FAS dnr 2012-0079, Anna Essén, Juniorforskare: Numeriska mått i vården–... Projektbeskrivning The Quantification of Healthcare: How Numbers Can Open & Close Doors to Improvement How could we improve healthcare if we don’t measure what and how we are doing? This is a commonly held view among policy makers, who argue that providers need to gather more data about the process and outcomes of care – numerical data that is—in order to make more well-informed decisions about what to do (e.g. IJQHI 2006; OECD 2010). This argumentation epitomizes a more general quantification movement permeating most sectors in today’s society (e.g. Espeland & Stevens 2009; Hood 1995; Porter 1995; Power 1994; Strathern 2000). Swedish health care has unique international position in this context thanks to our tradition of gathering and linking information about citizens in national databases of various kinds. Recently, Swedish health care quality registries have gained international attention as ‘gold mines’ due to their voluminous numerical data about intervention and diagnosis-specific aspects of processes and results of care. It is argued that the registry-data can improve decision-making during the individual meeting between physicians and patients as well as provide valuable statistics about the effectiveness of care at aggregate level (Porter & Teisberg 2006; Rosén 2011). The Swedish government has recently announced large investments in Swedish registries in order to improve the Swedish health care delivery system, encouraged by authoritative voices from the industry, academia and clinical practice (BCG 2010; Klareskog & Lindblad 2011; Regeringen 2011; SKL 2011). Being a researcher one is alarmed by the lack of critical reflection in the midst of this enthusiasm. What are we doing here? The expanded production of numerical measures in health care surely has benefits, but don’t we also need to consider the risks associated with this – seemingly inevitable – development? While such questions are virtually absent at health policy level and in the popular healthcare debate in Sweden1, there is an emerging interdisciplinary academic literature on quantification that has begun to address these issues. This literature suggests that numbers are not merely descriptions of an external reality. Numbers direct attention, persuade and create new categories for apprehending the world in various sectors (Miller 1994; Scissons 2002; Espeland & Stevens 1998; 2009). The quantification literature is largely critical, emphasizing e.g. how measures may become aspirations if practitioners start caring about numbers rather than the reality that they describe and that measures tend to be internalized even in cases where the measures are considered highly questionable (Jennings 2010; Sauder & Espeland 2009, cf Foucault 1977). This echoes classic philosophical arguments about how calculation produces a ‘mechanized objectivity’ (Porter 1995) with no place for passion and intrinsic values (Nussbaum 1986; Weber 1978). Do numbers merely simplify and exclude worlds, then? Cannot numbers also ‘open’ doors to worlds and passions? Indeed, as argued by Espeland & Stevens (2009), while measurement can narrow our appraisal of value, it can also help us see complicated things in ways that make it possible to intervene in them and it can make visible people and aspects that may formerly have been invisible. Few empirical studies have however fleshed out this two-edged nature of quantification in practice. Against this background, the present project investigates how the use of numbers can both reduce and expand thinking and action in healthcare practice. That is, how, why and when can the use of numbers reduce variability and complexity (standardize care work) and how does it increase variability and complexity (create more diverse, individualized processes)? Both these dimensions are necessary given the ambition to ensure fairness and minimal standards of care while also adjusting processes to each patient. The project assumes that there is no unidirectional, causal relationship between absolute traits of numbers themselves and action. Drawing on performativity and practice theory (Bourdieu 1990; Feldman & Pentland 2003; Schatzki et al. 2001), numbers are rather viewed as both products and producers of practice, and their implications depend on how they are performed and the authority users assign them in practice.

1 Notable exceptions in Swedish trade/daily press include (Canivet & Bengtsson 2011; Järhult 2011, cf. debate in Läkartidningen).

Theoretical Background – the Reactivity of Measures The healthcare and overall management and accounting literature encompass a large body of publications dealing with the necessity and appropriate designs of performance measurement systems (e.g. Behn 2003; Blumenthal 2012; IJQIHC 2006; Kaplan & Norton 1992; Murray & Frenk 2008; Neely et al. 2005; Otley 2003; Porter & Teisberg 2006; Porter 2010; Roski & McClellan 2011; Thier & Gelijns 1998)2. The arguments for expanding measurement in healthcare include the need to reduce unwanted variations in care delivery and facilitating research that can identify in/effective care processes and interventions. These arguments are quite established and I will in this section rather devote space to the nascent but growing interdisciplinary scholarship dealing with the unintended and negative consequences that quantification efforts may be associated with, as this diverse “quantification literature” provides several important inroads to the present project. Building on insights from sociology, political science, philosophy and critical accounting, the quantification literature is clear about the importance of understanding numbers as more than neutral recordings of reality (e.g. Bowker & Star 1999; Carruthers & Espeland 1991; Desrosieres 1998; Espeland & Stevens 1998; 2009; Feldman & March 1981; Hacking 1992; Kalthoff 2005; Miller, 1994; Porter 1995; Scissons 2002). While this assumption appears obvious, it is worth articulating as many measurement efforts in organizations and society are based on a paradoxical view of numbers as natural descriptions of the world AND as vehicles for inducing changes in performances (Elg 2007; Sauder & Espeland 2009). The Swedish healthcare debate, for example, refers to measures both as evidence in research and as tools for incentivizing behavioral changes in clinical practice. The question is: is it reasonable to expect a sharp temporal break between the act of measurement and the reactivity that follows measurement? The quantification literature answers no to this question, suggesting instead that the mere existence of a measurement platform and the act of describing reality in certain numerical terms may impact individuals’ behavior, including how they make sense of situations and what they choose to do. This has been discussed in terms of the reactivity of measures. Reactivity is typically viewed as a methodological problem, as it refers to the mechanism through which a measure “modifies the phenomenon under study, which changes the very thing that one is trying to measure” (Campbell 1957: 298). Hence, reactivity blurs the distinction between the act of measuring and its object, which “contaminates” results. Quantification scholars such as Sauder & Espeland (2009) however view reactivity as an inevitable result of human reflexivity and they emphasize the need for investigating 1) what and how mechanisms shape the reactivity of measures and 2) the effects of such reactivity. The limited existing work on this topic has identified self-fulfilling prophecies as one mechanism shaping the reactivity of measures. This mechanism operates by confirming the expectations or predictions that are embedded in measures, which in turn reinforces the validity of the measures. For example, Jennings (2010) illustrate how schools with bad quality scores (low rankings) actually became inferior to other schools in the study as the low rankings led to a reduced number of student and teacher applicants (cf. Booher-Jennings 2005). Another mechanism identified by scholars is commensuration, which refers to the act of making disparate entities comparable through a shared metric (Espeland & Stevens 1998; 2009). Commensuration unites objects because all entities measured bear a common relationship to each other, but it also distinguishes by creating hierarchal relationships and by creating new or reinforcing old categories, which makes it a powerful mechanism that can change individuals’ cognition. For example, in Sauder & Espeland (2009), the creation of categories such as average vs. outlier and high vs. low according to a single scale in the school

2 The pro-measurement healthcare literature includes a critique against today’s systems, which focus on process (guideline compliance) and mainly include medical aspects of care. To address these flaws, scholars suggest an addition of new measures that reflect the health outcome of care and that are patient-centric, e.g. patient reported measures of subjective wellbeing and health related quality of life (Dolan et al 2009; Lee et al. 2011). Some studies further emphasize that the implication of measures depend on how they are used (Øvretveit 1993; Thor et al 2007) and warn against using the ‘wrong’ measures in the ‘wrong’ way (Lilford et al 2004; Ryan & Blustein 2012; Webb 2011). The basic assumption in these studies, however, is that measurement is desirable and the main focus is on how measurement could be developed.

setting translated into definitions and values such as good vs. bad, over- vs under-achiever and normal vs. deviant, thus ultimately leading to moral implications. These mechanisms are highly relevant to explore in the healthcare setting given the attenuated efforts to evaluate, compare (rank) and even reward care providers financially based on their measurable results in order to reduce unwanted differences (e.g. Blumenthal 2012; Lee et al. 2011; Porter & Teisberg 2006). The risk of opposite effects such as fortified differences between high- and low-performers warrant more attention. Further, while making aspects commensurable is the point of measuring health outcomes in care, there is little academic discussion about how numerical comparisons may have negative implications by concealing important non-measurable differences or exaggerating unimportant but measurable differences (noteworthy exceptions in the health care literature include Ingelfinger 1973; May 1985; Narins et al 2005; Lee & Walter 2011). Quantification studies further suggest that mechanisms such as self-fulfilling prophecies and commensuration can generate changes in organizational behavior, including shifts in procedures and resource allocation as well as manipulative strategies. For example, studies of educational settings illustrate that organizations, especially those on the cusp of tiers, may react to measures by increasingly investing resources in aspects being measured (even if other aspects are considered more important and in line with the organization’s self-defined mission). Educational studies also highlight that the implementation of measures can generate gaming strategies in which individuals manipulate the numbers to manage their ‘quantitative’ appearance rather than the reality that the measures are intended to depict (Booher-Jennings, 2005; Espeland & Sauder 2009; Jennings, 2010). Of course, many initiatives in healthcare are, again, implemented with the ambition to make care providers ‘react’ to measures by making changes in resource allocation, roles and procedures. The point is that this may occur in many unintended and undesirable ways, which may have implications on the work, organization and distribution of power within and across organizations. While being a controversial issue, the risk of physicians, managers and patients ‘playing to the test’ by tweaking their documentation e.g. to improve their image is further important to bring to the fore and empirically investigate. Summing up, the present project draws on the quantification literature and its emphasis of the risks mentioned above. Of particular importance in this context is the alleged tendency of measures to impose a single norm of excellence, which, according to the quantification literature, tends to reduce organizations’ chances of tolerating heterogeneous goals and a plurality of values (e.g. Espeland & Stevens 2009), that is, un undesirable reduction of variability. The present project however also seeks to extend the quantification literature. For one, the mechanisms that shape the reactivity of measures and the implications they may generate are related to the origins, purposes and work behind the implementation of measures, and the authority the measures are ascribed in situ. Obviously, many the recent critical studies mentioned above draw on empirical cases where external parties have imposed measures, and settings in which the producers of numbers are not the primary users of numbers. The need for more empirical research is hence warranted and repeatedly asked for by quantification scholars (e.g. Elg 2007; Espeland & Stevens 2009; Sauder & Espeland 2009). Perhaps more importantly, new theoretical perspectives are needed in order to advance our understanding of the potential role and implications of measures in work and organization. Today’s discussion about measures is polarized, with ‘critical’ quantification scholars on one side, and optimistic performance measurement enthusiasts on the other. Both sides however assume that achieving universality implies an erasure of local variety and that measures contain a form of mechanical objectivity, which is opposed to objectivity grounded in expert opinion. For example, critical authors such as Porter (1995) argue that the calculative authority grounded in quantification is impersonal and constraining, thereby limiting discretion and replacing “trust in persons with trust in numbers” (Espeland & Stevens 2009: 420). Measurement enthusiasts frame this as the potential of measures to balance the undesirable variation, the unexamined reliance on professional judgment and risk for bias in today’s care delivery systems (e.g. Porter 2012). While using different terms, both sides hence reproduce the incommensurability and the disjoint origins of the measurable and the non-measurable, the universal and the local, the stable and the unstable.

The present project seeks to transcend this polarization by viewing the measurable (universal, formal, explicit) and the non-measurable (local, informal, tacit, implicit) as co-existing and mutually dependent. As argued by Berg & Timmermanns (2000), they are self-reproducing dualities: the formal has its Other, the informal, which it simultaneously creates, rejects and incorporates. From this view, the infusion of an order does not replace a preexisting disorder. Rather, with the production of an order, a corresponding disorder comes into being. Similar arguments are forwarded in studies of the enactment of technology-based protocols in practices and routines, where authors highlight how a technology never works purely according to the ideologies of technological rationality: messy, real-time work is a prerequisite for any technology to persist (see e.g. Orlikowski (2000); Feldman & Pentland (2003); Suchman (1987)). The present project will draw particularly on the conceptualization of Pentland & Feldman (2005), which suggests that artefacts such as measures can only serve as a template for behavior. Artefacts always interact with the ostensive aspect of the practice in question (how the practice is described by participants, which in turn is influenced by the norms and values of their practice) and, most importantly, the performative aspect of the practice (how it is enacted in day-do-day situations). Pentland and Feldman highlight the emergent and unexpected engagements with artefacts in action, emphasizing that agency is distributed across the different dimensions of any practice as, for example, artifacts not only shape but is also shaped by actual performances. That is, the interaction between the different dimensions of the practice is important. Drawing on this conceptualization, the present project will be able to empirically investigate several of the one-sided claims made in the optimistic as well as critical literature about measurement in organizations. For example, the project will take seriously the risk that measures may lead to an elimination of expert discretion and a reduction of variability in the work and organization of care, but it will also be open to other possibilities, assuming that measures can change the location and expression of agency and discretion in ways that increase variability, depending on how users enact the measures in daily practice. Further, the project will not assume that numbers, per definition, will assume a superior authority and thus discipline will in far reaching ways. It will also try to observe when and how measures encourage users to think critically about the ontological status of numbers and thus lead to a questioning of the authority of numbers. In this way, the project will provide insights about the interplay between the authority in different forms of knowledge, and about the relationship between the formal and informal, the universal and the local, and the stable and the unstable in work and organization. Hopefully, this will open up worlds with more axes than merely “more” or “less” rationalized and standardized, which is a needed step toward a more sober analysis and judgment of the infusion of measurement and other forms of ‘order’ in healthcare, organizations and society in general. Study Design, Method, Time plan The project will focus empirically on Swedish Rheumatology (theoretical sampling, Mason 2002), a setting with long history of creating and using numerical measures in the Swedish Rheumatology Quality registry (SRQR). SRQR was initiated 1995 by practitioners seeking to create a research database. However, external actors are increasingly using the numbers to compare care delivery at national level. (Hence, there has been a shift from quantification employed to describe relations and make predictions, to quantification used to judge and control these relations, a small but fatal one according to Foucault, 1977). SRQR includes numerical data about more than 40 000 patients and it is today one of the most advanced quality registries in Sweden in terms of the range of services offered to its users (enabling patients, professionals, managers, policy makers and the life-science industry to produce and analyze numerical data in various ways). The present project departs from questions raised by a pre-study performed in 2010-2011. The pre-study3 focused on the historical development 3 The prestudy included: A) 65 interviews with rheumatologists, patients, rep. from the life science/consulting industry concerning their perception of the transformation of rheumatologist practice during the last decades, particularly in relation to external developments and the creation and use of SRQR. B) Documentation (> 200 pages in total): protocols from registry steering committee meetings 1995-2010, press, applications and awards related to the registry. Concerning: the development of SRQR and its recognition. C) SRQR aggregated data about the growing number of patients included in the registry since its start, and performance variations across Swedish regions and patient populations (health outcomes produced).

of SRQR and found that various micro- and macro-level forces have shaped the ongoing re-invention of this IT-based measurement platform since 1995. What was indicated, but beyond the scope of the pre-study, was however a great diversity in physicians’ and patients’ current use of SRQR during patient encounters. Furthermore, the expanding engagement in SRQR by external parties at county council and national level between 1992-2012 also indicated a need to study the use of data across hierarchial levels.4 Studying the production & utilization of numbers across sites and hierarchical levels. In order to generate rich data about how one measurement structure is used (performed) differently in every-day practice by patients and professionals at different sites, the present project will perform a multiple case study (Eisenhardt, 1989) including 3 Swedish rheumatologist clinics: A (at a large university hospital) B (rural clinic) & C (private practice) (within case sampling, Miles & Huberman 1994). The rationale underlying this case selection is to maximize diversity, to respond to the call to elicit conditions that produce variation in the implications of measures across organizational settings (Espeland & Stevens 2009). The use of numbers will also be studied at several hierarchical levels, as the transmission of numbers across levels may have important implications (Feldman & March 1981). The data-generation methods used per level are summarized in table 1 (see further table 2). Table 1. Methods used to study how numbers are enacted at different levels.

Micro-level Production & use of numbers in the care of individual patients. 1st year: Interviews: patients, physicians, nurses, N=50. Observations: patient-physician encounters at 3 clinics, 9 weeks total. Annual follow-up interviews.

Meso-level Production & use of numbers at clinical/hospital level. Observations: group-meetings at 3 clinics, N=24 (appr. 12 meetings 1st year, 6 meetings/year 2015-2016). Interviews: clinical/hospital managers, N=10, including annual follow-up. Documentation: reports, strategy documents. Statistics: Registry data.

Macro-level Production & use of numbers at national level. Documentation: Continuous analysis of NBHW, SALAR, Ministry of Health reports & documentation from the Swedish Rheumatology Specialist Association/ SRQR meetings. Interviews: 10 interviews with representatives from these organizations.

Interviews will initially be performed with patients, specialists, nurses and manager/s at each clinic (appr. 15-20 interviews per clinic). Interviews will provide opportunities to arrange for observations at several levels: of patient encounters (involving 2 selected physicians and their encounters with different patients, appr 3 weeks per clinic); of strategy meetings at each clinic and if possible, at hospital level (appr. 6/clinic/year). Protocols from such meetings and other strategy documents will also be gathered. This will be repeated over time, however less extensively the two last rounds (see table 2). Finally, quantitative data (from SRQR) about each clinic will be gathered (first and last year to trace quantitative performance development, the relative position of the clinics in national comparisons and the quantitative volume of data produced. Interviews, observations and documentation generation will be semi-structured (Lofland et al. 2006; Spradley 1979) and will concentrate on the production, view and usage of numbers at different organizational levels. For instance, data generation will focus onM); 2) post-hoc usage of numerical data in decision making at clinical/hospital management level (to define the current situation, to position the clinic in relation to other clinics, to identify appropriate measures regarding resource allocation, the organizational structure including authority structures, responsibilities, rewards etc). Areas of interest will include: in what situations do the numbers appear to: a) dominate, having more authority than e.g. verbal patient/professional narratives; b) serve as a springboard to multi-faceted discussions about issues other than those measured; c) be subject to revision; and d) be ignored or rejected? Further, in what situations are efforts to manipulate the data without addressing the

4 Several Swedish scholars have critically studied the drive forces behind and carriers of the shift towards transparency in Swedish healthcare e.g. (Bejerot & Hasselblad, 2011; Blomgren 2007; Blomgren & Waks 2011; Jonnergård & Erlingsdóttir 2012; Sahlin-Andersson 2006). Less empirical studies have however focused on the actual production & use of numbers, at different levels, in situ, and the implications this may have on variability in care.

underlying condition observable/or at risk? I am aware that studying the use of numbers at the three clinics will be a sensitive issue and, in line with the ethnographic tradition (Lofland et al. 2006), I will make efforts to get to know the people at each clinic (some of which I have already met and introduced myself to) to create a relaxed atmosphere. I will inevitably take part in the production of meaning in e.g. interviews, and in general, I see the study as a collaborative project between the participants and me. I will be sensitive to their interests and to what questions they see as relevant, and regularly arrange for workshops where we can discuss emerging findings. I will also invite participants to co-author trade press articles with me. To understand how numbers are understood and used by external actors at national and county council levels, interviews (approx. 10) with relevant representatives from county councils, Ministry of Health & Social Affairs, National Board of Health & Welfare (NBHW) & Swedish Association of Local Authorities & Regions (SALAR) will be performed in 2013 (see table 2, note selective follow-up interviews in 2016) and relevant documentation produced by these organizations (eg reports, national evaluations) will be continuously gathered. Finally, I will continuously follow debates about quality registries in documentation from meetings held by the Swedish Rheumatology Association/SRQR adminstration, in medical trade press (the Journal of the Medical Association, Dagens Medicin) and in the emailing list CFAM (focusing on primary care physicians, however including a vivid debate about measurement and indicators). This will provide access to marginal views and resistance that may be concealed in other forums. In summary, the method is designed to enable a study of the production, use & implications of numbers across hierarchical levels and over time. Triangulation of different data sources will generate a broad understanding of how the view and use of numbers relate to e.g. conditions at the clinic, the user’s position, changes in the numerical platform, policy changes5, or changes in knowledge culture. The data-analysis process will be iterative (abductive, Alvesson & Sköldberg, 2008; Miles & Huberman 1994), consisting of a continuous inductive empirical theme analysis in parallel with the consultation of relevant theory. Hence, the project will generate context-dependent knowledge, which cannot be automatically be generalized. However, by conceptualizing the mechanisms identified and relating them to previous theory, the project will be able to confirm or disconfirm widely held assumptions about the role of measures in the literature, and provide new perspectives on the way through which measures may have implications on the variability of care delivery. (Flyvbjerg 2011). Relevance. The quantification literature calls for research about the reactivity of numbers given the increasing pressures to measure and compare in today’s society (e.g. Sauder & Espeland 2009). The Swedish healthcare setting is a pertinent empirical example of this, as it is characterized by extensive research & development efforts focusing on new reimbursement approaches and care models that assume extensive measurement (e.g. Krohwinkel Karlsson & Winberg 2012; Witell & Elg 2007), while implicitly bracketing the issues related to the work behind measurement. The increasing number of commentaries that warn about the potential dangers of expanding the use of measures however suggest that there is an emerging resistance towards this development (e.g. Canivet & Bengtsson 2011; Järhult 2011). There is hence a need for the present empirical project, which takes one step back, investigating the actual use of numbers in daily practice and highlighting the promises and perils that certain uses of measurement may bring about. The project will furthermore contribute to the debate about healthcare working conditions, which is today characterized by blurred distinctions between ‘measurement’, ‘administration’, ‘information technology’ and ‘managerialism’. While these terms are often discussed as threats towards professional health and freedom (Hansen et al. 2008; Persson & Anell 2000; Scott et al 2000), the present project will ask: when are measures are perceived as a resource and administrative burden respectively? Does this change when measures are aggregated and transmitted across levels? Further, do users at higher hierarchical levels see numbers as factual and objective, while the production of the numbers at operational level may involve uncertainty, ambiguity, subjective priorities, trade-offs and manipulation? In general: to what kind of culture does an increasing measurement 5 SRQR will change during the study period, as new versions are launched every year. Furthermore, several changes in national health care structures are planned: the deregulation of rheumatologist care (Patient Choice) and new reimbursement models.

focus contribute? A culture where the analyses of numbers occur at the expense of other ways of knowing, or where workers learn to be mindful of the limitations of all representations, including numerical models? The project will begin to answer these and other questions, thus providing knowledge about several issues that should interest FAS: i) The competence and behavior (e.g. secrecy, learning, innovation, competition, collaboration, critical thinking) required and encouraged by the use of measures; ii) how measures contribute to new ways of organizing work and to the reinforcement of old/development of new roles (among care employees, consumers, purchasers) (Work Organization);

iii) The influence of measures on participants’ definition of their professional responsibility and role (Labor Market);

iv) The use of measures in relation to participants’ perceived flexibility, stress and happiness at work (Work & Health).

v) All of the above helps us think broadly about the effectiveness of the healthcare and the different kinds of health and healthy behaviors (measurable and non-measurable) that certain measurement structures can contribute to (Public Health).

Table 2 Time plan. Documentation in daily and trade press will be generated continuously.

Spring 2013 Preparing data generation, reviewing pre-study material & relevant literature. Autumn 2013 Data generation (1): Interviews, observations at the 3 clinics, interviews with external

actors, documentation generation. Data analysis (A) in parallel with data generation: identifying recurrent issues in need of further exploration (nVivo). Presenting article ideas at meetings/seminars (see collaborators) & communicating results to practitioners, patients and politicians at arranged workshops.

Spring 2014 Data analysis (B): Creating narratives and timelines for each case. Identifying commonalities and differences between and within the three cases (cross-case analysis). Cross-checking findings through triangulation of data sources. Discussing preliminary findings with participating clinics. Iterating between identifying emerging themes and developing theoretical frameworks. Drafting articles together with collaborators.

Autumn 2014 Extended analysis and writing up results, submitting articles to peer-reviewed journals, reporting of results to practitioners and politicians. Collaborating with partners to compare settings.

Spring 2015 Data-generation (2): follow-up interviews & observations at the 3 clinics to see development over time.

Autumn 2015 Data analysis (C): same as Autumn 2014, including analysis of survey-results. Spring 2016 Data-generation (3) follow-up observations and interviews at the 3 clinics and follow-

up interviews with relevant external actors. Autumn 2016 Same as Autumn 2014 (resubmitting articles). Integrating results in value-based care

curricula at KI and management/accounting courses at SU. Publishing results in Swedish trade press (Läkartidningen, Dagens Medicin) & debate articles in daily press (SVD, DN).

COLLABORATORS. The project has several collaborators in Sweden and abroad. (1) Practitioners: P2I Care – a research team at Karolinska Institutet developing a generic platform for Swedish registers and participating clinics. The Institute will provide facts and is a powerful vehicle enabling me

to communicate the results to practitioners and decision makers beyond participants (2) Peers studying quantification: Michael Sauder, associate sociology professor at Harvard School of Public Health, has extensive experience of studying quantification in health care and education (e.g. Sauder & Espeland 2009). At an initial meeting held in January 2012, we set up a preliminary agenda for joint comparison of empirical material and co-write papers, enabling cross-cultural differences/similarities to emerge. Studies in the Consumption of Accounting (SICA) project, Stockholm University, accounting section. SICA’s seminars will provide important feedback on my work in progress (3) The project Operationalizing Value in Rheumatology (OVR). Participants: Harvard Business School, ISC (Michael Porter and team); Karolinska Institutet (Rheumatologists, Patients, Epidemiologists). OVR will provide information about measurement development in chronic care. I will feed a critical view on measurement into this practice-oriented project. (4)The programme Management Innovation Practices (MIP). Stockholm University, Management & Organization. MIP studies management practices (quantification can be viewed as one such practice). I will participate at weekly seminars and co-write papers with members of the program. Senior advisors who have agreed to provide feedback on work in progress: Saskia Bengtsson (Primary care physician, Jönköping CC), engaged in the public debate about measurement in Sweden (see ref list); Kathryn Mc Donald (Professor, Stanford School of Medicine, leads “the Indicator project” at the dept of Health Policy) with whom I plan to meet at an initial seminar in May 2013; Hans Winberg (General secretary, Leading healthcare, Handelshögskolan Stockholm) who has invited me to participate in the LHC:s network of researchers; Eva Bejerot (Professor, Psychology, Stockholm University) has previously studied quality registries (see reference list)). Ethics. Ethical approval will be sought (e.g. for observing patient-physician encounters, and retrieving registry data). References Alvesson M & Sköldberg K (2008) Tolkning och reflektion. Vetenskapsfilosofi och kvalitativ metod. 2:a upplagan. Studentlitteratur. BCG (2010) From Concept to Reality: Putting value-based care into practice in Sweden, Report Boston Consulting Group, available at http://www.bcg.dk/documents/file64538.pdf Behn RD (2003) Why Measure performance? Different Purposes Require Different Measures. Public Administration Review, 63 (5): 586-606. Bejerot E & Hasselblad H (2011) Professional autonomy and pastoral power: the transformation of quality registers in Swedish Healthcare. Public Administration Vol. 89 (4):1604–1621. Bengtsson S (2011) Stoppa avväpningen av vårdens professioner, Dagens medicin, 03-11. Berg M & Timmermans S (2000) Order and Their Others: On the Constitution of Universalities in Medical Work, Configurations 8 (1): 31–61. Blomgren M & Waks C (2011) Öppna jämförelser inom hälso- & sjukvården: En utmaning mot den professionella byråkratin?, Arbetsmarknad & Arbetsliv, vol. 17 (4): 95-108. Blumenthal D (2012) Perspective Performance Improvement in Health Care — Seizing the Moment, New England Journal of Medicine. April 25, 2012 (10.1056/NEJMp1203427) Booher-Jennings J (2005) Below the Bubble: “Educational Triage” and the Texas Accountability System. American Educational Research Journal 42: 231-268. Bourdieu P (1990) The Logic of Practice, Tr. R. Nice, Stanford: Stanford University Press. Bowker GC & Star SL (1999). Sorting Things out – Classifications and its Consequences. London: MIT Press. Campbell DT (1957) Factors relevant to the validity of experiments in social settings. Psychological Bulletin, Vol 54(4), Jul 1957, 297-312 Canivet C & Bengtsson S (2011) Kvalitetsregistren passar inte våra patienter, Dagens samhälle: 13 oktober. Carruthers BG & Espeland WN (1991) Accounting for rationality: double-entry bookkeeping and the rhetoric of economic rationality. Am J Soc 91: 31-96. Desrosieres A (1998) The Politics of Large Numbers: A History of Statistical Reasoning. Cambridge, Mass, Harvard University Press. Desrosieres A (2001) How ‘Real’ Are Statistics? Four Possible Attitudes, Social Reseach 68, 339-355. Dolan P, Lee H, King D, Metcalfe R (2009) Valuing health directly. BMJ 2009; 339: b2577.

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