High-risk pregnancy (HRP) is a pregnancy complicated by factors that can adversely affect outcomes of the mother or the infant. Health insurers use algorithms to identify members who would benefit from additional clinical support. We aimed to build machine learning algorithms to identify pregnant patients and triage them by risk of complication to assist care management. In this retrospective study, we trained a hybrid Lasso regularized classifier to predict whether a patient is currently pregnant using claims data from 36735 insured members of Independence Blue Cross (IBC), a health insurer in Philadelphia. We then train a linear classifier on a subset of 12,243 members to predict whether a patient will develop gestational diabetes or gestational hypertension. These algorithms were developed in cooperation with the care management team at IBC and integrated into the dashboard. In small user studies with the nurses, we evaluated the impact of integrating our algorithms into their workflow. We find that the proposed model predicts an earlier pregnancy start date for 3.54% (95% CI 3.05-4.00) for patients with complications compared to only using a set of pre-defined codes that indicate the start of pregnancy and never later at the expense of a 5.58% (95% CI 4.05-6.40) false positive rate. The classifier for predicting complications has an AUC of 0.754 (95% CI 0.764-0.788) using data up to the patient's first trimester. Nurses from the care management program expressed a preference for the proposed models over existing approaches. The proposed model outperformed commonly used claim codes for the identification of pregnant patients at the expense of a manageable false positive rate. Our risk complication classifier shows that we can accurately triage patients by risk of complication.

Long-term care service for old people is in great demand in most of the aging societies. The number of nursing homes residents is increasing while the number of care providers is limited. Due to the care worker shortage, care to vulnerable older residents cannot be fully tailored to the unique needs and preference of each individual. This may bring negative impacts on health outcomes and quality of life among institutionalized older people. To improve care quality through personalized care planning and delivery with limited care workforce, we propose a new care planning model assisted by artificial intelligence. We apply bandit algorithms which optimize the clinical decision for care planning by adapting to the sequential feedback from the past decisions. We evaluate the proposed model on empirical data acquired from the Systems for Person-centered Elder Care (SPEC) study, a ICT-enhanced care management program.

Machine learning tools are used in a variety of fields, from sales to medicine. But getting tech into the workplace is just one step -- these tools are only successful if they're integrated into workflows, and if people trust them enough to depend on them. A key to successful adoption is back-and-forth dialogue between technology developers and end users, according to new research from MIT Sloan professorKate Kellogg,Sara Singer of Stanford University, Ari Galper of Columbia University, and Deborah Viola of Westchester Medical Center. The paper was published in Health Care Management Review. Deploying workplace tools is often seen as one-directional -- developers make them and hand them off to users.

An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital--before they to return to the doctor's office with further complications. But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that aren't optimized for their patients. Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.

An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital -- before they to return to the doctor's office with further complications. But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that aren't optimized for their patients. Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.

As pandemic-driven social distancing and self-isolation measures permeate society, safeguarding vulnerable people's mental and physical well-being is more important than ever. This trend had already led to a surge in technologies aimed at enabling loved ones and caregivers to monitor seniors and engage with them remotely, but the COVID-19 crisis has lent an air of urgency to the endeavor. A quick glance across this landscape shows fall-detection contraptions, targeted social networks, and more. Artificial intelligence (AI) in particular is gaining a firmer foothold, with AI-powered social companions and fancy wearables designed to track all manner of activity or changes in behavior. Fledgling Israeli startup MyndYou is using AI to help care providers assess and monitor elderly patients from afar, with a platform centered on passive data collection, automated engagement, and remote intervention.

Note: This is part one of a two-part series on IBM Watson Care Manager for specialty courts. Our goal at the Juvenile Court of Montgomery County, Ohio, is to keep kids alive until they're old enough to make decisions on their own. We feel as if almost every child is directly or indirectly at risk today because of drugs, abuse and other issues. When we can't rely on the family to take care of them, it's our role to become like a surrogate parent. We give them a chance to survive until they're an adult and able to make it on their own.

An unnamed regionally-managed health care provider partnered with ThingWorx's machine learning platform to detect patterns in data that would lead to better patient care and reduce costly readmissions for patients with ischemic heart disease, according to a case study provided by Thingworx. The solution predicts high-risk patients and provides caregivers insight into why flagged patients should receive extra care across their network using health care IoT. The unspecified health care network includes two major hospitals and a network of outpatient and preventative care providers. It has more than 1,000 patient beds, a home health care service, preventive medicine, rehabilitation services, a network of primary care physicians and a range of outpatient services. According to Thingworx, its client is one of the largest health providers in the country.

An unnamed regionally-managed health care provider partnered with ThingWorx's machine learning platform to detect patterns in data that would lead to better patient care and reduce costly readmissions for patients with ischemic heart disease, according to a case study provided by Thingworx. The solution predicts high-risk patients and provides caregivers insight into why flagged patients should receive extra care across their network using health care IoT. The unspecified health care network includes two major hospitals and a network of outpatient and preventative care providers. It has more than 1,000 patient beds, a home health care service, preventive medicine, rehabilitation services, a network of primary care physicians and a range of outpatient services. According to Thingworx, its client is one of the largest health providers in the country.