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BACKGROUND: Outpatient delivery of geriatric rehabilitation (GR) might contribute to preserving the accessibility and quality of GR, whilst dealing with an increasing demand for healthcare in an aging population. However, the application of outpatient GR differs between GR facilities. This study aimed to gain insight into factors influencing outpatient GR utilization. METHODS: In this case study, 24 semi-structured interviews were conducted with physicians, physiotherapists, nurse practitioners, occupational therapists, and managers in GR. Interviews were transcribed and analyzed using summative content analysis. RESULTS: Various patient-related barriers for using outpatient GR were mentioned including lacking social support and limited capacities and self-management skills. Additionally, professional-related barriers included a lack of awareness and consensus among care professionals regarding the possibilities and potential advantages of outpatient GR. Yet, most perceived barriers were related to efficiency and organization of outpatient GR (e.g., reimbursement system, lacking practical guidance). Still, most participants were in favor of increasing outpatient GR because of expected advantages for patients, GR organizations, and society. CONCLUSIONS: Despite experienced barriers, there seems to be agreement on the need to increase outpatient GR application. It is recommended to use the present findings to develop and evaluate new ways of organizing and reimbursing outpatient GR.
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Fundamental algorithms such as sorting or hashing are used trillions of times on any given day1. As demand for computation grows, it has become critical for these algorithms to be as performant as possible. Whereas remarkable progress has been achieved in the past2, making further improvements on the efficiency of these routines has proved challenging for both human scientists and computational approaches. Here we show how artificial intelligence can go beyond the current state of the art by discovering hitherto unknown routines. To realize this, we formulated the task of finding a better sorting routine as a single-player game. We then trained a new deep reinforcement learning agent, AlphaDev, to play this game. AlphaDev discovered small sorting algorithms from scratch that outperformed previously known human benchmarks. These algorithms have been integrated into the LLVM standard C++ sort library3. This change to this part of the sort library represents the replacement of a component with an algorithm that has been automatically discovered using reinforcement learning. We also present results in extra domains, showcasing the generality of the approach.
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We describe an analysis of genome variation in 825 P. falciparum samples from Asia and Africa that identifies an unusual pattern of parasite population structure at the epicenter of artemisinin resistance in western Cambodia. Within this relatively small geographic area, we have discovered several distinct but apparently sympatric parasite subpopulations with extremely high levels of genetic differentiation. Of particular interest are three subpopulations, all associated with clinical resistance to artemisinin, which have skewed allele frequency spectra and high levels of haplotype homozygosity, indicative of founder effects and recent population expansion. We provide a catalog of SNPs that show high levels of differentiation in the artemisinin-resistant subpopulations, including codon variants in transporter proteins and DNA mismatch repair proteins. These data provide a population-level genetic framework for investigating the biological origins of artemisinin resistance and for defining molecular markers to assist in its elimination.
