This work tries to answer the following question: can healthcare be engineered using lean management tools? Lean is known to achieve successful results when implemented in the manufacturing sector. Typical results are operational cost reduction, cycle time reduction, and higher customer satisfaction. The service sector, however, has seen mixed results. For the last two decades, educators and healthcare professionals are trying to implement lean tools in healthcare. Some reported success and many did not, for variety of reasons. In this paper, we search the literature and reveal the special nature of healthcare services, success factors, and barriers facing implementation of lean in healthcare. We then conduct a survey of 18 elite Jordanian hospitals to study the case holistically. Statistical analysis of the survey results confirmed some of what the literature revealed; organizational leadership seems to be the most dominant factor, followed by knowledge of employees about lean, training, and patient satisfaction (customer focus). Another important finding, not captured by the literature, is that lean implementation success depends on educating physicians about continuous improvement and lean and ensuring they are part of the improvement team. Based on the revealed enablers and obstacles, we created a full lean implementation framework. This framework was then used along with selected engineering tools to implement lean in a major hospital successfully. Implementation results showed 60% of reduction in cycle time, 80% reduction in operational cost, and many other benefits.
In a highly competitive world, all industrial and service sectors work relentlessly to save cost and enhance market standing. Healthcare organizations face even more challenges in keeping up with the competition and the cost of providing medical services. Delivering a high-quality medical service at an acceptable cost is the objective of owners and managers [
Healthcare professionals used various lean tools for the last two decades [
Kovacevic et al. [
Mannon [
Typical lean studies try to run current operations with less human efforts, less space, less amounts of raw materials, and less cycle time [
Many researchers focused on the nature of healthcare systems and its interaction with any implemented methodology; Machado et al. [
Radnor et al. [
Poksinska et al. [
Some researchers studied the reasons behind implementation failure. A good literature review on implementation barriers and enablers can be found by Leite et al. [
D’Andreamatteo et al. [
This research consists of two phases. In the first phase, we create and distribute a survey designed to reveal reasons for success and failure of engineering and lean initiatives. In the second phase, we use knowledge gained thus far to create a framework that ensures implementation success. We then implement the framework in a major Jordanian hospital as pilot study to prove findings.
Throughout this work, we will focus on lean’s effects on financial gains and patient satisfaction. Financial gains are achieved by saving money or adding income. Lean saves money when it leads to reduced cycle time, reduced raw material used, less quality issues, and less number of workers needed to do the job. Lean helps healthcare organizations to add income whenever it results into saving space or enhancing capacity to deal with more patients. Patient satisfaction is achieved by providing faster and effective service at a reasonable cost. We use the “customer focus” concept in this paper to highlight efforts that lead to patient satisfaction.
While both measures are tracked separately, they are really connected through focusing on quality. By decreasing cycle time and resources needed and by simplifying the process, we are really improving the speed and effectiveness of service, thus improving the patient satisfaction.
The healthcare industry continuously searches for new ideas to enhance the performance and improve financial standing. In this report, we study the effect of lean implementation on healthcare.
While lean practices are proven to create successful improvements in manufacturing environments, it does not have the same effect in the healthcare industry. In fact, over the last two decades, some healthcare organization saw some levels of success with lean implementation, and some did not.
The questions this research tries to answer are as follows: Can healthcare be engineered using lean management tools? What are the factors that support lean implementation and what are factors that hinder lean implementation in healthcare settings? What can be done to ensure successful implementation of lean in healthcare organizations?
To answer the three questions listed in our problem statement, we start by summarizing the literature areas regarding lean implementation in healthcare. Table
Summary of literature review on lean implementation.
Area of research | Subarea | Authors | |
---|---|---|---|
Leadership skills and involvement | Achanga et al. [ | Kundu and Manohar [ | |
Aij [ | Maijala et al. [ | ||
Doss and Orr [ | Najem et al. [ | ||
Hamid [ | Steed [ | ||
Kumar et al. [ | Vermaak [ | ||
Holistic implementation of lean | Bateman et al. [ | Machado et al. [ | |
Costa and Godinho Filho [ | Parkhi and Suresh [ | ||
Guillebaud [ | Poksinska et al. [ | ||
Employee involvement | Hamid [ | ||
Harmon et al. [ | Vermaak [ | ||
Organizational culture | Achanga et al. [ | ||
Hamid [ | Kundu and Manohar [ | ||
Kumar et al. [ | Najem et al. [ | ||
Customer focus | Hamid [ | Kumar et al. [ | |
Waste reduction, customer satisfaction, cycle time reduction, administrative benefits, employee satisfaction, better performance quality, etc. | Folinas and Faruna [ | Papadopoulas [ | |
Graban [ | Rexhepi and Shrestha [ | ||
Kanamori et al. [ | Shazali at al. [ | ||
Hussain and Malik [ | Womack and Jones [ | ||
Bateman et al. [ | Manos et al. [ | ||
Costa and Godinho Filho [ | Patri and Suresh [ | ||
Fillingham [ | Radnor et al. [ | ||
Leite et al. [ | Teich and Faddoul [ | ||
Aij [ | |||
Kovacevic et al. [ | Mannon [ | ||
Fillingham [ | Øvretveit [ | ||
Nembhard [ | Shortell et al. [ | ||
Barnabè et al. [ | Maijala et al. [ | ||
D’Andreamatteo at al. [ | Rees and Gauld [ | ||
de Souza [ | Savage et al. [ | ||
Costa and Godinho Filho [ | Hussain and Malik [ | ||
Dahlgaard et al. [ | Shazali at al. [ |
The listed literature is supported by countless research that can be found in specialized research databases such as PubMed under areas or subareas in Table
It is obvious that, under certain managerial scenarios, success is possible. To identify best environment for success, a structured survey was distributed and answered by 60 representatives working in 18 hospitals. The survey was answered by nurses, administrative personnel, physicians, and quality managers. The survey was designed to test multiple hypotheses according to the research model set forth. The hypotheses used in the model are as follows: H1: good leadership practices have a positive impact on lean implementation success and will lead to financial gains H2: good leadership practices have a positive impact on lean implementation success and will lead to patient satisfaction H3: employee involvement has a positive impact on lean implementation success and will lead to financial gains H4: employee involvement has a positive impact on lean implementation success and will lead to patient satisfaction H5: customer focus has a positive impact on lean implementation success and will lead to financial gains H6: customer focus has a positive impact on lean implementation success and will lead to patient satisfaction H7: external forces have a positive impact on lean implementation success and will lead to financial gains H8: external forces have a positive impact on lean implementation success and will lead to patient satisfaction H9: employee training has a positive impact on lean implementation success and will lead to financial gains H10: employee training has a positive impact on lean implementation success and will lead to patient satisfaction H11: holistic implementation approach has a positive impact on lean implementation success and will lead to financial gains H12: holistic implementation approach has a positive impact on lean implementation success and will lead to patient satisfaction
These relationships can be seen in Figure
Research model.
The six factors used in the survey were selected from the listed literature and were selected tested against good management sense. The healthcare industry, just like any other service, thrives on these six soft factors. Prior to running the surveys, interviews with management and quality professionals in all 18 hospitals indicated reasons for implementation failure. All the indicated reasons were related to these six factors. This is in line with the literature.
The six independent factors for implementation success are elaborated as follows: good leadership practices refer to full support and follow-up during implementation. Good leadership practices include coaching, mentoring, and authorizing needed training.
Employee involvement here focusses on involvement of physicians, since nurses and administrative staff are typically involved in any initiative, and their involvement is linked directly to the success of lean implementation in many published research papers. Physicians involvement, however, are not studied as much.
Customer focus refers to the level of interest in customer satisfaction in every change made in the healthcare organization. Most changes are made for the sake of minimizing operational cost or maximizing profit. This, however, may come at delays or less quality of services provided to the customer. A company with high customer focus thinks about the level of service provided to the customers as well as quality of services.
External forces refer to the pressure healthcare organizations face from the market. Competitive pressure and local regulations may cause the healthcare organization to look for initiatives that cut cost in every possible way. This, however, may hinder implementation success.
Finally, holistic approach refers to applying lean fully, not just one simple tool or just in one department. Applying an initiative holistically means taking into considerations all organizational and individual related factors and issues.
To ensure credibility of results, all healthcare organizations involved in this research were picked based on the following criteria: Involved in lean implementation or used lean tools for at least one year Have reports of performance measurements before and after implementation Mature enough and has been in business for at least 5 years The respondent has been involved in performance management or performance improvement for at least 3 years.
The criteria are set to guarantee seriousness of implementation in these organizations.
As mentioned Introduction, this work includes two phases, and the second phase includes two parts: part one cares about creating a framework to guide successful implementation. This framework is built based on our findings from the literature and the survey results. In part two, we implement lean in a major Jordanian hospital using a collection of engineering tools.
From the literature review, one may be able to conclude that lean tools can be used in a healthcare facility to minimize cost, enhance patient care and patient satisfaction, speed up the healthcare process, etc.
The following lean tools can be used effectively in healthcare organizations: 5S (sort, straighten, shine, standardize, and sustain): 5S tools aim at making the working place clean, orderly, and free of clutter. Such tool builds great working ethics and reveals a clean operation. Healthcare organizations should always use 5S all the time as it represents a good fit with healthcare processes and objectives. Visual management: The working place should be very visible. Every mistake is seen clearly. Inventories are exposed. Good work is seen to all. This can be achieved by using visual aids. Visual management tries to provide work related information to anyone without asking an employee, without using a computer or holding a meeting. This can be achieved by using colored signs, warning lights, instructional signs, painted floors, etc. The seven harmful wastes: according to Womack and Jones [ Waiting: employee or equipment idle time Transportation: any movement that does not add value Overprocessing itself: doing more work than necessary Motion: wasted walking or movement Poor quality: errors or rework Inventory: storing excess inventory of anything Overproduction: producing more, sooner, faster than required by the next step in the process Reduction of these wastes in healthcare settings leads to faster service, less mistakes, and less cost. Standardized work: standardized work stands for performing each process with the least cost, least number of steps, and best possible outcome. This can be achieved by establishing a continuous improvement process in place and by utilizing other lean tools such as Value stream mapping (VSM): value stream maps are used to draw the process from the patient point of view. These maps reveal the seven wastes and help making the process better. Load balancing: most healthcare organizations see imbalanced work loading, most of the time. Some departments are swamped with work while others do not have as much workload. Utilization of healthcare workers should be balanced. Lean utilizes most of the tools mentioned thus far to balance the load among departments and ensures a smooth flow of work. In this research, we use a combination of engineering tools along with lean tools. Such tools can be of help to make the transformation to lean effective. Many engineering tools can be used, including the following: Prioritization tools: many prioritization tools can be used including Root cause analysis tools: many tools can be used to arrive to the root cause of a problem, including Simulation: simulation is used to represent the process, represents changes in the process, and performs “what-if “analysis. In this research, we use Arena® software to perform simulation. Performance evaluation tools:at the end of each improvement project, performance improvements need to be quantified, measured, and presented. In this research, we propose using an index that quantifies the improvement in cost, cycle time, customer satisfaction, and quality improvement. The performance improvement index (PII) can be calculated as follows:
Each of the four quantities is assigned a value of 1, 5, or 10. A value of 1 means no improvement. A value of 5 means good improvement and a value of 10 means great improvement. So, for any improvement project, if we do not expect a change in customer satisfaction because of this project, then CSI = 1. If this project causes great cost savings (say more than 50% of the current cost), then CI = 10. Similarly, if the quality is expected to improved and mistakes will be reduced by a good fraction, then QI = 5. Finally, if we expect cycle time to also decreased by a good fraction (no more than 50%), then CTI = 5. As a result, PII = 1
It can be noticed that PII will get values between 1 and 10000 and that the higher the value of PII, the better the indicator we get for quality initiative success.
Initial sample included all governmental hospitals and 25 private hospitals in Jordan, with a total number of 31 hospitals. Initial number of survey respondents was 105. Introductory interviews that lasted two weeks revealed that only 18 hospital and 66 respondents are eligible to take the survey, based on eligibility criteria set forth. In the real sampling phase, only 60 healthcare professionals responded with answered surveys, which include 13 major questions with multiple branch for each question. Surveys and interview lasted for a total of four week.
The sample consists of 15 physicians with managerial posts in their hospitals, 23 nurses currently assigned administrative roles, 9 laboratory technicians currently assigned administrative roles, and 13 quality managers. All respondents were selected because they run some administrative tasks at their hospital, and they have been involved with performance improvement initiatives for at least two years.
The sample size is considered representative since participating hospitals represent all hospitals in the country with lean implementation experience.
All questions were close type and has a five-point Likert scale for each branch of each question, where 1 = totally disagree and 5 = totally agree.
The sample consists of 15 physicians, 23 nurses, 9 technicians, and 13 quality management professionals. All respondents have been involved with performance improvement initiatives for at least two years.
The survey consists of four parts: the first part collects personal information about those filling the survey. The second part includes questions that ensure lean implementation at the hospital. The third part includes questions about the six indicators shown in Figure
Statistical analysis and instrument validity used in this research is similar to those used by Salhieh and Abdallah [
Construct validity was performed as per Hair et al. [
As seen in Table
EFA and CFA analyses.
Standardized loading | Eigenvalues | Construct reliability | Average variance extracted | ||
---|---|---|---|---|---|
Leadership | 6.83 | 0.877 | 0.815 | ||
Financial gains | 0.712 | 32.621 | |||
Patient satisfaction | 0.623 | 28.442 | |||
Employee involvement | 3.11 | 0.790 | 0.786 | ||
Financial gains | 0.802 | 17.348 | |||
Patient satisfaction | 0.703 | 21.622 | |||
Customer focus | 5.54 | 0.755 | 0.722 | ||
Financial gains | 0.669 | 11.655 | |||
Patient satisfaction | 0.813 | 17.278 | |||
External forces | 1.50 | 0.686 | 0.568 | ||
Financial gains | 0.724 | 8.546 | |||
Patient satisfaction | 0.821 | 5.629 | |||
Employee training | 4.95 | 0.823 | 0.788 | ||
Financial gains | 0.771 | 15.243 | |||
Patient satisfaction | 0.728 | 18.418 | |||
Holistic approach | 6.13 | 0.884 | 0.803 | ||
Financial gains | 0.690 | 30.326 | |||
Patient satisfaction | 0.825 | 25.621 | |||
Goodness of fit indicators: |
CFA performs multiple statistical calculations and benchmarks each value of such statistics to a benchmark value. Each benchmark represents a standard, and if all benchmarks fit the standards, the model is said to be fit. First statistic is standard deviation of all effect in the model to be more than 0.5 [
The calculated CFA values are all listed in Table
The final step of analysis is to calculate the hypothesis significance. The goal is to create a model showing which independent factor has more effect on the results than other factors. The point here is to create a framework toward implementing lean successfully.
Results of hypothesis tests are shown in Table
Hypotheses testing calculations.
Hypothesis | Conclusion | |
---|---|---|
Leadership | ||
H1 | 1.2 | Very significant |
H2 | 3.5 | Very significant |
Employee involvement | ||
H3 | 19.3 | Very significant |
H4 | 22.1 | Very significant |
Customer focus | ||
H5 | 46.2 | Significant |
H6 | 16.9 | Very significant |
External forces | ||
H7 | 70 | Significant |
H8 | 96 | Marginal |
Employee training | ||
H9 | 55.9 | Significant |
H10 | 41.3 | Significant |
Holistic approach | ||
H11 | 12.6 | Very significant |
H12 | 9.8 | Very significant |
From Table
This is in line with many published literatures, with one exception: “employee involvement.” In this research, we used this term to highlight the role of physician involvement. Most published research focused on educating and involving nurses, laboratory technicians, and administrative staff but not physicians. This study found out that physicians could be great implementation assets or major obstacles. Educating physicians about lean and coaching them on their role can be a significant factor in successful implementation.
Since the outcome of lean implementation is not always success and those who succeed use certain success factors, it is clear that we have to create an implementation framework that provides high probability of success. Many researchers faced similar situation and came up with frameworks [
Based on our findings so far, we concluded that lean can be implemented successfully in healthcare organizations, given that implementation follows a holistic implementation approach that uses success factors revealed from hypothesis testing. This approach is built into a framework as shown in Figure
Proposed implementation framework.
The framework consists of several steps as follows: Establish a need to improve financial standing or patient satisfaction: improvement initiatives such as lean are need-driven. If management does not see a major need for improvement in the financial performance or patient satisfaction levels, no support will be provided for lean implementation. Ensure upper management involvement and continuous support: once the need has been established, lean cannot survive without continuous support from upper management. Leadership influence here is a major factor. Involvement of upper management in performance reviews and implementation audit reviews is vital. Build a holistic implementation approach: this starts with understanding the culture and level of resistance for new initiatives and then, a good understanding of the internal environment for the organization with a focus on strengths and weaknesses points. The initiative will be implemented everywhere in the organization, and it is essential to start by establishing a good feeling of the as-is status. Perform internal marketing campaign to all employees to convince them on the new initiative: it is vital to tell everyone why we need lean? Why is it the way to prosper? What is their role? How did others become successful with it? Internal marketing campaign is performed through emails, portals, employee meetings, news leaflets, etc. Perform training for all and include physicians: after knowing the weaknesses, quick training need analysis (TNA) can be established. And, training programs should be performed to everyone as awareness sessions, while members of the lean team are offered specialized training. Select an implementation team: part of the holistic approach is the involvement of everyone; however, a small lean team will be responsible for implementation success and follow-up. This team should include nurses, physicians, laboratory technicians, and administrative staff. Train and empower the implementation team: the team has to have access to upper management. It has to have high visibility in the organization. Design an implementation approach: while the implementation is supposed to be holistic, implementation should start in areas where it will show highest success levels or quick wins. Expected values of PII for any initiative may help to select where to start, or we can just use any prioritization tool to select where to start. It helps to start implementing lean in areas of lowest expected resistance, but the plan should include all areas in the organization. As much as possible, use the following tools in this sequence: 5S Visual management Eliminating the seven wastes Simulation Implementing standardized work VSM Load balancing Also, as needed, use any of the root-cause analysis tools in any step. Audit implementation: one of the important failure reasons is the lack of follow-up. An annual audit plan needs to be set for all areas. The lean team will be responsible for auditing the implementation progress. Calculate PII and announce the results. Review and continuously improve: a quarter review (every three months) is suggested to take place between lean team and the upper management. The review will act as a tool to enhance audit results and to set continuous improvement plans. The review will highlight areas with high PII results and will set the stage for future improvement initiatives.
The framework is designed to ensure implementation success; it takes into consideration obstacles and enablers covered in the literature and the survey performed in this research. The design of the framework does not contradict with any literature known to the authors or listed in this work, but the value of the framework will not be quantified unless it is experimented on a real case study. The use of the framework has to show a positive impact on financial gains for the hospital, improved patient satisfaction, and most importantly better performance quality, which can be measured in less mistakes and more effective treatments.
To test the findings of our survey and the framework, a major hospital (more than 250 beds and more than 1000 employee) was used as a case for this research. The hospital was selected for the following reasons: The hospital is accredited by JCI (Joint Commission International) Most processes are considered standardized since they have gone through cycles of improvements The hospital is ISO 9001 certified Upper management are always eager to use new improvement ideas
Steps 1 and 2 of the framework were not performed since upper management appreciates any improvement initiative. We began with step and decided to use the framework hospital-wide but to start by selecting areas with immediate need, as it will be shown next.
In step 4, we started a marketing campaign. The mission was not difficult since employees of the hospital are used to quality initiatives mandated by JCI and ISO 9001, but staff were not familiar with lean and its effectiveness in healthcare, so many marketing activities took place to tell everyone how well did some health organizations do with lean.
Next, we established a hospital-wide training program. The training was an introductory course in lean performed for everyone. The idea of the short training is to be informative and to tell people that everyone will be part of the movement. Next, we selected a team of 12 (3 physicians, and the rest are nurses and technicians). This team was trained for two weeks on lean and problem solving tools and tactics.
The real work starts in the next few steps. The team selected to work in the emergency room (ER) area for the following reasons: Two of the team members work in that area. ER has the highest number of complaints from customers. Most medical insurance issues are seen in the ER. ER has high density of employees (technicians, nurses, and physicians). Patients do not easily find their path once they arrive to the ER and tend to need guidance. ER has operation room, triage room, 10 beds, X-ray room, reception, accounting department, pharmacy, and lab. The utilization is not the same for each of these areas, and high imbalance can be seen. Operational cost is considered very high by management, since 10% of the payroll is related to ER. In addition, the overhead cost concerning the operations room, triage room, beds area, and the labs is about 20% of the hospital overhead cost.
A comprehensive review of using lean tools in ER can be found in [
The process in the emergency room is described by the flow chart in Figure
Flow chart for the process at the ER.
To fully understanding the process, a team of three technicians collected data for 7 days of full operation. The average waiting and walking distances taking place in the ER is shown in Table
Average time and walking distance in the ER.
Task | Walking (steps) | Time (min) |
---|---|---|
Patient arrival | 30 | 1 |
Check vital signs in triage room | 5 | |
Fill out patient history | 2 | |
Wait for empty bed | 36 | 14 |
Nurse inspects patient | 20 | 4 |
Wait for physician | 10 | |
Full check | 7 | |
X-ray | 380 | 13 |
Lab | 174 | 25 |
Wait for physician | 10 | |
Diagnosis | 3 | |
Walk to pharmacy | 98 | 5 |
Pick up prescription | 10 | |
Payment collection | 106 | 5 |
Check out | 10 | 5 |
Total | 854 | 119 |
To build the simulation model, we need two things: process flow (Figure
Probability distribution for patients’ interarrival times.
Patient arrival | Shift A | Shift B | Shift C |
---|---|---|---|
Time | 8:00–15:00 | 15:00–22:00 | 22:00–8:00 |
Distribution | Exponential | Exponential | Exponential |
Mean | 2.36 | 2.17 | 7.79 |
Chi-squared test | 0.58 | 0.75 | 0.29 |
Result | Do not reject | Do not reject | Do not reject |
The simulation model is ready to run after all process data is entered for the ER (Figure
Initial simulation model for the process at the ER.
The baseline model revealed that most of the process is spent in waiting or walking. The model also revealed high utilization rate for the nurses in the ER bed area and the X-ray but low utilization in other areas.
Finally, we created a value stream map for the process at the ER prior to make any improvements. The full map can be seen in Figure
VSM for the process at the ER.
The VSM chart, while simple in nature, reveals that half the time spent by the patient in the ER is not adding any value; only 65 minutes are used to add value in this process, the remainder of his time is a form of waste.
After collecting all needed data and information about the process in the ER, the team started brainstorming sessions with the following two goals: Eliminate none value adding time as much as possible Lower the total time for the ER experience to all patients
The length of the cycle time for patients in ER can be seen as a result of many reasons. A fishbone diagram was created by the team to reveal possible root causes and areas for improvement. The diagram is shown in Figure
Fishbone diagram for the long cycle time at the ER.
The following can be deducted from all the tools used so far: The layout of the ER does not help; patients walk too much inside the facility. Patients walk from one room to another aimlessly without a clear guide. The assignment of tasks for nurses and physicians is not done in any systematic manner. Some resources are highly utilized and some are not. This imbalance needs to be resolved. Some mistakes take place due to paper work and documents that flow in a none electronic manner. Patients spend good amount of time waiting for available resource.
Based on this holistic picture we created for the current process and the team is ready to use improvement tools.
The following were recorded as brainstormed ideas: Since most of the interior building sections are made of Gibson boards, the medical observation room (where the beds are located) is suggested to be enlarged and some of the partitions that split it from the laboratories to be eliminated. Since the medical observation room is now much bigger, the triage room can now have a door leading to the waiting room, which in turns leads to the medical observation room. All the rooms will have visual aids and signs showing patients where to sit and where to go and what to do. Eliminating the reception area: this area is currently staffed with two technicians who are swamped with miscellaneous, unorganized work. Instead, the team suggested naming one, well-trained technician or nurse as a joker (someone who can fit in many places). This joker will have two main jobs: (i) greet the patients as they come into the ER and guide them between steps (ii) assign tasks to all those working in the ER in a manner that guarantees smooth flow of patients. Cross train employee (nurses and technicians) so that any employee can help other areas when needed and under the supervision of the joker. Every area that has high level of work will push a button that flags a red light observed by the joker, so that he himself goes and helps or sends someone who does not have high load of work. Merging some tasks for those who have low utilization: for instance, the lab technicians are now responsible for data entry of patients since they were found to have the least workload. The process will be focused on two things: (i) no patients waiting for service and (ii) no empty bed in the observation room. These two items will be focus of the joker and will guarantee smooth flow of patients with minimal waiting. Distributing employees in the ER so that patients always have a visible employee. This high customer exposure guarantees higher customer satisfaction and resolve customer issues swiftly and quickly. Killing the majority of paper work: this can be done by adding computers in every area that has customer contact, such as triage room, medical observation room, pharmacy, lab, and X-ray. The nurse in the triage room establishes a new live page (file) for every patient that walks into the ER, and then any employee or physician who does anything to that patient enters what he performed and forwards his file to the next department. By the time the patient walks to the next department, he finds them ready for him, with little or no waiting. For Example, by the time he arrives to pharmacy, he finds his medications prepared and ready for pick up. The patient’s file is closed by the accounting department. Minimizing the possibility of any mistake (mistake proofing): this can be achieved by establishing controls in the computer program that prevent those who use the patient file from forwarding his file to the next department without going through the proper procedure. This is a simple programming matter. Making the flow of patients (patient arrival ⟶ triage ⟶ observation room ⟶ lab ⟶ X-ray ⟶ pharmacy ⟶ accounting department ⟶ patient departure) in a “U” shape. This can be achieved by ensuring the arrangement of these departments is in a sequence with a corridor in the middle. The “U” shape arrangement guarantees high visibility and allows all to serve the customer well and observes any problem as it happens. The furthest department form ER is the X-ray room. The team suggested relocating it to a closer location, but this suggestion will take 4 months of implementation. Simplifying the entire process: for example, the physician is considered a precious resource, so he is freed from doing all paper work, and this became the job of less utilized technicians. In addition, the joker became responsible for faster patient movement inside the facility. In addition, much of the paper work has been eliminated. In short, physicians, nurses, and technicians are more involved now in value-adding work. Providing every patient with a simple card before he or she exits to ask him or her about their experience at the ER. Eliminating all the clutter including all paperwork, all unneeded towels, and covers from the ER area. Connecting the hospital warehouse with the ER through an automated request system, so that any time the ER needs supplies of anything, a request is pushed, and the warehouse supplies the department with its needs. This request system is made the responsibility of one of the technicians.
The above lean ideas were incorporated, and the current process was changed toward a faster service and better customer experience. These ideas are summarized in Table
Lean ideas in the case hospital.
Idea number | Lean tool | Benefits |
---|---|---|
1 | Waste reduction (waiting and movement) | Walking and waiting reduction |
2 | Waste reduction (waiting and movement) | Walking and waiting reduction |
3 | Visual management | Better customer satisfaction |
4 | Process standardization | Process simplification |
5 | Employee empowerment | Process simplification |
6 | Process flow | Faster processing better customer satisfaction |
7 | Process flow | Faster processing better customer satisfaction |
8 | Process flow | Faster processing better customer satisfaction |
9 | Process flow | Better customer satisfaction |
10 | 5S | Better customer satisfaction |
11 | Mistake proofing | Better customer satisfaction |
12 | 5S | Better customer satisfaction |
13 | Eliminating movement waste | Faster process |
14 | 5S | Faster process |
15 | Customer focus | Better customer satisfaction |
16 | 5S | Faster process |
17 | 5S | Faster process |
After gaining approval for suggested process changes, all changes were incorporated in the original simulation model except relocating the X-ray room. The management asked that this change be delayed for future considerations.
Simulations model results revealed the following: The maximum employee utilization is 92% The minimum employee utilization is 67% The observation room bed utilization 73% Total average cycle time of patients in the ER is 33 minutes which is considered ideal in global standards. A decrease of number of employees working in the ER by 3 nurses and technicians.
Simulation produces these important performance results by randomly selecting values from the distribution of each input piece of data, then running the process in animated mode and observing each time how much each resource is idle or utilized, and how long each patient is waiting in the ER. This makes simulation a powerful tool to predict almost all process outputs once distributions are defined for input data.
A full week of data collection, after the changes took place, revealed that actual average time of patients in ER is 46 minutes, and the new total average walking distance is 406 steps, which is considered a great improvement from the old status.
The team enforced an audit process that guarantees proper implementation of all suggested improvements. In addition, a quarter review is set in place to be the upper management platform of follow-up.
The case hospital is an ideal candidate to perform the experiment of lean implementation since it has a leadership that is willing to try any initiative under the umbrella of continuous improvement. In addition, the most employees are trained on quality initiatives, which makes it even better.
All suggested ideas were implemented with the best possible efforts. The resulted new times and walking distance are shown in Table
Average time and walking distance in the ER after lean implementation.
Task | Walking (steps) | Time (min) |
---|---|---|
Patient arrival | 25 | 1 |
Check vital signs in triage room | 5 | |
Fill out patient history | 2 | |
Wait for empty bed | 17 | 1 |
Nurse inspects patient | 15 | 3 |
Wait for physician | 1 | |
Full check | 5 | |
X-ray | 235 | 10 |
Lab | 35 | 10 |
Wait for physician | 1 | |
Diagnosis | 1 | |
Walk to pharmacy | 51 | 2 |
Pick up prescription | 1 | |
Payment collection | 18 | 3 |
Check out | 10 | |
Total | 406 | 46 |
In addition to improvements seen in cycle time and walking distances, ER saw a number of other benefits including the following: Minimizing all waiting times that lowers patient satisfaction. Minimizing all types of walking in the ER. Better customer experience and customer satisfaction. This was seen immediately through customer satisfaction cards that was added to the process and collected during a whole week after implementation. Better quality performance and less mistakes. Mistake proofing of the process and the automation of all information flow reduced the mistakes by an average of 80% as seen in the measurements taken a week after lean implementation and comparing that week with historical data. Some of the mistakes that was eliminated or reduced include the following: Misdiagnosis Poor monitoring Lab errors Contaminated or misplaced test samples Operational cost was reduced by 55% due to the following: Reduced number of employees by 3 Cycle time is now 60% less than it used to be Capacity increase Less mistakes and rework Less paperwork and less material used
The 55% estimate is our first estimate based on one-week worth of work after the implementation. Estimate of cost savings included salary of 3 employees, estimation of all the paper and material saved, and the estimated time spent performing rework.
This estimate is in line with simulation output and is expected to be firmed up after the first few quarter reviews: Better employee skills. Employees are now more empowered and loyal. Higher level of pride among employees.
We can easily calculate PII according to the following equation:
As a result,
This is a great result, and this project will be used as a benchmark inside the case hospital as they plan for similar performance improvements in many areas especially the warehouse, operations rooms, etc.
While the manufacturing sector has been enjoying benefits of lean implementation for decades, effective implementation in healthcare organization is not easy but achievable. Taking into consideration internal and external forces and thinking holistically is vital, a smart implementation strategy has to be designed to overcome implementation obstacles.
This research agrees with the literature on the importance of leadership and support of upper management, but an important finding of this research is the role of physicians in lean implementation. They can be a major obstacle if not involved in a proper manner.
This paper shows that implementation success in healthcare can be achieved similar to that in the manufacturing industry, but the service nature of healthcare organizations needs to be taken into consideration. Once a good understanding of internal environment is created, a proper holistic approach can be implemented. Continuous follow-up is the final requirement for implementation success.
The case hospital revealed how easy implementation becomes when the upper management is on board, but if this is not the case, then following the proposed framework guarantees success, even if it takes longer.
The use of PII as a measure of success is necessary, since it quantifies performance improvements, and it can be used to show various levels of success in different areas in each organization.
The most important conclusion is the answer to the main question in this research: yes, healthcare can be engineered to be lean, by following the proposed framework.
Future work can focus more on the role of physicians. How can they be a driving force in such initiatives? And how can they enhance follow-up and continuous improvement efforts?
Future work can also extend the findings of this research and the implementation framework to other quality initiatives, by using the proposed framework including the use of PII and simulation. This is in line with recently published research in other quality initiatives such as six sigma [
Future work can link lean implementation success with accreditation and internal culture; culture of accredited healthcare organization may be different from that of unaccredited organizations, but is this a factor in implementation success? A question to be answered by future research.
Finally, future work can extend the fruitful finding of this research to policy makers who are responsible for healthcare management. Lean tools, especially 5S, visual management, and standardization should become standard procedures at all healthcare institutions. Policy-makers can also limit the maximum time and maximum walking distances for patients, especially ER patients.
This research was proven by a case study in a large accredited hospital. Results may vary in smaller size hospitals. Smaller hospitals are run with limited number of employees and tend to scrutinize every operational cost more than large hospital. Future research may test the model used in this research in hospitals with less than 50 beds.
Another category of hospitals that was not studied in this research is the unaccredited hospitals category. Unaccredited hospitals do not have the same operational standards of accredited hospitals and may not value lean management in the same manner.
The case and survey was implemented in Jordan, where physicians have great administrative power. Physicians were included in the survey and in the research team. In different countries, physicians may have limited administrative power; therefore, their participation may not be needed in such surveys.
Future research can tackle these research limitations.
Five words starts with the letter “S.” Each word represents an action under lean management umbrella. The five words are sort, straighten, shine, standardize, and sustain
Confirmatory factor analysis, a statistical tool used to ensure analysis validity
Comparative fit index, a statistical tool used to ensure analysis validity
Improvements in operational cost after implementing a new initiative
Improvements in cycle time after implementing a new initiative
Improvements in customer satisfaction after implementing a new initiative
Value stream mapping, a method to draw the process in lean management
Exploratory factor analysis, a statistical tool used to ensure analysis validity
Emergency room
Joint Commission International
Performance improvement index
Improvements in quality performance after implementing a new initiative
Tucker–Lewis index, a statistical tool used to ensure analysis validity
Training need analysis.
For the sensitivity of interviews, many of the organizations surveyed asked not to disclose any of the data used. As a result, data used to support the findings of this study are confidential and cannot be made available.
The author declares that there are no conflicts of interest regarding the publication of this paper.