Six Sigma Approach to Increase the Quality of a Drug Safety Contact Center

Venkatraman Kumar

12.22.2014

Drug Safety (or) Pharmacovigilance is the pharmacological science relating to the collection, detection,

assessment, monitoring, and prevention of adverse effects with pharmaceutical products. As such,

pharmacovigilance heavily focuses on adverse drug reactions, or ADRs, which are defined as any

response to a drug which is noxious and unintended, including lack of efficacy. Ultimately,

pharmacovigilance is concerned with identifying the hazards associated with pharmaceutical products

and with minimizing the risk of any harm that may come to patients.

An Adverse event (AE) is any undesirable experience associated with the use of a medical product in a

patient. Adverse Event reporting is a sine qua non for any pharmaceutical company that markets its

drug. It involves the receipt, triage, data entering, assessment, distribution, reporting (if appropriate),

and archiving of AE data and documentation. Most of the pharmaceutical companies outsource their

Adverse Event monitoring to a third party drug safety center.

A Drug Safety Contact Center is a place where a reporter reports any Adverse Event or Product

Complaint associated with the consumption of a particular drug through a mail or a call. The reporter

himself can be the consumer or he can call on behalf of a consumer. For an AE case to be valid, it must

be ensured that there is an identifiable patient, an identifiable reporter, a suspect drug, and an adverse

event. In this white paper we will discuss the impact of Six Sigma methodology in improving the call

handling quality of a third party drug safety contact center.

One of the leading US based pharmaceutical company has outsourced its Adverse Event Response

System to a Drug Safety Contact Center (DSCC) with an expectation of 97% accuracy in the Adverse

Event cases triaged through the call handlers & entered in the database. The DSCC was operating from

Asia and was unable to meet its client’s quality expectation owing to which there were regulatory

compliance issues. The DSCC thus wanted its Process Excellence team to drive a quality improvement

initiative using Six Sigma Methodology.

Six Sigma stands for a measure of customer quality - and it stands for a philosophy of giving customers

what they want each and every time (zero defects, or as close as you can get). It is also a methodology

that can be used to change processes and company culture to enable companies to deliver Six Sigma

quality.

Six Sigma is a quality methodology that uses the best from existing Total Quality Management together

with Statistical Process Control and Measurement, and strong Customer Focus, and therefore impacts on

three key areas: the process, the processor, and the customer. Successful implementation requires

Strategy Management and Cultural Change across the entire process.

The DMAIC model of Six Sigma is a systematic method for analyzing & improving existing business

processes. It is an integral part of a Six Sigma initiative, but in general can be implemented as a

standalone quality improvement procedure or as part of other process improvement initiatives.

It consists of five phases:- Define the problem, improvement activity, opportunity for improvement, the

project goals, and customer (internal and external) requirements, Measure the process performance,

Analyze the process to determine root causes of variation and poor performance (defects), Improve

process performance by addressing and eliminating the root causes & Control the improved process and

future process.

The Six Sigma team prepared a project charter which provides an overview of the project and serves as an agreement between management and the Six Sigma team regarding the expected project outcome. The team then studied & mapped the current state of the Drug Safety Contact Center.

Call Received by the Health Care

Professional

Documentation of the Call if an

Adverse Event is reported

Quality Review for the

documented Adverse Event

Case

FIGURE A

A high-level process mapping for the existing process was made as shown in Figure A. The next step was to define the measurement system. Any attribute in the deliverable sent to the customer, which does not meet the customer’s requirements, or which is not as per the Customer’s Standards was defined as a defect. For the purpose of calculating DPMO (Defects per Million Opportunities), the opportunity, which is a product or process characteristic that adds or deducts value from the product, was defined, based on the opportunity definition by the client.

During this phase, the key processes in the Drug Safety Contact Center that affects the CTQ (in this case AE case documentation quality), was identified to be call handling, documentation & quality review. Measurements related to the CTQ were made in these phases using a Data Collection Plan as shown in Figure B. The Capability of the current process was verified as shown in Figure C. A data collection plan was devised & the measurement system (which is the Quality Review team) was validated by conducting an ‘Attribute Agreement Analysis’ as shown in Figure D.

FIGURE B

60

HighLow

Z.Bench = 2.68

> 0.50.10.050

NoYes

P = 0.000

1.000.980.960.940.920.900.88

Target USL

Actual (overall) capability is what the customer experiences.

spec limits.

percentage of parts from the process that are outside the

-- The defect rate is 0.37%, which estimates the

< 0.05).

-- The process mean differs significantly from the target (p

Conclusions

Upper Spec 1

Target 0.97

Lower Spec *

Customer Requirements

Mean 0.93167

Standard deviation 0.025495

Actual (overall) capability

Pp *

Ppk 0.89

Z.Bench 2.68

% Out of spec 0.37

PPM (DPMO) 3678

Process Characterization

Capability Analysis for QUALITY

Summary Report

Does the process mean differ from 0.97?

Actual (overall) Capability

Are the data below the limit and close to the target?

Comments

How capable is the process?

FIGURE C

standard 88.7% of the time.

The appraisals of the test items correctly matched the

100%< 50%

YesNo

88.7%

4

3

2

1

1007550250

88.7%

very difficult to assess.

the study were borderline cases between Yes and No, thus

-- High percentage of mixed ratings: May indicate items in

are being passed on to the consumer (or both).

many Yes items are being rejected, or too many No items

-- High misclassification rates: May indicate that either too

or incorrect standards.

problems, such as poor operating definitions, poor training,

Low rates for all appraisers may indicate more systematic

indicate a need for additional training for those appraisers.

-- Low accuracy rates: Low rates for some appraisers may

measurement system can be improved:

Consider the following when assessing how the

Overall error rate 11.3%

Yes rated No 1.5%

No rated Yes 76.2%

ways)

Mixed ratings (same item rated both 5.2%

Misclassification Rates

87.9

86.9

87.5

92.4

Attribute Agreement Analysis for Rating

Summary Report

% Accuracy by Appraiser Comments

Is the overall % accuracy acceptable?

FIGURE D

Process performance was assessed using Cause-and-Effect diagrams, to isolate key problem areas, to study and to identify if there is a relationship between the variables. Extensive brain-storming sessions were held with the team members to evolve these diagrams. Figure E shows the Cause-and-Effect diagram for the process.

FIGURE E

Quality of Drug Safety Call Center

Mother Nature Man Method

Measurement Machine Material

Caller Accent

Call Handler

QC'er

Trainer

Training Plan

Dashboard

Call Handler KPI

Experienced Call Handler

Database & IT infrastructure

AVAYA phone

Cooperation from Caller

QC form

Head Phones & Mic

PRIMA Form

PQC & IQC Scorecard

Supervisor

SOP

Internal Assessment

AEM form

Source Document

Regulatory Compliance

Measurement Sysytem Validation

The probable causes that can lead to quality nonconformance in a process during different phases of a Drug Safety Contact Center were listed. The Failure Mode and Effect Analysis (FMEA) was subsequently carried out to arrive at a plan for prevention of causes for failure. FMEA is a risk mitigation tool that helps prevent the occurrence of problems by identifying the potential failure modes in which a process or product may fail to meet specifications, and rating the severity of the effect on the customer. FMEA thereby provides an objective evaluation of the occurrence of causes & determines the ability of the current system to detect when those causes or failure modes will occur. Based on the above factors, a Risk Priority Number (RPN) for each failure mode was calculated. All the highlighted causes in the Cause & Effect diagram were those causes with RPN greater than 200.

An extensive Focus Group Discussion (FGD) was held to list the possible solutions for the root causes identified. A Prioritization matrix was then prepared to sort the improvements identified through the FGD & FMEA according to its impact and effort requirement. These improvements include developing process control and error proofing tools amongst others. Figure E shows the improvements implemented in the process. A pilot was also initiated for the improvement of reducing the opportunity fields from 240 to 50. This pilot was conducted for 2 Weeks before full scale implementation. The other improvements were instantly implemented in the system.

FIGURE F

The process improvements that were introduced resulted in the reduction of field errors and the target

of 97% accuracy in the Adverse Event cases entered on the customer database was achieved. A control

plan was then created as shown in Figure F to sustain the improvement. The improvement in the

accuracy was proved to be statistically significant using the 2-Sample t Test as shown in Figure H. The

revised capability of the process is shown in Figure I. The project was successfully completed and a sign

off was obtained from the project champion & the project sponsor.

FIGURE G

100.00%97.50%95.00%92.50%90.00%

Quality Befo

Quality Afte

mean of Quality Afte (p < 0.05).

The mean of Quality Befo is significantly less than the

> 0.50.10.050

NoYes

P = 0.011

0.00-0.01-0.02-0.03-0.04

results of the test.

samples. Look for unusual data before interpreting the

-- Distribution of Data: Compare the location and means of

-0.0083156.

that the true difference is between -0.044184 and

the difference from sample data. You can be 90% confident

-- CI: Quantifies the uncertainty associated with estimating

less than Quality Afte at the 0.05 level of significance.

-- Test: You can conclude that the mean of Quality Befo is

Sample size 24 8

Mean 0.94375 0.97

90% CI (0.9327, 0.9548) (0.95481, 0.98519)

Standard deviation 0.031459 0.022678

Statistics Quality Befo Quality Afte

-0.02625

(-0.044184, -0.0083156)

Difference between means*

90% CI

* The difference is defined as Quality Befo - Quality Afte.

2-Sample t Test for the Mean of Quality Befo and Quality Afte

Summary Report

Distribution of Data

Compare the data and means of the samples.

Mean Test

Is Quality Befo less than Quality Afte?

90% CI for the Difference

Does the interval include zero?

Comments

FIGURE H

29%

> 0.50.10.050

NoYes

P = 0.190

> 0.50.10.050

NoYes

P = 0.021

Before

LSL USL

AfterActual (overall) capability is what the customer experiences.

-- The process mean changed significantly (p < 0.05).

(p > 0.05).

-- The process standard deviation was not reduced significantly

Conclusions

Before: Quality Befo After: Quality Afte

0.97 * 1

Lower Spec Target Upper Spec

Customer Requirements

Mean 0.94375 0.97 0.02625

Standard deviation 0.031459 0.022678 -8.78E-03

Capability

Pp 0.16 0.22 0.06

Ppk -0.28 0.00 0.28

Z.Bench -0.97 -0.24 0.74

% Out of spec 83.49 59.29 -24.19

PPM (DPMO) 834862 592938 -241924

Statistics Before After Change

Reduction in % Out of Spec

to 59.29%.

% Out of spec was reduced by 29% from 83.49%

Before/After Capability Comparison for Quality Befo vs Quality Afte

Summary Report

Was the process standard deviation reduced?

Did the process mean change?

Actual (overall) Capability

Are the data inside the limits?

Comments

FIGURE I

The thrust on Six Sigma Quality has helped in creating and sustaining customer focus in the Drug Safety Contact Center, leading to an improved customer satisfaction by achieving the customer expectation of 97% quality. This project has also reduced the rework time in the Call Handling value chain. At the same time, active participation of the team members from all levels in the Six Sigma projects has evolved a culture of effective and creative team work. The goal is to achieve Six Sigma level not only in product quality, but also in the other client specified metrics of on-time delivery and estimate compliance.

1. http://en.wikipedia.org/wiki/Pharmacovigilance

2. http://www.who.int/medicines/areas/quality_safety/safety_efficacy/pharmvigi/en/

3. Forrest W. Breyfogle Implementing Six Sigma: Smarter Solutions Using Statistical Methods

4. 6σ OJT Six Sigma Application – GEPS Playbook, GE Power Systems