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 Table of Contents  
ORIGINAL ARTICLE
Year : 2015  |  Volume : 2  |  Issue : 1  |  Page : 9-14

Improvement in the postoperative visual outcome following cataract surgeries by reducing the average residual spherical error


Department of Phaco and Refractive, NABH accredited Eye Hospital, Bengaluru, Karnataka, India

Date of Web Publication8-Jul-2015

Correspondence Address:
Dr. Suman Shree Ramaswamy
Nethradhama Super Speciality Eye Hospital, (A Unit of Nethradhama Hospitals Pvt Ltd), No. 256/14, Kanakapura Main Road, 7th Block, Jayanagar, Bengaluru - 560 082, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2319-1880.160235

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  Abstract 

Background and Context: Lean Six Sigma (LSS) in healthcare was conceived as a national demonstration project by Quality Council of India to launch and manage their continuous quality improvement programs.
Aims and Objectives: To explore the potential benefits of applying LSS principles in the clinical domain to enhance patient satisfaction and as a clinical performance indicator.
Materials and Methods: Through the supplier, inputs, process, outputs and customers (SIPOC) were identified. On the supplier side there were surgical coordinators, surgeon, optometrists and medical store. Input and process are important in that patient selects lens based on an estimate, intra ocular lens and refraction as analyzed by an optometrist. The define, measure, analyze, improve and control (DMAIC) cycle was used in taking the project further.
Results: The residual refractive error was reduced from 0.35D to 0.33D and eventually to 0.25D (3 sigma). Project helped the hospital in terms of tangible and intangible benefits. Number of cataract surgeries surged by 17.75%, giving rise to an increase in financial accrual from rupees 11.5 Cr to 13.5 Cr.
Conclusion: A control plan was deployed through visual display of the process flow in out-patient department and monthly review of visual outcomes during clinical audit using average, standard deviation and sigma presented as key performance indicators, to ensure no drift in performance post the closure of the project.

Keywords: Analyze phase, clinical performance indicators, control phase, define phase, define, measure, analysis, improve and control, improve phase, Lean Six Sigma, measure phase, postoperative visual outcome, supplier, inputs, process, outcome, customer


How to cite this article:
Ramaswamy SS, Ganesh S, Jain KH, Krishna DG. Improvement in the postoperative visual outcome following cataract surgeries by reducing the average residual spherical error. J Nat Accred Board Hosp Healthcare Providers 2015;2:9-14

How to cite this URL:
Ramaswamy SS, Ganesh S, Jain KH, Krishna DG. Improvement in the postoperative visual outcome following cataract surgeries by reducing the average residual spherical error. J Nat Accred Board Hosp Healthcare Providers [serial online] 2015 [cited 2019 Mar 25];2:9-14. Available from: http://www.nabh.ind.in/text.asp?2015/2/1/9/160235


  Introduction Top


A cataract is a clouding that develops in the crystalline lens of the eye in its envelope, varying in degree from slight to complete opacity and obstructing the passage of light. The most accurate surgical technique for cataract is phaco-emulsification with foldable intraocular lens (IOL) implantation. Power of IOL is measured through biometry.

It was observed in clinical audit December'2009 that the postcataract surgery residual refractive error was 0.35D (Diopters) as against the ideal spherical power of 0 ± 0.25D according to voice of consultants (VOCs).

Patients expect freedom from spectacles for distance vision after cataract surgery. Residual refractive error implies defective delivery of promised results that is, "absence of spectacles for distant vision." This can lead to potential dissatisfaction of patients. The minimum cost borne by the patient due to poor quality is Rs 750/- (for bifocal glasses) and Rs 3000/- (for the progressive ones).

Lean, a generic process management philosophy derived mostly from the Toyota Production System is a set of principles that accelerates the speed of all processes across the enterprise. Lean is renowned for its focus on reduction of wastes in order to improve overall customer value. [1]

Six Sigma approach to quality management [2] essentially recognizes opportunities and eliminates defects as defined by customers, reduces variation which hinders our ability to reliably deliver high quality services (war on variance), enables data driven decisions, powerful framework for effective problem solving (quality tools) and highly prescriptive cultural infrastructure effective in obtaining sustainable results. Although, there are many quality improvement tools in healthcare management, LSS are two new and popular tools to be used in the healthcare industry. [3]

Hence a Lean Six Sigma (LSS) Green Belt Project [4] was initiated for "improving the postoperative visual outcome following cataract surgeries by reducing the average residual spherical error."


  Materials and Methods Top


The define, measure, analysis, improve and control [3] cycle, a diagnostic approach was used to achieve the objectives of the study. In the define phase (defining the problem), VOCs (internal customers) during the clinical audit identified potential area for improvement in surgical outcome.

A high level process map - supplier, inputs, process, outcome, customer (SIPOC) for the IOL implantation process was constructed. The SIPOC served to fix the process boundaries and identify internal customers for the project. Further, a cross functional team consisting of the appropriate representatives from all the functions/departments were identified. A detailed responsibility, accountability, consultant and inform (RACI) chart to balance the roles of the team members was developed. [5]

The project charter that served as the memorandum of understanding for the project between the hospital management and the team emerged. This included:

Problem/opportunity statement

It was found that average residual refractive error is 0.36D as against the expected spherical power of 0D.

Business case

It was determined that the defect (pain) was residual refractive error after cataract surgery implying defective delivery of promised results that is, "absence of glasses for distant vision," which was the critical to quality characteristic. The postcataract surgery residual refractive error was 0.35D (1.3 Sigma) as against the ideal spherical power of 0 ± 0.25D (3 Sigma).

The defect (pain) was defined as postoperative refraction below -0.5D and above +0.5D. Thus, the average residual spherical error is aimed to be reduced to ±0.25D (upper limit +0.5D and lower limit -0.5D).

One satisfied cataract patient brings in three new surgical patients and one dissatisfied patient leads to loss of nine surgical patients. The cost of poor quality to the patient and estimated earnings with satisfied customer was calculated.

Goal statement
"To decrease the average residual spherical error from 0.36D to 0.25D and to improve the process sigma level from 1.3 to 1.6 by end of the project and achieve 3 sigma (93.3% yield) eventually."

Project scope

Project assumptions

  • Inclusions: Patients undergoing phaco-emulsification with foldable IOL implantation and foldable IOL's from +16.0D to +24.0D.
  • Exclusions: Intra-operative complications, calculated IOL power not implanted, steroid responders, postlaser-assisted in situ keratomileusis and corneal refractive surgery.


Process boundaries

  • Process start point: The biometry done by the optometrist before surgery.
  • Process stop point: The refraction done by the optometrist 15 days after the surgery.


Project constraints

Patients who do not come for the 15 days postoperative checkup or lost for follow-up.

Impacted functions

Biometry, cataract surgery and IOL selection.

In the measure phase (measuring the problem) retrospective data analysis was done. During the process firstly "as is" value stream mapping [6] with value-added (VA), nonVA (NVA) and value enabled (VE) were mapped. Elimination of NVA in order to hasten and make process flawless generated the "should be" process flow chart.

The data collection method and factors to be measured was defined. The team collected following set of data for 3 months.

  • Data from the biometry (Y) report in the medical record.
  • Spherical power data at 15 th day postsurgery from hospital information system (HIS).
  • Intra ocular lens label from the consent form in the medical record.


The mean, the variance (standard deviation) and sigma level of the process was measured which determined the factors that influenced the outcome of the process. The histogram was made which helped in defining the frequency of occurrence of a factor.

In the analysis phase (analyzing the problem) the hypothesis testing was conducted with ANOVA and t-test at 95% confidence interval to find out if there was difference among the biometrists, surgeons and IOL selected for surgery.

  1. Among the biometrists: (Null hypothesis true: There was no significant difference among the biometrists).
  2. Surgeons and IOL selection. It showed that: (Null hypothesis true: There was no significant difference among the surgeons), and
  3. Among the IOLs selected for the surgery: (Null hypothesis false: Consequently the alternate hypothesis was true, there was significant difference between the IOLs implanted.


Statistical quality tools used were brainstorming, fishbone analysis, hypothesis testing, cause and effect (CE) matrix and root cause analysis Y = f(x). Analysis and segregation of causes into noncontrollable and controllable causes was done. The question that the analyze phase seeks to answer is "why is this problem occurring?" (but no steps for the improvement are taken during this phase). A question asking method - 5 why's was used to explore the cause and effect relationship (CE matrix-X and Y) underlying a particular problem. This is done for the controllable causes only. The potential root cause (X) having maximum impact on the process emerged.

Pilot study

Data were collected for the pilot study with replaced lenses and hypothesis testing and 2 t-tests were done. Following the results and discussion of clinical outcomes in the clinical audit, it was decided that the IOL's with high variance will be replaced with the one giving low variance within the same group or package following evaluation matrix, which considered effectiveness, ease to implement and cost of replacement.

ANOVA and 2 t-test with 95% confidence interval was done. It was found that the null hypothesis was true for:

  • Biometrist variance (implies not significant).
  • Surgeon variance (implies not significant).


Alternate hypothesis is true for variance in IOL's.

  • Implies a significant difference between the lenses.
  • Hydrophobic IOL is better than hydrophilic IOL.


In the improve phase the process map of the "should be" process was created. The counter measure matrix was determined to strengthen the process.


  Results Top


The IOL are classified into two kinds, viz. hydrophobic and hydrophilic lenses. In order to study which type of IOL is contributing to the residual spherical error, hypothesis testing was conducted to study the existence of variance between hydrophobic and hydrophilic lenses.

The result obtained from hypothesis testing proved that there is significant difference between hydrophobic and hydrophilic lenses:

  • Average of hydrophobic was better than average of hydrophilic lenses (0.287 vs. 0.732).
  • Variance of hydrophobic was better than that of hydrophilic lenses (0.157 vs. 0.534).
  • Average of hydrophilic lenses is beyond the target average.


The baseline data for residual spherical error was collated to calculate relevant statistical values such as mean, standard deviation and process capability. The baseline performance is presented in [Table 1].
Table 1: Baseline data for residual spherical error

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Further analysis of hydrophobic IOL's with 95% confidence Interval and 2 t-tests was done which showed that there is significant variation between the silicon versus acrylic and domestic versus imported [Figure 1].
Figure 1: Measure phase

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Based on the inferences derived during the measure phase, the team refined its project definition to "maintain the postoperative refraction between -0.5D and +0.5D."

The hospital established its profitability in terms of high patient retention and increased patient satisfaction.

Application of the tools and techniques and derivations of solutions

In the analyze phase, the team conducted structured brainstorming to determine the root cause for the residual refractive error. The root causes were displayed on a fishbone diagram. Analysis and segregation of the causes were done into controllable causes, noncontrollable causes and direct improvement causes [Figure 2].
Figure 2: Fishbone diagram

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Five why's method was used to explore the cause and effect relationship underlying a particular problem (for controllable causes only). The potential root causes were based on frequency of occurrence. These root causes were prioritized using a customized CE matrix.

The team brainstormed to determine solutions to counteract the validated root causes. It was decided that the team should focus on only those counteractions that resulted in a reduction in impact, minimization of occurrence and elimination of the root cause. The brainstorming session yielded more than one solution for each validated root cause.

Following are the immediate action taken for identified cause

  • A-scan appointments were distributed uniformly throughout the day to avoid crowding.
  • Training was provided for the biometrists for daily calibration check.
  • Dedicated and spacious biometry room facilitated better positioning of the patient and enhanced comfort of optometrist.
  • Change of the IOL's was done based on the surgery package.


The team evaluated all the possible solutions for each respective root cause and selected the best solution (evaluation matrix). Each proposed solution was evaluated and prioritized as the effectiveness of the solution in counteracting the cause, ease of implementation and cost of implementation. The team decided to test the solutions in a controlled environment through a pilot study. The final solutions were approved for implementation by the project champion and process stakeholders [Figure 3].
Figure 3: Improve phase

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The "should be" process map is created [Figure 4].
Figure 4: Process mapping

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The results were rewarding. The residual refractive error was reduced from 0.35D (measure phase) to 0.33D (improve phase).

Data collection plan during control phase was as follows:

  • Adhered to standard protocols.
  • Uniformity in data collection for biometry and refraction.


Data retrieval from the electronic medical record in the HIS.

The goal was to ensure that the gains obtained during improve phase were maintained long after the project ended. It was necessary to make sure all employees were trained and informed about the project's results.

Continuous quality improvement phase November 2010-March 2012

In CQI phase the sigma level improved consistently from 1.7 to 2.6 [Figure 5]. Since this project proved to be a very useful tool to measure cataract surgical outcomes, it became a dynamic key performance indicator.
Figure 5: Continuous quality improvement (CQI) phase

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  Discussion Top


The study was done in the Cataract Surgery Department as it would have a high impact due to the high volume of cataract surgeries and to explore the possibility of meeting patients expectations and achieving excellence in the visual outcome.

Micahel L George [5] in his book LSS for service has elucidated how stanford hospital implemented LSS principles and developed a common surgical tray for 6 different cardiac surgeons and eliminated unnecessary complexity adding to costs, time and enormous waste in preparing 6 different surgical trays and reduced annual material cost by 25 million $.

Patient centric outcome of the study are improved quality of vision for the patient, reduced number of postoperative visits thereby saving time for patient and surgeon, surgeon able to accommodate more number of patients for OPD and surgery, Reduced postoperative inflammation/complications (stability of lens) and improvement in the flow of patients waiting for biometry.

The clinical outcomes are the hospital evolved a system of evaluating the product through clinical trial before introducing new lens, The LSS project served as a tool for clinical audit. It made the teams objectively know the reason for the residual refractive error in lenses and enabled the hospital to benchmark with best industry practices.

The other additional benefits are it has increased surgeon and counselor satisfaction through delivery of quality vision as promised, conversion to hydrophobic lenses from hydrophilic lenses thereby improving patient satisfaction and increasing revenue, the project has enabled the hospital to take precautionary steps before entering into memorandum of understanding with purchase and utilization of any new equipment or lens and a centralized A-scan report storage.

Sustainability after the project were visual display of the process flow in the OPD, credentialing of biometrists for accurate biometry, credentialing of surgeon for phaco with foldable IOL, daily calibration of biometry equipment, counseling more number of hydrophobic lenses for better visual outcome, monthly review of the visual outcomes (average, standard deviation and sigma) of various IOL's implanted during the clinical audit. This is presented as a key performance indicator of cataract surgical outcome. clinical evaluation of any new lens before the introduction and increased number of IOL suppliers.

The intangible benefits realized by the team through executing this project were collaborative spirit demonstrated by the team, data driven approach and word of mouth appreciation by patients.

Sigma from 2010 to 2014

Diligent capturing of the refractive outcomes post operatively and making intelligent changes in the correction factor for the power of intra-ocular lens to be implanted during cataract surgery or changing them helped us reach 3 sigma performance in the year 2013-2014 [Figure 6]. Hence a clinical Green belt Lean Six sigma project was accomplished successfully.
Figure 6: Sigma

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  Conclusion Top


Through the successful implementation of LSS principles in a clinical process, it acts as an analytical tool for clinical audit. Hospital-wide display of process mapping has reinforced the importance of the accuracy of biometry and selection of IOL implant as key factors for the success of cataract surgeries.


  Acknowledgments Top


We sincerely acknowledge the continuous guidance of Dr. Giridhar Gyani, secretary general Quality Council of India (QCI). Support from Mr. Avik Mitra, Mr. Suresh Lulla, Mr. Subramaniyam, Mr. Anirudh Chakravarty is duly acknowledged. The national demonstration project conducted by QCI and Qimpro consultants gave us an opportunity to participate in this quality revolution and implemented LSS in clinical service. All surgeons, optometrists, administration staff of Nethradhama who participated in the study deserve due acknowledgement for their support.

 
  References Top

1.
DelliFraine JL, Langabeer JR 2 nd , Nembhard IM. Assessing the evidence of six sigma and lean in the health care industry. Qual Manag Health Care 2010;19:211-25.  Back to cited text no. 1
    
2.
Lean Six Sigma - Green Belt Program by Qimpro Consultants.  Back to cited text no. 2
    
3.
George ML. Lean Six Sigma for Service, How to Use Lean Speed and Six Sigma Quality to Improve Services and Transactions. Tata McGraw-Hill; 2003.  Back to cited text no. 3
    
4.
Murphree P, Vath RR, Daigle L. Sustaining lean six sigma projects in health care. Physician Exec 2011;37:44-8.  Back to cited text no. 4
    
5.
Pocha C. Lean six sigma in health care and the challenge of implementation of six sigma methodologies at a veterans affairs medical center. Qual Manag Health Care 2010;19:312-8.  Back to cited text no. 5
    
6.
Maclnnes RL. The Lean Enterprise. 1 st ed. GOAL/QPC; 2002.  Back to cited text no. 6
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
 
 
    Tables

  [Table 1]



 

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Abstract
Introduction
Materials and Me...
Results
Discussion
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Acknowledgments
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