Medicaid and CHIP cover about half (48%, or about 6.8 million) of children with special health care needs in the U.S. as of 2016 (Figure 1).1 Box 1 below describes children with special health care needs.  Medicaid or CHIP is the sole source of coverage for 41 percent of these children.  Another seven percent have Medicaid/CHIP to supplement private insurance.  Medicaid provides a wide range of medical and long-term care services, many of which are not covered at all or only available in limited amounts through private insurance, and makes coverage affordable for many children with special health care needs and their families.

This issue brief compares the demographics, health status, access to care, and coverage affordability of Medicaid/CHIP children with special health care needs to those with private insurance and those who are uninsured.  Medicaid plays a key role for children with special health care needs by making coverage affordable and covering services that private coverage typically does not. Consequently, legislative proposals that would cap and reduce federal Medicaid funding may pose a particular risk to children with special health care needs and their providers.  While Congress did not pass such legislation in 2017, these proposals may resurface in 2018, and the Trump Administration’s FY 2019 proposed budget continues to advance these proposals.  A companion brief describes Medicaid’s role for children with special health care needs.

Children with special health care needs who have Medicaid/CHIP are significantly more likely to live in low income families, below 200% of the federal poverty level, compared to those with private insurance only.  Nearly eight in 10 (78%) Medicaid/CHIP-only children with special health care needs live in families with incomes below 200% of the federal poverty level (FPL, less than $41,560/year for a family of three in 2018), compared to 17% of those with private insurance only. Nearly six in 10 (57%) of  children with special health care needs who have both Medicaid/CHIP and private insurance live in a family with income below 200% FPL.  Medicaid/CHIP children with special health care needs are about 10 times less likely, and those with both Medicaid/CHIP and private insurance are about three times less likely, than those with private insurance only to live in a family with income of 400% FPL or higher ($83,120/year for a family of three in 2018, 5% vs. 16% vs. 49%) (Figure 2).

Medicaid/CHIP-only children with special health care needs are significantly more likely to be a member of a racial or ethnic minority group compared to those with private insurance only, while over half (53%) of those with both Medicaid/CHIP and private insurance are non-Hispanic white.  Nearly seven in 10 (68%) children with special health care needs with private insurance alone are non-Hispanic white compared to less than four in 10 (38%) Medicaid/CHIP children with special health care needs (Figure 3).

Medicaid/CHIP children with special health care needs are significantly more likely to have multiple chronic conditions and to be in fair or poor health compared to those with private insurance only. Close to four in 10 (37%) Medicaid/CHIP-only children with special health care needs have four or more chronic conditions, compared to less than a quarter (23%) of those with private insurance only.  Over half (51%) of those with both Medicaid/CHIP and private insurance have four or more chronic conditions. Some of the chronic conditions reported by children with special health care needs include autism, intellectual disability, asthma, depression, anxiety, attention deficient hyperactivity disorder, cerebral palsy, cystic fibrosis, diabetes, muscular dystrophy, brain injury, heart conditions, and epilepsy.  Medicaid/CHIP-only children with special health care needs are three times as likely (9%), and those with both Medicaid/CHIP and private insurance are four times as likely (12%), to be in fair or poor health compared to those with private insurance alone (3%) (Figure 4).

As a result of their poorer health, Medicaid/CHIP children are significantly more likely to have multiple health care service needs during the course of a year and to have their daily activities affected by their health compared to those with private insurance only.  Medicaid/CHIP-only children with special health care needs are significantly more likely (25%) to require three or more health care services, such as specialist care; prescription medication; speech, occupational, or behavioral therapy; or mental health treatment or counseling, compared to those with private insurance alone (17%).  Medicaid/CHIP-only children with special health care needs are nearly twice as likely (43%) to have four or more functional difficulties compared to those with private insurance alone (24%).  Over half (55%) of those with both Medicaid/CHIP and private insurance have four or more functional difficulties. Functional difficulties experienced by children with special health care needs include problems with bodily functions, such as breathing, swallowing, or chronic physical pain; activities or participation, such as self-care, mobility, learning, or communicating; and emotional or behavioral factors, such as anxiety, depression, or making friends.  Medicaid/CHIP-only children with special health care needs are significantly more likely (31%) to have health conditions that usually or always affect their daily activities somewhat or a great deal compared to those private insurance only (18%) (Figure 5).

As a result of their greater health needs and functional limitations, Medicaid/CHIP children with special health care needs are significantly more likely to have their education impacted by their health status.  Those with Medicaid/CHIP only (30%) are significantly more likely to have a current special education plan compared to those with private insurance alone (24%). Medicaid/CHIP-only children with special health care needs also are significantly more likely (13%) to miss more than two weeks (11 or more days) of school due to illness or injury compared to those with private insurance alone (7%) (Figure 6).

Despite their greater needs, Medicaid/CHIP-only children with special health care needs are significantly more likely than those with private insurance alone to report that their coverage meets their needs.  For example, Medicaid/CHIP-only children with special health care needs (87%) are significantly more likely than those with private insurance only (56%) to report that their coverage is adequate. Two-thirds (66%) of children with both Medicaid/CHIP and private insurance report that their coverage is adequate, perhaps due to this group’s greater health needs. Medicaid/CHIP-only children with special health care needs also are significantly more likely to report that their insurance always allows them to see all needed health care providers, compare to those with private insurance alone (77% vs. 71%).  While access to mental health services can be challenging across payers due to provider shortages, Medicaid/CHIP-only children with special health care needs (64%) are significantly more likely to report that their insurance always meets their mental or behavioral needs compared to those with private insurance alone (41%) (Figure 7).

Medicaid/CHIP children with special health care needs have high rates of access to care; their access is not as high as those with private insurance alone but significantly better than those who are uninsured.  Nearly four in five (78%) Medicaid/CHIP-only children with special health care needs report a usual source of care when sick compared to 89% of those with private insurance alone.  The rates for access to a usual source of preventive care (92% for Medicaid/CHIP-only vs. 97% for private insurance only) and for at least one preventive visit in the last year (86% for Medicaid/CHIP-only vs. 91% for private insurance only) are high for both groups. Medicaid/CHIP children with special health care needs also experience significantly better access to care on these measures compared with those who are uninsured (Figure 8).

Medicaid/CHIP children with special health care needs are significantly more likely to report that their coverage is affordable compared to those with private insurance alone.  Medicaid/CHIP-only children with special health care needs (82%) are more than four times as likely to report that their out-of-pocket health care costs are always reasonable compared to those with private insurance alone (19%).  Over four in 10 (43%) of those with both Medicaid/CHIP and private insurance report that their out-of-pocket costs are always reasonable. Medicaid/CHIP-only children are more than 12 times less likely (3%) to incur annual out-of-pocket costs of $1,000 or more, compared to those with private insurance alone (36%) (Figure 9).

Insurance affordability may be a particular concern for families of Medicaid/CHIP children with special health care needs because they are significantly more likely to have difficulty covering basic household needs compared to those with private insurance only.  Families of Medicaid/CHIP-only children with special health care needs are more than two and a half times as likely (53%) to find it somewhat or very often hard to cover basic needs like housing or food since their child’s birth compared to those with private insurance alone (20%) (Figure 9). For an example of how Medicaid helps make private coverage affordable for children with special health care needs, see Gabriel’s story in Box 2 below.

Insurance affordability also is important because the demands of caring for children with special health care needs can impede their families’ ability to work, an outcome that is significantly more likely among Medicaid/CHIP children with special health care needs compared to those with private insurance only. Medicaid/CHIP-only children with special health care needs are nearly twice as likely (18%) to live in families who had to reduce their work hours or stop working due to their child’s health needs, compared to those with private insurance alone (10%). Over one-quarter (27%) of those with both Medicaid/CHIP and private insurance live in families who had to reduce their work hours or stop working due to their child’s health needs (Figure 10).

Families of Medicaid/CHIP children with special health care needs may be less able to work because they are significantly more likely to devote time to meeting their child’s health needs compared to those with private insurance only.  They are three times as likely (12%) to have family members who spend five or more hours per week providing their health care at home, and those with both Medicaid/CHIP and private insurance are over four times as likely, compared to those with private insurance alone (4%).  Medicaid/CHIP-only children with special health care needs are three times as likely (3%) to have family members devoting five or more hours per week coordinating their health care, and those with both Medicaid/CHIP and private insurance are five times as likely, compared to those with private insurance alone (1%) (Figure 10).

Medicaid/CHIP children with special health care needs experience greater health needs, comparable access to care, and greater affordability compared to those with private insurance alone.  Their families are more likely to have to limit their work hours or stop working as a result of their health and more likely to devote time providing or coordinating their health care.  In addition to filling gaps in private insurance and making coverage affordable, Medicaid is the sole source of coverage for many children with special health care needs in low and middle income families. Medicaid also fills in the gaps in private insurance and makes coverage affordable for children with some of the greatest health care needs.  Medicaid/CHIP children with special health care needs experience significantly better access to care on these measures compared with those who are uninsured.

Proposals to cap and reduce federal Medicaid funding may pose a particular risk to children with special health care needs because these children use services more intensively, and often incur greater costs, compared to other children.  While Congress did not pass such legislation in 2017, these proposals may resurface in 2018, and the Trump Administration’s FY 2019 proposed budget continues to advance these proposals. Families of children with special health care needs, their providers, states, and other stakeholders may want to consider the implications of these proposals, given the important role that Medicaid plays for children with special health care needs.

source: https://www.kff.org/medicaid/issue-brief/how-do-medicaid-chip-children-with-special-health-care-needs-differ-from-those-with-private-insurance/

It depends who you ask.

Back in the 1950s, the fathers of the field Minsky andMcCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task.

That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not.

AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.

AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud.

At a very high level artificial intelligence can be split into two broad types: narrow AI and general AI.

Narrow AI is what we see all around us in computers today: intelligent systems that have been taught or learned how to carry out specific tasks without being explicitly programmed how to do so.

This type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past. Unlike humans, these systems can only learn or be taught how to do specific tasks, which is why they are called narrow AI.

There are a vast number of emerging applications for narrow AI: interpreting video feeds from drones carrying out visual inspections of infrastructure such as oil pipelines, organizing personal and business calendars, responding to simple customer-service queries, co-ordinating with other intelligent systems to carry out tasks like booking a hotel at a suitable time and location, helping radiologists to spot potential tumors in X-rays, flagging inappropriate content online, detecting wear and tear in elevators from data gathered by IoT devices, the list goes on and on.

Artificial general intelligence is very different, and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or to reason about a wide variety of topics based on its accumulated experience. This is the sort of AI more commonly seen in movies, the likes of HAL in 2001 or Skynet in The Terminator, but which doesn't exist today and AI experts are fiercely divided over how soon it will become a reality.

Special report: How to implement AI and machine learning (free PDF)

A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C Müller and philosopher Nick Bostrom reported a 50 percent chance that Artificial General Intelligence (AGI) would be developed between 2040 and 2050, rising to 90 percent by 2075. The group went even further, predicting that so-called ' superintelligence' -- which Bostrom defines as "any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest" -- was expected some 30 years after the achievement of AGI.

That said, some AI experts believe such projections are wildly optimistic given our limited understanding of the human brain, and believe that AGI is still centuries away.

There is a broad body of research in AI, much of which feeds into and complements each other.

Currently enjoying something of a resurgence, machine learning is where a computer system is fed large amounts of data, which it then uses to learn how to carry out a specific task, such as understanding speech or captioning a photograph.

Key to the process of machine learning are neural networks. These are brain-inspired networks of interconnected layers of algorithms, called neurons, that feed data into each other, and which can be trained to carry out specific tasks by modifying the importance attributed to input data as it passes between the layers. During training of these neural networks, the weights attached to different inputs will continue to be varied until the output from the neural network is very close to what is desired, at which point the network will have 'learned' how to carry out a particular task.

A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with a huge number of layers that are trained using massive amounts of data. It is these deep neural networks that have fueled the current leap forward in the ability of computers to carry out task like speech recognition and computer vision.

Download now: IT leader's guide to deep learning

There are various types of neural networks, with different strengths and weaknesses. Recurrent neural networks are a type of neural net particularly well suited to language processing and speech recognition, while convolutional neural networks are more commonly used in image recognition. The design of neural networks is also evolving, with researchers recently refining a more effective form of deep neural network called long short-term memory or LSTM, allowing it to operate fast enough to be used in on-demand systems like Google Translate.

Another area of AI research is evolutionary computation, which borrows from Darwin's famous theory of natural selection, and sees genetic algorithms undergo random mutations and combinations between generations in an attempt to evolve the optimal solution to a given problem.

This approach has even been used to help design AI models, effectively using AI to help build AI. This use of evolutionary algorithms to optimize neural networks is called neuroevolution, and could have an important role to play in helping design efficient AI as the use of intelligent systems becomes more prevalent, particularly as demand for data scientists often outstrips supply. The technique was recently showcased by Uber AI Labs, which released papers on using genetic algorithms to train deep neural networks for reinforcement learning problems.

Finally there are expert systems, where computers are programmed with rules that allow them to take a series of decisions based on a large number of inputs, allowing that machine to mimic the behavior of a human expert in a specific domain. An example of these knowledge-based systems might be, for example, an autopilot system flying a plane.

The biggest breakthroughs for AI research in recent years have been in the field of machine learning, in particular within the field of deep learning.

This has been driven in part by the easy availability of data, but even more so by an explosion in parallel computing power in recent years, during which time the use of GPU clusters to train machine-learning systems has become more prevalent.

Not only do these clusters offer vastly more powerful systems for training machine-learning models, but they are now widely available as cloud services over the internet. Over time the major tech firms, the likes of Google and Microsoft, have moved to using specialized chips tailored to both running, and more recently training, machine-learning models.

An example of one of these custom chips is Google's Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which useful machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which they can be trained.

These chips are not just used to train up models for DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, as well as services that allow the public to build machine learning models using Google's TensorFlow Research Cloud. The second generation of these chips was unveiled at Google's I/O conference in May last year, with an array of these new TPUs able to train a Google machine-learning model used for translation in half the time it would take an array of the top-end graphics processing units (GPUs).

As mentioned, machine learning is a subset of AI and is generally split into two main categories: supervised and unsupervised learning.

Supervised learning

A common technique for teaching AI systems is by training them using a very large number of labeled examples. These machine-learning systems are fed huge amounts of data, which has been annotated to highlight the features of interest. These might be photos labeled to indicate whether they contain a dog or written sentences that have footnotes to indicate whether the word 'bass' relates to music or a fish. Once trained, the system can then apply these labels can to new data, for example to a dog in a photo that's just been uploaded.

This process of teaching a machine by example is called supervised learning and the role of labeling these examples is commonly carried out by online workers, employed through platforms like Amazon Mechanical Turk.

See also: How artificial intelligence is taking call centers to the next level

Training these systems typically requires vast amounts of data, with some systems needing to scour millions of examples to learn how to carry out a task effectively -- although this is increasingly possible in an age of big data and widespread data mining. Training datasets are huge and growing in size -- Google's Open Images Dataset has about nine million images, while its labeled video repository YouTube-8M links to seven million labeled videos.ImageNet, one of the early databases of this kind, has more than 14 million categorized images. Compiled over two years, it was put together by nearly 50,000 people -- most of whom were recruited through Amazon Mechanical Turk -- who checked, sorted, and labeled almost one billion candidate pictures.

Unsupervised learning

In contrast, unsupervised learning uses a different approach, where algorithms try to identify patterns in data, looking for similarities that can be used to categorise that data.

An example might be clustering together fruits that weigh a similar amount or cars with a similar engine size.

The algorithm isn't setup in advance to pick out specific types of data, it simply looks for data that can be grouped by its similarities, for example Google News grouping together stories on similar topics each day.

Reinforcement learning

A crude analogy for reinforcement learning is rewarding a pet with a treat when it performs a trick.

In reinforcement learning, the system attempts to maximize a reward based on its input data, basically going through a process of trial and error until it arrives at the best possible outcome.

An example of reinforcement learning is Google DeepMind's Deep Q-network, which has been used to best human performance in a variety of classic video games. The system is fed pixels from each game and determines various information, such as the distance between objects on screen.

By also looking at the score achieved in each game the system builds a model of which action will maximize the score in different circumstances, for instance, in the case of the video game Breakout, where the paddle should be moved to in order to intercept the ball.

With AI playing an increasingly major role in modern software and services, each of the major tech firms is battling to develop robust machine-learning technology for use in-house and to sell to the public via cloud services.

Each regularly makes headlines for breaking new ground in AI research, although it is probably Google with its DeepMind AI AlphaGo that has probably made the biggest impact on the public awareness of AI.