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auto;\r\n}\r\n.custom_widget_MicrosoftFooter_c-uhff-nav_1w55e_69 {\r\n margin: 0 auto;\r\n max-width: calc(100rem + 10%);\r\n padding: 0 5%;\r\n box-sizing: inherit;\r\n &:before,\r\n &:after {\r\n content: ' ';\r\n display: table;\r\n clear: left;\r\n }\r\n @media only screen and (max-width: 1083px) {\r\n padding-left: 0.75rem;\r\n }\r\n .custom_widget_MicrosoftFooter_c-heading-4_1w55e_97 {\r\n color: #616161;\r\n word-break: break-word;\r\n font-size: 0.9375rem;\r\n line-height: 1.25rem;\r\n padding: 2.25rem 0 0.25rem;\r\n font-weight: 600;\r\n }\r\n .custom_widget_MicrosoftFooter_c-uhff-nav-row_1w55e_113 {\r\n .custom_widget_MicrosoftFooter_c-uhff-nav-group_1w55e_115 {\r\n display: block;\r\n float: left;\r\n min-height: 0.0625rem;\r\n vertical-align: text-top;\r\n padding: 0 0.75rem;\r\n width: 100%;\r\n zoom: 1;\r\n &:first-child {\r\n padding-left: 0;\r\n @media only screen and (max-width: 1083px) {\r\n padding-left: 0.75rem;\r\n }\r\n }\r\n @media only screen and (min-width: 540px) and (max-width: 1082px) {\r\n width: 33.33333%;\r\n }\r\n @media only screen and (min-width: 1083px) {\r\n width: 16.6666666667%;\r\n }\r\n ul.custom_widget_MicrosoftFooter_c-list_1w55e_155.custom_widget_MicrosoftFooter_f-bare_1w55e_155 {\r\n font-size: 0.6875rem;\r\n line-height: 1rem;\r\n margin-top: 0;\r\n margin-bottom: 0;\r\n padding-left: 0;\r\n list-style-type: none;\r\n li {\r\n word-break: break-word;\r\n padding: 0.5rem 0;\r\n margin: 0;\r\n }\r\n }\r\n }\r\n }\r\n}\r\n.custom_widget_MicrosoftFooter_c-uhff-base_1w55e_187 {\r\n background: #f2f2f2;\r\n margin: 0 auto;\r\n max-width: calc(100rem + 10%);\r\n padding: 1.875rem 5% 1rem;\r\n &:before,\r\n &:after {\r\n content: ' ';\r\n display: table;\r\n }\r\n &:after {\r\n clear: both;\r\n }\r\n a.custom_widget_MicrosoftFooter_c-uhff-ccpa_1w55e_213 {\r\n font-size: 0.6875rem;\r\n line-height: 1rem;\r\n float: left;\r\n margin: 0.1875rem 0;\r\n }\r\n a.custom_widget_MicrosoftFooter_c-uhff-ccpa_1w55e_213:hover {\r\n text-decoration: underline;\r\n }\r\n ul.custom_widget_MicrosoftFooter_c-list_1w55e_155 {\r\n font-size: 0.6875rem;\r\n line-height: 1rem;\r\n float: right;\r\n margin: 0.1875rem 0;\r\n color: #616161;\r\n li {\r\n padding: 0 1.5rem 0.25rem 0;\r\n display: inline-block;\r\n }\r\n }\r\n .custom_widget_MicrosoftFooter_c-list_1w55e_155.custom_widget_MicrosoftFooter_f-bare_1w55e_155 {\r\n padding-left: 0;\r\n list-style-type: none;\r\n }\r\n @media only screen and (max-width: 1083px) {\r\n display: flex;\r\n flex-wrap: wrap;\r\n padding: 1.875rem 1.5rem 1rem;\r\n }\r\n}\r\n.custom_widget_MicrosoftFooter_social-share_1w55e_275 {\r\n position: fixed;\r\n top: 60%;\r\n transform: translateY(-50%);\r\n left: 0;\r\n z-index: 1000;\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 {\r\n list-style: none;\r\n padding: 0;\r\n margin: 0;\r\n display: block;\r\n flex-direction: column;\r\n background-color: white;\r\n width: 3.125rem;\r\n border-radius: 0 0.4375rem 0.4375rem 0;\r\n}\r\n.custom_widget_MicrosoftFooter_linkedin-icon_1w55e_311 {\r\n border-top-right-radius: 7px;\r\n}\r\n.custom_widget_MicrosoftFooter_linkedin-icon_1w55e_311:hover {\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-email-image_1w55e_325:hover {\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_social-link-footer_1w55e_333:hover .custom_widget_MicrosoftFooter_linkedin-icon_1w55e_311 {\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_social-link-footer_1w55e_333:hover .custom_widget_MicrosoftFooter_social-share-email-image_1w55e_325 {\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_social-link-footer_1w55e_333 img {\r\n width: 1.875rem;\r\n height: auto;\r\n transition: filter 0.3s ease;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list_1w55e_359 {\r\n width: 3.125rem;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-rss-image_1w55e_365 {\r\n width: 1.875rem;\r\n height: auto;\r\n transition: filter 0.3s ease;\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li {\r\n width: 3.125rem;\r\n height: 3.125rem;\r\n padding: 0.5rem;\r\n box-sizing: border-box;\r\n border: 2px solid white;\r\n display: inline-block;\r\n text-align: center;\r\n opacity: 1;\r\n visibility: visible;\r\n transition: border 0.3s ease; /* Smooth transition effect */\r\n border-left: none;\r\n border-bottom: none; /* Apply bottom border to only last item */\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-linkedin_1w55e_405 {\r\n background-color: #0474b4;\r\n border-top-right-radius: 5px; /* Rounded top right corner of first item*/\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-facebook_1w55e_413 {\r\n background-color: #3c5c9c;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-xicon_1w55e_419 {\r\n background-color: #000;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-reddit_1w55e_425 {\r\n background-color: #fc4404;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-bluesky_1w55e_431 {\r\n background-color: #f0f2f5;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-rss_1w55e_437 {\r\n background-color: #ec7b1c;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-mail_1w55e_443 {\r\n background-color: #848484;\r\n border-bottom-right-radius: 5px; /* Rounded bottom right corner of last item*/\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li.custom_widget_MicrosoftFooter_social-share-list-mail_1w55e_443 {\r\n border-bottom: 2px solid white; /* Add bottom border only to the last item */\r\n height: 3.25rem; /* Increase last child height to make in align with the hover label */\r\n}\r\n.custom_widget_MicrosoftFooter_x-icon_1w55e_459 {\r\n filter: invert(100%);\r\n transition: filter 0.3s ease;\r\n width: 1.25rem !important;\r\n height: auto;\r\n padding-top: 0.3125rem !important;\r\n}\r\n.custom_widget_MicrosoftFooter_bluesky-icon_1w55e_473 {\r\n filter: invert(20%) sepia(100%) saturate(3000%) hue-rotate(180deg);\r\n transition: filter 0.3s ease;\r\n padding-top: 0.3125rem !important;\r\n width: 1.5625rem !important;\r\n}\r\n.custom_widget_MicrosoftFooter_share-icon_1w55e_487 {\r\n border: 2px solid transparent;\r\n display: inline-block;\r\n position: relative;\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li:hover {\r\n border: 2px solid white;\r\n border-left: none;\r\n border-bottom: none;\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li.custom_widget_MicrosoftFooter_social-share-list-mail_1w55e_443:hover {\r\n border-bottom: 2px solid white; /* Add bottom border only to the last item */\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li:hover .custom_widget_MicrosoftFooter_label_1w55e_519 {\r\n opacity: 1;\r\n visibility: visible;\r\n border: 2px solid white;\r\n box-sizing: border-box;\r\n border-left: 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rounding using Microsoft Teams","conversation":{"__ref":"Conversation:conversation:1245748"},"id":"message:1245748","revisionNum":21,"uid":1245748,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:329980"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Using virtual rounding enables providers to stay healthy and ensures they can continue to provide a high standard of care during the COVID-19 crisis. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":36094},"postTime":"2020-03-23T07:12:58.233-07:00","lastPublishTime":"2020-08-05T11:31:07.751-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Using virtual rounding enables providers to stay healthy and ensures they can continue to provide a high standard of care during the COVID-19 crisis. \n \n As a Microsoft Teams Technical Specialist on healthcare, this week has shown me some of the real challenges facing some of the top healthcare clients in the US and around the world. From the very real challenges of optimizing telework, to finding ways to better optimize online diagnosis software, I was grateful to be comfortable at home, while our Healthcare workers are on the front lines of fighting COVID-19. In talking to my healthcare customer, I was humbled and astounded by the very real challenge of supply shortages that are plaguing our nation and how providers are putting themselves at risk. \n \n The Problem \n Currently in the US and many countries around the world, COVID-19 has been putting our providers at risk with issues such: \n \n Worldwide shortage of personal protective equipment (PPE) such as masks, gloves, etc. \n Healthcare providers being at risk from lack of PPE \n Influx of patients with patient loads anticipated to keep climbing. \n \n Note: This solution is a sample and may be used with Microsoft Teams for dissemination of reference information only. This solution is not intended or made available for use as a medical device, clinical support, diagnostic tool, or other technology intended to be used in the diagnosis, cure, mitigation, treatment, or prevention of disease or other conditions, and no license or right is granted by Microsoft to use this solution for such purposes. This solution is not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgement and should not be used as such. Customer bears the sole risk and responsibility for any use. Microsoft does not warrant that the solution or any materials provided in connection therewith will be sufficient for any medical purposes or meet the health or medical requirements of any person. \n \n How virtual rounding may help \n Hospital and emergency room providers make dozens, and often hundreds of “rounds” per day. These quick check-ins on patients are intended to provide a status check on how the patient is doing and ensure that the patient’s concerns are addressed. While rounding is an essential practice to ensure patients are being monitored by multiple types of providers, they represent a huge drain on PPE, because for each visit, from each provider, a new mask, and new set of gloves must be used. But…what if patients could still have frequent check-ins with all necessary providers, while not draining the hospital of PPE, and perhaps even exposing providers to less risk of exposure to COVID-19? Allowing for virtual rounding as an option may help reduce risk and drain on PPE. \n Virtual rounding relies on an easy concept. Most rounds will be conducted virtually, through a Microsoft Teams meeting between the provider and the patient. \n Here’s how it looks to the patient: \n \n Each hospital/emergency room is set-up with a laptop or tablet with Microsoft Teams \n Before a new patient enters the room, the nurse or other staff will press Join Meeting. \n When the patient enters the room, they will see a meeting screen with only them inside. This screen will notify them when a provider drops in. \n When the provider drops in, the provider and the patient can engage one-on-one with secure audio and video. When they are done, the session will end, and the screen will go back to only the patient. \n Additional providers may drop in throughout the patient’s time in the room, and the patient will be notified as they drop in. If the meeting is ended on the patient side, providers can still ring the device to bring the patient back into the meeting. \n \n \n \n Features on the device and the Microsoft Teams application are locked down to ensure no company information is available to the patient, and that the patient cannot accidentally share PHI. \n Here’s how it looks to the provider: \n \n Providers are shown a list of rooms in Microsoft Teams, and are able to select a room to drop in on. \n Providers click Join Meeting. If the patient room is not already in the meeting, providers can click \"Ask to Join\" in the participants list to ring the patient room. \n Providers meet with the patient and then leave the meeting once complete. \n Providers can then select another patient to drop in on, communicate with other providers on the floor through Microsoft Teams, or follow-up with patients in-person if needed. \n \nInterface can be customized to your needs or branding. \n \n Features \n \n A patient interface can block the patient from doing most anything besides talking to a provider who has “dropped in”. \n An intuitive system for providers can be configured to match your hospital/clinic setup. \n Security features maintain patient privacy and prevent inadvertent exposure of PHI data. \n \n Impact \n Providers are working under the assumption they could contract COVID-19. They want to help as many patients as possible before this happens. However, if we can disrupt the cycle of providers having heavy exposure to patients with active COVID-19 cases, especially in situations where they don’t have proper PPE, providers may be able to visit more patients. \n \n Through the use of virtual rounding through Microsoft Teams, providers may be physically exposed to fewer patients per day, which may limit their risk factors for contracting COVID-19 (and other communicable ailments) and could keep these providers able to care for patients longer. \n Through the use of virtual rounding in Microsoft Teams, the use of PPE may go down. This could ensure PPE remains in hospitals for crucial situations and help reduce future risks to providers. \n Through the use of virtual rounding in Microsoft Teams, providers may spend less time walking between patient rooms, increasing their efficiency during this time of increased demand during COVID-19. \n \n How does it work? \n \n Each \"patient room\" is setup with a Microsoft 365 account, and the device is logged into the Teams application (and ideally locked down with Intune MDM Kiosk policies). \n Custom Teams policies are applied to patient room accounts to disable non-essential features. \n Teams meetings are setup for each patient room account (with that account as a host), lasting a large amount of days (hopefully past the end of COVID-19). \n Teams are created and available to providers for each building/site, with individual channels for sub-locations such as floors. \n SharePoint lists are created and added to each channel as a tab listing the meeting join links. \n \n Demo \n \n \n How you can roll this out in your environment \n Want to get started with this in your environment? The sample code and documentation can be found here. As always, we suggest you test scenarios out like this in a non-production environment and work with a certified Microsoft partner to build out an enterprise scale solution. \n Please contact your Microsoft Account team for more information. \n \n FAQ: \n What if the patient needs in-person care, such a temperature check, or blood test? \n Microsoft Teams is not a substitute for these things, however, it may be used as a triage to determine if additional in-person testing is necessary. It also may limit the frequency of daily in-person check-ins by moving them to a virtual platform. \n How can we ensure this solution is secure? \n Microsoft Teams is a natively secure application; however, we are suggesting putting it in front of a patient instead of an employee. Microsoft Teams policies and Conditional Access policies should be implemented to ensure a secure patient experience. Please see the documentation for more details. \n Is this HIPAA compliant? \n Please see this link to learn how Microsoft 365 and Microsoft Teams offer compliance with HIPAA. 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In his role Michael engages a number of large enterprise customers in regulated industries. A frequent request that has been sent to Michael is the ability to automate the provision of selective Microsoft Teams with guest access. Michael has helped customers to achieve this using services that come with Microsoft 365. To help spread the word on approaches that customers can look at as they consider their Teams guest access options Michael has published a series of posts detailing the approach and services leveraged to attain selective Teams provisioning with guest access. \n Follow the links below to access the great work done by Michael Mukalian. You can also follow him on LinkedIn here to get his latest updates. \n Automating the Provisioning of Selective Teams with Guest Access: Introduction \n \n Introduction \n History & Background \n Part 1: Application Authentication — Azure AD Application Registrations \n Part 2: Request Storage — SharePoint \n Part 3: Request Process Automation — Microsoft Power Automate & Microsoft Graph \n Part 4: Enhanced User Interface — Power Apps (coming soon) \n Part 5: Next Steps — Security (coming soon) \n \n Thanks for visiting – Michael Gannotti LinkedIn | Twitter | Facebook | Instagram \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1410","kudosSumWeight":7,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMDYyNTM0LTE2MTM3NWlENkE3RENFQTAzODcxNTk2?revision=2\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMDYyNTM0LTE2MTM3NWlENkE3RENFQTAzODcxNTk2?revision=2\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMDYyNTM0LTUwNmkyNENBNDQyMEZFMzBCQzZE?revision=2\"}"}}],"totalCount":3,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:831553":{"__typename":"Conversation","id":"conversation:831553","topic":{"__typename":"BlogTopicMessage","uid":831553},"lastPostingActivityTime":"2023-07-06T06:38:01.336-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04MzE1NTMtMTMwMjU4aUJGOEU2NDhDMjI2N0I5MDY?revision=5\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04MzE1NTMtMTMwMjU4aUJGOEU2NDhDMjI2N0I5MDY?revision=5","title":"podcasting.png","associationType":"TEASER","width":1280,"height":720,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04MzE1NTMtMTI5Mzk1aUQ0RjNEMDhGMEI1MDZGOTU?revision=5\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04MzE1NTMtMTI5Mzk1aUQ0RjNEMDhGMEI1MDZGOTU?revision=5","title":"microphone.jpg","associationType":"BODY","width":460,"height":594,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04MzE1NTMtNTA2aTI0Q0E0NDIwRkUzMEJDNkQ?revision=5\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04MzE1NTMtNTA2aTI0Q0E0NDIwRkUzMEJDNkQ?revision=5","title":"me - Copy.jpg","associationType":"BODY","width":610,"height":584,"altText":null},"BlogTopicMessage:message:831553":{"__typename":"BlogTopicMessage","subject":"Office 365 Podcasting – Show Me How","conversation":{"__ref":"Conversation:conversation:831553"},"id":"message:831553","revisionNum":5,"uid":831553,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:880"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" In the HLS Show Me How video I walk the viewer through the steps of setting up a SharePoint Podcast Library, configuring the information displayed, enabling the Library RSS, and then how to consume through different methods like….. Microsoft Teams! ","introduction":"","metrics":{"__typename":"MessageMetrics","views":39544},"postTime":"2019-08-30T09:49:13.399-07:00","lastPublishTime":"2019-09-05T06:15:20.026-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" In the last several weeks I have been asked by several customers how they can deliver audio podcasting. In their organizations there are a number of individuals, including executives, who wish to record and disseminate audio recordings with their thoughts on the business, technology, and more. Certainly, with Microsoft Stream they have a GREAT vehicle for the dissemination of audio through the video medium (simply put in a single slide/picture as a placeholder). In these cases however the customer wanted to specifically disseminate .mp3 files. The good news is it is easily accomplished with Microsoft SharePoint Online that allows for it’s consumption via a number of clients ranging from the browser, podcast software, Outlook, and even Microsoft Teams (my favorite!). \n In the HLS Show Me How video I walk the viewer through the steps of setting up a SharePoint Podcast Library, configuring the information displayed, enabling the Library RSS, and then how to consume through different methods like….. Microsoft Teams! \n \n Resources: \n \n Manage RSS feeds for a site or site collection \n Create a document library in SharePoint \n Set up an Asset Library to store image, audio, or video files \n \n Thanks for visiting – Michael Gannotti LinkedIn | Twitter | Facebook | Instagram \n 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"height":569,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzY2MjQxLWlRQnJieg?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzY2MjQxLWlRQnJieg?revision=7","title":"clipboard_image-1-1736825769490.png","associationType":"BODY","width":181,"height":19,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzY2MjQxLVVWWXlkYg?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzY2MjQxLVVWWXlkYg?revision=7","title":"image.png","associationType":"BODY","width":883,"height":283,"altText":""},"BlogTopicMessage:message:4366241":{"__typename":"BlogTopicMessage","subject":"Cancer Survival with Radiology-Pathology Analysis and Healthcare AI Models in Azure AI Foundry","conversation":{"__ref":"Conversation:conversation:4366241"},"id":"message:4366241","revisionNum":7,"uid":4366241,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:2256982"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" The integration of radiology and pathology is transforming predictive analytics in healthcare. By combining MRI imaging with histopathology (H&E slides), this multimodal approach leverages pre-trained models from Azure AI Foundry to seamlessly connect macro-level and micro-level insights. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":2396},"postTime":"2025-01-14T11:53:42.116-08:00","lastPublishTime":"2025-01-22T09:03:38.610-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" @Alberto Santamaria-Pang, Principal Applied Data Scientist, HLS AI and Adjunct Faculty at Johns Hopkins Medicine \n @Peter Lee, Applied Scientist, HLS AI and Adjunct Assistant Professor at Vanderbilt University \n @Ivan Tarapov, Group Manager, HLS AI \n 1. Introduction \n The fusion of radiology and pathology is transforming predictive analytics in medical and biomedical imaging. By integrating these two complementary modalities, healthcare professionals can unlock powerful predictive capabilities that enhance survival predictions and risk assessments, ultimately leading to better patient outcomes. \n Imagine a future where a seamless diagnostic ecosystem provides a comprehensive view of patient data. Such an approach empowers clinicians with a holistic understanding, enabling more precise and informed decision-making. \n Traditionally, relying on a single imaging modality, whether radiology or pathology, has left gaps in the diagnostic process. Each modality alone often lacks the complete information necessary for accurate diagnoses and effective treatment planning. The major limitation of classical radiomics lies in its inability to correlate clinically relevant data across different imaging modalities. This prevents the full integration of diverse datasets needed to capture the complexity of a patient's condition. \n In addition, classical radiomics has struggled to incorporate broader patient data, such as genomic profiles or clinical histories, and to bridge the gap between macro-level imaging insights and micro-level biological information. Integrating radiology and pathology helps address these challenges, offering a more robust, comprehensive diagnostic framework. This integrated approach not only fills critical gaps but also sets the stage for advancements in personalized medicine by providing a more detailed and precise understanding of patient health. \n In this blog we will show you how one can use the recently launched healthcare AI models in Azure AI Foundry to begin designing and testing systems that can reason about these two modalities together. \n It is important to highlight that the models available on the model catalog in Azure AI Foundry portal are not designed to generate diagnostic-quality results. Developers are responsible for further developing, testing, and validating their appropriateness for specific tasks and eventually integrating these models into complete systems. The objective of this blog is to demonstrate how this can be achieved efficiently in terms of data and computational resources. \n 2. The Problem \n We will guide you through a practical approach to predicting cancer grades while fitting a survival model using radiological MRI images and H&E-stained histopathology slides for brain tumor analysis. Inspired by the work of Chen et al. (2020) [1] and Can et al. (2022) [2], we’ll outline a step-by-step method for deploying and testing models from the model catalog in Azure AI Foundry. You’ll learn how to extract and fuse features from these complementary modalities, train adapters to link multi-modality input images with survival outcomes and validate the resulting predictive insights. An overview of this process is illustrated in Figure 1 below. \n \n \n \n \n \n Figure 1. Overview of the computational pipeline for training Radiology, Pathology, and Multi-Modal adapters to predict Hazard Risk Scores. These scores are subsequently used to infer cancer grading and survival outcomes. \n \n \n \n \n \n \n\n Sample code and Jupyter Notebook is available in our Samples Repository: https://aka.ms/healthcare-ai-examples-rad-path . Please refer to it if you want to reproduce this experiment or use the principles employed here to build and test your own multimodal systems using healthcare AI models in Azure AI Foundry. \n 3. The Data \n We have used TCGA-GBMLGG dataset which serves as a valuable resource for studying malignant brain tumors, specifically gliomas, by combining data from glioblastoma multiforme (GBM) and lower-grade gliomas (LGG). GBM, classified as WHO grade IV, is the most aggressive type of brain tumor, known for its rapid growth, invasive nature, and resistance to therapy, leading to poor prognosis. In contrast, LGG, encompassing WHO grades II and III, grow more slowly and generally have a better initial prognosis, though they can progress to higher grades over time. \n This dataset is particularly well-suited for survival prediction, as it integrates multimodal data: diagnostic brain MRI scans, including T1, T2, and FLAIR sequences, alongside histopathology images of H&E-stained tumor regions. Each subject's data is annotated with categorical tumor grades (Grade 0, 1, or 2) and survival durations (Figure 2). By combining these complementary data modalities, clinical researchers can uncover deeper insights into the relationships between tumor characteristics and patient outcomes, fostering advancements in glioma research and personalized medicine. \n . MRI and Histopathology images for grades 0, 1, and 2.\n Exploring the Relationship Between Tumor Staging and Survival \n Further in this blog, we explore how different tumor stages correlate with survival outcomes in glioma patients, while exploring cost-effective approaches to building and testing predictive models using the healthcare AI models in Azure AI Foundry. Our analysis leverages data from 170 subjects, categorized into three tumor grades: \n \n Grade 0 (Early Stage): 40 subjects \n Grade 1 (Intermediate Stage): 53 subjects \n Grade 2 (Advanced Stage): 77 subjects (slight predominance of higher-grade tumors) \n \n To gain better context of the problem and understand the data better we have visualized survival outcomes in Figure 3. This figure contains so-called Kaplan-Meier survival curves which are used in cancer survival analysis to estimate the probability of survival over time, accounting for censored data (e.g., patients alive at the end of study or patients lost to follow-up). This analysis reveals several key trends: \n \n Survival Variability in Early-Stage Tumors Patients with Grade 0 (Early Stage) tumors show the widest range of survival outcomes. This variability reflects the diverse responses to early interventions and the significant role of individual biological factors in determining prognosis. \n Narrowing Survival Range in Higher Grades As tumor grade increases, the survival range becomes progressively narrower. For patients with Grade 2 (Advanced Stage) tumors, survival periods are shorter and more predictable, highlighting the severe prognosis associated with higher-grade tumors. \n Impact of Tumor Severity on Survival Outlook A clear negative correlation exists between tumor grade and survival duration. Higher tumor grades, such as Grade 2, are consistently linked to shorter survival periods, emphasizing the critical importance of early detection and timely treatment. These findings underscore the value of predictive modeling in identifying high-risk patients and supporting early intervention strategies to improve outcomes. \n \n Please note that this following figure is for illustrative purposes only and a larger dataset should be used to draw statistically meaningful conclusions. \n . Kaplan-Meier survival model representing survival years across each grade corroborates and quantifies this decreasing survival trend with advancing tumor severity.\n 4. Introducing model catalog in Azure AI Foundry \n The model catalog in Azure AI Foundry provides a range of pre-trained foundation models accessible via the cloud, enabling organizations to efficiently test, develop, deploy, and scale custom AI applications. The Health and Life Sciences section of the catalog includes specialized healthcare models like MedImageInsight [1] for radiology and Prov-GigaPath [2] for pathology, specifically designed to process and analyze complex medical imaging data (Figure 4). To accomplish our task we can use the Azure AI model catalog to deploy individual models as real-time endpoints, extract features from single modalities, and implement feature fusion to test survival outcome predictions. This is particularly relevant for applications that use radiology and pathology data, often in drug discovery contexts. \n Health and Life Sciences models from Azure AI Foundry's catalog (green box), highlighting Prov-GigaPath and MedImageInsight (red).\n MedImageInsight and Prov-GigaPath: A Dynamic Duo \n When MedImageInsight and Prov-GigaPath are used together, they create a unified embedding across different medical imaging modalities, enabling powerful multi-modal predictive capabilities. This synergy is especially effective for tasks such as survival prediction and risk stratification, offering a more comprehensive and accurate view of insights from these modalities. Here’s how these models complement each other: \n \n MedImageInsight (MI2): Extracts macro-level anatomical features from radiology images such as MRI and CT scans, enabling insights into tissue structure, density, and morphology. \n Prov-GigaPath (PGP): Focuses on micro-level cellular features from pathology images like histology slides, capturing cellular morphology and tissue architecture. \n Combined Approach (MI2+PGP): Integrates anatomical insights from radiology with cellular context from pathology, enabling a holistic analysis of tissues and tumors for enhanced predictive accuracy. \n \n Deploying these models is straightforward through the model catalog in Azure AI Foundry. Each model is assigned a URL and an API key upon deployment, making integration seamless. In our Jupyter Notebook, \"mi2-deploy.ipynb\" we demonstrate how to programmatically deploy MedImageInsight. This process can be easily adapted for deploying Prov-GigaPath, offering a flexible and scalable solution for multi-modal imaging analysis. \n 5. Training a Multi-Modal Adapter Model \n As previously discussed, combining radiology and pathology embeddings offers both macro- and micro-level insights. We apply this integrated approach to train the multimodal adapter to leverage these combined features. \n . Multimodal framework integrating radiology (MRI) and histopathology (H&E-stained) images to predict Hazard Risk Score.\n The workflow, illustrated in Figure 5, begins with cutting-edge feature extraction: MI2 processes images from different radiology sequences such as T1, T2, and FLAIR, while PGP extracts features from pathology images. These extracted features represented by embedding vectors are passed through modality-specific adapters—Radiology Adapter and Pathology Adapter—to refine and standardize the representations, bringing them into the same embedding space. The outputs are then fused through a Multi-Modal Adapter, merging macro-level anatomical insights with micro-level cellular details into a unified representation. \n This integration results in a predictive model capable of capturing both the big picture and the fine-grained details, culminating in a survival hazard score – a continuous variable that researchers can use to estimate the survival of a subject. \n Task Adapters – the trusty aides of foundation models \n In the practical application of embedding models, adapters emerge as essential components to adapt unseen downstream tasks. These compact neural networks are designed to take embeddings as input and output either classes or new embeddings. Adapters enhance classification performance and uncover correlations across different modalities, making them indispensable for multi-modal analyses. \n Figure 5 illustrates the use of three adapters in this workflow: \n \n Radiology Adapter: Receives a 4096-sized vector, representing a concatenation of embeddings from four slices across different radiological modalities. \n Pathology Adapter: Processes a feature tensor of size 1536×14×14, generated by the PGP model from histopathology slide regions of interest (ROIs). \n Multi-Modal Adapter: Combines the outputs of the radiology and pathology adapters—refined into a unified 1024-sized vector—and converts this into a final hazard score. \n \n These three adapters are trained together to minimize a Cox Proportional Hazards (Cox) survival loss function, with the primary goal of ranking individuals by their relative risks (hazard ratios) rather than predicting exact survival times or cancer grades. Unlike fully supervised tasks, this training process estimates risk by ordering individuals based on survival times. The loss function reinforces accurate ranking by focusing the model on meaningful, consistent risk-based ordering. \n This approach is particularly advantageous over directly predicting cancer grades because it accounts for tumor heterogeneity and molecular phenotype variability, which often result in overlapping features between grades. By emphasizing relative risk ranking instead of fixed grade predictions, the model captures nuanced differences in survival outcomes and accommodates the continuous spectrum of tumor biology, offering a flexible and robust framework for survival analysis. \n Model performance is evaluated using the Concordance Index (C-index), which measures how well the predicted risk scores align with actual survival outcomes in a test dataset. Although not part of the optimization process itself, the C-index serves as a key metric during training, providing insight into the model’s ranking accuracy. \n By optimizing the survival loss and leveraging the C-index for evaluation, the three adapters learn to associate imaging features from different modalities with risk-based ordering. This enables the model to effectively represent the complex relationships between multi-modal imaging data and hazard scores, facilitating nuanced and reliable predictions of relative risk. \n Model Output: Converting Hazard Scores to Cancer Grading \n Hazard scores represent relative risk compared to other patients. Since higher cancer grades generally correlate with worse outcomes, hazard scores can effectively serve as a surrogate for disease severity, allowing continuous risk values to be segmented into discrete categories for more intuitive clinical interpretation and research insights. \n In real-world datasets, hazard score distributions often overlap between categories (e.g., cancer grades). For example, patients with intermediate-grade cancer may have hazard scores that intersect with both low- and high-grade groups, complicating classification based on strict thresholds. For the purposes of this discussion, we assume minimal overlap between hazard score distributions for different cancer grades [1]. Under this assumption, hazard scores can be divided into percentiles to define risk categories: \n \n Low risk (Grade 0): Scores up to the 33rd percentile. \n Intermediate risk (Grade 1): Scores in the 34th to 66th percentile. \n High risk (Grade 2): Scores in the 67th to 100th percentile. \n \n In practice, however, the assumption of minimal overlap may not hold, as hazard scores frequently exhibit significant overlap across categories due to tumor heterogeneity and biological variability. In such cases, probabilistic approaches or alternative classification strategies, such as Bayesian models or soft classification methods, provide a more flexible framework. These methods accommodate uncertainty and better reflect the complex, continuous nature of cancer biology, enabling more accurate and clinically relevant predictions. \n 6. Evaluating the Cox Model: Survival Prediction and Cancer Grading \n By now you have learned the principles behind the multi-modal analysis using foundation models which should equip you with tools required to build, test, and validate such adapters of your own. The job of building survival prediction model is not complete though! \n This part of the blog explains how to get to the survival prediction model from the hazard score estimator, however it relies on material that pertains to the field of survival analysis and is grounded in the corresponding mathematical framework. We will provide a high-level overview of the principles, and if you are interested in learning further about the underlying math we will direct you to the appropriate external sources that can help you understand how and why certain concepts are applied. If you are curious, read on! \n Once you have trained the adapters as described in the section above, we acquire the ability to predict a hazard score for each subject. Recall from previous section that hazard scores are not absolute survival probabilities, but rather relative ranking. We translate these relative risk rankings (which are computed in relation to other patients in the dataset) into meaningful survival probabilities over time so that we are able to predict the chance of survival of a given subject beyond certain time period given only imaging data as input. Although our hazard score indicates whether a patient is at a higher or lower risk compared to others, it does not directly provide a survival percentage so to bridge the gap, we would rely on the framework defined by Cox Proportional Hazards model. \n The Cox PH model describes how an individual’s risk (hazard) at any time point is related to a baseline hazard function and the individual’s hazard score. Specifically, it models the hazard function as: \n \n Here, h 0 (t) is the baseline hazard function, which represents the underlying risk of an event (in our case the mortality from the tumor) for a reference subject. The hazard score serves as a multiplier that shifts the baseline hazard up or down based on the patient’s features (the representations of MRI scans and digital pathology slides in our case). \n So, to convert these features into a survival probability at time t, we would first estimate the baseline survival curve from the training set. The lifelines library that we use in the Jupyter notebook that we will publish provides a method for such estimation. \n However, for the sake of simplicity and interpretability, in the provided code sample we use a different method to estimate the ability of our model to help in estimating survival probabilities. \n Instead of explicitly estimating the baseline hazard and converting the hazard scores into time-dependent survival probabilities, we used the scores to rank patients and classify them into risk groups. Since brain cancers are typically categorized into Grade 0, Grade 1, and Grade 2, we segmented our predicted scores into three strata using the 33 rd and 66 th percentiles as described in the previous section, thus mapping the continuous hazard scores into clinically interpretable categories that can be compared directly to traditional grading systems. \n Using this stratification we applied the Kaplan-Meier fitter to each group in the test set and generated nonparametric survival curves, offering a visual and intuitive comparison of survival outcomes across risk categories. In our testing we compare Kaplan-Meier curves generated for the test set based on ground truth grading data to those generated for the same test set based on our model’s hazard score data. \n With a small dataset of 135 subject (675 images) for training and 35 subjects (175 images) for testing, we achieved a Concordance Index (or C-index – an evaluation of how well our hazard score ranks patients’ hazard compared to survival given by the ground truth, measured on an interval of [0...1]) of 0.8097, indicating that the hazard scores output by our model provide rather meaningful prognostic information. Figure 6 presents Kaplan-Meier curves for both the actual and predicted stratifications in blue and orange respectively. Although the predicted curves capture the overall trend (Grade 2 < Grade 1 < Grade 0) the limited data size introduces more pronounced step changes in the curves. It is important to recognize that our example is illustrative, leveraging a relatively small dataset. Real-world applications would likely employ larger, more diverse populations and integrate additional modalities, such as genomic data, to enhance the model’s predictive accuracy and generalizability. The efficiency of foundation models as feature extractors from complex data demonstrated here, make it much easier for clinical researchers to experiment with these approaches, making them a versatile tool for clinical and research applications. \n Survival model from the test set across grades, comparing the actual grade with the predicted grade.\n This system is tailored for practical applications and optimized for efficiency, making it attractive for testing and developing real-world use cases. This efficiency makes it ideal for applications requiring rapid turnarounds or operating within limited computational environments. To explore its implementation and adaptability across diverse datasets, Jupyter Notebook tutorial [3] offers a clear, hands-on guide, enabling users to effectively apply this approach in real-world scenarios. \n 7. Conclusion \n Our exploration of AI models in radiomics highlights how the dynamic duo of MedImageInsight [4] and Prov-GigaPath [5] utilizes modern cloud technologies to advance healthcare innovation. By combining radiology and pathology embeddings from our sandbox dataset to build a hazard prediction model, we demonstrated the potential of this approach for survival analysis. In real-world applications, larger datasets and more diverse models will further enhance these capabilities, enabling more robust and accurate predictions. \n The key insight is clear: true innovation in healthcare AI emerges from the synergy of advanced tools, technical expertise, and clinical understanding. By leveraging healthcare AI models in Azure AI Foundry developers gain streamlined access to these models and tools, making it easier to integrate multimodal data and deliver actionable results. This convergence is not merely about improving analytical accuracy – it is about accelerating the pace at which meaningful insights reach the bedside, empowering clinicians to make more informed decisions and ultimately enhance patient outcomes. \n References \n \n R. J. Chen, M. Y. Lu, J. Wang, D. F. K. Williamson, S. J. Rodig, N. I. Lindeman, and F. Mahmood, \"Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis,\" IEEE Transactions on Medical Imaging, vol. 41, no. 4, pp. 1–14, Apr. 2022. \n Cui, C., Liu, H., Liu, Q., Deng, R., Asad, Z., Wang, Y., ... & Huo, Y. (2022, September). Survival prediction of brain cancer with incomplete radiology, pathology, genomic, and demographic data. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 626-635). Cham: Springer Nature Switzerland. \n Sample code: https://aka.ms/healthcare-ai-examples-rad-path \n MedImageInsight: https://aka.ms/mi2modelcard \n Prov-GigaPath: https://aka.ms/provgigapathmodelcard Azure AI Foundry Documentation \n Azure AI Model Catalog – Foundation Models \n MedImageInsight Paper: https://arxiv.org/abs/2410.06542 \n GigaPath Paper: https://www.nature.com/articles/s41586-024-07441-w \n Prov-GigaPath GitHub Repository: https://aka.ms/healthcare-ai-examples-rad-path \n TCGA-GBMLGG Dataset: https://github.com/mahmoodlab/PathomicFusion/tree/master/data/TCGA_GBMLGG \n Cox Proportional Hazards model: https://pmc.ncbi.nlm.nih.gov/articles/PMC7876211/ \n Kaplan-Meier estimator: https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator \n Lifelines survival analysis model: https://lifelines.readthedocs.io/en/latest/index.html \n \n The healthcare AI models in Azure AI Foundry are intended for research and model development exploration. The models are not designed or intended to be deployed in clinical settings as-is nor for use in the diagnosis or treatment of any health or medical condition, and the individual models’ performances for such purposes have not been established. You bear sole responsibility and liability for any use of the healthcare AI models, including verification of outputs and incorporation into any product or service intended for a medical purpose or to inform clinical decision-making, compliance with applicable healthcare laws and regulations, and obtaining any necessary clearances or approvals. 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snacks3.png","associationType":"BODY","width":1280,"height":720,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTQyOTEyLTU4MTg4N2k2NEU5QTZGMzc3OTM2NUY2?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTQyOTEyLTU4MTg4N2k2NEU5QTZGMzc3OTM2NUY2?revision=8","title":"ladysaiyan_0-1715879965366.png","associationType":"BODY","width":518,"height":362,"altText":null},"BlogTopicMessage:message:4142912":{"__typename":"BlogTopicMessage","subject":"🦸🏾♀️Analyze your communications: Superhero to Organization Leader Analysis Prompt evolution","conversation":{"__ref":"Conversation:conversation:4142912"},"id":"message:4142912","revisionNum":8,"uid":4142912,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:1142700"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" In this snackable you will learn how you can analyze your emails, meetings, and chats to understand who your superhero, anime character, and leadership style is. This video goes through the evolution of prompting on a fun topic to one that is focused on professional growth. \n To see all HLS Copilot Snacks video click here. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":2351},"postTime":"2024-05-16T10:19:36.060-07:00","lastPublishTime":"2024-09-26T07:36:10.561-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n \n HLS Copilot Snacks are the perfect way to learn how to use Copilot to boost your productivity and reduce your workload. \n \n In this snackable you will learn how you can analyze your emails, meetings, and chats to understand who your superhero, anime character, and leadership style is. This video goes through the evolution of prompting on a fun topic to one that is focused on professional growth. \n To see all HLS Copilot Snacks video click here. \n \n \n Prompts Used within Word \n \n 🦸:female_sign:Superhero \n \n Look at the emails and Teams meetings that I have sent in the last two weeks. Use these messages to distill a personal brand voice document that I can use to inform copilot of my personal voice and style. Based on the above analysis, what superhero represents my style of work best? What would my super powers be? Can you format that as a dossier for superheroes -- listing my real name, and most similar existing superhero, and include the rest of the information formatted and edited in dossier style? \n \n \n Anime Character\n \n Look at the emails and Teams meetings that I have sent in the last two weeks. Use these messages to distill a personal brand voice document that I can use to inform copilot of my personal voice and style based on the above analysis, what (Genre: example Magical pretty girl) Anime Character would represents my style of work best and why? What would my abilities and personality flaws be? Listing my real name and most similar existing Anime Character. Also give me a anime character friend group, they should be from different anime, include a table with |Name | Anime| strengths | flaws | why they are my friends | how they help me or make my flaws worst. Also be sure to identify my rival anime character and a short story of how we became enemies but may end up being friends. \n \n \n Leadership Style, Strengths, and Goals\n \n Look at the emails, teams meetings, meeting transcripts, documents, teams chats that I've sent in the last year. Use these messages to distill a leadership style document that I can use to inform copilot of my personal voice and style based on the above analysis. Give me a breakdown of my top 5 strengths in a Table with the following formation | Strength | description | how it helps me in my role | how it could be misunderstood | 2 ways I can build this strength. In another table give me a breakdown of 3 areas for improvement in the following formation | Improvement area | description | how it impacts my role | how it could be misunderstood | 3 ways I can improve. I would also like 3 OKRs based on the above analysis that I could reach in the next 6-9 months. \n \n \n \n \n Have questions you would like to have us address in a snackable? Let us know! \n Thanks for Visiting – Jasmine Hoegh, PMP, Connect with me on LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2944","kudosSumWeight":6,"repliesCount":3,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTQyOTEyLTU4MTg5N2kzRUNBMDE3N0ZDMjRBQjNE?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MTQyOTEyLTU4MTg4N2k2NEU5QTZGMzc3OTM2NUY2?revision=8\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3d3dy55b3V0dWJlLmNvbS93YXRjaD92PVpLcmo0cnRmRC1rfDB8MjU7MjV8fA","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://www.youtube.com/watch?v=ZKrj4rtfD-k","thumbnail":"https://i.ytimg.com/vi/ZKrj4rtfD-k/hqdefault.jpg","uploading":false,"height":225,"width":400,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3964454":{"__typename":"Conversation","id":"conversation:3964454","topic":{"__typename":"BlogTopicMessage","uid":3964454},"lastPostingActivityTime":"2023-10-26T21:53:02.277-07:00","solved":false},"User:user:1279603":{"__typename":"User","uid":1279603,"login":"Olesya_Melnichenko","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0xMjc5NjAzLW82bFJmWQ?image-coordinates=0%2C360%2C3840%2C4200"},"id":"user:1279603"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTgyOWlFNDkwRjJENzU2NjA3MENG?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTgyOWlFNDkwRjJENzU2NjA3MENG?revision=9","title":"Olesya_Melnichenko_2-1698344788484.png","associationType":"TEASER","width":1485,"height":574,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTg5MWlEQjJCQzMxNjA2QjM2OTU1?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTg5MWlEQjJCQzMxNjA2QjM2OTU1?revision=9","title":"Figure 1.png","associationType":"BODY","width":1829,"height":697,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTgxN2lDQTQ4QTU5NTc0QjkxQjQ4?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTgxN2lDQTQ4QTU5NTc0QjkxQjQ4?revision=9","title":"Olesya_Melnichenko_0-1698342989710.png","associationType":"BODY","width":1434,"height":502,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTgyNmk1MENEODk5NDlDMEU1QjY3?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTgyNmk1MENEODk5NDlDMEU1QjY3?revision=9","title":"Olesya_Melnichenko_1-1698344474584.png","associationType":"BODY","width":1485,"height":574,"altText":null},"BlogTopicMessage:message:3964454":{"__typename":"BlogTopicMessage","subject":"Designing multi-step cohort creation flow for biomedical datasets","conversation":{"__ref":"Conversation:conversation:3964454"},"id":"message:3964454","revisionNum":9,"uid":3964454,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:1279603"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n With the recent advancements in natural language processing offered by Large Language Models, there is a potential for empowering researcher to interrogate dataset independently using Natural Language. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":3076},"postTime":"2023-10-26T21:53:02.277-07:00","lastPublishTime":"2023-10-26T21:53:02.277-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" In the realm of biomedical research and healthcare analytics, one common task is the selection of specific cohorts of patients from larger datasets. These cohorts often serve as the foundation for various studies, clinical trials, and population analyses. The ability to construct patient cohort accurately and efficiently is crucial for gaining insights, conducting research, and improving patient care. \n \n There are several things specific to the healthcare field that make cohort generation more challenging. Many datasets have restricted access and, in some cases, even data schemas are not available to researchers. Also, a dataset might contain Protected Health Information (PHI), so thorough validation of request and its result is required. In addition to the listed above, low-code / no-code users make a significant part of researchers selecting patient cohorts, so we can’t assume their ability to write or validate queries even if data schemas can be shared. In current reality it might take hours of phone calls or emails between a researcher and a data custodian to check whether dataset has required data to support researcher’s study. \n \n With the recent advancements in natural language processing offered by Large Language Models (LLMs) such as GPT-4, there is a potential for empowering researcher to interrogate dataset independently using Natural Language (NL), reducing the reliance on manual translation and data custodian involvement, and therefore increasing the overall efficiency of data retrieval. The focus of our exploratory work was to evaluate how cohort selection experience might be improved by adding steps to clarify data requirements using LLMs. In this article, we describe the flow and the specific design that emerged from our effort to model an LLM-powered cohort builder. \n \n Low code user use case \n During our work we were focused on researcher experience and used the following set of assumptions: \n \n Researcher has domain knowledge and defines the requirement for the cohort using natural language. \n Researcher is a low-code / no-code user. \n Researcher does not have direct access to data and may not have knowledge of the dataset structure (for example number of tables, table schemas, etc.). Cohort building backend (System) is responsible for final query validation and execution of an actual query on data. \n \n \n Initial experiment: from natural language to runnable query \n \n One of the well know use cases of LLMs application is code generation. Therefore, we attempted to generate SQL queries directly from natural language requests. Our expectation was to obtain a query that could be directly executed on a database. However, even with strict instructions and examples in the prompt we encountered several problems with this approach: \n \n Fake columns added to a query - an additional field (that is not present in the table schema) consistently appeared in the WHERE clause. \n The JOIN structure was often inadequate. \n Logical operations, such as AND, OR and NOT, were applied incorrectly in some cases. \n \n These issues might be fixed by researcher if LLMs are used in a copilot mode and researcher is familiar with dataset structure, but in our use case we cannot rely on that. Additionally, a resulting string containing a SQL query needs to be parsed and validated again before running on a real database, at least to make sure there was no malicious code injected. \n \n Multi-step approach: introducing request card \n \n To address the limitations encountered in the initial experiment, we developed a machine-friendly structure that (a) limits the variability of fields and (b) preserves the logic of the original request – the request card. To create the card, we used a two-step LLMs-powered approach that follows human logic: \n \n Extract inclusion and exclusion criteria from a natural language request. \n Generate a request card using inclusion and exclusion criteria from previous step and requiring to use a predefined dictionary of “concept keys”. \n \n The interactive process keeps user in the loop for validation of the generated request query that is still human-readable. Once researcher is satisfied with the request card, it will be passed to a subsequent processing for (i) mapping the concept keys to the data table schema, (ii) values validation and alignment if required, (iii) converting result into a schema- and technology- dependent query (like SQL) specific to the target system. The format of request card preserving logical context (see detailed discussion below) makes card conversion a simple task that doesn’t require LLMs and researcher validation. \n \n This approach allows the researcher to validate that the card accurately reflects the criteria and to refine it if needed without the need to understand SQL syntax. It also transfers responsibility to write and validate queries from low-code users to System. Suggested cohort selection flow is presented on Fig.1. \n \n \n Figure 1: Multi-step cohort selection flow. Researcher types in cohort description in natural language. The request is transformed into eligibility criteria lists that might be validated by researcher. Then criteria lists are transformed into request card representing request logic in disjunctive normal form. When approved by researcher the request card is passed to a module converting card to SQL query. Researcher validation is not expected at this stage, cohort building tool is responsible for validation. When query is executed, its results are presented to researcher for cohort size validation. \n \n Step 1: From natural language to criteria lists \n \n The goal of the first step is to get researcher’s request structured by creating separate inclusion and exclusion lists. Eligibility criteria lists are an integral part of clinical trials description, and this format is well known in healthcare and biomedical field. Some request-response pairs are presented at the end of the next section, responses were produced by GPT-4 model. \n \n \n Figure 2: Transforming Natural Language request into eligibility criteria. \n \n Step 2: From criteria lists to request card \n \n A crucial challenge in transitioning between steps was maintaining the logical context of the original natural language request. To overcome this, we employed the Disjunctive Normal Form (DNF). It’s well known that any logical statement can be converted into an equivalent DNF (see Logic And Declarative Language by M. Downward or any other textbook on Mathematical Logic). By representing the request in DNF, we preserved the logical context and ensured that the generated queries accurately reflected the intended criteria. \n \n The implementation of DNF may vary, but for the purpose of simplified explanation let’s assume that [key: value] pair represents a particular expression for a field (key) having exact value, without going into detail of comparison operator implementation. With that assumption the request card will have the following format and logical representation: \n \n [ {\"include\": {[key: value], [key: value], …}, \"exclude\": {[key: value], [key: value], …}}, \n\n {\"include\": {[key: value], [key: value], …}, \"exclude\": {[key: value], [key: value], …}}, \n\n…] \n \n In this structure, that all the members of “include\" group are predicated with AND, all the members of “exclude\" group are predicated with AND NOT, and all further [\"include”:{…}, \"exclude”:{…}] pairs are appended with OR predicate. \n For example: \n \n \n \n [{\"include\": { \n “keyA”: “valueA”, \n “keyB”: “valueB”}, \n \"exclude\": { \n “keyC”: “valueC”, \n “keyD”: “valueD”}}, \n \n {\"include\": { \n “keyX”: “valueX”, \n “keyY”: “valueY”}, \n \"exclude\": { \n “keyZ”: “valueZ”}}\n] \n \n(keyA=valueA \nAND keyB=valueB \n \nAND NOT keyC=valueC \nAND NOT keyB=valueB) \nOR \n\n(keyX=valueX \nAND keyY=valueY \n\nAND NOT keyZ=valueZ) \n \n \n \n To limit the variability of fields in DNF we specified a list of allowed fields in our prompt. The prompt was based on work of Cliff Wong et al. (Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology. Proceedings of the 8th Machine Learning for Healthcare Conference, PMLR 219). To illustrate the transformation of a human query into Disjunctive Normal Form (DNF), let's consider a couple of examples. Assume we have the following list of allowed keys: gender, race, age, drug, condition, procedure. \n \n \n Figure 3: Transforming criteria lists into Disjunctive Normal Form. \n \n Example 1 \n Human Query: \"Find all women over 20 years of age who are not taking heparin.\" \n \n Applying the DNF transformation, the query can be represented as follows: \n (gender = \"woman\") AND (age > 20) AND NOT (drug = \"heparin\") \n In the transformed query, the inclusion criteria (women and age > 20) are combined using the logical AND operator, while the exclusion criteria (heparin) are negated using the logical NOT operator. \n \n Eligibility lists and request card generated by GPT-4 model \n \n \n \n \n Request \n \n \n Criteria lists \n \n \n Request card \n \n \n \n \n Find all women over 20 years of age who are not taking heparin. \n \n \n Inclusion criteria: \n - women over 20 years of age \n Exclusion criteria: \n - taking heparin \n \n [{\"include\": { \n \"gender\": \"female\", \n \"age\": \">20\"}, \n \"exclude\": { \n \"drug\": \"heparin\"}}\n] \n \n \n \n \n Example 2 \n Human Query: \"Retrieve all patients who have diabetes or hypertension and are older than 50.\" \n \n Applying the DNF transformation, the query can be represented as follows: \n ((age > 50) AND (condition = \"diabetes\")) \n OR \n ((age > 50) AND (condition = \" hypertension \")) \n In this example, the inclusion criteria are combined using the logical OR operator, allowing for the selection of patients with either diabetes or hypertension. The age criterion is represented separately and combined with the condition criteria using the logical AND operator. \n \n Eligibility lists and request card generated by GPT-4 model \n \n \n \n \n Request \n \n \n Criteria lists \n \n \n Request card \n \n \n \n \n Retrieve all patients who have diabetes or hypertension and are older than 50. \n \n \n Inclusion criteria: \n - have diabetes or hypertension \n - older than 50 \n Exclusion criteria: \n \n \n [{\"include\": { \n \"condition\": \"diabetes\", \n \"age\": \">50\"}, \n \"exclude\": {}}, \n\n {\"include\": {\n \"condition\": \"hypertension\", \n \"age\": \">50\"}, \n \"exclude\": {}}\n] \n \n \n \n \n Thoughts on validation and prompt evaluation \n \n The approach that we suggest provides several points where human or system validation can be performed. The main goal of human validation of request card should be confirmation that the card matches the intent of natural language request. This check point allows making changes/refinements if needed. \n \n We observed situations when keys not presented on the allowed list were injected into request card, see key ‘location’ in Example 3 below. This request card is considered describing researcher request correctly. It will be passed to a module (not powered by LLMs) responsible for key validation, as well as query generation for a given key-data schema correspondence, with no researcher involvement. Keeping request card matching researcher request (instead of matching it to data schema) might be very useful when search is done across multiple datasets with different schemas. \n \n Example 3 \n Request card created by GPT-4 model, key location doesn’t belong to the allowed list of keys \n \n \n \n \n Request \n \n \n Criteria lists \n \n \n Request card \n \n \n \n \n Find all women over 60 years of age undergone knee replacement in Overlake Hospital \n \n \n Inclusion criteria: \n - women \n - over 60 years of age \n - undergone knee replacement \n - in Overlake Hospital \n \n [{\"include\": { \n \"gender\": \"female\", \n \"age\": \">60\", \n \"procedure\": \"knee replacement\", \n \"location\": \"Overlake Hospital\"}, \n \"exclude\": {}}\n] \n \n \n \n \n The last validation point, when cohort size is returned, allows researcher to review the cohort size and its sufficiency for research purposes, and iterate through previous steps if needed. \n \n We do not present evaluation results for prompts here since it is early exploratory work. Proper evaluation should be performed before using them in production settings. One possible evaluation approach would be to create a test set of requests with corresponding manually structured request cards and use them to compute precision and recall. Definition of match in this case might vary (for example exact vs. fuzzy) and will require careful consideration. There are other things to be taken into consideration such as variability in the list of allowed keys, each step vs. request card only evaluation, etc. \n \n Acknowledgements \n We are grateful to Christopher Gooley for giving us access to infrastructure that made experimenting with LLMs fast and simple, to Jinpeng LV for sharing knowledge on prompt engineering and to Cliff Wong for discussions on request card format. \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"13822","kudosSumWeight":6,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTgyOWlFNDkwRjJENzU2NjA3MENG?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTg5MWlEQjJCQzMxNjA2QjM2OTU1?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTgxN2lDQTQ4QTU5NTc0QjkxQjQ4?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTY0NDU0LTUxOTgyNmk1MENEODk5NDlDMEU1QjY3?revision=9\"}"}}],"totalCount":4,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:2621451":{"__typename":"Conversation","id":"conversation:2621451","topic":{"__typename":"BlogTopicMessage","uid":2621451},"lastPostingActivityTime":"2023-06-28T19:16:44.616-07:00","solved":false},"User:user:1122402":{"__typename":"User","uid":1122402,"login":"James_Havens","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-1.svg?time=0"},"id":"user:1122402"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTMwMjc5N2lGQTU2Q0UxMDE2NTRERENB?revision=53\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTMwMjc5N2lGQTU2Q0UxMDE2NTRERENB?revision=53","title":"paint_by_numbers_splash_picture.jpg","associationType":"TEASER","width":4000,"height":3000,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTMwMjc5OGlCMDFGNTc5MDc3RDQ0M0Ux?revision=53\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTMwMjc5OGlCMDFGNTc5MDc3RDQ0M0Ux?revision=53","title":"paint_by_numbers_splash_picture.jpg","associationType":"BODY","width":4000,"height":3000,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTM3NTU5NGk5NjFCMjY3MkFGNzI2MDg4?revision=53\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTM3NTU5NGk5NjFCMjY3MkFGNzI2MDg4?revision=53","title":"James_Havens_0-1653603177714.png","associationType":"BODY","width":1733,"height":1188,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTM2NTY0Nmk5QkJGM0ZFOTk2QkI3MjU1?revision=53\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTM2NTY0Nmk5QkJGM0ZFOTk2QkI3MjU1?revision=53","title":"James_Havens_1-1650482501230.png","associationType":"BODY","width":1640,"height":1521,"altText":null},"BlogTopicMessage:message:2621451":{"__typename":"BlogTopicMessage","subject":"Microsoft Purview- Paint By Numbers Series (Part 0) - Overview","conversation":{"__ref":"Conversation:conversation:2621451"},"id":"message:2621451","revisionNum":53,"uid":2621451,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:1122402"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n How do you set up new Sensitive Information Types (SITs) in Microsoft Compliance Center or modify an existing Sensitive Information Type. \n \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":10029},"postTime":"2021-08-19T10:15:36.080-07:00","lastPublishTime":"2023-06-28T19:16:44.616-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n If you wish to jump to a more relevant section of this series for what you are attempting to accomplish, use the links below. \n \n OVERVIEW (Part 0) \n 0 - Microsoft Purview - Paint By Numbers Series (Part 0) - Overview \n 0a - Microsoft Purview - Paint By Numbers Series (Part 0a) - Permissions \n \n DATA CLASSIFICATION (Part 1) \n 1 - Microsoft Purview - Paint By Numbers Series (Part 1) - Sensitive Information Types \n 1a - Microsoft Purview - Paint By Numbers Series (Part 1a) - Exact Data Match (old UI) \n 1b - Microsoft Purview - Paint By Numbers Series (Part 1b) - Trainable Classifiers \n 1c - Microsoft Purview - Paint By Numbers Series (Part 1c) - Exact Data Match (new UI) \n \n INFORMATION PROTECTION (Part 2) \n 2 - Microsoft Purview - Paint By Numbers Series (Part 2)- Information Protection \n 2a - Microsoft Purview - Paint By Numbers Series (Part 2a) - Adding the ability to add Container and Site labels \n 2b - Microsoft Purview - Paint By Numbers Series (Part 2b) - Adding a Sensitivity Label to a Container and/or Site \n 2c - Microsoft Purview - Paint By Numbers Series (Part 2c) - Default Labels \n 2d - Microsoft Purview - Paint By Numbers Series (Part 2d) - Required Labels \n 2e - Microsoft Purview - Paint By Numbers Series (Part 2e) - Using Multiple Sensitivity Labels \n 2f - Microsoft Purview - Paint By Numbers Series (Part 2g) – TBA \n 2g - Microsoft Purview - Paint By Numbers Series (Part 2g) – Recommended Labeling of Files and Emails \n \n DATA LOSS PREVENTION (Part 3) \n 3 - Microsoft Purview - Paint By Numbers Series (Part 3) - Data Loss Prevention for Exchange \n 3a - Microsoft Purview - Paint By Numbers Series (Part 3a) - DLP for Endpoint (SIT-based) \n 3b - Microsoft Purview - Paint By Numbers Series (Part 3b) - DLP for Endpoint (Label-based) \n 3c - Microsoft Purview - Paint By Numbers Series (Part 3c) - Data Loss Prevention with Teams Chat \n \n RECORD MANAGEMENT (Part 4) \n 4 - Microsoft Purview - Paint By Numbers Series (Part 4) - Records Management \n \n PREMIUM eDISCOVERY (Part 5) \n 5 - Microsoft Purview - Paint By Numbers Series (Part 5 - from 2021) - Premium eDiscovery (old walk through) \n 5 - Microsoft Purview - Paint By Numbers Series (Part 5) - Overview and Settings \n 5a - Microsoft Purview - Paint By Numbers Series (Part 5a) - Case Creation and Case Settings \n 5b - Microsoft Purview - Paint By Numbers Series (Part 5b) - Data Sources and Collections \n 5c - Microsoft Purview - Paint By Numbers Series (Part 5c) - Review Sets \n 5d - Microsoft Purview - Paint By Numbers Series (Part 5d) - Communication \n 5e - Microsoft Purview - Paint By Numbers Series (Part 5e) - Holds \n 5f - Microsoft Purview - Paint By Numbers Series (Part 5f) - Processing \n 5g - Microsoft Purview - Paint By Numbers Series (Part 5g) - Exports \n 5h - Microsoft Purview - Paint By Numbers Series (Part 5h) - Jobs \n \n INSIDER RISK MANAGEMENT (Part 6) \n 6 - Microsoft Purview - Paint By Numbers Series (Part 6) - Insider Risk Management - Overview \n 6a - Microsoft Purview - Paint By Numbers Series (Part 6) - Insider Risk Management - Permissions \n 6b - Microsoft Purview - Paint By Numbers Series (Part 6) - Insider Risk Management - Settings \n 6c - Microsoft Purview - Paint By Numbers Series (Part 6) - Insider Risk Management - Policies \n 6d - Microsoft Purview - Paint By Numbers Series (Part 6) - Insider Risk Management - Alerts \n 6e - Microsoft Purview - Paint By Numbers Series (Part 6) - Insider Risk Management - Cases (investigations) \n \n VIVA (Part 7) \n 7 - Microsoft Purview - Paint By Numbers Series (Part 7) - Viva and Purview Workloads \n \n INFORMATION BARRIERS (Part 😎 \n 8a - Microsoft Purview - Paint By Numbers Series (Part 8a) - Information Barriers and Teams Chat \n \n COMPLIANCE MANAGER (Part 9) \n 9a - Microsoft Purview - Paint By Numbers Series (Part 9a) - Compliance Manager - Overview \n 9b - Microsoft Purview - Paint By Numbers Series (Part 9b) - Compliance Manager - Run an Assessment \n 9c - Microsoft Purview - Paint By Numbers Series (Part 9c) - Compliance Manager - Improvement Actions \n \n \n Microsoft Defender for Cloud Apps (MDCA) and Data Loss Prevention (DLP) (Part 10) \n 10 - Microsoft Purview - Paint By Numbers Series (Part 10) - Compliance Manager - Microsoft Dender for Cloud Apps and DLP - Overview \n 10a - Microsoft Purview - Paint By Numbers Series (Part 10a) - Compliance Manager - Microsoft Defender for Cloud Apps - MS Teams Browser DLP \n \n Overview of this Blog Series \n This blog series is meant to address several specific use cases in the Compliance space as well as provide step-by-step approach to run basic tests against those use cases. \n \n We will start with the overview and permissions of the Purview stack. Then, as you look at the diagram below, you will see that the blog series starts with the Sensitive Information Type (SIT) as a foundation and then all other parts of compliance are built on that foundation. Once you understand how to create and modify a SIT, you can then create an Exact Data Match, if you want. Once you’ve done Part 1/1a you can then do Parts 2-5 (and future parts) in the order provided or you can perform them in the order relevant to your needs. \n \n \n \n \n \n In each Part of this blog, I will explain the Use Case related that Compliance function. Please note there is more than one use case for each of these functions, but for simplicity, I am only addressing one per for each. \n \n Microsoft Purview (April 19, 2022) \n As of April 19, 2022, Microsoft Compliance is now known as Microsoft Purview. Please see the link at the bottom of this page for more information about this renaming and what it means \n \n \n \n \n Disclaimer \n This document is not meant to replace any official documentation, including those found at docs.microsoft.com. Those documents are continually updated and maintained by Microsoft Corporation. If there is a discrepancy between this document and what you find in the Compliance User Interface (UI) or inside of a reference in docs.microsoft.com, you should always defer to that official documentation and contact your Microsoft Account team as needed. Links to the docs.microsoft.com data will be referenced both in the document steps as well as in the appendix. \n All of the following steps should be done with test data, and where possible, testing should be performed in a test environment. Testing should never be performed against production data. \n \n Target Audience \n The Sensitive Information Type (SIT) section of this blog series is aimed at Compliance officers who need to identify any PII and PHI data in their environment. \n \n Document Scope \n This document is meant to provide an overview of the Microsoft Purview Suite (formerly Compliance Suite) to an administrator who is “net new” to Microsoft E5 tools. \n \n Out-of-Scope \n This document does not cover any other aspect of Microsoft E5 Compliance, including: \n \n Sensitive Information Types \n Exact Data Matching \n Sensitivity Labeling \n Data Protection Loss (DLP) for Exchange, OneDrive, Devices \n Microsoft Cloud App Security (MCAS) \n Records Management (retention and disposal) \n Advanced eDiscovery (AeD) \n Insider Risk Management \n Privacy Management \n \n It is presumed that you have a pre-existing of understanding of what Microsoft E5 Compliance does and how to navigate the User Interface (UI). \n \n Overview of Document \n \n Overview \n Microsoft Purview \n Target Audience \n Document Scope \n Out of Scope \n Use Case \n Definitions \n Notes \n Requirements \n Appendix and Links \n \n \n Use Case \n None \n \n Definitions \n \n Data Classification\n \n The core of the Compliance tool is the Microsoft Information Protection (MIP) engine. This engine allows for indexing of existing data and then track any changes made to that data via the Compliance tool set (example – information label that data with sensitivity and governance labels). \n \n \n \n \n \n Information Protection\n \n This is the tool that is used to apply labels, watermarks, headers, footnotes and encryption to emails, files, etc These labels carry with files/emails inside and outside the organization. \n \n \n \n \n \n Data Loss Prevention\n \n This component of the solution blocks emails and files being leaked or stolen from the organization, whether it be from the endpoint devices (windows 10/11 and Mac) or from Exchange Online, SharePoint Online, Teams, or OneDrive. \n \n \n \n \n \n Information Governance\n \n This tool applies Retention and Disposal polices to files and emails inside of a tenant, thereby helping the organization meet its regulatory needs. \n \n \n \n \n \n Advanced eDiscovery\n \n This is the Microsoft eDiscovery tool. It performs more than just search, hold and export. It provides for granular eDiscovery functions, such as review, redaction, searches against existing holds, etc. \n \n \n \n \n \n Insider Risk Management\n \n This tool brings in the telemetry from the organization’s tenant and onboarded devices to provide a baseline of “behavior” of for users. Examples of this baseline, as it relates to compliance workloads, are copying data, deleting data, uploading data to the cloud, etc. \n \n \n \n \n Notes \n \n Replication times for changes in Purview polices take affect:\n \n DLP policies will take approximately 15 minutes to take affect \n Other Compliances items could take 24-48 hours for other changes to take affect \n \n \n \n \n Requirements \n None \n \n Appendix and Links \n \n The future of compliance and data governance is here: Introducing Microsoft Purview - Microsoft Security Blog \n \n \n \n Microsoft Purview solution catalog - Microsoft Purview | Microsoft Docs \n \n \n \n Microsoft Purview compliance portal - Microsoft Purview | Microsoft Docs \n \n \n Note: This solution is a sample and may be used with Microsoft Compliance tools for dissemination of reference information only. This solution is not intended or made available for use as a replacement for professional and individualized technical advice from Microsoft or a Microsoft certified partner when it comes to the implementation of a compliance and/or advanced eDiscovery solution and no license or right is granted by Microsoft to use this solution for such purposes. This solution is not designed or intended to be a substitute for professional technical advice from Microsoft or a Microsoft certified partner when it comes to the design or implementation of a compliance and/or advanced eDiscovery solution and should not be used as such. Customer bears the sole risk and responsibility for any use. Microsoft does not warrant that the solution or any materials provided in connection therewith will be sufficient for any business purposes or meet the business requirements of any person or organization. \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"11442","kudosSumWeight":6,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTMwMjc5N2lGQTU2Q0UxMDE2NTRERENB?revision=53\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTMwMjc5OGlCMDFGNTc5MDc3RDQ0M0Ux?revision=53\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTM3NTU5NGk5NjFCMjY3MkFGNzI2MDg4?revision=53\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yNjIxNDUxLTM2NTY0Nmk5QkJGM0ZFOTk2QkI3MjU1?revision=53\"}"}}],"totalCount":4,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:838781":{"__typename":"Conversation","id":"conversation:838781","topic":{"__typename":"BlogTopicMessage","uid":838781},"lastPostingActivityTime":"2019-10-31T07:20:14.846-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04Mzg3ODEtMTMwMjQwaUQ3MTRBQkREMjJDQjREMTc?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04Mzg3ODEtMTMwMjQwaUQ3MTRBQkREMjJDQjREMTc?revision=3","title":"records.png","associationType":"TEASER","width":1280,"height":720,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04Mzg3ODEtNTA2aTI0Q0E0NDIwRkUzMEJDNkQ?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04Mzg3ODEtNTA2aTI0Q0E0NDIwRkUzMEJDNkQ?revision=3","title":"me - Copy.jpg","associationType":"BODY","width":610,"height":584,"altText":null},"BlogTopicMessage:message:838781":{"__typename":"BlogTopicMessage","subject":"Office 365 Records Management and Compliance Resource Documentation","conversation":{"__ref":"Conversation:conversation:838781"},"id":"message:838781","revisionNum":3,"uid":838781,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:880"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" In this post I provide the resource documentation I put together focusing on Office 365 Records Management and related Compliance/Security documentation. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":20413},"postTime":"2019-09-05T05:57:02.324-07:00","lastPublishTime":"2019-09-05T05:57:02.324-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" The other day I had a discussion with a customer around the use of Microsoft 365 for records management. We covered front end SharePoint Online administration for records management and as is usual in these conversations moved into a discussion around their regulatory compliance requirements. At the end of our discussions and demonstrations I provided the customer asked if I could provide links to Microsoft official documentation around Records management and compliance. \n In this post I provide the resource documentation I put together focusing on Office 365 Records Management and related Compliance/Security documentation. \n \n Records management in Microsoft 365 \n Implement Records Management \n Overview of file plan manager \n Overview of retention labels \n View label activity for documents \n Overview of event-driven retention \n View the data governance reports \n Protect information \n Overview of sensitivity labels \n Overview of data loss prevention \n Overview of retention policies \n Supervision policies in Office 365 \n Watch videos from the Microsoft Data Governance team \n Search for content in Office 365 \n Manage legal investigations in Office 365 \n Overview of Data Investigations in Microsoft 365 \n Search the audit log in the Security & Compliance Center \n Microsoft Compliance Manager \n Secure SharePoint Online sites and files \n \n Thanks for visiting – Michael Gannotti LinkedIn | Twitter | Facebook | Instagram \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1618","kudosSumWeight":5,"repliesCount":5,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04Mzg3ODEtMTMwMjQwaUQ3MTRBQkREMjJDQjREMTc?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS04Mzg3ODEtNTA2aTI0Q0E0NDIwRkUzMEJDNkQ?revision=3\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3800475":{"__typename":"Conversation","id":"conversation:3800475","topic":{"__typename":"BlogTopicMessage","uid":3800475},"lastPostingActivityTime":"2025-02-02T16:24:42.749-08:00","solved":false},"User:user:1332922":{"__typename":"User","uid":1332922,"login":"Ramya_Gangula","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-1.svg?time=0"},"id":"user:1332922"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyMmkxMjM5MzQzMjBGNkNFRjY1?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyMmkxMjM5MzQzMjBGNkNFRjY1?revision=6","title":"Ramya_Gangula_0-1681929689139.png","associationType":"BODY","width":2704,"height":1134,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyM2lDQ0E2MUEzOTg5RTM5QTJB?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyM2lDQ0E2MUEzOTg5RTM5QTJB?revision=6","title":"Ramya_Gangula_1-1681929689150.png","associationType":"BODY","width":1244,"height":1304,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyNGk4QUYxQThGNkE2Q0MzODcx?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyNGk4QUYxQThGNkE2Q0MzODcx?revision=6","title":"Ramya_Gangula_2-1681929689157.png","associationType":"BODY","width":1118,"height":526,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyNWlGMkYwMDlGNzA3Q0QzNTlD?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyNWlGMkYwMDlGNzA3Q0QzNTlD?revision=6","title":"Ramya_Gangula_3-1681929689163.png","associationType":"BODY","width":1294,"height":756,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyNmk5RkMzQjYxMkQ3ODZENjkw?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODAwNDc1LTQ2MjQyNmk5RkMzQjYxMkQ3ODZENjkw?revision=6","title":"Ramya_Gangula_4-1681929689167.png","associationType":"BODY","width":974,"height":752,"altText":null},"BlogTopicMessage:message:3800475":{"__typename":"BlogTopicMessage","subject":"Utilizing Azure Key vault with Private link in DevOps","conversation":{"__ref":"Conversation:conversation:3800475"},"id":"message:3800475","revisionNum":6,"uid":3800475,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:1332922"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Azure Key Vault is a cloud service that provides secure storage and access to secrets such as API keys, passwords, certificates, or cryptographic keys. To enhance security and disable public access, Azure Key Vault can be integrated with Private Endpoint powered by Azure Private Link. This private endpoint uses a private IP address from your VNet and brings the service into your VNet, effectively eliminating exposure from the public Internet by traversing traffic between your virtual network and the service over the Microsoft backbone network. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":10789},"postTime":"2023-04-20T12:05:06.274-07:00","lastPublishTime":"2023-04-24T14:33:44.291-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Azure Key Vault is a cloud service that provides secure storage and access to secrets such as API keys, passwords, certificates, or cryptographic keys. To enhance security and disable public access, Azure Key Vault can be integrated with Private Endpoint powered by Azure Private Link. This private endpoint uses a private IP address from your VNet and brings the service into your VNet, effectively eliminating exposure from the public Internet by traversing traffic between your virtual network and the service over the Microsoft backbone network. \n \n Prerequisites to achieve this scenario. \n \n Self-Hosted Agent in Azure DevOps \n Azure Key Vault \n Virtual Network \n Subnet in the Virtual Network \n Set Access policy for the service connection with Get and List permissions to access secrets from Yaml/Library section. \n \n \n Accessing Key Vault from yaml with Private Endpoint enabled \n \n Ensure that the Key Vault private endpoint connection is approved. This document provides detailed steps for integrating Key Vault with Azure Private Link. \n \n \n \n The job below is running on a self-hosted agent using the service connection to get access to Azure Key Vault. I have added a cmdLine task to create a file and retrieve secrets from Key Vault. \n \n \n \n Below is the output of retrieving secrets from Azure Key Vault after running the Azure DevOps pipeline. \n \n \n \n Link Secrets from Azure Key vault as variables \n \n I have whitelisted the IP address (in my case, ADO is hosted on Central United States) from the Azure Key Vault Networking section to link secrets. 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","introduction":"","metrics":{"__typename":"MessageMetrics","views":8260},"postTime":"2020-09-18T08:25:22.165-07:00","lastPublishTime":"2020-09-18T08:44:45.639-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" More and more life science organizations are looking to leverage cloud-based solutions that can be used anywhere, on any device, to support \"good practice\" quality guidelines and regulations (GxP). To carry out their digital transformation, customers in regulated industries trust Microsoft cloud services such as Microsoft 365, Azure, and Dynamics 365 to shorten their time to market, reduce costs, increase operational efficiency, and accelerate scientific innovation. \n \n Each year Microsoft invests billions of dollars in designing, building, and operating innovative cloud services. But in this highly regulated industry, for you to even consider our services, we must earn and retain your trust. Microsoft cloud services are built around key tenets of security, privacy, transparency, and compliance; and we invest more each year to increase the confidence of our life sciences customers in Microsoft cloud services. \n \n Microsoft aims to ensure the confidentiality, integrity, and availability of data, documents, and GxP applications for life science organizations. With each service, customer data benefits from multiple layers of security and governance technologies, operational practices, and compliance policies to enforce data privacy and integrity at specific levels. \n \n Over time, we intend to make it easier for life sciences organizations to use Microsoft cloud services for their full portfolios of applications. We believe that this GxP guidance document is a key step toward that goal. Given the shared responsibilities of the cloud model, life science customers rely on the fact that Microsoft has implemented appropriate technical and procedural controls to manage and maintain the cloud environment in a state of control. Microsoft’s quality practices and secure development lifecycle encompass similar core elements as would be found in many life sciences customers’ internal Quality Management Systems and meet or exceed industry standards. \n \n This guide should help demonstrate that you can develop and operate GxP applications on Microsoft Azure, Dynamics 365, and Microsoft 365 with confidence and without sacrificing compliance with GxP regulation. We look forward to working with you to help you achieve your digital transformation initiatives. \n - Daniel Carchedi – Sr. Director Business Development & Strategy Life Sciences \n \n GxP Guidelines for Microsoft 365, Dynamics, and Azure are attached below \n \n Webinars to check out: \n The future of digital collaboration in pharma and biotech: The Microsoft 365 Journey \n Accelerate your journey to the cloud: Move your GxP regulated workloads to Microsoft Azure \n Register for the GxP Cloud Compliance Summit October 20th and 21st \n \n 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