JAMA network open
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Inadequate privacy disclosures have repeatedly been identified by cross-sectional surveys of health applications (apps), including apps for mental health and behavior change. However, few studies have assessed directly the correspondence between privacy disclosures and how apps handle personal data. Understanding the scope of this discrepancy is particularly important in mental health, given enhanced privacy concerns relating to stigma and negative impacts of inadvertent disclosure. Because most health apps fall outside government regulation, up-to-date technical scrutiny is essential for informed decision making by consumers and health care professionals wishing to prescribe health apps. ⋯ Data sharing with third parties that includes linkable identifiers is prevalent and focused on services provided by Google and Facebook. Despite this, most apps offer users no way to anticipate that data will be shared in this way. As a result, users are denied an informed choice about whether such sharing is acceptable to them. Privacy assessments that rely solely on disclosures made in policies, or are not regularly updated, are unlikely to uncover these evolving issues. This may limit their ability to offer effective guidance to consumers and health care professionals.
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Comparative Study
Association of Medicare Spending With Subspecialty Consultation for Elderly Hospitalized Adults.
High use of subspecialty care is an important source of health care spending. Medical subspecialty care in particular may duplicate the scope of practice of the primary attending physicians for patients hospitalized for medical conditions. Under value-based payments, which aim to control overall spending during an episode of hospitalization (including Part B physician fees), subspecialty consultations may be a target for hospitals working to reduce costs. ⋯ The substantial variation in the use of subspecialty consultative care suggests potential opportunities for cost savings.
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Comparative Study
Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques.
Competence in cataract surgery is a public health necessity, and videos of cataract surgery are routinely available to educators and trainees but currently are of limited use in training. Machine learning and deep learning techniques can yield tools that efficiently segment videos of cataract surgery into constituent phases for subsequent automated skill assessment and feedback. ⋯ Time series modeling of instrument labels and video images using deep learning techniques may yield potentially useful tools for the automated detection of phases in cataract surgery procedures.
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Since the introduction of the Hospital Readmission Reduction Program (HRRP), readmission penalties have been applied disproportionately to institutions that serve low-income populations. To address this concern, the US Centers for Medicare & Medicaid introduced a new, stratified payment adjustment method in fiscal year (FY; October 1 to September 30) 2019. ⋯ The new, stratified payment adjustment method for the HRRP was associated with a reduction in penalties across hospitals included in the program; the greatest reductions were observed among hospitals in the low-SES group, lessening but not eliminating the previously unbalanced penalty burden carried by these hospitals. Additional public policy research efforts are needed to achieve equitable payment adjustment models for all hospitals.
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The US Patient Protection and Affordable Care Act of 2010 (ACA) was enacted in 2010 with several provisions that targeted reducing numbers of uninsured Americans. ⋯ Proportions of ED visits and hospital discharges by uninsured patients decreased considerably after the implementation of the 2014 ACA insurance provisions. Despite these changes, approximately 1 in 10 ED visits and 1 in 20 hospital discharges were made by uninsured individuals in 2014 to 2016. This suggests that continued attention is needed to address the lack of insurance in US hospital visits, particularly among people aged 18 to 64 years who have less access to government-sponsored insurance.