# Talk2Care: Facilitating Asynchronous Patient-Provider Communication with
Large-Language-Model

Ziqi Yang<EMAIL_ADDRESS>University of MichiganUSA43017-6221 ,  Xuhai Xu
<EMAIL_ADDRESS>Massachusetts Institute of TechnologyUSA ,  Bingsheng Yao
<EMAIL_ADDRESS>Rensselaer Polytechnic InstituteUSA ,  Shao Zhang
<EMAIL_ADDRESS>Northeastern UniversityUSA ,  Ethan Rogers
<EMAIL_ADDRESS>Northeastern UniversityUSA ,  Stephen Intille
<EMAIL_ADDRESS>Northeastern UniversityUSA ,  Nawar Shara
<EMAIL_ADDRESS>MedStar Health Research InstituteUSA ,  Guodong
(Gordon) Gao<EMAIL_ADDRESS>John Hopkins UniversityUSA and  Dakuo Wang
<EMAIL_ADDRESS>Northeastern UniversityUSA

(2018)

###### Abstract.

Despite the plethora of telehealth applications to assist home-based older
adults and healthcare providers, basic messaging and phone calls are still the
most common communication methods, which suffer from limited availability,
information loss, and process inefficiencies. One promising solution to
facilitate patient-provider communication is to leverage large language models
(LLMs) with their powerful natural conversation and summarization capability.
However, there is a limited understanding of LLMs’ role during the
communication. We first conducted two interview studies with both older adults
(N=10) and healthcare providers (N=9) to understand their needs and
opportunities for LLMs in patient-provider asynchronous communication. Based
on the insights, we built an LLM-powered communication system, Talk2Care, and
designed interactive components for both groups: (1) For older adults, we
leveraged the convenience and accessibility of voice assistants (VAs) and
built an LLM-powered VA interface for effective information collection. (2)
For health providers, we built an LLM-based dashboard to summarize and present
important health information based on older adults’ conversations with the VA.
We further conducted two user studies with older adults and providers to
evaluate the usability of the system. The results showed that Talk2Care could
facilitate the communication process, enrich the health information collected
from older adults, and considerably save providers’ efforts and time. We
envision our work as an initial exploration of LLMs’ capability in the
intersection of healthcare and interpersonal communication.

Older adults, Patient-provider communication, Large-language-model

††copyright: acmcopyright††journalyear: 2018††doi: XXXXXXX.XXXXXXX††ccs:
Human-centered computing Human computer interaction (HCI)††ccs: Applied
computing Health informatics Figure 1. Overview of Talk2Care System. The
system consists of two modules. 1) The patient module: An LLM-powered VA
interface (in purple) that generates natural conversation with home-based
older adults to collect health information and forward it to healthcare
providers; 2) The provider module: A dashboard interface (in green) that
summarizes the key information from the older patient conversation to assist
providers who are responsible for communication (e.g., nurses and patient
navigators). Note that Talk2Care does not provide specific healthcare advice.
Our current implementation does not involve an actual electronic health record
(EHR) system, which can be a promising future direction.

##  1\. Introduction

Among the healthcare needs of the population, the requirements of older adults
stand out as particularly unique and crucial. A significant proportion of
older adults live with chronic diseases or conditions, which require long-term
monitoring and regular interaction with healthcare providers (NCO, 2023).
According to a recent report from the National Council on Aging (NCOA), nearly
95% of older adults in the US have at least one chronic health condition, and
around 80% have two or more conditions (NCO, 2023). The numbers translate to
more than 50 million seniors requiring healthcare support. On the one hand,
older people often experience a gradual decline in cognitive and physical
abilities, which can make regular outpatient visits increasingly challenging.
On the other hand, healthcare providers are already overloaded and have
limited bandwidth (Wang et al., 2021; Vugs et al., 2023). Asking them to visit
patients’ homes to check their health is not a scalable solution due to the
significant workload (Theile et al., 2011). As such, accessible home-based
healthcare, which bridges the gap between older adults and their healthcare
providers, emerges as one of the promising solutions. It can offer a familiar
and convenient environment to older adults while saving healthcare providers’
efforts.

Researchers have explored a wide range of home-based telehealth applications
to support asynchronous communication between older adults and healthcare
providers who are mainly responsible for communication (e.g., nurses, patient
navigators, medical office staff), remote health monitoring (Malasinghe et
al., 2019; Halloran et al., 2019; Bates et al., 2011; Johnson and Miller,
2022; Crossley et al., 2011), healthcare plan management (Arguello et al.,
2023), and patient-provider collaborative review (Chung et al., 2016; Chung,
2017; Schroeder et al., 2017). However, these platforms only support specific
data formats and contexts that are tailored to common diseases. When it comes
to various daily health communication scenarios (e.g., unexpected health
concerns), older adults often have to refer to asynchronous communication
methods such as a basic text or voice messaging system, as healthcare
providers are often not available in real-time for a call (e.g., with a busy
schedule or outside working hours). These asynchronous messages are sometimes
lost due to hasty schedules (Laukka et al., 2020). Meanwhile, older adults
generally have lower technology proficiency compared to younger generations,
which is another common obstacle to accessing and using these tools (Haase et
al., 2021; Smith, 2014; Kruse et al., 2020; Foster and Sethares, 2014). For
healthcare providers with certain patient follow-up protocols (e.g., post-
surgery follow-up), making manual phone calls is one of the most common
practices, yet it costs a significant amount of workload, and sometimes older
adults may not be reachable.

This calls for the need for a better asynchronous communication system between
older adults at home and healthcare providers. The recent technological boost
of large language models (LLMs) brings us new opportunities to better achieve
the goal. The HCI community has explored the application of LLMs for various
healthcare practices, such as mental health support (Kumar et al., 2023;
Lamichhane, 2023), health information seeking (Yunxiang et al., 2023; Wei et
al., 2023; Montagna et al., 2023), and public health interventions (Jo et al.,
2023). However, there is little previous work leveraging LLMs for patient-
provider communication. In our case, we can leverage its outstanding power of
natural language conversation generation and information summarization. For
older adults, an LLM-based voice assistant (VA) may provide convenience and
easy accessibility. Through natural conversation, the LLM-powered VA may help
older adults better convey their information asynchronously to providers when
needed, such as in cases with unexpected health concerns. Meanwhile, providers
can also use LLMs to automate the information-gathering and summarization
process with high-quality data collected from older adults.

However, the role of LLMs in the asynchronous communication between older
adults and healthcare providers is still unclear. To address this gap, we
first conducted two semi-structured interviews to understand communication
challenges and older adults’ and providers’ perceptions towards LLMs-based
communication systems. Our studies involved both older adults with various
health conditions (N=10), as well as health providers (N=9). Our results
revealed a set of asynchronous communication challenges between older adults
and healthcare providers. Both groups thought that having an LLM-powered
asynchronous communication system would be beneficial.

Leveraging the insights from our interview study, we designed and implemented
a pilot LLM-based system, Talk2Care, to support diverse home-based healthcare
communication scenarios. The system consists of two modules. (1) A patient-
facing module: A VA interface that provides a natural conversation experience
with older patients to collect health information. (2) A provider-facing
module: A dashboard interface for health providers that summarizes and
highlights key information from patient-VA conversations. It is worth noting
that our system does not aim to provide any specific health advice due to the
early-stage exploratory nature of this work and ethical concerns. As discussed
later, the ethical concerns must eventually be addressed, but in this work, we
explore the value of LLM-driven communication as a first step.

We evaluated the pilot Talk2Care in two cases close to real-world scenarios,
one primarily designed for older adults (unexpected health concerns) and one
for health providers (post-surgery follow-up). We conducted two user studies
with both sides to evaluate the usability of Talk2Care. Our results showed
that our system can facilitate the asynchronous communication process, enrich
the health information collected from older adults, and save providers’
efforts and time considerably.

Our primary contributions are:

  * •

We conducted two interview studies with both older adults and healthcare
providers to understand the communication challenges and opportunities for
LLMs to assist patient-provider communication.

  * •

We designed and developed a pilot LLM-powered system, Talk2Care – with a VA
interface for older patients and a dashboard interface for providers – to
facilitate home-based patient-provider communication for older adults.

  * •

We evaluated our systems with two common home-based healthcare scenarios via
two user studies with older adults and providers to demonstrate the usability
and effectiveness of Talk2Care.

  * •

We revealed potentials and design implications for future LLM-powered systems
to facilitate and mediate remote patient-provider communication in a human-AI
collaborative workflow.

##  2\. Related Work

We first provide an overview of telehealth and patient-provider communication
solutions (Section 2.1). We then briefly overview intelligent voice assistants
for older adults’ healthcare (Section 2.2) and large language models for
healthcare (Section 2.3).

###  2.1. Telehealth and Patient-Provider Communication Solutions

Telehealth, or telemedicine, refers to the use of digital communication
technologies to provide healthcare services remotely (Tuckson et al., 2017).
In the past decade, research has shown the advantages of telehealth in saving
patients’ and providers’ time and effort compared to in-person visits (Dorsey
and Topol, 2016; Brophy, 2017; Crossley et al., 2011; Chen et al., 2019),
especially during the pandemic isolation (Blandford et al., 2020). Researchers
have explored novel and more accessible tools to enrich the amount of patient
health information in presented artifacts for patient-provider collaborative
review and remote patient management, such as mobile health applications (Ding
et al., 2019; Xu et al., 2019; Wang et al., 2020; Lu et al., 2021; Xu et al.,
2022b), sensor systems and instruction platforms (Halloran et al., 2019;
Boulanger et al., 2013; Pandit et al., 2019; Grönvall and Verdezoto, 2013;
Kayser et al., 2023; Xu et al., 2022a, 2023a), personal coaches (Arguello et
al., 2023), online platforms for healthcare consultation (Vinker et al., 2007;
Umefjord et al., 2008). However, issues such as poor system integration,
technical barriers, and limited information have resulted in unsatisfactory
usability and inconsistency for both patients and providers (Dorsey and Topol,
2016; Shaver, 2022; Donelan et al., 2019; Vugs et al., 2023; Kruse et al.,
2020; Park et al., 2012). Especially for older adults who have relatively low
health and technical literacy, most of the existing solutions still cannot
provide adequate accessibility and usability (Kruse et al., 2020; Rao and
Joshi, 2020; Smith, 2014; Foster and Sethares, 2014). Therefore, a great
number of older patients, still refer to traditional communication methods
such as phone calls (Dorsey and Topol, 2016; Rao and Joshi, 2020). Similarly,
healthcare providers also often find these telehealth tools hard to use
(Jawhari et al., 2016; Kasim et al., 2023; Kawakami et al., 2022). Making
phone calls and leaving messages are still popular communication methods for
providers in cases such as hospital discharge follow-up (Mark et al., 2022;
Kasim et al., 2023). In this paper, we explore a novel, easy-to-use, and
natural communication solution for both home-based older adults and healthcare
providers.

###  2.2. Intelligent Voice Assistants for Older Adults Healthcare

Voice assistants (VAs), due to their simplicity, convenience, and good
accessibility, have been designed and used as a home-based healthcare
technology for patients and older adults (Brewer et al., 2022; Harrington et
al., 2022). In recent years, the HCI community has conducted extensive studies
on VAs to support older adults’ needs at home, including health information
seeking (Pradhan et al., 2020; Brewer et al., 2022; Bérubé et al., 2021),
learning (Desai and Chin, 2023), assisting home health aides (Bartle et al.,
2022, 2023). Researchers either leveraged commercially available VAs (e.g.,
Google Home and Amazon Alexa) (Pradhan et al., 2020; Brewer et al., 2022;
Harrington et al., 2022), or built their own novel VAs with customized
features (e.g., ADELE (Spillane et al., 2018), Anne (Stara et al., 2020)).
Even with simple pre-scripted or rule-based conversational capabilities, these
studies have shown the advantage of leveraging VAs for older adults’ home-
based healthcare, motivating us to explore further opportunities for more
intelligent VAs.

However, few recent studies have explored the combination of VAs and more
intelligent techniques. For example, Natural Language Processing (NLP) methods
have been used to build VAs in travel apps and browsers to simplify user
interaction (Poran et al., 2022; Cambre et al., 2021). Recently, Zhang et al.
proposed SpeechGPT, an LLM with intrinsic cross-modal conversational abilities
(Zhang et al., 2023). Rubenstein et al. presented AudioPaLM, a unified speech-
text LLM (Rubenstein et al., 2023). However, little is known about whether
these could be applied to real-world contexts, especially for home-based older
adults. Our work seeks to leverage this voice-based interaction paradigm to
build an interactive and easy-to-use LLM-powered VA, focusing on personalized
information gathering and asynchronous communication with healthcare
providers.

###  2.3. Large Language Models for Healthcare

Recently, the technology boost of Large Language Models (LLM), such as ChatGPT
(OpenAI, 2022), has shown its great potential in engaging in, scaffolding, and
processing natural conversations (Xiao et al., 2020, 2023b; Shen et al., 2023;
Hämäläinen et al., 2023; Wang et al., 2023; Liu et al., 2023). The latest
studies have discussed and explored its application for individual patients’
in health information seeking (Yunxiang et al., 2023; Xiao et al., 2023a),
mental health support (Lamichhane, 2023; Kumar et al., 2023; Xu et al.,
2023b), personal health coaching and management (Wei et al., 2023; Montagna et
al., 2023) and health education (Montagna et al., 2023; Kung et al., 2023).
For healthcare professionals, researchers have also leveraged LLMs to support
public health interventions (Jo et al., 2023), clinical pre-screening (Hamer
et al., 2023; Wang et al., 2021), risk prediction (O’Caoimh et al., 2015; Gatt
et al., 2022; Kennedy et al., 2014; Beede et al., 2020) and information
processing (Kocaballi et al., 2020; Nair et al., 2023; Cascella et al., 2023;
Nori et al., 2023; Agrawal et al., 2022; Arbatti et al., 2023). However,
multiple studies have raised concerns about using LLMs for healthcare
scenarios, due to their inconsistency, potential errors, and bias in clinical
work (Kocaballi et al., 2020; Iliashenko et al., 2019; Bérubé et al., 2021).
Due to these concerns, in our work, we focus on facilitating communication,
but not on providing any specific healthcare advice.

Despite the great potential of LLM for promoting healthcare work and
experiences, little work aims to address the challenges of patient-provider
communication mentioned in Section 2.1, and little is know about how LLM might
help patient-provider communication and benefit both stakeholders. Close to
our research, Jo et al. conducted interviews with stakeholders in an LLM-
powered system that supports public mental health interventions for people
living alone (Jo et al., 2023). The study revealed that the system could
reduce the workload for mental health practitioners, while providing mental
support for end users in large-scale healthcare services. However, the
existing solution still failed to meet the personalized requirements of users
at home because it followed a uniform setup(“one-size-fits-all”), which lacked
the necessary flexibility for customization (Jo et al., 2023). With our pilot
system, we aim to facilitate patient-provider communication by providing a
more personalized interaction experience for older adults, together with
efficient information analysis assistance for healthcare providers.

##  3\. Challenges in Patient-Provider Communication & LLM Opportunities

To better understand the communication gap between older adults and their
healthcare providers and how LLMs can play a role in facilitating
communication, we conducted two semi-structured interview studies, one with
older adults (Section 3.1), and the other with healthcare providers (Section
3.2). Understanding the challenges and needs of their communication can guide
the design of our LLM-powered system to facilitate communication.

###  3.1. User Study 1: Interview with Older Adults

We started by investigating the role LLMs might play from the end-user
perspective when used with a home-based VA interface. Our results indicate
that older adults often need to communicate with providers, such as nurses and
doctors (Section 3.1.3). However, providers had very limited bandwidth for
real-time communication, and existing asynchronous methods were hard to use
due to technology complexity. Even for real-time communication like phone
calls, some important information could be lost (e.g., providers might only
note down a subset of what they said). Older participants also expressed
strong interest in the LLM-powered VA system (Section 3.1.4), with the
opportunity to offload providers, provide mental support, and offer better
accessibility.

####  3.1.1. Participants

After obtaining the IRB approval, we recruited older adults (N=10) from a
local university-maintained participant pool of older adults with snowball
sampling. All the older adults resided in single-family homes and were 65
years of age or older. Participants reported one or more chronic conditions
and urgent care histories; they had rich experience communicating with their
healthcare providers in recent years. Out of the ten older adult participants,
two of them had experience using AI, six knew about the concept but had not
used AI, and two others were unfamiliar with the concept. Table 1 summarizes
their detailed demographic information111We show the age of participants by
age range for deidentification purposes..

P# | Age | Gender | Chronic/Occasional Condition | Surgery/Accident History
---|---|---|---|---
P1 | 65-69 | Female | Migraine, hypertension, former depression, dementia risk | Joint surgery
P2 | 75-79 | Female | Hypertension, arthritis, stomach issue, anxiety disorder | Thumb injury
P3 | 65-69 | Female | Generally healthy, sinus infection, cervical dystonia | /
P4 | 80-84 | Female | Generally healthy, former alopecia | Ankle injury
P5 | 65-69 | Female | Generally healthy | Throat & shoulder surgery, body injury
P6 | 80-84 | Female | Atrial fibrillation, occasional pain | /
P7 | 70-74 | Female | Generally healthy, usual aches and pain | /
P8 | 70-74 | Male | COPD, sleep apnea, atrial fibrillation | /
P9 | 65-69 | Male | Generally healthy, long-term COVID influence | Collarbone injury
P10 | 70-74 | Female | Arthritics, occasional depression, pain and hypertension | /

Table 1. Demographics of Older Adult Participants

####  3.1.2. Interview Design and Procedures

Our interview sessions followed a semi-structured format. Our key interview
questions covered three perspectives: (1) What were older adults’ needs and
challenges in communicating with their healthcare providers at home? (2) How
might an LLM-based VA system facilitate communication and address these needs
and pain points? (3) What concerns and risks would they have when applying
such an AI-powered system? Based on these starting points, the interviewer
followed up with interviewees based on their responses. The specific questions
can be found in Appendix A.1 and Appendix A.2. The study lasted 25-30 minutes.
Participants were compensated $25 for their time. The interviews were recorded
via Zoom and transcribed afterward. Thematic analysis was employed to analyze
the data. Two authors coded interview transcripts iteratively until they
reached a consensus. We summarize our findings in the following sections,
following the order of the three perspectives described above.

####  3.1.3. Communication Needs and Challenges

Participants reported a variety of examples where they needed to communicate
directly with their healthcare providers at home, such as daily questions
about new symptoms and urgent care (e.g., COVID or flu symptoms), confusion or
changes about an existing condition (e.g., occasional pain or blood pressure
change, prescription instruction), and post-surgery-related questions. Many
cases were consistent with, and supported by, prior research (Grönvall and
Verdezoto, 2013; Vugs et al., 2023; Donelan et al., 2019; Mitzner et al.,
2009). Most of these communication needs were case-specific and personalized.
Participants’ current solutions included visiting/calling healthcare services,
communicating through the patient portal with providers, discussing with
friends or family, and seeking online resources as alternatives. Our interview
revealed a set of challenges of these existing solutions.

Lack of Health Provider Availability. Despite the wish to get quick and in-
time responses from the healthcare system and providers, 5 participants
mentioned that it was hard to get to healthcare providers because they were
often too busy during working hours. For example, “I can tell you the
frustration of just simply making a phone call to the provider… then you’re on
hold for maybe 30 minutes, which is crazy.” (P10) The in-person visiting
option posed more challenges. Besides the traveling difficulties, the waiting
time for an appointment with providers often took several weeks or even
months. This suggests the strong need for asynchronous communication.

Information Loss during Communication. Even in cases where participants got to
talk to providers, some information was often ambiguous or even lost during
communication. For example, P1 mentioned that nurse practitioners “never put
down exactly what I say… They may not realize that it is very significant”. P8
gave another example where the messages from providers and lab results were
often lack of explanations and hard to understand. Such an information loss
caused the participant to pay extra effort to describe things or request
explanations one more time when they talked to doctors next time, which
reduced communication efficiency and brought confusion for older adults.

Lack of Digital Literacy for Communication Tools. Some participants were more
familiar with technology and learned to use digital patient communication
portals to leave messages to providers, which could address these challenges
to some extent. However, lack of digital literacy is a widely known issue
(Pradhan et al., 2020; Smith, 2014; Foster and Sethares, 2014). Only a limited
number of participants (4 out of 10) knew how to use such a digital
technology. Yet they often had to wait for a long time or even did not get a
response from providers, again due to the limited bandwidth of healthcare
providers.

Reliability Concerns of Alternative Resource. Since they could not get in-time
communication with health providers, 4 participants reported resorting to
alternative resources by searching online or talking to families. Consistent
with previous work (Brewer et al., 2022; Bérubé et al., 2021), they expressed
concerns about the reliability of these resources. “I’d rather just go to the
doctor. That’s their area of expertise.” (P1) This became another strong
motivation for us to facilitate communication between older adults and
providers.

####  3.1.4. Opportunities for An LLM-facilitated VA System

To further understand how LLMs may help older adults with their communication,
we introduced the concept of an LLM-based VA system. We also presented
examples of how such a system could have natural conversations, collect
information and conditions, and forward the information to healthcare
providers for further decision and contact. We highlighted explicitly that our
system would not provide any specific health advice, but the system could
provide general information about clinics, procedures, or term explanations.
Overall, older participants expressed strong interest in such an AI-powered
system to help them address the communication challenges. We summarize their
comments below.

Offloading for Healthcare Providers. As introduced in Section 3.1.3, one of
the key communication challenges was the unavailability of healthcare
providers. Older adults hoped that the system could help save providers’
efforts. “There may only be one or two nurses who can respond to all of those
phone calls. Whereas with a computer, and assuming it can respond to 100
people at the same time, it would take pressure off of the healthcare
providers.” (P8) Although this comment included cases where providers gave
specific health advice (which our system could not achieve), this could still
potentially help providers with tedious procedure work and give them the
bandwidth to respond to patients’ actual inquiries. As we will show in the
next Section 3.2, older adults’ thoughts echoed with providers’ comments.

Mental and Emotional Support. Although we made it clear that the system would
not provide specific health advice, 3 participants still thought it could give
immediate responses and provide emotional support. For example, P4 lived alone
and thought “so in this case, I have like, somebody answer me for accompany…
this would be interesting to get the experience.”. P10 agreed that the
conversation system would be helpful when having health concerns: “Sometimes
you just wanted to talk to the person… or hear a voice, even if it’s not a
human voice”. Moreover, the support could also be expanded to other family
members. For example, P1 shared her post-surgery experience and thought that
if such a system existed, it would “give my daughter enough comfort to let me
be in our apartment alone after” (P1).

Better Accessibility. Participants commented that the natural VA system could
address the challenges of the technology barrier of navigating digital
communication portals. Multiple participants mentioned the accessibility
advantage of a VA compared to other options, especially equipped by the
intelligence from LLMs. For example, P1 had a potential dementia risk and said
“I can use my phone and tablet… now that maybe it will begin to decrease…
there’s going to come a time when I’m not going to be able to remember how to
use it.” She thought a conversational-based system could provide better
usability and accessibility.

Function Expectations. In addition to the natural conversation, information
summary, and forwarding features, participants also suggested a number of
expectations, including having pre-set personalized instructions from doctors
(P8), providing high-level suggestions and references (P1, P6), and delivering
results notification and administrative information (P2, P10). We selected
practical suggestions for our system implementation in Section 4.

####  3.1.5. Concerns and Risks

We also investigated the potential concerns of older adults when using the
intelligent system. We found the common ethical concerns for AI systems (Zhuo
et al., 2023) and privacy concerns of VAs from 3 participants (Pradhan et al.,
2020). For example, P5 was concerned that they would accidentally reveal
sensitive personal information: “because it’s a little bit [listening to]
everything”. However, many participants did not have such concerns, as long as
it “follows the criteria that the federal regulations” (P2). Besides, some
participants were also concerned about the learning curve of the system, which
we will evaluate through a user study in Section 5.2.

###  3.2. User Study 2: Healthcare Providers

The results of our Study 1 reveal the needs and opportunities of an LLM-
powered system from the perspective of older adults. We further investigated
these questions from the perspectives of healthcare providers. Our results
indicate that healthcare providers often need to communicate with older adults
in various scenarios (Section 3.2.3). However, they run into the challenges of
older adults’ limited availability, low communication efficiency, and the
information loss problem. Providers were excited about the potential of an
LLM-powered system for both older adults and themselves, which could be used
to save their time and assist in information processing (Section 3.2.4).

####  3.2.1. Participants

To better understand the perspectives of different roles in the patient
communication process, we focused on healthcare providers who are responsible
for communication with patients. By emails and snowball sampling, we recruited
9 nurse practitioners, doctors who are primary care physicians or specialists,
and other professionals who have close contact with patients. Participants
have frequent communication with patients at home, and most of them have close
communication with older adult patients. Six of the nine participants had
experience using AI, and three others knew about the concept. Table 2
summarizes their demographics and expertise.

PP# | Gender | Role, Responsibility, Specialty |  Years of Experience in Healthcare
---|---|---|---
PP1 | Female | Primary care physician, specialist in diabetes | 10-20 Years
PP2 | Female | Nurse practitioner in emergency department | 0-2 Years
PP3 | Female | Dentist, health consultant | 0-2 Years
PP4 | Male | Palliative medicine physician | ¿20 Years
PP5 | Male | Primary care physician, internist, pediatrician, hospice and palliative care specialist | ¿20 Years
PP6 | Male | Internal medicine physician | 2-5 Years
PP7 | Female | Clinical research assistant & medical records coordinator | 5-10 Years
PP8 | Female | Palliative care nurse practitioner, former patient navigator | ¿20 Years
PP9 | Female | Pediatrician, physician in ER | ¿20 Years

Table 2. Demographics of Healthcare Provider Participants

####  3.2.2. Interview Design and Procedures

We designed a semi-structured interview process, with a focus on three
perspectives: (1) What were healthcare providers’ needs and challenges in
communicating with their patients at home, especially with older adults? (2)
How might an LLM-based system facilitate their communication with patients and
address these needs and challenges? (3) What concerns and risks would they
have when applying such a system? The specific questions can be found in
Appendix A.2. The interviewer followed up with the providers based on their
responses. The study lasted 25-30 minutes. Providers were compensated $25 for
their time. Similar to our procedure in Section 3.1, we recorded the Zoom
interview, and two researchers used thematic analysis on the transcribed data.
We summarize our findings in the following sections from the three
perspectives listed above.

####  3.2.3. Communication Needs and Challenges

Providers mentioned a variety of scenarios that required patient-provider
communication. The majority were about older patients’ daily care inquiries
and the follow-up of previous treatments or surgeries. Sometimes long-term
patient education also requires communication. Providers did use digital tools
to manage information and reminders, yet for older adults, phone calls were
still the most common method. Our interview results suggest several prominent
challenges.

Hard-to-Reach Older Patients and Limited Bandwidth. In Section 3.1.3,
participants mentioned that providers were hard to reach. However,
interestingly, providers felt the same. Four providers found patients,
especially older adults, were hard to reach via phone calls. They often needed
to call multiple times to finally reach patients, and sometimes they just
could not succeed. For example, PP2 (provider participant, same below)
mentioned “So it’s like a dead end, even though the result could be very
alarming for us.” Meanwhile, providers mentioned that they are “understaffed
and overworked” since they usually have too many patients to handle (PP8).
Their bandwidth was limited, so they could not spend all their time reaching
out to older patients or going through a standardized in-person visit. As a
result, providers had to prioritize other tasks, and communication often got
inevitable delays. The challenges of limited availability were prominent on
both sides, suggesting the strong need for asynchronous communication methods.

Ineffective Communication with Existing Asynchronous Methods. Existing
asynchronous solutions have been trying to address this limited availability
challenge. Tools such as patient portals or EHR systems are set up as an
alternative to manual phone calls. However, providers commented that the
messages from patients were usually unclear. These messages were usually too
limited or overly long for providers to make the right decision. For example,
“It’s our physician-patient portal, and [older patients] will send things that
are either very short and unclear, or really long and [still] very unclear.
And then I often have to call them because I don’t have the time to write them
another, you know, page of questions that they’re just going to mess up
again.” (PP5) Meanwhile, the design of current EHR systems has too much
redundant information for providers to process “There’s so much information
that you need to disregard.” (PP5) This may explain the information loss issue
mentioned by older adults in Section 3.1.3.

Extra Effort Communicating with Older Adults. Out of the nine providers, seven
also mentioned the challenges during the real-time communication with older
adults: Older adults often had limited health literacy and higher
accessibility needs. Four of the providers felt that they needed to pay extra
attention to language and spend more time explaining concepts to make sure
that the information was delivered to and understood by older adults. “There
are a lot of special considerations for advocating older adults, like talking
[in a] slower pace or using non-medical terms that any patient can
understand.” (PP2) This suggests that our patient-facing VA system for older
adults needs to use simple language.

####  3.2.4. Opportunities for An LLM-facilitated Communication System

To investigate how LLM-based AI can be used to help providers address
communication challenges, we introduce the concept of LLM and our system. We
briefly introduced the concept of both the patient-facing VA system for older
adults and the LLM-powered information dashboard that summarizes the health
information of older adults. Similar to Study 1, we also explicitly mentioned
that the AI system would not provide any specific health advice. Overall,
providers were excited about the potential of the system after learning the
concept. We summarize the opportunities below.

Communicate Efficiency Promotion. In Section 3.1.4, older adults expressed the
hope that such an LLM-powered system could help offload providers. This was
confirmed by our Study 2. Providers agreed that using a VA system & an
information dashboard could significantly save their effort. “[It] definitely
can reduce the time and energy for us.” (PP2) “[The system] could be very
helpful for those local community clinics that may cover tens or even hundreds
of patients.” (PP1) Providers could leverage such an intelligent communication
system to reduce the procedural process and focus on more critical tasks.

Information Processing Assistance. Moreover, providers also mentioned that
such a system could help to address the information loss problem (Section
3.1.3 and 3.2.3) since it could record things and provide analysis results.
“Sometimes we weren’t able to capture the full experience… AI will come in if
it’s being recorded… and capture that data.” (PP7) Providers would like to see
a concise summary of the conversation as they saw during our study (PP1, PP2,
PP3), which could help them with the triage and prioritization of the right
next step. These comments could guide the design of our dashboard system in
Section 4.2.

Experience Improvement for Older Adults. Consistent with the comments from
older adults, providers, from their own perspective, also agreed that the VA
system could help with the conversation experience for older adults. “[The VA
system] might eliminate that feeling of judgment, and the patient will
probably answer more accurately, as opposed to sitting across from another
person who could potentially, you know, unintentionally making them feel
uncomfortable because of the certain questions and answers that are required.”
(PP7) Providers also agreed with the accessibility advantage of a VA system
over text-based solutions. “[For older patients,] reading the text and things
like that can be a problem. So having that voice feature would be quite
helpful.” (P3) These comments strengthen our motivation for the VA system for
older adults.

Function Expectation. Providers also brought up a few features that the
communication system would be good to have. For the VA system, several
providers mentioned the necessity to provide “closed loop communication”.
“That would be helpful and increase the accuracy of the information.” (PP2)
Meanwhile, providers also mentioned the expectation of such an AI system to be
integrated with EHR systems (PP1, PP4). Some providers also brought up the
importance of recording conversation audio to assist with certain diagnoses.
We set some of these suggestions into account for our system design in Section
4.

####  3.2.5. Concerns and Risks

Providers had a strong agreement with our design of not providing health
advice due to the concern about AI reliability. Moreover, they also expressed
concerns about the risks of misinterpretation (PP2, PP5), AI-embedded bias
(PP3, PP7), and sensitive/triggering topics, such as cancers or sex crimes
(PP4). Meanwhile, some were also worried about how AI might influence patient-
provider interaction in the long run (PP4, PP9). Some of these ethical
concerns were beyond the scope of this paper, and we will discuss potential
solutions in Section 6.

##  4\. Talk2Care System

The insights from older patients and health providers shed light on the design
and implementation of our pilot Talk2Care system to facilitate patient-
provider communication. Figure 1 presents an overview of our pilot system.
Talk2Care consists of two parts: First, the patient component: an LLM-powered
VA interface that asks and answers older patients’ questions to collect their
health information, which will be forwarded to healthcare providers for future
decisions and contact (Section 4.1). Second, the provider component: a
dashboard interface that employs LLM to summarize patients-VA conversations
and present important information while keeping original details to save
effort and time for healthcare providers (Section 4.2).

###  4.1. Patient Component: LLM-powered Voice Assistant

Figure 2. The Component of Talk2Care System for Home-based Older Adults. The
VA interface has multi-turn personalized conversations with the older adult to
collect related health information. The LLM-powered Question Generation Module
is responsible for taking the older adult’s words and generating questions for
effective information collection. The prompt design of this module is detailed
in Figure 3. Another LLM-powered Content Loopback Module is to make sure that
key information from the older adult (e.g., pain level) is accurate by double-
checking the content, a common healthcare communication practice. The older
adult’s information, conversation protocol, and conversation log are stored in
the information database.

The results in Section 3.1.4 confirmed the advantage of an LLM-powered VA
system for its convenience and accessibility for home-based older patients. We
build a VA interface to collect information health information (e.g., detailed
symptom descriptions). The interface has a multi-turn conversation ability so
that they can have continuous and natural back-and-forth conversations with
older patients, generating appropriate and personalized follow-up questions to
collect important information. Figure 2 provides an overview of this component
of Talk2Care system, including the two modules for conversation generation
(Section 4.1.1 and 4.1.2), the VA hardware (Section 4.1.3), and the database
(Section 4.1.4).

####  4.1.1. Question Generation Module

The core part of the VA system is to generate high-quality questions to
collect key health information from older adults, which can then be forwarded
to healthcare providers for decision-making. To achieve this goal, we leverage
GPT-3.5-Turbo over GPT-4.0 due to the fast speed of GPT-3.5. LLMs are known to
generate offensive or biased texts, sometimes even harmful content (Zhuo et
al., 2023; Hartvigsen et al., 2022). To address the ethical concerns, we
conduct a series of prompt designs to improve the question generation
referring to prompt optimization practices from previous works (Wei et al.,
2023; Pro, 2023). Specifically, one researcher conducted a set of preliminary
prompt designs and tested them with GPT-3.5. Then, another two researchers,
one with technical expertise and the other with healthcare expertise, acted as
quality checkers for the content and provided feedback. This process was
iterated through multiple rounds until all researchers were satisfied with the
generated content. 222Despite these efforts, we acknowledge that this could
not address the ethical concerns completely. We show more discussion in
Section 6.

Finally, we propose a set of five important content factors to be integrated
as a complete prompt, as illustrated in Figure 3. (1) Patient Information.
This part includes the older adult’s basic information, such as their name,
gender, age, and living situation. Moreover, it also includes their health
situations such as chronic conditions and medical history. This information
was added to the database during the initial setup of the system. (2)
Conversation Protocol. To ensure the validity of the information collection
questions, the VA should follow a standard protocol employed by providers for
a specific task (e.g., daily healthcare needs in Figure 3). Different
scenarios may require different protocols (e.g., post-survey follow-up vs.
daily care needs). Therefore, the information database stores a set of
protocols collected from online resources, researchers, and providers. We
separate the task summary and question protocol into two chunks to provide a
flexible mapping between tasks and protocols. 333Later in our user study in
Section 5, we pre-determined the protocol depending on the scenarios. Future
work can explore automatic protocol selection depending on the task, as we
will discuss in Section 6. (3) System Setting. This part instructs the role of
the VA (as a communication facilitator), its responsibilities (i.e.,
collecting health information but not giving specific health advice), and its
communication style (i.e., concise, health-focused, and easy-to-understand, as
suggested in Section 3.2.3). The specific wording of our prompts can be found
in Figure 3. (4) Conversation History. This part includes the conversation
history between the older adult and the VA, which can augment the VA’s memory
and personalize the conversation experience. (5) Response Optimization. To
optimize the VA’s response to the older adult, this part is designed to
improve the quality of each response and question generated by GPT-3.5. For
each conversation session, (1) - (4) are only used at the beginning of the
generation, while (5) is added in the prompt across all conversation rounds.

Figure 3. Prompt Design of High-Quality Question Generation for Health
Information Collection. The input prompt consists of five parts: 1) patient
information, 2) conversation protocol, 3) system setting, 4) conversation
history, and 5) response optimization. For multi-turn conversation, 5) will be
repeated for each round of conversation. The colored texts are parameters that
can be extracted from the information database (see Figure 2). Note that the
conversation protocol needs to be set by researchers or healthcare providers
to ensure question validity. This figure shows an example of daily-care
protocol.

####  4.1.2. Content Loopback Module

In addition to the question generation, our interviews with providers (Section
3.2) suggest the importance of content confirmation, especially for important
health-related information. Therefore, we design a simple content loopback
module. When a question asks about specific values (e.g., the pain level), the
module loops the value back to users and asks them a second time to confirm.

####  4.1.3. Voice Assistant Hardware

We experimented with multiple audio devices, including Raspberry Pi and
Arduino boards. Finally, we decided to deploy our system on the Amazon Alexa
platform by wrapping it as an Alexa Skill, so that we can leverage the well-
integrated ecosystem. We use Alexa Echo Dot as the hardware. Voice-to-text and
text-to-voice service provided by Amazon Alexa is employed to transcribe the
older adult’s input into texts and convert LLM-generated texts into audio,
bridging the text content and natural speech interaction.

####  4.1.4. Information Database

We employ a cloud-based database to store the older adult’s information,
conversational protocols, as well as conversation log. These contents are used
to construct the prompt for both the question generation module (1, 2, and 4
in Section 4.1.1) and the content loopback module. The database is encrypted
to protect each individual’s privacy.

####  4.1.5. Conversation Flow

Depending on the scenarios, the conversation can be initiated either by older
patients (e.g., with specific health concerns and care needs) or by the VA
(e.g., health condition follow-up requests). In each session, the VA will lead
the conversation to ask the patient about symptoms and iteratively gather
detailed information to pass to healthcare providers following the prompt
instructions. For example, in the post-surgery scenario, the VA will first ask
the patient about the overall health conditions and any pain or discomfort. If
the patient mentions any pain or discomfort, the VA will ask about details
(with loopback confirmation), such as pain level, positions of discomfort, and
any actions that may influence the symptoms. If the patient has any confusion
about the information, the VA explains the concepts accordingly but does not
provide direct medical advice.

####  4.1.6. Exception Handling

We designed the system and prompts to handle common exceptions in voice
recognition and user input to minimize unsatisfactory user experience in
abrupt failure or termination. We use Alexa skill re-prompts (Ale, 2021) and
conversation history to handle unexpected pauses, so that users could have
continuous conversations with the VA after pauses. If voice recognition has
errors, users could interrupt the VA and start again.

Some users might seek immediate medical advice from the system, and according
to prompts, the system generates responses such as “I’m not a doctor, but it
would be best to consult with your healthcare provider about which painkiller
to take. I can pass this information along to the nurse so they can assist you
further. ”(VA) Since we aim to take advantage of LLMs, we do not restrict the
tasks that the VA can complete, so it also provides answers to less related
but reasonable questions such as “What is hospice and palliative care?”. The
strategies appeared to be successful in our user study in Section 5.2.

###  4.2. Provider Component: LLM-powered Information Dashboard

Figure 4. The Component of Talk2Care System for Healthcare Providers. The
information dashboard summarizes and highlights key older adults’ information.
The main content on the dashboard is generated by three LLM-powered modules:
(1) The Content Summary Module formats the conversation log and user
information into a clinical note structure. (2) The Information Highlight
Module color-codes the parts in the conversation log that require attention.
(3) The Risk Prediction Module suggests the health risk (low, moderate, and
high) based on the current conversation log. Providers can take notes or
further actions on the dashboard, which are then stored in the information
database.

Other than the LLM-powered VA system for older adults, we further developed a
dashboard interface for healthcare providers to review the information in the
patient-VA conversation and take further actions if necessary. Figure 4
illustrates an overview of this component of Talk2Care, including three
modules for information summary (Section 4.2.1), context highlight 4.2.2), and
risk prediction (Section 4.2.3), together with the dashboard (Section 4.2.4)
and the database (Section 4.2.5).

####  4.2.1. Conversation Summary Module

Our interview results with experts suggest that providing a summary of the
conversation between the older adult and the VA system is very helpful
(Section 3.2.4). This module aims to extract the key messages from the
conversation log and format them into a clinical notes structure. To achieve
this goal, we also leverage GPT-3.5 with a series of prompt designs.

By leveraging provider training tutorials and literature (AAP, 2023; PIH,
2021; Pro, 2023), our prompt iteration process is close to the one introduced
in Section 4.1.1. The final prompt design structure, as shown in Figure 5,
also resembles the one of question generation for older adults (Figure 3),
including five factors. (1) Patient Information. This is the same information
of the older adults from the information database. (2) Conversation Protocol.
Other than providing the task summary and question protocol, the prompt
further emphasizes the key information to help the LLM narrow down its focus
and improve the quality of the summary. This piece usually comes together with
each question protocol. (3) System Setting. This part instructs the role of
the AI assistant (as a text summary tool), its responsibilities (i.e.,
extracting health information and patient questions), and output format (i.e.,
concise clinical note structure, Section 3.2.4). The specific wordings are
shown in Figure 5. (4) Conversation Log. The complete log of the conversation
session between the older adult and the VA. (5) Summary Optimization. Through
our pilot tests, we noticed the need to adopt an in-context learning paradigm
to help the LLM generate high-quality summaries. Therefore, this part shows a
summary example to ensure the output is well-structured.

Figure 5. Prompt Design of Patient-VA Conversation Summary for Healthcare
Providers. Similar to Figure 3, the input prompt consists of five parts: 1)
patient information, 2) conversation protocol, 3) system setting, 4)
conversation log, and 5) summary optimization. For multi-turn conversation, 5)
will be repeated for each round of conversation. This figure continues the
example of the protocol of daily care.

####  4.2.2. Information Highlight Module

The healthcare providers in our interviews also mentioned the importance of
having the raw conversation log (Section 3.2.4), enabling them to check the
raw data when necessary. We design a highlighting feature to save their
effort. The prompt setup is similar to the conversation summary module
introduced in Section 4.2.1, with the main difference being the system setting
(the responsibility is to find and return the important quotes of the older
adult) and output optimization (no longer needed).

####  4.2.3. Risk Prediction Module

There has been a wide range of research on risk prediction in healthcare
(e.g., (Kennedy et al., 2014; O’Caoimh et al., 2015; Gatt et al., 2022)). We
envision our dashboard should also include such a module to help providers
allocate limited resources appropriately. For the completeness of our
Talk2Care system, we design an LLM-powered risk prediction module. The prompt
design is also similar to the conversation summary module in Section 4.2.1,
with the main difference being the system setting (the responsibility is to
predict the risk level of the older patient: low, moderate, and high) and
output optimization (ask for the prediction reasoning). However, we recognize
the ethical risks of mis-predictions. Evaluating the prediction performance of
the LLM goes beyond the scope of our paper. Additionally, health providers
need to implement safety protocols to standardize risk management. Therefore,
our evaluation of Talk2Care in Section 5 mainly focuses on the usability of
the system design.

####  4.2.4. Dashboard Interface

We develop our dashboard interface with the React framework. Our design
simplifies the interface of existing EHR systems (Adv, 2023; Epi, 2023) and
mainly focuses on the new features we propose. Figure 6 presents the
continuous example of the older adult’s information, following Figure 3 and
Figure 5. Providers can easily navigate to read the conversation summary, the
raw conversation logs and highlights, and the communication session history.
For each session, they can take follow-up actions or write notes under the
summary.

Figure 6. LLM-powered Dashboard Interface Layout for Healthcare Providers. The
dashboard presents the conversation summary (center), raw log, and its
highlights (right). The colored dot represents the risk prediction (left and
center). Providers can take specific follow-up actions or write down notes
under each conversation summary, after which they can mark one session as
done.

####  4.2.5. Information Database

The information database of the dashboard is shared with the database of the
VA system. It provides the older patient’s information, conversation log, and
health protocol for prompt input. It also saves providers’ action logs or
notes for future visualization.

####  4.2.6. Interaction Flow

Depending on the specific scenarios, the providers first input instructions
and protocols into the system, which are used for both the older adult’s and
the provider’s components. When new conversation sessions are stored in the
database, the providers are notified. Using the LLM-powered dashboard, they
can check the conversation content, take notes, and make decisions on the
appropriate follow-up actions.

Combining the two components together, we aim to build Talk2Care as a complete
system to facilitate the communication between home-based older adults and
healthcare providers.

##  5\. Evaluation

To evaluate the design of our Talk2Care pilot system for stakeholders, we
proposed two specific healthcare scenarios to put Talk2Care in realistic
settings (Section 5.1). Then, we conducted two user studies with both older
adults (Section 5.2) and healthcare providers (Section 5.3) to measure the
usability of Talk2Care design.

###  5.1. Scenario Setup

Based on our interview feedback in Section 3, we designed two scenarios
corresponding to the communication needs of older adults at home and
healthcare providers, one with older adults as initiators (Section 5.1.1), and
the other with providers as initiators (Section 5.1.2).

####  5.1.1. Scenario A: Daily Care Needs

One of the common communication needs of older adults at home is when they
have unexpected health concerns and want to communicate with providers. We
designed a character, Mary, a 75-year-old female patient living alone. Mary
has been coughing and feeling tired since this morning. She suspects it may be
related to COVID-19, but the home-test result turns out to be negative. Mary
does not want to travel to the clinic unless necessary, because it takes too
much time and energy, and she wants to avoid spreading the potential virus. So
she wants to ask for advice on going to urgent care or staying at home.
Meanwhile, Mary has high blood pressure, so she is worried about the complex
situation and wants to check if there may be any problems.

On the provider side, we designed another character, Tom, a medical office
assistant working at a clinic, and his primary responsibility is to answer
patient phone calls every day. He gets a number of calls from patients asking
about various things, such as lab results, prescriptions, random health-
related questions, and requests for appointments. After Tom has all the
information from the phone calls, he needs to prioritize them and take the
next steps.

####  5.1.2. Scenario B: Post-Surgery Follow-up

For providers, one common communication need is to follow up with older
patients about their symptoms after being discharged from the hospital.
Following the character above, John, a 72-year-old male, just went through a
small joint surgery on the knee two days ago. He feels good overall, but there
is still some pain in the knee. Meanwhile, John has two different kinds of
painkillers: aspirin and ibuprofen. He is not sure which one to take.

On the provider side, Emily is a postoperative care nurse working at a busy
hospital. Part of her primary responsibility is to follow up with patients
after their surgeries, ensuring a smooth recovery process. Emily has to call a
number of patients to ask about their current situation and questions. John is
one of Emily’s patients, so Emily needs to follow up with him about the knee
surgery. After Emily has all the information, she will prioritize them and
take the next steps.

###  5.2. User Study 3: Voice Assistant System with Older Adults

This user study aims to evaluate the older adult’s component of Talk2Care. Our
evaluation results indicate that the pilot system can provide good usability
(Section 5.2.3), help older adults provide more health information (Section
5.2.4), and offer better mental support (Section 5.2.5).

####  5.2.1. Participants

With the university IRB approval, we recruited the same set of 10 participants
from our User Study 1 (Table 1). We confirmed that all participants have no
hearing or speaking disabilities that might impact their interaction with the
VA.

####  5.2.2. Study Design and Procedure

We compared Talk2Care against the most common asynchronous communication
method by leaving a message to providers. Specifically, we introduced the two
scenarios one by one. After introducing each scenario, older adults were asked
to play the role of the character (Mary or John) by writing down (or speaking
aloud) the message to leave for providers. Then, they had a conversation with
the VA system, with the same goal of conveying the information to providers.
After going through the two scenarios, they completed a short evaluation
questionnaire and a semi-structured exit interview. Note that the order of the
two scenarios was counterbalanced. Two examples of the conversation log
between the older adult and the VA can be found in Appendix B.1 and B.2.
Figure 7 shows an example of the user study. The study lasted 25-30 minutes.
Participants were compensated $25 for their time.

Our questionnaire includes the System Usability Scale (SUS) (Brooke, 1995),
the comparison against the basic method of leaving a message, the health
support experience, and a few VA-specific experience questions, using a
5-point Likert scale. In the interview session, we collected user feedback on
what they liked and disliked regarding their interaction experience, as well
as their future expectations about the system. The interviews were recorded,
transcribed, and processed with Thematic analysis by two researchers. We
summarize our findings in the following sections.

Figure 7. User Study 3 Scenario with an Older Adult Participant. Figure 8.
Subjective Ratings on Different Perspectives of The Usability of The VA
System. Error bars show the standard deviation. The average SUS score is
75.5$\pm$17.1, indicating good usability.

####  5.2.3. Good System Usability

During the study, all participants were able to successfully complete both
scenarios following the instructions. They agreed that the two designed
scenarios were close to their daily care needs situations (4.2$\pm$1.0 out of
5). Meanwhile, participants found the VA system easy to use. The average SUS
score was 75.5$\pm$17.1 (out of 100), indicating good usability. “[It’s]
almost like talking to anybody. I mean, talk to people on the telephone. It’s
not that different.” (P10) They also found that the VA system’s generated
questions were simple to understand (4.6$\pm$0.5), and that the system could
understand their words accurately (4.3$\pm$0.8). Participants agreed that the
system was reliable (4.2$\pm$1.0) and the overall privacy concern was not high
(2.4$\pm$1.4). These results were consistent with the findings in Section
3.1.4 about the advantage of the VA system’s accessibility, and supported that
the older adult’s component of Talk2Care enjoyed satisfactory usability.

####  5.2.4. Interactive Conversation Gathered More Health Information

During the study, participants tried the traditional method of leaving a
message, so that they could compare it against the method of talking with the
VA. Our results showed that older adults thought the new method could better
support their communication with providers (4.3$\pm$0.7), and enhance the
descriptions of their health information (3.4$\pm$1.3). “It reminded me of
additional symptoms that I went through” (P9). “I wouldn’t necessarily have
thought to include that in my message. [But it asked] for some pieces of
information.. that are significant to the doctor. It’d be more efficient” (P1)
This was a strong advantage of Talk2Care: With the help of natural and
interactive conversation, older adults were able to better communicate and
give more health information to providers. This had the potential to enhance
the communication efficiency significantly. As we will show in the next user
study with providers (Section 5.3), providers also confirmed that more useful
information could be collected with the help of our system.

####  5.2.5. Better Healthcare and Mental Support

As mentioned in Section 3.1.4, older adults thought our system might provide
better mental support, even though it was intentionally designed to not give
specific health advice. This comment was validated after they used our system.
Participants gave high ratings on mental support (4.0$\pm$1.3) and on the
support for their home-based healthcare needs (4.2$\pm$1.0). In particular,
P2, previously working as a nurse in a local clinic, provided some insights.
“A lot of people are sometimes afraid to ask a doctor. They think it’s
foolish, so I think that gives them the opportunity to ask without being too
threatening by picking up a phone and having to talk to a secretary and go
through all of that.” This provided an interesting effect of AI-mediated
communication in the patient-provider scenario. We will have more discussion
in Section 6.

####  5.2.6. Expectations for More Features and Information

Participants also had more specific expectations of the system after using the
system. A dominating direction was to have the system provide more
administrative information, integrate with the healthcare system, and help
manage their personal health information. For example, P7 and P9 suggested the
VA could provide an estimated time that it takes for the healthcare provider
to respond. Participants also mentioned that the system could play the role of
“an entry point of the different specialists” within the EHR system to
minimize the communication effort. As we will show in the next user study,
providers also mentioned similar thoughts. This is a promising future to
deploy Talk2Care in real healthcare systems.

###  5.3. User Study 4: System Interface Study with Healthcare Professionals

Our Study 3 evaluated the first half of the Talk2Care system for older adults.
This study focused on evaluating our dashboard design’s usability with
providers. Our results demonstrate that the pilot system provides good
usability (Section 5.3.3), and that it can effectively support providers’
communication (Section 5.3.4) and information processing (Section 5.3.5).

####  5.3.1. Participants

Our study was also IRB-approved. We recruited the same set of 9 healthcare
providers from our User Study 2 (Table 2). All providers have the experience
of using one or multiple EHR systems.

####  5.3.2. Study Design and Procedure

Similar to Study 3, we compared Talk2Care against the most common asynchronous
communication method of receiving a message from older patients to providers.
We briefly introduced the dashboard interface to providers. After they got
familiarized with the system, we introduced the two scenarios to them one by
one. Since providers had different responsibilities and specialties, they were
not asked to play the role but to suggest the characters (Emily and Tom) about
the appropriate actions. In each scenario, they were first asked to give
suggestions on the next steps with the traditional communication method, i.e.,
after receiving patient messages corresponding to the scenario. Then, they
navigated through the dashboard interface with the same goal of processing
patient information. Note that the dashboard of the two scenarios was the
same, except for the different content (scenario A: Figure 6, scenario B:
Figure 9). After going through the two scenarios (with a counterbalanced order
across participants), they completed a short evaluation questionnaire and a
semi-structured exit interview. The study lasted 25-30 minutes. Providers were
compensated $25 for their time.

Our questionnaire includes the NASA Task Load Index (TLX) (Hart and Staveland,
1988), the System Usability Scale (SUS) (Brooke, 1995), the comparison against
the basic method of processing messages, the work support experience, and a
few dashboard user experience questions. Other than TLX had a 7-point Likert
scale, all other questions had a 5-point scale. In the interview session, we
collected providers’ feedback on their experience regarding using the
dashboard to facilitate their communication with older adults. We also asked
about their future expectations and concerns about the system. Like other
studies, two researchers processed the interview transcriptions with Thematic
analysis. We summarize our findings below.

Figure 9. Dashboard Interface Example for Scenario B: Post-Surgery Follow-up.
The example for Scenario A can be found in Figure 6, with the same interface
and different scenario-specific content.

####  5.3.3. Good System Usability

After navigating through the interface for the two scenarios, providers
commented that the system was easy to understand and convenient to use. They
also agreed that the scenarios and questions were realistic to their actual
scenarios, and that the system established a good workflow. The system has an
overall SUS score of 85.8$\pm$9.8, indicating very good usability. The NASA
TLX results were also consistent with their comments (see Figure 10), with low
ratings on mental demand (2.2$\pm$1.0 out of 7), physical demand
(1.4$\pm$0.5), temporal demand (1.7$\pm$0.9), effort (2.0$\pm$1.1), and
frustration (1.1$\pm$0.3), as well as high ratings on performance
(6.1$\pm$0.6).

Figure 10. Subjective Ratings on Different Perspectives of The Usability of
The Dashboard System Design. The average SUS score is 85.8$\pm$9.8, indicating
very good usability.

####  5.3.4. Better Communication Support

Providers also thought the system was helpful in their communication with
older adults (4.6$\pm$0.5 out of 5) and offloading their work (4.4$\pm$0.9).
Six participants mentioned cases where the system could save time for them.
For example, PP2, PP3 and PP9 were convinced that the VA system could save
their time spent on the phone calling. PP1 also mentioned that the whole
workflow might also reduce unnecessary outpatient visits. These results were
consistent with the advantages mentioned by providers in Study 2 before using
our system (Section 3.2.4).

####  5.3.5. Better Information Processing Support

Moreover, also consistent with Study 2, more than half of the providers
mentioned the advantage that the LLM-powered dashboard could assist in triage
and help prioritize their tasks, improving efficiency in the process. “Through
the summary and the list of the patients, I can see who’s the most critical
one that I need to take action right away or wait a little bit longer.” (PP2)
Meanwhile, the summary and highlights could help them organize notes for
documentation and speed up the review process (PP2, PP3, PP4, PP7, PP9). This
was also backed up by the high ratings on the system reliability
(4.1$\pm$0.6). Providers also liked the feature of referring to the raw log,
which was important for clarification and confirmation (PP1, PP3).

####  5.3.6. More Information through Patient-VA Conversation

In Study 3, patients commented that talking to the intelligent VA system could
help them reveal more health information (Section 5.2.4). This was also
confirmed by providers in Study 4. Providers agreed that the summary and log
contained more information compared to the commonly seen short messages in the
traditional patent portal. “When they’re writing, they’re probably going to
put less detail than when they’re talking.” (PP9) Compared with some
questionnaire practices, they also thought that the information could help
narrow down the problem and get providers more prepared for future visits. “I
definitely think this helps narrow in. Here [dashboard section] on what the
patient is trying to get to. It takes away some of the fluff.” (PP9) With the
sufficient information provided, providers also agreed that the communication
procedures would be optimized to reduce the back and forth (PP4, PP8). This
helps to address the information loss challenge, so that they could make
decisions in a shorter time and have quicker responses for patients (PP2,
PP7).

####  5.3.7. Frustration Reduction

Providers also mentioned an interesting advantage of our system. With LLM-
powered VA playing the intermediate communication role, providers could reduce
the mental load of talking with patients. “The nurse is managing a lot of
that. And that’s pretty soul-sucking, sometimes, and he or she has to be good
at keeping their temper and managing people’s expectations, all kinds of
things like that they that they don’t teach you don’t think in nursing
school.” (PP5) This reveals another advantage of AI working as a conversation
mediator. We have more discussion about this in the next section.

####  5.3.8. Expectations for More Features and Information

In addition to the positive comments, providers also brought up more
expectations. Four providers mentioned the need to integrate the system with
EHR systems, which was also brought up by older adults in Study 3. Also
consistent with older adults’ expectations, providers mentioned that the
system could offer pre-set valid suggestions and explanations from providers.
Meanwhile, we only covered two protocols for the two scenarios, and providers
expected a future intelligent system could incorporate more detailed protocols
and collect more detailed health information from older adults (PP2, PP3,
PP7). With the concerns about AI making mistakes, some providers also
suggested that the system should enable them to edit content to correct
errors, if any. “That’s where the human component comes in. Knowing that you
still have to make sure you’re not missing anything.” (PP8) These are great
directions to improve towards a deployable Talk2Care in the future. We discuss
more about this in Section 6.

##  6\. Discussion

Our User Studies 3 and 4 demonstrate the effectiveness of the LLM-powered
Talk2Care to facilitate communication between home-based older adults and
healthcare providers. In this section, we discuss the effects of AI-mediated
communication (Section 6.1), the future potential of deploying Talk2Care in
real-world scenarios (Section 6.2), and the important ethical concerns and
safety considerations (Section 6.3). We also discuss the limitations of our
current work (Section 6.4).

###  6.1. AI-Mediated Communication

In multiple stages of our user studies, older adults and providers mentioned
how the role of the LLM-powered system can support their communication needs
and reduce mental load. Even if they do not receive specific health advice,
older adults still feel mentally supported by talking to our VA system that
could forward their information to providers (Section 5.2.5), and some might
feel less judged compared to talking to a real healthcare provider (Section
3.2.4). For providers, other than saving their effort and time, such an
intelligent system can also reduce their mental load for patient communication
(Section 5.3.7). These results suggest the potential of AI as a mediator role
in human-human communication, which can help to take over some less favorable
parts during the commutation and improve both sides’ experience. At the same
time, since the healthcare setting is close to a professional scenario, such
an AI mediator also introduces minimal impact on patient-provider
relationships. This work could serve as an example to support the promising
future of AI-mediated communication (Mieczkowski et al., 2021; Hohenstein and
Jung, 2018). For more personal communication scenarios, such as sharing health
information across a family, it remains as an open question on how such an AI
mediator may affect human relationships.

###  6.2. Potentials of Real-World Deployment

Our studies suggest that Talk2Care has a strong potential to be deployed in
the real world. However, there are a number of features that Talk2Care needs
to support and be evaluated before deployment (Section 5.2.6 and 5.3.8).
First, both older adults and providers suggested the idea for our system to be
integrated with EHR systems, which could enable our system to provide more
personalized conversation with older adults with more validated and detailed
question protocols, update their health information in the system more
seamlessly, support providers with a smoother working flow, and further
facilitate the communication between older adults and multiple providers.
Meanwhile, there are also a few more important features that need to be
implemented to better support the two sides. Some functions of the current
system need to be automated, such as selecting the question protocol based on
the specific scenarios, providing reliable resources of information for older
adults, and reminders for providers for unread or delayed inquiries. In
addition, enabling older adults to refer back to their conversation history
(both with AI and providers) can serve as another way to reduce the
information loss problem, and enabling both adults and providers to edit and
correct any part of AI outcomes could enhance the robustness of the system.

###  6.3. Ethical and Safety Concerns of LLM-powered Healthcare Communication

Although Talk2Care shows a number of promising advantages, ethical and safety
concerns must be addressed adequately and thoroughly before any real-world
deployment. Our participants mentioned a range of ethical concerns that need
to be considered in future work (Section 3.1.5 and 3.2.5). Talk2Care is
intentionally designed to not provide health advice due to the concern about
AI reliability in healthcare (Butow and Hoque, 2020; Iliashenko et al., 2019;
Morley et al., 2020). Nevertheless, it can still make mistakes at multiple
stages of communication, such as misunderstanding older adults’ meaning of a
sentence, misleading older adults when providing their health information, or
mis-predicting the severity level so that providers’ priorities are biased.
Meanwhile, the impact of such a remote communication system on in-person
outpatient visits is also unclear. Although providers can ask patients to come
in for more tests after the system mediates the communication, this system may
reduce older adults’ desire to leave home and increase health risks. Much
future work is needed to closely and carefully address these concerns to
ensure a reliable, safe, and robust healthcare communication system.

###  6.4. Limitations

There are a few limitations in our work. First, our sample sizes for the user
studies are limited (10 older adults and 9 healthcare providers). The results
obtained from our study may not generalize to other populations. Second, our
current system is a proof-of-concept prototype. The protocols we used in the
study need manual effort and can be more detailed. As we discussed in Section
6.2, it needs more future work to achieve a smooth deployment. Third, we
didn’t evaluate the performance of different modules in Talk2Care, such as the
quality of the generation question or summary, or the accuracy of the risk
prediction. Comprehensive and systematic evaluation is necessary before
scaling up such a system.

##  7\. Conclusion

In this paper, we designed and implemented Talk2Care, a novel LLM-powered
pilot system to facilitate communication between older adults at home and
healthcare providers. To better understand the communication challenges and
opportunities of LLM-based systems, we first conducted two semi-structured
interview studies, one with older adults (N=10), and the other with healthcare
providers (N=9). The interview results reveal the need for an AI-based
asynchronous communication system and shed light on our design of Talk2Care.
Our pilot system consists of two components. The patient component is an LLM-
powered VA system that generates high-quality questions to collect health
information from older adults and send it to providers for further decision-
making. The provider component is a dashboard that summarizes and highlights
key information from the patient-VA conversation to save providers’ time and
assist their analysis process. We then conducted two more user studies to
evaluate Talk2Care. Our results showed that Talk2Care may address the
challenges mentioned by patients and providers, facilitate their
communication, enrich the health information shared by older adults, and
reduce the efforts of providers. Our work serves as an example of LLMs’
application in the intersection of healthcare and interpersonal communication.

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##  Appendix A Appendix: Interview Scripts

###  A.1. Interview Scripts for Older Adults

We begin with a brief introduction of our research and study procedure.

Background

  1. (1)

Could you please tell me a bit more about your health conditions, such as any
chronic diseases, surgeries or accidents?

  2. (2)

Could you please tell me about your home setting, like do you live alone or
with family?

Recent Healthcare Visit Experience

  1. (1)

Could you please give a recent example of your healthcare experience with a
provider, such as visiting a doctor?

    1. (a)

What difficulties and inconveniences did you encounter?

Patient-Provider Communication

  1. (1)

In this process, could you please tell me more about what and when you
communicated with a healthcare provider at home or seeked their support? [What
questions and needs do you have?]

    1. (a)

If the health providers are not available, what’s your alternative? Any other
solutions you refer to?

Personalization/Conversation

  1. (1)

In this process, do you have any personalized questions or concerns that you
feel can only be addressed by communicating with a healthcare provider(e.g.
but not by a friend or online searching)?

Attitudes of VA for patient-provider communication

  1. (1)

Do you know about voice assistants such as Alexa? What about AI? How about
ChatGPT? If not, I can make a brief introduction.

  2. (2)

In your case, if there is such an AI voice assistant to help you communicate
with a healthcare provider by …, do you think it will be helpful? [What do you
think about it? What do you like/dislike about it? What other features would
you like or dislike?]

Closing Question

  1. (1)

Do you have any questions for us about the study?

###  A.2. Interview Scripts for Healthcare Providers

We begin with a brief introduction of our research and study procedure.

Background

  1. (1)

Could you please tell me a bit more about your work responsibilities
communicating with patients at home, such as monitoring their health
conditions or answering patient questions?

Patient-Provider Communication

  1. (1)

In this process, could you please tell me more about what difficulties and
challenges you encountered communicating with patients, especially older
adults?

  2. (2)

For your difficulties, did you seek any support from technologies?

    1. (a)

Automated calling systems?

    2. (b)

Pre-designed surveys?

Attitudes of AI for patient-provider communication

  1. (1)

Do you know about voice assistants such as Alexa? What about AI? How about
ChatGPT? If not, I can make a brief introduction.

  2. (2)

In your case, if there is such an AI voice assistant to help you gather
information from your patients by [referring to their difficult cases
mentioned above]
Or: In your case, if there is such an AI that summarizes the conversation and
highlights the key information,
Do you think it will be helpful?

    1. (a)

What do you think about it?

    2. (b)

What do you like/dislike about it?

    3. (c)

What other features would you like or dislike?

Closing Question

  1. (1)

Do you have any questions for us about the study?

##  Appendix B Appendix: Conversation History

###  B.1. Post-Surgery Example of Conversation Log Between The Older Adult and
Our Voice Assistant

Voice Assistant |  Hello! I’m Talk2Care. How are you feeling after your surgery? Any discomfort or concerns?
---|---
Patient |  I’m feeling good overall, thanks for asking. But I have a little pain.
Voice Assistant |  I’m glad to hear that you’re feeling good overall. I’m sorry to hear about the pain. On a scale of 1 to 10, how would you rate your pain?
Patient |  I’m not sure. Can you give some examples of how to rate my pain?
Voice Assistant |  On a scale of 1 to 10, with 1 being no pain and 10 being the worst pain imaginable, how would you rate your current pain level?
Patient |  I would probably rate 2. I think it’s not too severe.
Voice Assistant |  I understand. On a scale of 1 to 10, with 1 being mild and 10 being severe, you rate your discomfort as a 2. Is that correct?
Patient |  Yes, that’s correct.
Voice Assistant |  Thank you for letting me know. Is the pain more towards the mild side or is it more severe?
Patient |  I would say it’s more mild, and actually I’m not sure about my painkiller. I have 2 different kinds, but I kind of want to ask my doctor which one I should use.
Voice Assistant |  I understand that you have two different painkillers. Would you like me to pass this information to the doctor and have them advise you on which one to use?
Patient |  Yes, please. And also I think one of them is aspirin and the other one is like a paste that I can put on my skin. Maybe you can ask my doctor about that?
Voice Assistant |  Thank you for letting me know about the medications. I’ll make sure to pass that information along to your doctor. Is there anything else I can help you with?
Patient |  No, thank you.
Voice Assistant |  Goodbye!

###  B.2. Daily Care Example of Conversation Log Between The Older Adult and
Our Voice Assistant

Patient |  Hello?
---|---
Voice Assistant |  Hello! I’m Talk2Care. How can I assist you today? Are you feeling unwell or have any health concerns?
Patient |  I have like cold or flu symptoms, I took a covid home test it was negative, but I, I’m not, I’m worried that I might have a false negative and then I might be having covid. I, I just don’t know should I go to the clinic and get their test or should I stay at home.
Voice Assistant |  I understand that you’re worried about the possibility of a false negative. Can you describe your symptoms in detail?
Patient |  I have a fever, I’m coughing a lot, very tired, and I have a headache.
Voice Assistant |  I understand that you’re experiencing a fever, coughing, fatigue, and a headache. It’s important to consult a healthcare provider for further evaluation. Would you like me to notify your doctor about your symptoms?
Patient |  Yes, please notify my doctor and ask should I be extra worried because of my high blood pressure.
Voice Assistant |  I understand your concern. It’s important to monitor your symptoms closely. Would you like me to inform your healthcare provider about your symptoms?
Patient |  Inform my doctor, thank you.
Voice Assistant |  I have informed your doctor about your symptoms. They will contact you directly for further guidance. Is there anything else I can assist you with?
Patient |  No, thank you.
Voice Assistant |  Goodbye!