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1. Access Is Necessary but Not Sufficient: Factors Influencing Delay and Avoidance of Health Care Services

   https://doi.org/10.1177/2381468318760298  

   Smith, Kyle T.; Monti, Denise; Mir, Nageen; Peters, Ellen; Tipirneni, Renuka; Politi, Mary C. (January 2018, MDM Policy & Practice)

   null (Ed.)

   Background: Despite recently expanded access to health insurance, consumers still face barriers to using their coverage to obtain needed health care. Objective: To examine the characteristics of those who delay or avoid health care due to costs. Methods: Participants were recruited via Amazon MTurk and completed a survey assessing demographic characteristics, financial toxicity, health care minimizer-maximizer tendencies, health insurance knowledge, numeracy, delaying/avoiding any care, and delaying/avoiding six common health care services (three preventive and three nonpreventive services). Validated measures were used when available. Delay/avoidance behaviors were categorized into delaying/avoiding any care, preventive care, and nonpreventive care. Logistic regression models examined 1) financial toxicity, 2) minimizer-maximizer tendencies, 3) numeracy, 4) health insurance knowledge, and 5) knowledge of preventive care coverage separately on three forms of delay/avoidance behaviors, controlling for chronic conditions, insurance status, and/or income where appropriate. Results: Of 518 respondents, 470 did not fail attention-check questions and were used in analyses. Forty-five percent of respondents reported delaying/avoiding care due to cost. Multivariable analyses found that financial toxicity was related to delaying/avoiding any care (odds ratio [OR] = 0.884, P < 0.001), preventive care (OR = 0.906, P < 0.001), and nonpreventive care (OR = 0.901, P < 0.001). A tendency to minimize seeking health care (OR = 0.734, P < 0.001) and lower subjective numeracy (OR = 0.794, P = 0.023) were related to delaying/avoiding any care. General health insurance knowledge (OR = 0.989, P = 0.023) and knowledge of preventive care coverage (OR = 0.422, P < 0.001) were related to delaying/avoiding preventive care. Conclusions: Many people delay or avoid health care due to costs, even when insured. Results suggest that there may be different reasons individuals delay or avoid preventive and nonpreventive care. Findings may inform interventions to educate consumers and support discussions about health care costs to facilitate appropriate health care utilization. 

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2. The association between the problem list and chronic pain management

   https://doi.org/10.1007/s44250-022-00016-4  

   Rylee, Tina L; Copenhaver, David; Drake, Christiana; Joseph, Jill (December 2023, Discover Health Systems)

   Abstract BackgroundRoughly a quarter of the US population suffers from moderate to severe chronic pain for at least six months in any given year. The complexity of managing chronic pain has encouraged providers to use innovative methods to address it. Research has shown that problem lists are potential tools that support the care of patients with diabetes and chronic kidney disease. ObjectivesTo examine the extent to which the inclusion of chronic pain in a problem list is associated with follow-up specialty pain care. MethodsThe association between chronic pain documentation on the problem list and specialty pain care was investigated in this retrospective cohort study using 4531 patient records. ResultsChronic pain documentation in the problem list was associated with higher odds of receiving specialty pain care. The most common diagnosis was chronic pain (69.7%). A migraine diagnosis was associated with decreased odds of receiving specialty care, and chronic pain syndrome was associated with increased odds of receiving specialty care compared with the other chronic pain groups. ConclusionDocumenting chronic pain on the problem list was associated with a higher likelihood of patients receiving specialty pain care. 

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3. Skin Deep

   https://doi.org/10.70312/QVNS  

   Nading, Alex (June 2025, Limn)

   What if skin care is really planetary care? 

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4. Life history, mating dynamics and the origin of parental care

   https://doi.org/10.1111/jeb.13959  

   Azad, Tamjeed; Alonzo, Suzanne H.; Bonsall, Michael B.; Klug, Hope (November 2021, Journal of Evolutionary Biology)

   Abstract Parental care, mating dynamics and life history co‐evolve. Understanding the diversity of reproductive patterns found in nature is a major focus of evolutionary ecology research. Previous research suggests that the origin of parental care of eggs will be favoured when egg and adult death rates and juvenile survival are relatively high. However, the previous research that explored the link between care and life history did not account for among‐species variation in mating dynamics. As mating dynamics are generally expected to influence care, we explore, theoretically, the life‐history conditions (stage‐specific rates of maturation and survival) that favour parental care across three mating scenarios: reproductive rate (1) is unaffected by males (assuming that some males are present), (2) increases as male abundance increases or (3) decreases as male abundance increases. Across scenarios, all forms of care were most strongly favoured when egg and adult death rates, juvenile survival and female egg maturation rates were relatively high. When reproductive rate was unaffected by male abundance or increased as male abundance increased, as we might expect in systems in which females are mate‐limited, all forms of care were most strongly favoured when male egg maturation rate (i.e. the rate at which male eggs develop, mature and hatch) was moderate or high. When greater male abundance inhibited reproduction, which might occur in systems with intense male–male competition, all forms of care were most strongly favoured when male egg maturation rate was low‐to‐moderate. These results suggest that life history affects the evolution of parental care, and sex‐specific life history can interact with mating dynamics to influence the origin of care. 

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5. Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review

   https://doi.org/10.1001/jama.2024.21700  

   Bedi, Suhana; Liu, Yutong; Orr-Ewing, Lucy; Dash, Dev; Koyejo, Sanmi; Callahan, Alison; Fries, Jason A; Wornow, Michael; Swaminathan, Akshay; Lehmann, Lisa Soleymani; et al (January 2025, JAMA)

   ImportanceLarge language models (LLMs) can assist in various health care activities, but current evaluation approaches may not adequately identify the most useful application areas. ObjectiveTo summarize existing evaluations of LLMs in health care in terms of 5 components: (1) evaluation data type, (2) health care task, (3) natural language processing (NLP) and natural language understanding (NLU) tasks, (4) dimension of evaluation, and (5) medical specialty. Data SourcesA systematic search of PubMed and Web of Science was performed for studies published between January 1, 2022, and February 19, 2024. Study SelectionStudies evaluating 1 or more LLMs in health care. Data Extraction and SynthesisThree independent reviewers categorized studies via keyword searches based on the data used, the health care tasks, the NLP and NLU tasks, the dimensions of evaluation, and the medical specialty. ResultsOf 519 studies reviewed, published between January 1, 2022, and February 19, 2024, only 5% used real patient care data for LLM evaluation. The most common health care tasks were assessing medical knowledge such as answering medical licensing examination questions (44.5%) and making diagnoses (19.5%). Administrative tasks such as assigning billing codes (0.2%) and writing prescriptions (0.2%) were less studied. For NLP and NLU tasks, most studies focused on question answering (84.2%), while tasks such as summarization (8.9%) and conversational dialogue (3.3%) were infrequent. Almost all studies (95.4%) used accuracy as the primary dimension of evaluation; fairness, bias, and toxicity (15.8%), deployment considerations (4.6%), and calibration and uncertainty (1.2%) were infrequently measured. Finally, in terms of medical specialty area, most studies were in generic health care applications (25.6%), internal medicine (16.4%), surgery (11.4%), and ophthalmology (6.9%), with nuclear medicine (0.6%), physical medicine (0.4%), and medical genetics (0.2%) being the least represented. Conclusions and RelevanceExisting evaluations of LLMs mostly focus on accuracy of question answering for medical examinations, without consideration of real patient care data. Dimensions such as fairness, bias, and toxicity and deployment considerations received limited attention. Future evaluations should adopt standardized applications and metrics, use clinical data, and broaden focus to include a wider range of tasks and specialties. 

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