number of visits by 9.9% within a space of seven years [1]. One of the reasons for this increase is that Qatar is one of the fastest growing economies in the world. As such large numbers of expatriates come to Qatar in search for jobs and business opportunities. This creates pressure on healthcare systems. As a result, there is a possibility of mismatches between population influx and the existing healthcare services. Therefore, health care organizations in Qatar should be prepared to expand and extend their service offerings in response to possible population explosions. In addition, there Farayi Musharavati is with the Department of Mechanical and Industrial Engineering, Qatar University, P.O.Box 2713, Doha, Qatar (phone: ; fax: ; farayi@qu.edu.qa). is need to continuously seek new strategies in creating and inventing methods, techniques and tools that lead to improvements in healthcare service levels. This paper contributes to this need by developing computer simulation models that can be used as decision making models for improving processes in healthcare systems. Computer simulation modeling was used to study, analyze and seek improvements in surgical patients flow in the cardiology department of a hospital in Qatar. The aim of the study was to identify possible areas of process improvements, with a particular focus on patient flow in the cardiology department. Simulation modeling was used to identify existing bottlenecks in the process systems and to evaluate opportunities and initiatives for improving patient flow in the cardiology department. The objective of the study was to reduce the length of stay (LoS) in the cardiology department. This objective was addressed through what-if analysis scenarios based on a discrete event simulation (DES) model that was developed using ARENA simulation software. The remainder of the paper is organized as follows: section II presents a theoretical background to the study, section III describes the methods used in the investigation, section IV discusses the results obtained from the DES model and finally concluding remarks are given in section V. II. THEORETICAL BACKGROUND The past two decades has seen a number of reports that discuss mismatches between progress in medical science and healthcare delivery [2]-[3]. One report estimated that systems failures in healthcare delivery were responsible for at least 90,000 deaths each year in the USA [2]. Another report revealed a wide gap between the quality of care that the health system should be capable of delivering today, given the recent advances in medical science and technology [3]. Consequently, a number of projects have been initiated to carry out the following tasks: (1) identify engineering applications that could contribute significantly to improvements in health care delivery in the short, medium, and long terms; (2) assess factors that would facilitate or impede the deployment of these applications; and (3) identify areas of research in engineering and other fields that could contribute to rapid improvements in performance [4]. Therefore, the need for improving healthcare systems can never be overemphasized. While many tools have been used to solve patient flow 18

2 problems in healthcare systems, discrete event simulation (DES) has been used extensively [5]-[7]. For example, ARENA simulation software was used to study the Emergency Department of Cooper Hospital University Medical Center [8]. In [8], DES modeling helped in reducing the length of patients stay at minimum cost. In [9], DES modeling showed that there was a possibility of reducing patients waiting times. Such results are quite encouraging and worth the pursuit. In applying DES modeling, the performance of any processing system can be evaluated through a number of performance metrics. Public literature shows that LoS is one of the most important performance measures of the service level of any subsystem of a healthcare system [5]-[7]. This is because the LoS is linked to a number of critical decision variables in processing systems. As such, the LoS provides a quick and reliable measure of the performance of healthcare systems. This measure can then be used, through what-if techniques, to decide on the course action among multiple alternatives. This paper will focus on reducing the LoS using DES modeling in Areana simulation software. III. MATERIALS AND METHODS In any given organization, operating systems are at the hub of the organization s output. This is also true for healthcare systems. Healthcare system function as a collection of subsystems (departments). This scenario may result in independent operating units, with the negatives consequences of poor operating efficiencies, ineffectiveness and poor systems performances. It is, therefore, necessary to use a systems approach in analyzing healthcare systems. In this study, the cardiology department is viewed as a system (department) with a number of sub-systems that work together for the specific objective of providing high quality service for cardiovascicular surgical patients. The focus in this paper is the total length of stay of patience in the cardiology department. In order to analyze patient flow in the cardiology department, two methods were used to address the issues related to LoS. These methods are described in this section. A. Case Study Data was collected from a case study hospital. These data were used to characterize system behavior, and patient flow patterns. Based on data collected, cardiac surgery patients were classified into three categories, namely; cardiac patient who visits the out-patient-department (OPD) to make new appointments (NAp), cardiac patients who visits OPD according to scheduled appointment (SAp), and emergency cardiac patients (ECP) who are directly sent to operating room for surgical treatments. For out-patience, doctors order admission of patients on appointment based on diagnoses and results obtained from electrocardiogram (ECG) and Lab tests. The average discrete probability distribution of these classes of patients (inclusive of male and female patients) computed over a ten-months-period data set are shown in Table I. TABLE I PERCENTAGE DISTRIBUTION OF PATIENTS BY TYPES Type of Patients NAp SAp ECP Percentage Distribution On the day of the appointment, nurses admit patients and the patient goes through a procedure in preparation of the surgery. Preparation includes fitness tests and anesthesia tests. Emergence patients are directly admitted and may spent a short time waiting for their turn to go to the operation room. Activities that the cardiac patients undergo in the cardiology department were categorized into eight sub-systems, namely; 1. consultations, 2. admissions, 3. ECG, 4. Lab Tests, 5. operation preparation, 6. surgical operation, 7. post operation care, and 8. discharge. The consultation sub-system involves doctor diagnosis of candidate patients. It consists of three doctors in one shift where diagnosis of the patient is carried out based one clinical check and tests obtained from laboratory and ECG document. After consultation, the doctor will either assign a medical prescription, which should be taken from the pharmacy, or assign a surgical treatment to the patient. The admissions subsystem involves patient reception, registration and appointment activities. Reception tasks include; receptionist checking the date and time of the appointment, and entering patient data in the patient s file. Registration tasks include; receive patient medical transform paper, open new medical record for the patient in the department, assigning appointment to any available doctor. The ECG subsystem involves tests that check for problems with the electrical activity of the heart. A doctor prepares a document of the graphical representation of heart bits. On the other hand, the Lab. tests involve blood sampling and analysis. Operation preparation involves fitness tests and anesthesia tests before the patient is admitted to the cardiac surgery operating room. In the cardiac surgery sub-system the patient is transferred to the operation room where initially anesthetic stage is carried out and surgery is conducted by 3-4 doctors, one equipment specialist and 3 nurses. After the surgery, the patient is sent to the post anesthesia care unit from which the patient will be transferred to the post operation care subsystem where the patient will spend 5-7 days under intensive care of doctors and nurses. The last subsystem involves the discharge process. After making sure that patient is risk free, doctors sign discharge papers and the process of patient discharge starts. The patient is then allowed to leave the hospital once the discharge conditions are met. The average percentage distribution of patients visiting these activity areas computed over a ten-months-period data set and inclusive of male and female patients are shown in Table II. 19

3 TABLE II PERCENTAGE DISTRIBUTION OF PATIENTS FOR VARIOUS SUB- SYSTEMS % Distribution Activities in Various Sub-systems Suspicion of Cardiac Problem Appoint. Admission Appoint. Surgery Urgent Consultation Surgeon Tests Diagnostic Tests Require Surgeon The case study consists of; (a) two waiting areas, divided according to gender as is the practice in Qatar, (b) three examination rooms, (c) two laboratories, and (d) one pharmacy with two windows (one for male patients and the other for female patients). The cardiology department operates in one shift with seven doctors, seven nurses, two receptionists, three appointment registrars, two lab analyzers, two Electrocardiograph (ECG) specialists, one pharmacy-clerk and one pharmacist. In conducting a surgery, for one cardiac operation 3 surgeons, 1 medical-equipment specialist and 3 nurses are seized in the operation room. In addition, 6 nurses work in the surgery department in different sections such as the pre-operative unit, post anesthesia care unit and post operative unit. B. Computer Simulation Modeling A DES model was created in ARENA simulation software for the purpose of conducting what-if analysis in order to study patient flows in the cardiology department. Experiments were conducted with these models to simulate the effects of alternative solutions to patient flows. The simulation model was implemented using the Arena 14 software package. In order to run the ARENA simulation model, collected data was fitted (input data modeling) into appropriate probability distributions. This was achieved by using Input Analyzer, a tool in ARENA simulation software. The results of this investigation are presented in section IV. IV. RESULTS AND DISCUSSIONS The process flow chart used to model the cardiology department is shown in Fig. 1. The service time distribution of patients visiting the eight activity areas computed over a tenmonth-period data set are shown in Table III. A number of delays were observed after studying the cardiology department. The delay time distribution of patients waiting to be served computed over a ten months period are shown in Table IV. In an attempt to reduce the LoS, the focus of the analysis was on improving the service times and improving the service levels throughout the cardiology department subsystems. Lab. Tests Discharge setup Recover Operation preps Post Op. Care Operation Fig 1. Process Flow Chart for the Cardiology Department Surgery TABLE III SERVICE TIME DISTRIBUTION Sub-System Service Time Distributions 1 Consultations TRIA (15, 20, 25) minutes 2 Admissions TRIA (30, 40, 50) minutes 3 ECG UNIF (10, 15) minutes 4 Lab. tests TRIA (15, 18, 21) minutes 5 Operation preparations UNIF (20, 30) minutes 6 Surgical Operation TRIA (3, 4, 5) hours 7 Post Operation Care UNIF (3, 6) days 8 Discharge Process TRIA (2, 3, 5) days TABLE IV DELAY TIME DISTRIBUTION Delay Nodes in the various Subsystems Delay Time Distributions NA p waiting for day of appointment UNIF (7, 10) days SA p waiting for surgery UNIF (6, 9) days Cleaning Catherization Room UNIF (20, 30) minutes Setting up Catherization Room UNIF (15, 20) minutes Cleaning Operating Room UNIF (20, 30) minutes Setting up Operating Room UNIF (15, 20) minutes Fig. 2 shows an overview of the Arena simulation model 20

4 (excluding the submodels). and from the improved system are shown with respect to the average number of patients in queues, and the average waiting times. In studying the cardiology department, it was found necessary to differentiate between LoS for outpatients who are waiting for surgical operations appointment (LoS outpatients ) and LoS for inpatients who are waiting for their surgical operation day (LoS inpatients ). Based on data collected from the simulation runs, it was observed that LoS outpatients improved from minutes to minutes, while the LoS inpatients improved from minutes to minutes. This represents potential reductions of 5.97% in LoS outpatients. and 24.08% in LoS inpatients. Fig. 2 Arena Simulation model Changes made improve the service times included: advanced computerization of information and data capture as well as improved communications and scheduling of appointments. Changes made to improve the service levels included; scheduling doctors according to the Bailey-Welch schedule instead of the traditional scheduling scheme, adding resources (e.g. adding I more receptionist, adding one more doctor to the team, adding one more nurse and adding one more operating room). TABLE V SUMMARIZED COMPARISON OF SUSSYSTEM PERFORMANCE DATA FOR THE ORIGINAL AND IMPROVED SYSTEMS V. CONCLUDING REMARKS In a healthcare system, patient flow analysis is crucial in improving the operating performance, patients satisfaction and the quality of service offered by the healthcare providers. In this paper it was illustrated that patient flow analysis can be conducted using computer simulation modeling techniques. It was shown that computer simulation modeling techniques can reveal areas of potential improvements when making process decisions in order to improve operating performances. Thus, computer simulation modeling techniques can be used to analyze and evaluate current processes, recommend process improvement changes through what-if analysis scenarios, and evaluate the impact of the proposed changes through simulation experiments. In addition, this research shows that simulation is an efficient tool for identifying problems and improving performance of healthcare systems. The simulation model is valuable to present the current work flow and to predict the bottleneck in healthcare systems. Original System Performance Measures Improved System Performance Measures Cardiology Department Sub-System Number of Patients in Queue Waitaiting Time (min) Number of Patients in Queue Waitaiting Time (min) 1 Consultations Admissions ECG Lab. tests Operation preperations Surgical Operation Post Operation Care Dischsrge Process Table V shows a summary of data collected from the Arena simulation model. Performance data from the original system The simulation models developed for the various subsystems discussed in this work demonstrates that the 21

5 resources in the pharmacy subsystem and the discharge process are major bottlenecks for the longer stay of patients in the cardiology system. Such valuable information can then be used as a starting point of investigation using other optimization techniques. Case study results indicated that on average there is a 5.97% potential reduction in LoS for home patients waiting for their surgery day. In addition, it was shown that there is a 23% potential reduction in LoS for admitted patients waiting for surgery. These results illustrate that significant reductions in LoS of cardiovascular patients can be achieved through process improvement techniques that are revealed by computer simulation modeling of a given system. This is because the times that the patients spend when in direct contact with the cardiology system simply translate into standard times for the required procedures. These standard times can only be improved by implementing advanced continuous improvement techniques on selected Kaizen events within the cardiology system. Such improvements are important since a significant reduction in the length of stay may translate into increased patient satisfaction and/or increase the access to the cardiac surgery department. In this case study, it was also found that the length of stay could be reduced by adding one pharmacist and one clerk in the discharge process. A promising improvement suggestion was to increase the availability of the surgery room (by for example increasing the number of operating rooms or facilitating the rooms in such a way that parallel surgeries could be performed). This would translate into an increase in the number of patients who will be served in the cardiology system. However, this action would also reduce the utilization rate for the surgery room. Department Using Discrete Event Simulation, Pediatric Emergency Care vol. 23, January 2007, pp [6] M. Gunal, M. Pidd, Understanding Accident and Emergency Department Performance using Simulation. Proceedings of the 2006 Winter Simulation Conference. Ed. Perrone, L, et al IEEE, pp [7] L.G. Connelly, A.E. Bair, Discrete event simulation of emergency department activity: a platform for system-level operations research. Acad Emerg Med, vol. 11, pp [8] S. Samaha, W.S. Armel, D.W. Starks, Emergency departments: the use of simulation to reduce the length of stay in an emergency department, in: Proc 2003 Winter Simul Conf., pp [9] R. Konrad, K. DeSotto, A. Grocela, P. McAuley, J. Wang, J. Lyons, M. Bruin, Modeling the impact of changing patient flow processes in an emergency department: Insights from a computer simulation study. Operations Research for Health Care, 2013 DOI: ACKNOWLEDGMENT This publication was made possible by a grant from the Qatar National Research Fund under its Undergraduate Research Experience Program award number UREP Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund. REFERENCES [1] Health system profile, Qatar, in Regional Health Systems Observatory world health organization report, 2006, ofile.pdf [2] To Err Is Human: Building a Safer Health System, in IOM (Institute of Medicine) report, L.T. Kohn, J.M. Corrigan, and M.S. Donaldson, ED. Washington, D.C.: National Academies Press. [3] IOM Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C.: National Academies Press. [4] P. Reid, W. D. Compton, J. H. Grossman, and G. Fanjiang, Ed, Building a Better Delivery System: A New Engineering/Health Care Partnership, National Academies Press Washington, D.C. [5] G. R. Hung, S. R. Whitehouse, C. O Neill, A. P. Gray, N. Kissoon, Computer Modeling of Patient Flow in a Pediatric Emergency 22