Why healthcare systems should adopt A.I. in healthcare delivery? (Part 1-Economic Benefits)

(This article was first posted on LinkedIn)

Keeping aside the buzz and hype about utilization of A.I.* in various disciplines and what mistakenly many assume it can accomplish, there are real merits in governments and decision-makers setting out strategies for adoption of A.I. in health service delivery. In this article, I will review the economic benefits of the application and in later articles, the other advantages.

Of those health systems analyzed by the Commonwealth Fund in their performance rankings, people in the U.S. and Australia had the highest out-of-pocket costs when accessing healthcare. This issue has arisen not for the lack of investment in healthcare by governments in these countries. In 2016, the healthcare spending in the U.S. increased by 4.3 percent to attain a figure of US$3.3 trillion or about US$10,348 per person. Of the US$3.3 trillion, US$1.1 trillion was spent on hospital care, US$92 billion on allied health services and about US$162.5 billion on nursing care facilities and retirement communities. Together these expenditures constituted approximately 45% of the 2016 health spend. Of the total health expenditure, individuals/households contribution matched the government expenditure (28% of the total health expenditure). In Australia in 2015-16, the total healthcare spend was AU$170.4 billion (a AU$6 billion increase compared to 2014-15). Of this, the government expenditure on public hospital services was AU$46.9 billion and on primary healthcare AU$34.6 billion. Expenditure by individuals accounted for 52.7% of non-government expenditure or 17.3% of total health expenditure. While governments continue to increase spending this hasn't really made a serious dent on out-of-pocket costs.

One of the key component of annual healthcare spending and pertinent to this article is the recurrent healthcare expenditure. Recurrent healthcare expenditure does not involve acquisition of fixed assets and expenditure on capital but largely expenditure on wages, salaries and supplements. In Australia, recurrent healthcare expenditure constitutes a whopping 94% of the total expenditure and in the US, about US$664.9 billion was spent on physician and clinical wages in 2016. Considering it is unlikely for wages to go down; it is hard to imagine recurrent healthcare expenditure decreasing and consequently total healthcare spend decreasing .

A.I. technology, which has been around for decades but has only recently received wide-spread attention, is increasingly being applied in various aspects of healthcare (primarily in the U.S.). While in an earlier article, I have argued how A.I. can never totally replace human clinicians**, many number of American hospitals are using A.I. technology to leverage their consultants expertise where in some cases the A.I applications are outperforming them. I won't discuss the technologies and application here (as it will be covered in my book chapter^ and in subsequent articles) but I will discuss the costs of development of these technologies from a healthcare point of view.
With traditional software development, the usual phases include discovery and analysis phase, prototype implementation and evaluation phase, minimum viable product and followed by product release. The costs associated with these phases, depending on the project sizes and complexity of the software, can constitute anywhere from US$10,000 to US$100,000^^. However, development of AI programs (here I will consider Machine Learning based programs not robotic applications, which adopt a different development model and consequently different cost models) have distinctive features to be considered in their development.

These aspects include acquisition of large data sets to train the system and fine-tuning the algorithms that analyze the data. Where significant data sets cannot be obtained, data augmentation can be considered. Costs will be incurred in acquiring the data sets if not available in prior. However, in the context of healthcare government agencies and hospitals can provision this data for developers at no costs (if the A.I. program is being developed/customized for their exclusive use). So the most cost impacting factor is whether the data is structured or not. Data doesn't have to be structured, there are several machine learning algorithms that are trained to analyze unstructured data. However, developing programs to review unstructured data incurs more costs. Even when structured data is available, there are processes like data cleansing and data type conversion, which add to the costs. The next distinctive feature is fine-tuning/customizing the machine learning algorithm to suit organization's requirements. As the healthcare context requires the program to have a high degree of accuracy (less false negatives and high true positive identification epidemiological speaking), many round of refinements of the algorithm will be required.

Even considering these distinctive features, which will add to the baseline costs ranging from US$50,000 to US$300,000^^; you are looking at a range of total costs of US$60,000 to US$500,000^^ (depending on the organisation requirements and complexity of the A.I. software). If we consider in the U.S. about US$664 billion (2016) and in Australia that AU$64 billion (2015-2016) was spent on hospital recurrent expenditure alone, a mere 0.016% allocation of the spend on developing A.I. technologies could fund development of at least 18 (hospital focused advanced machine learning based) applications per annum. The ROI is not just economic but also improvement in patient outcomes because of avoidance of medical errors, improved medical/laboratory/radiological diagnosis and predictability of chronic disease outcomes. Considering the rapid advances machine learning based program have made in medical prediction, diagnosis and prognosis^ , governments and healthcare organizations should seriously consider focus on supporting the development and deployment of A.I.technologies not only for the serious dent these applications can make on recurrent health expenditure but also how they can significantly improve patient access.

Footnotes

* I have a distaste for the term 'A.I.' which I have explained the reasons for in an earlier article but use this term as it is widely recognizable and accepted to portray computational intelligence based products.

**https://www.linkedin.com/pulse/role-human-clinicians-automated-health-system-dr-sandeep-reddy/

^ 'Use of Artificial Intelligence in Healthcare' (book working title: E-Health, ISBN 978-953-51-6136-3; Editor: Thomas.F.Heston, MD)

^^ This estimation does not take into consideration deployment, insurance and marketing costs.