February 2020 | ISE Magazine 33

Working in healthcare can cause major men-

tal, physical and organizational stress, which

in turn can lead to clinical hazards. Artiﬁcial

intelligence may help, though recent research

on such advanced technology is largely devoted

to patient safety; regulatory bodies conducting

such studies, such as the U.S. Food and Drug Administration

(FDA), focus on improving an AI system’s performance purely

from a statistical viewpoint. What’s missing is a need to in-

tegrate AI and cognitive ergonomics to make it more user-

friendly for medical workers.

Cognitive ergonomics – the gathering of knowledge about

human perception, memory and mental processes – has been

neglected in the healthcare domain and the evaluation of AI

from a cognitive ergonomics perspective is not well-estab-

lished. Though the effect of clinicians’ heavy mental load on

patient safety is known, there is no framework to guide the

graphical user interface (GUI) of complex AI systems and their

inﬂuence on clinicians’ and patients’ thoughts and feelings.

Here we seek to adopt a systems approach to develop and pro-

pose a conceptual framework across AI, cognitive ergonomics

and patient safety.

The gap between cognitive ergonomics, AI

Cognitive ergonomics is a component of human factors and

ergonomics aimed at ensuring effective interaction between

technology and humans. In this article, we discuss human-

system interaction from a cognitive ergonomics perspective

in healthcare. In this interaction, cognitive ergonomics con-

centrates on mental processes such as thinking, reasoning and

problem-solving, as well as psychological or behavioral inter-

actions. In cognitive ergonomics, these aspects are studied in

the context of work and other systems.

Due to the increasing complexity of healthcare, researchers

are focused on the application of AI primarily in improving

its ability to provide accurate diagnoses. AI systems have long

held the promise of reﬁning the prediction of diseases, such as

guiding imaging for pulmonary embolisms.

A healthcare AI system has two dimensions that impact

cognitive workload: Complexity of the algorithm and user in-

terface. Many researchers have tried to simplify its underlying

algorithm, yet no signiﬁcant steps have been taken to improve

the AI systems interface. Thus, its impact on human cognition

remains uncertain.

A well-designed graphical interface can help an AI user lo-

cate relevant information, then interpret and prioritize it. The

signiﬁcance of cognitive ergonomics and human factors in the

intersection of healthcare and AI has not yet been studied and

cognitive load has been assumed rather than measured. There

is therefore a need to use the methods of cognitive ergonom-

ics in healthcare artiﬁcial intelligence systems. We propose a

framework to simplify a GUI of healthcare AI systems and

understand its impact on clinicians’ cognitive load.

Meaningful outcome and cognitive load

Those who use advanced AI, such as clinicians, might face

difﬁculties in understanding and interpreting the outcomes of

the technology. This may be due to the users’ inability to form

a conceptual model of the information presented on a com-

puter screen or a device, and their lack of understanding of

the system’s working principle. Addressing confusion with AI

technology and its interface will not only aid in safer applica-

tion but can foster better satisfaction.

Nevertheless, a benchmark for examining the cognitive

load generated by a healthcare AI system is not deﬁned in the

FDA’s premarket clearance program. In an early-phase study

AI in healthcare:

Improving human

interface for

patient safety

Better standards needed to make

artiﬁcial intelligence user-friendly

for clinicians

By Avishek Choudhury

34 ISE Magazine | www.iise.org/ISEmagazine

AI in healthcare: Improving human interface for patient safety

of 326 hospitalized patients, the FDA approved a pre-

dictive algorithm, WAVE, which indicates vital signs’

abnormalities and has led to a reduction in the average

duration of patient instability. Though this was judicious

under current regulatory standards, the approved system

was not tested for the usability and complexity of its GUI.

Is the system’s interface simple or intuitive enough for an

inexperienced clinician to implement and understand in a

chaotic environment? It is uncertain whether WAVE has

reduced clinicians’ cognitive load.

The FDA should rigorously conﬁrm and test surrogate

endpoints of new technologies to prohibit the introduc-

tion of AI systems with questionable GUI into a chaotic

healthcare environment where human life is at risk. The

agency must ensure that implementing an AI system not

only improves diagnostic capabilities but also minimizes

a clinician’s time spent analyzing and interpreting com-

plex outcomes; such necessary measures could be valu-

able for premarketing authorization. Unfortunately, no

AI algorithms or systems that received regulatory clear-

ance have been tested for their impact on cognitive load.

With the increase of technology in healthcare, clini-

cians and patients are encountering AI devices that involve

complex and unfamiliar GUIs. The industry needs to con-

duct comprehensive and informative research to develop user-

friendly AI devices and systems by considering such design

needs as human memory limitation, perception and attention.

GUI design considerations

Here are aspects of GUI design that should be considered

when approving new AI technology for healthcare use:

Human memory limitation. This partly involves reten-

tion theory and cognitive load theory. The human memory

allows an average person to retain seven (plus or minus two)

sets of information at a time. Thus, to ensure better retention,

health information should be divided into smaller units that

do not exceed the limits of 7 ± 2 per AI system display. Dis-

playing fewer sets of information per screen can reduce clini-

cians’ need to memorize data, which in turn helps minimize

the cognitive load on them and their patients. In addition,

using suitable colors in designing GUI is shown to enhance

information retention by 50%.

Perception. This aspect employs the schema theory and

Gestalt law, illustrated in Figure 1. A schema signiﬁes the con-

ceptual representation of how an individual decodes informa-

tion and extracts contextual knowledge. For instance, when

we see an image showing sun, blue sky and birds ﬂying, we

interpret it as daytime and associate a feeling of happiness. In

addition, users’ prior experience and knowledge plays a cru-

cial role in enhancing their perceiving ability (transference and

mental imagery).

Transference denotes the users’ anticipation of the behavior

of an AI system based on past experience with other computer

interfaces, such as text placement and the location, appearance

and functionality of buttons and icons. Mental imagery refers

to the conceptual representation of how things look.

Gestalt law is explained in the table below; some law of Ge-

stalt complements schema theory as shown in Figure 1.

Attention. In the placement of texts and images, the left-

to-right theory can be applied in designing an effective AI

interface. This suggests allocating critical information on the

top left corner of the screen. This is due to the responsiveness

Laws of Gestalt Meaning

Balance/symmetry Asymmetrical images are perceived as incomplete

Continuation Eyes typically follow a direction as shown in GUI

Closure Shapes that are not fully closed are perceived as

incomplete

Figure-ground Different colors are perceived as different entities

Focal point Each AI system’s display should have a focal point

Isomorphic

correspondence

Different users perceive a same image differently

Prägnanz Simple GUI design are considered as good forms

Proximity Objects that are clustered are perceived as a group

Similarity Similar objects grouped together attract more

attention

Simplicity Users understand faster if the information is

simple

Unity/harmony

arrangement of the

objects

Objects are perceived as one group if there is a

similar arrangement of the objects

FIGURE 1

Schema and Gestalt

A look at the fundamental functioning of human perspective.

February 2020 | ISE Magazine 35

where people tend to interpret data from upper left to lower

right. Additionally, the placement of texts and images should

be applied considering the human visual ﬁeld, which is di-

vided into two different segments, as shown in Figure 2. Thus,

the placement of texts or images according to humans’ visual

ﬁeld minimizes the users’ cognitive load.

Clinicians may experience difﬁculties whenever there are

multiple parallel tasks overwhelming their senses. For exam-

ple, an alarm noise in an emergency ﬂoor or a patient talking

in an outpatient setting can be distracting if auditory infor-

mation is also running on the AI system. Such multitasking

should be avoided to reduce their cognitive load. Their sense

of sight might be impaired when background images or texts

are embedded under pieces of informative texts. Thus, AI-en-

abled applications should avoid such embedded content. And

the need to process information simultaneously by two motor

systems increases cognitive load, such as listening to a patient’s

distress while using a complex AI system.

Trust and meaningful use

As governing bodies decide which downstream features mat-

ter for AI systems, they should also keep in mind these sys-

tems will fail as a technology without acceptance and trust

from clinicians despite having good analytical performance.

Studies have shown that trust is built in a continuous manner,

demanding two-way interactions between the user and the

technology.

Initial trust is essential for ensuring the adoption of new

technology. Trust is inﬂuenced by a user’s ﬁrst impression and

is built based on that person’s personality and institutional cues.

Once trust is developed, it must be nourished to be sustained.

In our context, continuous trust depends on the functioning

of the AI system in reducing users’ cognitive load and yielding

clinically meaningful outcomes.

It has been believed that trust in technology is determined

by human characteristics (personality and ability), environ-

mental characteristics (culture, task and institutional factors)

and technology characteristics (performance, process and pur-

pose). However, the impact of meaningful use on trust has

been neglected. Meaningful use,

just as it is imposed on the applica-

tion of EHR technology, should be

imposed on AI systems to improve

the quality of care.

The meaningful use of AI sys-

tems means that speciﬁc AI should

be implemented and interpreted in a

speciﬁc manner. Depending on the

functioning of the AI algorithm, not

all systems can be generalized across

a healthcare system.

For instance, the WAVE platform

algorithm is based on ﬁve vital signs: heart rate, respiration,

oxygen saturation, temperature and blood pressure. Since such

measures are common across health systems, the system could

be employed by multiple diverse health systems. However,

other AI-based platforms, especially those based on institu-

tion-speciﬁc EHR or image datasets, may not translate across

other EHRs. Moreover, Al trained on speciﬁc datasets, such

as patients from a speciﬁc institution, may not be generally ap-

plied across broader populations.

Increasing AI interoperability may necessitate developers to

deliver more speciﬁed data to conﬁrm that predictive algo-

rithms will achieve reliable, replicable and valid results. In-

deed, regulators should focus on balancing the clarity of pre-

dictive models without impeding the proprietary interests and

intellectual property of algorithm developers.

Healthcare AI systems are just getting evaluated and being

made available for clinical use, so the inﬂuence of the exist-

ing regulatory framework on patient outcomes is yet to be

determined. It is also uncertain what impact the 21st Cen-

tury Cures Act passed to relax regulatory standards for low-

risk health technology will have on the value and quality of

predictive algorithms. The FDA’s Digital Health Innovation

Action Plan, issued in 2017, launched a precertiﬁcation pro-

gram to analyze clinical outcomes of AI-based algorithms.

Such efforts should be acclaimed but improved based on our

recommended norms.

Some developers may disparage the overregulation and

standardization of a vaguely understood ﬁeld. Indeed, a pledge

to regulate healthcare AI systems will emerge over time and

impose ﬁnancial costs to stakeholders. Policymakers should

also be sensitive to the stability between regulation and in-

novation in this evolving ﬁeld. 

Avishek Choudhury is a Ph.D. student and research assistant in the

School of Systems and Enterprises at Stevens Institute of Technology

in Hoboken, New Jersey. He received his master’s degree in industrial

and systems engineering from Texas Tech University. His research in-

terests include healthcare systems, artiﬁcial intelligence, clinical decision-

making, patient safety and network science. He is an IISE member.

FIGURE 2

Human visual ﬁeld

Where the eyes and the brain connect to process information.

36 ISE Magazine | www.iise.org/ISEmagazine

AI in healthcare: Improving human interface for patient safety

5 examples of how technology is changing healthcare

Artiﬁcial intelligence, machine learning, wearable sensors and

virtual reality have taken root in various aspects of industrial and

systems engineering, nowhere more inﬂuential than in healthcare.

Here is a sample of the new technological processes in the works:

Seeing through a robot’s eyes helps those

with motor impairments live independently

An interface system using augmented reality technology could help

individuals with profound motor impairments operate a robot to

feed themselves and perform routine personal care tasks such as

scratching an itch and applying skin lotion. The interface displays

a “robot’s eye view” of surroundings to help users interact with the

world through the machine.

The system, described in the journal PLOS ONE, could help

make robots useful to people by using standard assistive computer

access technologies such as eye trackers and head trackers.

“Our results suggest that people with profound motor deﬁcits

can improve their quality of life using robotic body surrogates,”

said Phillip Grice, a recent Georgia Tech Ph.D. graduate and ﬁrst

author of the paper.

Researchers used a PR2 mobile manipulator manufactured by

Willow Garage, a wheeled robot with two arms and a “head” and

the ability to manipulate objects such as water bottles, washcloths,

hairbrushes and even an electric shaver.

The study made the PR2 available across the internet to 15

participants with severe motor impairments. They learned to

control the robot remotely using their own assistive equipment to

operate a mouse cursor to perform a personal care task. Eighty

percent of the participants were able to manipulate the robot to pick

up a water bottle and bring it to the mouth of a mannequin.

In a second study, researchers provided the PR2 and interface

system to Henry Evans, a California man with limited control of

his body who tested the robot in his home for seven days. He not

only completed tasks, but also devised novel uses combining the

operation of both robot arms at the same time – one to control a

washcloth and the other to use a brush.

“The system was very liberating to me, in that it enabled me to

independently manipulate my environment for the ﬁrst time since

my stroke,” Evans said.

Source: Georgia Tech

AI can detect 26 skin conditions as accurately

as dermatologists, better than primary care doctors

Skin conditions are among the most common kind of ailment,

resulting in an estimated 25% of all patient treatments worldwide

and up to 37% of clinical patients having at least one skin

complaint.

With dermatologists facing enormous caseloads and general

practitioners tending to be less accurate in identifying skin

conditions, researchers at Google investigated an AI system

capable of spotting the most common dermatological disorders

seen in primary care. Their paper, “A Deep Learning System for

Differential Diagnosis of Skin Diseases,” reports accuracy across

26 skin conditions when presented with images and metadata

about a patient’s case, which they say is on par with U.S. board-

certiﬁed dermatologists.

Google software engineer Yuan Liu and Google Health technical

program manager Dr. Peggy Bui explained that dermatologists

don’t give just one diagnosis for any skin condition but instead

generate a ranked list of possible diagnoses narrowed by lab tests,

imaging, procedures and consultations. The system does the same

by processing inputs that include one or more clinical images of

the skin abnormality and up to 45 types of metadata.

To test the system’s accuracy, researchers compiled diagnoses

from three U.S. board-certiﬁed dermatologists and the AI system’s

ranked list of skin conditions achieved 71% and 93% top-1 and

top-3 accuracies, respectively. When the system was compared

with clinicians, the team reported its top three predictions

demonstrated a top-3 diagnostic accuracy of 90%, or comparable

to dermatologists (75%) and “substantially higher” than primary

care physicians (60%) and nurse practitioners (55%).

Source: Google

Photo by Henry Clever/Phillip Grice, Georgia Tech

Henry Evans, a California man who helped Georgia Tech

researchers with improvements to a web-based interface,

uses a robot to shave himself. The team worked to develop

robotic body surrogates to help people with profound motor

deﬁcits interact with the world.

February 2020 | ISE Magazine 37

Photo by Sarah Freeman, University of Georgia

AI is learning to read mammograms

Researchers in the United States and Britain have found that

artiﬁcial intelligence can help doctors do a better job of ﬁnding

breast cancer on mammograms.

According to an article in the journal Nature, the new system

for reading mammograms is not yet available for widespread

use. Google paid for the study and worked with researchers from

Northwestern University in Chicago and two British medical

centers, Cancer Research Imperial Centre and Royal Surrey County

Hospital.

The system performed better than radiologists in diagnosing

known cases from images. Reviewing scans of 3,000 women in

the U.S., the system produced a 9.4 percent reduction in false

negatives, when cancer is missed, and a 5.7 percent reduction

in false positives, where there is no cancer. A study of 25,000

mammograms from Britain showed that AI reduced false negatives

by 2.7 percent and false positives by 1.2 percent.

About 33 million screening mammograms are performed each

year in the United States. The test misses about 20 percent of

breast cancers, according to the American Cancer Society, and

false positives are common.

“There are many radiologists who are reading mammograms

who make mistakes, some well outside the acceptable margins

of normal human error,” said Dr. Constance Lehman, director of

breast imaging at the Massachusetts General Hospital in Boston.

To train computers to read the mammograms, the authors used

scans from about 76,000 women in Britain and 15,000 in the

United States whose diagnoses were already known.

Sources: New York Times, www.nature.com

Virtual reality could boost ﬂu shot rates

Researchers at the University of Georgia and the Oak Ridge

Associated Universities in Oak Ridge, Tennessee, have conducted

a study using virtual reality simulation to show how ﬂu spreads

and its impact on others as a way to encourage more people to get

vaccinated. It is mostly aimed as a communication tool at the “ﬂu

vaccine avoidant” 18- to 49-year-old adults.

The research, “Using Immersive Virtual Reality to Improve

the Beliefs and Intentions of Inﬂuenza Vaccine Avoidant 18- to

49-year-olds,” was published by the journal Vaccine.

The Centers for Disease Control and Prevention reports only

26.9% of 18- to 49-year-olds in the U.S. received a recommended

annual inﬂuenza vaccination during the 2017-18 ﬂu season. The

low current acceptance of ﬂu vaccination makes it important to

identify more persuasive ways to educate them.

The 171 participants in the study were divided into four groups,

each exposed to a different form of persuasion: A virtual reality

experience, a video based on the VR platform, an e-pamphlet with

text and images from the video, and a control group provided only

with the CDC recommendations.

The virtual reality group wore headsets that enabled them

to experience events and controllers enabling them to actively

participate in the story. It resulted in participants showing

greater conﬁdence that a vaccination would protect others, more

positive beliefs about ﬂu vaccine and increased intention to get a

vaccination.

Source: University of Georgia

Wireless sensors on patients’ skin tracks health

Stanford engineers have developed wireless sensors that can

detect physiological signals emanating from the skin and beam

wireless readings to a receiver clipped onto clothing.

The sensors can be applied like Band-Aids on various areas. To

demonstrate the technology, researchers stuck sensors on the wrist

and abdomen of one test subject to monitor pulse and respiration

by detecting how the skin stretched and contracted. Sensors on

the elbows and knees tracked arm and leg motions by gauging

tightening or relaxation of the skin when muscles ﬂexed.

Zhenan Bao, the chemical engineering professor whose lab

described the system in an Aug. 15 article in Nature Electronics,

thinks this wearable technology, called BodyNet, will ﬁrst be

used in medical settings such as monitoring patients with sleep

disorders or heart conditions. The goal is to create an array of

wireless sensors on the skin that work with smart clothing to

accurately track a wide variety of health indicators.

“We think one day it will be possible to create a full-body skin-

sensor array to collect physiological data without interfering with a

person’s normal behavior,” said Bao.

Sources: Stanford University, Nature Electronics

University of Georgia faculty members tested methods of

delivering effective ﬂu vaccination messages through print,

video and virtual reality.