Mammography and Decision Aid Use for Breast Cancer Screening in Older Women.

INTRODUCTION: Decision aids for breast cancer screening are increasingly being used by physicians, but the association between physician practice decision-aid use and mammography rates remains uncertain. Using national data, this study examines the association between practice-level decision-aid use and mammography use among older women. METHODS: Physician practice responses to the 2017/2018 National Survey of Healthcare Organizations and Systems (n=1,236) were linked to 2016 and 2017 Medicare fee-for-service beneficiary data from eligible beneficiaries (n=439,684) aged 65-74 years. In 2021, multivariable generalized linear models estimated the association of practice decision-aid use for breast cancer screening and advanced health information technology functions with mammography use, controlling for practice and beneficiary characteristics. RESULTS: Overall, 60.1% of eligible beneficiaries had a screening mammogram, and 37.3% of physician practices routinely used decision aids for breast cancer screening. In adjusted analyses, advanced health information technology functions (OR=1.19, p=0.04) were associated with mammography use, but practice use of decision aids was not (OR=0.95, p=0.21). Beneficiary clinical and socioeconomic characteristics, including race, comorbidities, Medicare and Medicaid eligibility, and median household income were more strongly associated with mammography use than practice-level decision-aid use or advanced health information technology functions. CONCLUSIONS: Health information technology‒enabled automation of mammography reminders and other advanced health information technology functions may support mammography, whereas breast cancer decision aids may reduce patients' propensities to be screened through the alignment of their preferences and screening decision. More resources may be needed for decision aids to be routinely implemented to improve solicitation of patient preferences and targeting of mammography services.

Primary care practices' ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation.

BACKGROUND: Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores. METHODS: Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score. RESULTS: In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71-0.88) but sensitivity and PPV were low (0.16-0.40). Practice-created scores had 0.02-0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity. CONCLUSIONS: Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.

Implementing Risk Stratification in Primary Care: Challenges and Strategies.

INTRODUCTION: Primary care risk stratification (RS) has been shown to help practices better understand their patient populations' needs and may improve health outcomes and reduce expenditures by targeting and tailoring care to high-need patients. This study aims to understand key considerations practices faced and practice experiences as they began to implement RS models. METHODS: We conducted semistructured interviews about experiences in RS with 34 stakeholders from 15 primary care practices in Oregon and Colorado and qualitatively analyzed the data. RESULTS: Three decisions were important in shaping practices' experiences with RS: choosing established versus self-created algorithms or heuristics, clinical intuition, or a combination; selecting mechanisms for assigning risk scores; determining how to integrate RS approaches into care delivery. Practices using clinical intuition found stratification time-consuming and difficult to incorporate into existing workflows, but trusted risk scores more than those using algorithms. Trust in risk scores was influenced by data extraction capabilities; practices often lacked sufficient data to calculate their perceived optimal risk score. Displaying the scores to the care team was a major issue. Finally, obtaining buy-in from care team members was challenging, requiring repeated cycles of improvement and workflow integration. DISCUSSION: Practices used iterative approaches to RS implementation. As a result, procedural and algorithmic changes were introduced and were influenced by practices' health IT, staffing, and resource capacities. Practices were most successful when able to make iterative changes to their approaches, incorporated both automation and human process in RS, educated staff on the importance of RS, and had readily accessible risk scores.

Perceptions of Risk Stratification Workflows in Primary Care.

Risk stratification (RS) in primary care is frequently used by policy-makers, payers, and health systems; the process requires risk assessment for adverse health outcomes across a population to assign patients into risk tiers and allow care management (CM) resources to be targeted effectively. Our objective was to understand the approach to and perception of RS in primary care practices. An online survey was developed, tested, and administered to 148 representatives of 37 primary care practices engaged in RS varying in size, location and ownership. The survey assessed practices' approach to, perception of, and confidence in RS, and its effect on subsequent CM activities. We examined psychometric properties of the survey to determine validity and conducted chi-square analyses to determine the association between practice characteristics and confidence and agreement with risk scores. The survey yielded a 68% response rate (100 respondents). Overall, participants felt moderately confident in their risk scores (range 41-53.8%), and moderately to highly confident in their subsequent CM workflows (range 46-68%). Respondents from small and independent practices were more likely to have higher confidence and agreement with their RS approaches and scores (p < 0.01). Confidence levels were highest, however, when practices incorporated human review into their RS processes (p < 0.05). This trend was not affected by respondents' professional roles. Additional work from a broad mixed-methods effort will add to our understanding of RS implementation processes and outcomes.

Specifications of Clinical Quality Measures and Value Set Vocabularies Shift Over Time: A Study of Change through Implementation Differences.

Clinical quality measures (CQMs) aim to identify gaps in care and to promote evidence-based guidelines. Official CQM definitions consist of a measure's logic and grouped, standardized codes to define key concepts. In this study, we used the official CQM update process to understand how CQMs' meanings change over time. First, we identified differences between the narrative description, logic, and the vocabulary specifications offour standardized CQMs' definitions in subsequent versions (2015, 2016, and 2017). Next, we implemented the various versions in a quality measure calculation registry to understand how the differences affected calculated prevalence of risk and measure performance. Global performance rates changed up to 5.32%, and an increase of up to 28% new patients was observed for key conditions between versions. Updates to definitions that change a measure's logic and choices to include/exclude codes in value set vocabularies changes measurement of quality and likely introduces variation by implementation.

The genome of deep-sea vent chemolithoautotroph Thiomicrospira crunogena XCL-2.

Presented here is the complete genome sequence of Thiomicrospira crunogena XCL-2, representative of ubiquitous chemolithoautotrophic sulfur-oxidizing bacteria isolated from deep-sea hydrothermal vents. This gammaproteobacterium has a single chromosome (2,427,734 base pairs), and its genome illustrates many of the adaptations that have enabled it to thrive at vents globally. It has 14 methyl-accepting chemotaxis protein genes, including four that may assist in positioning it in the redoxcline. A relative abundance of coding sequences (CDSs) encoding regulatory proteins likely control the expression of genes encoding carboxysomes, multiple dissolved inorganic nitrogen and phosphate transporters, as well as a phosphonate operon, which provide this species with a variety of options for acquiring these substrates from the environment. Thiom. crunogena XCL-2 is unusual among obligate sulfur-oxidizing bacteria in relying on the Sox system for the oxidation of reduced sulfur compounds. The genome has characteristics consistent with an obligately chemolithoautotrophic lifestyle, including few transporters predicted to have organic allocrits, and Calvin-Benson-Bassham cycle CDSs scattered throughout the genome.