International Journal of Health Care Quality Assurance Small and big quality in health care: Paul Martin Lillrank

Article information: To cite this document: Paul Martin Lillrank , (2015),"Small and big quality in health care", International Journal of Health Care Quality Assurance, Vol. 28 Iss 4 pp. Permanent link to this document: http://dx.doi.org/10.1108/IJHCQA-05-2014-0068 Downloaded on: 23 April 2015, At: 03:11 (PT) References: this document contains references to 0 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 48 times since 2015*

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Small and big quality in healthcare

Introduction Quality is a central concept in engineering, management and service production. It has a positive ring to it; nobody willingly admits to producing poor quality. However, in general speech and among practitioners, quality is used in a wide variety of meanings. The World Health Organization defines (WHO, 2006, p.9) six quality areas or dimensions: safe; effective; patient centric; efficient; timely and equitable. These, however, are more like a general mission statement. In healthcare, there is no existing international gold standard for developing and comparing quality (Blozik et al., 2012). Absent adequate quality metrics induce patients to base their choice on location, price, experience or image - a serious impediment to purchase-provider models and quality development through benchmarking. In healthcare, as in other walks of life, you can’t manage what you can’t measure and you can’t measure what you can’t define. Management as a technology depends on measurement epistemology, which in turn requires a clear ontology of the relevant phenomena to be measured and managed. This article analyzes quality and suggest guidelines for developing definitions. First, quality and its development in industry from small q (error-free production) to Big Q (satisfying customers) is reviewed. Second, the specific general and healthcare service quality problems in particular are discussed. Third, methodological guidelines and a three-step health care quality definition are postulated. Finally, implications and future research are discussed. Quality - A brief intellectual history Originally, quality refers to the characteristics of things and events in contrast to volume; i.e., quality is different from quantity. In the pre-industrial world, quality was an individual product’s innate characteristic associated with its functionality or aesthetic pleasure (Juran, 1995). With mass production, design was separated from production, which built identical items in large volumes. When the task is to produce parts for standardized assembly, the question arises, can they all be made similarly to given specifications so that they fit together without adjustment? Thereby, industrial quality came to signify production control: can a production process accomplish exactly what it is planned to do? If not, there are deviations from specifications, which carry waste, rework and complication costs. The unit of analysis; i.e., the entity that can have the attribute ‘quality’ became a series of identical copies. The phenomena hampering control is variation. Production systems operating as they are designed to do, exhibit random variation arising from common causes. Additionally, there is variation that arises from specific, or assignable causes, discrete events or external disturbances. Examining one defective piece does not necessarily reveal the problem’s origin. Specific causes require local solutions, while common causes call for system redesign. If data are collected on relevant measures along a production run and put in a time-series then statistical analysis can reveal whether the problem’s source is common or specific. This method, known as Statistical Process Control (SPC) was developed by Walter Shewhart (1931) and expanded by Deming (1982, 1994) and Juran (1988, 1989). It was based on ontology (deviations from targets are caused by random and specific variation), epistemology (deviations are counted and/or measured as distance from target) and technology (first eliminate assignable variation causes, then reduce random variation). Quality management reduced poor quality costs, improved economic performance and became a technology that could reach beyond the obvious (Oakland, 1999). Shewhart’s methods were put to good use in the US during WWII and after in Japan with remarkable success. Soon it was realized that merely eliminating deviations from

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repetitive manufacturing processes was not enough. First, there is the human factor; i.e., production systems are designed and operated by people, who may or may not pay attention or follow procedure. Manufacturing requires much idiosyncratic planning and back-office work that is not easily standardized and subject to SPC. Quality requires management support and an administration. Therefore Japanese manufacturers, drawing on contemporary Western thinkers on participative management, such as Likert, Drucker and Maslow, devised company-wide quality control, later renamed Total Quality Management (TQM) (Ishikawa, 1985). Second, during and immediately after WWII, goods were scarce. Having them produced to engineering specifications without deviations was good enough. As product volume and type proliferated, came increasing customer choice. It was no longer obvious that design engineers had sufficient understanding on what customers wanted. Quality was expanded to incorporate capturing customer requirements and turning them into technical specifications. Quality was seen as fitness for use (Juran, 1988, p.15) and customer satisfaction became a metric. Quality was no longer to be confined to the manufacturing manager concerned about defects, but to the sales, marketing and design managers and ultimately to the CEO. Thus, a divide between production ‘small q’ and ‘Big Q’ management emerged (Juran, 1988, p.47). Figure 1 here As illustrated in Figure 1, quality constitutes the relations between: 1. 2. 3. 4.

Specifications and output: small q, Requirements and outcomes: Big Q, Requirements and specifications: design decisions, Outputs and outcomes: user experience.

Ontology, epistemology, and technology With quality expanding from small q to Big Q, several things happened to ontology, epistemology and technology. Ontology is a philosophy that inquires into the essence of things; what can be said to exist. The small q ontology is the relation between specifications and actual output, the production system’s ability to predictably do what it is supposed to do based on requirements that are sufficiently known ex ante, before production. In a factory, small q operates in a controlled environment where variation can be brought under control. With methods, such as Failure Mode and Effect Analysis (FMEA), quality problem effects can be established. Thereby small q is equal to managing production risks. Big Q postulates that the ontological relation is between customer requirements and how they are fulfilled after experiencing a product. This relation is called customer satisfaction. It has an ontology that is significantly larger, fuzzier and more complicated than conformance to specifications. Customer satisfaction can be influenced by many things, such as, obviously whether the product is error-free and by its design; i.e., how requirements are translated into technical specifications; functionality, price, perceived value, and trade terms. Thus quality, as a product attribute meaning error free execution, became confused with the attributes functionality, grade and variety (style); and through them with cost and value. The way a customer uses and experiences a product can vary in ways not necessarily known, or knowable to a producer. Thereby Big Q has to deal with uncertainty. Epistemology is a philosophy that inquires into what can be known and how things can be measured. The small q epistemology is straightforward. The designer sets the specifications before production. Typically, specifications are given as physical variables, such as dimensions and material properties and expressed as blueprints, recipes, process

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models or prototypes. If requirements are given in dimensions accurate to 0.01 mm then the output is measured in the same terms. The output is compared to the specifications and deviation magnitude can be determined. The Big Q epistemology comes into the realm of human behavior. Customers have needs and wants, which they are able to articulate in various ways and terms at various times. Requirements capture; i.e., understanding what customers want and translating it into technical and actionable specifications, is a major problem. Customer satisfaction is an expressed state that may, or may not influence behavior, such as repeat purchase, willingness to pay a premium, or brand loyalty (Kotler et al., 2008). While the small q specifications can be known ex ante (before production), the Big Q satisfaction issues, particularly with new products, can be known only ex post after a product is launched, bought and used. It leads to management technologies that manage after the fact. Technology, in this context, means ways to exploit some known phenomena for a purpose (Arthur, 2009). Management is a technology that applies physical, behavioral and economic means to influence relevant phenomenon, such as cost, quality, productivity and flexibility. The small q and Big Q technologies are different. Small q has a solid technology set, such as SPC, Six Sigma, Quality Assurance, Quality Improvement and Quality Systems (Mitra, 1998). Beyond these, Big Q incorporates virtually everything known to business administration. A standard Total Quality Management textbook (e.g., Foster 2001), includes chapters on strategy, marketing, teamwork, production, human resources and customer relations; all labeled as quality technologies. The early TQM proponents envisioned a company where everything would be aligned to quality. While small q could and still can deliver its promises and thrives under Six Sigma, Big Q could not. The powerful analytical SPC tools were restricted to production, while the managerially relevant, but technically diffuse customer needs and satisfaction issues attracted top manager attention. The small q – Big Q constellation reflects the perennial dilemma between rigor and relevance. The issues that can rigorously studied and to which powerful technologies can be developed, tend to be parts of the totality of running a successful business. The relevant ‘big, hairy, and audacious’ (Collins and Porras, 1994) issues are fuzzy and hard to turn into recipes of continuous success. While relevant, conceptual expansion led to quality confusion that continues to present. Total Quality Management overpromised and under-delivered, and soon fell into disrepute within management research. Functionality, grade, style and value Owing to conceptual confusion, quality lost its ontological integrity and became confused with other generic product and service attributes, functionality, grade and variety (style). Functionality means what a product or service can do, or what can be done with it to what effect; e.g., a knife’s function is its ability to cut. As cuts can be applied to several materials and used for several purposes; i.e., a knife is multi-functional. A peeler also cuts but it’s designed to cut as potatoes, etc., into even slices. Functionalities are designed and can be expressed in several terms, such as efficiency, strength, ease of use, aesthetic pleasure and capability to impress others, all depending on intended use. If small q is confused with functionality then control gets confused with design. Grade means different levels of the same functionality. A knife can be long or short, but it is still a knife. Paper comes in grades, measured as grams per square meter. Grades are developed to specific customer segments and corresponding requirements. Different grades typically have different production costs. If you pay more, you get more. If small q is confused with grade, it also gets confused with price. Variety means alternatives within a functionality and a grade (Ashby, 1956). Variety should not be confused with variation; i.e., a deviation from a given target. Therefore, variety is better called style, such as colors or ornaments. There is no right and wrong style, some

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prefer blue, others red (Lillrank, 2003). Style is particularly important in services where it reflects service personnel’s attitudes and behaviors that cannot be standardized. While functionality, grade and style are designed for products and services, quality, in its core industrial meaning, is about execution. When design is done, can it be produced as intended? Durability and reliability are quality in an execution time-line. How long can a product be expected to function as intended, or a style remain fresh? Functionality, grade, style and small q are logically independent dimensions. Imagine hand tools: knife, screwdriver and wrench. They have different intended uses. Each can come in several grades such as size and material strength and in color styles. Each functionality-grade-style combination can be high or low small q quality depending on how well it has been manufactured to its own specifications. As an economic term, value denotes the difference between what you give and what you get (Zeithaml, 1988). Value is not a product but a transaction. On the get side, there are functionality, grade, style and small q, on the give side is the price paid, trade terms and ownership cost. Quality is not value, rather an element in the value equation. Mixing functionality, grade, style and small q leads to confusion about cost and value. Improving functionality typically costs more if it entails using materials that are more expensive or elaborate. In manufactured products, however, advances in technology may bring improved functionality at lower costs. A modern smartphone has better functionality than the old analog handsets, at a fraction of the cost. Grade usually means increased cost. High service levels typically imply personal attention, which require more staff resources. But for small q, it is universally true that quality is free (Crosby, 1979). It is cheaper to do things right the first time. The technologies used for functionality, grade, style and small q are different. Functionality builds on each product’s science and engineering. Designing grades and styles requires knowing customer preferences and willingness to pay. Small q builds on the generic tools, SPC and Quality Assurance and Improvement, applicable to all production types. Quality in services Defining service quality, paradoxically, is easier than for manufactured products, while service quality management is more difficult. The literature distinguishes between the whats and the hows; i.e., service functionality and how it is executed (Grönroos, 2007). The hows, however, cannot always be expressed with the same accuracy as manufactured goods, hence the service quality management problems (Dotchin and Oakland, 1994). A basic position is that service quality is the relation between customer perceived expectations and customer perceived outcomes. The ontology of service quality is the relation between perceptions before and after a service event, and correspondingly, epistemology deals with how to measure and compare such perceptions. The ‘what is a service?’ question has been much debated. The traditional view is that the core issue is immateriality, associated with heterogeneity, inseparability and perishability (IHIP) (Fitzsimmons and Fitzsimmons, 2006). The service dominant logic (SDL) school of thought (Vargo and Lush, 2004; Lush and Vargo, 2006; Sampson and Froehle, 2006) postulates a different view. Customers and providers co-create services. Therefore, customer participation in production is the core element differentiating products and services. Services as a co-created value adds a new dimension. While small q focus is ex ante and in Big Q ex post, in service production quality ex nunc, right now, is important. The resource integration model (Moeller, 2010) offers an elegant IHIP and SDL combination. Both customers and producers bring their resources together in a service agreement based on a value proposition, which leads to a joint service process, from which value follows. Heterogeneity applies principally to customer resources and needs, as individuals and their situations are different. Perishability applies to producer resources, as reserved capacity perishes, if there are no customers. Immateriality is in the

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service contract, since a service trade is made based on promises that are not yet real. Production is inseparable, as the customer needs to be present, either in person or through personal information or possessions (Wemmerlöv, 1991). In services, functionality is the core service, what is accomplished by combining producer and customer resources in the cocreation act. Grade equals service level; i.e., issues such as facilities, amenities and personal attention. Style reflects front-line personnel unspecified service attitudes and manners. Small q, as in manufacturing, is execution following ex ante specifications. In personal services, the Big Q elements include a serious daily dilemma. Small q requires specifications, standards and strict adherence. Style, on the contrary, requires situational adjustment to customer needs and varying situations, which may not always be captured in service specifications (Lillrank, 2002). Quality in health care service production Health service quality is based on the Hippocratean notion: at least, do no harm (Donabedian, 2003). This is a small q definition. A healer should not intentionally put a patient in harm’s way. A small q approach dominates patient safety discussions. An avoidable error is something in which there should have been better knowledge ex ante, but execution failed. Thus much regulatory work screening pharmaceuticals and devices and imposing quality standards on production, follow the industrial logic. It is apparent, that simply doing no harm is not enough. Patients seek help and a cure for their medical problems. Therefore the question has turned to medical intervention functionality, what outcomes are accomplished with what therapies and cures (Institute of Medicine, 2001, Berry and Benapudi, 2007)? A fundamental problem with health service production is that it includes both pre-industrial craft production and standardized industrial processes. Christensen et al., (2009) differentiate between intuitive medicine building on clinical intuition and experience; precision medicine, where an exact diagnosis can be made and a standard procedure applied; and supported networks, self-service and peer support. Each has different ontologies, epistemologies and technologies. There have been several attempts to establish essential quality parameters for health care (Rashid and Jussof, 2009; Mosadegrad, 2013). Azam et al., (2012) reviewed the literature and produced hospital service quality parameters: tangibles, reliability, responsiveness, communication, credibility, security, competence, understanding, access, assurance, waiting time, physical appearance, support services, clinical quality, respect, religious needs, dignity, food, structure, atmosphere, personalization, security and convenience. Consequently, the aggregated to-do list includes tasks, such as professionalism, facility management, nurse satisfaction, change management knowledge, TQM projects, quality function deployment, integrated care pathways, infection control, value chains, IT access and doctor-patient relationships. All such parameters are relevant to the healthcare mission. However, coherent measurements and technologies are difficult to construct without defining specific ontology and epistemology and the corresponding differences in functionality, grade, style and small q. It is no wonder that much quality management literature is complaints about implementation difficulties. Methodology This conceptualization can now be used to establish some methodological rules. The starting point is that health services need to be managed to fulfill their mission, accomplish maximal health value to patients, given the circumstances. To do this, measures are needed. Without metric, it is impossible to know what works and what doesn’t, to learn from both success and failure. The metric does not necessarily have to be quantitative, but it needs to be based on understanding the phenomenon being measured and its relevance to expected outcomes. The

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method includes the following steps. First, the unit of analysis (UoA) should be established; i.e., the entity that is supposed to have quality attributes. In healthcare, the ultimate UoA is a patient care episode and its outcomes; i.e., what happened to the patient’s medical condition after something was done (Solon et al., 1967; Lillrank et al., 2011). An episode starts at the first perception of need for help and ends when the case is closed. In chronic conditions, the case only closes with death. In the same way as manufactured products are connected parts; episodes are encounters, activities and interventions that can be examined separately and together as a system. Since there are several diseases and problems, episodes need to be based on a diagnostic grouping. Individuals are different, each patient and each patient episode is different. However, some diagnostics and procedures are similar across patients. Thus the UoA can be, like in industry, tasks that need to be performed identically to the same specifications. For example, blood samples are collected and examined in the same ways for several diseases. Patient episodes can be seen as process flows and the tasks as repetitive processes (Figure 2). The flow process owner is the person or team in charge of a patient; the repetitive process owner manages that function. Figure 2 here Second, the ontology relations need to be established. Are they measureable small q specifications – outputs relations; design relations between expectations and specifications; experience relations between outputs and outcomes; or Big Q relations between expectations and outcomes (Fig.1)? Third, the epistemology needs to be clarified. Who can know what, at which time and how is this expressed? Fourth, what relevant performance objectives can be established, given the UoA, ontology, and epistemology? Fifth, which are the principal technologies associated with each category? Three healthcare-quality types From these methodological guidelines, a conceptual healthcare quality model can be constructed. There are three distinct healthcare quality types: (i) clinical decision-making; (ii) patient safety; and (iii) patient experience (Table I). Table I here The clinical decision-making task is to examine a patient case, collect relevant information from various sources including anamnesis and tests, determine the problem’s nature and possible cause (diagnosis), and propose a cure or a care plan. Following the resource integration terminology (Moeller, 2010), a clinical decision combines perishable provider resources and heterogeneous patient resources to produce a service proposition, a care plan. A clinical decision is akin to case-by-case service design. Therefore, it is primarily about functionality: is the diagnosis correct, does the cure fit the patient and will it lead to expected outcomes? Care plans can be designed at various grades or service levels, depending on the patient’s situation. How and when can this be known? In routine cases, such as the common cold or minor trauma, the outcomes can be predicted ex ante if standard procedures are followed. However, the definitive proof of efficacy can be only ex post, particularly in complicated cases where decisions are taken under uncertainty and all relevant input factors and possible outcomes cannot be fully known ex ante. Clinical decisions can be examined individually, or in aggregates summing across similar diagnoses. Comparing specific diagnoses for comparable populations can illuminate systematic biases in clinical decisionmaking. Since there is uncertainty in clinical decisions, it can’t be assumed or demanded that a clinician should be right every time. Therefore, the relevant performance objectives need to

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be relative; case-mix adjusted benchmarking with others and comparisons to historical track records. There are several generic management technologies to improve clinical decisionmaking: improve education and diagnostic techniques, strengthen professional ethics; develop best practices to collect all relevant information from patients and relatives; provide opportunities to consult seniors (telemedicine); establish check-lists and collect data on outcomes to support learning. Patient safety is a small q issue about execution. It includes all executions, including standard procedures, waiting times and information to which specifications exist ex ante. When a care plan has been established, is it followed and are best-known practices, such as hygiene, followed rigorously? The patient safety epistemology and technology are industrial and reasonably straightforward, because what needs to be done is knowable ex ante. Quality assurance systems can be built to monitor compliance; adverse events and near misses can be reported, counted, evaluated and changed through continuous improvement. More complicated issues arise if patient safety is endangered by the patients’ own activities. Like in other small q issues, zero defect is a realistic performance target. There is no excuse to allow the preventable to happen. For clarity, not all patient episodes have a clear-cut initial diagnosis and an ensuing care plan. In some instances, particularly with complex cases, diagnosis is an ongoing process and treatment plans are adjusted frequently (Bohmer, 2009). On an episode timeline, decisions and implementation can be mixed. As the service marketing literature points out, perceptions matter. It is also well known, that a patient’s mental state can have a significant impact on outcomes. Patient experience, however, is ontologically, epistemologically and technically different from clinical decision-making and patient safety. Therefore, it must be treated as a specific issue. The ontology of satisfaction is a positive mental state that can be described in various terms. It can be known by asking and observing behavior. Various survey instruments, such as SERVQUAL (Parasuraman et al., 1994), can be used to track patient expectations and compare them with experiences. Satisfaction is difficult to manage, because it is driven by subjective preferences, pre-existing mental states and variable perceptions. It is highly heterogeneous, different people have different preferences. Some may want the unblemished truth about their conditions, while others prefer blissful ignorance. Different people may experience the same thing differently. It is important to collect data on patient satisfaction averages and its development over time. Empirical research can identify generic satisfaction drivers, such as waiting time or information. Still the link to technology is weak. Patient satisfaction is managed ex nunc. The management technologies applicable to patient experience are akin to style; i.e., personal attitudes and behaviors, responsiveness and caring that are not easily captured in service manuals. This brings up issues such as situational awareness, social competence, organizational culture, staff empowerment and trust. Achieving good experiences in each case is a day-to-day struggle (Lee, 2006; Berry and Seltman, 2008). Discussion The proposed model uses the conceptual categories, quality relations ontology, measurement epistemology and improvement technologies to distinguish between three distinct healthcare quality types: (i) clinical decision making as a care design episode; (ii) patient safety as execution without defects; and (iii) patient experience. This classification corresponds neatly to the NHS quality definition: ‘care that is effective, safe, and provides as positive an experience as possible (Swinglehurst et al., 2014, p.65). The historical and conceptual contribution is that these three issues can’t be managed and measured with similar tools or in similar ways. Expanding the quality concept has been laudable in its effort to tackle the relevant problems, rather than only the manageable. But attempts to integrate fundamentally

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different concepts create confusion and an overwhelming task range, metrics and sometime contradictory requirements. Only what is clearly separated can be properly joined (Kauffman, 2008). Different stakeholders manage the three types. Obviously, clinical decision-making is the medical professions’ task with a little help from IT systems and team practices. Patient safety is and must be everybody’s concern. Patient experience is primarily, but not solely, for those with direct interaction with patients and their significant others. The model presented here is a conceptual construct. As such, it is not subject to a true-false judgment. Rather, its logical coherence (internal validity) and its coverage (external validity) need to be critically examined: is something significant left out? The method can be tested by applying it to build quality systems, establish multi-dimensional quality registers and frame improvement activities. It is useful, if it can bring clarity to the quality confusion. References Arthur, B. (2009), The Nature of Technology - What It Is and How It Evolves, Allen Lane Ltd., London. Ashby, W.R. (1956), An Introduction to Cybernetics, Methuen Ltd., London. Azam, M., Rahman, Z, Talib, F. and Singh, J.K. (2012), ‘A critical study of quality parameters in health care establishment, Developing and integrated quality model’, International Journal of Health Care Quality Assurance, Vol. 25 No 5, pp. 387-402. Berry, L.L. and Benapudi, N. (2007), ‘Health care: A fertile field for service research’, Journal of Service Research, Vol. 10 No 2, pp. 111-122. Berry, L.L. and Seltman, K.D. (2008), Management Lessons from the Mayo Clinic, McGrawHill Ltd., New York. Blozik, E., Nothacker, M., Bunk, T., Szecsenyi, J., Ollenschläger, G. and Scherer, M. (2012), ‘Simultaneous development of guidelines and quality indicators – How do guideline groups act? A worldwide survey’. International Journal of Health Care Quality Assurance, Vol. 25 No 8, pp. 712-729. Bohmer, R. (2009), Designing Care - Aligning the Nature and Management of Health Care, Harvard Business Press Ltd., Boston. Christensen C., Grossman J. and Hwang J. (2009), The Innovator’s Prescription – Disruptive Solutions for Health care, McGraw-Hill Ltd., New York. Collins, J. and Porras, J. (1994), Built to Last: Successful Habits of Visionary Companies, Harper Collins Ltd., New York. Crosby, P.B. (1979), Quality is Free, Mentor Books Ltd., New York. Deming, W. E. (1982), Out of the Crisis, MIT Press Ltd., Cambridge. Deming, W.E. (1994), The New Economics for Industry, Government, Education, MIT, Center for Advanced Educational Services, Cambridge. Donabedian, A. (2003), An Introduction to Quality Assurance in Health Care, Oxford University Press Ltd., Oxford. Dotchin, J. A. and Oakland J.S. (1994), ‘Total Quality Management in services. Part two: Service quality’, International Journal of Quality and Reliability Management, Vol. 11 No 3, pp. 27-42. Fitzsimmons, J. and Fitzsimmons, M. (2006), Service Management - Operations, Strategy, Information Technology, 5th ed., McGraw Hill Ltd, New York. Foster, S.T. (2001), Managing Quality – An Integrative Approach, Prentice Hall Ltd., Upper Saddle River. Grönroos C. (2007), Service Management and Marketing - Customer Management in Service Competition, 3rd ed., Wiley & Sons Ltd., New York. Institute of Medicine (2001), Crossing the Quality Chasm: A New Health System for the 21st century, National Academy Press Ltd., Washington.

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World Health Organization. (2006), ‘Quality of Care: A Process for Making Strategic Choices in Health Systems’, http://www.who.int/management/quality/assurance/ QualityCare_B.Def.pdf >, accessed May 2014. Zeithaml, V. (1988), ‘Consumer perceptions of price, quality, and value - A means-end model and synthesis of evidence’, Journal of Marketing, Vol. 52 July, pp. 2-22. Table I: Healthcare quality types What it is? Ontology

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Clinical decisionmaking

Patient safety Patient experience

How an individual patient care episode is designed How a care plan or a task is executed A patient’s subjective perception of a care episode

How it is known? Epistemology Ex post outcomes

Conformance to ex ante specifications Interviews, surveys, observed behavior

Relevant objectives

Principal technologies

Relative to benchmarks and past performance

Education, consultations, professionalism, decision aids

Zero defect Compliance

Quality Assurance and Improvement Service culture and values, recruiting

Situational Style

Figure 1: Big and small quality relations

Big Q Requirements

Specifications

Design Decisions

Figure 2: Flow and function

Production small q

Output

Outcomes Customer Experience

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Repetition of functional task

Patient episode

Small and big quality in health care.

The purpose of this paper is to clarify healthcare quality's ontological and epistemological foundations; and examine how these lead to different meas...
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