The use of mobile applications can be seen as an important and effective way to monitor adherence and support in the self-management of complications associated with chemotherapy treatments. Notwithstanding, these applications should be tested outside the academic environment, outreaching this group of people to effectively investigate its applicability, allowing the assessment of the impact of this "new" technological intervention process. The use of mobile applications can be seen as an important and effective way to monitor adherence and support in the self-management of complications associated with chemotherapy treatments. Notwithstanding, these applications should be tested outside the academic environment, outreaching this group of people to effectively investigate its applicability, allowing the assessment of the impact of this "new" technological intervention process. We introduce a system devoted to automatically produce structured data in radiotherapy to (i) relate clinical outcomes with any variable; and (ii) optimize resources and procedures. We have designed a detailed workflow for a patient to follow during radiotherapy treatments. Four elements of Oncology Information Systems (OISs) can be mainly interrelated in our system (a) task lists to be accomplished by the staff; (b) forms to fill in at each step of the workflow; (c) generation of reports; and (d) a system to trigger new tasks, forms or reports when an needed, either automatically or manually. We handle the data dumped into reports with Visual Basic for Word code to store structured data for patients in electronic medical records (EMRs). These EMRs can be further analyzed, generating clinical real-world data in real time, i.e., at any step of the process. Our system was implemented about the beginning of 2019, producing a database filled with a pool of 1,184 patients in a year. Although one year is not the department by driving an automatized paperless workflow; and allows for an automatized and effortless collection of structured data throughout the radiotherapy process. This study reviewed the competency and threshold standards for allied health professionals to identify the inclusion of digital health competencies. A nine-stage, sequential meta-synthesis of professional standards was undertaken. Statements relevant to digital health were extracted, categorised by discipline, and coded to the level in the standards, skills or knowledge and level of learning. Eighteen standards were analysed. Of these, fourteen standards contained a total of thirty-five statements related to digital health in the themes of data governance and technologies, but not data translation. Only four disciplines included more than two statements related to digital health. The study highlighted four key gaps in the Standards. Statements in competency and threshold standards for allied health professionals lack reference to digital health, with predominantly information management statements. The statements are ambiguously worded, and could be interpreted to only refer to paper records management, and when there is a reference to digital health, it is more likely to be a skill as opposed to knowledge, typically at the indicator or cue level, and largely a lower level of learning (Bloom's). The lack of digital health in standards may result in limited instruction in already full tertiary education curriculum. Digital health represents a major gap in competency statements for all allied health disciplines, signifying the need for a national approach to developing quality and specific digital health competencies, to support allied health graduates being prepared to work in the digital health age. Digital health represents a major gap in competency statements for all allied health disciplines, signifying the need for a national approach to developing quality and specific digital health competencies, to support allied health graduates being prepared to work in the digital health age. Electronic Health Records (EHRs) contain scanned documents from a variety of sources such as identification cards, radiology reports, clinical correspondence, and many other document types. We describe the distribution of scanned documents at one health institution and describe the design and evaluation of a system to categorize documents into clinically relevant and non-clinically relevant categories as well as further sub-classifications. Our objective is to demonstrate that text classification systems can accurately classify scanned documents. We extracted text using Optical Character Recognition (OCR). We then created and evaluated multiple text classification machine learning models, including both "bag of words" and deep learning approaches. We evaluated the system on three different levels of classification using both the entire document as input, as well as the individual pages of the document. Finally, we compared the effects of different text processing methods. A deep learning model using ClinicalBERT performed best. This model distinguished between clinically-relevant documents and not clinically-relevant documents with an accuracy of 0.973; between intermediate sub-classifications with an accuracy of 0.949; and between individual classes with an accuracy of 0.913. Within the EHR, some document categories such as "external medical records" may contain hundreds of scanned pages without clear document boundaries. https://www.selleckchem.com/products/sodium-ascorbate.html Without further sub-classification, clinicians must view every page or risk missing clinically-relevant information. Machine learning can automatically classify these scanned documents to reduce clinician burden. Using machine learning applied to OCR-extracted text has the potential to accurately identify clinically-relevant scanned content within EHRs. Using machine learning applied to OCR-extracted text has the potential to accurately identify clinically-relevant scanned content within EHRs.The mammalian skin is equipped with a highly dynamic stratified epithelium. The maintenance and regeneration of this epithelium is supported by basally located keratinocytes, which display stem cell properties, including lifelong proliferative potential and the ability to undergo diverse differentiation trajectories. Keratinocytes support not just the surface of the skin, called the epidermis, but also a range of ectodermal structures including hair follicles, sebaceous glands, and sweat glands. Recent studies have shed light on the hitherto underappreciated heterogeneity of keratinocytes by employing state-of-the-art imaging technologies and single-cell genomic approaches. In this mini review, we highlight major recent discoveries that illuminate the dynamics and cellular mechanisms that govern keratinocyte differentiation in the live mammalian skin and discuss the broader implications of these findings for our understanding of epithelial and stem cell biology in general.