After seeing the new Netflix documentary “The Social Dilemma,” millions of people are having an eye-opening discussion on how social media platforms affect human behaviour. In short, artificial intelligence (AI) is only a neutral technology.
The usage of it may benefit our healthcare sector by helping to support us with social media, as was said by Dr. Darren Schulte, MD, the chief executive officer of Apixio.
By using artificial intelligence that has grown startlingly effective at assessing, predicting, and influencing user behaviour, the company will realise new and enhanced customer and company experiences. In the film, the new realities that have emerged from these unintended effects include addiction, social alienation, increased despair, and even more self-harm and death.
Users are concerned with the social-media fueled repercussions that they will experience when they seek to social media for affirmation. Large technology firms are hugely profitable because they are able to gather and analyse their user data while also influencing the behaviour of users in order to benefit advertising.
While the film may seem to blame machine learning algorithms for many of the problems the film illustrates, these algorithms are not necessarily malicious. The algorithms are either being trained to do one thing or another.
Although the widespread application of AI algorithms in healthcare has the potential to transform health care, increasing patient outcomes and reducing waste and costs, it could also enable personalised medicine, expedite the discovery of new treatment and preventative measures, and improve overall population health.
Computer algorithms originally developed to study social dilemmas such as the Prisoner’s Dilemma and the Tragedy of the Commons may now be trained to examine data from patients, health care professionals, and medical equipment (like wearables).
Algorithms are also capable of analysing physiological functions (such as lab tests and vital signs) to create a more accurate understanding of a person’s overall health, the way he or she behaves, and any health-related habits or behaviours over time.
We can find better treatments, update clinical recommendations, and uncover new medicines to enhance the health of the entire population by compiling anonymous, aggregated individual data with anonymous, aggregated population data.
Let’s examine these 11 ways artificial intelligence might support healthcare applications.
Tackling emerging illnesses, such as COVID-19, is a worthwhile endeavour. The treatment of COVID-19 patients has, so far, been difficult because of all the testing and trial-and-error. Nevertheless, the information on the effects of such medicines has had a sluggish spread in the medical community throughout the world.
There is just data on the patients that the hospitals and clinicians are treating. With no coordinated mechanism for exchanging patient data, no clear decisions are made. As an example, America’s providers have been unable to profit from the Asia and Europe-based expertise and experience where the virus spread early.
We may learn which therapies have worked best for particular patient groups by mining medical information collected from millions of people.
It might be noted that people who have already contracted the illness may have certain features, which may make them more prone to progress to the worst stages of the disease. Preventing infection means identifying those who are vulnerable to it earlier so that we may then focus on appropriate therapies.
Let us look at what happens when we manually process this data: It promotes the speed of spread and death toll. With AI, we are able to bring this information to light at a much faster rate, and this may help mitigate the effect of the next new disease.
Take better care of your patients. Also, understanding how COVID-19 spreads and how quickly is difficult. Typically, scientists utilise the metric known as R0 (which is a reminder of the term “naught”) to express the average number of persons infected by a single infectious individual.
Because multiple different organisations utilise various models and data, it has been difficult to forecast the growth of COVID-19 using R0. However, asymptomatic individuals can spread the illness without knowing that they are sick.
A system that uses artificial intelligence (AI) may help to mitigate this problem and assist in improving patient monitoring by taking into consideration patient medical records with test results as well as contact tracing data which points to the presence of infection. The ability to use this data at scale, along with knowledge about the source, enables health officials to identify regions where intensive testing programmes and restricting shelter-in-place measures may be put in place to control the spread of illness.
Deliver improved-quality treatment. The health care providers desire to give their patients with the finest possible treatment. The main problem they confront is finding a way to quantify the quality and patient outcomes based on empirical evidence. Many health information, such as electronic health records (EHRs), laboratory findings, and imaging tests, are distributed across several sources. This makes it difficult to collect and evaluate patient data.
Physician offices and hospitals may detect trends among patients by consolidating this data and establishing systems that allow them to utilise AI to mine it for insights.
for example, clinicians might use appointment reminders, transportation resources, give telemedicine options, or do other interventions to help patients remain involved in their own care if they observe that people with specific characteristics tend to fail to follow-up on significant health problems
For both insurers and their customers, the biggest concern is with quality of treatment and ensuring that customers get the best possible outcome at the lowest possible cost.
A recent technology known as artificial intelligence may be used to enable insurers to track and assess patient outcomes, which range from a primary care physician to a specialist to a hospital for surgery and into a rehabilitation centre, for example. In this scenario, insurance companies can cooperate with health care providers to introduce novel ways to enhance patient outcomes and success rates.
Identify and respond to current issues. Doctors often have just access to the medical information for the patient in front of them during a routine patient interaction. Determining the patient’s history only gives a partial look at the variables that could foreshadow a health decline. Due to the dispersion of data across multiple systems, clinicians frequently do not have all the relevant data on hand.
Artificial intelligence can be used to reveal a larger signal that a patient’s health is deteriorating over time.
Analyzing aggregate data across a big population enables AI to predict various health problems, such as diabetes and heart disease.
Doctors can use this information to assess future problems and start precautionary measures. As notifications in the Electronic Health Record (EHR), certain systems can alert clinicians to these findings. It gives clinicians the opportunity to adopt a direct approach in preventing disease development.
Allow patients to get individualised care. There has been an emphasis on personalised medicine for a long time, with the hopes of transforming the “one-size-fits-all” approach to healthcare into a customizable strategy tailored to each person. But until we have access to aggregated data and insights that AI can give, this will be almost impossible.
To trigger interaction and drive advertising money, consider the AI social media businesses utilise to design and connect with personas. We could give this information to providers if we can figure out unique personal health care personas for each individual.
Tools like alerts, nudges, signals, or other forms of communication may potentially be used to urge patients to adopt improved health habits.
Let’s use an example: Doctors may give medication reminders, nutrition suggestions, or other information pertinent to their unique health issue to patients who are at risk.
Reduce the number of diagnostic and treatment mistakes. To be quite frank, even the finest healthcare practitioners miss key facts and make mistakes, especially when they are under the added strain of accommodating a greater number of patients each day.
Social networks like Facebook are able to expose data about their users to court advertising. Algorithms, on the other hand, can properly identify and cure medical problems. AI can point out variables that might have a confusing effect on the patient’s overall health, allowing the doctor to give consideration to the full health profile of the individual when making judgments.
Another possible advantage of AI is that it can assist in identifying possible medication interactions that could put patients at danger. All of this can significantly minimise the likelihood of medical errors that cause patients injury, and even the likelihood of allegations of malpractice.
Early intervention is beneficial for people who are at high risk. Of the individuals in the United States, only 5% account for half of all health care spending. Early diagnosis, management, and prevention are important for “high-utilizer” chronic diseases because of the demanding and continuous care these individuals require.
Identifying those who may be interested in buying a new lawnmower helps doctors to target those patients who are at risk of developing costly medical requirements. Using artificial intelligence, companies can do risk assessments, which allows them to execute preventative and early intervention measures.
A special feature, such as detecting a distinct obesity signal that predicts the likelihood of developing Type II diabetes, may also be present in an algorithm. Furthermore, algorithms might identify individuals with high blood pressure who are at increased risk of a heart attack, stroke, or kidney disease.
These insights can be provided during an interaction, at the point of care. Once the data has been recorded into the EHR, a physician will be notified to the danger and be able to take steps to counter disease progression or confounding circumstances.
Utilize data-based referrals to identify the most effective treatment routes. Physicians traditionally use existing ties to refer patients needing surgery or physical treatment to a surgeon or physical therapist they know.
However, this does not necessarily guarantee that patients will receive the best care possible for their specific condition. Is the clinic ready to treat patients with additional conditions? Or do they only do standard surgeries?
With artificial intelligence, clinicians may refer to the optimal option for each patient, based on verified proof of success and achievement, instead of on unverified ties.
AI might assist primary care physicians find orthopaedic experts and rehabilitation providers with demonstrated outcomes in treating patients with diabetes, along with those specialists’ associated equipment.
Eliminate wasteful expenditures. According to an OECD study, 30% of all healthcare spending, around $935 billion, is waste. It is estimated that almost $80 billion in excess and unnecessary care has been provided in the last two decades.
For the sake of greater insurance coverage, providers order tests, services, and procedures that are neither necessary or helpful, all to try to protect themselves against charges of underperformance and to meet insurance requirements (e.g., ordering x-rays before an MRI when an injury is clearly soft tissue related or sending patients for multiple repeat mammograms before conducting an ultrasound to evaluate a suspicious lump).
Companies and insurers that mine data using algorithms are able to concentrate on utilising tests and treatments that show high value or which are absolutely essential for certain cases. These examples provide a variety of answers to the question, “Is it necessary for patients on particular drugs to have blood tests done every 90 days?” Would a wellness visit have a positive impact on patients?
AI is better able to lead clinicians toward appropriate tests and treatment options when used as a benchmark for the general population. That, in turn, reduces needless diagnostics and expedites the route to improved health for patients.
AI has the ability to help healthcare organisations save money by determining which diagnostics are most successful and cost-efficient, possibly saving patients and insurance companies millions per year on useless treatments and testing.
Increase the pace of pharmaceutical discovery. Currently, there is a lengthy and difficult road to new medicines, vaccines, and therapies. With trials lasting an average of seven years, it takes on average 10 years for new medicines to travel from discovery to marketing. As new vaccines are discovered, it can take up to 12 years until they are widely used (which puts optimism for a COVID-19 vaccine by the end of the year into context).
Because of the limited availability of advanced data and analytics in the process, the procedure moves very slowly.
The use of AI might significantly increase the speed of developing new medicines and vaccines, saving lives.
A significant absence of data analytics meant that clinicians had a difficult time devising efficient COVID-19 procedures. As a result, patients were unable to obtain the therapy they needed.
Algorithms help speed up this analysis, and the resulting medication will be available sooner to those who need it.
Operational costs are lowered. About one-sixth of physicians’ time is spent on administrative labour, with 33% spending 20 hours or more per week on paperwork and administrative activities. Furthermore, there are additional operational activities, such as coding, documentation, and reporting done by support personnel that are not included in this figure.
When everything is said and done, this may be a costly investment that takes away from time spent in direct, in-person contact with patients.
AI can assist with this by automating manual procedures like prior authorizations, which reduces retrospective chart reviews, and therefore increases operational efficiency by lowering operational expenses. Patients who have accurate, up-to-date medical data are empowered to make faster, better decisions.
AI’s administrative-side savings result in cheaper costs for patients and health insurers, as well as more funds that may be allocated to better patient care.
When personal data impacts human behaviour, and the resulting consequences are unpleasant, social media becomes a problem.
Generally speaking, technology is impartial. However, algorithms may be misused by those with harmful motivations or goals.
Like with social media, we may also utilise these algorithms to help us deal with anxiety, loneliness, and depression.
To design a survey, you first have to consider the algorithm’s objectives, training, testing, and user feedback data. Data and insights are needed to effectively manage both individual and public health in the 21st century.
The only insights we have into healthcare are guesswork until we make use of data-driven insights.
The use of data analysis algorithms allows clinicians to develop a personalised healthcare plan that is uniquely tailored to each individual. Tapping into collective wisdom and information acquired from millions of patient data enables the physician to enhance the quality of care overall and decrease health care expenditures.
Image Credit: karolina grabowska; pexels
