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Accounting for accountable care: Value-based population health management

Linda F Hogle Department of Medical History & Bioethics, University of Wisconsin-Madison, Madison, WI, USA

Abstract Accountable Care Organizations (ACOs) are exemplars of so-called value-based care in the US. In this model, healthcare providers bear the financial risk of their patients’ health outcomes: ACOs are rewarded for meeting specific quality and cost-efficiency benchmarks, or penalized if improvements are not demonstrated. While the aim is to make providers more accountable to payers and patients, this is a sea-change in payment and delivery systems, requiring new infrastructures and practices. To manage risk, ACOs employ data-intensive sourcing and big data analytics to identify individuals within their populations and sort them using novel categories, which are then utilized to tailor interventions. The article uses an STS lens to analyze the assemblage involved in the enactment of population health management through practices of data collection, the creation of new metrics and tools for analysis, and novel ways of sorting individuals within populations. The processes and practices of implementing accountability technologies thus produce particular kinds of knowledge and reshape concepts of accountability and care. In the process, account-giving becomes as much a procedural ritual of verification as an accounting for health outcomes.

Keywords Affordable Care Act, big data, dataveillance, population health, risk, US healthcare

This article concerns the way populations are constructed through the processes of dataveillance and within the set of institutional relations designed to produce value and accountability. Value-based care (VBC), defined as health outcomes achieved per dol- lar spent, is becoming a widely embraced policy strategy to contain healthcare costs while improving patients’ care experience (Porter, 2010; Porter and Teisberg, 2006).

Correspondence to: Linda F Hogle, Department of Medical History & Bioethics, University of Wisconsin-Madison, 1135 Medical Sciences Building, 1300 University Avenue, Madison, WI 53706, USA. Email: [email protected]

840429 SSS0010.1177/0306312719840429Social Studies of ScienceHogle research-article2019

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As one physician remarked: ‘For healthcare providers, value-based care isn’t just an operational incentive anymore, it’s an imperative for basic survival. [It is] vitally important to redesign health system services for population health’ (Michael Blackman, MD as cited in Garvin, 2016). Implicit in this new conceptual landscape is an actuarial way of thinking, involving both economic and health risk calculations when consider- ing how to achieve outcomes. Risks affect the health of individuals, populations and healthcare organizations.

To ensure that ‘value’ outcomes of improved cost and care are achieved, policy ana- lyst Elliott Fisher contended, an arrangement was needed in which providers share risks, rewards and penalties with payers. This would make providers more accountable for the outcomes of their patients, (Fisher et al., 2007). In the US, this led to a new institutional form, the Accountable Care Organization (ACO). The inherent political rationalities on which ACOs are built manage risk by compiling comprehensive dossi- ers on individuals within defined populations and redistributing accountability for their health outcomes. The complex notion of accountability in contemporary American medicine is situated within a particular historical, political and sociotechnical moment with assemblages of technologies, concepts and practices that constitute value-based care. It takes a unique form in the US, with its market-based healthcare system and no guaranteed access to care.1

My central argument is that accountability has become a foil through which systems of managing population health become entangled with particular concepts of value and risk, in ways that are consequential for population health and clinical care. ACOs consist of particular kinds of virtual populations to be managed with targeted interventions. This relies on the ability to make visible individuals who may be harbingers of risk and reas- sembling them into unique categories. Yet this rests on certain assumptions about what characteristics constitute current and future risk and how best to ameliorate it.

The administrative and algorithmic techniques to classify individuals and sort them into groups will determine who may receive what kind of care – something that is very much at stake in current political efforts to dismantle certain patient protections provided by recent health law. In the process, relations among providers, payers and patients are also reordered: Basing care on monetary incentives (and penalties) for the outcomes of patients puts providers in the position of arbitrating financial and health risks, blurring their role with that of insurers. Boundaries between clinical care and public health are blurred as population health management becomes a matter of data-intensive sourcing about individuals in their everyday lives, and individuals are viewed as risk objects in relation to others within unconventionally defined populations (Jacobson and Dahlen, 2016). At the same time, infrastructures established to document accountability facilitate data-intensive sourcing of personal health information for broader purposes of data col- lection beyond healthcare.

My analysis contributes to STS by bringing together perspectives from valuation studies and the social study of risk practices to consider the complexities of crossed economic domains and changing legal and financial practices (Birch, 2017; Dussauge et al., 2015; Power, 2007, 2016). Hilgartner (1992) distinguishes objects of risk (peo- ple or things potentially experiencing harm) and risk objects (potential causes of harm). From a value-based perspective, patients are both, since their health risks may also be

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financial risks to the ACO. The ability of ACOs to identify potential sources of risk is thus critical.

I use the analytical frame of assemblage – that is, the arrangements of practices, tech- nologies, and theories that configure action in a sociotechnical space – to analyze inter- actions shaping the way accountability is manifested in particular institutional forms in the US (Law and Urry, 2004; Ruppert, 2011). This enables a broader view of value-based healthcare concepts and activities not only in relation to each other, but also in relation to activities and structures that pre-figured the current situation, plus phenomena in other social domains, such as increased dataveillance in consumer and finance domains. The assemblage includes the big data analytics, changing health information technology (HIT) infrastructures, novel cost accounting techniques, historical and political policy contexts, and intensified public health focus on social and behavioral influences on health as much as genomics. VBC in the US would likely not have existed in its current form without the interaction among these parts, and within the context of a market-based healthcare system.

Relations in an assemblage are dynamic: Actants may enter or depart, laws may be enacted or repealed, benchmarks may move, and measurement tools may be adapted by local users. I show how interactions in such an unsettled (and potentially unsettling) assemblage enact concepts such as ‘accountable care’ and ‘population health manage- ment’ through practices such as data-intensive sourcing (Hoeyer, 2016; Kitchin and Lauriault, 2014). At the same time, population databases created for accountable care materialize particular kinds of subjects within framings of future risk and value. Showing how a particular American version of accountability came to be manifested as algorith- mic risk sorting of defined populations, I address a fundamental STS question of how new forms of knowledge and models of social control develop together.

Managing population health entails acquiring much more data about patients, of many more types, collected and analyzed in new ways. Such intensified data sourcing includes getting data from sources beyond that which is usually considered to be ‘health-related’ (Hoeyer, 2016; Hogle, 2016a; Van Dijk, 2014). As I show, information about individuals ‘in the wild’, rather than in experimental or treatment spaces in the clinic, is used to identify, characterize, and intervene in individuals’ health with care coordination, pre- vention efforts aimed at behaviors, and more.

At the same time, as value-based partnerships, ACOs have to prove they are provid- ing care that has relative worth according to federally-set benchmarks that measure both quality and cost-efficiency. To do this requires metrics that order data in particular ways to demonstrate improved outcomes with which to support their claims to shared sav- ings. Yet ‘data are not simply “collected”, but are the result of multiple sociotechnical arrangements of technological and human actors that configure agency and action’ (Ruppert, 2011: 7). It is important, then, to attend to the practices of collecting, measur- ing, analyzing, sorting and representing data in interaction with value-based payment and reporting structures. Metrics for measuring outcomes are performative, not only in terms of generating potential interventions with selected members of populations, but also in terms of the institutional forms, work practices, and meanings that emerge; in particular, they perform understandings of what comes to count as ‘population health’, ‘health risk’, and ‘accountability’. The vision may have been to deliver the so-called

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‘Triple Aim’ goals – to improve population health and individuals’ care experience while reducing per capita costs (Berwick et al., 2008) – but as I show, the way account- ability practices are being enacted has other effects.

Data analysts are going further than collecting genomic or clinical data to create risk classifications, using big data analytics to associate social variables with health, deriving new categories such as ‘superutilizer’, ‘nonadherent’, ‘socially isolated’, or ‘aging- focused household’. Here the STS literature on classifications is helpful (Bowker and Star, 1999). Social classifications take on meanings that arise through interactions of scientific, administrative and popular definitions, and change the way individuals experi- ence themselves (Hacking, 2006). In the ACO case, patients may be unaware of how they have been classified (or that they are being classified), but the sorting may nonethe- less be consequential for their care (Pasquale, 2014; Solove, 2004). In a time of national policy precarity, practices of measuring and sorting risk thus become a key focus for study. In VBC, the locus of responsibility becomes more fluid and interactive among the state, various kinds of public and private corporate entities, and the patients themselves. As such, it problematizes simplistic understandings of neoliberalism. While explicitly a market-based model with incentivized competition at its core, responsibility and account- ability are more complex in the new models and need to be examined.

This article proceeds in two parts. First, I provide background on the US system, reviewing current and emerging financial and legal infrastructures and including key laws marrying healthcare payment and delivery with information technologies. These prefigurations shape the form that accountability takes in the US in distinct ways. Second, I analyze emerging practices to produce accountability using data-intensive sourcing. As I show, the question of to whom ACOs are accountable and for what purposes is debatable.

Methods

This article overviews policy accountability practices in process. I do not include activi- ties or responses of patients, although this is an area ripe for STS analysis (cf. Lupton and Michael, 2017). Rather, I focus on providers and payers, the focus of value-based practice changes. My data come from document analysis of policies and recommenda- tions from expert governmental and nongovernmental policy advisory bodies (includ- ing the Institute of Medicine [IOM], Centers for Medicare & Medicaid Innovation Center, among others), laws (the ACA, the Health Information Technology for Economic and Clinical Health Act, and other relevant laws) to situate the emergence of ACOs politically and historically. Third-party white papers plus promotional literature from analytics companies and VBC consultancies, and reports from ACOs shed light on how concepts of accountability are framed by various actors. I also interviewed representa- tives of data analytics companies, and practicing professionals in health informatics, public health and hospital administration, primarily at clinical medical informatics con- ferences and workshops between 2015–2017. Population health management and patient stratification tools (both commercially available and those designed in-house by providers) were demonstrated at these meetings, using actual patient and provider data. Patient identifiers were masked, but patient-specific and provider-specific scores and

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comparisons were demonstrated. My observations illuminate the way accountability is being interpreted and assumptions are being built in to concepts about financial and health risk, and ultimately, into products that will be used to characterize individuals and stratify populations.

Background: Conditions of possibility for value-based care

Value-based care concepts are being introduced into many existing healthcare systems, but each is historically and politically distinct. There are different stakes for providers, payers, and patients. I therefore begin by reviewing salient features of the US system.

Roots of the cost-quality-outcomes conundrum

Most significantly, the US consistently has among the highest costs of care in the world, yet worse health indicators than many other countries: the US ranks 50th out of 55 coun- tries based on cost per person, longevity and health indices (Du and Lu, 2016; Office of Economic Co-Operation and Development (OECD), 2017). Reform efforts since the sec- ond half of the 20th century attempted to remedy this conundrum, but have been thwarted by the fact that without a single-payer system, millions of Americans do not have insur- ance or are not covered by federal plans, and care is paid on a fee-for-service basis through many providers operating under various kinds of contracts.

Under fee-for-service, payers (typically government or private insurers) pay, based on multiple negotiated rates, for healthcare services when ordered by clinicians. The para- dox of this volume-based model is that patients who are more sick and who are using more services bring in more revenue for providers. As a result, there is overuse of some services, especially those with higher revenue margins, while there is under-use by those who cannot afford certain procedures or medications. Providers are left to pay for expended services for uninsured or under-insured patients.

Providers have little incentive to contain costs, since their income comes from ser- vices. Payers, on the other hand, are often more focused on containing cost than improv- ing quality. Payers are usually unwilling to increase their financial burden and risk, and quality initiatives may not pay off until far in the future. Private insurers experience sig- nificant churn (up to a quarter of their customers change insurers each year), so the return on investment may not be considered worth it. Policymakers saw this essential conflict as a lack of accountability on both sides, and it became the basis of policies to change pay- ment and clinical practices. Yet, previous organizational experiments to cap costs (such as Health Maintenance Organizations [HMOs]) were highly unpopular, and efforts to stand- ardize quality resulted in a proliferation of thousands of quality measures without substan- tially improving health indices (IOM, 2006, 2016). Pay-for-performance (P4P) models tried to incentivize change by paying physicians a bonus to meet or exceed performance benchmarks, but benchmarks mostly dealt with practice efficiency rather than quality, and had mixed results (Grossbart, 2006; Porter and Teisberg, 2006). In sum, efforts to improve quality or reduce cost have been neither comprehensive nor effective.

Between 2008 and 2016, attempts to devise a federally subsidized system for universal access to care, which might have helped standardized payment and quality requirements,

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met with intense resistance from insurance and medical product industries for which existing, predictable payment models were deeply entrenched, and from those who wanted to maintain private markets rather than move to a public, single payer. Ultimately the Affordable Care Act (PL 111-148, more commonly known as the ACA) was a middle (albeit potholed) road. Individuals not otherwise covered could gain access to insurance with subsidies (for some). Until the ACA, private insurers could refuse to cover individu- als they deemed to be too risky, such as those likely to require more care and incur more costs because they had pre-existing or chronic conditions, had unhealthy lifestyles, or had other characteristics associated with higher risks. Likewise, physicians could refuse to accept certain patients, such as those who had insurance with low provider reimburse- ment rates (in particular, Medicare). While the ACA guaranteed the possibility of access to health insurance, there is no guarantee of access to care: individuals can still refuse to buy insurance and physicians can still limit their patient panels. Payers can no longer refuse patients due to pre-existing conditions. Plus, of those millions of people added to insurers’ rolls, many previously had no insurance (so likely had not sought care). The result was a new mixture of patients, many of whom were sicker and about whom there was little medical history, hence a different and potentially riskier pool. As I show, payers and providers are adapting their strategies accordingly, using value-based programs to adjust formulas for this new landscape.

Most Americans buy health insurance through their employers (who partially subsi- dize the payments and either contract with insurance companies to provide coverage or take on the financial risk themselves with their own insurance plan). For older and poorer citizens, the Department of Health and Human Services (DHHS) Center for Medicare and Medicaid Services (CMS) contracts with private insurance companies to provide care. Individuals older than 65 who have worked and paid payroll taxes are covered under the Medicare program, while the poor and some disabled are covered under the Medicaid program (these are adults and children, but all must be US citizens, and being poor alone is insufficient for eligibility).2 While Medicaid was expanded by the ACA to extend insurance to low-income individuals nationally, as of 2012, states are allowed to opt out (Kaiser Family Foundation, 2018) – as of this writing, eighteen states have opted out. As a result, policies and gaps in care vary among states. Finally, about 9% of Americans are still uninsured (28.8 million) compared to 16% (48.6 million) in 2010 (Zamitti et al., 2017). Medicare patients are sicker, older, and the most costly. Payment for their care constitutes about one-third of hospital revenues under the current fee-for- service system, one-quarter of this being for in-patient hospital care. Medicare patients alone accounted for about 15% of the entire federal budget in 2015, making this program a prime target for cuts during debates over federal budget deficits. Unlike private insur- ers, who can create risk pools with which to determine differential policy rates and ben- efits for members (based on variables such as health status, age, or other factors), Medicare must pay for ‘reasonable and necessary’ care for all who qualify. Of course, the interpretation of these terms can vary in practice. The Center for Medicare & Medicaid Services (CMS) sets reimbursement rates for services, drugs and devices, which also influence the rates of private payers. Providers complain that their costs for Medicare patients are often not fully reimbursed (about 12–48% of charges). This has led to a type of gaming the system called ‘upcharging’ or ‘upcoding’ (classifying patients using more

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lucrative diagnostic billing codes to enhance cost recovery), or outright fraud (claims for services not rendered, altering medical records) (Dafny and Dranove, 2009). It is unsur- prising, then, that Medicare is a ripe target for alternative payment plans. Roughly half of ACOs operate in a Medicare Shared Savings Program (CMS- Aug 2017).

As for providers, just under a quarter of hospitals are for-profit, about 58% are private but not-for-profit and the remainder are state or locally-owned. In contrast to other coun- tries, this affects how ‘value’ is tied to revenues and practices. The institutions that per- form worse on both quality and cost metrics typically care for greater numbers of vulnerable patients, particularly elderly minority and Medicaid patients. These so-called ‘safety-net’ hospitals consistently look worse on quality and cost measures because of the complexity of cases, high costs and low revenues (Jha et al., 2011).

The upshot of diverse providers and payers for Americans is that healthcare varies widely across regions as they receive different levels of quality, cost, and comprehen- siveness of care. Employers, payers (especially CMS) and providers (especially for- profit) are intent on lowering their financial risk, particularly with the new mix of patients, and anything that lowers costs provides competitive advantage. Risk-sharing initiatives become attractive in this scenario.

It is within these historical and political-economic environments that American healthcare is transitioning to value-based care, materialized in innovations such as the ACO. The VBC concept evolved long before passage of the ACA, but serves the so- called ‘Triple Aim’ goals that were a key feature of the law. Framing best care as best value is a very different way of thinking about managing a population’s health than uni- versal care or healthcare as a human right, but fits with the market-economy basis of American healthcare. Focusing on value also decenters debates about rights to care that continue to plague American politics. At the time of writing, efforts to dismantle the law are ongoing; however, infrastructures already installed to execute value-based care will affect clinical care and public health for years to come.

The road to value-based care is a digital trail

Beyond structures for providing and paying for care, there are relevant histories of laws, policies, technologies and assumptions about quality paired with legislative require- ments to embed information technologies bringing cost and quality together, rebranded as ‘value’ (for a more thorough discussion, see Hogle, 2016b).

In the 1990s, quality care became a priority in the US not only due to poor national indices, but also because of heightened concerns about medical error and institutional liability. Yet measuring ‘quality’ is tricky because the term itself is ambiguous. The most frequently cited definition of quality is: ‘the degree to which health services for individu- als and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge’ (IOM, 1990). This vague definition allowed con- siderable leeway to institute a plethora of policies, incorporating different values regard- ing risk under different political and social environments.3

By 2007, recommendations from the IOM (2007) to standardize quality measures were accompanied by calls for the expanded use of electronic health records (eHR) and for more data, with which to provide evidence of outcomes, to be collected in each

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clinical encounter.4 The US had been slower than other countries to take up eHR. However, promoters of the rapidly-evolving healthcare information technology (HIT) field argued that electronic data capture was crucial to facilitate the transfer of patient data across clinics, compare treatments or physician practices for the purpose of evi- dence-based medicine, and to conduct operations or outcomes research (IOM, 2011).

Subsequently, policymakers in President Bush’s administration flagged electronic medical records as a critical national infrastructure need. By 2009, the Health Information Technology for Economic and Clinical Health (HITECH) Act was passed, requiring pro- viders to adopt HIT to achieve ‘meaningful use’ of health information. Providers must prove that they are using certified eHR to communicate electronically with patients and other providers in ways that can be quantitatively measured. The stated aim was to enhance data exchange for purposes of care coordination, population health management and consumer engagement. In 2018, ‘meaningful use’ was rebranded as ‘promoting interoperability’, to underscore data sharing. New scoring and measurement policies were proposed as a condition for participation in Medicare, with penalties for not sharing data. This directly links to the 21st Century Cures Act of 2016 (dubbed the ‘Cures Act’), which penalizes data blocking and mandates open application program interfaces (APIs, the means through which software applications can interact).5 Together, provisions in the HITECH and Cures Acts laid the infrastructural foundation for facilitating exchange of data about patients among providers, payers, but also third parties, including data aggre- gators and analytics companies.

It was also during President Bush’s tenure (2001–09) that healthcare policymakers began arguing that care should be ‘value-based’: services should have worth relative to outcomes, not based on cost-cutting alone or simply adding more quality measures. This occurred in an era of political efforts to ‘downsize government’ and contain federal costs for welfare and social programs. The trifecta of information technology and data analyt- ics, a renewed emphasis on quantifiable quality measures, and cost containment pres- sures in this political climate came together at the starting block in the race to healthcare reform in 2010.

To ensure that value-based principles would be put into practice, the ACA built in measures to hold providers accountable for improving patient health outcomes as a con- dition of receiving payment. Additionally, the Medicare Access and Childrens’ Health Insurance Program Reauthorization Act of 2015 (MACRA) specified alternative pay- ment models for Medicare.6 The models offered providers financial incentives if they could demonstrate improvements in their patients’ outcomes and in cost-efficiency.7 MACRA further stipulated that to get incentives providers must also abide by the mean- ingful use provisions of the HITECH Act, which, as described above, expanded elec- tronic data use and sharing. The CMS goal was to convert 50% of providers to some form of alternate payment model by the end of 2018 – an aggressive timeline.

The reformulation of care-as-value was fully embraced by the time the report ‘Best Care at Lower Cost’ appeared (IOM, 2013). Significantly, it advocated the creation of a national, searchable database of information about individuals, real-time collection of data during each clinical encounter, and greater use of genomics and social indicators. To deal with such complex, large datasets, advocates heralded the nascent field of big data analytics in many policy reports (Manyika et al., 2011). Notably, the report was authored

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by the Roundtable for Evidence-Based Medicine (charged with best practices in knowl- edge production), which then renamed itself the Roundtable on Value and Science-driven Healthcare. Members include health policy experts plus representatives from CMS, the Office of National Coordinator for Health IT, the pharmaceutical industry, and the head of the largest US electronic medical record firm.

To make such sweeping transformations in a system with deeply entrenched payment structures and revenue streams would take drastic measures to change the way care was paid for. As Berwick et al. (2003) put it, ‘systematic changes will not come forth quickly enough unless strong financial incentives are offered to get the attention of managers and governing boards’ (p. 8). A ‘carrot and stick’ approach was built into alternative payment systems to incentivize ‘high-value’ care, but, importantly, new models shift responsibil- ity for costs and risks. Whereas payers (public and private) previously shouldered most of the financial risk of patients becoming or staying ill, shifting the responsibility to providers for both patients’ health risks and their own financial risk would make provid- ers bear consequences for their actions, in the value-based way of thinking. In contrast to earlier efforts to change payment and service delivery models, risk-sharing constitutes a substantial rethinking of accounting systems, organizational relationships, and expertise, entailing novel institutional forms and practices for analyzing and managing care and its co

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