Artificial intelligence and machine learning have never been higher, so much so that they can overshadow the real applications and actual outcomes companies are working on. But larger-than-life promises or hype might have an eclipsing effect around the actual, realistic benefits it provides to almost any organization in a wide variety of industries generating a large volume of data.
The use case benefits are real, and it’s time for your company to start harnessing them. But before your organization can receive value from A.I. and ML, it must get a thorough understanding of the role it can play in your business, the problems it can solve, and how it can align with your company’s objectives or intended outcome.
The problem is that this is where a lot of A.I. and ML Proof of Concept initiatives have stalled and not made a lot of headways. To overcome this, companies need a place to start from. Smaller companies can take a cue card from tech giants such as Google, which has started to make bets to solve large-scale healthcare issues.
Large Tech Companies are Investing in Healthcare A.I.
Google A.I., which has been doing AI-related research and collaborative projects in the field of healthcare and biosciences says: “Machine learning has dozens of possible application areas, but healthcare stands out as a remarkable opportunity to benefit people — and working closely with clinicians and medical providers, we’re developing tools that we hope will dramatically improve the availability and accuracy of medical services.”
Artificial intelligence (A.I.) is poised to revolutionize healthcare operations, health research, the delivery of medical care, and how patients are supported to maintain health. It is and will be a critical part of long-term healthcare solutions.”
A.I. is already infused into a variety of industrial processes, apps, and systems that people have daily interactions with, making the field of healthcare primed for an A.I. expansion. It will go beyond just using A.I. to support activities such as detecting diseases and medical diagnoses.
This is just the tip of the iceberg. The bigger picture of healthcare is similar to other commercial industries, administration, logistics, business processes, and customer relations. These areas are where use case applications of A.I. have already started to improve costs and efficiencies. Because healthcare costs remain under tremendous pressure to get lowered or brought down, these are some areas that organizations should be exploring and investing in today to start making changes using A.I.
The United States spends over $10,000 per capita, or 18% of its GDP, on healthcare.” According to data from BGV, Global healthcare spending is projected to increase at an annual rate of 4.1% from 2017 to 2021, driven by an aging population, developing market expansion, and rising labor costs. Beyond digital disruption, there has been a unique opportunity for innovative startups to emerge and build technologies that tackle specific issues with the healthcare ecosystem.
The Value of A.I.
So, while already some healthcare organizations are seeing benefits from applying A.I. to its operations, logistics, admin processes, and even improving customer engagement, A.I. also shows promise augmenting areas such as pathology and radiology interpretation. With the increasing velocity of data and the fact the number of patient data sources will continue to expand, A.I. can be used to support data processing, visualization, and decision-making support.
The application of A.I. through (ML) and natural language processing (NLP) can bring a tremendous amount of value across the current healthcare continuum to deliver improved outcomes. The use of these technologies in healthcare will also help support new models of “value-based care,” and with the increase of Big Data – it can be leveraged to drive more personalization and transformation in healthcare to patients. Growth in the A.I. health market is expected to reach $6.6 billion by 2021—that’s a compound annual growth rate of 40 percent.
Deep Learning and Computer Vision Use Cases
Computer vision has already seen radical advances through an (A.I.) technique known as deep learning, or sometimes referred to as deep neural networks. It makes use of your pocket technologies that could have been seen as science fiction a decade ago.
If these new and advanced computer vision systems can have the accuracy of classifying different cars or breeds of dogs in images, Google’s engineers and scientists asked themselves, “might those same systems be capable of learning to identify disease in medical images?”
Varun Gulshan Ph.D., Research Engineer describes that in ophthalmology, they began exploring computer-aided diagnostic screening for a disease of the eye called diabetic retinopathy. Diabetic retinopathy is the fastest-growing cause of preventable blindness globally. Usually the test is conducted by highly trained doctors examining a retinal scan of a patient’s eye. In the field of digital pathology, Google has been researching and developing deep learning algorithms that might assist pathologists in detecting breast cancer in lymph node biopsies.
In the last several years, there has been a lot of progress in improving the diagnostic accuracy of medicines using (A.I.) and (ML). According to research from Big Market Research, the A.I. intelligence medical market is forecasted to exceed $18.12 billion by 2025. According to an article in A.I. for health, (A.I.) and (ML) are faster and more accurate in detecting anomalies in scans than humans. By using (A.I.) and (ML) programs to detect anomalies that a human eye could miss are improving diagnoses and setting up patients to get better patient care. Here are few examples:
Researchers at Stanford have created an algorithm for chest scans that is just as accurate as radiologists are and can interpret results in a fraction of the time.
Doctors using Viz.ai shave crucial hours off diagnosis time by using the technology to quickly and accurately detect blood clots in stroke patients before major damage can be done.
In the U.S., more than 25% of health care expenditures are due to administrative costs, far surpassing all other developed nations. One important area where A.I. could have a sizeable impact is medical coding and billing, where A.I. can develop automated approaches. The key to detangling the current healthcare system’s cost structure issue lies in the transfer of time-consuming human tasks to machines.
While enabling patients to self-service their care needs wherever possible. This can help with reducing the amount of human labor that is required to keep more people living healthier lives. According to a report from Accenture, when combined, key clinical health A.I. applications can potentially create $150 billion in annual savings for the United States healthcare economy by 2026.
Detecting Healthcare Fraud
Detecting and discovering patterns of anomalous and concerning behavior among a gigantic number of healthcare providers is difficult and very time-consuming (sometimes taking months or years to complete). Locating the source of illegal prescriptions and monitoring it more closely is an additional problem or challenge.
There have been all kinds of healthcare fraud abuses and scams, such as the prescription and distribution of opioids, which is the source of one of the nation’s deadliest substance abuse epidemics. (ML) can help discover anomalous and potentially fraudulent providers, which would be difficult and time-consuming for humans to sort through and detect.
By aggregating claims data, it’s possible to get a full view into opioid purchases. With machine learning programs and algorithms, healthcare companies can see patterns across that data and when they deviate.
This allows healthcare organizations to shift to preventing and detecting fraud versus a “pay and chase” approach. “Alleged fraud and false billings collectively accounted for 13 million illegal opioid dosages in the U.S.” and also included 23 pharmacists and 19 nurses. The types of data sources can consist of; Electronic health records (EHR), health level 7 standard messaging, medical devices, desktops, servers, storage, network, portals, billing systems, patient management systems.
New Drug Discovery
Big pharma has been struggling with developing new therapeutics. Over the past decades, developing drugs has become increasingly complex and expensive, leaving many patients with significant unmet needs.
Clinical trial success rates hover around the mid-single-digit range. The pre-tax R&D cost to develop a new drug (once failures are incorporated) is estimated to be greater than $2.5B, from $200 million 30 years ago. The rate of return on drug development investment has been decreasing linearly year by year, and some analyses estimate that it will hit 0% before 2020.
Regulatory oversight and not having large enough patient data sets have played a role in the increased costs. Koller’s company is trying to change that; Insitro is trying to revolutionize pharmaceutical R&D by leveraging machine learning for drug discovery and therapies. Insitro, in a matter of months, raised over $100 million from big-name investors including ARCH, Foresite Capital, Andreessen Horowitz, and the firm that manages Jeff Bezos’ personal V.C. investments.
The first human genome sequencing under the Human Genome Project (HGP) took nearly 13 years and cost 2.7 billion. Since then, the time and costs associated with individual sequencing genomes have dramatically decreased due to technological advances.
Healthcare organizational providers are starting to use genome sequencing and patient data to optimize the care for an individual by tailoring it from the individual’s unique genetic profile. The new advances and development in precision medicine have been unlocked from genome sequencing and the explosion of using Big Data and cloud (ML) techniques.
Machine learning algorithms can identify patterns and make predictions using cloud computing data lakes and data warehouses that clean (creating a single source of truth’ in the data) and store vast amounts of data, enabling the integration of multiple health care systems together. To provide better and more targeted care to an individual’s electronic health record.
Oncology and cancer research has gotten the most investment into precision medicine by studying cancer genetics. In some instances, cancer treatments can be suggested based on genetic drivers of cancer and not on the physical location of cancer itself within the patient’s body. As more healthcare organizations start to invest, experiment and integrate emerging technologies into their systems, traditional healthcare models will be disrupted and changed.
While there are plenty of use cases and evidence of the promising benefits of using Artificial Intelligence and Machine Learning applications in the healthcare industry, and each year more funding continues to flow into this sector, there are still a bunch of challenges for healthcare and tech startups beyond the mere improvement of technology to address a large potential market. Some of these challenges have been mentioned down below, these are –
- Getting widespread adoption
- Implementation of these tools
- The healthcare ecosystem
- Current business models
- Incentive alignments with the Payer-payee relationships
Healthcare organizations have to learn to trust algorithms to use them, meaning they want to see clinical validations of them. As of now, some are still cautious or hesitant about going into complete adoption of (A.I.) tools without having a large body of proof that verifies the outcomes.