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Looking for Content Requests for HLS Modern Workplace Fire Away Friday's
As Health and Life Sciences moves forward with a regular rhythm for our Modern Workplace Fire Away Friday's we want to make sure that content we deliver matches your needs. To that end we would love to have you submit your requests here for content in the collaboration area. Looking for Teams best practices? Let us know. Looking for new ways to aggregate and visual data for your org? Let us know. We look forward to your requests and then we will see you online...live!
Understanding the Role of SASSA Grants: A Discussion
How have https://onlinesassastatuscheck.co.za/ Grants impacted poverty alleviation and social welfare in South Africa? Join the conversation to share your insights, experiences, and analysis of the role SASSA plays in supporting vulnerable populations across the country.
"Enhancing Service Delivery at NSFAS through Microsoft Technologies"
By integrating Microsoft technologies, https://applyfornsfas.co.za/ can streamline application processes, enhance communication with applicants, and automate administrative tasks, resulting in improved efficiency, transparency, and service delivery to students in need.
Excel Template
Hi, I would like a suggestion on how to create an excel sheet to monitor sample reception monthly and yearly. I would like to include variables such as total samples received monthly, number of samples tested, number of positive and the district the samples were received from. I am not really a tech guru but I follow instructions. ThanksDevelopment of Health Care Strategy
I have started a new career with Home Health Care organisation that is engaged in: Working for the community of the vulnerable aged and pensioners Enhancing Later life Health of people Fitness for the future In current circumstances of Coronavirus pandemic, my job will be very challenging. It involves developing strategies for avoiding the virus and curing those who are sick due to the virus. The aged are most at risk and need special care and attention, almost like intensive care of patients at home. Dementia care strategy - focus on people living with dementia, people not living with dementia, families and friends of people living with dementia, colleagues and volunteers. Hand Hygiene strategy - ensuring care and safety of every individual from the attacks of micro-organisms, bacteria, chain of infection and the good health of the hands. Diabetes care strategy - focus on people who are suffering from Type 1 Diabetes and Type 2 Diabetes, managing diabetes by medication and blood glucose monitoring, administering and injecting insulin, elderly diabetics, diet and weight management. A check-list of the following procedures may be followed in Hand Hygiene as a matter of great emergency today: - Train and educate everyone in hand washing correctly. Many presentations have been illustrated by government agencies, including posters published by WHO. - Use of soaps for washing and bathing the hands, face, feet and other parts of the skin that are exposed. The infection is spread by physical touch and by coughing and sneezing when droplets touch other people within a very short distance. - Proper hand and face drying. Wet towels and tissues may carry the viral bacteria, they must not be touched and must be put in washing machine and dryer. Hot air drying may be the safe method. - Skin related problems. Care must be taken when treating skin that shows rash, inflammation, burns, sores and pores. Water may attract more bacteria and micro-organisms. An antiseptic soap and cream may be useful. - Frequently unwashed parts of the hands. Water should not be run over or splashed in high volume, as that will leave unwashed and dry parts. Best way to clean is that which shows shinning by rubbing and massaging all over gently. This will ensure that treated water goes beyond the hair and cleans the skin thoroughly. - Home care and hand hygiene. Ensure that the surface of everything is sparkling clean, washed and shinning. Take care of glass, door and window handles, pots and pans and other utensils, wash up all the dishes, cups, saucers, tumblers put in the sink or left on tables and worktops, clean up also the mobile phones, laptops and computer keyboards and screens, chairs, settee and furnishing. - Disposable gloves. Wear them properly and ensure they are fit and new, dispose of them properly and carefully after use so that nobody touches them and they don't touch surface of things. It is better to wash the hands with soap after removing the disposable gloves.
24 hour time slots from a specific time point
Hi! Is there a formula to make 24 hour time slots from a specific time? For example, 3/3/25 @ 0810. The 1st 24 hour box would be (3/3/25 @ 0810 - 3/4/25 @ 0810), 2nd 24 hour box (3/4/25 @ 0810 - 3/5/25 @ 0810), etc. Also, once those 24 hour prefilled dates and times are created for 15 days, is it possible to take a shreadsheet with dates and time entries and place them into the correlating 24 hour time slots from a specific time? For example, if an entry was dated and timed 3/3/25 @ 0935, and 3/4/25 @ 0700, both of those would fall into the 1st 24 hour box and so on. Thank you in advance for saving me hundreds of hours doing this by hand!Did Microsoft make an effort to lift poverty in South Africa?
What specific initiatives has Microsoft undertaken to address poverty in South Africa, and how do these efforts compare with government programs like https://srd-sassa-gov.co.za/ and other social assistance initiatives aimed at alleviating poverty and promoting economic empowerment?AI and Machine Learning Revolutionizing Healthcare
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the healthcare landscape, bringing about a new era of personalized, efficient, and data-driven care. These technologies are revolutionizing various aspects of healthcare, from diagnosis and treatment to drug discovery and patient management. Diagnosis and Treatment: AI and ML algorithms are being used to analyze medical images, such as X-rays, CT scans, and MRIs, with unprecedented accuracy. This allows for earlier and more accurate diagnosis of diseases like cancer, heart disease, and neurological disorders. Additionally, AI-powered systems can analyze patient data, including medical history, lab results, and genetic information, to predict the risk of developing certain diseases and recommend personalized treatment plans. Drug Discovery and Development: AI and ML are playing a crucial role in accelerating drug discovery and development. These technologies can analyze vast amounts of data to identify potential drug targets and predict the efficacy and safety of new drugs. This can significantly reduce the time and cost of bringing new drugs to market. Patient Management and Monitoring: AI-powered chatbots and virtual assistants are being used to provide patients with 24/7 support and information. These systems can answer patients' questions, schedule appointments, and even monitor their health status remotely. Additionally, AI algorithms can analyze patient data to identify those at risk of complications or readmission, allowing for early intervention and improved outcomes. Administrative Tasks and Workflow Optimization: AI and ML can automate many administrative tasks in healthcare, such as scheduling appointments, processing claims, and managing medical records. This frees up healthcare professionals to focus on providing direct patient care. Additionally, AI-powered systems can analyze data to identify inefficiencies in workflows and suggest improvements, leading to increased efficiency and cost savings. Challenges and Ethical Considerations: Despite the numerous benefits, AI and ML in healthcare also present challenges and ethical considerations. Data privacy and security are critical concerns, as AI systems rely on vast amounts of patient data. Additionally, ensuring fairness and avoiding bias in AI algorithms is crucial to prevent discrimination and ensure equitable access to healthcare. Conclusion: AI and ML are revolutionizing healthcare, offering the potential to improve patient outcomes, reduce costs, and increase efficiency. However, it is important to address the challenges and ethical considerations associated with these technologies to ensure their responsible and equitable implementation. As AI and ML continue to evolve, the future of healthcare promises to be more personalized, data-driven, and accessible than ever before.Introducing Scalable and Enterprise-Grade Genomics Workflows in Azure ML
Genomics workflows are essential in bioinformatics as they help researchers analyse and interpret vast amounts of genomic data. However, creating a consistent and repeatable environment with specialized software and complex dependencies can be challenging, making integration with CI/CD tools difficult, too. Azure Machine Learning (Azure ML) is a cloud-based platform that provides a comprehensive set of tools and services for developing, deploying, and managing machine learning models. Azure ML offers great repeatability and auditability features natively that not many workflow solutions offer. It provides a highly integrated and standardised environment for running workflows, ensuring that each step is executed in a consistent and reproducible manner. This feature is particularly useful for genomics workflows that require the use of multiple tools and software packages of certain versions with specific dependencies. In this blog post, we will show how Azure ML can run genomics workflows efficiently and effectively, in addition to being an end-to-end platform for machine learning model training and deployment. Figure 1 illustrates an example of such a workflow. Figure 1: A sample genomics workflow running in Azure ML, consisting of 3 steps. A reference genome input dataset flows into the indexer step, while the sequence quality step gets its data from a folder of DNA sequences (".fastq" files). Azure ML has comprehensive audit and logging capabilities that track and record every step of the workflow, ensuring traceability and repeatability. One of the critical features of Azure ML to achieve these capabilities is its support for users to be able to specify https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?tabs=cli#create-an-environment for each workflow step, which guarantees consistent environment execution. These environments can be versioned and centrally shared. Workflow steps within pipelines then can refer to a particular environment. Figure 2 shows one such environment, bwa, version "5". As we make modifications in the environment definition, the new version will be registered as "6", however, we will still be able to continue to use older versions. Figure 2: An example Azure ML environment, defining a Docker image containing the BWA bioinformatics software package. This is the 5th version of this environment registered under the name, "bwa". Like environments, Azure ML supports user created pipeline https://learn.microsoft.com/en-us/azure/machine-learning/concept-component that can be centrally registered for reuse in other pipelines, also versioned, and with an audit log of their usage. Runs are logged together with standard out and error streams generated by the underlying processes, automatically. https://learn.microsoft.com/en-gb/azure/machine-learning/how-to-use-mlflow-configure-tracking?tabs=cli%2Cmlflow and adding custom tags to all assets and runs are supported, too. This feature ensures that the results are consistent and reproducible, saving users’ time. An example versioned component is shown in Figure 3. Figure 3: An Azure ML component named "BWA Indexer". It is a self-contained, re-usable, versioned piece of code that does one step in a machine learning pipeline: running the bwa indexer command, in this instance. Versioning is not limited to environments and pipeline components. Another essential feature of Azure ML is its support for versioning all input https://learn.microsoft.com/en-gb/azure/machine-learning/concept-data and genomic data, including overall pipeline input, and as well as intermediate step and final outputs, if needed. This feature enables users to keep track of dataset changes and ensure that the same version is used consistently across different runs of the workflow, or in others. There are many genomics workflow engines which are very good with multiple parallel execution when it comes to processing files in parallel. However, Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-parallel support parallel running both at the file-level (one by one, or 3 files at a time etc) and at the file chunk-level (50 MB of data per process, or 20 KB of text per node etc) where appropriate as supported by the consuming application, enabling the processing of large genomic datasets efficiently across elastic compute clusters that can auto-scale. Pipelines can even also https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-attach-compute-targets#local-computer for test/development phases, and of course support powerful CPU and GPU-based VMs, https://learn.microsoft.com/en-gb/azure/machine-learning/how-to-use-low-priority-batch?tabs=cli or on-demand compute clusters, https://learn.microsoft.com/en-gb/azure/machine-learning/quickstart-spark-jobs?tabs=cli, and other compute targets such as https://learn.microsoft.com/en-gb/azure/machine-learning/how-to-attach-kubernetes-anywhere, making it flexible for different use cases. Azure ML has integrations with https://learn.microsoft.com/en-us/azure/machine-learning/how-to-devops-machine-learning and https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning?tabs=userlevel for CI/CD, making it easy to deploy and manage genomics workflows in a production environment, which in turn makes GenomicsOps possible. Well established pipelines ready for "production use" can be published, and called on-demand or from other Azure services including the https://learn.microsoft.com/en-us/azure/data-factory/transform-data-machine-learning-service. This means we can create a schedule for running pipelines automatically, or whenever data become available. Thanks to its Python SDK, command line utility (https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli?tabs=public), REST-API, and user-friendly UI, it makes it possible to develop pipelines and initiate pipeline runs from any preferred means, also providing easy monitoring and management of workflows. That said, event-based triggers and notifications are also supported. For instance, one can set up an email alert that will be triggered whenever a genomics pipeline finishes execution. As compute and storage are de-coupled, any pipeline input or output stored in an Azure ML datastore or blob storage can also be accessed by Azure ML’s https://learn.microsoft.com/en-gb/azure/machine-learning/quickstart-run-notebooks Notebooks for any upstream or downstream analysis. Azure ML is a managed PaaS service, making it an accessible and easy to set up platform for genomics researchers and bioinformaticians. Additionally, it has a https://learn.microsoft.com/en-gb/azure/machine-learning/how-to-setup-vs-code for local development and has a https://learn.microsoft.com/en-gb/azure/machine-learning/concept-workspace for managing pipeline projects, enabling collaboration, and Azure role-based access control (RBAC). In conclusion, Azure ML comes with advanced security features, including AD authentication, public & private endpoints, subscription-based event triggers, storage backed by the Azure Storage Service that comes with encryption at rest and in transit, and application insights, making it a reliable and already proven enterprise platform that can also be natively used for genomics research. For a more detailed tutorial that shows how to set up and run the example workflow shown in Figure 1, as well as for all the source code for creating the aforementioned sample environments and components, please check out this GitHub repository: https://github.com/truehand/azureml-genomicsCan Remote Patient Monitoring address many challenges in healthcare?
People who are old might need medical care and observation more often than younger people. That is to be expected as seniors are more prone to diseases and physical injury. Regular diseases aside, elders are also prone to long-term illnesses like dementia or Parkinson's disease. Moreover, lots of them even find it difficult to drive to a clinic or use smartphone applications to order a cab. It is estimated that the end of this decade will see tens of millions of people reach or surpass eighty years of age. The frailty increases drastically as one crosses eighty and they need constant support and care. Experts have warned that the United States is about to reach a tipping point in the area of eldercare, and the country is grossly unprepared. That is mainly because of the rapidly increasing cost of care, coupled with a dearth of home caregivers. In light of this looming problem, https://www.osplabs.com/remote-patient-monitoring-system/ technology has been touted as a promising solution to address the problem of senior care. As the name suggests, remote patient monitoring systems enable caregivers and providers to monitor a patient’s health situation outside clinical settings. The idea of a https://www.osplabs.com/remote-patient-monitoring-system/ based device is to collect the health information of a patient in real-time and transmit it to a physician. The physician can view the health vitals and know about the patient’s health situation. In other words, the doctor and patient need not even be in the same city. If the doctor sees abnormal health data, he or she can immediately notify the patient about it and have an in-person visit arranged. This is an excellent way for a single physician to observe the health vitals of multiple seniors from a remote location and ensure timely care. Recent innovations in remote healthcare monitoring have given immense hope to many physicians and people alike. Let’s look at some innovative remote health monitoring systems that help with senior care - Fall Detector As mentioned earlier, seniors who are of 80 years and above are frail and are at greater risk of accidents and physical injury. A little slip and fall could have catastrophic results. So, a fall detector installed at home alleviates this risk and allows family members to breathe easy. A fall detector is like a camera but without the usual video feed. It detects movement patterns of people in the room and knows if someone fell. It is powered by an advanced artificial intelligence algorithm and knows the difference between a person who fell, and someone who performs activities like bending over, squatting, or anything else. If it detects a fall, it automatically notifies people from a pre-determined list through push notifications or alerts. Moreover, since it doesn’t have a regular video feed, it ascertains privacy for the person being observed. This type of home health monitoring system also ensures peace of mind to the families of elders who might not live with them. Wearable Diagnostic Band This is a band to be worn on the wrist. It detects vitals like body temperature, heart rate, blood pressure, and electrocardiogram at regular intervals throughout the day. In case of deviation from established benchmarks of normalcy, the device automatically notifies the families of the user and also a designated caregiver. This type of patient monitoring software is excellent for seniors who can’t afford home care, and whose family doesn’t live with them. This device could also work as a kind of hospital monitoring system and enable physicians to track patient health in real-time.Part 11 Compliance guide for Dynamics or Dynamics 365
Hello, i wanted to ask if there any compliance guides from Microsoft or other organizations, for FDA Part 11 compliance specifically for Dynamics or Dynamics 365. I have seen some Part 11 Compliance docs for Office 365, Azure, and SharePoint specifically, but nothing for Dynamics or Dynamics 365. I wasn't sure if something existed or not. thank you, mark
Help Creating an Excel File to Calculate Student Commutes to Clinical Sites and Filter Site Details
Hello, I’m hoping someone can help me create an Excel document for a fairly complex need. I oversee a large number of students across my state and am trying to ensure fairness in the clinical rotations they are assigned to. I would like to set up an Excel spreadsheet that can: House student names along with their home addresses. List multiple clinical site addresses that students may rotate to. Calculate and display the commute time and distance (in miles) from each student’s home address to each potential clinical site. Additionally (if possible), I would love to be able to filter the clinical sites based on certain characteristics, such as: Types of MRI scans performed at the site Patient volume (high volume vs slower paced) Type of location (small town hospital, large city hospital, or mobile MRI unit) If the filtering features are too complicated, I would at least like help setting up the commute calculations between home addresses and multiple site addresses. I appreciate any guidance or ideas. Thank you so much in advance for your help!AI-powered tool predicts gene activity in cancer cells from biopsy images
To determine the type and severity of a cancer, pathologists typically analyze thin slices of a tumor biopsy under a microscope. But to figure out what genomic changes are driving the tumor's growth -; information that can guide how it is treated -; scientists must perform genetic sequencing of the RNA isolated from the tumor, a process that can take weeks and costs thousands of dollars. Now, Stanford Medicine researchers have developed an artificial intelligence-powered computational program that can predict the activity of thousands of genes within tumor cells based only on standard microscopy images of the biopsy. The tool, described online in Nature Communications Nov. 14, was created using data from more than 7,000 diverse tumor samples. The team showed that it could use routinely collected biopsy images to predict genetic variations in breast cancers and to predict patient outcomes. This kind of software could be used to quickly identify gene signatures in patients' tumors, speeding up clinical decision-making and saving the health care system thousands of dollars." Olivier Gevaert, PhD, professor of biomedical data science and senior author of the paper The work was also led by Stanford graduate student Marija Pizuria and postdoctoral fellows Yuanning Zheng, PhD, and Francisco Perez, PhD. Driven by genomics Clinicians have increasingly guided the selection of which cancer treatments -; including chemotherapies, immunotherapies and hormone-based therapies -; to recommend to their patients based on not only which organ a patient's cancer affects, but which genes a tumor is using to fuel its growth and spread. Turning on or off certain genes could make a tumor more aggressive, more likely to metastasize, or more or less likely to respond to certain drugs. However, accessing this information often requires costly and time-consuming genomic sequencing. Gevaert and his colleagues knew that the gene activity within individual cells can alter the appearance of those cells in ways that are often imperceptible to a human eye. They turned to artificial intelligence to find these patterns. The researchers began with 7,584 cancer biopsies from 16 different of cancer types. Each biopsy had been sliced into thin sections and prepared using a method known as hematoxylin and eosin staining, which is standard for visualizing the overall appearance of cancer cells. Information on the cancers' transcriptomes -; which genes the cells are actively using -; was also available. A working model After the researchers integrated their new cancer biopsies as well as other datasets, including transcriptomic data and images from thousands of healthy cells, the AI program -; which they named SEQUOIA (slide-based expression quantification using linearized attention) -; was able to predict the expression patterns of more than 15,000 different genes from the stained images. For some cancer types, the AI-predicted gene activity had a more than 80% correlation with the real gene activity data. In general, the more samples of any given cancer type that were included in the initial data, the better the model performed on that cancer type. "It took a number of iterations of the model for it to get to the point where we were happy with the performance," Gevaert said. "But ultimately for some tumor types, it got to a level that it can be useful in the clinic." Gevaert pointed out that doctors are often not looking at genes one at a time to make clinical decisions, but at gene signatures that include hundreds of different genes. For instance, many cancer cells activate the same groups of hundreds of genes related to https://www.news-medical.net/health/What-Does-Inflammation-Do-to-the-Body.aspx, or hundreds of genes related to cell growth. Compared with its performance at predicting individual gene expression, SEQUOIA was even more accurate at predicting whether such large genomic programs were activated. To make the data accessible and easy to interpret, the researchers programmed SEQUOIA to display the genetic findings as a visual map of the tumor biopsy, letting scientists and clinicians see how genetic variations might be distinct in different areas of a tumor. Predicting patient outcomes To test the utility of SEQUOIA for clinical decision making, Gevaert and his colleagues identified breast cancer genes that the model could accurately predict the expression of and that are already used in commercial breast cancer genomic tests. (The Food and Drug Administration-approved MammaPrint test, for instance, analyzes the levels of 70 breast-cancer-related genes to provide patients with a score of the risk their cancer is likely to recur.) "Breast cancer has a number of very well-studied gene signatures that have been shown over the past decade to be highly correlated with treatment responses and patient outcomes," Gevaert said. "This made it an ideal test case for our model." SEQUOIA, the team showed, could provide the same type of genomic risk score as MammaPrint using only stained images of tumor biopsies. The results were repeated on multiple different groups of breast cancer patients. In each case, patients identified as high risk by SEQUOIA had worse outcomes, with higher rates of cancer recurrence and a shorter time before their cancer recurred. The AI model can't yet be used in a clinical setting -; it needs to be tested in clinical trials and be approved by the FDA before it's used in guiding treatment decisions -; but Gevaert said his team is improving the algorithm and studying its potential applications. In the future, he said, SEQUOIA could reduce the need for expensive gene expression tests. "We've shown how useful this could be for breast cancer, and we can now use it for all cancers and look at any gene signature that is out there," he said. "It's a whole new source of data that we didn't have before." Scientists from Roche Diagnostics were also authors of the paper. Funding for this research was provided by the National Cancer Institute (grant R01 CA260271), a fellowship of the Belgian American Educational Foundation, a grant from Fonds Wetenschappelijk Onderzoek-Vlaanderen, the Fulbright Spanish Commission and Ghent University
Survey opportunity | Data governance, compliance and risk management requirements
With the ever-increasing number of regulatory standards (like GDPR, HIPAA, FISMA) along with the increasing threat to backed-up data and the process of recovery, data governance & risk management for backup and DR has become an area to be focused on. The Azure BCDR product group has been exploring various use cases in this area and would appreciate any and every input from stakeholders who have worked with customers in regulated industries such as healthcare and life sciences. If you or your end customers have been involved in processes around data governance, compliance or risk management (for Azure in general), we would love to connect with you and learn more about these processes, challenges faced and overall experience of various stakeholders. Please help us out by filling up this short survey! Your inputs would greatly help us prioritize the right set of product investments. Link to survey: https://aka.ms/DataGovernanceAndComplianceSurveyHow Emerging Opportunities team at Microsoft uses Azure DevOps for project management
We want to share how we created a project management system with Scrum practices based on Azure DevOps. We chose the Scrum template from Azure DevOps that supports Work Items - Epics, Features, Product Backlog Items (PBIs) and Tasks as shown below. Epic represents the overall Project. Features are used to organize specific objectives within the project. To build the features, we need User Stories that group work into logical collections of activities. Tasks captures the actual work that needs to be done to satisfy the User story. During the planning of Sprint, each PBI in the current Sprint is given an estimated effort. Effort is defined as a relative estimate of the amount of work required to fully implement a PBI. To set the effort, you can use any numeric unit of measurement. e.g., powers of 2 (1, 2, 4, 😎 and the Fibonacci sequence (1, 2, 3, 5, 8, etc.). How do we know we are progressing in a Sprint and Sprint-over-Sprint? Sprint Burndown - By reviewing a sprint burndown report, we can monitor how much work remains in a sprint backlog, understand how quickly our team has completed tasks, and predict when our team will achieve the goals of the sprint. We defined it by the number of Tasks under each PBI. To achieve this, the measurement to do a task should be consistent. In our case, Task should be a work that can be completed in a day or less. If a Task, was not marked as Done in 3 days, it was classified as a blocker. This enabled us to easily find any blockers and focus on resolving them to keep the project moving forward. Velocity - We track the velocity to determine how much work we can perform sprint-over-sprint. Velocity provides an indication of how much work a team can complete during a sprint based either on a count of work items completed or the sum of estimates made to Effort (PBIs). By using Azure DevOps for project management, the built-in reporting for burndown charts and Sprint planning allows us to analyze progress, have reasonable expectations from the team, focus on prioritizing and keep everyone fully informed. Resources: https://docs.microsoft.com/en-us/azure/devops/boards/sprints/scrum-overview?view=azure-devops-2020
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