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Healthcare has higher barriers to adopting data science than other industries. State-of-the-art analytics solutions are already available, but few of them are in use by clinicians.

At University of Virginia Health System, health leaders worked to establish a culture of data-driven decision-making with clinicians, with data science guides clinicians in finding opportunities for improvement, designing and implementing interventions, and evaluating impacts.

Bommae Kim, senior data scientist at Hackensack Meridian Health and until last year with UVA Health, also as a senior data scientist said a key challenge to wider adoption is lack of interest.

"Due to their disinterest or ambivalence to data science, it may be difficult to find opportunities to work with clinicians to begin with," she said.

Kim, who along with Dr. Jonathan Michel, director of data science at University of Virginia Health, will speak on the topic next month at HIMSS21. She said a lack of trust and a lack of understanding are two other challenges to adoption of analytics solutions

"Clinicians may disagree with analytics results due to lack of trust in data science," she said. "It may also be challenging to introduce advanced analytics due to the level of data literacy."

She explained the key opportunities for clinicians adopting data science depends on the analytics maturity and executive leadership support at the organization.

"Of the multiple aspects to consider, I'd like to point out actionability in finding opportunities," said Kim. "Unless strong clinician support is already in place, it would be extremely challenging to succeed in purely clinical topics, for example sepsis."

She noted those clinical topics are certainly important to any health system but may not be readily actionable for many reasons.

On the other hand, Kim noted some topics are highly relevant to clinicians yet not purely clinical--LOS and readmissions, for instance.

"Their causes and interventions are not necessarily clinical, unlike sepsis, and clinicians seem more open to data scientists' suggestions in less-clinical domains," she said. "I would consider them more actionable topics. Once a strong relationship is built with clinicians, it'll be easier to move to more clinical domains with their support."

She explained UVA Health Data Science often engages with clinicians by presenting data analysis about their patients and workflows as for their project or interest. Such sessions naturally lead clinicians to data-driven decision making.

"Through such engagement, we built trust and improved data literacy among clinicians," said Kim.

"Moreover, in the process data scientists learned what clinicians truly want and need. What they ask for may not be what they truly want or need. With improved clinician trust and data literacy and a better understanding of clinician needs, we were able to move toward more advanced analytics."

Jonathan Michel and Bommae Kim will address the use of data science among clinicians at HIMSS21 in a session titled "Making Prescriptive Analytics Work for Clinicians." It's scheduled for Thursday, August 12 from 1-2 p.m. in room Wynn Lafite 2

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Cathie Anderson| The Sacramento Bee via Associated Press

SACRAMENTO, Calif. Cases of COVID-19 rose sharply last year in Reno, Nevada, when a heavy layer of wildfire smoke settled over the city, according to scientists at the Desert Research Institute, and they and other scientists are postulating that there is a link between air pollution and increased susceptibility to the new coronavirus.

Our results showed a substantial increase in the COVID-19 positivity rate in Reno during a time when we were affected by heavy wildfire smoke from California wildfires, said Daniel Kiser, a co-lead author of the study published in the Journal of Exposure Science and Environmental Epidemiology. This is important to be aware of as we are already confronting heavy wildfire smoke ... with COVID-19 cases again rising in Nevada and other parts of the western U.S.

Kiser, an assistant research scientist of data science at the institute, said he became interested in studying the effect of the microscopic particulate matter from wildfires after reading a Canadian scientists article on the dual effect of confronting both issues at the same time.

In the preface to her work, senior scientist Sarah Henderson of the British Columbia Center for Disease Control, wrote: As we enter the wildfire season in the northern hemisphere, the potential for a dangerous interaction between SARS-CoV-2 and smoke pollution should be recognized and acknowledged. This is challenging because the public health threat of COVID-19 is immediate and clear, whereas the public health threat of wildfire smoke seems distant and uncertain in comparison. However, we must start preparing now to effectively manage the combination of public health threats.

Kiser is hoping that his research results will motivate people to get vaccinated and to wear masks to reduce their exposure to the virus and to tiny wildfire particulate matter that measures 2.5 micrometers or less.

Thats about 1/30th the size of a human hair at its largest. Scientists refer to it as PM 2.5 for short.

To analyze the relationship between this fine wildfire ash and COVID-19 positivity rates, Kiser and his team collected data from the Washoe County Health District and the regions big hospital system, Renown Health.

He said they discovered that the PM 2.5 was responsible for a 17.7% increase in the number of COVID-19 cases that occurred during a period of prolonged smoke that took place between Aug. 16, 2020, and Oct. 10, 2020.

Washoe Countys 450,000 residents, many of whom live in Reno, experienced 43 days of elevated PM 2.5 during that period, researchers said, compared with 26 days for residents of the San Francisco Bay Area.

We had a unique situation here in Reno last year where we were exposed to wildfire smoke more often than many other areas, including the Bay Area, said Dr. Gai Elhanan, co-lead author of the study and an associate research scientist of computer science at the institute. We are located in an intermountain valley that restricts the dispersion of pollutants and possibly increases the magnitude of exposure, which makes it even more important for us to understand smoke impacts on human health.

The relationship between COVID-19 positivity rates and air pollution in general has gained interest among scientists around the world, and Kiser and Elhanan cite research papers from Europe and Asia that explore the phenomenon as well.

Kent Pinkerton, an expert on air pollution on the faculty at the University of California, Davis, said theres concern among physicians and scientists about the impact of climate change on cardiopulmonary health, a topic hes currently addressing in an article hes submitting to a medical journal.

Hotter temperatures, climate change, wildfires, air pollution, all seem to have some association with a greater risk of COVID-19 cases, Pinkerton said. If youre susceptible to air pollution, such as particulate matter, it could be that you just have a situation where youll be also much more susceptible to viral particles that might be in the air that youre breathing. Its not that the air pollution makes the COVID-19 cases more likely to happen, but it may simply be a reflection of just the fact that, where areas of high pollution are, ... the risk for COVID-19 cases may be greater.

Pinkerton said he read a paper on a study out of Turkey, which was submitted to a medical journal, and researchers there also found a terrible upswing in COVID-19 cases linked to increased air pollution.

No one has yet found the mechanism that increases the risk, Kiser and Pinkerton said, but there have been some hypotheses.

Could the new coronavirus be hitching rides on the PM 2.5 and managing to remain virulent as it is breathed into peoples lungs? Certainly, PM 2.5 has been found in the smallest air sacs of peoples lungs.

Kisers team cites a study out of Northern Italy where researchers found the new coronavirus on particulate matter, and Pinkerton noted that the pathogen has been detected in water supplies and in sewage.

We know that dust from the Mongolian desert, that comes across the Pacific Ocean, can carry at least biological material, whether it be viral or bacterial, Pinkerton said. What people have argued about is that the dust can be a carrier for microorganisms.

It raises questions, Pinkerton added, of how long a virus can survive.

Kiser and Pinkerton said researchers also have postulated that the PM 2.5 irritates nasal, throat and lung passages, creating inflammation that makes those areas ripe for infection. Some research has even suggested that the PM 2.5 increases the presence of a histamine receptor to which the COVID-19 virus attaches, Kiser said.

Elhanan said: We believe that our study greatly strengthens the evidence that wildfire smoke can enhance the spread of SARS-CoV-2. We would love public health officials across the U.S. to be a lot more aware of this because there are things we can do in terms of public preparedness in the community to allow people to escape smoke during wildfire events.

In fact, the U.S. Centers for Disease Control and Prevention have a website about wildfire smoke and COVID-19 that provides tips on how to prepare for wildfire season, including identifying high-efficiency air filters and maintaining a supply of N95 respirators which filter out particulates.

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Researchers link heavy wildfire smoke in Reno to increased risk of contracting COVID-19 - Reno Gazette-Journal

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News Release

Tuesday, July 20, 2021

Microsoft Azure has joined the National Institutes of Healths Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative as the newest cloud service provider to support biomedical research. The addition of this latest industry partner will further the STRIDES Initiatives aim to accelerate biomedical research in the cloud by reducing economic and process barriers as well as providing cost-effective access to cloud platforms, training, cloud experts, and best practices for optimizing research in the cloud.

In just a few years, the STRIDES Initiative has expanded access to critical infrastructure and cutting-edge cloud resources for NIH researchers, as well as NIH-funded researchers at more than 2,500 academic institutions across the nation. To date, NIH has helped more than 425 research programs and projects leverage cloud resources through the STRIDES Initiative. Collectively, researchers have used more than 83 million hours of computational resources to access and analyze more than 115 petabytes of high-value biomedical data in the cloud. This is equivalent to 2.3 million four-drawer filing cabinets full of text.

By leveraging the STRIDES Initiative, the National Library of MedicinesSequence Read Archive (SRA) one of the worlds largest, publicly available genome sequence repositories migrated over 43 petabytes of next generation sequencing data to the cloud, easing access for millions of researchers. Using the cloud, researchers can nowsearch the entire catalog of genomic data and take advantage of the computational tools for analysis.

The cloud can help democratize access to high-value research data and the most advanced analytical technologies for all researchers. Expanding our network of providers and access to the most advanced computational infrastructure, tools, and services provides the agility and flexibility that researchers need to accelerate research discoveries, said Andrea T. Norris, Director of NIHs Center for Information Technology and NIH Chief Information Officer. Partnering with Microsoft Azure as a cloud service provider furthers our goals to enhance discovery and improve efficiency in biomedical research.

We often risk losing the value of biomedical data because of the sheer volumes being generated and digitized around the world. By leveraging cloud and artificial intelligence capabilities, biomedical researchers are able to quickly identify and extract critical, lifesaving insights from this sea of information, said Toni Townes-Whitley, President, U.S. Regulated Industries, Microsoft. We are honored to collaborate with the NIH to help researchers solve some of todays biggest medical challenges, in support of a healthier and more sustainable global population.

A central tenet of the STRIDES Initiative is that data made available through these partnerships will incorporate standards endorsed by the biomedical research community to make data Findable, Accessible, Interoperable, and Reusable (FAIR).

NIH has an ambitious vision of a modernized, FAIR biomedical data landscape, said Susan K. Gregurick, Ph.D., Associate Director for Data Science and Director of the Office of Data Science Strategy at NIH. By partnering with Microsoft Azure, which has over three decades of experience in the cloud space, we can strengthen NIHs data ecosystem and accelerate data-driven research and discovery.

Microsoft Azure joins Google Cloud and Amazon Web Services in supporting the STRIDES Initiative.

About the NIH Office of Data Science Strategy: The Office of Data Science Strategy (ODSS) leads implementation of the NIH Strategic Plan for Data Science through scientific, technical, and operational collaboration with the institutes, centers, and offices that comprise NIH. The office was formed in 2018 within the Division of Program Coordination, Planning, and Strategic Initiatives, which plans and coordinates trans-NIH initiatives and research supported by the NIH Common Fund. More information is available at the Office of Data Science Strategy website: datascience.nih.gov.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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NIH expands biomedical research in the cloud with Microsoft Azure - National Institutes of Health

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Most of the public discourse around artificial intelligence (AI) policy focuses on one of two perspectives: how the government can support AI innovation, and how the government can deter its harmful or negligent use. Yet there can also be a role for government in making it easier to use AI beneficiallyin this niche, the National Science Foundation (NSF) has found a way to contribute. Through a grant-making program called Fairness in Artificial Intelligence (FAI), the NSF is providing $20 million in funding to researchers working on difficult ethical problems in AI. The program, a collaboration with Amazon, has now funded 21 projects in its first two years, with an open call for applications in its third and final year. This is an important endeavor, furthering a trend of federal support for the responsible advancement of technology, and the NSF should continue this important line of funding for ethical AI.

The FAI program is an investment in what the NSF calls use-inspired research, where scientists attempt to address fundamental questions inspired by real world challenges and pressing scientific limitations. Use-inspired research is an alternative to the traditional basic research, which attempts to make fundamental advances in scientific understanding without necessarily a specific practical goal. NSF is better known for basic research in computer science, where the NSF provides 87% of all federal basic research funding. Consequently, the FAI program is a relatively small portion of the NSFs total investment in AIaround $3.3 million per year, considering that Amazon covers half of the cost. In total, the NSF requested $868 million in AI spending, about 10% of its entire budget for 2021, and Congress approved every penny. Notably, this is a broad definition of AI spending that includes many applications of AI to other fields, rather than fundamental advances in AI itself, which is likely closer to $100 or $150 million, by rough estimation.

The FAI program is specifically oriented towards the ethical principle of fairnessmore on this choice in a moment. While this may seem unusual, the program is a continuation of prior government funded research into the moral implications and consequences of technology. Starting in the 1970s, the federal government started actively shaping bioethics research in response to public outcry following the APs reporting on the Tuskegee Syphilis Study. While the original efforts may have been reactionary, they precipitated decades of work towards improving the biomedical sciences. Launched alongside the Human Genome Project in 1990, there was an extensive line of research oriented towards the ethical, legal, and social implications of genomics. Starting in 2018, the NSF funded 21 exploratory grants on the impact of AI on Society, a precursor to the current FAI program. Today, its possible to draw a rough trend line through these endeavors, in which the government is becoming more concerned with first pure science, then the ethics of the scientific process, and now the ethical outcomes of the science itself. This is a positive development, and one worth encouraging.

NSF made a conscious decision to focus on fairness rather than other prevalent themes like trustworthiness or human-centered design. Dr. Erwin Gianchandani, an NSF deputy assistant director, has described four categories of problems in FAIs domain, and these can each easily be tied to present and ongoing challenges facing AI. The first category is focused on the many conflicting mathematical definitions of fairness and the lack of clarity around which are appropriate in what contexts. One funded project studied the human perceptions of what fairness metrics are most appropriate for an algorithm in the context of bail decisionsthe same application of the infamous COMPAS algorithm. The study found that survey respondents slightly preferred an algorithm that had a consistent rate of false positives (how many people were unnecessarily kept in jail pending trial) between two racial groups, rather than an algorithm which was equally accurate for both racial groups. Notably, this is the opposite quality of the COMPAS algorithm, which was fair in its total accuracy, but resulted in more false positives for Black defendants.

The second category, Gianchandani writes, is to understand how an AI system produces a given result. The NSF sees this as directly related to fairness because giving an end-user more information about an AIs decision empowers them to challenge that decision. This is an important pointby default, AI systems disguise the nature of a decision-making process and make it harder for an individual to interrogate the process. Maybe the most novel project funded by NSF FAI attempts to test the viability of crowdsourcing audits of AI systems. In a crowdsourced audit, many individuals might sign up for a toole.g., a website or web browser extensionthat pools data about how those individuals were treated by an online AI system. By aggregating this data, the crowd can determine if the algorithm is being discriminatory, which would be functionally impossible for any individual user.

The third category seeks to use AI to make existing systems fairer, an especially important task as governments around the world are continuing to consider if and how to incorporate AI systems into public services. One project from researchers at New York University seeks, in part, to tackle the challenge of fairness when an algorithm is used in support of a human decision-maker. This is perhaps inspired by a recent evaluation of judges using algorithmic risk assessments in Virginia, which concluded that the algorithm failed to improve public safety and had the unintended effect of increasing incarceration of young defendants. The NYU researchers have a similar challenge in minddeveloping a tool to identify and reduce systemic biases in prosecutorial decisions made by district attorneys.

The fourth category is perhaps the most intuitive, as it aims to remove bias from AI systems, or alternatively, make sure AI systems work equivalently well for everyone. One project looks to create common evaluation metrics for natural language processing AI, so that their effectiveness can be compared across many different languages, helping to overcome a myopic focus on English. Other projects looks at fairness in less studied methods, like network algorithms, and still more look to improve in specific applications, such as for medical software and algorithmic hiring. These last two are especially noteworthy, since the prevailing public evidence suggests that algorithmic bias in health-core provisioning and hiring is widespread.

Critics may lament that Big Tech, which plays a prominent role in AI research, is present even in this federal programAmazon is matching the support of the NSF, so each organization is paying around $10 million. Yet there is no reason to believe the NSFs independence has been compromised. Amazon is not playing any role in the selection of the grant applications, and none of the grantees contacted had any concerns about the grant-selection process. NSF officials also noted that any working collaboration with Amazon (such as receiving engineering support) is entirely optional. Of course, it is worth considering what Amazon has to gain from this partnership. Reading the FAI announcement, it sticks out that the program seeks to contribute to trustworthy AI systems that are readily accepted and that projects will enabled broadened acceptance of AI systems. It is not a secret that the current generation of large technology companies would benefit enormously from increased public trust in AI. Still, corporate funding towards genuinely independent research is good and unobjectionable especially relative to other options like companies directly funding academic research.

Beyond the funding contribution, there may be other societal benefits from the partnership. For one, Amazon and other technology companies may pay more attention to the results of the research. For a company like Amazon, this might mean incorporating the results into its own algorithms, or into the AI systems that it sells through Amazon Web Services (AWS). Adoption into AWS cloud services may be especially impactful, since many thousands of data scientists and companies use those services for AI. As just an example, Professor Sandra Wachter of the Oxford Internet Institute was elated to learn that a metric of fairness she and co-authors had advocated for had been incorporated into an AWS cloud service, making it far more accessible for data science practitioners. Generally speaking, having an expanded set of easy-to-use features for AI fairness makes it more likely that data scientists will explore and use these tools.

In its totality, FAI is a small but mighty research endeavor. The myriad challenges posed by AI are all improved with more knowledge and more responsible methods driven by this independent research. While there is an enormous amount of corporate funding going into AI research, it is neither independent nor primarily aimed at fairness, and may entirely exclude some FAI topics (e.g., fairness in the government use of AI). While this is the final year of the FAI program, one of NSF FAIs program directors, Dr. Todd Leen, stressed when contacted for this piece that the NSF is not walking away from these important research issues, and that FAIs mission will be absorbed into the general computer science directorate. This absorption may come with minor downsidesfor instance, a lack of clearly specified budget line and no consolidated reporting on the funded research projects. The NSF should consider tracking these investments and clearly communicating to the research community that AI fairness is an ongoing priority of the NSF.

The Biden administration could also specifically request additional NSF funding for fairness and AI. For once, this funding would not be a difficult sell to policymakers. Congress funded the totality of the NSFs $868 million budget request for AI in 2021, and President Biden has signaled clear interest in expanding science funding; his proposed budget calls for a 20% increase in NSF funding for fiscal year 2022, and the administration has launched a National AI Research Taskforce co-chaired by none other than Dr. Erwin Gianchandani. With all this interest, bookmarking $5 to $10 million per year explicitly for the advancement of fairness in AI is clearly possible, and certainly worthwhile.

The National Science Foundation and Amazon are donors to The Brookings Institution. Any findings, interpretations, conclusions, or recommendations expressed in this piece are those of the author and are not influenced by any donation.

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How the National Science Foundation is taking on fairness in AI - Brookings Institution

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Data Science Platform Market is latest research study released by Adroit Market Research evaluating the market risk side analysis, highlighting opportunities and leveraged with strategic and tactical decision-making support (2021-2028). The market Study is segmented by key a region that is accelerating the marketization. The report provides information on market trends and development, growth drivers and the changing investment structure of the Data Science Platform Market. Some of the key players profiled in the study areMicrosoft, IBM, Google, MathWorks, Cloudera, Altair Engineering, SAS, Wolfram, Alteryx, and SAP. Moreover, the other potential players in the data science platform market are RapidMiner, Dataiku, Civis Analytics, Databricks, and Anaconda.

By end users/application, market is sub-segmented as: by Application (Logistics, Marketing, Sales, Customer Support, Human Resource, and Others), Industry Vertical (IT & Telecom, BFSI, Retail, Healthcare, Government & Defense, and Others)

Breakdown by type, the market is categorized as: by Platform (Solutions and Services (Managed Services and Professional Services))

Regional Analysis for Data Science Platform Market includes: North America, US, Canada, Mexico, Europe, Germany, France, U.K., Italy, Russia, Nordic Countries, Benelux, Rest of Europe, Asia, China, Japan, South Korea, Southeast Asia, India, Rest of Asia, South America, Brazil, Argentina, Rest of South America, Middle East & Africa, Turkey, Israel, Saudi Arabia, UAE & Rest of Middle East & Africa

The Data Science Platform Market study covers on-going status, % share, upcoming growth patterns, development cycle, SWOT analysis, sales channels & distributions to anticipate trending scenarios for years to come. It aims to recommend analysis of the market by trend analysis, segment breakdown, and players contribution in Data Science Platform market upliftment. The market is sized by 5 major regions i.e., North America, Europe, Asia Pacific (includes Asia & Oceania separately), Middle East and Africa (MEA), and Latin America and further broken down by 18+ jurisdiction or countries like China, the UK, Germany, United States, France, Japan, India, group of Southeast Asian & Nordic countries etc.

Players profiled in the report:Microsoft, IBM, Google, MathWorks, Cloudera, Altair Engineering, SAS, Wolfram, Alteryx, and SAP. Moreover, the other potential players in the data science platform market are RapidMiner, Dataiku, Civis Analytics, Databricks, and Anaconda.

Consumer Traits Includes Following Patterns**

Consumer Buying patterns (e.g., comfort & convenience, economical, pride)

Customer Lifestyle (e.g., health conscious, family orientated, community active)

Expectations (e.g., service, quality, risk, influence)

Major Highlights from the Data Science Platform Market factored in the Analysis

Data Science Platform Market Measures & Parameters Addressed in Study: The report highlights Data Science Platform market features such segment revenue, weighted average selling price by region, capacity utilization rate, production & production value, % gross margin by company, consumption, import & export, demand & supply, cost bench-marking of finished product in Data Science Platform Industry, market share and annualized growth rate (Y-o-Y) and % CAGR.

Major Strategic Data Science Platform Market Developments: Activities such as Research & Development (R&D) by phase, ongoing and completed Merger & Acquisition (M&A) [deal value, purpose, effective year], Joint ventures (JVs), Technological tie-ups, Suppliers partnerships & collaborations, agreements, new launches etc taken by Data Science Platform Industry players during projected timeframe of study.

The Data Science Platform Market report provides the rigorously studied and evaluated data of the top industry players and their scope in the market by means of various analytical tools. To gain a deep dive analysis; qualitative commentary on changing market dynamics {drivers, restraints & opportunities}, PESTLE, 5-Forces, Feasibility study, BCG matrix, SWOT by players, Heat Map analysis etc have been provided to better correlate key players product offering in the market.

1. Data Science Platform Market Overview

Market Snapshot

Definition

Product Classification

2. Data Science Platform Market Dynamics

Drivers, Trends, Restraints

Market Factors Analysis

3. New Entrants and Entry-barriers

4. Standardization, Regulatory and collaborative initiatives

Manufacturing Process Analysis

Industrial/Supply Chain Analysis, Sourcing Strategy and Downstream Buyers

5. Data Science Platform Market Competition by Manufacturers

6. Data Science Platform Market Value [USD], Capacity, Supply (Production), Consumption, Price, Export-Import (EXIM), by Region (2016-2020)

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7. Data Science Platform Revenue (Value), Production, Sales Volume, by Region (2021-2028)

8. Data Science Platform Market Trend by Type {, HKP::LT;500ml, 500-1000ml & HKP::GT;1000ml}

9. Data Science Platform Market Analysis by Application {Offline Store & Online Store}

10. Data Science Platform Market Manufacturers Profiles/Analysis

Market Share Analysis by Manufacturers (2020)

Manufacturers Profiles (Overview, Financials, SWOT etc)

Connected Distributors/Traders

Marketing Strategy by Key Manufacturers/Players

Thanks for reading Data Science Platform Industry research publication; you can also get individual chapter wise section or region wise report version like America, LATAM, Europe, Nordic nations, Oceania or Southeast Asia or Just Eastern Asia.

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Adroit Market Research is an India-based business analytics and consulting company incorporated in 2018. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a markets size, key trends, participants and future outlook of an industry. We intend to become our clients knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

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Data Science Platform Market 2020: Potential Growth, Challenges, and Know the Companies List Could Potentially Benefit or Loose out From the Impact of...

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