critique a six sigma study

Table of Contents 1. Executive summary 1 2. Define Phase 2 3. Measure Phase 4 4. Analyze phase 7 5. Improve phase 10 6. Control Phase 10 7. Conclusion 11 References 12

This project details the use of the DMAIC methodology in the management of a project aimed at improving the service delivery at Bellevue Hospital Center in New York. The objective of the project is to reduce congestion in the medical facility by 40%. This will not only improve their medical service delivery, but also ensure that high quality services are availed to New York citizens.

After an intensive investigation, the ER, wards, ICU unit, pharmacy, laboratory, surgery theatres and the morgue were identified as the seven areas of concern. To ascertain their suitability, these medical facility units were subjected to “acceptable or defect” test where all were found to be “defected.” Excel was then used to analyze descriptive statistics of the hospital departments where Theatres, pharmacy and ward were found to have high defects rates.

To detect the area that contribute highest variations in the hospital value chain, every department was subjected to statistical test of variation (One-Way ANOVA). Apart from revealing that theatres contribute to the highest system variability, the analyses still confirmed theaters, Pharmacy and wards (in that order) contributing to the highest level of congestion in the hospital.

The project therefore present a quick act to the above medical units in order to greatly reduce the level of congestion in Bellevue Hospital Centre. From this study, we were able to find serval recommendations for the hospital.

Project Charter

Project Mission

The decentralization of medical services from the state headquarters should enable our medical services to reach more New Yorkers. Therefore, the main objective of this project is to improve medical service delivery to all New Yorkers without regard to their race, creed, religion or socio-economic status. This is to be achieved by reducing congestion in all our medical facilities and in the process, improve the efficiency and quality of service delivery/care to the citizens. The improvement and subsequent expansion of the Bellevue Hospital Center’s ward operations would create employment in the area.

Problem Statement

In the last couple of years, the Bellevue Hospital Center has seen a rise in the total time taken to server patients. The resulting congestion in the wards has led to long queues because the hospital has been admitting 21% more patients than its designed capacity. The congestion has led to an increase in doctor to patient ratio to 1:75, which has adversely affected the level of service delivery in the facility.

Goal Statement

The goal of this project is to reduce the level of congestion in the medical facility by at least 40% to increase the quality and accessible medical services to patients.

SIPOC Diagram

Inputs

Suppliers

Customers

Output

Processes

A larger and more spacious ER

Eight standard Wards

A 25-bed capacity ICU unit

One general and Two Specialized Theatres

A Well-Stocked Pharmacy

A well-equipped laboratory

A 55-capacity morgue

Collect Information

Understand the social needs

Present to stakeholders

Agreeing and delivering per requirements

Handing over to the stakeholders

Medical consumables suppliers

Labor

Consultancy services

Resources

Contractor

Cement manufacturers

Steel makers

Bed fabricators

Medical equipment suppliers

Medical consumables suppliers

Patients

Medical interns

Supply chain

Affected citizens

The government

Routine checkup clients

Transport companies

Ambulance services

Define Defect, Opportunity, Unit and Metrics

If a project does not meet the desired specifications and requirements, it is said to be defective (Santra, 2015).The development project at hand is expansion and overhaul renovation of a medical hospital. If on completion the improvements do not meet the requirements of the people, it is a defect. The seven areas of concern are the ER, the wards, an ICU unit, a pharmacy, a laboratory, surgery theatres and a morgue each with a weighted deliverable of 14.3%, adding up to 100.1%. Any of them that do not meet the standards is a defect.

Opportunity is the chance to have a defect and each defect is one opportunity. In this case, we have seven units of the medical facility that have a chance of being defective. The total opportunities are the multiples of the deliverable units and the opportunity (7units*14.3 = 100.1).

A unit is a specifically defined single deliverable. In this case, we have seven units as mentioned earlier. The six areas of concern are the units and each of them gives one unit (Santra, 2015). For instance, the ER makes a unit.

The metrics are the derived measurements of the project. We shall use defects per unit, defects per opportunity and total opportunities. They are simple to use, calculate and understand (Tennakoon & Palawatta, n.d).

Data Collection Plan

Validating the Measurement System

The averages for acceptances are as follow; Auditors average is 78.65, Community leaders average is71.5, Difference is 7.15, Expert’s Average is 10.21, Standard Deviation is 1.43

Data Collected

Five individuals who were, one hired external expert, two quality assurance officers at the state government, while two are community professionals, did the above assessment. Measurement System Analysis (MSA) is an experimental process and require more views from different quotas of different levels of understanding to make independent judgments on quality (McCarty, 2005).

Process Capability

Short-term establishment of numerous errors correct the long-term problems (Kurma& Sharma, 2012). Therefore, the errors we shall establish during the near future shall be corrected to save the long-term effects. The data we shall collect, and the measurement system we shall use to analyze the data shall be the tools to help establish the tolerance, error of contribution and by the results obtained, we shall act accordingly. We shall invest in technology and controls in the end where the clusters are higher, thereby lowering the standards set in the health services delivery.

The main objective of the analyze phase of DMAIC is to get to the root cause of a problem (De Mast & Lokkerbol, 2012). In this phase our focus is to analyze the root cause of congestion in the hospital by identifying the exact departments where the problems emanated from the possible reasons for the congestions. The tools and techniques used in this case will help us to find clues on the root problems and those factors would later be used for quality improvement. In this phase the Y=f(x) relationship will play a significant role in ascertaining to us the root causes of congestion at the hospital. In this phase, we shall analyze the process using a statistical analysis of means and variations (we used Excel to analyze the descriptive statistics of every hospital department suspected of contributing to inefficiency and congestions). This was then followed by gathering information on the root causes in order to establish if a cause and effect relationship exists in this case scenario. A verification of the cause and effect relationship is an important step of this analyze phase.

The initial in the analysis phase involved the identification of all potential contributors to variation in the healthcare value chain. Our analysis focused on the identification of the contributors that leads to the longest length of stay or delay in the hospital (delay signifies congestion in this case). The identification of all critical factors (in this case denoted by the x’s) that possess the most statistically significant impact in the entire process variation was the key element of the DMAIC process’ analyze phase.

Data analysis

The data will show whether the units of the medical facility are acceptable or defect. The answer will be either 14.3 or 0, being acceptable or unacceptable respectively. The data followed a Y=f(x) Relationship in which all the seven units received bad scores.

Based on Data Collected previously

The graph shows that the lowest cumulative of points that corresponds to higher defect rates can be found in Theatres, Pharmacy and Wards in that order with Theatres contributing to the highest level of congestion in the hospital.

Statistical test of variation -One way ANOVA

The key is to check the process that leads to the highest level of variation in the process. To detect the areas contributing the highest variations in the hospital value chain, we subjected every key element to a statistical test of variation. In this case, we used Excel to analyze the test statistics of the data points.

The analysis shows that ER department has zero error rate, 0 variance and 0 standard deviation. These two figures reveal that ER department has a zero variability and hence the lowest level of congestion (or no congestion at all).

A statistical analysis of the wards reveals a variance of 61.347, standard deviation of 7.8324 and a standard error rate of 3.5027. The mean is 8.58. These figures reveal a high level of variability in the hospital wards which suggests a high level of congestion.

A statistical analysis of the theaters reveals a variance of 40.898, standard deviation of 6.3951 and a standard error rate of 2.86. These figures reveal a high level of variability in the hospital wards which suggests a high level of congestion.

The analysis shows that laboratory department has zero error rate, 0 variance and 0 standard deviation. These two figures reveal that laboratory department has zero variability and hence the lowest level of congestion (or no congestion at all).

A statistical analysis of the pharmacy reveals a variance of 61.347, standard deviation of 7.8324 and a standard error rate of 3.5027. The mean is 5.72. These figures reveal a high level of variability in the hospital wards which suggests a high level of congestion.

The analysis shows that ICU department has zero error rate, 0 variance and 0 standard deviation. These two figures reveal that ER department has zero variability and hence the lowest level of congestion (or no congestion at all).

The analysis shows that the Morgue department has zero error rate, 0 variance and 0 standard deviation. These two figures reveal that ER department has zero variability and hence the lowest level of congestion (or no congestion at all).

In order to improve the congestion situation at the hospital solutions were made such as;

· A larger and more spacious ER.

· Building eight standard Wards.

· Adding 25-bed more to the ICU unit.

· Building One general and Two Specialized Theatres.

· A Well-Stocked Pharmacy.

· A well-equipped laboratory.

· Adding a 55-capacity more to the morgue.

· Hiring more staff.

Immediate results were astonishing. After six months of the project charter the reduction in congestion reach a 22.7%. Improving the efficiency and quality of service delivery/care to the patents. The enhancement and subsequent expansion of the Bellevue Hospital Center’s operations attracted more interns and doctors. The level of congestion in the medical facility reached a 40% reduction by December 2017. To keep the result reach from returning to the previous status precautions need to be take in the control phase. Areas that represents the biggest variation were Theatres, Pharmacy and Wards thus focusing on them to continue per requirements. Using the management control system (MCS) to gathers information to assess the performance of different departments in the hospital. Reporting any anomalies in performance directly to the stakeholders so it can be corrected.

Box, G.E.P., Luceño, A., del Carmen Paniagua-Quinones, M. (2009). Statistical Control by

Monitoring and Adjustment, second ed. Wiley, New York

De Koning, H., De Mast, J. (2006). A rational reconstruction of Six Sigma’s Breakthrough

Cookbook. International Journal of Quality and Reliability Management 23(7), 766–787

De Mast, J., and Lokkerbol, J. (2012). “An analysis of the Six Sigma DMAIC method from the

perspective of problem solving”. International Journal of Production Economics 139(2) 604–614

De Mast, J. (2011). The tactical use of constraints and structure in diagnostic problem solving.

Omega 39(6), 702–709

Kumar, A. & Sharma, N. (2012).Six Sigma DMAIC Methodology: A Powerful Tool for

Improving Business Operations.AMR, 488-489, 1147-1150.

http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1147

McCarty, T. (2005).The Six Sigma black belt handbook. New York: McGraw-Hill.

Santra, A. (2015). DMAIC Approach for Defect Predictability and Control.IJHIT, 8(10), 261

268. http://dx.doi.org/10.14257/ijhit.2015.8.10.24

Tennakoon, B. & Palawatta, T.A Case Study on Application of DMAIC to Improve Delivery

Efficiency.SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.2706992

Expert’s View Casualty Wards Theatres Laboratory Pharmacy ICU Morgue Totals 14.3 14.3 0 14.3 0 14.3 14.3 71.5 Auditor 1 Casualty Wards Theatres Laboratory Pharmacy ICU Morgue Totals 14.3 0 14.3 14.3 0 14.3 14.3 71.5 Auditor 2 Casualty Wards Theatres Laboratory Pharmacy ICU Morgue Totals 14.3 14.3 0 14.3 14.3 14.3 14.3 85.8 Leader 1 Casualty Wards Theatres Laboratory Pharmacy ICU Morgue Totals 14.3 0 0 14.3 0 14.3 14.3 57.2 Leader 2 Casualty Wards Theatres Laboratory Pharmacy ICU Morgue Totals 14.3 14.3 0 14.3 14.3 14.3 14.3 85.8