The World Health Organization issued its first global report on artificial intelligence in late June, highlighting concerns of algorithmic bias in health care applications of AI. It accompanies a growing number of news stories exposing AI's shortfalls. AI has come of age through the alchemy of cheap parallel (cloud) computing combined with the availability of big data and better algorithms. Problems that seemed unconquerable a few years ago are being solved, at times with startling gains -- think instant language translation capabilities, self-driving cars, and human-like robots. AI's arrival to health care, however, has been markedly slower.

May 13, 2021 – Artificial intelligence (AI) can shed light on trends within low-risk member populations so that health plans can prevent them from …

Regional Cancer Care Associates, based in New Jersey, has more than 20 locations throughout New Jersey, Connecticut, Maryland, Pennsylvania and the Washington area. Staff realized they needed a risk-stratified list of patients for COVID-19 vulnerability that nurses could manage through phone calls and by coordinating services with other providers. Because of staffing challenges, the list had to identify only the high-risk patients who staff needed to manage first, not the entire population or those patients who could wait a bit longer for nurse outreach. "Even though we already had an indigenous and independent scoring logic/mechanism for patient risk, this was mainly based on a combination of comorbidities that differentiated it from the usual scoring techniques," explained Lani M. Alison, vice president of quality and value transformation at RCCA. "Thus," she said, "there was a need to further stratify the risk patients for COVID-19 vulnerability and to establish a patient-centered assessment and outreach." On another note, staff observed challenges in assigning these patients and a defined patient roster to care coordination executives or support staff, which was hindering a patient-centric outreach approach, Alison added.

A large public research university figured out how to tap the power of artificial intelligence and human intelligence to produce impressive gains in student success. Over the past ten years, the University of South Florida (USF) has experienced dramatic improvement in student success, as measured by the first-year persistence rate, the four-year graduation rate, and the six-year graduation rate. Each of those metrics has improved substantially. To promote persistence and completion, the university implemented a wide array of programs, practices, and policies based on a campus Student Success Task Force Report released in April 2010. These initiatives included many of the standard student success initiatives in place at many other colleges and universities, including living learning communities, the professionalization of academic advising, gradual increases in admissions requirements, course redesigns, expansion of on-campus housing, and promotion of student engagement.

Racial and ethnic disparities in access to healthcare in the United States is well-known and documented [1]. Health disparities are defined to be differences in health ou tcomes and causes among different groups of people. Health equity is achieved when everyone has the same opportunity to be as healthy as possible. We have a very good handle on the types of health disparities i n the US healthcare system, but the causes for these disparities are complex [2, 3] - such as inco me, education, socioeconomic conditions, neighborhood and community influence, public policy, and so cietal structure. Achieving health equity also necessitates a complex set of programs and interventions, a nd several public and private initiatives have tried to address this problem over the past decades.

The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning (a subset of artificial intelligence) plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data and records and the treatment of chronic diseases. As computer scientist Sebastian Thrum told the New Yorker in a recent article titled "A.I. Versus M.D., "Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful." Despite warnings from some doctors that things are moving too fast, the rate of progress keeps increasing. And for many, that's as it should be. "AI is the future of healthcare," Fatima Paruk, CMO of Chicago-based Allscripts Analytics, said in 2017. She went on to explain how critical it would be in the ensuing few years and beyond -- in the care management of prevalent chronic diseases; in the leveraging of "patient-centered health data with external influences such as pollution exposure, weather factors and economic factors to generate precision medicine solutions customized to individual characteristics"; in the use of genetic information "within care management and precision medicine to uncover the best possible medical treatment plans." "AI will affect physicians and hospitals, as it will play a key role in clinical decision support, enabling earlier identification of disease, and tailored treatment plans to ensure optimal outcomes," Paruk explained. "It can also be used to demonstrate and educate patients on potential disease pathways and outcomes given different treatment options.

The clinical implementation of Artificial Intelligence (AI) is the most viable means of uniting the interests of the healthcare industry's capital constituents: the patient, the payer, and the provider. AI can improve healthcare outcomes while reducing costs when used to address patient compliance, chronic care management, genome sequencing, and physician diagnostics by classifying treatment options. Its widespread clinical deployment is poised to transform the healthcare industry into one that maintains wellness instead of merely combating illness. Maximizing AI's clinical value depends on the proper execution of four interrelated steps, each of which represents emerging developments within the industry: The proper implementation of each of these steps will ensure a future in which AI substantially contributes to decreased costs of chronic care and patient non-adherence, while achieving patient objectives in accordance with contemporary physician economics. Their implementation will also provide physicians with a vital support tool for conducting remote diagnostics, treatment classifications and accelerated care management.

It is not clear how to target patients who are most likely to benefit from digital care management programs ex-ante, a shortcoming of current risk score based approaches. This study focuses on defining impactability by identifying those patients most likely to benefit from technology enabled care management, delivered through a digital health platform, including a mobile app and clinician web dashboard. Anonymized insurance claims data were used from a commercially insured population across several U.S. states and combined with inferred sociodemographic data and data derived from the patient-held mobile application itself. Our approach involves the creation of two models and the comparative analysis of the methodologies and performances therein. We first train a cost prediction model to calculate the differences in predicted (without intervention) versus actual (with onboarding onto digital health platform) healthcare expenditure for patients (N = 1,242). This enables the classification of impactability if differences in predicted versus actual costs meet a predetermined threshold. A random forest machine learning model was then trained to accurately categorize new patients as impactable versus not impactable, reaching an overall accuracy of 71.9%. We then modify these parameters through grid search to define the parameters that deliver optimal performance. A roadmap is proposed to iteratively improve the performance of the model. As the number of newly onboarded patients and length of use continues to increase, the accuracy of predicting impactability will improve commensurately as more advanced machine learning techniques such as deep learning become relevant. This approach is generalizable to analyzing the impactability of any intervention and is a key component of realising closed loop feedback systems for continuous improvement in healthcare.

Home care agencies might want to think twice about how they handle workers who are chronically late to client visits. "When we started Honor, we thought, clearly, if a care pro is late, that's terrible," Seth Sternberg, CEO of the San Francisco-based company, told Home Health Care News. "And then we learned that's not true." That counterintuitive lesson about late workers came from analyzing data gathered through Honor's proprietary technology platform. About three years after launching, Honor now is looking closely at its data and adjusting operations accordingly, in a variety of ways.

Artificial intelligence is gaining ground in healthcare. In 2012, there were fewer than 20 artificial intelligence startups focused on healthcare; last year there were almost 70, according to CB Insights. Additionally, the AI for healthcare sector is expected to drive overall AI market growth over the next six years, according to a MarketsandMarkets report. The overall AI market is expected to grow at a compound annual growth rate of 62.9 percent from 2016 to 2022, when it's projected to reach $16.6 billion. Here, six health IT company executives discuss how AI will impact healthcare over the next 20 years.