Improving the Safety of Computed Tomography Through Automated Quality Measurement: A Radiologist Reader Study of Radiation Dose, Image Noise, and Image Quality.

OBJECTIVES: The Centers for Medicare and Medicaid Services funded the development of a computed tomography (CT) quality measure for use in pay-for-performance programs, which balances automated assessments of radiation dose with image quality to incentivize dose reduction without compromising the diagnostic utility of the tests. However, no existing quantitative method for assessing CT image quality has been validated against radiologists' image quality assessments on a large number of CT examinations. Thus to develop an automated measure of image quality, we tested the relationship between radiologists' subjective ratings of image quality with measurements of radiation dose and image noise. MATERIALS AND METHODS: Board-certified, posttraining, clinically active radiologists rated the image quality of 200 diagnostic CT examinations from a set of 734, representing 14 CT categories. Examinations with significant distractions, motion, or artifact were excluded. Radiologists rated diagnostic image quality as excellent, adequate, marginally acceptable, or poor; the latter 2 were considered unacceptable for rendering diagnoses. We quantified the relationship between ratings and image noise and radiation dose, by category, by analyzing the odds of an acceptable rating per standard deviation (SD) increase in noise or geometric SD (gSD) in dose. RESULTS: One hundred twenty-five radiologists contributed 24,800 ratings. Most (89%) were acceptable. The odds of an examination being rated acceptable statistically significantly increased per gSD increase in dose and decreased per SD increase in noise for most categories, including routine dose head, chest, and abdomen-pelvis, which together comprise 60% of examinations performed in routine practice. For routine dose abdomen-pelvis, the most common category, each gSD increase in dose raised the odds of an acceptable rating (2.33; 95% confidence interval, 1.98-3.24), whereas each SD increase in noise decreased the odds (0.90; 0.79-0.99). For only 2 CT categories, high-dose head and neck/cervical spine, neither dose nor noise was associated with ratings. CONCLUSIONS: Radiation dose and image noise correlate with radiologists' image quality assessments for most CT categories, making them suitable as automated metrics in quality programs incentivizing reduction of excessive radiation doses.

When Can We Rely on Real-World Evidence to Evaluate New Medical Treatments?

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. The RWE label has been used to refer to a variety of departures from the methods of traditional randomized controlled trials. Recognizing this complexity and potential confusion, the National Academies of Science, Engineering, and Medicine convened a series of workshops to clarify and address questions regarding the use of RWE to evaluate new medical treatments. Those workshops identified three specific dimensions in which RWE studies might differ from traditional clinical trials: use of real-world data (data extracted from health system records or data captured by mobile devices), delivery of real-world treatment (open-label treatments delivered in community settings by community practitioners), and real-world treatment assignment (including nonrandomized comparisons and variations on random assignment such as before-after or stepped-wedge designs). For any RWE study, decisions regarding each of these dimensions depends on the specific research question, characteristics of the potential study settings, and characteristics of the settings where study results would be applied.

When Can We Trust Real-World Data To Evaluate New Medical Treatments?

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. RWE studies often rely on real-world data (RWD), including data extracted from healthcare records or data captured by mobile phones or other consumer devices. Global assessments of RWD sources are not helpful in assessing whether any specific RWD element is fit for any specific purpose. Instead, evidence generators and evidence consumers should clearly identify the specific health state or clinical phenomenon of interest and then consider each step between that clinical phenomenon and its representation in a research database. We propose specific questions regarding potential error or bias affecting each of those steps: Would a person experiencing this clinical phenomenon present for care in this setting or interact with this recording device? Would this clinical phenomenon be accurately recognized or assessed? How might the recording environment or tools affect accurate and consistent recording of this clinical phenomenon? Can data elements from different sources be harmonized, both technically (same format) and semantically (same meaning)? Can the original data elements be consistently reduced to a useful clinical phenotype? Addressing these questions requires a range of clinical, organizational, and technical expertise. Transparency regarding each step in the creation of RWD is essential if evidence consumers are to rely on RWE studies.

Challenges and Opportunities to Updating Prescribing Information for Longstanding Oncology Drugs.

A number of important drugs used to treat cancer-many of which serve as the backbone of modern chemotherapy regimens-have outdated prescribing information in their drug labeling. The Food and Drug Administration is undertaking a pilot project to develop a process and criteria for updating prescribing information for longstanding oncology drugs, based on the breadth of knowledge the cancer community has accumulated with the use of these drugs over time. This article highlights a number of considerations for labeling updates, including selecting priorities for updating; data sources and evidentiary criteria; as well as the risks, challenges, and opportunities for iterative review to ensure prescribing information for oncology drugs remains relevant to current clinical practice.

Changing home care aides: Differences between family and non-family care in California Medicaid home and community-based services.

In California Medicaid home-and-community-based services (HCBS), recipients' family members receive payment as home care aides (HCAs). We analyzed data on first-time HCBS recipients to examine factors associated with the likelihood of switching HCAs within the first year of services. Those with family HCAs were less than half as likely to change than those with non-family HCAs and racial/ethnic minorities with non-family HCAs had the highest switching rates. Lower wages and local unemployment were associated with switching of non-family HCAs but not family HCAs. Policymakers can foster continuity of home care by paying family members for home care and raising worker wages.

Learning healthcare systems: a perspective from the US.

Changes under way in the healthcare environment have the potential to accelerate the pace at which evidence is incorporated into practice. Motivated by new payment models that hold clinicians accountable for cost and quality, healthcare organisations in the US are developing their capacity to become learning healthcare systems that are able to generate, adopt and apply evidence to support quality improvement and high-value care. The pace at which healthcare organisations will make progress will depend on whether they perceive a return on their investments, the availability of internal and external resources to help them make this transformation, and the external pressures on them to be accountable for managing the cost and quality of their patient care.

Concentration of Physician Services Across Insurers and Effects on Quality: Early Evidence From Medicare Advantage.

BACKGROUND: A growing proportion of Medicare beneficiaries is covered by private insurers through Medicare Advantage, yet little is known about how these plans are structured in terms of relationships with physicians and implications for quality of care. OBJECTIVE: The objective of this study was to assess whether greater physician concentration of services across insurers was associated with higher quality in Medicare Advantage (MA), overall and particularly among MA insurers serving a high proportion of vulnerable enrollees. RESEARCH DESIGN: A retrospective cohort design with regression analysis. DATA SOURCES: The primary dataset was 2014 MA encounter records submitted by insurers to the Centers for Medicare and Medicaid Services, covering 600,329 physicians across 119 insurers. These data were merged with Centers for Medicare and Medicaid Services data on MA contract quality rating as well as physician characteristics in the Medicare Data on Provider Practice and Specialty file. MEASURES: Two measures were generated to capture the concentration of physician services across insurers: the percentage of a physician's Medicare services which was through MA (MA penetration); and the percentage of a physician's MA services with a specific insurer (insurer share of MA services). RESULTS: Greater MA penetration and insurer share of MA services were each associated with higher MA plan quality. The relationship between insurer share and quality was stronger in contracts with a relatively high percentage of disabled enrollees. CONCLUSION: Greater physician concentration of services across MA insurers was associated with a higher quality of care overall and especially among vulnerable enrollees.

Language-Concordant Primary Care Physicians for a Diverse Population: The View from California.

Purpose: The population with limited English proficiency (LEP) in California is growing. We sought to determine whether enough primary care physicians (PCPs) have the language skills to meet patient needs. Methods: The authors determined the number of PCPs who self-report proficiency in the five most common non-English languages spoken in California (Spanish, Cantonese, Mandarin, Tagalog, and Vietnamese) using Medical Board of California data from 2013 to 2015. The authors estimated LEP populations during 2011-2015 using Census data. They calculated PCP supply (the ratio of PCPs/100,000 LEP individuals) compared to a federal standard to judge adequacy. They performed a sensitivity analysis adjusting the percentage of LEP patients in a bilingual physicians' practice from 100% to the percentage of LEP individuals in California who spoke that language. Results: Of 19,310 PCPs in California, 15,933 (83%) provided information about languages they speak. There were 5,203 (33%) Spanish-, 486 (3%) Cantonese-, 986 (6%) Mandarin-, 956 (6%) Tagalog-, and 671 (4%) Vietnamese-speaking PCPs. PCP supply, compared to a federal standard, was adequate if we assumed that bilingual PCPs only care for LEP patients. However, if one assumes the number of LEP patients in a PCP's practice reflects the percentage in the general population, there is a large PCP undersupply for all languages. Conclusion: Estimates of access to language-concordant PCPs for LEP individuals are sensitive to assumptions about the percentage of LEP patients in a PCP's panel. Ensuring language-concordant access will require deliberate effort to match LEP patients with bilingual PCPs.