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The three dialogues in this contribution concern 21st century application of life-like robots in the care of older adults. They depict conversations set in the near future, involving a philosopher (Dr Phonius) and a nurse (Dr Myloss) who manages care at a large facility for assisted living. In their first dialogue, the speakers discover that their quite different attitudes towards human-robot interaction parallel fundamental differences separating their respective concepts of consciousness. The second dialogue similarly uncovers deeply contrasting notions of personhood that appear to be associated with respective communities of nursing and robotics. The additional key awareness that arises in their final dialogue links applications of life-like robots in the care of older adults with potential transformations in our understandings of ourselves - indeed, in our understandings of the nature of our own humanity. This series of dialogues, therefore, appears to address a topic in nursing philosophy that merits our careful attention.

Systematic observations of people suffering from dementia of the Alzheimer's type (DAT) reveal they regress in behavior and become childlike. These observations have been used to structure clinical research and therapeutic interventions for dementia patients. However, no concise framework explains successful caregiving. Models for care exist but they lack an adequate framework for the long-term care of a person with DAT. This state of the science review describes what is known about cognitive functioning in people with DAT. It examines studies based on cognitive functioning. It also then relates this information to an emerging theory tentatively identified as a "cognitive developmental approach" which may be useful for understanding people with dementia and for predicting caregiver requirements.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877–0.892 vs. C-statistic = 0.871; 95%CI = 0.863–0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.