Amazon Web Services (AWS) launched Amazon HealthLake--a new HIPAA-eligible platform that lets healthcare organizations seamlessly store, transform, and analyze data in the cloud. The platform standardizes unstructured clinical data (like clinical notes or imaging info) by in a way that makes it easily accessible and unlocks meaningful insights--an otherwise complex and error-prone process. For example, Amazon HealthLake can match patients to clinical trials, analyze population health trends, improve clinical decision-making, and optimize hospital operations. Amazon already has links in different parts of the healthcare ecosystem--now that it's taking on healthcare AI, smaller players like Nuance and Notable Health should be worried. Amazon has inroads in everything from pharmacy to care delivery: Amazon Pharmacy was built upon its partnerships with payers like Blue Cross Blue Shield and Horizon Healthcare Services, Amazon Care was expanded to all Amazon employees in Washington state this September, and it launched its Amazon Halo wearable in August.
A UVA Health data science team is one of seven finalists in a national competition to improve healthcare with the help of artificial intelligence. UVA's proposal was selected as a finalist from among more than 300 applicants in the first-ever Centers for Medicare & Medicaid Services (CMS) Artificial Intelligence Health Outcomes Challenge. UVA's project predicts which patients are at risk for adverse outcomes and then suggests a personalized plan to ensure appropriate healthcare delivery and avoid unnecessary hospitalizations. CMS selected the seven finalists after reviewing the accuracy of their artificial intelligence models and evaluating how well healthcare providers could use visual displays created by each project team to improve outcomes and patient care. Each team of finalists received $60,000 and will compete for a grand prize of up to $1 million.
AI is undoubtedly changing the healthcare industry, making it more efficient and driving better outcomes for patients. COVID-19 has served as an accelerator of adoption – a catalyst in helping the industry catapult itself forward, taking advantage of the best technology has to offer. Barriers to adoption persist, however, as many applications of AI in healthcare remain uncharted territory. The vast majority of the world's health systems are not using their data and AI to make helpful predictions that inform decision making, creating tremendous opportunity to use data and AI to help make more insightful healthcare decisions. But the challenge is in finding common, replicable use cases. To start, healthcare providers are looking to understand how the disparate clinical data they gather can be organised better into an efficient pipeline that can be used to tap into accurate, predictive data intelligence.
The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The detection and mitigation of biased models is a very delicate task which should be tackled with care and involving domain experts in the loop. In this paper we introduce FairLens, a methodology for discovering and explaining biases. We show how our tool can be used to audit a fictional commercial black-box model acting as a clinical decision support system. In this scenario, the healthcare facility experts can use FairLens on their own historical data to discover the model's biases before incorporating it into the clinical decision flow. FairLens first stratifies the available patient data according to attributes such as age, ethnicity, gender and insurance; it then assesses the model performance on such subgroups of patients identifying those in need of expert evaluation. Finally, building on recent state-of-the-art XAI (eXplainable Artificial Intelligence) techniques, FairLens explains which elements in patients' clinical history drive the model error in the selected subgroup. Therefore, FairLens allows experts to investigate whether to trust the model and to spotlight group-specific biases that might constitute potential fairness issues.
As healthcare providers have faced unprecedented workloads (individually and institutionally) around the world, the pandemic response continues to cause seismic shifts in how, where, and when care is provided. Longer-term, it has revealed the need for fundamental shifts across the care continuum. As a physician, I have seen first-hand the challenges of not having the right data, at the right time, in the right format to make informed shared decisions with my patients. These challenges amplify the urgency for trusted partners and solutions to help solve emergent health challenges. Today we're taking a big step forward to address these challenges with the general availability of Microsoft Cloud for Healthcare.
AIMed UK 2020 virtual summit took place early on. In the opening keynote session: Deployment of artificial intelligence (AI) in the UK and across the world, Professor Neil Sebire, Chief Research Information Officer at the Great Ormond Street Hospital for Children National Health Service (NHS) Foundation Trust talked about some of the considerations healthcare organization need to have as they plan to deploy AI tools at scale. Professor Sebire said healthcare organization ought to think about what is required, in terms of infrastructure, when it comes to dealing with healthcare data. Often, it's great to have talks focusing on electronic health records (EHRs) but these data warehouses do not facilitate utilization. What the healthcare system needs is a place which not only keeps all the data but also permits algorithm development; planning the deployment and scaling of AI, and everything else.
Savana, the healthtech startup accelerating health science with big data, announced the successful completion of its $15 million B Series led by Cathay Innovation. Other investors include Seaya Ventures, the lead investor in the previous $5 million round, as well as new investors, such as MACSF, the French mutual insurer for health professionals. Savana develops artificial intelligence (AI) solutions that provide doctors and pharmacists with life-saving insights into patients' healthcare by unlocking critical information buried within the free text of clinical notes. Savana's SaaS platform leads the world in its field with over 400 million Electronic Medical Records processed in English, Spanish, German and French. Founded in 2014 in Spain, the company's industry-leading technologies and services have been adopted by health systems across Western Europe, the United States and Canada.
Awareness of artificial intelligence (AI) is increasing throughout health care. Relative to pharmacy, the American Society of Health-Systems Pharmacists Foundation's "Pharmacy Forecast 2020: Strategic Planning Advice for Pharmacy Departments in Hospitals and Health Systems" specifically lists the emergence of AI in its report, inspiring industry discussion and guiding the strategic planning processes of health system pharmacy leadership across the country.1 For pharmacy, AI provides information on drug interactions, drug therapy monitoring, formulary selection, costs, usage trends, and more. "AI is already transforming health care but will become increasingly valuable as investments in systems that can capture and manage data are made and clinical informatics entities work more collaboratively to address current data shortfalls," said Doug Zurawski, PharmD, senior vice president of clinical strategy at Kit Check, Inc, maker of radio frequency identification (RFID)/AI technology for hospital pharmacies to help with medication management. "It is incumbent upon us, in this industry and in this field, to take the lead and learn more, invest in systems that support AI and machine learning, and prepare for the future with access to artificial intelligence."
Plopping down on a mattress, primping in front of a mirror or sitting on a toilet: In coming years, any of these activities could generate the most intimate data about your health, via sensors, wearables, machine-learning algorithms and data-mining systems. Though they promise to make health care more personalized, our nonstop interactions with digital technologies and analytics are upending traditional notions of patient confidentiality. In the U.S., the core federal law restricting the use and protecting the disclosure of health data is the Health Insurance Portability and Accountability Act, or HIPAA. Congress enacted it in 1996, when much of the health system was paperbound and fax-reliant. The law's age is showing.
Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.