Implementation of Six Sigma Method To Improve Hospital Discharge Process: A Before-And- After Study With The Control Group In A Large Hospital


 Background: Delayed hospital discharge is a complex process that can impact hospital and service delivery capacity. The improvement of this process requires structural reforms and coordination with different wards, individuals, and the hospital.The present study aimed to use the Six Sigma method to reduce hospital discharge time. Methods: This pre-post study was conducted based on an experimental design from 2016 to 2020. A series of Six Sigma-driven interventions based on the Define-Measure-Analyze-Improve-Control (DMAIC) cycle was employed in a 1,000 bed tertiary care hospital to decrease discharge waiting time. Two wards in the hospital were allocated to the intervention and control groups. Three months of pre-intervention data were compared with 16 months of post-intervention in each group. The data were analyzed in Stata software (version 14.1). The level of statistical significance was set at 0.000.Results: There was a significant difference (P<0.000) between pre and post-intervention, as well as experimental and control groups. The discharge time points in the intervention and control groups before the intervention were reported as 438 and 411 min, respectively. After the intervention, the discharge time reached 246 min (3.20 h decrease) in the intervention group (P<0.000) and 475 min in the control group (P<0.574). The waiting time in stations 2, 3, and 4 reached zero after the intervention(P<0.000). The trend of discharge time after the intervention from October 2017 to March 2020 demonstrated that the changes were stable (184±25.56 min).Conclusions: As evidenced by the obtained results, the Six Sigma methodology can be an effective change management tool for the improvement of discharge time. The findings suggested that the use of electronic discharge and focusing on physician readiness for writing a discharge order would have the greatest impact.


Introduction
The waiting time or the time patient spend to receive medical services is recognized as a factor that reduces patients' satisfaction with the quality of health services, which in turn, plays an esentially role in quality management (1). Patient discharge is the nal interaction of patients and their companions with the hospital system. Prolongation of this process causes nancial, psychological, and health damages (2). Based on the conducted studies, early discharge of patients can reduce the length of stay (LOS), which in turn, increases the admission capacity for new patients and the satisfaction of discharged patients (3,4). Furthermore, pressures to decrease cost have led hospitals to adopt strategies for the reduction of LOS and improvement of hospital throughput over the past decade (3,4).
One strategy implemented by hospitals to combat overcrowding is investments in new construction and additional sta ng.
Process improvement is another strategy which offers the potential for greater return on investment (6). It seems that tackling this issue requires proper management based on science and evolution, rather than nancial resources (7). As a result, various approaches have been used to improve the quality and management of health organizations, among which lean management system is a comprehensive management approach.
Lean management, originally termed the Toyota Production System (TPS), was developed in other industries, such as the health care system (5). Six-sigma is a business improvement strategy employed to improve business pro tability to drive out waste, reduce costs of poor quality, and improve the effectiveness and e ciency of all operations so as to meet or even exceed customer's needs and expectations (6). It is a set of tools and a way of thinking about how to more effectively assess and study clinical ow and operations to achieve better results for patients, providers, and healthcare delivery systems (7).
Six-sigma projects use a De ne-Measure-Analyze-Improve-Control (DMAIC) structure which is considered by many practitioners to be the primary reason for 6σ's success [12]. There are several studies in the literature which have reported that six sigma tackles numerous problems in health care(8-10); moreover, it reduces LOS (11) and turnaround times in the laboratory (12). Furthermore, similar trends have been observed in decreased LOS of inpatients and waiting time in other countries (13,14). In light of the aforementioned issues, the present study aimed to use the Six Sigma method for the reduction of hospital discharge time in a large Iranian hospital.

Study Design and Setting
The present study was performed in the central laboratory of Çukurova University Teaching Hospital, Adana, Turkey. This hospital has approximately 1000 beds. The laboratory has been accredited by the Joint Commission International since 2006, making it the rst accredited clinical laboratory in a university hospital in Turkey. This research project was conducted in Imam Reza Hospital complex (IRHC) that is a tertiary care teaching hospital with 1000 beds located in Mashhad, Iran. The IRHC provides care to around 6,000 inpatients and 19,000 emergency patients monthly. The study used a quasi-experimental two-group pretestposttest design and was designed to reduce patient discharge time.
To select the wards of intervention and control groups, a team was formed consisting of ward secretaries (n=2), head nurses with at least 10 years of work experience(n=2), hospital quality improvement o cer, hospital admission and medical records o cer, as well as hospital billing o cer and faculty members of health system management and health information management (n=3). The participants were contacted via phone calls and were asked to attend the meeting after explaining the study objectives and introducing the researchers. Moreover, the faculty members of health system management were invited to ensure more widespread coverage of the issue.
Four wards were selected during a meeting based on the following criteria: 1) the same discharge time or a maximum of 15% standard deviation with the total discharge time of the hospital, 2) the same LOS with a maximum of one-day difference. Accordingly, wards of general surgery, orthopedics, heart-1, and heart-2 were selected. Thereafter, general surgery and heart-1 were selected as the intervention group and orthopedics and heart-2 as the control group. The study was approved by the IRHC administration in accordance with the quality assurance policy.

Intervention
The hospital throughput project was conducted at IRHC from July-October 2017 with ongoing key performance metrics monitoring, following a process improvement framework developed using Six Sigma principles. Six Sigma relies on a structured approach to uncover the root cause of a problem using the De ne, Measure, Analyze, Improve and Control (DMAIC) method by de ning the problem, measuring the defect, analyzing the causes, improving the process by removing major causes, and controlling the process to ensure that defects do not recur( 18).
A multidisciplinary team (value team) was formed at the outset, including a physician, chief medical resident, director of bed management, manager of billing, patient care unit manager, head nurses of wards, unit clerk representative, assistant director of health information management, and manager of the transport services. The hospital administrator was the assigned Six Sigma expert responsible for team building and project management. Following the DMAIC approach, the team set out to de ne the scope of the project and decided to focus on the improvement of the administrative processes that contributed to delayed discharge. To identify the hospital discharge process and de ne the problem, the discharge process was broken down into seven logical sub-processes called seven stations.
A time study (stopwatch) was used to measure each step in the process to determine the time consumed by each of the seven sub-processes to prepare the discharge summary, and the discharge process of 140 patients was measured. In the next step, to discuss the solutions and approve the new discharge process, the method of expert discussion and focused group discussion was used. The team, through extensive discussions, completed a root cause analysis for delayed discharge. Moreover, they outlined barriers and wastes and proposed changes from the perspective of each stakeholder, and interventions were implemented according to Table 1.

Data gathering and Measurements
Dataset for hospital inpatients' analyses included 140 patients in the pre-intervention phase (November 2016-February 2016) and Page 4/14 randomly selected from each ward in the intervention and control groups in different months. Data related to patient discharge time (time from discharge order to patient leaving the hospital) was examined.
The inclusion criteria for patients were the medical records of patients who were discharged from the selected wards during the study period. On the other hand, the exclusion criteria entailed: patients who died and those who were discharged from the hospital without settlement. For all patients, the start and end time points of each station in the discharge process were checked and recorded in a data table. The main sources of data for the extraction of the start and end times of each station were the hospital information management system and the patient's physical medical record. In addition, to check data accuracy, the original data was matched with other medical record documents, such as the date and time in the physician order, nursing report sheet, and summary sheets. verbal consent was obtained from participamts that was approved by the ethics committee. In addition to the ethical approval, all methods were performed in accordance declaration of Helsinki.

Statistical Analysis
Categorical variables were summarized by number and percent, while continuous variables were described as mean and standard deviation. The comparability of the intervention and control groups was assessed by comparing some characteristics of their patients using the Chi-Square test for categorical variables and independent t-tests for continuous variables. Outcome variables (waiting times) before and after the intervention in each group were compared using an independent t-test since the patients were different before and after the intervention. In each group, the mean difference and its 95% con dence interval were calculated for all stations. Intervention and control groups were considered statistically signi cantly different in any station if their con dence intervals exclude each other. All statistical analyses were carried out using Stata software (version 14.1), and the level of statistical signi cance was set at 0.05.
Individual control chart was used to analyze trends in average daily discharge time, special cause variations (non-routine events), and common cause variations (routine events), and assess the process for stability (statistical control). To further assess variation in the process, the following were also measured: six sigma scores (number of short-term standard deviations between the center of a process and the closest speci cation limit), yield (percentage of discharge times meeting team goal of 212 min), defects per million (number of times that discharge time exceeded the target per million discharge opportunities).

Results
The discharge process was broken down into seven logical sub-processes called seven stations (Fig. 1).
The hospital data from 280 patients admitted to selected wards during the pre and post-intervention were analyzed. In the modi ed discharge process, four steps were eliminated. The most important change in the patient discharge process was changing the ward secretary to HIM experts. In the electronic patient discharge, all discharge processes are performed without the presence of the patient and the medical record at the inpatient ward. In this process, the patient is discharged using the POS system, which is available to the HIM expert, and all these processes are performed in the relevant inpatient ward. The HIM expert of each department should correct the defects of the medical records and deliver them to the medical records department within 48 h after the discharge. There were no signi cant differences between patients' variables before and after the intervention in intervention and control groups ( Table 2). After the interventions, it was found that the discharge time (totally) decreased by 3.20 h in the post-intervention, as compared to the pre-intervention phase (P < 0.000) in the intervention group (Table 3).
As illustrated in Table 3, there was a statistically signi cant reduction in the average waiting time for patient discharge in the intervention group in all seven stations (P < 0.05). The implementation of electronic discharge eliminated the stations of 2, 3, and 4 in the intervention group (Fig. 1). In the control group, the average waiting time for patient discharge was reduced in ve stations (1, 3, 4, 5, and 7); nonetheless, this reduction was statistically signi cant only in station 7. However, in stations 2 and 6, the average waiting time was statistically signi cantly increased (P < 0.05).

Control phase
The application of the Six Sigma methodology not only improved the process performance but also process improvement in ensuring the sustainability of results in the long run. The most daunting challenge in any improvement initiative is the sustainability of the archived results(1). Therefore, control charts were used to monitor the ongoing performance of key variables.
After the implementation of all of the improvements in the process, a decrease was observed in the mean duration of the discharge process as demonstrated in Fig. 1 which shows the control chart before and after improvement. The Six Sigma score ranged within 0.87-1 in the pre-intervention phase. After the intervention, the Sigma reached 2.125. Furthermore, errors in the intervention group decreased from 26 − 10 (Table 4).

Discussion
Hospital overcrowding poses a daunting challenge to the healthcare system since limited bed capacity and admission congestion exert enormous negative impacts on quality and safety (15). The constrained bed capacity can be effectively addressed by the management of discharge timing; nonetheless, signi cant institution-speci c barriers exist in its implementation (16). For instance, different staff and wards are involved in this process, including physicians, nurses, ancillary service staff, patients, their families, and the billing department. Therefore, it requires great coordination between them. The present study validated the application of Six Sigma DMAIC methods to reduce and optimize the discharge process with a speci c focus on orthopedic and surgical wards.
Although some pieces of evidence on the effectiveness of Six Sigma in the healthcare setting have been criticized for weak methodological design (17), the present study included a large pre and post-intervention analysis of a total of 280 patient records. Others have demonstrated that increasing early discharges using a structured framework for quality improvement is achievable and sustainable. For example, Beck et al. used the Lean methods to modify work ow and attain early discharges (20). Along the same lines, Patel et al. used an Institute for Healthcare Improvement PDSA (Plan-Do-Study-Act) framework to achieve early discharges (21). A feature common to all these successful interventions is a standardized forum for communication, such as a multidisciplinary huddle. Other elements central to the success of these interventions include accurate measurement with data analysis, use of checklists, feedback, and identi cation of patients the day before discharge (21,22). One of the key points of interest in this study was the increase in discharge time in control wards, compared to that in the intervention group. This nding can be attributed to changes in the number of patients or ward staff.
One of the notable strengths of this study was the corrections made to reduce the duration of discharge, and it was indicated that these corrections had good stability. The majority of similar studies lacked a control group and long-term follow-up (3,21,23).
Nevertheless, the results pointed to a positive impact on the reduction of patient discharge time due to the application of suggested recommendations for 16 months. As a result of this breakthrough improvement, more patients will be managed in the particular department. This, in turn, increases the number of admissions, room turnover, hospital pro tability, and patient satisfaction.
The results of the current study also demonstrated the contribution of the multidisciplinary team members of the hospital to the reduction of discharge time. The ndings of this study would be of great help to hospital administrators and policy planners in expediting decisions about the implementation of six sigma method to improve the quality of care in hospitals. All organizations experience resistance to change. Physicians as one of the most important and highly constrained resources in hospitals are mostly autonomous in the management structure and immune to incentives typically available in other organizations. Owing to the autonomous nature of their profession, it is di cult for physicians to accept standardization, especially when it goes against their own interests. Physicians do not feel comfortable adopting a standardization initiative unless there is transparent evidence regarding its impact on patient outcomes (7) Furthermore, a major cause of the long discharge process was poor communication between different stakeholders (e.g., treating physicians, consultants, nurses, pharmacy staff, and the accounting department). Since the discharge process is a highly peopledependent process, it was imperative to observe the complexity of communication in this process (24). It is recommended to involve physicians in the analysis and development of solutions through participation in the improvement team or holding workshops to discuss quality improvement issues with other stakeholders to encourage collaboration in the proposed methods to sustain and control the improvements.
In addition, one of the main processes modi ed in this study was the use of an electronic discharge system which has been mentioned in various studies as a tool to ensure complete (25) and reliable discharge summary (26), expedite the discharge time (27), and increase staff satisfaction(28).

Limitations
The coverage of this study and obtained improvements were limited to two wards of the research setting. Therefore, an appropriate precaution needs to be taken while generalizing the results. Moreover, another limitation of the study is related to the pre-post intervention observational design that limits the ability to control for all possible confounders. Patient acuity and sta ng levels of different stakeholders are possible confounders that were not taken into account. Furthermore, the outcomes were retrieved from a database that included timestamps that were manually entered by clerks and prone to error.
2 . Dyers RE, Evans J, Ward GA, Du Plooy S, Mahomed HJSAMJ. Are central hospitals ready for National Health Insurance? ICD coding quality from an electronic patient discharge record for clinicians. 2016;106(2):181-5. Figure 1 Discharge process ow map (after implementation of intervention)