The Determinants and Implications of Firms\u27 Workforce Composition: The Case of Home Health

This paper presents and tests a new model that highlights the role of reputation in determining firms\u27 workforce composition and strategy. Facing demand uncertainty, firms in labor-intensive service industries, such as health care, often rely on temporary workers. Past research has shown that firms that employ more temporary workers when facing greater demand fluctuations. However, this strategy is challenged by accumulating evidence that permanent and temporary workers are not perfectly interchangeable in the production of quality. This paper examines the strategies of firms facing this trade-off: temporary workers provide flexibility in responding to demand fluctuations but can lower reputation through a decline in quality. Through a model where demand is stochastic and linked to firms\u27 reputation for quality, this paper predicts that firms\u27 workforce composition depends on their reputation. Using novel and rich data from a large multi-state US home health provider, I provide evidence consistent with the theory. First patients visited more by permanent nurses were less likely to be rehospitalized. I use patient\u27s differential distances to the nearest proportion of permanent nurse visits. Second, measuring firms\u27 reputation by the establishment of a strong referral base, I find that low-reputation firms, such as new firms, decreased the share of temporary nurses with demand fluctuations. These results imply that low-reputation firms forgo short-term profitability in favor of long-term reputation gains through improvements to service quality

The Determinants And Implications Of Firms\u27 Workforce Configuration: The Case Of Home Health

A fundamental challenge for labor-intensive firms facing demand uncertainty is how to configure their workforce to improve quality and performance. This dissertation investigates determinants of firms\u27 workforce configuration, with a focus on the mix of temporary and permanent nurses, and its implications on patient outcomes, a central measure of performance in health care. This is a timely topic since many industries including health care are increasingly using alternative work arrangements. This dissertation uses novel and rich proprietary data from a large US freestanding home health company. The data provide detailed information on both the consumption and production sides of home health care delivery: utilization, referral sources, risk factors and health outcomes of patients as well as nurses’ work logs and human resources characteristics. In Chapter 1, I investigate the effect of firms’ labor mix on patient readmission, a main quality marker in post-acute care, using exogenous variation in full-time nurses’ activeness in the patient’s neighborhood and unavailability of nearest full-time nurses. I find that patients who received one standard-deviation higher proportion of full-time nurse visits were 7 percent less likely to be readmitted. In Chapter 2, I investigate the effect of reputation on firms’ labor mix strategy under demand uncertainty. Firms face a trade-off in using temporary nurses: they provide flexibility in responding to demand fluctuations but may impede the establishment of reputation through lower quality of service. I present and test a model of firm’s labor mix choices where the labor mix dynamically shapes the market’s perception of the firm’s quality (i.e. reputation), and the firm’s demand is in turn stochastically linked to its reputation. I find evidence consistent with the model: firms with lower reputation, especially young firms, increased the percentage of full-time nurses with demand volatility. Young firms trade off short-term profitability for long-term reputation gains. In Chapter 3, I investigate the effect of firms’ workforce assignment on readmission through care discontinuity or handoffs. Using exogenous variation in workflow interruption caused by providers’ inactivity, I find handoffs to increase the likelihood of readmission. One in four readmissions during home health would be avoided if handoffs were eliminated

The Effect of Workforce Assignment on Performance: Evidence from Home Health Care

In this study of more than 43,000 home health episodes following a hospitalization, handoffs between skilled nursing providers—a marker of discontinuity of care—substantially increased hospital readmissions, and were more detrimental for sicker patients. The estimates imply that a single handoff increases the likelihood of 30-day hospital readmission by 16% and that one in four hospitalizations during home health care could be avoided if handoffs were eliminated

DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models

Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition) with text prompts alone may encounter limitations due to the inherent constraints of verbal description. To this end, we introduce DreamStyler, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer. DreamStyler optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality. In addition, with content and style guidance, DreamStyler exhibits flexibility to accommodate a range of style references. Experimental results demonstrate its superior performance across multiple scenarios, suggesting its promising potential in artistic product creation

AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks

To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image. However, because of the low semantic correspondence between arbitrary content and artworks, the attention module repeatedly abuses specific local patches from the style image, resulting in disharmonious and evident repetitive artifacts. To overcome this limitation and accomplish impeccable artistic style transfer, we focus on enhancing the attention mechanism and capturing the rhythm of patterns that organize the style. In this paper, we introduce a novel metric, namely pattern repeatability, that quantifies the repetition of patterns in the style image. Based on the pattern repeatability, we propose Aesthetic Pattern-Aware style transfer Networks (AesPA-Net) that discover the sweet spot of local and global style expressions. In addition, we propose a novel self-supervisory task to encourage the attention mechanism to learn precise and meaningful semantic correspondence. Lastly, we introduce the patch-wise style loss to transfer the elaborate rhythm of local patterns. Through qualitative and quantitative evaluations, we verify the reliability of the proposed pattern repeatability that aligns with human perception, and demonstrate the superiority of the proposed framework.Comment: Accepted by ICCV 2023. Code is available at this https://github.com/Kibeom-Hong/AesPA-Ne

Quantitative Analysis of Warnings in Building Information Modeling (BIM)

Building Information Modeling (BIM) provides automatic detection of design-related errors by issuing warning messages for potential problems related to model elements. However, if not properly managed, the otherwise useful warning feature of BIM can significantly reduce the speed of model processing and increase the size of models. As the first study of its kind, this study proposes to apply the Pareto analysis to investigate BIM warnings in terms of type and frequency. Based on warning data collected from three California healthcare projects, the analysis revealed that the 15-80 rule applies across the case projects and their design phases—15% of the warning messages are responsible for nearly 80% of the warnings. Two other noteworthy findings include: (1) only the schematic design phase indicates a different Pareto rule of 25-80, as well as warning pattern from other design phases due to its unique purpose; and (2) the decisions of individual design teams are a major variable in the pattern of warning types. Lastly, time estimation for warning corrections is proposed based on learning curve theory to support efficient BIM warning management practices. The results and warning classifications presented in this study are expected to contribute to the design management and modeling practices of design teams involved in large, complex projects.Keywords: Building information modeling, Design errors, Design management, Decision making, Pareto analysi

Genomic GPS: using genetic distance from individuals to public data for genomic analysis without disclosing personal genomes

Genomic global positioning system (GPS) applies the multilateration technique commonly used in the GPS to genomic data. In the framework we present here, investigators calculate genetic distances from their samples to reference samples, which are from data held in the public domain, and share this information with others. This sharing enables certain types of genomic analysis, such as identifying sample overlaps and close relatives, decomposing ancestry, and mapping of geographical origin without disclosing personal genomes. Thus, our method can be seen as a balance between open data sharing and privacy protection.This research was supported by the National Research Foundation of Korea (NRF) grant (grant number 2019R1A2C2002608) and the Bio & Medical Technology Development Program of the NRF (grant number 2017M3A9B6061852) funded by the Korean government, Ministry of Science and ICT. E.E. was supported by National Science Foundation grants 0513612, 0731455, 0729049, 0916676, 1065276, 1302448, 1320589, 1331176, and 1815624, and National Institutes of Health grants K25-HL080079, U01- DA024417, P01-HL30568, P01-HL28481, R01-GM083198, R01-ES021801, R01- MH101782, and R01-ES022282. E.E. was supported in part by the NIH BD2K award, U54EB020403. We acknowledge the support of the NINDS Informatics Center for Neurogenetics and Neurogenomics (P30 NS062691)

IL-15 promotes self-renewal of progenitor exhausted CD8 T cells during persistent antigenic stimulation

In chronic infections and cancer, exhausted CD8 T cells exhibit heterogeneous subpopulations. TCF1+PD-1+ progenitor exhausted CD8 T cells (Tpex) can self-renew and give rise to Tim-3+PD-1+ terminally differentiated CD8 T cells that retain their effector functions. Tpex cells are thus essential to maintaining a pool of antigen-specific CD8 T cells during persistent antigenic stimulation, and only they respond to PD-1-targeted therapy. Despite their potential as a crucial therapeutic target for immune interventions, the mechanisms controlling the maintenance of virus-specific Tpex cells remain to be determined. We observed approximately 10-fold fewer Tpex cells in the spleens of mice chronically infected with lymphocytic choriomeningitis virus (LCMV) one-year post-infection (p.i.) than at three months p.i. Similar to memory CD8 T cells, Tpex cells have been found to undergo self-renewal in the lymphoid organs, prominently the bone marrow, during chronic LCMV infection. Furthermore, ex vivo treatment with IL-15 preferentially induced the proliferation of Tpex cells rather than the terminally differentiated subsets. Interestingly, single-cell RNA sequencing analysis of LCMV-specific exhausted CD8 T cells after ex vivo IL-15 treatment compared with those before treatment revealed increased expression of ribosome-related genes and decreased expression of genes associated with the TCR signaling pathway and apoptosis in both Tpex and Ttex subsets. The exogenous administration of IL-15 to chronically LCMV-infected mice also significantly increased self-renewal of Tpex cells in the spleen and bone marrow. In addition, we assessed the responsiveness of CD8 tumor-infiltrating lymphocytes (TILs) from renal cell carcinoma patients to IL-15. Similar to the data we obtained from chronic viral infection in mice, the expansion of the Tpex subset of PD-1+ CD8 TILs upon ex vivo IL-15 treatment was significantly higher than that of the terminally differentiated subset. These results show that IL-15 could promote self-renewal of Tpex cells, which has important therapeutic implications

The Determinants and Implications of Firms\u27 Workforce Configuration: The Case of Home Health

A fundamental challenge for labor-intensive firms facing demand uncertainty is how to configure their workforce to improve quality and performance. This dissertation investigates determinants of firms\u27 workforce configuration, with a focus on the mix of temporary and permanent nurses, and its implications on patient outcomes, a central measure of performance in health care. This is a timely topic since many industries including health care are increasingly using alternative work arrangements. This dissertation uses novel and rich proprietary data from a large US freestanding home health company. The data provide detailed information on both the consumption and production sides of home health care delivery: utilization, referral sources, risk factors and health outcomes of patients as well as nurses’ work logs and human resources characteristics. In Chapter 1, I investigate the effect of firms’ labor mix on patient readmission, a main quality marker in post-acute care, using exogenous variation in full-time nurses’ activeness in the patient’s neighborhood and unavailability of nearest full-time nurses. I find that patients who received one standard-deviation higher proportion of full-time nurse visits were 7 percent less likely to be readmitted. In Chapter 2, I investigate the effect of reputation on firms’ labor mix strategy under demand uncertainty. Firms face a trade-off in using temporary nurses: they provide flexibility in responding to demand fluctuations but may impede the establishment of reputation through lower quality of service. I present and test a model of firm’s labor mix choices where the labor mix dynamically shapes the market’s perception of the firm’s quality (i.e. reputation), and the firm’s demand is in turn stochastically linked to its reputation. I find evidence consistent with the model: firms with lower reputation, especially young firms, increased the percentage of full-time nurses with demand volatility. Young firms trade off short-term profitability for long-term reputation gains. In Chapter 3, I investigate the effect of firms’ workforce assignment on readmission through care discontinuity or handoffs. Using exogenous variation in workflow interruption caused by providers’ inactivity, I find handoffs to increase the likelihood of readmission. One in four readmissions during home health would be avoided if handoffs were eliminated

Exploration of errors in variance caused by using the first-order approximation in Mendelian randomization

Mendelian randomization (MR) uses genetic variation as a natural experiment to investigate the causal effects of modifiable risk factors (exposures) on outcomes. Two-sample Mendelian randomization (2SMR) is widely used to measure causal effects between exposures and outcomes via genome-wide association studies. 2SMR can increase statistical power by utilizing summary statistics from large consortia such as the UK Biobank. However, the first-order term approximation of standard error is commonly used when applying 2SMR. This approximation can underestimate the variance of causal effects in MR, which can lead to an increased false-positive rate. An alternative is to use the second-order approximation of the standard error, which can considerably correct for the deviation of the first-order approximation. In this study, we simulated MR to show the degree to which the first-order approximation underestimates the variance. We show that depending on the specific situation, the first-order approximation can underestimate the variance almost by half when compared to the true variance, whereas the second-order approximation is robust and accurate.Y