Improvements in the Manufacturing Industry Versus Health Care Industry

Introducingqualityimprovement.pdf

Home >Business & Finance homework help >Management homework help >Improvements in the Manufacturing Industry Versus Health Care Industry

EDITORIAL

Introducing quality improvement

In 1950, a New York University professor received an

unusual letter. It came from Japanese industrial leaders

requesting help to improve manufacturing quality in

their country. The professor taught statistics and had

used it to improve farm crops and military equipment.

At the time, Japanese products were considered ‘cheap’,

and the leadership wanted to change that reputation.

The professor agreed to help. He spent the next 20 years

working with Japanese industries and founded quality

improvement (QI) Science along the way. The rest is his-

tory: Japanese products became known for superior

quality in the likes of Toyota and Sony. The professor’s

name was Dr. Edward Demings.

Some 60 years later, Pediatric Anesthesia launches a

themed issue and a section in subsequent issues dedi-

cated to QI, following the journal Pediatrics in this

regard (1). Why now? All over the world, health care

faces a critical economic crossroad: How do we deliver

better and more care at lower cost? Deming’s message to

Japan’s leadership many years ago: improving quality

will reduce expenses while increasing productivity. This

has been proven time and again in every industry. Now

is the time for us to learn and apply QI and model the

way for others in our institutions. The QI section of the

journal aims to teach QI and illustrate its application

through original articles. To begin this journey, I will

describe the basic elements of QI, differentiate it from

QA (quality assurance), and describe the format for

publication of QI articles.

Quality assurance contains two types of knowledge:

subject matter knowledge and profound knowledge (2).

The former is the knowledge basic to the field. If the

field is pediatric anesthesiology, it is medical knowledge

(e.g., developmental physiology, pharmacology, anes-

thesia equipment, pediatric diseases). As pediatric anes-

thesiologists, we possess subject matter knowledge in

our field. Profound knowledge consists of four elements:

appreciation of a system, understanding variation,

action learning, and change management (2). Profound

knowledge is common to all industries, whereas subject

matter knowledge is industry specific. For pediatric

anesthesiologists to conduct QI, it is necessary to learn

and apply profound knowledge in our work. Let us dive

into the four elements of profound knowledge.

Appreciation of a system: a system is an interdepen-

dent group working together toward a common pur-

pose. The purpose of the system must be clear to

everyone in the system. The vast majority of product

defects originate from system failures and not individual

failures. Thus, understanding the system and getting every-

one in the system to work together is key to successful QI.

In health care, three broad systems exist: microsystems,

mesosystems, and macrosystems. For pediatric anesthe-

siology, generally speaking, the microsystem is the

operating room, the mesosystem is the hospital, and the

macrosystem is the government. The product defect can

arise from one or more failures in these systems. All too

often, the leaders in these systems point fingers at each

other. Improving quality requires the QI team to iden-

tify the role of each system in the defect and to get the

parties to work together.

Understanding variation: high quality is all about

reducing variation in the product. An entire field of sta-

tistics evolved for QI that is distinct from medical statis-

tics (3). All manufacturing industries utilize QI statistics;

hospitals are increasingly using it. In the 1920s, Demings

studied under Walter Shewhart of Bell Laboratories, the

founder of QI statistics, which included statistical pro-

cess control, common and special causes of variation,

and the control chart. Statistical process control views

products as resulting from defined work processes,

which contain variation. Common cause variation is the

variation that is inherent to the process (the ‘noise’).

Special cause variation is not part of the process but

arises from a special situation. A control chart plots the

product specification over time; QI statistics are applied

to determine the variation and significant change. The

QI team aims to favorably shift the average (significant

change) and to reduce common and unintended special

cause variation.

Action learning: QI follows the scientific method:

hypothesis, inductive and deductive reasoning, study

design, and experimentation. Inductive reasoning

generates a hypothesis about the system that predicts a

response in which an experiment can be designed to test

the hypothesis. Deductive reasoning analyzes the experi-

mental data to validate or refute the hypothesis. In QI

science, this methodology appears in the roadmap and

plan-do-study-act (PDSA) cycle. The roadmap

begins with a smart aim (purpose) followed by key driv-

ers (hypotheses) about the system and designing

interventions (experiments) to test the key drivers. The

experiments are known as PDSA cycles. In the PDSA

cycle, the first step is to plan the test of the new process.

Second is to do the test; third is to study the results of

the test. And fourth is to act on the results: if the test

yields positive results, additional PDSA cycles are

performed to evaluate the sustainability over time and

569

generalizability to other parts of the system. For exam-

ple, if a process change yields positive results in one

operating room on one day, additional PDSA cycles are

conducted on other days (sustainability) and in other

operating rooms (generalizability) before the process is

permanently adopted.

Change management: Of the profound knowledge

elements, change management is perhaps the most

important. Systems and people inherently resist change,

whether from fear of the unknown, inertia, distrust, or

lack of motivation or time. Excellent communication

skills by the QI project leader along with overt support

by the leadership of the system remain absolutely

essential to overcome resistance and drive process

improvement. QI and leadership have to go hand in

hand.

QI vs QA: There are many differences between QI

and QA despite clinicians referring to the two inter-

changeably. QA involves systematic measurement and

comparison with a standard. QA is goal directed to meet

the standard and is widely used in benchmarking. It is

retrospective and says nothing about how to improve

the product if it is substandard. QI involves a formal

approach to the analysis of performance and the system-

atic efforts to increase it. The approach applies the

elements of profound knowledge as well as systematic

measurement. QI is process directed and is both pro-

spective and retrospective. In essence, QI encompasses

QA along with the methodology to improve the product

if it is substandard.

Format of QI articles: Although QI utilizes the scien-

tific method, it requires a different format for writing an

article than a research article. QI focuses on making care

better at local sites, rather than on generating new, gen-

eralizable scientific knowledge. QI contains a strong

‘local context’, whereas research is not supposed to be

dependent on the site of the study. However, systems in

health care around the world share similar features, and

thus, many aspects of QI at one institution are generaliz-

able. Given the local context, special statistics, action

learning, and change management issues, the research

format was not suitable to QI articles. In 2005, an

author group drafted a standardized format for QI arti-

cles, which was refined through a systematic vetting pro-

cess with input from an expert panel and public

feedback and approved as the Standards for Quality

Improvement Reporting Excellence (SQUIRE) (4). QI

submissions to Pediatric Anesthesia are expected to use

the SQUIRE format, exemplified by the articles in this

issue, and detailed on the website (5).

Now that we have a legitimate format and the journal

Pediatric Anesthesia to communicate QI for our spe-

cialty, we hope you will get started in this journey to

learn, conduct projects, and share the trials and tribula-

tions that go with it. We eagerly look forward to receiv-

ing original QI articles and scholarly reviews.

Conflict of interest

No conflicts of interest declared.

C. Dean Kurth Cincinnati Children’s Hospital Medical Center, University of Cincin-

nati College of Medicine, Cincinnati, OH, USA Email: [email protected]

doi:10.1111/pan.12167

References

1 Kemper AR, Moyer VA, First LR. Introduc-

ing Quality Reports. Pediatrics 2001; 127:

187–188.

2 Langley GJ, Moen RD, Nolan KM et al. The

Improvement Guide, 2nd edn. San Francisco,

CA: Jossey-Bass, 2009: 13–72.

3 Wheeler DJ. Understanding variation. The

Key to Managing Chaos. 2nd edn. Knoxville,

TN: SPC Press, 2000: 1–111.

4 Davidoff F, Batalden P, Stevens D et al.

SQUIRE Development Group. Introducing

Quality Reports, 2008; 17: i3–i9.

5 http://squire-statement.org/guidelines/.

570

Editorial

Copyright of Pediatric Anesthesia is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.