Standard, routine and non-routine processes in health ...

Introduction

Standard, routine and non-routine processes in health care

Facing ageing populations and rising aspiration levels, the supply of health-care services needs to be increased. This can basically be done through increasing resources and/or spending them more wisely, that is, improving productivity. Productivity is a general measure of the relationship between input and output. While productivity as a concept is easy to understand, in practice it is difficult to measure. The problems are mostly related to the output-side. Qualitative improvements are difficult to account for since they may result in fewer units produced and/or lower costs. Further, should all activities and outcomes be counted as output or only those that fulfill requirements or achieve their intended purpose? This is the question where productivity and quality intersect. Quality management (QM) and the more specific management philosophy total quality management (TQM) have been implemented in health care with variable success (Westphal et al., 1997; Roland et al., 2001). TQM includes principles, such as customer focus, continuous improvement, and teamwork; practices, such as collecting information and analyzing processes; and techniques, such as statistical process control and various problem-solving methods (Dean and Bowen, 1994). While many of these ideas have a non-controversial ‘‘motherhood and apple pie’’- character, they nevertheless have their origin and best applications in mass manufacturing. A lot of effort has been put into developing normative ideas about best practices in management and codifying them into quality systems and excellence models. However, the definition of quality itself remains problematic (Reeves and Bednar, 1994). Quality has a dual definition as both ‘‘conformance to requirements’’ and ‘‘fitness for use’’. The former is based on the assumption that requirements can be specified with sufficient accuracy ex ante, deviations from requirements can be measured and their cost impact calculated. The latter implies that quality is subjective and measurable as customer satisfaction, which, as Zeithaml (1988) has argued, is a very subjective and elusive proposition. These two definitions are based on

Paul Lillrank and Matti Liukko

The authors Paul Lillrank is Professor, Quality Management Department of Industrial Engineering and Management, Helsinki University of Technology, Helsinki, Finland. Matti Liukko is Director, Health and Welfare, The Finnish Association of Local and Regional Authorities, Helsinki, Finland. Keywords Process management, Quality, Variance Abstract Quality management methods have been introduced into health care with variable success. Industrial approaches, such as standardization, are not always applicable professional services, because of fundamental differences in conceptions of aims and the predictability of the results of action. Processes in health care can be classified into standard, routine and non-routine depending on the level of repetition and amount of variation, variety and uncertainty. Quality problems are different in each type: standard processes may produce deviations from targets, routines errors in classification, and non-routines failures in interpretation. Different management approaches for each type are discussed. A metaphor to assist discussion, The Broom, is introduced. Electronic access The Emerald Research Register for this journal is available at www.emeraldinsight.com/researchregister The current issue and full text archive of this journal is available at www.emeraldinsight.com/0952-6862.htm International Journal of Health Care Quality Assurance Volume 17 . Number 1 . 2004 . pp. 39-46 # Emerald Group Publishing Limited . ISSN 0952-6862 DOI 10.1108/09526860410515927

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International Journal of Health Care Quality Assurance Volume 17 . Number 1 . 2004 . 39-46


different epistemological and ontological views of the world. The basic assumptions underlying conformance quality can be summarized as: . Quality is knowable ex ante and explicable in product or service specifications. . Objectivistic epistemology: the ‘‘real world’’ is ‘‘out there’’ independently of human perceptions. . Conformance with specifications through reduction of variation improves quality and benefits producers, customers and society. . Positivistic methodology: characteristics of the ‘‘real world’’ can be observed and by measurement turned into ‘‘facts’’.

They also correspond to the differences between natural science-based medicine and social science-based nursing. The management implications of these differences are significant. Conformance quality is usually associated, although not necessary logically linked, with an assumption that production is undertaken in repetitive processes that are closed or semi-closed systems. They are functional and rationally designed to achieve a-political and non-controversial aims. Progress and continuous improvement are possible and desirable. Subjective quality, on the other hand, is associated with an assumption that processes are seldom repetitive but vary from case to case, eluding inter-process comparisons and the use of detailed measurements. Processes are seen as open systems where unexpected inputs often are valid concerns that can not be excluded or ignored. Where manufacturers see progress in terms of error causes to be eliminated for linear improvement towards zero defect, service producers see the world as issues to be managed in continuous cycles of renewal and regress. The practical implications of these philosophical differences are significant. Should management focus on eliminating variation in measurable and repetitive processes or try to achieve mutual consensus in the management of unique projects? If the answer is the quite obvious: ‘‘both’’, how should resources be allocated between these tasks and what kind of management and leadership would these tasks require? The purpose of this paper is to discuss the difference between objective and subjective quality by looking at what we see as a most significant determinant of these two views: the level of repetition and uncertainty in various processes. Health care is a mixture of both worlds. Services are co-produced by specialists and their clients and perceptions of quality emerge from such interactions. On the other hand, there are sub-processes, such as surgical procedures or diagnostic tests, where standard processes are expected. However, attempts to improve quality by eliminating variation from a whole patient process by means of standardization and system closure may destroy the very value of service.

These philosophical underpinnings are not easily applied to professional services, such as health care. In these areas many quality characteristics are knowable ex ante only in very general terms, such as getting well or avoiding pain. Quality emerges in an interaction between providers and caretakers where the effects of normal recovery are not easily distinguished from effects of rehabilitation. It is thereby no coincidence that within the quality movement a rift has opened between the manufacturing-based TQM and the academically-oriented service-marketing-school (Silvestro, 1998). The basic assumptions of subjective quality in service management are: . Quality is, in addition to minimum defects, also a matter of choice between functionally equivalent alternatives, which may not be knowable ex ante. . Subjectivist epistemology: perceptions of the world are socially constructed. . Aims are political and controversial. . Phenomenological methodology: focus is on perceptions, perspectives, negotiations of meaning and sensemaking. The boundaries of the two worlds fall rather neatly along the demarcation lines of Western philosophy since the days of Aristotle and Plato: . objective vs subjective; . rational vs empirical; . classical vs romantic; . positivism vs phenomenology; and . linear progress vs cycles of birth and death. 40




We want to emphasis that the world is not black-and-white. There is no clear empirical way of drawing the line between subjective and objective perceptions of quality. Distinctions between repetitive and non-repetitive organizations cannot be made on an overriding sectoral basis. Rather, processes in health care include elements, some of which are highly repetitive and therefore subject to the manufacturing-based TQM apparatus, while some others are unique and better suited to be treated as customer service projects. The aim of this paper is to clarify the conceptual problems involved. This paper is organized as follows. First, following an argument developed by Lillrank (2003) we discuss processes, dividing them into standard, routine, and non-routine. Second, variation and variety are conceptually separated. Third, we apply these concepts to quality problems in health care, making the distinction between defects, errors, and failures. Finally, we introduce the Quality Broom as a metaphor to aid the discussion of various process types and their management in health care.

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an assessment phase of examination and diagnosis; an algorithm in terms of treatment schemes and protocols; activities such as clinical interventions and nursing; and finally output as a patient who has received help.

An unarticulated assumption of classical quality management is that processes are identical or near identical repetition of activities towards a predetermined aim (Lillrank, 2003). These may be called standard processes. Such processes have boundaries, accept only pre-defined input, and have algorithms based on more or less accurate theories to turn input into output. There is a ‘‘one best way’’ for each activity and the task of management is to find it and turn it into a standard operating procedure (SOP). Given targets should be achieved if an SOP is meticulously followed. If this is not the case, deviations may have been caused by system-external disturbances or internal deficiencies in assessment, algorithm or action. Routines are another type of processes. They have two or more types of inputs, and two or more types of alternative outputs. The essential thing in managing routine processes is not mindless, defect-free repetition, but assessment and classification of input, and selection from a finite set of alternative algorithms and actions. In health care the classifications are diagnoses, and the alternatives treatment protocols. The overall aim of a routine is usually clear, but it can be achieved through alternative types of actions. There is no ‘‘one best way’’ that could with certainty be known before the fact, but several alternative routes. If aims are not achieved, the primary cause lies in inappropriate selection between alternative lines of action. In contrast with repetitive work, we can define routine work as operations, where target values are quite fixed, or at least reasonably well-known, but input conditions vary. Let us think about a rather basic health-care activity. Before surgery patients’ skin must be cleaned. The target, a clean skin, can be defined with some precision. A detailed manual listing all the features of clean skin can be developed. The input, soap and antibacterial liquids can be standardized to some extent, as well as the procedures of cleaning. However,

Processes and systems The classical writers on quality management (Deming, 1981; 1994; Juran, 1992; Ishikawa, 1985; for a summary see Hackman and Wageman, 1995) assume that all activities are carried out as processes, that processes are systems and parts of larger systems, and that all processes exhibit variation. In system theory the basic process model is input-black box-output (Jackson, 2000). Managerial cybernetics add assessment that accepts or rejects input, conversion rules that determine proper action, and actuators that deliver output (Beer, 1981). Processes are thus bundles of human and material resources that have input, assessment, algorithm, action and output. Applied to health care we can say that a process has human resources, such as: . physicians and nurses; . material resources such as facilities, equipment and medical supplies; . patients’ conditions as inputs; 41




the actual condition of the skin of a certain patient can not be standardized. Therefore the actual skin cleaning operation must be adjusted to fit the actual state of the input, the condition of the patient. It other words, an assessment of the particular patient’s condition is made, an algorithm is activated to derive a plan that guides action. Routine operations, we believe, constitute the majority of activities in health care. Non-routine processes differ from routines in that input is vague and not readily classified into categories to which certain algorithms and actions could be linked. Therefore the assessment of input is an interpretation, which must be derived through search for new information, iterative reasoning or trial-and-error. Non-obvious diagnostic processes are often of this kind. If the input can be categorized into a diagnosis, the process may continue as a routine; otherwise uncertainty and ambiguity remain. In sum, standard processes are applicable in situations where events are repeated in an identical fashion. Routines are associated with somewhat similar, but not identical, repetition. Non-routine processes emerge where events are dissimilar. Standard, routine and non-routine processes may coexist in the same systems. Emergencies, such as treating victims of accidents, may as an overall process be non-routine, but consist of several sub-routines and standardized support processes.

that are internal to the system and thereby essential descriptions of it. The managerial implications of the theory of variation are significant. Most of the observed variation in manufacturing comes from system-internal common causes. Variation occurs because management has insufficient means to monitor and control compliance with established standards and/or the system is designed based on insufficient theoretical knowledge about products and processes. Following this thesis, quality management turned its focus from putting blame on workers to examining the design of systems and processes. Quality is achieved, first, by eliminating specific causes, i.e. closing the system from unwarranted external influences, and second, by reducing internal variation by fact-based process improvement and redesign. Variety, in contrast, has always been associated with something good. Variety means the number of functionally equivalent alternatives available to a chooser. It is based on the assumption that customers prefer products and service that are suited to their individual needs and tastes. In health care, variety is a set of choices between alternative treatments that can be assumed to have equivalent basic therapeutic effects. If there is, say, only one type of medication available in a given therapeutic or price category, several patients are forced to accept sub-optimal choices. The health-care system does not have the ‘‘requisite variety’’ (Ashby, 1956) demanded by its environment. With increased variety a better fit between treatment and patients’ individual needs becomes possible. Consequently, a great deal of quality improvement in services aims at increasing the variety set customers can choose from.

Variation and variety The statistical theory of variation, developed by Shewhart (1931) and expanded and popularized by Deming (1981), is based on the insights that: . All processes exhibit variation. . Variation leads to deviation from targets and is the primary cause of poor quality. . Variation is associated with a loss function: when an actual manufacturing step produces a value that deviates from the target, costs in terms of rejection or trouble later in the process are incurred. . Variation originates in two distinct types of causes: specific causes that are external to the production system; and common causes

Quality problems in health care Equipped with the concepts of standard, routine, and non-routine processes, and the differentiation between variation and variety, the question of quality problems in health care can be approached. Depending on process type, 42




problems can be labeled deviations, errors, and failures. The formal aim of health care is to treat patients based on scientifically derived and verified methods. Compliance with established best practices and scientific findings is stressed through professional training, reinforced through vicarious learning, and occasionally audited formally. Malpractice legislation and insurance mechanisms guarantee patients some protection in the cases of harm following the traditions of product liability legislation. There is growing evidence, however, that this is not enough. According to research done in the USA and elsewhere, a lot of harm is done to patients under treatment. Two studies (Brennan et al., 1991; Thomas et al., 2000) have shown that adverse events occur to approximately 3-6 per cent of patients. Since following established practices could have prevented most of them, they constitute deviations from targets. The quality management theory and practice suggest that standards can reduce the number of deviations. This world view, as mentioned, is based on the assumption of identically repeated processes aiming at non-controversial, measurable targets. Accordingly, evidence-based medicine tries to value treatments according to scientific proof. In situations where this is not applicable, this logic does not hold. Health outcomes are evaluated mainly through added life expectancy corrected for quality of life. It is usually evaluated by panels aiming at combining expert and layman views. However, from an individual point of view, the quality of life can be evaluated only by the patient. The aims of health-care processes can thus to some extent be defined exactly, but to a large degree they are subjective and controversial. The crucial characteristics of health care, as well as other high-end professional services, is that the relevant system is difficult to define and its boundaries are hard to close. In open systems risk assessment is troublesome, since many unpredictable things can happen, and, what is worse, they can not always be excluded or ignored. Indeed, attempts to close out essential input from patients and their families by using standard interview and observation techniques has been subject to much criticism. In health care a patient case may be different

from the ordinary to the extent that mindless repetition is not an option, textbooks and manuals may not offer profound theory. In such conditions decisions must be taken under bounded rationality, that is, neither all relevant input information, nor theoretical knowledge of the effects of certain interventions is available at the point of decision. In a routine process even a competent physician with the best of intentions can misinterpret a clinical picture, for example classify a tumour as malignant when in fact it is not, and choose actions that at the point of decision appear rational, but which after the fact turn out to have been inappropriate. This cannot be said to be a deviation from a target, but an error. In a non-routine situation the amount of relevant input is unknown. Some of it consists of vague clues or weak signals that may not always receive appropriate attention. Should that happen, treatment may be inappropriate or not conducted at all. This constitutes a failure. In sum, we can say that quality problems in health care are different in different kinds of processes: . Non-compliance with a standard process produces a deviation from target. . Inappropriate selection from known alternatives in a routine process produces an error. . Inproper assessment of input in a non-routine process produces a failure.

Process management in health care The practice of health care consists of many types of activities. A repetitive process carries great possibilities. If a situation repeats itself, it can be observed, measured, and experimentally manipulated. The best-known set of actions can be determined and standardized to be followed every time. Although individual patient processes differ at least slightly from one another, types can be grouped into routines, each having its typical internal variation. Diagnoses-related groups (DRG) can be used to evaluate and manage the use of resources in each group. Routines can be defined through check-lists, best-practice guidelines, and examples, that, however, leave 43




ample room to consider situational variation and variety. In such instances routinization reduces confusion and increases predictability. Non-routine situations are best managed by indirect means, such as competence, improvement and professional values, visions and missions. Learning may occur in two directions. To a novice intern most cases appear like confusing non-routines, which eventually simmer down into safe routines, which again over the years may turn into standard processes. On the other hand, an insightful practitioner may start to question standards and routines and accept a more complex but accurate view of the world. Standard, routine and non-routine processes are useful as analytical categories. In practice, however, they cannot be distinguished between with empirical accuracy for several reasons. Processes are complex entities, essentially rather arbitrary classifications in two directions. First, any process can be divided vertically into sub-processes, sub-sub-processes and so on all the way to microbiological events; and conversely put into larger process contexts all the way to societal, historical or global processes. Second, any process description represents a horizontal cut-off in a chain of events, such as the process of surgical interventions, the process of a surgery ward, the hospital process, the health care process including GPs and rehabilitation, and the total life history of a patient. Therefore process analysis must always start with a definition of the unit of analysis. Processes are standard, routine or non-routine depending on the experience, competence and resources available, thereby situationally determined. Therefore process analysis must consider the availability and competence of resources.

themselves as artists thriving in non-routine environments and consider attempts at standardization as infringements, even insults to their professional competence. Both camps tend to create a great deal of anxiety and resistance in the other. Obviously, some kind of common language would be useful. The starting-point is to assume that the variation and variety of inputs into a process are variables, and that the certainty of interventions producing expected outcomes is also a variable. Summed up, these constitute the overall level of uncertainty. Where symptoms are clear-cut, easy to understand and classify, certainty prevails and standards can be established. Where the clinical picture is confusing, ambiguous and not easy to classify, uncertainty prevails and actions must be based on experience and intuition. This kind of continuum can be illustrated with the metaphor of a broom (Lillrank, 2002), as in Figure 1. A broom is made out of three components, a hard unyielding stick; a soft, flexible part made of a bundle of straws; and a connector between these. The stick provides rigidity and reach, the straws flexibility and tenacity. Applying this image to an organization, the broomstick represents repetitive, standardized operations where each step in terms of inputs, conditions and activities is identical or nearly so, and outcomes are known with reasonable certainty. The bundle of straws represents non-routine systems with a variety of flexible movements. The connector represents routines where rigidity and flexibility are brought together. As Figure 1 The quality broom

The Quality Broom as a metaphor Health care consists of several different types of processes with their own targets and logic. Attempts to improve quality may start from identifying repetitive processes and standardizing them as far as possible. At the other extreme, some expert clinicians see 44




we move along the broom from left to right, standardized and manageable operations start to become increasingly mixed with vague and unknown things that cannot be explicitly described in a manual. Most processes can be placed at some position on the broom based on their average type of activity. However, individuals performing in these processes may be running back and forth on the broom as they carry out their work. For example, even the most accomplished clinician with a golden touch in sorting out the messiest non-routines must occasionally wash up and settle down to follow the most meticulous standard operating procedures. On the other hand, nurses’ aides, whose daily work is mostly routine, may occasionally detect and bring forth weak signals that otherwise would have been lost. The managerial implication of the broom-metaphor is to determine where various processes and activities are located in terms of uncertainty, and thereby to what extent they may be standardized, routinized, or left to empowered local experts. Management needs to comprehend the whole broom, understand where order meets chaos, manuals and their interpretation, and where the swinging twigs are forcefully tied together and attached to the stick. Managers need to decide what should be strictly regulated and what should be left to empowered individuals and groups, what should be made explicit at any effort, and what could and should be left to the realm of tacit knowledge; when to manage inputs and when only look for output. The broomstick can be managed with quality systems and standards, while the most viable way to impact on the other end is through organizational culture, the creation of professional pride, which should not focus only on clinical wizardry, but also on the pleasure of running a ‘‘tight ship’’ that is respected in its community. A great deal of trouble follows, if processes are interpreted as being different from what they on closer examination actually are. If reality is on the left on the broom but perceptions on the right, unnecessary fuss is created and a ‘‘chaos trap’’ (Brown and Eisenhardt, 1998) is set. Conversely, if processes are perceived as located on the stick-end, when in fact they are more complex,

managers fall in the ‘‘bureaucracy trap’’ applying rigid procedures where they are not warranted. We have used the broom metaphor in several seminars and training sessions for health-care professionals. While it, at first look, may appear silly, we have on several occasions observed how it reduces anxiety and focuses planning and improvement activities in a constructive direction.

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improvement?’’, Journal of Management in Medicine, Vol. 15 No. 3, pp. 188-201. Shewhart, W.A. (1931), Economic Control of Quality of Manufactured Product, Van Nostrand, New York, NY. Silvestro, R. (1998), ``The manufacturing TQM and service quality literatures: synergistic or conflicting paradigms?’’, International Journal of Quality & Reliability Management, Vol. 15, pp. 303-28. Thomas, E.J. and Brennan, T.A. (2000), ``Incidence and type of preventable adverse events in elderly patients: population based review of medical records’’, British Medical Journal, Vol. 329, pp. 741-4. Westphal, J.D., Gulati, R. and Shortell, S.M. (1997), ``Customization or conformity? An institutional and network perspective on the content and consequences of TQM adoption’’, Administrative Science Quarterly, Vol. 42, pp. 366-94.

Zeithaml, V.A. (1988), ``Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence’’, Journal of Marketing, Vol. 52, pp. 2-22.

Further reading Berry, L.L. and Parasuraman, A. (1990), Marketing Services, Competing Through Quality, Free Press, New York, NY. Gitlow, H.S. (2001), Quality Management Systems ± A Practical Guide, St Lucie Press, Boca Raton, FL. Oakland, J. (2000), Statistical Process Control, Butterworth-Heineman, Oxford. Zbaracki, M.J. (1998), ``The rhetoric and reality of total quality management’’, Administrative Science Quarterly, Vol. 43, pp. 602-36.

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