The Digital Transformation Journey of a Large Australian Hospital: A Teaching Case

With the vision of a seamless, state-wide approach to patient management, the Department of Health within the Queensland State Government of Australia embarked on a digital transformation journey. This involved the configuration and rollout of an integrated electronic medical record system (ieMR) with computerized provider order entry, ePrescribing, decision support, analytics, and research functionalities, together with new devices and work practices, to create a multi-hospital, whole-of-state digital health ecosystem. Drawing on multiple perspectives, including executives and front-line clinicians who are both optimistic and pessimistic towards the ieMR, this teaching case describes the digital transformation of the lead site, Princess Alexandra Hospital, and their experience in becoming Australia’s first large digital hospital. This case has been informed by a multi-year qualitative study involving the collection of primary (observations and interviews) and secondary data (publicly available project records) before and after the implementation. This case is relevant to undergraduate and postgraduate students in information systems, executive management, and clinical/health informatics

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated method for connecting scientific literature to synthesis insights. Starting from natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for arbitrary materials. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties, and that the model's behavior complements existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds.Comment: Added new funding support to the acknowledgments section in this versio

Using Perturbation Theory to Compute the Morphological Similarity of Diffusion Tensors

Computing the morphological similarity of diffusion tensors (DTs) at neighboring voxels within a DT image, or at corresponding locations across different DT images, is a fundamental and ubiquitous operation in the postprocessing of DT images. The morphological similarity of DTs typically has been computed using either the principal directions (PDs) of DTs (i.e., the direction along which water molecules diffuse preferentially) or their tensor elements. Although comparing PDs allows the similarity of one morphological feature of DTs to be visualized directly in eigenspace, this method takes into account only a single eigenvector, and it is therefore sensitive to the presence of noise in the images that can introduce error in to the estimation of that vector. Although comparing tensor elements, rather than PDs, is comparatively more robust to the effects of noise, the individual elements of a given tensor do not directly reflect the diffusion properties of water molecules. We propose a measure for computing the morphological similarity of DTs that uses both their eigenvalues and eigenvectors, and that also accounts for the noise levels present in DT images. Our measure presupposes that DTs in a homogeneous region within or across DT images are random perturbations of one another in the presence of noise. The similarity values that are computed using our method are smooth (in the sense that small changes in eigenvalues and eigenvectors cause only small changes in similarity), and they are symmetric when differences in eigenvalues and eigenvectors are also symmetric. In addition, our method does not presuppose that the corresponding eigenvectors across two DTs have been identified accurately, an assumption that is problematic in the presence of noise. Because we compute the similarity between DTs using their eigenspace components, our similarity measure relates directly to both the magnitude and the direction of the diffusion of water molecules. The favorable performance characteristics of our measure offer the prospect of substantially improving additional postprocessing operations that are commonly performed on DTI datasets, such as image segmentation, fiber tracking, noise filtering, and spatial normalization

Revealing the Root Causes of Digital Health Data Quality Issues: A Qualitative Investigation of the Odigos Framework

Digital health data quality is a critical concern in the healthcare industry, jeopardizing the secondary use of data for revolutionizing population health, and hindering patient care and organizational outcomes. Limited published evidence exists for explaining why these data quality issues emerge. The Odigos framework is a notable exception asserting that data quality issues emerge from three worlds: material world (e.g., technology artifact), personal world (e.g., technology users/use), and social world (e.g., organizations/ institutions) but has yet to systematically unpack the elements within these worlds. Through deductive and inductive analysis of interview data from a case study of the Emergency Department of Australia’s first large digital hospital, we apply and extend the Odigos framework by identifying elements emanating from the three worlds and their interrelationships as root causes of data quality issues. These elements can then be used by hospitals to develop strategies to proactively improve their digital health data quality

Digital Health Data Imperfection Patterns and Their Manifestations in an Australian Digital Hospital

Whilst digital health data provides great benefits for improved and effective patient care and organisational outcomes, the quality of digital health data can sometimes be a significant issue. Healthcare providers are known to spend a significant amount of time on assessing and cleaning data. To address this situation, this paper presents six Digital Health Data Imperfection Patterns that provide insight into data quality issues of digital health data, their root causes, their impact, and how these can be detected. Using the CRISP-DM methodology, we demonstrate the utility and pervasiveness of the patterns at the emergency department of Australia's major tertiary digital hospital. The pattern collection can be used by health providers to identify and prevent key digital health data quality issues contributing to reliable insights for clinical decision making and patient care delivery. The patterns also provide a solid foundation for future research in digital health through its identification of key data quality issues, root causes, detection techniques, and terminology

Comparative and functional genomics provide insights into the pathogenicity of dermatophytic fungi

ABSTRACT: BACKGROUND: Millions of humans and animals suffer from superficial infections caused by a group of highly specialized filamentous fungi, the dermatophytes, which exclusively infect keratinized host structures. To provide broad insights into the molecular basis of the pathogenicity-associated traits, we report the first genome sequences of two closely phylogenetically related dermatophytes, Arthroderma benhamiae and Trichophyton verrucosum, both of which induce highly inflammatory infections in humans. RESULTS: 97% of the 22.5 megabase genome sequences of A. benhamiae and T. verrucosum are unambiguously alignable and collinear. To unravel dermatophyte-specific virulence-associated traits, we compared sets of potentially pathogenicity-associated proteins, such as secreted proteases and enzymes involved in secondary metabolite production, with those of closely related onygenales (Coccidioides species) and the mould Aspergillus fumigatus. The comparisons revealed expansion of several gene families in dermatophytes and disclosed the peculiarities of the dermatophyte secondary metabolite gene sets. Secretion of proteases and other hydrolytic enzymes by A. benhamiae was proven experimentally by a global secretome analysis during keratin degradation. Molecular insights into the interaction of A. benhamiae with human keratinocytes were obtained for the first time by global transcriptome profiling. Given that A. benhamiae is able to undergo mating, a detailed comparison of the genomes further unraveled the genetic basis of sexual reproduction in this species. CONCLUSIONS: Our results enlighten the genetic basis of fundamental and putatively virulence-related traits of dermatophytes, advancing future research on these medically important pathogens

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts