HBR | AI Will Change Health Care Jobs for the Better

Smart machines tend to elicit both awe and deep anxiety. This is especially true in health care, where people’s hopes and fears tend to get magnified quickly. Consider three issues that get a lot of attention: the use of medical records, the “human touch” in medical care, and the future of jobs in the industry. On balance, are people more glass half-full or half-empty? Our research points to an optimism that may surprise expert observers.

Consumers Want What Works

When it comes to privacy of medical data, of course people don’t want their records hacked by criminals. But consumers are ready to share their information when it’s in their clear interests to do so, and when it’s shared with the right people. More and more people are turning to wearable health devices, for example. According to a recent Accenture survey, 88% of consumers are willing to share the data from their “wearables” with either their doctor, a nurse, or other health care professionals. And 72% are fine with their health insurers having that information. (On the other hand, only 38% are willing to share that data with their employers.)

We see similar results when it comes to the human touch. Of course, in many situations, nothing can substitute for the human touch. But in many cases, convenience and efficiency trump personalized care. In our survey, two-thirds of respondents said they would use AI-based after-hours services, and 63% said they would use AI agents to help them navigate the health care system. In addition, more than half of those surveyed said they would be likely to use AI-based systems to diagnose their symptoms and to receive emergency advice. Overall, 75% of U.S. consumers surveyed said that AI technological advances (including mobile apps, wearable monitoring devices, and smart scales) were important to them to help them manage their health.

What About Jobs?

When it comes to the impact of smart machines on jobs, we see ample evidence to put hopes over fears. Take the question of potential worker displacement through automation. Accenture Research recently concluded a study of future workforce trends, including in health care, and the data point to significant growth for the industry, both in terms of jobs and revenues, as AI moves beyond rudimentary automation and enables greater collaboration between humans and machines. The data predict that, from 2018 to 2022, employment in health care will increase by 15% while revenues will surge by 49%.

Much of that growth will come from three new ways in which smart machines will enable humans to improve performance.

The first is by amplifying people’s natural abilities, enhancing their insight and intuition through the use of powerful analytics and copious historical data. A Harvard-based team of pathologists, for example, recently developed an AI-based technique to identify breast cancer cells with greater precision. Using the new method, they were able to increase their accuracy from 96% to 99.5%. That might not sound like much of change, but with nearly 1.7 million new cases of breast cancer diagnosed globally each year, the improvement translates to 68,000 to 130,000 more women receiving accurate diagnoses.

The second way is by interacting with people through novel types of interfaces such as voice, emotion, or gesture recognition. In elder care, robots can help overburdened caregivers by reminding patients to take medication, lead them through physical and cognitive exercises, and provide them with companionship. Chatbots can help mental health professionals serve many more patients a day. Machine learning tools can help with the screening and treatment for autism. Philips has developed an AI-based tool, Illumeo, for radiologists. One of the tool’s features is that it displays contextual information about a patient alongside the images, so a radiologist doesn’t have to hunt for that information. It can also anticipate the radiologist’s needs. It might, for example, recognize the anatomy of the radiological images and automatically suggest the correct tool set such as one that can measure and analyze blood vessels. The software is also able to learn how a particular user prefers to look at the images — what’s known as a radiologist’s “hanging protocol.”

The third way that smart machines are helping humans is by embodying physical attributes that work to extend people’s capabilities beyond their natural limits. A good example of embodiment in the health care field is robot-assisted surgery: Instead of using a scalpel, the surgeon sits at a console and nudges a joystick that controls robotic arms. This AI-assisted technology has been a huge asset for certain types of surgeries that require incredible precision — for instance, when a doctor has to remove an overgrown retina membrane that’s only a hundredth of a millimeter thick. By eliminating human jitters and involuntary tremors, robot-assisted surgery can help significantly improve the success rate of such operations.

The above examples are just a few of the many that illustrate the power of human-machine collaborations in which each party does what it does best: people’s intuition, creativity, teamwork, and social skills combined with a machine’s precision, speed, scalability, and quantitative capabilities. Such collaborations are the future, enabling companies to reimagine their work processes, and this transformation is happening in one industry after another. That’s cause for considerable optimism.

Source: Harvard Business Review