- September 26, 2018
- Posted by: vmayo
- Category: Artificial intelligence
Consulting firm Frost & Sullivan reports that the healthcare Artificial Intelligence (AI) market is set to experience a compound annual growth rate of 40 percent through 2021, largely because AI has the potential to improve healthcare outcomes by 30 to 40 percent while simultaneously cutting the costs of treatment in half.
“AI systems are poised to transform how we think about disease diagnosis and treatment,” says Frost & Sullivan Transformational Health Industry Analyst Harpreet Singh Buttar. “Augmenting the expertise of trained clinicians, Artificial Intelligence systems will provide an added layer of decision support capable of helping mitigate oversights or errors in care administration.”
The value of Artificial Intelligence in the healthcare space not limited to clinical settings, however. By facilitating medical diagnostics, improving pharmaceutical marketing, and reducing medication non-adherence, AI-powered technologies are driving much-needed change at nearly every stage of the patient journey.
Facilitating medical diagnostics
Diagnostic errors play a role in around 10 percent of patient deaths and between 16 and 17% of all hospital complications. As exceptionally skilled as most healthcare providers (HCPs) are, in many ways, the human mind remains fallible.
As Andrew Beck, Director of Bioinformatics at Beth Israel Deaconess Medical Center Cancer Research Institute points out, “Identifying the presence or absence of metastatic cancer in a patient’s lymph nodes is a routine and critically important task for pathologists, [but] peering into [a] microscope to sift through millions of normal cells to identify just a few malignant cells can prove extremely laborious using conventional methods.”
That’s why Beck and his team built an automated diagnostic tool using a deep learning algorithm trained to differentiate between cancerous and noncancerous cells. In an evaluation conducted in 2016, the automated tool achieved a diagnostic success rate of 92 percent — just 4 percentage points lower than human pathologists. What’s more, when Beck’s team combined human pathologists’ analyses with the analyses of the automated tool, the diagnostic success rate rose to a remarkable 99.5 percent.
Ultimately, Beck believes that his experiment barely scratches the surface of what a hybrid — that is, human and algorithmic — approach has to offer to medical diagnostics. “Our results…show that what the computer is doing is genuinely intelligent and that the combination of human and computer interpretations will result in more precise and more clinically valuable diagnoses to guide treatment decisions.”
Improving healthcare marketing
Once a patient has been diagnosed, the next step is to find a therapy that will cure — or at least mitigate the effects of — their condition. HCPs obviously have an out sized influence over which therapeutic regimen a patient adopts, but the importance of “Ask your doctor about [Drug X]” direct-to-patient messaging shouldn’t be underestimated.
Unfortunately, the pharmaceutical sector often finds itself talking past its core constituencies. In fact, one study found that as many as 45% of patients believe that pharmaceutical companies don’t understand their real needs. Not unlike the challenges of traditional medical diagnostics, this disconnect is first and foremost a problem of scale.
In the digital age, gauging patient behavior — the first step toward delivering relevant, tailored messaging — involves aggregating information drawn from a wide variety of data sets, including medical data like hospital records, lab results and HCP notes and general data like media preferences, internet usage, and demographic information. Healthcare marketers must then draw out salient narratives and insights from their aggregated data — “connecting the dots,” so to speak — not just once, but on a rolling basis over the course of a campaign.
The reality is that executing such a data-driven approach at scale requires a superhuman amount of computing capacity. Not even the best, most experienced team of marketers is capable of organizing and analyzing millions of data points, which is where machine learning-based predictive analytics tools become invaluable.
By leveraging a properly-trained predictive analytics algorithm, a marketing team can gain unparalleled insight into their target patients, facilitating messaging based not on broad-strokes segmentation, but on analyses of the intricate — and often imperceptible to the human eye/mind — ways that a patient’s past behavior, personal characteristics and current position in the patient journey interact.
Reducing medication non adherence
Between 1988 and 1994, roughly 38 percent of adults living in the United States were taking at least one prescription drug at NYGoodHealth online pharmacy. Over the subsequent two decades, that figure grew to 49 percent, driven in large part by a 100 percent increase in the number of adults taking three or more prescription drugs.
All told, according to research presented to the American Hospital Association in October 2016, “Total net spending on prescription drugs…has accelerated over the past year to $309.5 billion annually, making prescription drugs the fastest growing segment of the U.S. healthcare economy.”
A significant fraction of this $309.5 billion is going to waste. As many as half of the 3.2 billion prescriptions written in the U.S. each year aren’t taken as directed — if they’re even taken at all. This non adherence leads to over $250 billion dollars of unnecessary costs or roughly 13 percent of the country’s total annual healthcare expenditures.
But just as pharmaceutical marketers can use algorithmic tools to refine their patient targeting, HCPs can use algorithmic tools to reduce this systemic waste by getting a better sense of which of their patients are most prone to medication non-adherence. Everything from demographics and payer type to out of pocket costs and the prescribing HCP’s area of specialty bear upon the likelihood of a patient deviating from their prescribed regimen and an Artificial Intelligence-based approach is a robust way to take all of these factors into account.
Armed with the predictive outputs of such systems, HCPs are able to pinpoint which patients need additional support in order to remain on course with their treatment. Granted, better, more targeted communication isn’t a comprehensive solution for medication non-adherence, but research published in Medical Care suggests that poor HCP-patient communication results in a 19 percent higher risk of non-adherence. In other words, it’s a start.
Overview of the Medical Artificial Intelligence (AI) Research
Recently AI techniques have sent vast waves across healthcare, even fueling an active discussion of whether AI doctors will eventually replace human physicians in the future. We believe that human physicians will not be replaced by machines in the foreseeable future. But AI can definitely assist physicians to make better clinical decisions. The increasing availability of healthcare data and rapid development of big data analytic methods have made possible the recent successful applications of Artificial Intelligence in healthcare. Guided by relevant clinical questions, powerful AI techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making.
Here you can understand how Artificial intelligence Works.