Health care reform is affecting our discipline. Four prominent themes provide a window into what’s to come—Outcomes Measurement, Learning Systems, Patient-Centered Care, and Transparency. Though the full impact of health care reform is not yet clear, it is important to consider the factors that are driving change, as mapping this emerging landscape may help us to prepare for the challenges that lie ahead.


Transformation of the policy and payment framework underlying health care is being driven by the need for systems that lead to better services and better outcomes at lower costs. The National Strategy for Quality Improvement in Health Care identified these three aims for achieving economically sustainable reform: better care, healthy people/healthy communities, and affordable care (U.S. Department of Health and Human Services, 2012). Health care costs have been rising more precipitously than inflation and population growth since the inception of Medicare in 1963 (Cai, 2013). Indeed, the United States spends more on health care per capita than any other nation ($8,233 per person in 2010; OECD, 2012). Yet, the United States ranks poorly on quality indicators of health care when compared to other high-income nations (The Commonwealth Fund, 2010). Furthermore, the provision of unnecessary services, inefficiently delivered services, excessive administrative services, missed prevention opportunities, and fraud cost the United States approximately $765 billion in 2009 (Institute of Medicine, 2011a). Accordingly, new systems and policies are being developed that are expected to incentivize quality and accountability, improve outcomes, curb fraud and waste, and ensure that members of society can afford and access needed services. To accomplish these aims, payment decisions and funding policies must be based on comprehensive and valid data that can be used to estimate the value of services. One critical concern for many health professionals is the growing expectation that, in order for them to be reimbursed, there must be evidence demonstrating that a service provides value. Evidence can be defined in many ways, and what constitutes acceptableevidence can be debated even further. Nevertheless, the need for research that advances the evidence-base and documents the value of our services could not be more critical.

Outcomes Measurement

Outcomes measurement plays a central role in health care reform (e.g., Institute of Medicine, 2010; Porter, 2010; Rogers & Mullen, 2012). Across the full spectrum of health care settings, systems are being engineered to calculate the value generated across individual, program, facility, and organizational levels. Payment is shifting from volume to value—”value-based purchasing,” such that, in future years, reimbursement will no longer be based exclusively, or perhaps at all, on the volume of services provided. Value is measured by dividing outcomes by cost. Cost can be estimated in a relatively straightforward manner based only on the short-term expenses directly related to the services provided for a specific condition during a circumscribed period (e.g., Ellis & Mauldin, 2012). However, with heightened national focus on prevention, the long-term financial consequences of quality variation in health care may be assuming a more prominent role in operationalizing cost. To calculate value, the benefits derived from a service or program must also be quantified, though it is often less clear how to measure outcomes.

Outcomes can include highly objective measures of binary phenomena (e.g., mortality) and comparisons of pre- and post treatment measures (e.g., laryngoscopic evaluation of nodules, performance measures of articulatory, lexical or grammatical accuracy), as well as scores that can be compared to normative data from standardized tests. Outcomes can also be based on perceptual judgments, such as global ratings of severity as judged by practitioners and patient-reported ratings of their own activity limitations, participation restrictions, or experience of disability. Regardless of approach, it is methodologically challenging to develop valid and clinically meaningful outcomes measures that (a) are sensitive to change across the severity continuum, (b) can be used reliably across settings, (c) minimize response burden, and (d) measure something that people in the population of interest notice and care about (e.g., PCORI Methodology Standard RQ-6, 2012). But, ultimately, outcomes measurement can only support quality improvement initiatives if the data can be amassed in a centralized repository (e.g., clinical registry, electronic medical record, outcomes database).

Learning Systems

While progress is being made to advance our understanding of what works best for whom under which circumstances, no discipline will be able to fully address this multifaceted question without large-scale databases that function as learning systems (Rogers & Mullen, 2012). With learning systems, data are collected as part of the service delivery enterprise and thus can provide the basis for continuous quality improvement initiatives in clinical settings. Due to the many factors that influence outcomes, very large sample sizes are required to determine the effects that patient and service delivery factors have on outcomes. One cannot meaningfully address what works best for whom under which circumstances by examining outcomes in isolation; rather, analyses must take into account contextual factors about the person receiving services and about the nature of the services provided. Thus, it is important that such databases include information about the person—”case-mix” data (e.g., patient characteristics such as health conditions, severity, demographic information)—and about the nature of the services delivered (e.g., service delivery context, type, intensity, and dosage of treatment). Because patient factors mediate the effects of services on outcomes, case-mix adjusted data are necessary to compare the effectiveness of different interventions and to model the benefit of providing specific services to specific populations. With large-scale databases, adequately powered analyses can be conducted to identify the effects that case-mix and service delivery factors have on outcomes. In the future, practitioners could conceivably access such a database during a clinical encounter to learn which approach had worked best for previous patients in the same case-mix group. By incorporating resource utilization and other cost information, learning systems can also provide the data needed to validly estimate the value of a service.

The vision of Learning Health Care Systems (Institute of Medicine, 2011b) is a very positive development for our discipline. In light of ongoing and projected shortages of faculty-researchers in communication sciences and disorders (CSD; ASHA, 2008), there is legitimate concern over how we will be able to address the sharp increase in the demand for clinical research to support evidence-based practice and the development of new reimbursement models based on client need. Investigator-initiated research has been the primary means through which scientific discovery has advanced in CSD. Now, with the potential of learning systems to help address many of our pressing clinical research questions, the research capacity of the discipline could be strengthened as every practitioner who contributes data would be helping to advance the evidence base.

This does not mean that all of our problems will be solved by learning systems, as we still need more (a) researchers developing and testing outcome measures; (b) researchers identifying the most predictive case-mix information for specific populations or conditions; (c) researchers examining the precision of our diagnostic measures and the efficacy of our interventions; (d) fully-elaborated taxonomies by which service delivery factors can be coded into databases; (e) researchers developing and analyzing large-scale databases; and (f) researchers focusing on practice-based research and health services research (e.g., implementation science, which aims to examine how services are delivered, how research findings and other evidence-based practices can be incorporated into routine practice, and how services can be optimized within the environments in which they are typically delivered to improve the quality, effectiveness, and efficiency of those services). Merely collecting the data will not solve all our problems either. Rather, it is critical that the information be made available to the public to support patient choice and to promote accountability and quality.

Patient-Centered Care

In 2001, the Institute of Medicine published a seminal report, Crossing the Quality Chasm: A New Health System for the 21st Century, which lays out a new perspective of health care that prioritizes patient needs and supports patient choice. The report concludes that reform around the edges will not solve the quality problem and outlines an ambitious agenda for redesigning the nation’s health care system to focus on six quality aims: safety, timeliness, efficiency, effectiveness, equity, and patient-centered care, (i.e., the “STEEEP” aims). Because management of health issues usually involves multiple health care providers and care settings, coordination, collaboration, and continuity of care are essential to achieving these aims. Unfortunately, professional silos and an overly narrow focus on disease and symptom management, rather than on the whole person, have led to a pervasive lack of care coordination and collaboration, which has been associated with substantial patient harm and suffering (e.g., Institute of Medicine, 2000; Welch, 2012). It is now widely recognized that Patient-Centered Care necessitates Interprofessional Education and Interprofessional Practice (e.g., Institute of Medicine, 2003; Reeves et al., 2010)—both of which are being promoted as critical areas in need of change if health, health care, and the nation’s health care system are to improve.

Care coordination is defined by the National Priorities Partnership (2012) as “a conscious effort to ensure that all key information needed to make clinical decisions is available to patients and their providers” (p. 18). The National Priorities Partnership ranked “Communication and Care Coordination” as a top priority in the National Strategy for Quality Improvement in Health Care report to Congress in 2012. Patient-Centered Care supports the active involvement of the recipients of services in making informed choices about treatment options and providers (e.g., International Alliance of Patients’ Organizations, 2006). Unfortunately, the nature of services provided for the same condition are highly variable across providers and seldom standardized, so even knowledgeable professionals don’t have much to go on when it comes to making choices about treatment options or service providers (e.g., Gawande, 2012). Furthermore, all too often, outcomes and quality data either do not exist or are not publicly available, so there is no basis upon which to support informed choice.


Outcomes data and other quality metrics must be transparent to impact quality and support patient choice. The push for transparency may be “the single most important step we can take to set health systems on a sustainable path” (Porter, Larsson, & Ingvar, 2012). The rationale driving this “market-based” approach is that “competition unleashes innovation, lowers costs, and boosts quality” but “for competition to achieve its promise of incentivizing higher-value health care, patients and payers must first be able to determine and elect higher value options—providers who deliver better outcomes at the same or lower costs” (Porter, Larsson, & Ingvar, 2012). But none of this is possible unless data are publicly reported, so that the outcomes of care are transparent and available to be used to inform choice and to guide clinical decision making.

While there will be many unforeseen changes, consideration of these four themes may help us map the emerging landscape in a way that can assist students, clinicians, faculty, and researchers to prepare for the challenges and opportunities that lie ahead.


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