Artificial intelligence (AI) can help minimize inefficiencies, ensuring substantially more stream-lined and cost-effective health ecosystems.
There has been much debate in the past decade around the potential applications of AI-driven technologies in a number of industries, including healthcare. While we are not yet at the stage of autonomous robots doing your house chores and driving you to work – the traditional perception of AI – there is strong evidence pointing to a number of ways in which AI can help tame healthcare costs.
1. Guiding treatment choice Healthcare providers begin to move towards a standardized format for recording patient outcomes, large sets of data will become available for analysis by AI-enabled systems which can track outcome patterns following treatment and identify optimal treatments based on patients’ profiles. In doing so, AI empowers clinical decision-making and ensures the right interventions and treatments are customized to each patient, creating a personalized approach to care. The immediate consequence of this will be a significant improvement in outcomes, which will eliminate costs associated with post-treatment complications – one of the key drivers of cost in most healthcare ecosystems across the world.
2. More efficient diagnosis Repetitive, uncomplicated tasks such as the analysis of CT scans and certain tests can be performed more accurately by AI-enabled systems, reducing physician error and enabling early diagnosis and interventions before conditions become critical. As an example, an Israeli start-up has developed AI algorithms that are equally or more accurate than humans for the early detection of conditions such as, coronary aneurysms, brain bleeds, malignant tissue in breast mammography and osteoporosis.
According to a recent article in Wired, AI has demonstrated 99% accuracy and is 30 times faster in reviewing and translating mammograms, enabling much earlier detection of breast cancer than humans are capable of. In cases such as osteoporosis, which costs the UK’s National Health Service approximately £1.5 billion annually (and that excludes the high costs of social care), the detection of vertebral fractures – an early indicator of impending osteoporosis which is commonly missed by human diagnosis – can substantially reduce the cost of this condition to health services.
3. Clinical trials optimization and drug development AI has the potential to enable faster development of life-saving drugs, saving billions in costs that can be transferred to health ecosystems. Most recently, a start-up supported by the University of Toronto programmed a supercomputer with an algorithm that simulates and analyses millions of potential medicines to predict their effectiveness against Ebola, saving costly physical tests and – most importantly - lives, by repurposing existing drugs. In clinical trials, AI can optimize drug development using biomarker monitoring platforms – biomarkers allow for gene-level identification of diseases – and millions of patient data points, which can be analyzed in seconds from a drop of blood using at-home devices.
4. Empowering the patient AI has the potential to truly empower us as individuals to make better decisions regarding our health. Vast numbers of people across the world already use wearable technology to collect everyday information, from their sleep patterns to their heart rate. Applying machine learning to this data could inform people at risk of certain diseases long before that risk becomes critical. Mobile apps are already providing granular-level patient profile information that could help people living with specific chronic conditions to better manage their disease and live healthier lives. All of this can lead to healthier populations and a reduction of the overall cost burden.
5. Cost effective
The average cost of colonoscopy in the U.S. is over $5,500. This is a cost borne either by the consumer or by the insurer depending on their insurance status. Depending on your unique condition, the procedure can cost several times more.
What’s bothersome, however, is the poor success rate in detecting polyps and tumors. Healthcare experts agree a doctor’s independent skill in determining polyps play a defining role in the success of the procedure. Some studies estimate the success rate of detecting benign polyps at 20 percent and adenoma at 12 percent.
This is a poor success rate for a procedure that can cost several thousands of dollars. So why do these tests cost so much with such low success rate? A typical colonoscopy test takes between 20 minutes to an hour. Even discounting the pre-test procedures, a doctor may only be able to perform 8-10 tests in a day at max efficiency.
As for the cost; In addition to the doctor’s fee, patients also incur significant costs to use the hospital facilities, take pre-test medications, and consultation. In short, a good chunk of money is charged because of the limited number of patients a hospital can take in a day, not for the actual costs of performing the test.
This is why the advances in artificial intelligence (AI) and machine learning are exciting from a healthcare perspective. AI can help computers scan thousands of images and identify patterns at a fraction of time. Add machine learning to this, and we are looking at technology that can dramatically improve sensitivity and accuracy over time. Leveraging the cloud, these technologies would be capable of comparing every scanned image against the millions of data points gathered from hospitals across the world.
AI can bring down the cost of a cancer screening test in two ways: it reduces the time it takes to perform a screening operation on one person (thereby increasing the number of procedures that can be conducted in a day), and also brings down the doctor’s fee since highly skilled endoscopists may no longer be required to perform screening tests.
Cloud, AI, and machine learning could dramatically alter the way medical tests are conducted and priced. The lower cost of performing such advanced tests could also push doctors into prescribing such tests at an earlier stage. This could help detect and stop deadly diseases including cancer much before they are detected today.