As AI gets better at handling complex tasks with incredible efficiency, we see it becoming increasingly useful in different industries. Robotics and AI-driven solutions are also permeating the healthcare industry with the promise to effect changes that will ensure significant improvements in healthcare delivery.
Artificial intelligence in healthcare is the use of software and machine-learning algorithms to analyze and process complex clinical data for the purpose of supporting clinical decisions, predicting diseases, improving patient outcomes and generally streamlining workflow in medical organizations.
It is estimated that through 2021, the healthcare AI market will reach $6.6 billion with an explosive CAGR of 40%. The popularity of artificial intelligence is only looking upwards and here we take a look at the future of healthcare AI.
AI in healthcare today
One of the most common use cases of AI in healthcare systems in the early detection of diseases. AI-driven technology is currently used to improve the accurate diagnosis of disease like cancer in their earliest stages.
According to the American Cancer Society, 1 in 2 women are wrongly diagnosed with cancer because a significant percentage of mammograms yield false results. But with AI, mammograms are reviewed and translated 30 times faster with up to 99% accuracy, eliminating unnecessary biopsies.
Another use case is the IBM Watson’s Genomic product used at the University of North Carolina Lineberger Comprehensive Cancer Center to perform big data analysis and determine specific treatment options for over 1,000 patients.
In the same vein, Google’s Cloud Healthcare API helps clinicians make better clinical decisions by creating invaluable insights from data in the EHR of patients. Artificial intelligence is also being adopted by pharmaceutical companies to help streamline the process of drug discovery and development.
Current Technology Trends In Healthcare
With the implementation of artificial intelligence, the healthcare industry is experiencing several changes in the way medical professionals care for patients to improve treatments, how physicians are trained and how pharmaceutical companies carry out clinical trials for drug development. You can check out https://www.withpower.com for more information.
Everything we thought we knew about medicine and health care is changing at an incredible pace and here are some notable technology trends pervading the health care space.
VR And AR
Both virtual and augmented realities promise to be of great use in the healthcare industry. With the help of these technologies, doctors can fine-tune their surgical skills by first playing out an entire surgical procedure before the actual operation. Different outcomes and possible complications will be taken into account to ensure a seamless procedure in the theater.
The use of AR and VR technologies in healthcare also extends to the training of surgeons. With these technologies, aspiring medical professionals will be able to practice and improve their skills in a shorter time. Using VR to train healthcare workers have been reported to improve skill retention by 75% and reduce skill fade by up to 52%.
Virtual and Augmented Reality can also be used with patients. Before a surgical procedure, doctors can walk their patients through the entire process to better prepare them for the operation. Patients with chronic or incurable health problems may require some degree of self-care, a quick walkthrough with VR will teach such patients exactly how to monitor their vital signs and administer some medications themselves.
Although VR and AR are still in their early stages, they promise great potential and the market for these technologies in the healthcare industry is estimated to reach $11.14 billion by 2025.
Patients Data Interoperability
While the COVID-19 pandemic ravaged the globe, the United States Department of Health and Human Services (HHS) stepped up and finalized its interoperability rules which gives patients free access to their health data via mobile phones and smart devices.
The interoperability rules give patients greater control over their electronic health records which they can share more freely. Also in 2020, Google launched its Cloud Healthcare Interoperability Readiness Program which is designed to help organizations navigate changes and better prepare for the new healthcare data interoperability rules.
With these changes came a leap in the number of patient records shared between providers, and more health systems are transitioning to cloud platforms.
For instance, the Epic Care Everywhere interoperability platform which allows the seamless sharing of patients data in real time saw more than 221 millions patient records shared, which represents nearly a 40% increase from the same period the year before.
Big Data Management
Although big data was already one of rising healthcare technology trends before 2020, the global pandemic showed how very important data analytics and management can be in the healthcare industry. Gathering, analyzing medical data and drawing actionable insights for better healthcare service delivery are some of the benefits of efficient data management.
The use of medical wearables and EHR interoperability are factors that facilitate data collection and reporting. Big data was incredibly instrumental in managing the pandemic and developing a vaccination strategy. Coronavirus outbreaks were contained in certain areas using contact tracing technology. Big data analytics is also very useful in academic medicine and research.
Customized Mobile Apps
There are billions of smartphone users across the globe, and every one of them spends at least 4 hours on their smartphones each day. This highlights the efficacy of using mobile apps to connect with people, which a lot of businesses are already keying into.
Healthcare providers can also employ this technique to facilitate better and seamless communications for better patients and doctors. The strategy of using customized mobile applications that integrate EHR/EMR as a point of contact for better patients and doctor communication offers great potential.
From check-ins to teleconsultations and e-prescriptions, mobile apps can bolster digital patient monitoring, reduce hospital visiting hours, eliminate unnecessary paperwork, improve patients care and facilitate better data collection.
Mobile applications can be customized for patients with features like pill and dose reminders, check blood-pressure, heartbeat or other reminders, remote doctor’s appointment, etc.
Remote Patient Monitoring Using Medical Wearable
Connected medical wearable for remote patient monitoring essentially extends the reach of healthcare systems. This is an incredibly useful method for remotely keeping track of dynamic vital signs.
The continuous monitoring of human vital signs in real-time—such as blood pressure, pulse, heart sound, respiration and temperature—can have a very positive impact in managing chronic health conditions like heart diseases, diabetes, asthma, cancer, etc.
With a wearable medical device, patients can monitor their vital signs and send the information to their physicians to be reviewed with immediate feedback. Wearable medical devices also help in data collection that helps in predicting possible health crises which prompts a timely response to mitigate risks and tackle diseases at their earliest stages.
How will AI change the healthcare workforce?
A study conducted by the McKinsey Global Institute (MGI) has known that AI and automation will affect jobs in virtually every sector but to varying degrees, and the healthcare sector is poised to experience the least amount of AI automation. In healthcare delivery, the potential for automation is evidently low because only about 35% of time spent is potentially automatable, and it differs by type of occupation.
The study also highlights that the imminent shortage of healthcare providers in the European healthcare can only be mitigated with automation. For instance, a 39% increase in all nursing occupations is expected by 2030 even as about 10% of nursing activities could become automated.
AI is posed to completely change the health care systems as we know it. Medical professionals will have more time to attend to patients and clinical solutions will be more patient-centric.
Clinicians will also have to work with machines which will require the development of certain skills and competencies. Perhaps one of the most obvious challenges with fully incorporating AI into the healthcare industry will be an inevitable skill gap and the need to rapidly build a workforce with the skills required to efficiently interact with AI.
Implications for the Healthcare Workforce
A research study by the American Hospital Association’s (AHA) Center for Health Innovation reveals that about 40% of the tasks performed by non-clinical staff and 33% of tasks performed by clinicians can be assigned to AI. Delegating these tasks to AI in the future will free up tremendous amounts of time for clinicians to focus on what really matters, and unfortunately loss of jobs for non-clinical staff.
New job roles
The entrance of machine learning and robotic process automation into the healthcare system affect a huge change in the traditional job roles of hospital staff. While there will inevitably be loss of jobs, there will also be a huge need for individuals with specific skill sets and competencies to handle the AI aspect of healthcare delivery. New job roles for positions such as AI engineers, data scientists, data engineers, data architects, etc. will emerge in the health care workforce.
Reduced operation cost
A report by Business Insider Intelligence, states that 30% of healthcare costs are associated with repetitive administrative tasks such as maintaining records, patient scheduling, insurance verification, billing, etc. With these tasks delegated to AI and robotic process automation, there will be a decrease in human error caused by manual handling and also a reduction in administrative staffing costs.
The Challenges Of AI In Healthcare
The future of healthcare with AI is not without its challenges, as with any new technology there are certain concerns on the reliability and safety of artificial intelligence. Here are some concerns that need to be addressed to enable widespread adoption of AI in healthcare.
AI design quality
Despite the potential for AI to effect tremendous changes in healthcare that will completely revolutionize the ecosystem, there has been a reluctance in fully embracing the technology by healthcare professionals, probably due to a slight mistrust in the quality and effectiveness of the solutions. As a budding technology in medicine, AI is yet to leave a trail of clinical evidence to prove its effectiveness and reliability. This is always a problem with new technologies.
Data security concerns
AI in healthcare certainly requires pools of high-quality data to function as well as it should, however this brings us to the challenges involved in accessing the amount of data required. To overcome this challenge, the entire health care system will have to be digitalized to expedite the generation and collection of high-quality data. Another apparent concern with this is the need for robust data-sharing policies between healthcare providers and at the same there should be systems to protect the very sensitive medical data from cybercriminals.
Investing in a skilled workforce
One of the biggest changes to expect from artificial intelligence in healthcare is the emergence of new job roles. There will certainly be a need for individuals with skills that cut across medicine, informatics, and data science. For the successful adoption of this technology in healthcare, medical organizations will have to invest heavily in new talents and create a workforce with the appropriate skill to efficiently interact with AI and medicine.
Compliance to regulations
Liability, risk management and responsibility for AI solutions are still very fuzzy. The successful adoption of AI also depends, to a great degree, on clear-cut regulations by health agencies on how the technology should be used to ensure the safety of patients. This will also enable medical professionals to better understand AI use cases, whether as a product or tool for complementing their skills, which will ultimately help in mitigating risks.
AI in the future
The ultimate goal of artificial intelligence is to build computers with the type of general intelligence that humans possess, and these computers will be capable of complex thinking and reasoning just like humans.
Looking back to how far we have come with technology; from the most primitive of machines like the abacus and the first programmable computer, the ENIAC, it’s quite evident that we have come very far.
However, building computers with the capacity to think and reason as efficiently as humans remains the single most ambitious undertaking ever proposed by scientists.
Computers as we know them today are generally specific in what they do. The smartest computers in the world possess what is known as specific intelligence, for instance, computers are programmed to perform specific tasks which are usually the only tasks they can perform as accurately as possible. So far, no one machine has the type of general intelligence—including abstract thinking, critical thinking, problem-solving, deductive reasoning, etc.—ascribed to humans.
The future of AI, however, is leaning towards computers with general intelligence to handle all sorts of jobs as efficiently as possible, and it’s feared that such machines will take over our jobs. But this is actually very unlikely, because there are major bottlenecks towards achieving this goal. And even in the distant future, AI will be unable to completely eradicate human labor, but instead it will inspire other inventions and drive growth in many new sectors which will essentially lead to the creation of new job roles.
Presently, about 90% of top businesses have invested in AI technologies, and more than half of businesses reported an improvement in productivity after adopting AI. And these businesses cut across different sectors including medical industry, transportation and automobiles, cybersecurity, e-commerce, manufacturing etc.
In the medical field, AI will successfully automate the repetitive task of clinicians and support them in diagnosis, formulating of treatment plans, recognizing risks and streamlining operations. The future of healthcare AI is ultimately to assist clinicians with certain tasks thus giving them more time to focus on what really matters. As for AI completely taking over the roles of physicians, that’s not happening, at least not in the near future.
The integration of AI solutions with medicine offers great potential, and we are only just getting started. The impact of AI on global health care promises to be phenomenal, but apparently there is still a lot of ground to cover. The first obvious step will be to address the major challenges preventing the general adoption of AI in the healthcare space. And when that happens, only then can we expect exponential growth in health AI and substantial advancements on a global scale.