Website update

content/en/_index.html

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 {{< /blocks/section >}}
 
+{{% blocks/lead color="primary" %}}
+See below for a map of all the places we have hosted datathons around the world!
 
+<iframe width="840" height="480" src="https://tmlunde.carto.com/builder/0d422c4d-df06-415a-9a47-8b41fe3e4931/embed?state=%7B%22map%22%3A%7B%22ne%22%3A%5B-33.137551192346145%2C-294.60937500000006%5D%2C%22sw%22%3A%5B59.88893689676585%2C211.64062500000003%5D%2C%22center%22%3A%5B19.642587534013032%2C-41.48437500000001%5D%2C%22zoom%22%3A2%7D%7D" title="Datathon Map" frameborder="0" allow="clipboard-write; encrypted-media; web-share" allowfullscreen></iframe>
+{{% /blocks/lead %}}

content/en/news/publications/2023/avoiding-odiao.md

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+
+---
+categories: ["Publications"]
+people: ["Hope Watson", "Jack Gallifant", "Yuan Lai", "Alexander P Radunsky", "Cleva Villanueva", "Nicole Martinez", "Judy Gichoya", "Uyen Kim Huynh", "Leo Anthony Celi"]
+title: Delivering on NIH data sharing requirements - avoiding Open Data in Appearance Only
+linktitle: Avoiding Open Data in Appearance Only
+date: 2023-06-27
+description: >
+ In January, the National Institutes of Health (NIH) implemented a Data Management and Sharing Policy aiming to leverage data collected during NIH-funded research. The COVID-19 pandemic illustrated that this practice is equally vital for augmenting patient research. In addition, data sharing acts as a necessary safeguard against the introduction of analytical biases. While the pandemic provided an opportunity to curtail critical research issues such as reproducibility and validity through data sharing, this did not materialise in practice and became an example of 'Open Data in Appearance Only' (ODIAO). Here, we define ODIAO as the intent of data sharing without the occurrence of actual data sharing (eg, material or digital data transfers).
+---
+
+<a href=https://informatics.bmj.com/content/30/1/e100771 target="_blank">BMJ Journals</a>
+
+>We introduce the term 'Open Data in Appearance Only' (ODIAO) which applies to datasets that are branded as open but are accessible through a complicated drawn-out costly process that prevents an average student from gaining access to the dataset before she/he graduates. Typically, only students of the richest universities are able to access and publish off these datasets.

content/en/news/publications/2023/equity-to-reimbursements.md

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+
+---
+categories: ["Publications"]
+people: ["Robin L. Pierce", "Jack Gallifant", "Leo Anthony Celi"]
+title: Reform Response - Tying Equity To Reimbursements
+linktitle: Tying Equity to Reimbursements
+date: 2023-08-07
+description: >
+ Check out our latest article here: <a href="https://www.healthaffairs.org/content/forefront/reform-response-tying-equity-reimbursements" target="_blank">HealthAffairs</a>
+---
+
+In December 2022, the Centers for Medicare and Medicaid Services proposed a bold initiative to tie reimbursements to health equity. The proposal puts forth a set of new and revised provisions targeting specific pathways that are set to enhance health equity. While it takes a much-needed step toward actualizing a commitment to health equity, more than good intentions are required to ensure its success. Several key decisions are yet to be made, which could derail this promising initiative's ability to make meaningful inroads toward health equity. In this paper, we point to critical considerations that need to be addressed before the intiative takes effect, including modifications to the roles of health care providiers, organizations, and policy makers.

content/en/news/publications/2023/guide-to-sharing-data-gdpr.md

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+
+---
+categories: ["Publications"]
+people: ["Jip W. T. M. de Kok", "Miguel A. Armengol de la Hoz", "Ymke de Jong", "Veronique Brokke", "Paul W. G. Elbers", "Patrick Thoral", "Alejandro Castillejo", "Tomas Trenor", "Jose M. Castellano", "Alberto E. Bronchalo", "Tobias M. Merz", "Martin Faltys", "Iwan C. C. van der Horst", "Minnan Xu", "Leo Anthony Celi", "Bas C. T. van Bussel", "Xavier Borrat"]
+title: A guide to sharing open healthcare data under the General Data Protection Regulation
+linktitle: Guide to sharing data under GDPR
+date: 2023-06-24
+description: >
+ The data bias within health records is too complex for a siloed small group of investigators to understand. Any health AI initiative without data sharing at its core is doomed to legitimize and even scale systemic inequities.
+---
+
+Check out the article here: <a href="https://www.nature.com/articles/s41597-023-02256-2" target="_blank">Nature</a>
+
+Some say they can't share data because of the GDPR when in fact they are using GDPR as an excuse not to share data. The real reasons are: 
+
+1. Why should others publish off "their" data?
+2. Data = money. Why give it away?
+3. Data = knowledge, knowledge = power, and as Cersei said, power = power.

content/en/news/publications/2023/role-of-ai-pharma.md

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+
+---
+categories: ["Publications"]
+people: ["Adrian Wong", "Erin Wentz", "Nicholas Palisano", "Manar Dirani", "Pansy Elsamadisi", "Farah Qashou", "Leo Celi", "Omar Badawi", "Lama Nazer"]
+title: Role of artificial intelligence in pharmacy practice - A narrative review
+linktitle: Role of AI in Pharmacy
+date: 2023-07-10
+description: >
+ With the growth of electronic health records, digital health platforms, and social media, there is an abundance of data that can be analyzed using AI to generate insights that can help pharmacy practice make informed decisions and provide personalized care to their patients. This can ultimately improve patient outcomes and reduce healthcare costs. This narrative review evaluates the potential roles that AI technology with three guided questions to determine how AI may improve patient outcomes.
+---
+
+Artifical intelligence is poised to upend every facet of healthcare delivery. We need to build capacity in data science among all healthcare personnel. Those at the frontlines of care have to be at the frontline of the AI revolution. We cannot afford to be at the backseat while the big companies drive.
+
+Check out the publication here: <a href="https://onlinelibrary.wiley.com/share/author/5BZZI56MSG39DAUS9JPJ?target=10.1002/jac5.1856" target="_blank">ACCP Journals</a>

content/en/news/upcoming/2023-08-Emory.md

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 weight: 7
 date: 2023-03-27
 description: >
-  Join us August 19-21 at Emory University
+  Join us August 18-21 at Emory University
 ---
 
->Information TBA
+>Get ready to witness the tantalizing power of cross-disciplinary collaboration as the Emory Datathon unveils the true potential of harnessing diverse perspectives and skills to shed light on bias in medical imaging, particularly in chest imaging and mammography. Embracing the tenets of Accessible Science, The Hive Mind and Shared Innovation, this electrifying datathon aims to revolutionize the discourse surrounding AI bias by shining a scorching spotlight on the staggering health disparities that affect marginalized populations. Join us as we dive headfirst into the world of cutting-edge data analysis and sizzling insights, all in the service of creating a more equitable future for all.
+
+Check out more info here: https://emory.healthdatathon.com/ 

content/en/news/upcoming/2023-08-New-York-City.md

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+
+---
+categories: ["Datathons"]
+title: "2023 Society of Critical Care Medicine: New York City"
+linkTitle: "August 2023: New York City"
+weight: 6.5
+date: 2023-07-05
+description: >
+ Join us August 5-6 at the Soceity of Critical Care Medicine datathon.
+---
+
+How do we build capacity in a field that is moving at warped speed and when learners far outnumber teachers, and only collective exerptise exists? We leverage hive learning where a diverse community learns together and learns from each other. This is the ethos of datathons. Please join us in New York City on August 5-6 for the 2023 Society of Critical Care Medicine datathon. We will focus on understanding the data first before we do any modeling. Machine learning should be 95% understanding the data - bias from medical devices, bias from who got into the database and who did not, bias from patients not monitored similarly - and 5% modeling. Otherwise, we get artificial intelligence that encodes health disparities.
+
+>Information: https://sccm.org/Research/Discovery-Research-Network/datascience/Datathon