Artificial Intelligence in Healthcare: Access and Equity

Artificial intelligence (AI) is a vast field that is developing fast. Its possible applications can be confusing and intimidating for patients. At the same time, we face challenges and needs in healthcare where AI can provide support and offers solutions. Emily Lewis, a digital health innovator, knows both the industry’s and the patient’s perspective and will shed light on different aspects of the topic. She is confident that we can use technology to re-humanize healthcare and bring connection.

In the fourth part of our series about “AI in Healthcare”, Emily illustrates how AI can compensate for a lack of health personnel and how AI can be used to overcome challenges and inequalities related to healthcare access.

AI has great potential in reshaping healthcare, especially in regions that are underserved or face specific challenges. Here are some ways in which AI can compensate for the lack of health personnel, address healthcare access challenges, and drive society towards better health equity.

Care Delivery

Disease Prediction and Prevention

AI algorithms can analyze electronic health records and lifestyle data to predict the likelihood of various diseases, enabling early intervention. In Radiology and Imaging, for instance, AI can assist in analyzing medical images, which can be particularly useful in areas where expert radiologists are scarce. This ensures timely diagnosis even in remote regions.

  • Butterfly Network: Butterfly Network has developed an AI-powered ultrasound device which can be used in resource-limited settings, such as in pregnant women in Africa, to track and monitor fetal development with biometrics.
  • Caption Health: Caption Health has the first AI-guided heart ultrasound technology which enables a broad set of healthcare providers to perform high-quality ultrasound exams wherever and whenever patients need them through their Caption Care service within patients’ homes.

Customized Treatment Plans

AI can assist in personalizing treatment based on the individual’s genetic makeup, environment, and lifestyle, ensuring better outcomes.

  • Vivante Health: Vivante Health personalizes care delivery for digestive conditions by including appropriate clinical interventions, utilizing cognitive behavioral therapy, diet and lifestyle recommendations, health articles and education, weekly live webinars, and a care team to help patients to focus on building healthier habits that are going to be the most meaningful in reducing their symptoms.

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Remote Patient Monitoring and Virtual Care Delivery

AI can be used to create virtual care platforms that allow patients to connect with providers from anywhere in the world. This can help to increase access to care for people who live in rural areas or who have difficulty traveling to see a provider. AI-powered virtual assistants can offer basic medical consultations, especially for common conditions, reducing the need for in-person visits. What’s more, AI models trained in local languages can assist in breaking down language barriers in healthcare delivery, easily translating from one language to another in real time in a culturally competent way. Wearable and Internet of Things (IoT) devices that utilize algorithms to track health metrics can alert individuals and healthcare professionals about potential health issues.

  • WellDoc: WellDoc is transforming healthcare with a chronic care platform that integrates personalized, real-time, and actionable insights into the daily lives of individuals, enabling improved health outcomes. Their AI-engine connects siloed data sources to deliver connected care across multiple conditions and comorbidities.
  • Eko: Eko is applying machine learning in the fight against heart and lung disease with AI-powered smart stethoscopes.
  • Soundable Health: Soundable Health is a digital health startup with AI-enabled tech, capturing audible biomarkers through smartphones to bring medical insights closer to empower stakeholders in health management. Their first product ProudP analyzes the sounds of urination and is currently in clinical trials.

Facilitating Quality Care

AI can also help to create more supportive and compassionate care environments. AI-powered chatbots can provide 24/7 support to patients, answering questions and providing emotional support. AI can also support continuity of care by assisting in transitions between different care settings (e.g., from hospital to home) and facilitating communication among different healthcare providers. AI can streamline guided clinical workflows and help reduce medical errors by flagging potential risks and inconsistencies, such as harmful drug interactions, that a busy healthcare provider might miss. This allows for safer, more accurate provision of care.

  • Alio: Alio provides medical-grade, actionable data to dialysis patients’ clinical care teams in real-time, from wherever the patient may be. Their platform monitors through a single, non-invasive, wearable device (Alio SmartPatch). The clinical care team receives notifications through a portal (web or electronic health record (EHR)) if patient metrics are abnormal (as defined by specific guidelines) and require follow-up.

Enhanced HCP Training

AI can be used to create virtual training programs for healthcare workers, enhancing their skills even if they are in remote locations. Generative AI can be especially helpful in teaching differential diagnosis and coming up with fictitious scenarios in which trainees need to navigate.

(source: khunkornStudio/Adobe Stock)

Public Health

Targeting of Higher Risk Areas

AI can analyze data to determine which areas or populations are at higher risk for specific health issues, enabling targeted public health campaigns.

Optimizing Resource Allocation

AI can predict patient influx, helping hospitals and clinics in underserved areas better allocate their resources (supply chain of medicines and medical supplies) and staff. Mobile apps powered by AI can collect health data from patients in remote areas, allowing for better epidemiological tracking and intervention.

  • Infermedica: Infermedica is automating primary care from symptom to outcome through their Medical Guidance Platform. Their flexible application programming interface (API) helps healthcare providers, telehealth systems, national healthcare systems, and healthcare practices to accurately analyze symptoms and steer patients to the appropriate level of care-reducing costs, increasing efficiency, improving the patient experience, and reducing unnecessary emergency department visits.

Effectiveness and Equity of AI-Powered Tools

To ensure that AI-powered tools are effective and equitable, developers in AI need to adhere to the following principles:

Industry Collaboration

Foster within-industry collaboration to establish frameworks and share best practices and resources for ethical AI development.

  • Examples: Coalition for Healthcare AI (CHAI) and the Alliance for Artificial Intelligence in Healthcare (AAIH)

Data Collection

Ensure diverse training datasets to help create algorithms that are effective across various populations.

Bias Detection

Design AI tools to detect and correct biases in medical data or previous clinical decision-making processes.

Transparency, Explainability, and Accountability

Ensure that tools can explain AI decision-making and recommendations in a way that medical professionals understand and that avoids perpetuating biases.

Collaborative In-Community Development

Involve the specific communities which will use the product. Understand their needs in the development phase and ensure that the tool is tailored (e.g. “fine-tuned”) to these needs and is sensitive to local dynamics.


The potential benefits of healthcare AI in addressing access and health disparities are vast. However, developers in AI need to realize that these benefits require a keen emphasis on human-centered design, thoughtful implementation, and continuous monitoring and oversight.

Artificial Intelligence in Healthcare: Governance and Ethical Guidelines

Artificial intelligence (AI) is a vast field that is developing fast. Its possible applications can be confusing and intimidating for patients. At the same time, we face challenges and needs in healthcare where AI can provide support and offers solutions. Emily Lewis, a digital health innovator, knows both the industry’s and the patient’s perspective and will shed light on different aspects of the topic. She is confident that we can use technology to re-humanize healthcare and bring connection.

In the third part of our series about “AI in Healthcare”, Emily addresses the question of governance and ethical guidelines for the use of AI in healthcare as well as privacy and data protection.

Applying principles of governance and ethics in healthcare AI practices is crucial given the sensitive and personal nature of health data, as well as the potential for AI to significantly impact patient care and outcomes. Here is an overview of the aspects that developers of AI systems need to take into account:

Ethical Principles

Clear ethical principles from which to govern need to be defined to build trust with the end users. Examples of these principles include:

  • Transparency: AI systems and their decision-making processes should be explainable and understandable to patients, healthcare providers, and regulators.
  • Beneficence and Non-maleficence: AI should be used to benefit patients and avoid harm.
  • Justice and Fairness: AI systems should be designed and operated to ensure that they are fair and do not discriminate.
  • Patient Autonomy and Consent: Patients must remain in control of their own healthcare decisions, and data should only be used with informed consent.
  • Privacy and Confidentiality: Patient data should be handled with the utmost care, and privacy should be maintained rigorously.

Governance Structures

A multidisciplinary governance committee ensures that the right partners are at the table to create governance structures. The group should include medical professionals, data scientists, ethicists, patient advocates, and legal experts. An example of an AI systems advisory committee within a health system environment may include stakeholders such as the Chief Operating Officer (COO), Ethics and Compliance, Chief Information Security Officer (CISO), Legal, and the Chief Strategy Officer (CSO) to make combined decisions and have a feedback loop. This group will establish infrastructure, protocols, and standards for the development, validation, and deployment of AI in healthcare settings.

(source: Nhan/Adobe Stock)

Data Privacy and Security

Much of the procedures in place should revolve around data privacy and security. Companies must ensure their customer data is stored, transmitted, and managed with a focus on security. Data should be encrypted in transit and at rest, with ongoing monitoring and alerting configured. Personally Identifiable Information (PII)/Protected Health Information (PHI) should not be stored. Platforms should meet a variety of stringent requirements for certification.

Patients should have control over how their data is used when they opt in to using solutions and should be able to opt out at any time for any reason.

Data Quality

Data quality needs to be ensured and biases need to be managed in the datasets used for training and validation. It is important to ensure that data used to train and test AI algorithms is collected and stored securely and responsibly, in compliance with relevant regulations. Advanced encryption standards (AES) where there is encryption at rest and in motion, data governance, data masking, and data loss prevention (DLP) need to be utilized. Anti-malware, intrusion detection, and firewalls have to be employed.

Infrastructure Security

Infrastructure security is also paramount. Those in charge of this aspect need to implement secure configurations, conduct periodic vulnerability assessments and address them regularly. Encryption, backups, configuration audits, and role-based identity and access management (e.g. virtual private networks (VPNs), multi-factor authentication) are important as well as having a security operations center (SOC) maintaining a 24/7 security team, monitoring services, and incident management.

Human-Centered Design

Partnership with those who will be using the product is paramount to make sure that the design is human-centered. It also makes for a shared responsibility and excitement. This includes also people not within the traditional care paradigm such as payers and regulators.

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Validation and Testing Processes

Rigorous validation and testing processes have to be implemented, ensuring that AI algorithms are safe, effective, and perform as intended. Those in charge need to monitor for biases and disparate impacts across different patient groups, and iteratively improve the algorithms based on these findings. Tools and techniques need to be developed for explaining the outputs of AI systems in terms that healthcare professionals and patients can understand. The capabilities and limitations of the AI system have to be clearly documented, and this information needs to be made accessible.

It goes without saying that each AI product intended to be used in healthcare is fully compliant from both a legal and regulatory perspective.

Training, Interpretation and Ethical Considerations

With each tool, healthcare professionals need to be trained on the use of the tool, the interpretation of its outputs, and the ethical considerations surrounding its use. Engagement with the broader public and clear and transparent communication about how AI is being used in healthcare is important, addressing concerns and misconceptions proactively. Patients also need to be educated about how AI is being used in their care, and informed consent has to be obtained where necessary.

Continuous Monitoring and Auditing

Each tool needs continuous monitoring and auditing as to understand how people are using the tool and engaging with it. Mechanisms for collecting feedback from healthcare professionals, patients, and other stakeholders need to be in place. This feedback is used to continuously improve the AI systems and their governance structures. Ongoing monitoring of AI systems ensures they are performing as expected and helps to quickly identify any issues.

The use of AI systems needs to be regularly audited, both internally and through third-party assessments, to ensure compliance with ethical principles and relevant regulations. Plans for handling any adverse events or outcomes related to the use of AI need to be developed and maintained, including clear lines of accountability and action steps.


Ultimately, privacy and security of health data, accessibility and usability, and human touch are important considerations in the deployment of AI solutions in healthcare. By systematically addressing these areas, healthcare organizations can work towards responsible, ethical, and effective use of AI, prioritizing patient well-being and societal values throughout the AI lifecycle.

Artificial Intelligence in Healthcare: Assisting an Ageing Population

Artificial intelligence (AI) is a vast field that is developing fast. Its possible applications can be confusing and intimidating for patients. At the same time, we face challenges and needs in healthcare where AI can provide support and offers solutions. Emily Lewis, a digital health innovator, knows both the industry’s and the patient’s perspective and will shed light on different aspects of the topic. She is confident that we can use technology to re-humanize healthcare and bring connection.

In the second part of our series about “AI in Healthcare”, Emily looks at how AI can help an ageing population in managing chronic diseases and long-term care needs. She takes a closer look at health education, care coordination and communication, as well as remote monitoring and telemedicine.

According to a 2022 study conducted by Pew Research Center, of seniors aged 65 or older in the United States, 61% report owning a smartphone, 44% report owning a tablet, 75% report being internet users, 64% report having broadband access at home, and 45% report using social media. This means that these seniors have the ability to use digital technology to access information, communicate with others, and complete tasks. In recent years, the percentage of digitally literate seniors has been increasing as more and more seniors are gaining access to computers, smartphones, and the internet.

While today’s octogenarians may be somewhat reluctant to use technology, the “baby boomer” generation, however, is proving much more receptive to it. Most important to note, however, is that as the boomers continue to age, there will be a much greater demand for scalable solutions to meet their growing healthcare needs outside of already overburdened healthcare systems.

The good news? AI holds significant potential to support the health and wellbeing of seniors of all generations. AI-powered digital health solutions can help seniors to stay independent, manage their chronic conditions, and access healthcare services more easily. What’s more, it can also greatly improve their quality of life, allowing them to maintain their independence longer. Here’s how:

(source: PIKSEL/iStock)

Health Education

AI can be used to educate senior patients about their conditions and treatments in more engaging and informative ways. Education can be personalized based on an individual patient’s needs, preferences, health status, educational background, language, and more. This can help to improve patient understanding of their condition and treatment plan.

Longevity AI

An Israeli digital health company that uses AI in their platform to help people live longer, healthier lives. Beyond their healthcare provider dashboard which allows hospitals, providers, and other medical organizations to track and monitor the health of their patients in real-time with clinical decision support, they also have a companion Lifestyle app specifically for patients which includes a personalized health plan and serves as a personal coach. The app provides the “next best action” to patients based upon their unique needs and preferences. It also provides complete insights from their personalized health data in an easy-to-understand way so that they can make more informed decisions about their lifestyle.

Enhancing Care Coordination and Communication

AI can assist in coordinating care, helping to manage multiple healthcare providers and appointments, and ensuring information is shared appropriately between providers. It can also improve communication between senior patients and healthcare providers. This could be through apps or chatbots that answer routine healthcare questions, refill prescriptions, schedule appointments, and allow for virtual consultations. Many of these AI-powered solutions can provide senior patients with 24/7 access to health information and support which can help to reduce stress and anxiety.

K Health

K Health is a US-based digital health company that offers an AI-powered primary care platform under a subscription model. Users can report their symptoms to the app, and the AI will compare those symptoms with data from millions of other people to give an idea of potential diagnoses. The platform also allows users to chat with doctors, obtain prescriptions, and even get lab tests ordered.

(source: DragonImages/iStock)

Remote Monitoring and Telemedicine

Wearable Devices

Through the use of AI wearable devices can now monitor patients’ health (e.g. vitals like heart rate, blood pressure, glucose levels, etc.) at home. This data can be analyzed in near-real-time, alerting patients, caregivers, or healthcare providers if there’s any anomaly. Such alerts can reduce the need for hospital visits by allowing the healthcare providers to intervene when necessary. The enablement of more timely, effective, proactive care means that seniors can therefore remain in their homes longer, reducing the need for costly hospital visits.

  • Examples include Apple Watch, Fitbit Versa, Samsung Galaxy Watch, Garmin Venu, and Withings ScanWatch.

Virtual Assistants

Many devices utilize AI to help seniors who have mobility or cognitive impairments perform tasks, stay organized, or control their environment.

  • Birdie: Birdie is a UK-based company who connects caregivers, families, and elderly individuals, providing reminders, monitoring health, and allowing families to stay updated on the well-being of their loved ones. Their data-driven product management strategy employs AI-powered analytics to analyze client medication usage and provide insights into patterns of behavior.
  • Papa: Papa is a US-based elder care company with a virtual assistant that can help seniors with tasks such as setting reminders, scheduling appointments, and making calls.

Medication Management

AI-powered apps can remind seniors when to take their medication, track their adherence, and notify caregivers or healthcare providers if they miss doses. This is particularly useful for seniors with chronic conditions requiring strict medication schedules. By addressing medication adherence issues up front, AI technology can reduce the cost of patient support programs and healthcare costs down the road.

  • AllazoHealth: AllazoHealth is a US-based company that provides predictive analytics to drive patient engagement, medication initiation, adherence, and health outcomes. Their AI platform empowers customers to identify risk factors for every patient and deliver the optimal content, channel, timing, and cadence for each individual, creating a truly one-to-one patient health experience.

Fall Detection and Prevention

Advanced AI systems can predict the likelihood of falls based on the person’s physical condition and their environment, helping to prevent injury.

  • CarePredict: CarePredict is a US-based company with a global operation with a wearable device that tracks various activities of daily living, such as eating, sleeping and walking. The device uses AI to enable fall detection and subsequently alerts caregivers or medical professionals if there is a fall. Their algorithms can also predict potential health declines or issues based on user behavior.
  • SafelyYou: SafelyYou is a US-based company that also operates in Canada with AI-powered video technology that is intended to be deployed in senior living facilities. Their technology conducts remote, hourly, and nighttime wellness checks to provide a clearer picture of patient risk and detect falls.

Cognitive Training and Mental Health

AI can offer support for mental health through apps and platforms that provide therapeutic techniques, mental health assessment, and even early detection of mental health issues based on user input and behavior. Monitoring for signs of depression or anxiety, solutions can then offer support or alert a caregiver or healthcare provider as necessary. AI applications can also provide cognitive training to help slow the progression of cognitive decline.

  • Koa Health: Koa Health based in Spain, with additional locations in the Netherlands, the UK, and the US, has developed a cognitive training app called Evermind that uses AI to assess seniors’ cognitive function and provide personalized exercises to improve memory, attention, and problem-solving.
  • Intuition Robotics: Intuition Robotics, based in Israel, has developed a robot called ElliQ designed to offer companionship, social engagement, reminders, and cognitive stimulation to senior citizens.


Ultimately, it is important to keep in mind that the objective of using AI in healthcare should always be to enhance the human aspects of care, not to replace humans themselves. This principle is especially important in older patient populations as seniors are not digital natives and therefore tend to prefer a more hands-on, humanized healthcare experience.

Artificial Intelligence in Healthcare: Assisting Socially Challenged Populations

Artificial intelligence (AI) is a vast field that is developing fast. Its possible applications can be confusing and intimidating for patients. At the same time, we face challenges and needs in healthcare where AI can provide support and offers solutions. Emily Lewis, a digital health innovator, knows both the industry’s and the patient’s perspective and will shed light on different aspects of the topic. She is confident that we can use technology to re-humanize healthcare and bring connection.

In the first part of our series about “AI in Healthcare”, Emily explains how the use of AI can help socially challenged populations (e.g. people diagnosed with autism spectrum disorder or psychiatric disorders) or seniors. Here’s how:

People with Autism Spectrum Disorder (ASD)

Early Detection and Intervention

AI systems can analyze patterns in children’s behavior to detect early signs of ASD. For example, research has shown that AI algorithms can be used to analyze a child’s use of language. For instance, an algorithm could detect unusual patterns or delays in language development. Additionally, some researchers are applying AI and computer vision techniques to analyze facial expressions and social interactions.

  • CanvasDx: Cognoa has developed a tool called CanvasDx for assessing the risk of Autism Spectrum Disorder (ASD) in children. The tool utilizes machine learning to analyze parent-provided home videos and data about a child’s behavior and development. The AI scoring of this data is similar to expert clinicians in distinguishing children with ASD. The software then generates a report indicating whether the child is at risk for ASD, which can aid healthcare providers in making an earlier diagnosis.
  • Early Autism Detection App: A team at Duke University has developed an app that uses machine learning to analyze eye movements and emotional responses of children while they watch videos on a smartphone or tablet. This app has shown potential in detecting signs of autism in children as young as 6 months old.

(source: AndreyPopov/iStock)

Social Skills Training

There are AI-based applications that use virtual reality and computer-based interaction to help individuals with autism improve their social skills. Moreover, AI-powered robots can be used to teach basic social interactions like maintaining eye contact, recognizing facial expressions, and understanding tone of voice. These solutions use AI to personalize the learning experience, simulate real-life social interactions, and help individuals improve their social and cognitive skills.

  • Robots: Social robots like Kaspar, Milo, and NAO have been used to teach social skills to children with autism. Although these toys are not cheap, they use AI to simulate social interactions and provide a controlled environment for the child to learn and practice skills such as turn-taking, eye contact, and emotional recognition.
  • Apps: Several apps use AI to customize learning for people with autism. For example, the app “Otsimo” provides AI-powered educational games designed according to the principles of Applied Behavior Analysis (ABA) therapy. The app’s AI learns from the child’s interactions and adjusts the difficulty level and content accordingly.
  • Virtual Reality: AI can be integrated with Virtual Reality (VR) to provide immersive social skills training. For example, the software “Floreo” uses VR to teach social skills and coping strategies in a fun, engaging environment. The software uses AI to adapt the virtual lessons based on the individual’s performance. Moreover, the SocialWiseVR app provides a suite of interactive immersive experiences whereby those with autism can experience first-hand a variety of social interactions, within the safety of the therapist environment. Example scenarios include going on a first date, being at a party, and dealing with authority figures. The solution uses a scaffold learning format with success gates applied to each scenario and a gamified interface to incentivize and reward learner’s progress.
  • AI Tutoring Systems: Systems like “The Social Express” and “Brain Power System” use AI to teach social norms and behaviors. These systems use video modeling and AI to assess the user’s understanding and adjust the teaching approach as needed.

Support for Day-to-Day Tasks

AI can provide personalized recommendations and reminders for people with autism to help them manage their daily routines and reduce anxiety.

People with Psychiatric Disorders

Early Warning Sign Detection

AI can monitor online activity and identify potential warning signs of a psychiatric crisis. For example, changes in social media activity or search engine use can indicate increased risk.

  • Ellipsis Health: This company uses AI to analyze natural speech to assess patients’ mental health. Their technology can detect signs of anxiety and depression, allowing for earlier and more proactive treatment.
  • Moodpath: This app asks daily questions to users to assess their wellbeing and screen for symptoms of depression. The AI uses these responses to generate a bi-weekly mental health assessment that users can share with healthcare professionals.

Symptom Management

Machine learning can analyze user data to predict and manage symptoms. For instance, apps can track a user’s sleep, activity, and self-reported mood to predict depressive episodes or anxiety spikes.

  • Woebot: This AI-driven chatbot uses principles of cognitive-behavioral therapy (CBT) to help users manage symptoms of depression, anxiety, and other mental health conditions. Woebot checks in with users daily and provides helpful tools and insights based on the user’s mood and needs.
  • Youper: An AI-powered emotional health assistant app that uses cognitive-behavioral techniques, mindfulness, and other evidence-based approaches to help users monitor and manage their emotional health. Youper allows users to track their moods, discover triggers, and improve mental health over time.


AI-powered chatbots can provide support between therapy sessions. These chatbots can offer cognitive behavioral therapy (CBT) strategies, relaxation exercises, and even crisis intervention resources.

  • Ginger: This is a complete behavioral health system combining human coaches, therapists, and AI chatbots. It provides personalized self-care activities and tracks users’ mood and behavior to offer insights and real-time care.
  • Talkspace: While primarily a platform for online therapy with licensed professionals, it also incorporates AI elements to match users with the best fitting therapists based on their needs.

(source: Andrey Popov/Adobe Stock)


Maintaining Independence

AI can assist seniors in managing their medication, scheduling appointments, and even completing chores through voice-activated assistants or robotics.

  • Medication Management: AI-based medication management systems like MedMinder and Hero can remind seniors to take their medications on time, dispense the correct dosage, and alert caregivers or family members if a dose is missed.

Ensuring Safety

AI can monitor seniors’ activities to detect irregularities or emergencies. For example, smart home technologies can detect falls or changes in routine that may indicate health issues. Wearable devices can monitor vital signs and alert medical professionals if necessary.

Social Interaction

AI-powered companions can provide conversation, remind seniors of appointments or tasks, and even detect mood or cognitive changes that might indicate a need for intervention.

  • AI Companions: Robotic companions such as ElliQ and Mabu provide social interaction and can also offer reminders for medication, appointments, and exercise.


It’s important to note that while AI holds great potential in these areas, there are also important considerations around privacy, accuracy, and the risk of over-reliance on technology. The technology is not a substitute for human care, but rather a tool that can supplement and enhance the care provided by humans.

Emily (Kunka) Lewis, MS, CCRP, CHES

Emily (Kunka) Lewis, MS, CCRP, CHES is an opinion leader in the digital transformation of healthcare. She is driven by a desire for the democratization of modern medicine. As an AI enthusiast she is convinced that technology and ingenuity will not only change the way research is currently conducted but will also enable better care solutions. From her point of view, artificial intelligence is meant to augment and supplement human capabilities, rather than replace them. Emily received a Master of Science in Clinical Research from Northwestern University. She is currently working as a Digital Transformation Project Lead at UCB.