The Patient Protection and Affordable Care Act of 2010 led to the largest expansion of healthcare coverage since the instantiation of Medicare and Medicaid in 1965. Concerningly, prior research suggests this large influx of new patient data across the various and highly dispersed sources may create new potential for malfeasance in the form of consumer fraud and identity theft. We exploit the phased expansion of Medicaid into different states at different times to assess whether this expansion subsequently drove increased fraud and identity theft. Using a difference in difference approach, we explore these data security-related aftereffects of the law. Surprisingly, results indicate a significant decrease in claims of consumer fraud after the expansion of Medicaid, with no robust effect on identity theft. In empirical extensions, we also find a material drop in data breaches and compromised records after the expansion of Medicaid. Taken in sum, these findings suggest that the expansion of Medicaid had a consequential effect on the security of consumer data and created significant positive externalities for consumers.

Recent developments in large language models open doors for Artificial Intelligence and robots to augment knowledge workers and teams in a variety of domains, such as customer service, data science, legal work, and software development. In this article, we review 317 articles from multiple disciplines and summarize the insights in a theoretical framework linking key robot attributes to human perceptions and behaviors. The robot attributes include embodiment, nonverbal and verbal communication, perceived gender and race, emotions, perceived personality, and competence. The outcomes include human perceptions, acceptance, engagement, compliance, trust, and willingness to help. We identify four differences between one human and one robot settings and team settings and use them as the springboard to generalize insights from the literature review to the design and impact of a robot in assisting humans in knowledge work teams. We report two high-level observations around the interplay among robot attributes and context dependent designs and discuss their implications.

INTRODUCTION:There is significant public health interest towards providing medical care at mass-gathering events. Furthermore, mass gatherings have the potential to have a detrimental impact on the availability of already-limited municipal Emergency Medical Services (EMS) resources. This study presents a cross-sectional descriptive analysis to report broad trends regarding patients who were transported from National Collegiate Athletic Association (NCAA) Division 1 collegiate football games at a major public university in order to better inform emergency preparedness and resource planning for mass gatherings. METHODS:Patient care reports (PCRs) from ambulance transports originating from varsity collegiate football games at the University of Minnesota across six years were examined. Pertinent information was abstracted from each PCR. RESULTS:Across the six years of data, there were a total of 73 patient transports originating from NCAA collegiate football games: 45.2% (n = 33) were male, and the median age was 22 years. Alcohol-related chief complaints were involved in 50.7% (n = 37) of transports. In total, 31.5% of patients had an initial Glasgow Coma Scale (GCS) of less than 15. The majority (65.8%; n = 48; 0.11 per 10,000 attendees) were transported by Basic Life Support (BLS) ambulances. The remaining patients (34.2%; n = 25; 0.06 per 10,000 attendees) were transported by Advanced Life Support (ALS) ambulances and were more likely to be older, have abnormal vital signs, and have a lower GCS. CONCLUSIONS:This analysis of ambulance transports from NCAA Division 1 collegiate football games emphasizes the prevalence of alcohol-related chief complaints, but also underscores the likelihood of more life-threatening conditions at mass gatherings. These results and additional research will help inform emergency preparedness at mass-gathering events.

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Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data will likely have to rely on artificial intelligence (AI). AI agents can help augment transplant decision making by discovering patterns and making predictions for specific patients that are not covered in the literature or in ways that are impossible for humans to anticipate by integrating vast amounts of data (e.g. trending across numerous biomarkers). Similar to other clinical decision support systems, AI may help overcome human biases or judgment errors. However, AI is not widely utilized in transplant to date. In this rapid review, we survey the methods employed in recent research in transplant-related AI applications and identify concerns related to implementing these tools. We identify three key challenges (bias/accuracy, clinical decision process/AI explainability, AI acceptability criteria) holding back AI in transplant. We also identify steps that can be taken in the near term to help advance meaningful use of AI in transplant (forming a Transplant AI Team at each center, establishing clinical and ethical acceptability criteria, and incorporating AI into the Shared Decision Making Model).

Patient access and adherence to chronic medications is critical. In this work, we evaluate whether disruptions related to Covid-19 have affected new and existing patients' access to pharmacological therapies without interruption. We do so by performing a retrospective analysis on a dataset of 9.4 billion US prescription drug claims from 252 million patients from May, 2019 through August, 2020 (about 93% of prescriptions dispensed within those months). Using fixed effect (conditional likelihood) linear models, we evaluate continuity of care, how many days of supply patients received, and the likelihood of discontinuing therapy for drugs from classes with significant population health impacts. Findings indicate that more prescriptions were filled in March 2020 than in any prior month, followed by a significant drop in monthly dispensing. Compared to the pre-Covid era, a patient's likelihood of discontinuing some medications increased after the spread of Covid: norgestrel-ethinyl estradiol (hormonal contraceptive) discontinuation increased 0.62% (95% CI: 0.59% to 0.65%, p<0.001); dexmethylphenidate HCL (ADHD stimulant treatment) discontinuation increased 2.84% (95% CI: 2.79% to 2.89%, p<0.001); escitalopram oxalate (SSRI antidepressant) discontinuation increased 0.57% (95% CI: 0.561% to 0.578%, p<0.001); and haloperidol (antipsychotic) discontinuation increased 1.49% (95% CI: 1.41% to 1.57%, p<0.001). In contrast, the likelihood of discontinuing tacrolimus (immunosuppressant) decreased 0.15% (95% CI: 0.12% to 0.19%, p<0.001). The likelihood of discontinuing buprenorphine/naloxone (opioid addiction therapy) decreased 0.59% (95% CI: 0.55% to 0.62% decrease, p<0.001). We also observe a notable decline in new patients accessing these latter two therapies. Most US patients were able to access chronic medications during the early months of Covid-19, but still were more likely to discontinue their therapies than in previous months. Further, fewer than normal new patients started taking medications that may be vital to their care. Providers would do well to inquire about adherence and provide prompt, nonjudgmental, re-initiation of medications. From a policy perspective, opioid management programs seem to demonstrate a robust ability to manage existing patients in spite of disruption.

The large-amplitude and lightly-damped oscillation of crane payloads is detrimental to safe and efficient operation. The problem is further complicated when the payload creates a double-pendulum effect. Previous researches have shown that single-mode oscillations can be greatly reduced by properly shaping the inputs to the crane motors. This paper builds on previous developments by thoroughly describing the double-pendulum dynamic effects as a function of payload parameters and the crane configuration. Furthermore, an input-shaping control method is developed to suppress double-pendulum oscillations created by a payload with distributed-mass properties. Experiments performed on a 10-ton industrial bridge crane verify the effectiveness of the method. A critical aspect of the testing was human operator studies, wherein numerous operators utilized the input-shaping controller to perform manipulation tasks. The performance improvements provided by the input-shaping controller, as well as operator learning effects, are reported.