The massive impact that Artificial Intelligence has had on society at large is a well-documented phenomenon. Virtually every industry has benefitted from AI’s proven ability to learn from experiential data, and the benefits to society have been multi-fold.
For all its touted advantages, however, AI adoption in the healthcare industry has been slow. There are several reasons contributing towards this, but the bottom line remains that healthcare technology is somewhat wary of adopting Artificial Intelligence in its hospital management systems. For all the hype surrounding AI and it’s the promise it holds for healthcare IT, there is hesitation when it comes to the actual implementation of AI in healthcare technology.
Limited Access to Patient Data
Electronic medical records (EMR) provide a wealth of information that AI can apply itself to. However, medical data has the limitation of being difficult to access. EMRs are not consistent across hospitals and facilities, thus leading to medical records that are incomplete, and localized, not lending itself well to artificial intelligence, which requires data sets that are large and high quality in nature.
Given the issue of EMR incompatibility and the fact that collection of these data sets is a cumbersome process, it follows that many hospitals will show a reluctance to incorporate AI into their healthcare IT setup. The high-performance AI algorithms are simply not a good fit for hospitals that do not have sufficient data and hence there is a lag in terms of AI adoption in healthcare IT, because of the quality of data.
Regulations in the Healthcare Industry
A significant factor contributing to the lag in terms of AI adoption is in the barriers created by data privacy regulations. No other industry is as intensive on data privacy as the healthcare technology industry. While these regulations are understandably necessary, given the sensitivity of medical data, these liability concerns may nevertheless pose a barrier to the adoption of AI.
In order for artificial intelligence to be deployed in the healthcare industry, then, these barriers will need to be lowered. This requires a serious look into regulation and calls for substantial scrutiny into both privacy regulations as well as liability concerns around data. The adoption of AI will call for innovation between the data suppliers, as well as the AI providers, but is precisely what is needed in order to kickstart the adoption of AI.
In order for the healthcare industry to be able to use AI-driven technology, training in AI will be required across the industry. Hospital management systems that incorporate AI will need to be understood and familiarized with, from doctors to nurses and other hospital staff. Education curricula will probably need to be revised in order to include artificial intelligence and data science.
Public awareness of artificial intelligence, particularly how it is used to make informed decisions regarding patient outcomes, goes a long way towards the acceptance of AI in the healthcare industry. When patients see physicians and nurses use AI-enabled healthcare technology to make insight-driven decisions, this increases acceptance of AI in healthcare; with an increased acceptance, the lag in adoption of AI is further reduced.
Limitations in Artificial Intelligence Algorithms
AI algorithms are sometimes difficult to comprehend in that the self-learning and predictive patterns they offer are hard to identify. Some AI algorithms function essentially as black boxes, meaning that it is difficult to understand precisely what their predictive analysis is being derived from; this leads to a situation known as algorithmic bias.
In order to reduce the lag in terms of AI adoption, the healthcare industry will have to develop large-scale initiatives that work towards eliminating the black box issue, creating more accountability in terms of understanding how algorithms work and mitigating the risk of algorithmic bias.
Artificial intelligence-enabled systems are being envisaged as harbingers of a new era where decision making depends largely on insights obtained by the analysis of big data. While this is definitely to the advantage of doctor and patient alike, care must be used to see that clinicians do not reach a stage of over-reliance on data for making informed decisions. There has to be an assurance of accountability and trust built into the systems that are AI-enabled. By working to prevent misuse of these systems and through awareness of AI, a hospital management system can gear itself up for the next level in healthcare technology, bringing benefit to both patient and physician.