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Bottlenecks in Healthcare AI Adoption

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Every sector has an opportunity to integrate artificial intelligence. Healthcare is taking the slower route, exercising caution and concern as AI advances other industries to new revenue and productivity heights. 

Why wouldn’t the sector want AI adoption if having a well of potentially unlimited data could better diagnose patients and streamline operational communications in healthcare facilities? Because of everything the industry encapsulates, the transition is more complex than most would consider.

The Massive Data Surface Area

Electronic health records (EHR) span countless electronic landscapes, including insurance databases, medical records and radiological laboratory imaging. There are also plenty of medical notes yet to be digitized, containing information an AI could find most insightful. However, the competitive and confidential nature of the healthcare industry prevents this data from meeting in the same silo.

It would be time-consuming and expensive to link, and many independent healthcare outfits are reluctant to join forces to inform machine learning algorithms. They want compensation for their efforts if they hand over their data. 

Personally identifying information (PII) and protected health information (PHI) are delicate resources. It’s a gray area to abide by health privacy regulations while feeding an AI dataset. Adversely, AI could always stay the most up-to-date with current compliance, so careful information entry may help it navigate this road safely.

However, if the industry champions this hurdle, AI datasets could know every known cure, prescription and remediation plan for every current medical situation. How can the sector overcome this massive spread of information? Regulations are the key.

AI in healthcare has little to no governmental benchmarks. Having them in place will quell some concerns from even the most prominent hospitals when delegating time and resources to this endeavor. Creating standards for these processes will be a joint, dedicated effort from regulatory bodies and health institutions. Trial-and-error testing with new AI trends like predictive analytics and enhanced security will take time, but standards will create cohesion and motivation while eliminating industry concerns.

The Skepticism of Patients

AI isn’t used enough in the industry to have enough patient feedback. It’s impossible to tell how patients react to artificial intelligence providing a diagnosis or recovery plan early in AI healthcare adoption. Some experts believe there would be requests for human doctors to be the mouthpiece for this information transfer.

Despite the accuracy AI could have over human doctors because of its constantly updating database, people haven’t warmed up to a world where technology replaces them. AI would not make physicians obsolete — human influences can always provide second opinions to its determinations. 

Also, people will inform and fine-tune AI after implementation to ensure efficiency and accuracy — this will overcome a related hurdle of a healthcare AI being overwhelmed with too much data. Human oversight will manage data scaling and input to ensure no false, outdated or unnecessary information causes determinations to be biased or misinformed. Patients may feel more comfortable if doctors relay this to patients.

Researchers must increase AI exposure to patients to gauge reactions and trust capability. Only through interactivity could they see the potential — reduced wait times, faster prescription filling, increased diagnostic accuracy and more balanced staffing to minimize burnout. This could prove especially beneficial, as 36% of caregivers say their jobs are highly stressful.

Trimming overhead with AI could advance lower- to middle-tier hospitals as they save countless dollars in expenses. This would allow them to invest in more expert staff and better equipment to propel them into a new future of better healthcare. These side effects could change patients’ minds if they saw the positive change unraveling before them.

The Unknowns of AI Decision Making

Though humans know what data they’re feeding into AI to inform decisions, artificial intelligence could predict or make assumptions that still bring surprises. Programmers and engineers exist to explain the technical side, but how AI connects the dots between its data points is still nebulous in ways.  

The concept is known as explainability. The question is how clinicians can work with AI if they can’t understand how they came to solutions, especially if humans have never conceived the answer in history. AI in healthcare could start suggesting cures for illnesses people didn’t have answers for. It could also identify trends or symptoms, making diagnostic leaps that extend outside human perception. 

Researchers want to uncover how this works and how medical professionals can develop strong relationships with AI resources while practicing a healthy dose of skepticism. If humans can’t figure out how an AI came to an impossible solution, how can institutions implement it reliably? Further research will solve this bottleneck by clarifying AI processing. 

However, another solution in conjunction with research is an overwriting of humanity’s perceptions and assumptions about AI. AI can make false equivalencies and determinations, but its ability to make accurate predictions are not unfounded — years of human research and contribution informs healthcare AI. Once this realization becomes normalized, AI adoption in health could become more seamless.

The Resistance to AI in Healthcare

Adopting infrastructure as innovative and industry-shifting as AI will revolutionize how health practitioners think about the field. Every technological shift requires proactive, optimistic discourse to illuminate how it will benefit the sector and its patients while avoiding as many roadblocks and legal issues as possible. 

Immense hesitation exists because nobody wants to encounter the potentially massive controversies and laborious efforts to implement AI. However, if utilized correctly, AI could bring healthcare to a new age of caring for humanity more effectively and accurately, increasing the quality of life for patients and staff worldwide.

Zac Amos is a tech writer who focuses on artificial intelligence. He is also the Features Editor at ReHack, where you can read more of his work.