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For the Record: Hospital Boosts Service Quality Using Data Analysis and Lean Six Sigma

Manufacturing errors result in defective products and increased costs, but there is no “reject pile” in healthcare. Medical errors cause tens of thousands of deaths worldwide each year and cost billions of dollars—and involve issues and processes complex enough to intimidate quality improvement professionals in any field.

The industry has been slow to adopt traditional improvement methods, with many citing healthcare’s complexity. But even in the business of saving lives, quality improvement depends on process management—and the experience of one hospital suggests that to redesign healthcare, you just start small.

That’s what a Lean Six Sigma (LSS) project team at a hospital in India did. The team examined its inpatient health information department (IP-HID) process and identified factors that could reduce the turnaround time (TAT) for patient health records. Using Minitab Statistical Software, the team implemented new HID procedures that addressed increasing labor costs and low productivity—moving the small facility toward world-class quality.

The Challenge


Two staff members and an attendant compose the hospital’s HID, which is divided into inpatient and outpatient services. One staff member and the attendant are responsible for outpatient services, which include registration, revisit, and admission, leaving one employee accountable for all inpatient services.

A project team evaluated the patient health record process, identified causes of delay, and reduced the turnaround time by 18 minutes using Lean Six Sigma methodologies and Minitab Statistical Software.

That person’s responsibilities include preparing patients’ health records, birth and death reports, and legal reports; retrieving health records; answering patient questions; updating the daily census; and attending medicolegal cases.

The TAT specification limit for the health records process was set at 40 minutes per batch size of 10 records. But with only one person performing all IP-HID tasks, the TAT had reached 55 minutes—a delay that postponed completion of other responsibilities in the department and prompted management to appoint an additional staff member to HID.

The team’s preliminary work indicated that preparing health records caused the longest time delay in the patient process—a concern easily addressed using LSS methodology. In order to decrease labor costs and increase productivity, the team set out to reduce the turnaround time for preparing records.

How Minitab Helped

The team applied the five-phase DMAIC (Define, Measure, Analyze, Improve, and Control) method to their project, and they began by defining the project goal—a TAT of less than 40 minutes—and identifying the relevant elements in the health records preparation process, including receiving, checking, sorting, and arranging patient information.

During the Measure phase of their project, the team gathered and assessed data using a control chart created with Minitab to verify the IP-HID process was stable and had no special-cause variation.

The control chart above plots the IP-HID process data in a time-ordered sequence. The points fall within the control limits, indicating the process is in control and exhibits only common-cause variation.

With confirmation that the process was statistically under control, the team tested the data for normality using an Anderson-Darling test and displayed the results in a probability plot.

This probability plot determines whether the sample data are normally distributed. Because the points lie close to a straight line, the plot shows that these data follow a normal distribution—a key assumption for performing a capability analysis.

The data points in the graph approximately followed a straight line, which indicates that the normal distribution fit the sample data. Since the data came from a stable process and fit the normal distribution, the team proceeded to perform a capability analysis in Minitab, which showed that the health records preparation process had room for improvement, but was capable of achieving a TAT under the specified time limit.

The project’s Analyze phase yielded a clearer picture of potential causes, as the team used data analysis to identify the root causes of delays in preparing health records. Lack of training, variation in the size of the forms, and varying location of patient’s details within the forms were identified, and the team conducted experiments in order to confirm each cause’s impact on TAT using 2- sample t-tests.

Additional root causes were verified using the GEMBA method, which relies on observations recorded during a specified period of time. These causes included unavailable stationery, lack of material handling devices, incomplete information from other departments, undetected mistakes in patient records, and improper ergonomic design of the work area.

During the Improve phase, the team addressed the issues identified during their analysis by implementing changes, including standardizing the size and format of the health record forms, using a kanban card system to signal steps in the process, creating checklists for IP-HID and HID staff preparing patient records sent, and adhering to a “5S” workplace ergonomics design. The team also conducted a three-day employee training program to introduce the changes and explain their expected impact on TAT.


After successful implementation of the changes, the team collected and analyzed data on the new IP-HID health record process, which revealed significant improvements.

Capability analysis revealed significant improvements in the IP-HID process, with the average TAT reduced to 34 minutes.

The team exceeded their goal, as the improvements resulted in an average TAT of 34 minutes. This achievement introduced the final stage of their DMAIC project, the Control phase. The team put measures in place to ensure the hospital sustained the improvements. Staff and team members completed a 5S audit sheet once per month, using checklists for every health record. They also recorded the TAT daily and plotted the data on a control chart to monitor the process for special cause variation. The team also provided LSS training to staff involved in additional improvement projects.

By incorporating quality improvement techniques in HID, the team demonstrated that these traditional improvement methods could work very well in a healthcare setting. The success of this small project created an opportunity to reduce waste and tackle bigger challenges affecting other hospital processes. It also brought a cultural change to the organization by showing staff members that they had the tools needed to achieve operational excellence.

This story was adapted from an article published in the 2013 Volume 8, Number 1 issue of the International Journal of Six Sigma and Competitive Advantage.


Corporate hospital in India


  • Established as a “Holistic Healthcare & Cure” Centre for Excellence
  • Specialize in allopathy, Ayurveda, and homeopathy
  • Facilities include 200 patient beds, intensive care unit, casualty department, pharmacy, and a nationally-accredited high-standard laboratory


Reduce the turnaround time for processing patient health records.


Minitab® Statistical Software


  • Process turnaround time reduced by 18 minutes
  • Increased department productivity
  • Reduced costs