Simulations of a Computational Model for a Virtual Medical Assistant

Aryana Collins Jackson

1 a

, Marl

`

ene Gilles

1 b

, Eimear Wall

2

, Elisabetta Bevacqua

1

ENIB, Lab-STICC UMR 6285 CNRS, Brest, France

2

out the direct help of medical professionals. Our medical assistant uses situational leadership to handle all

interaction with a caregiver, which works by identifying the readiness level of the caregiver in order to match

them with an appropriate style of communication. The agent system (1) obtains caregiver behavior during the

procedure, (2) calculates a readiness level of the caregiver using that behavior, and (3) generates appropriate

agent behavior to progress the procedure and maintain a positive interaction with the caregiver.

1 INTRODUCTION

Due to advancements in virtual agents and

telemedicine, caregiving in various capacities

can now be done at a distance. For example, virtual

agents can perform health assessments (Montenegro

individual present must assume a caregiving role, and

they will need guidance on preserving the patient’s

health. During these situations, a permanent virtual

medical assistant could be useful (Nakhal, 2017).

The assistant will communicate with the caregiver

and will also communicate to a team of medical ex-

perts standing by on a different site monitoring the

procedure. Because of the possible latency and dis-

ruptions in communication between the remote site

and the site where the experts are, the virtual assistant

https://orcid.org/0000-0002-4385-5258

b

https://orcid.org/0000-0003-1806-1672

c

https://orcid.org/0000-0002-7117-3748

while maintaining the health of the patient (Hjort-

dahl et al., 2009; Henrickson et al., 2013; Yule et al.,

2008; Flin et al., 2010). Both the caregiver and the

agent must work towards a common goal: the com-

pleted procedure that preserves the health of the pa-

tient. To foster this positive working alliance be-

readiness levels involve varying degrees of ability,

which refers to a person’s competence when complet-

ing tasks, and willingness, which refers to their con-

fidence and interest in completing those tasks. In this

scenario, the virtual agent assumes the role of leader,

and the caregiver assumes the role of follower. Situa-

tional leadership is covered in more depth in section 2.

Previously when situational leadership has been

studied, it has been implemented (Sims et al., 2009;

Figure 1: A basic representation of the agent framework.

Hersey et al., 1988; Lourdeaux et al., 2019) or simu-

lated for intended use in human-human relationships

(Bosse et al., 2017; Bernard, 2020). Virtual leaders

have used different methods of guiding humans and of

choosing behavior that is personalized to the individ-

ual humans. However, our work is the first to present

both a computational model of situational leadership

and an agent framework in which a virtual agent leads

humans (see Figure 1 for a basic representation of the

agent framework and Figure 6 for a detailed view).

havior.

While our work is conducted with the context of a

medical procedure in mind, the model itself is flexible

enough to be applicable to any situation in which a

human being must be led through a series of steps by

a virtual assistant.

In this paper, we detail what situational leadership

is and how it works, we discuss the current state of

the art regarding virtual leaders, we detail our com-

putational model of situational leadership, we explain

noted with an R and a number) are listed below

(Hersey et al., 1988):

Readiness levels R1 and R2 need the leader’s help

while readiness levels R3 and R4 should be able to

complete tasks without the leader’s help.

When a leader employs situational leadership,

confidence (Bedford and Gehlert, 2013). The lead-

ership styles that correspond to these readiness levels

are (Hersey et al., 1988):

Note that low relationship behavior is required for

followers with low ability and low willingness be-

cause the person may not be interested in being en-

couraged. For followers with high ability and low to

some willingness, the agent motivates the person be-

after receiving tasks, etc. (Collins Jackson et al.,

2019; Bosse et al., 2017; Hersey et al., 1988):

Situational leadership is an effective method of

managing followers because it accounts for follow-

ers with both high and low ability and willingness.

This model has been proven effective in an educa-

tional environment in which medical instructors im-

plemented leadership styles in their classes and with

individual students (Sims et al., 2009), work environ-

ments in which managers adopted leadership styles

els using situational leadership, and existing work in-

volves human-human interaction only (Bosse et al.,

2017; Bernard, 2020). Rarely are agents in positions

of leadership in human-agent interaction. Our work

models the work on situational leadership and also

provides a new method in which agents and humans

can interact by allowing an agent to lead a human.

Our model permits an agent to maintain a positive in-

teraction with a follower and interact with the human

in a way that is most beneficial for them in addition to

cal procedure. Within the medical domain, there has

been a lot of work regarding human-human relation-

ships in the emergency room. The person leading a

medical procedure holds an important role in that they

manage both the procedure tasks, the health of the pa-

tient, and the interaction with the caregiver(s). Thus

having the trust of the caregivers and being compe-

tent at their work are two of the most important qual-

ities a leader can have (Hjortdahl et al., 2009). Addi-

tional qualities that a medical leader should embody

include communication, negotiation, autonomy, cre-

variety of examples of virtual agents created for use

specifically in medical situations: agents which act

as liaisons between patients and physicians using pre-

scripted speech (Bickmore et al., 2015), agents which

are meant to connect emotionally with patients for

mental health benefits (Yang and Fu, 2016), agents

which act as companions for the elderly (Montenegro

et al., 2019), and agents which conduct psychiatric in-

terviews (Philip et al., 2020). In each of these exam-

ples, user engagement with the agent was dependent

veloped (Bosse et al., 2017; Bernard, 2020). While

these models have involved human-human interaction

only, they have provided a foundation for computing

readiness level and therefore determining leadership

style from various behavioral parameters. One model

is based entirely on follower behavior and allows the

leader to adapt their leadership style by monitoring

a follower’s advancement through the different readi-

ness levels (Bosse et al., 2017). This work in particu-

lar formed the backbone of our own research.

mance value possible of that criterion before the fol-

lower could be considered to have low ability or low

willingness.

In previous research, the extent to which a follower

exhibits thirty-three different behaviors determines

readiness level (Bosse et al., 2017). That model is

based on an interaction between a student and a su-

pervisor, and thus many of these behaviors were either

irrelevant to a medical scenario (such as feeling over-

work is based on this research, in order to adapt it to

do an action out of order;

sen to do an action that does not exist in the cur-

rent task;

the wrong resource for the action;

to take a resource when one is required or tries to

(as devised by our medical professional);

tion because they do not know how to proceed;

than five seconds before beginning an action;

clarifying question; the follower has asked a ques-

tion to ensure what they are doing is correct.

The values for the parameters in Table 1 were devised

by consulting with the medical doctor on our team.

The procedure that these values were created for is the

diagnosis of abdominal pain, and with these values,

we assume that the case is not urgent.

Because our context is rather specific, it was im-

most important ability criteria are Error: action out-

side task and No resource chosen because they indi-

cate that the follower does not understand the current

task and how to reach the end goal. Each are given

the highest weight of 0.25. If the follower is consid-

ered to be high-ability, these two errors change their

performance values considerably with low persistence

values of 0.1 and 0.2 respectively.

Question for help is the next most important cri-

terion in the ability domain because it indicates that

action outside task and No resource chosen.

Unlike the previous three criteria, there is room for

human error in Wrong resource chosen where a fol-

lower may accidentally reach for the wrong resource,

hence the higher persistence value of 0.7.

Error: action in task, Action duration too short,

and Action duration too long have the lowest weights.

The most important thing is that the task has been

completed, and so errors out of order do not matter

so much as long as the task is being completed. Per-

fore they should be taken quite seriously. Because of

their low weight, short or long action duration times

on their own do not generally affect the overall ability

performance unless they happen repeatedly through-

out the procedure.

Finally, Hesitation and Question for reassurance

are weighted equally in the willingness domain. Each

of these are equally indicative of a follower’s willing-

ness. Additionally, the persistence for both is set quite

high at 0.9 because a follower’s willingness should be

calculated from their history rather than from a single

task.

As shown in Table 1, there are two different val-

ues for persistence, one for followers are deemed to

sequences. When a follower displays high-ability be-

havior, the mistakes they make should have more im-

pact on the performance value. The Persistence: high

values are used when a follower begins the proce-

dure with high ability or willingness. When the abil-

ity performance drops below the first ability threshold

of 0.8525, then the Persistence: low values are used

instead. Persistence: low values are also used for fol-

lowers who begin the procedure with low ability or

willingness.

The first step to determining readiness level is to

persistence and e represents the extent (Bosse et al.,

2017).

v

= (p

Equation 1 requires the performance value at time

performance values are used in place of v

. Val-

ues of 0 or 1 are established for each criterion which

correspond to the follower’s previous experience and

knowledge, called the follower profile. The follower

profile establishes which readiness level the individ-

ual has prior to beginning the procedure and is based

on previous procedures, external evaluations of the

follower, and self-evaluations. For a follower profile

high ability and variable willingness (see Figure 2).

The default performance values for all the criteria in

the ability domain are 1, and the default performance

values for the criteria in the willingness domain are 0.

Calculating the performance value v for Error: action

in task for a follower of style 3 who has made an error

at the very beginning of the procedure would look like

this: (0.1 ∗ 1)+ ((1 − 0.1) ∗ (1 − 1)) = 0.1.

The performance value v is then fed back into the

model in equation 1 the next time readiness level is

mining whether a follower has low or high ability and

willingness. For the first, the performance thresholds

shown in Table 1 are used to determine whether a fol-

lower has high or low ability and willingness. When

using the performance thresholds in equation 1 for

both e

and v

value in equation 2, we achieve the overall ability per-

formance threshold of 0.8525 and the overall willing-

ness performance threshold of 0.625. If a follower has

The second threshold is defined by ability and

willingness equal to or greater than 0.95. This value

was devised by examining example follower behavior

and determining which were able to self-lead without

the leader’s help. Followers with both an ability and

a willingness value equal to or greater than 0.95 are

considered to be in R4. See Figure 2 for a visualiza-

tion of the thresholds and readiness levels.

the performance values needed in order to move between

them.

As shown, each readiness level is color-coded

uniquely. A follower’s ability is contained on the ver-

tical axis while willingness is described on the hori-

zontal axis. Once the initial thresholds of 0.825 and

0.625 are reached for ability and willingness respec-

tively, a follower is considered to be R3. Because

R4 is designated for completely self-sufficient follow-

ers, the thresholds for R4 are higher. Ensuring that

and 2. A follower with an existing readiness level of

R1 and a follower with an existing readiness level of

R4 perform the exact same steps; they each try to do

an action that is outside the scope of the task for ac-

tions 5, 7, and 9. As shown, the performance value

v dips for both the R1 and R4 follower, but the er-

rors have a greater effect on the R4 follower who is

supposed to be an expert.

Note that the extent values for each criterion re-

main the same until there is another opportunity to

follower has the opportunity to make or not make an-

other error (i.e., when it is time for the caregiver to

begin a new action). The performance values v are

only calculated when e changes. The overall ability

and willingness values are updated when the individ-

ual v values change.

Finally, there are two possible issues that should be

avoided when using this model: (1) the skipping

of readiness levels (followers that move directly be-

the high-willingness threshold but whose ability

is high will need their ability value lowered to en-

sure they move from R2 to R1;

still considered to be R1. The procedure continues,

and at action 18, the follower’s willingness passes the

threshold for high willingness. Since their willingness

performance has increased above the threshold, the

follower can now be considered to be in R2. Readi-

ness level R2 is defined by low to some ability and

high willingness, and so the follower’s ability value is

artificially decreased to 0.25, a value we chose in or-

der to allow the system to “remember” the follower’s

performance history somewhat. In Figure 4, we can

values as needed, the system acts similarly to dynamic

range compression in which signals are limited once

they reach a certain threshold (Kates, 2005). Unlike

compression, we lower the values in order to allow the

follower to progress themselves through to the next

readiness level. This is how we ensure that not only

do followers progress from one readiness level to the

next without skipping levels, but we also ensure that a

follower does not move between readiness levels too

quickly.

included in this section are not exhaustive; many more

parameters in our model returned accurate readiness

levels.

The scenarios use a sample of a procedure which

comprises a total of 22 actions. The first 9 actions

have the least difficulty, actions 10-12 have the most

difficulty, actions 13-14 have the least difficulty, and

actions 15-22 have medium difficulty.

Figures 5a-5p display the 16 scenarios and are

color-coded according to Figure 2: green represents

R1, pink represents R2, blue represents R3, and yel-

are paired with appropriate non-verbal behavior in the

behavior planner (Collins Jackson et al., 2020).

plans of actions for agents or instructions provided

to users for assisting them. With Mascaret, a whole

medical procedure with roles, resources, and trajecto-

ries can be formalized and virtually executed to assist

a caregiver to go through this same procedure in the

real world.

(a) R1: Ability rises, will-

ingness remains low.

(b) R1: Willingness rises

above the threshold.

(c) R1: Ability and willing-

ness rise above the thresh-

olds.

(d) R1: Ability and willing-

ness remain low.

tificially raised).

old.

(m) R4: Ability drops be-

low the threshold, willing-

ness remains high (but is ar-

tificially lowered).

(n) R4: Ability remains

high, willingness drops be-

low the threshold.

(o) R4: Ability and willing-

ness drop below the thresh-

olds.

(p) R4: Ability and willing-

ness remain high.

remains the same, four starting in each readiness level. The solid brown line indicates the overall ability performance, and the

dotted green line indicates the overall willingness performance. Green shading refers to R1, pink shading refers to R2, blue

shading refers to R3, and yellow shading refers to R4.

To allow Mascaret to interact with the user in

a natural way, we introduce an embodied conversa-

planner which generates the communicative inten-

tions of the agent, a behavior planner which trans-