Healthcare. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

What is quality improvement and howcan it transform healthcare?Paul B Batalden, Frank Davidoff. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Transformation of healthcarequality improvement

Many in healthcare today are inter-ested in defining qualityimprovement. We propose defin-

ing it as the combined and unceasingefforts of everyonehealthcare profes-sionals, patients and their families,researchers, payers, planners and educa-torsto make the changes that will leadto better patient outcomes (health), bet-ter system performance (care) and betterprofessional development (learning;fig 1). This definition arises from ourconviction that healthcare will not realiseits full potential unless change makingbecomes an intrinsic part of everyonesjob, every day, in all parts of the system.Defined in this way, improvementinvolves a substantial shift in our idea ofthe work of healthcare, a challenging taskthat can benefit from the use of a widevariety of tools and methods (table 1).

Although all improvement involveschange, not all changes are improvement.If healthcare is going to benefit fully fromthe science of disease biology, we need tobe sure that the changes we makesystematically incorporate generalisablescientific knowledge. To guide our designof change, we need to characterise thesettings in which care is actually deliv-ered (microsystems, mesosystems andmacrosystems) in sophisticated ways.Moreover, to know that change is produ-cing improvement, we need accurateand powerful measurements of what is

happening. We propose a simple formulathat illustrates the way in which theseforces combine to produce improvement(fig 2).

Each of the five elements in thisequation is driven by a different knowl-edge system (table 2). The generalisablescientific knowledge we need (element#1) is constructed from empirical studiesthat work to control context as a variable,thus minimising or eliminating its effecton what is being studied. A knowledge ofparticular contexts (element #2) is devel-oped by enquiry into the identity of localcare settingstheir processes, habits andtraditions. Knowledge on the effect ofimprovements on system performance(element #3) requires special types ofmeasurement, techniques that includetime in the analysis, as all improvementinvolves change over time; gaining thisknowledge also requires the use ofbalanced measures that accurately reflectthe richness and complexity of the phe-nomena under scrutiny. The + symbol(element #4) represents knowledgeabout the many modalities, includingstandardisation, forcing functions, aca-demic detailing, and so on, which areavailable for applying and adapting gen-eralisable evidence to particular contexts.The R symbol (element #5) representsthe knowledge required for executionwhat you need to know to make thingshappen, the drivers of change, in a

particular place. It requires knowingwhere power resides and how it isasserted; it requires knowledge of thestrategic aims, the usual ways of con-ducting work in that setting, the ways inwhich people are recognised andrewarded, and the ways in which theyare held accountable for their work.

Acquiring these five kinds of knowl-edge requires both scientific and experi-ential learning. Reflection on the natureof these five knowledge systems, howthey grow and change, and the ways inwhich they work together to move evi-dence into practice will be essential if weare going to learn about learning. Doingso will generate a kind of metaknow-ledge that will be essential over the longrun in becoming progressively better atimprovement.

Of course, better knowledge by itselfdoes not guarantee improved perfor-mance; if these five knowledge systemsare going to be effective, we need to paycareful attention to the way in which wedeploy them. It is one thing to expect aspecially commissioned QI team to beactively engaged in designing and testingthe many changes needed for betterpatient and population outcomes, bettersystem performance and better profes-sional development; it is quite another toexpect everyone involved in healthcare todo so, and do so all the time. For theuniversal practice of change testing tohappen, all those involved in supervising

Betterpatient (andpopulation)outcomes

Betterprofessionaldevelopment

Bettersystem

performance

Everyone

Figure 1 Linked aims of improvement.

Table 1 Illustrative tools and methods in improvement

Domain of interest Helpful tools and methods

Healthcare as processes within systems Diagrams that illustrate flow, inter-relationship and cause-effect; narrative descriptions; case examples

Variation and measurement Data recorded over time and analysed on run charts and control charts

Customer/beneficiary knowledge Measurements of illness burden, functional status, quality of life; recipients assessment of the quality of their care

Leading, following and making changes inhealthcare

Building knowledge, taking initiative or adaptive action, reviewing and reflecting; developing both leadership andfollower-ship skills

Collaboration Managing conflict, building teams and group learning; acquiring specific communication skills (eg, SBAR)

Social context and accountability Documenting unwanted and unnecessary variation; widespread public sharing of information

Developing new, locally useful knowledge Making small tests of change (PDSA cycles)

2 EDITORIAL

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the work of healthcarefrom the front lineto the front officesmight, for example, beexpected to offer specific, expert supportand guidance to those they supervise asthey design and execute tests of change.The model asserting that better healthoutcomes, better care delivery and betterprofessional development are inextricablylinked (fig 1) recognises that mutualsupport and stimulation among these threedomains invites both sustainability andunending creativity in their efforts.

Drawing everyone actively into theprocess of testing change, all the time,presumes that everyone will develop abasic understanding of the standards oftheir work, as well as the skills they needto test changes in that work. Makingimprovement happen also requires lea-dership that enables connections between

the aims of changes and the design andtesting of those changes; that pays seriousattention to the policies and practices ofreward and accountability; and unshake-able belief in the idea that everyone inhealthcare really has two jobs when theycome to work every day: to do their workand to improve it.

Qual Saf Health Care 2007;16:23.doi: 10.1136/qshc.2006.022046

Authors affiliations. . . . . . . . . . . . . . . . . . . . . . .

Paul B Batalden, Center for Evaluative Sciences,Dartmouth Medical School, Hanover, NewHampshire, USAFrank Davidoff, Institute for HealthcareImprovement, Cambridge, Massachusetts, USA

Correspondence to: Dr P B Batalden, Center forEvaluative Sciences, Dartmouth Medical School,Hanover, NH 03755, USA

Competing interests: None declared.

1Generalisable

scientificevidence

+4 5

2Particularcontext

3Measured

performanceimprovement

Figure 2 Formula illustrating the way in whichknowledge systems combine to produceimprovement.

Table 2 Characteristics of five knowledge systems involved in improvement

Knowledge system Illustrative features

1. Generalisable scientific evidence Controls and limits context as a variable; tests hypotheses

2. Particular context awareness Characterises the particular physical, social and cultural identity oflocal care settings (eg, their processes, habits and traditions)

3. Performance measurement Assesses the effect of changes by using study methods that preservetime as a variable, use balanced measures (range of perspectives,dimensions), analyse for patterns

4. Plans for change Describes the variety of methods available for connecting evidence toparticular contexts

5. Execution of planned changes Provides insight into the strategic, operational and human resourcerealities of particular settings (drivers) that will make changes happen

Diabetes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Specialists versus generalists in the era ofpay for performance: A plague o bothyour houses!David Aron, Leonard Pogach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The focus on comparisons of specialists and generalists ismisguidedgood diabetes care depends upon a team

The comparison of outcomes amonggeneralists and specialist remains amatter of considerable and sometime

acrimonious debate. A number of recentstudies, usually using intermediate out-comes, have resulted in differing conclu-sions as to who provides the best care.Confounding factors, including referralbiases, shared care, and illness burdenremain methodological challenges andboth groups continue to argue the point.1 2

Methodological shortcomings aside, thepaper in this issue by McAlister et al3 (seepage 6) is novel in that it uses all-causemortality for patients with new onsetdiabetes as a criterion by which to comparespecialists and generalists. All-cause mor-tality is perhaps the ultimate summary

outcome, and one that has previously beenproposed as a quality measure for assessingquality of outpatient care for systems ofcare.4 The provocative finding of thecurrent study is that specialist care isassociated with a survival disadvantage.This survival disadvantage occurreddespite the seemingly better performanceof specialists in process measures ofdiabetes quality such as use of statins,antiplatelet agents, and ACE inhibitors,and was robust across several sets ofanalyses.

The provocative finding of the currentstudy is that specialist care wasassociated with a survival disadvan-tage

In the era of public reporting and payfor performance in the UK and US, thispaper raises a number of issues for howand whether to assess quality measure-ment among different clinical groups,using diabetes as the example. First,sample size limitations would precludethe use of mortality comparisons at theindividual physician and probably at thesystem level as well. Furthermore, it isclear from both the current manuscriptand prior work that even system levelcomparisons require high quality dataand rigorous risk adjustment.4

Additionally, short term mortality ratesmay not be actionable. Consequently, theuse of intermediate outcomes and processmeasures that have been demonstrated toreduce mortalityor at least reduce theadverse macrovascular and microvascularoutcomes that result in shortened lifeexpectancywill undoubtedly continueto constitute the primary approach toquality assessment in diabetes.

In that regard, certain medications thathave been shown in randomised clinicaltrials to decrease cardiovascular morbid-ity and mortality, such as angiotensinenzyme converting inhibitors,5 statins6

and aspirin,7 can and should be able tobe successfully prescribed equally well bygeneralists and subspecialists for mostpatients. However, the situation withintermediate outcomes of glycaemia,blood pressure, and cholesterol is more

EDITORIAL 3

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transform healthcare?What is ''quality improvement'' and how can it

Paul B Batalden and Frank Davidoff

doi: 10.1136/qshc.2006.0220462007 16: 2-3 Qual Saf Health Care

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