William F. Lawrence

Measuring patient-centered communication in cancer care: A literature review and the development of a systematic approach

Patient-centered communication (PCC) is a critical element of patient-centered care, which the Institute of Medicine (Committee on Quality of Health Care in America, 2001) promulgates as essential to improving healthcare delivery. Consequently, the US National Cancer Institutes Strategic Plan for Leading the Nation (2006) calls for assessing the delivery of PCC in cancer care. However, no comprehensive measure of PCC exists, and stakeholders continue to embrace different conceptualizations and assumptions about how to measure it. Our approach was grounded in the PCC conceptual framework presented in a recent US National Cancer Institute monograph (Epstein & Street, 2007). In this study, we developed a comprehensive inventory of domains and subdomains for PCC by reviewing relevant literature and theories, interviewing a limited number of cancer patients, and consulting experts. The resulting measurement domains are organized under the six core functions specified in the PCC conceptual framework: exchanging information, fostering healing relationships, recognizing and responding to emotions, managing uncertainty, making decisions, and enabling patient self-management. These domains represent a promising platform for operationalizing the complicated PCC construct. Although this study focused specifically on cancer care, the PCC measurements are relevant to other clinical contexts and illnesses, given that patient-centered care is a goal across all healthcare. Finally, we discuss considerations for developing PCC measures for research, quality assessment, and surveillance purposes. United States Department of Health and Human Services, National Institutes of Health, National Cancer Institute (2006). The NCI Strategic Plan for Leading the Nation: To Eliminate the Suffering and Death Due to Cancer. NIH Publication No. 06-5773.

Predicting EuroQoL EQ-5D preference scores from the SF-12 Health Survey in a nationally representative sample.

Purpose . To predict the EuroQoL EQ-5D utility index from the SF-12 Health Survey for a US national sample of adults. Methods . The authors used the 2000 Medical Expenditure Panel Survey to examine the relationship between instruments. Linear regression was used to predict EQ-5D scores from Physical Component Summary (PCS) and Mental Component Summary (MCS) scores of the SF-12. A prediction model was derived in one half of the sample and validated in the other half. Results . Complete responses to both measures were available for 14,580 adults; 7313 (50.2%) surveys were used for the derivation set. The 2-variable model predicted 61% of the variance in EQ-5D scores and provided reasonable ability to predict mean EQ-5D scores from mean PCS and MCS scores. Confidence intervals are dependent on sample size and variance of PCS and MCS scores. Conclusions . EQ-5D scores can be reasonably predicted from the SF-12. This model allows researchers to estimate utility data for use in decision and cost-utility analyses.

Enhancing measurement in health outcomes research supported by Agencies within the US Department of Health and Human Services

Many of the Institutes, Agencies and Centers that make up the US Department of Health and Human Services (DHHS) have recognized the need for better instrumentation in health outcomes research, and provide support, both internally and externally, for research utilizing advances in measurement theory and computer technology (informatics). In this paper, representatives from several DHHS agencies and institutes will discuss their need for better instruments within their discipline and describe current or future initiatives for exploring the benefits of these technologies. Together, the perspectives underscore the importance of developing valid, precise, and efficient measures to capture the full burden of disease and treatment on patients. Initiatives, like the Patient-Reported Outcomes Measurement Information System (PROMIS) to create health-related quality of life item banks, represent a trans-DHHS effort to develop a standard set of measures for informing decision making in clinical research, practice, and health policy.

Models in the Development of Clinical Practice Guidelines

Clinical practice guidelines facilitate implementation of high-value health care when they are based on consideration of the benefits and harms most relevant to practitioners, their patients, and society. The Institute of Medicine has specified that guidelines should be based on systematic reviews that consider the quality, quantity, and consistency of the relevant evidence (1). This approach is supported internationally (2). In practice, findings from systematic reviews may not apply directly to the guideline development setting. For example, cancer screening guideline panels may need to determine not only whether they should recommend screening for a specific condition but, if so, the ages at which to start and stop, the frequency, and the test method. For many screening strategies, data to directly address these questions are lacking. One way to bridge the gap between primary evidence and guideline development is by using models. Models are mathematical frameworks that integrate available data to estimate the health consequences of alternative intervention strategies in patient populations. There are different classes of models with different goals, methodological approaches, or both (310). Models have been used to examine the natural history of disease, explain disease occurrence trends, and interrogate harmbenefit tradeoffs of competing policies. Some models express the entire disease process and outcomes at a population level; others (microsimulation models) attempt to construct a virtual population in which persons progress through a disease process. We focus on modeling to estimate the harmbenefit tradeoffs of different disease management strategies. This requires the existence of a calibrated model of disease progressionthat is, a representation of disease progression that is shown to be consistent with observed data. For example, in cancer screening we need a model of disease without screening that yields projections of disease incidence similar to those observed in the absence and presence of screening. Although the use of models is increasing in guideline development, many guidelines are created without them. In some cases, they are not needed because guideline questions can be adequately addressed by using published primary evidence. However, in many cases, an understanding of how modeling can provide useful information is lacking. This article proposes that models play an important role in integrating and extending the evidence on outcomes of health care interventions. We provide recommendations of when models are likely to be valuable, based on gaps between published research studies and guideline questions. We also discuss aspects of model quality. Finally, we provide direction for how a modeling study should be designed and integrated into the guideline development process. Examples of Models We use 2 examples from cancer screening to illustrate our primary points. Screening trials provide primary evidence on benefit but are unable to compare the full range of screening strategies; represent a screening program as it would be broadly implemented; estimate benefits over a lifetime horizon; or fully assess benefits, harms, and costs. To overcome these limitations, models have been used to provide this critical information to support the development of cancer screening recommendations (11). Colorectal Cancer Screening Colorectal cancer is one cancer type for which there is broad consensus on screening efficacy. Trials of fecal occult blood tests (FOBTs) have shown significant decreases in colorectal cancer deaths (12). However, disease management and testing technologies have changed since the trials began nearly 40 years ago. For example, new FOBT variants, including Hemoccult SENSA (Beckman Coulter) and immunochemical tests, are available, and use of colonoscopy for screening has increased. No randomized studies of these newer approaches have been conducted, although estimates of their performance have been published (12). A study used 2 models to calculate the number of life-years gained (measure of benefits) and the number of diagnostic colonoscopies (measure of harms and resource use) and to compare different screening ages and intervals for available screening tests (13). The models superimposed candidate screening tests with established performance characteristics on representations of adenoma onset, progression to colorectal cancer, and cancer progression. The models reproduced disease incidence trends in published screening trials (13). The results provided evidence for starting screening at age 50 years rather than 40 or 60 years and for stopping at age 75 years rather than 85 years. They supported a 10-year screening interval for colonoscopy and a 1-year interval for high-sensitivity FOBTs. In the models, the Hemoccult II FOBT (Beckman Coulter) had an inferior harmbenefit ratio compared with more recent FOBTs (13). Screening strategies recommended by the U.S. Preventive Services Task Force (USPSTF) were informed by the model results (14). Mammography Screening Many mammography screening trials have been conducted worldwide (15). Most indicated that screening reduces breast cancer mortality, but the trials enrolled women of different ages, used varying screening intervals, and had limited numbers of screening rounds. They also used film rather than the newer digital mammography technology and predated contemporary cancer therapies, such as tamoxifen and trastuzumab (15). Therefore, the trials were of limited value for contemporary settings. To inform guideline development (16), a modeling study was used to compare benefits and harms of mammography screening with different starting and stopping ages and screening intervals (17). The models combined previously estimated disease natural history with sensitivity estimates of current mammography tests. Many screening approaches were evaluated. The models indicated that a strategy of biennial mammography for women aged 50 to 69 years maintained an average of 81% of the benefit of annual mammography with half the number of false-positive results. For younger starting ages, the models indicated that initiating biennial screening at age 40 (vs. 50) years reduced mortality by an additional 3%, consumed more resources, and yielded more false-positive results. The USPSTF used the model results to inform its recommendations for biennial screening between ages 50 and 74 years, with individualized decision making before age 50 years and after age 74 years (16). Which Gaps Between Primary Evidence and Guidelines Can Be Addressed by Using Models? In both examples, a clear gap existed between the primary evidence from randomized, controlled trials (RCTs) and the evidence needed to develop clinical guidelines, and modeling bridged the gap. The examples help outline 4 areas in which models can be useful (Table 1). Table 1. Four Areas Where Models Can Bridge the Gap Between Primary Evidence and Guideline Development Apply New or Updated Information on Disease Risk, Tests, and Treatments Estimates of mortality benefit from breast cancer screening are derived from trials that started decades ago. However, advances in treatment have decreased disease-specific mortality over this same period, thus potentially reducing benefits of early disease detection. Models can project outcomes by using more recent mortality rates from information about the effect of new treatments on mortality and contemporary life tables (13, 17). In addition, screening tests may change over time. Digital mammography has largely replaced film, and new immunochemical FOBTs have been developed since the initial trials. Models calibrated to disease incidence under older screening technologies can incorporate these newer data sources to provide outcome estimates that are more relevant to current practice. Explore a Wide Range of Possible Intervention Strategies Many potential screening strategies can be considered that are defined by ages at which to start and stop, intervals, methods, and referral criteria. However, trials can test only a few screening strategies and have limited follow-up. For example, the 7 mammography trials in the systematic review used by the USPSTF had a median of 4 to 5 screening rounds (15), whereas population screening programs often use longer periods. To inform the USPSTF guidelines, the mammography models compared 20 strategies (17). For colorectal cancer, with its many possible screening methods, the models compared 145 strategies (13). Assess Important Benefits, Harms, and Costs Over the Lifetime of the Population Models can extend beyond published studies to evaluate new measures of harmbenefit tradeoffs and extrapolate effects on health outcomes beyond the study time horizons. Costs can also be incorporated. Although trials of FOBTs had follow-up exceeding 10 years, they underestimated the lifetime incidence and mortality effects of screening because adenoma detection and removal by colonoscopy after an abnormal FOBT result may prevent colon cancer decades later. Outcomes projected by the colorectal and breast cancer screening models reflected complete screening schedules over lifetime horizons. They included not only the numbers of screening tests, unnecessary biopsies, cancer cases, and cancer deaths prevented but also outcomes difficult to directly measure in trials, including the number of life-years gained and overdiagnosis. Project Outcomes for the Conditions for Which the Guideline Is Intended Participants in trials are often highly selected. Specific populations of interest not enrolled or underrepresented in trials may include racial or ethnic minorities, persons younger or older than most trial participants, and those with comorbid conditions. Also, health care conditions and outcomes in settings or countries where trials have been done may differ from those where guidelines apply. For example, prostate cancer incidence rates are higher in the Unit

Psychological Distress in U.S. Women Who Have Experienced False-Positive Mammograms

BackgroundIn the United States, approximately 10.7% of all screening mammograms lead to a false-positive result, but the overall impact of false-positives on psychological well-being is poorly understood.Materials and methodsData were analyzed from the 2000 U.S. National Health Interview Survey (NHIS), the most recent national survey that included a cancer control module. Study subjects were 9,755 women who ever had a mammogram, of which 1,450 had experienced a false-positive result. Psychological distress was assessed using the validated K6 questionnaire and logistic regression was used to discern any association with previous false-positive mammograms.ResultsIn a multivariate analysis, women who had indicated a previous false-positive mammogram were more likely to report feeling sad (OR = 1.18, 95% CI, 1.03–1.35), restless (OR = 1.23, 95% CI, 1.08–1.40), worthless (OR = 1.27, 95% CI, 1.04–1.54), and finding that everything was an effort (OR = 1.27, 95% CI, 1.10–1.47). These women were also more likely to have seen a mental health professional in the 12 months preceding the survey (OR = 1.28, 95% CI, 1.03–1.58) and had a higher composite score on all items of the K6 scale (P < 0.0001), a reflection of increased psychological distress. Analyses by age and race revealed that, among women who had experienced false-positives, younger women were more likely to feel that everything was an effort, and blacks were more likely to feel restless.ConclusionIn a random sampling of the U.S. population, women who had previously experienced false-positive mammograms were more likely to report symptoms of anxiety and depression.

Chapter 10: Deciding Whether to Complement a Systematic Review of Medical Tests with Decision Modeling

Limited by what is reported in the literature, most systematic reviews of medical tests focus on “test accuracy” (or better, test performance), rather than on the impact of testing on patient outcomes. The link between testing, test results and patient outcomes is typically complex: even when testing has high accuracy, there is no guarantee that physicians will act according to test results, that patients will follow their orders, or that the intervention will yield a beneficial endpoint. Therefore, test performance is typically not sufficient for assessing the usefulness of medical tests. Modeling (in the form of decision or economic analysis) is a natural framework for linking test performance data to clinical outcomes. We propose that (some) modeling should be considered to facilitate the interpretation of summary test performance measures by connecting testing and patient outcomes. We discuss a simple algorithm for helping systematic reviewers think through this possibility, and illustrate it by means of an example.

Health Outcomes Assessment in Cancer: Current Measurement Strategies and Recommendations for Improvement

Measuring the outcomes of cancer care has become increasingly important both in clinical practice and in health policy. Responsiveness to patient-centered needs, preferences, and outcomes is one of the hallmarks of quality healthcare.Health-related quality of life (HR-QOL) measures can be considered within a framework based upon: (i) whether the measure is a generic instrument applicable across a wide range of health conditions, or whether it is specific to cancer or a specific cancer site; (ii) whether it measures a single domain of health or multiple domains; and (iii) whether or not the measure is preference based. Judicious selection of a set of instruments from within different areas of this framework can provide a detailed description of relevant aspects of a patient’s health for a wide variety of research and clinical needs.Current health outcomes research is focused not only on the development of improved measures of health, but also on how to expand the use of these measures from research settings into clinical practice and health policy in ways to improve the process and outcomes of cancer care. Shared decision-making tools incorporating HR-QOL data can assist patients in clarifying decision alternatives for difficult cancer treatment decisions. Observational studies of HR-QOL of cancer patients can help patients better understand potential outcomes of their choices. HR-QOL measures are being used in quality of care initiatives.Cancer care is composed of a spectrum of services, ranging from prevention and early detection, through to diagnosis and treatment, as well as end-of-life care. As the importance of the patient’s perspective has become more clearly recognized, health outcomes measures have become more widely used and can contribute to improved care across the spectrum of cancer services. While further research needs to focus on developing better measures of health, it is equally imperative that future research focus on methods to incorporate health outcomes measurement into practice in ways to improve clinical practice, health policy, and ultimately to improve the outcomes of care of patients with cancer.

An overview and discussion of the Patient-Centered Outcomes Research Institute's decision aid portfolio

Decision aids (DAs) help patients make informed healthcare decisions in a manner consistent with their values and preferences. Despite their promise, DAs developed with public research dollars are not being implemented and adopted in real-world patient care settings at a rate consistent with which they are being developed. To appraise the sum of the parts of the portfolio and create a strategic imperative surrounding future funding, the Patient-Centered Outcomes Research Institute (PCORI) tasked the Duke Evidence Synthesis Group with evaluating its DA portfolio. This paper describes PCORIs portfolio of DAs according to the Duke Evidence Synthesis Groups analysis in the context of PCORIs mission and the field of decision science. The results revealed a diversity within PCORIs portfolio of funded DA projects. Findings support the movement toward more rigorous DA development, assessment and maintenance. PCORIs funding priorities related to DAs are clarified and comparative questions of interest are posed.

Starting the conversation.

Comparative effectiveness research (CER) has drawn increasing attention since the passing of the Medicare Prescription Drug, Improvement, and Modernization Act of 2003. In section 1013 of this Act, the Agency for Healthcare Research and Quality (AHRQ) was authorized to conduct and support research with a focus on outcomes, comparative clinical effectiveness, and appropriateness of pharmaceuticals, devices, and health care services. The interest in this research has skyrocketed with the

Cost-effectiveness Analysis Based on the Atypical Squamous Cells of Undetermined Significance/Low-Grade Squamous Intraepithelial Lesion Triage Study (ALTS)

1.1 billion Federal investment in CER with the 2009 American Recovery and Reinvestment Act (ARRA). In 2003, MEDLINE reports 20 articles using the term “comparative effectiveness”; twice as many articles published using this term in the first month of 2011 alone. But, what is CER, and is this research really something new? A number of organizations have defined CER, but two are worth particular mention. The Federal Coordinating Council for Comparative Effectiveness Research, established under ARRA, defines comparative effectiveness as follows: