Quality Improvement at the Health System Level: Measurement, Risk Stratification, Disparity Populations, and Governance

Hospital value-based payment programs and disparity in the United States: A review of current evidence and future perspectives

Beginning in the early 2010s, an array of Value-Based Purchasing (VBP) programs has been developed in the United States (U.S.) to contain costs and improve health care quality. Despite documented successes in these efforts in some instances, there have been growing concerns about the programs' unintended consequences for health care disparities due to their built-in biases against health care organizations that serve a disproportionate share of disadvantaged patient populations. We explore the effects of three Medicare hospital VBP programs on health and health care disparities in the U.S. by reviewing their designs, implementation history, and evidence on health care disparities. The available empirical evidence thus far suggests varied impacts of hospital VBP programs on health care disparities. Most of the reviewed studies in this paper demonstrate that hospital VBP programs have the tendency to exacerbate health care disparities, while a few others found evidence of little or no worsening impacts on disparities. We discuss several policy options and recommendations which include various reform approaches and specific programs ranging from those addressing upstream structural barriers to health care access, to health care delivery strategies that target service utilization and health outcomes of vulnerable populations under the VBP programs. Future studies are needed to produce more explicit, conclusive, and consistent evidence on the impacts of hospital VBP programs on disparities.

Quality measurement for cardiovascular diseases and cancer in hospital value-based healthcare: a systematic review of the literature

BACKGROUND

This systematic literature review identifies hospital value-based healthcare quality measures, measurement practices, and tools, as well as potential strategies for improving cardiovascular diseases and cancer care.

METHODS

A systematic search was carried out in the PubMed, Embase, CINAHL, and MEDLINE (OvidSP) databases. We included studies on quality measures in hospital value-based healthcare for cardiovascular diseases and cancer. Two reviewers independently screened titles and abstracts, conducted a full-text review of potentially relevant articles, assessed the quality of included studies, and extracted data thematically. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and four validated tools were used for methodological quality assessment.

RESULTS

The search yielded 2860 publications. After screening the titles and abstracts, 60 articles were retrieved for full-text review. A total of 37 studies met our inclusion criteria. We found that standardized outcome sets with patient involvement were developed for some cardiovascular diseases and cancer. Despite the heterogeneity in outcome measures, there was consensus to include clinical outcomes on survival rate and disease control, disutility of care, and patient-reported outcome measures such as long-term quality of life.

CONCLUSION

Hospitals that developed value-based healthcare or are planning to do so can choose whether they prefer to implement the standardized outcomes step-by-step, collect additional measures, or develop their own set of measures. However, they need to ensure that their performance can be consistently compared to that of their peers and that they measure what prioritizes and maximizes value for their patients.

TRIAL REGISTRATION

PROSPERO ID: CRD42021229763 .

Can artificial intelligence improve cancer treatments?

Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.

Enhancement of infection prevention case review process to optimize learning from defects

Hospitals continue to struggle with preventable healthcare-associated infections. Whereas the focus is generally on proactive prevention processes, performing retrospective case reviews of infections can identify opportunities for quality improvement and maximize learning from defects. This brief article provides practical information for structuring the case review process using readily available health system platforms. Using a structured approach for case reviews can help identify trends and opportunities for improvement.