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Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI’s potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

This cross-sectional study evaluates how policy changes for Medicare Advantage plans affected supplemental benefit availability across geographic social vulnerability, membership diversity, and rebates from 2019 to 2022.

When he again was paroled, on April 29, he told authorities he wanted to serve his three years of probation in Wisconsin. Ken Jones, public relations director for the Mississippi Department of Corrections, said Smith was granted permission to travel to Milwaukee. Though Smith apparently did not notify Wisconsin authorities that he was in the state, he did file from Milwaukee monthly reports, from May to July, with Mississippi probation officials about his activities, Jones said. He declined to release the reports to The Journal. Honor System Unheard of Here John Barian, a spokesman for the Milwaukee regional probation office, said it was unlikely that Smith would have been accepted here for probation supervision because of his earlier convictions and extradition to Mississippi in 1990.

Background The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality-of-life (QOL) questionnaire, EORTC QLQ-C30. The QLU-C10D contains ten dimensions (Physical, Role, Social and Emotional Functioning; Pain, Fatigue, Sleep, Appetite, Nausea, Bowel Problems), each with four levels. To be used in cost-utility analysis, country-specific valuation sets are required. Objective The aim of this study was to provide Australian utility weights for the QLU-C10D. Methods An Australian online panel was quota-sampled to ensure population representativeness by sex and age (≥ 18 years). Participants completed a discrete choice experiment (DCE) consisting of 16 choice-pairs. Each pair comprised two QLU-C10D health states plus life expectancy. Data were analysed using conditional logistic regression, parameterised to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each QOL dimension-level coefficient to the coefficient on life expectancy. Results A total of 1979 panel members opted in, 1904 (96%) completed at least one choice-pair, and 1846 (93%) completed all 16 choice-pairs. Dimension weights were generally monotonic: poorer levels within each dimension were generally associated with greater utility decrements. The dimensions that impacted most on choice were, in order, Physical Functioning, Pain, Role Functioning and Emotional Functioning. Oncology-relevant dimensions with moderate impact were Nausea and Bowel Problems. Fatigue, Trouble Sleeping and Appetite had relatively small impact. The value of the worst health state was -0.096, somewhat worse than death. Conclusions This study provides the first country-specific value set for the QLU-C10D, which can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30, prospectively and retrospectively.

Objectives To develop a value set reflecting the United States (US) general population’s preferences for health states described by the Functional Assessment of Cancer Therapy (FACT) eight-dimensions preference-based multi-attribute utility instrument (FACT-8D), derived from the FACT-General cancer-specific health-related quality-of-life (HRQL) questionnaire. Methods A US online panel was quota-sampled to achieve a general population sample representative by sex, age (≥ 18 years), race and ethnicity. A discrete choice experiment (DCE) was used to value health states. The valuation task involved choosing between pairs of health states (choice-sets) described by varying levels of the FACT-8D HRQL dimensions and survival (life-years). The DCE included 100 choice-sets; each respondent was randomly allocated 16 choice-sets. Data were analysed using conditional logit regression parameterized to fit the quality-adjusted life-year framework, weighted for sociodemographic variables that were non-representative of the US general population. Preference weights were calculated as the ratio of HRQL-level coefficients to the survival coefficient. Results 2562 panel members opted in, 2462 (96%) completed at least one choice-set and 2357 (92%) completed 16 choice-sets. Pain and nausea were associated with the largest utility weights, work and sleep had more moderate utility weights, and sadness, worry and support had the smallest utility weights. Within dimensions, more severe HRQL levels were generally associated with larger weights. A preference-weighting algorithm to estimate US utilities from responses to the FACT-General questionnaire was generated. The worst health state’s value was −0.33. Conclusions This value set provides US population utilities for health states defined by the FACT-8D for use in evaluating oncology treatments.

Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure. To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer's core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure. QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis. CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker-Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA. CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

Inflated construction industry wages were labeled \"the country's No. 1 economic problem\" by key businesss leaders meeting in a private session yesterday with Government officials.