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US President Joe Biden yesterday announced a goal for 70 percent of the adult US population to have received at least one COVID-19 vaccine shot by July 4.

Cities are playing a key role in this historic vaccination effort, not only in terms of logistics and administration but also with respect to the critical component of resident engagement.

To maximise vaccine uptake, local governments are working to mitigate any resident concerns; to counter misinformation and distrust; and to clear up confusion about practicalities. To do this effectively they need to understand in close to real-time and at scale how citizens are feeling about vaccines.

Thats why nineteen US cities and counties, including Los Angeles, Philadelphia, New Orleans and Newark, are using advanced sentiment analysis to help shape and scale their vaccine programmes.

The initiative is a collaboration between Israeli start-up Zencity and the Harvard Kennedy Schools Ash Center, with funding from the Robert Wood Johnson Foundation and support from Bennet Midland.

Through the programme, the cities and counties are using Zencitys tools to collect and analyse organic feedback from publicly available sources such as social media posts, online channels and local news sites, alongside proactive resident input from community surveys.

Zencity uses artificial intelligence (AI) to classify and sort the data to identify key topics, trends, anomalies, and sentiment.

Each city will receive a report including insights on how opinions about the vaccine break down across demographic groups; trends and themes in community sentiment toward vaccination; misinformation that might need to be addressed; and recommendations for how to communicate about vaccines. Each citys results are benchmarked against the average results from the cohort.

Assaf Frances, Director of Urban Policy, Zencity,said: These results will enable cities to make data-informed decisions as they continue to navigate vaccine rollout. This could mean anything from making the appointment scheduling process more accessible if the results show that logistical hurdles have been a major barrier to mass vaccination, to providing more education around vaccine safety and efficacy to a particular segment of the population where the data is showing more hesitancy.

Deana Gamble, Communications Director, City of Philadelphia, told Cities Today: Were currently in a pivotal moment where vaccine supply has never been greater yet there is still a significant amount of vaccine hesitancy, especially among communities of colour. We need to provide accurate and up-to-date information to those who are still unsure about the benefits of getting the vaccine and how to do so.

With this in mind, Philadelphia has launched the six-month #VaxUpPhilly marketing campaign.

Gamble said one key insight from Zencity was that Philadelphia residents report similar levels of intention to get the vaccine as the cohort average, but they are more likely to wait longer.

This speaks to intention to get vaccinated yet less urgency with residents indicating that they require more information or evidence, specifically by seeing more people they know get the vaccine, Gamble commented. This shows us that the education efforts of our #VaxUpPhilly campaign including use of myth busters and trusted, credible messengers are critical.

Philadelphia faced controversy early in its vaccine rollout. In January, the city cut ties with Philly Fighting COVID, a young start-up which was running the citys largest vaccination site, after it emerged the company had cancelled testing efforts and become a for-profit entity, and concerns were raised about its privacy policy. Philly Fighting COVID said it had the best intentions and had not sold or shared any data but the incident was still damaging for the city.

Gamble said: We certainly acknowledge the mistakes the administration made working with the group which has necessitated rebuilding trust with the public about our vaccination programme. The insights gleaned from Zencity can help us better communicate with residents, which can help us overcome the challenges caused by Philly Fighting COVID.

Liana Elliott, Deputy Chief of Staff for New Orleans Mayor LaToya Cantrell, said that although New Orleans vaccine rollout is going well, we also are hitting our plateau a little bit earlier than we thought.

Understanding nuances around vaccine sentiment can help the city push through this.

Generally, the hesitancy that we thought we were going to find was not nearly as prevalent in the communities that we expected, Elliott commented, noting lower levels of concern than anticipated in communities of colour and more of a tendency for conservative white men to have reservations.

Further, as in Philadelphia, while many people are willing to get vaccinated, some dont want to go first.

Elliott said: We worked really hard to make sure that we are working with our community partners and getting proactive about talking to people about the vaccine and bringing vaccine events into communities.

This includes encouraging people to share when they have been vaccinated on social media, urging hospitality businesses to incentivise and support staff vaccinations and making the inoculation process a positive one. For example, a brass band played to mark the opening of the vaccination site at the Ernest N. Morial Convention Center and local bars hosted shots for shots events, which Elliott described as very New Orleans.

These approaches have really encouraged people to go check it out and just go get [their vaccination] done, she said.

The Zencity analysis has also helped New Orleans to shape vaccine messages and understand who are the trusted ambassadors best placed to deliver them.

Research published in March by global communications company Edelmanfound that US residents most trust doctors, scientists and public health officials about vaccine information and are more likely to trust someone like themselves or their organisations CEO than a government official. However, Zencity data showed that New Orleans Mayor LaToya Cantrell is one of the most trusted messengers for residents.

Feedback also highlighted some ways the city needed to simplify appointment booking. It then analysed sentiment to check the improvements were working, and this is a continuous process.

If we start seeing more chatter about [something being] hard or [people not knowing] when or where to go, then that means something is broken in that chain of communication we have got to go back and fix it, Elliott said.

She added that a key benefit of the programme with Zencity is: It really helps us confirm that what we are seeing and experiencing anecdotally and locally as staff is in fact holding up across not only our city but across the country and across all the other cohort cities as well.

Sometimes its not necessarily that it informs or changes how were doing things but it affirms that were going the right way and that what were doing is working, she said.

A national report on getting residents on board with vaccinations will be published by Zencity, Harvard Kennedy Schools Ash Center, the Robert Wood Johnson Foundation and Bennett Midlandlater this month.

Image: City of New Orleans

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How US cities are using artificial intelligence to boost vaccine uptake - Cities Today

Artificial intelligence seems to be the next big thing in many industries today. The technology is infiltrating every sector and transforming the tasks that computers perform into a lot of hype. Starting from fitness-focused smartphone apps that adapt to womens menstrual cycle to autonomous vehicles that use sensors and software to dodge at stray animals, artificial intelligence has influenced every part of human life. It has evolved from being just a trend to a core ingredient virtually across every aspect of computing. In the modern world, businesses across diverse sectors use artificial intelligence as a tool to meet their goals, be it customer service through an intuitive chatbot or streamlining video production through synthetic voiceovers. For a term that dates back to 1956 and celebrates its 65th birthday this year, artificial intelligence has performed and revolutionized more than how anybody imagined.

As years passed, humans gained great faith in technology and machines, which eventually accelerated artificial intelligence adoption. Today, the role of artificial intelligence in an enterprise has become so important that it has touched every facet of business, and its crucial place will significantly grow over the coming years. Artificial intelligence, though revolutionary in itself, is an enabler that needs to be used effectively to achieve business objectives. Businesses are using AI agents to engage customers, rapidly create content, analyze transactions and detect fraud. Even though it comes with a lot of flaws, the speediness, customized content, and target recommendations, overweigh the cons.

AI-driven technologies have the potential to enhance our lives as both learners and workers. Researchers and developers are continuously improving them to mimic human behaviors in routines like learning, problem-solving, and processing language. While they are growing to be strong imitators of humans, they still lack essential human traits such as wisdom, insight, humor, and empathy. Fortunately, the next generations AI will carry all the objectives that humans think are essential for machines to cope up with them. The technological capabilities will attempt to solve real-world issues, moving beyond doing repetitive and routine works.

While big guys like Amazon, Apple, Google, and Microsoft scramble to infuse their products with artificial intelligence, other companies are hard at work developing their own intelligent technologies and services. The rise of digitization and the thirst for automation are fuelling the demand for AI solutions. Not just companies, even the governments are focusing on deep research in the field of finding, investing, and growing local talent to make their country the AI hub. Artificial intelligence companies are also initiating to deliver a robust service to their customers by using sub-technologies like machine learning, deep learning, edge computing, business intelligence, etc. as their prominent business principle. In a nutshell, artificial intelligence is used as a tool to integrate multiple sources of data or a vast amount of data, data security, real-world applications, predictions, cloud operations, etc. With the arena of AI technologies at the beginning, the world has experienced so much so far. The future is anticipated to be more sophisticated and personalized with the help of artificial intelligence.

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Artificial Intelligence is the Most Disruptive Technology of the Century - Analytics Insight

JERSEY CITY, N.J., May 5, 2021 /PRNewswire/ --ElectrifAi, one of the world's leading companies in practical artificial intelligence (AI) and pre-built machine learning (ML) models, announced today its collaboration with Temple Health,which is a leadingPhiladelphia-based academic health system that is driving medical advances through clinical innovation, pioneering research and world-class education.Temple Health will leverage ElectrifAi's pre-built machine learning models for spend and contract to drive operational efficiency, cost savings, spending control, increased revenue and risk reduction.

ElectrifAi's 17 years of practical machine learning expertise with regard to spend analytics, contract management, customer/patient engagement and machine learning models will help optimize and improve the operations of Temple Health.

Edward Scott, CEO of ElectrifAi said: "For years, our customers in financial services, telecommunications and retail have been leveraging practical machine learning. It was only a matter of time before we integrated pre-built machine learning models into the healthcare environment. The healthcare community can now accelerate their machine learning efforts with our solutions to drive revenue uplift, cost reduction as well as profit and performance improvements in today's fast-changing business climate."

"ElectrifAi's advanced technology will significantly facilitate efficient contracting and financial accounting for Temple Health, with increased data-driven granularity," said Michael A. Young, MHA, FACHE, President and CEO of Temple University Health System and Temple University Hospital. "We look forward to a productive working relationship."

About ElectrifAi

ElectrifAi is a global leader in business-ready machine learning models. ElectrifAi's mission is to help organizations change the way they work through machine learning: driving revenue uplift, cost reduction as well as profit and performance improvement. Founded in 2004, ElectrifAi boasts seasoned industry leadership, a global team of domain experts, and a proven record of transforming structured and unstructured data at scale. A large library of Ai-based products reaches across business functions, data systems, and teams to drive superior results in record time. ElectrifAi has approximately 200 data scientists, software engineers and employees with a proven record of dealing with over 2,000 customer implementations, mostly for Fortune 500 companies. At the heart of ElectrifAi's mission is a commitment to making Ai and machine learning more understandable, practical and profitable for businesses and industries across the globe. ElectrifAi is headquartered in New Jersey, with offices located in Shanghai and New Delhi. To learn more visitwww.electrifAi.netand follow us on Twitter@ElectrifAiand onLinkedIn.

About Temple Health

Temple University Health System (TUHS) is a $2.2 billion academic health system dedicated to providing access to quality patient care and supporting excellence in medical education and research. The Health System includes Temple University Hospital (TUH);TUH-Episcopal Campus; TUH-Jeanes Campus; TUH-Northeastern Campus; Temple University Hospital Fox Chase Cancer Center Outpatient Department; TUH-Northeastern Endoscopy Center; The Hospital of Fox Chase Cancer Center, together with The Institute for Cancer Research, an NCI-designated comprehensive cancer center; Fox Chase Cancer Center Medical Group, Inc., The Hospital of Fox Chase Cancer Center's physician practice plan; Temple Transport Team, a ground and air-ambulance company; Temple Physicians, Inc., a network of community-based specialty and primary-care physician practices; and Temple Faculty Practice Plan, Inc., TUHS's physician practice plan. TUHS is affiliated with the Lewis Katz School of Medicine at Temple University.

Temple Health refers to the health, education and research activities carried out by the affiliates of Temple University Health System (TUHS) and by the Katz School of Medicine. TUHS neither provides nor controls the provision of health care. All health care is provided by its member organizations or independent health care providersaffiliated with TUHS member organizations. Each TUHS member organization is owned and operated pursuant to its governing documents.

Non-discrimination notice: It is the policy of Temple University Hospital and The Hospital of Fox Chase Cancer Center, that no one shall be excluded from or denied the benefits of or participation in the delivery of quality medical care on the basis of race, ethnicity, religion, sexual orientation, gender, gender identity/expression, disability, age, ancestry, color, national origin, physical ability, level of education, or source of payment.

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Temple University Health System Selects ElectrifAi's Practical Artificial Intelligence Solutions to Improve Financial Performance and Reduce Risk -...

[author: Doug Austin, Editor of eDiscovery Today]

Even though were far from achieving critical mass in the legal profession when it comes to the use of predictive coding technologies and approaches in electronic discovery, the use of predictive coding for document review especially relevancy review to support discovery is certainly the most common use of artificial intelligence (AI) and machine learning technologies. Some of you reading this blog post may be old pros at this point when it comes to the use of predictive coding while others of you still have yet to dip your toes into the predictive coding pool.

But applying machine learning technology to support document review (which is predictive coding) is far from the only discovery-related workflow and use case where AI and machine learning technology can be applied. There are several others that forward-thinking organizations are looking to also implement to streamline workflows in the discovery life cycle.

With that in mind, here are three use cases of AI and machine learning technology you may not know:

Information Governance and Defensible Deletion

How could we forget one of the forgotten ends that I discussed last week?

An effective Information Governance program has become a must have to achieving effective discovery downstream. Why? Because of the Big Data challenge (that I also discussed here last year).

When the amount of data in the world grows 1,630 times over 20 years, that tells you all you need to know about why InfoGov has become such an important part of not just the discovery lifecycle, but also the management of information for all organization activities. Data is simply overwhelming without a program to effectively manage it including removing the information your organization doesnt need on a timely basis.

AI and machine learning technologies have become key to helping organizations understand their data sooner. More and more, organizations are implementing AI technologies and classifiers to support records management and defensible disposal initiatives within the organization.

For example, identification of redundant, obsolete, or trivial (ROT) data (which can be defensibly deleted) within your organization can be automated by AI technologies and extend human identification of ROT data to other ROT data that doesnt have to be identified by humans.

As its more important than ever to identify ROT data to eliminate the risk of breaches, the downstream benefits are huge for using AI in this way.

Investigations and Sentiment Analysis

Corporate investigations are on the rise and the reasons for them expanded even more during the pandemic. For example, in a September 2020 survey conducted by the Association of Certified Fraud Examiners (ACFE), 74% of surveyed certified fraud examiners indicated that preventing, detecting, and investigating fraud in the COVID-19 era had become even more challenging than it was before the pandemic.

Sentiment analysis involves finding key words and phrases relating to sentiments or feelings that indicate potential detection of employees engaged in fraud or harassment (for example, words that indicate anger or frustration). AI and machine learning technologies help automate the identification of communications more likely to indicate activities that may require investigation.

Data Privacy and Identification of PII

Unless youve been living under a rock, youre aware that the data privacy landscape within the world continues to change. In the past few years, weve seen the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) go into effect, among other data privacy laws, and plenty of other laws have at least been enacted around the country and the world. There is not only more information to manage within organizations, the expectations for protecting sensitive data have also expanded for all organizations, whether or not they have litigation.

As a result, the identification of personally identifiable information (PII) has become important in a variety of situations. Organizations are even having to implement brand new workflows to respond to Data Subject Access Requests (DSARs) from individuals where they request information about the way companies handle their personal data. The ability to identify PII within an organization has become paramount to support these requests and other data privacy requirements.

Once again, the application of AI and machine learning technologies can help here.

Social security numbers, phone numbers, drivers license numbers, and credit card numbers are easier to identify through pattern matching, but names, addresses, and health information are much more difficult to differentiate.

Take my last name for example Austin--which also happens to be a city in Texas. AI can help differentiate the true matches to data related to me based on context from the false ones that are just related to this capital city.

Identification of PII is an iterative process that leverages AI technology to identify that PII much more quickly throughout an organizations data corpus to address ever changing data privacy requirements.

Conclusion

Leveraging AI and machine learning technologies in legal and eDiscovery is about much more than predictive coding today its about more use cases than ever as legal professionals learn how to fully utilize (and harness) the technology properly.

The AI/machine learning train for legal and eDiscovery use cases is building up steam are you on it?

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Three Use Cases of AI and Machine Learning Technology You May Not Know - JD Supra

The worlds firstin-vitro fertilization(IVF) baby was born in the United Kingdom in 1978. The success of the artificially made baby gave hopes to the human community on their fertilization and parenthood. After four decades of intense research and trials into the field, doctors now useartificial intelligenceto help parents get babies successfully. Scientists are working onembryoanalysis with computeralgorithmsto help build families.

More than 80 million couples are affected by infertility across the globe. Around one in seven couples have trouble conceiving, which means there is a high demand for solutions such asin-vitro fertilization. Creating anembryois a process where the ovum from the female ovary and sperm from the male are fused outside the body in a laboratory. Theembryois then placed in the females ovary for the development ofin-vitro fertilization. It has been reported that more than 5 million babies have been born from thein-vitro fertilizationmethod. Unfortunately, not all IVF treatments turn out to be successful. Couples who need IVF to conceive a child are well aware that even the most advanced assisted reproductive technology doesnt always guarantee a baby. Therefore, doctors are seeking the help ofartificial intelligenceto pick theembryothat is most likely to succeed. In a normal in-vitro cycle, around 70% of the embryos are abnormal, resulting in a miscarriage or a baby with a lifelong genetic disorder. Butartificial intelligencecan change the routine by detecting theembryothat is more likely to grow without any problem.

Selecting the successful embryo is the toughest process inin-vitro fertilization. Currently, the tools available for making this decision are limited, highly subjective, time-consuming, and often extremely expensive. Therefore,embryologists use their experience, observation skills, and gut feeling to choose theembryothat is most likely to be successful. To change the routine and makein-vitro fertilizationprocess more accurate, scientists are seeking help fromartificial intelligence. The technology assists embryologists to make a consistent choice.Artificial intelligencesystem could learn how embryos develop over time and then uses the information to select the best embryos. The trained AIalgorithmcan find the successfulembryojust by looking at its image.

As more and more companies and medical institutions are coming forward to try their hand on thisartificial intelligenceespoused in IVF, we take you through some of the recent significant developments in the field.

Embryonics use AI to identify the most successful embryo: Embryonics, an Israeli AI fertility company has usedartificial intelligenceto increase the fertility rate and avoid the odds of successful implantation of theembryo. At the company, a group ofalgorithmspecialists, data scientists, and embryologists are developing analgorithmthat could predict theembryoimplanting probability. They have trained thealgorithmto analyze IVF time-lapsing imaging of developing embryos. The team is using medical imaging withdeep learningto curate datasets from tens of thousands of IVF cycles, including time-lapse videos of embryos. Embryonics is planning to streamline this fertility process by conducting clinical trials at several sites in the United States after obtaining the US Food and Drug Administrations approval.

Austin Fertility Center seeks AIs help for embryo analysis: Austin Fertility Center in the United States is also usingartificial intelligenceto non-invasively analyze embryos and determine whether they are euploid or aneuploid. The center has successfully applied AI inembryoselection with the help ofdeep learningthrough computer vision. They use 2D statistic images of embryos created through past IVF cycles at Ovation Fertility IVF laboratories to train thealgorithm. Austin Fertility Center also said that the method has shown 32% improvement in the prediction of successful implantation.

VIOLET, a tool that beats human analysis in embryo selection: Scientists from CARE Fertility, one of the leading independent providers of fertility treatment in the United Kingdom has joined hands with Canadian med-tech partner Future Fertility onembryoanalysis. The duo has researched to know howartificial intelligencecan be used as a more accurate tool to predict human egg fertilization andembryodevelopment. Recently, they also launched VIOLET, an AIalgorithmthat has outperformed human analysis, predicting human egg fertilization and blastocyst embryo development with 77% and 62% accuracy respectively.

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Why Artificial Intelligence is the Magic Tool in Fertility Treatment? - Analytics Insight