Rising to meet the formidable challenge of the timely diagnosis of dementia, research scientists from Regenstrief Institute, IUPUI and the medical schools of Indiana University and University of Miami are conducting the Digital Detection of Dementia study, a real-world evaluation of the use of an artificial intelligence (AI) tool they developed for early identification of Alzheimer's disease and related dementias in primary care, the setting where most adults receive healthcare. Individuals identified as cognitively impaired will be referred for diagnostic services. The AI tool, called a passive digital marker, is a machine learning algorithm the researchers developed, trained and tested. The tool uses natural language processing to cull unstructured information in combination with structured data from a patient's electronic health record. These could include mention of memory issues, a notation of vascular concerns, comorbid conditions or other factors potentially linked to dementia. "Between 50 to 80 percent of dementia cases are unrecognized by the healthcare system in the U.S. And, if you include patients living with mild cognitive impairment, that number might actually climb to higher than 80 percent of cases that are not recognized," said Regenstrief Institute and Indiana University School of Medicine faculty member Malaz Boustani, M.D., MPH, senior author of the Digital Detection of Dementia study protocol paper, published in the peer-reviewed journal Trials.

Rising to meet the formidable challenge of the timely diagnosis of dementia, research scientists from Regenstrief Institute, IUPUI and the medical schools of Indiana University and University of Miami are conducting the Digital Detection of Dementia study, a real-world evaluation of the use of an artificial intelligence (AI) tool they developed for early identification of Alzheimer's disease and related dementias in primary care, the setting where most adults receive healthcare. Individuals identified as cognitively impaired will be referred for diagnostic services. The AI tool, called a passive digital marker, is a machine learning algorithm the researchers developed, trained and tested. The tool uses natural language processing to cull unstructured information in combination with structured data from a patient's electronic health record. These could include mention of memory issues, a notation of vascular concerns, comorbid conditions or other factors potentially linked to dementia. "Between 50 to 80 percent of dementia cases are unrecognized by the healthcare system in the U.S. And, if you include patients living with mild cognitive impairment, that number might actually climb to higher than 80 percent of cases that are not recognized," said Regenstrief Institute and Indiana University School of Medicine faculty member Malaz Boustani, M.D., MPH, senior author of the Digital Detection of Dementia study protocol paper, published in the peer reviewed journal Trials.

While the pandemic is still raging, the chaos of the past 18 months has calmed a bit, and the dust is starting to settle. Now the time has come for healthcare CIOs and other health IT leaders to look forward and plan their IT investments – shaped, in no small part, by the lessons of the recent past. According to new research from HIMSS Media, the average overall 2021 IT budget is nearly $13 million, with 15% on average being allocated to IT security. While that may be a lot of money, there are many technological areas yearning for more investment. Today, Healthcare IT News launches a new feature article series, Health IT Investment: The Next Five Years.

IMAGE: As a fellow of the 4th Intercontinental Academia (ICA): Intelligence and Artificial Intelligence, Regenstrief Institute Research Scientist Suranga Kasthurirathne, PhD, is studying the role of operationalizing artificial intelligence (AI) within... view more INDIANAPOLIS -- Regenstrief research scientist and Indiana University School of Medicine faculty member Suranga Kasthurirathne, PhD, has been selected as a fellow of the 4th Intercontinental Academia (ICA): Intelligence and Artificial Intelligence. He and the other outstanding early and midcareer researchers chosen as fellows will work together on cross-disciplinary projects while being mentored by some of the most renowned scientists from around the world, including Nobel Prize winners. Through its fellowship program, the ICA seeks to create a global network of future research leaders. Each fellow proposes a project. Dr. Kasthurirathne's focuses on the role of operationalizing artificial intelligence (AI) within learning health systems.

Researchers at Regenstrief Institute and Indiana University created decision models capable of predicting which patients might need more treatment for their depression than what their primary care provider can offer. The algorithms were specifically designed to provide information the clinician can act on and fit into existing clinical workflows. Depression is the most commonly occurring mental illness in the world. The World Health Organization estimates that it affects about 350 million people. Some people may be able to manage their depression on their own or with guidance from a primary care provider.

Depression is a worldwide health predicament, affecting more than 300 million adults. It is considered the leading cause of disability and contributor to the overall global burden of disease. Detecting people in need of advanced depression care is crucial. Now, a team of researchers at the Regenstrief Institute found a way to help clinicians detect and identify patients in need of advanced care for depression. The new method, which uses machine learning or artificial intelligence (AI), can help reduce the number of people who experience depressive symptoms that could potentially lead to suicide.

Using data from a statewide health information exchange, researchers have created machine learning algorithms that are able to identify patients who need advanced treatment for depression. According to Regenstrief Institute and Indiana University researchers, identifying cases of depression that require advanced care can be challenging for primary care physicians. However, they contend that their models--which leverage diagnostic, behavioral and demographic data, as well as past visit history from an HIE--can help PCPs predict which patients may be more at risk for adverse events from depression. Researchers created models for the entire patient population at Eskenazi Health, the public safety net healthcare system for Marion County, Indiana, as well as several different high-risk patient populations. "This study demonstrates the ability to automate screening for patients in need of advanced care for depression across an overall patient population or various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors and past visit history," conclude researchers in a recent article published in the Journal of Medical Internet Research.

Take a look around any big hospital and you'll find plenty of imposing technology: surgical robots, artificial organs, wireless brain sensors, three-dimensional imaging visualizations. But you have to look harder to find what's been touted for years as the future of medicine: artificial intelligence, or the use of computers to reason, learn and make critical decisions in patient care, with little or no human involvement. The medical field's lofty dreams of unleashing the power of artificial intelligence to transform medicine have yet to materialize in a major way. The thought of replacing doctors with machines remains a science-fiction fantasy. Even so, health-information experts say artificial intelligence has its place and can perform valuable tasks, from helping doctors identify diseases earlier to matching call-center customers at an insurance company with the person most qualified to help.

Health Catalyst and the Regenstrief Institute are working together to commercialize nDepth, Regenstrief's natural language processing technology. Indianapolis-based Regenstrief developed the technology to harness unstructured data. Salt-Lake City-based Health Catalyst, a data warehousing and analytics company, has been in the business of extracting data to boost care quality since it launched in 2008. It was developed within the Indiana Health Information Exchange, the largest and oldest HIE in the country. Regenstrief fine-tuned nDepth through extensive and repeated use, searching more than 230 million text records from more than 17 million patients.

Developing effective tools against cancer has been a long, complicated endeavor with successes and disappointments. Despite all, cancer remains the leading cause of death worldwide. Now, machine learning and data analytics are being recruited as tools in the effort fight the disease and show significant promise according to two recent papers. In one paper – An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer – researchers from MIT and Stanford "propose models that use machine learning and optimization to suggest regimens to be tested in phase II and phase III trials." Their work, published in March in Management Science, could help cut costs and speed clinical trials.