Measuring time utilization of pharmacists in the Birmingham Free Clinic dispensary.

BACKGROUND: Free and charitable clinics are a critical part of America's healthcare safety net. Although informatics tools have the potential to mitigate many of the organizational and service-related challenges facing these clinics, little research attention has been paid to the workflows and potential impact of electronic systems in these settings. In previous work, we performed a qualitative investigation at a free clinic dispensary to identify workflow challenges that may be alleviated through introduction of informatics interventions. However, this earlier study did not quantify the magnitude of these challenges. Time-motion studies offer a precise standard in quantifying healthcare workers' time expenditures on clinical activities, and can provide valuable insight into system specifications. These data, informed by a lean healthcare perspective, provide a quality improvement framework intended to maximize value and eliminate waste in inefficient workflow processes. METHODS: We performed a continuous observation time-motion study in the Birmingham Free Clinic dispensary. Two researchers followed pharmacists over the course of three general clinic sessions and recorded the duration of specific tasks. Pharmacists were then asked to identify tasks as value-added or non-value-added to facilitate calculation of the value quotient, a metric used to determine a workflow's level of efficiency. RESULTS: Four high-level workflow categories occupied almost 95 % of pharmacist time: prescription (Rx) preparation (39.8 %), clinician interaction (21.5 %), EMR operations (14.8 %), and patient interaction (18.7 %). Pharmacists invested the largest portion of time in prescription preparation, with 21.8 % of pharmacist time spent handwriting medication labels. Based on value categorizations made by the pharmacists, the average value quotient was found to be 40.3 %, indicating that pharmacists spend more than half of their time completing tasks they consider to be non-value-added. CONCLUSIONS: Our results show that pharmacists spend a large portion of their time preparing prescriptions, primarily the handwritten labeling of medication bottles and documentation tasks, which is not an optimal utilization of pharmacist expertise. The value quotient further supports that there are many wasteful tasks that may benefit from workflow redesign and health information technology, which could result in efficiency improvements for pharmacists.

Learning to encode cellular responses to systematic perturbations with deep generative models.

Cellular signaling systems play a vital role in maintaining homeostasis when a cell is exposed to different perturbations. Components of the systems are organized as hierarchical networks, and perturbing different components often leads to transcriptomic profiles that exhibit compositional statistical patterns. Mining such patterns to investigate how cellular signals are encoded is an important problem in systems biology, where artificial intelligence techniques can be of great assistance. Here, we investigated the capability of deep generative models (DGMs) to modeling signaling systems and learn representations of cellular states underlying transcriptomic responses to diverse perturbations. Specifically, we show that the variational autoencoder and the supervised vector-quantized variational autoencoder can accurately regenerate gene expression data in response to perturbagen treatments. The models can learn representations that reveal the relationships between different classes of perturbagens and enable mappings between drugs and their target genes. In summary, DGMs can adequately learn and depict how cellular signals are encoded. The resulting representations have broad applications, demonstrating the power of artificial intelligence in systems biology and precision medicine.

Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. Mol Cancer Res; 16(2); 269-78. ©2017 AACR.

Development of a chemically defined medium and discovery of new mitogenic growth factors for mouse hepatocytes: mitogenic effects of FGF1/2 and PDGF.

Chemically defined serum-free media for rat hepatocytes have been useful in identifying EGFR ligands and HGF/MET signaling as direct mitogenic factors for rat hepatocytes. The absence of such media for mouse hepatocytes has prevented screening for discovery of such mitogens for mouse hepatocytes. We present results obtained by designing such a chemically defined medium for mouse hepatocytes and demonstrate that in addition to EGFR ligands and HGF, the growth factors FGF1 and FGF2 are also important mitogenic factors for mouse hepatocytes. Smaller mitogenic response was also noticed for PDGF AB. Mouse hepatocytes are more likely to enter into spontaneous proliferation in primary culture due to activation of cell cycle pathways resulting from collagenase perfusion. These results demonstrate unanticipated fundamental differences in growth biology of hepatocytes between the two rodent species.