Research Collection
Doctoral Thesis
Conversational Agents (CAs) for Health Care for Chronic
Diseases (HCD): Personalization and the CA-Patient Relationship
Author(s):
Schachner, Theresa Publication Date:
2021
Permanent Link:
https://doi.org/10.3929/ethz-b-000473931
Rights / License:
In Copyright - Non-Commercial Use Permitted
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Conversational Agents (CAs) for Health Care for Chronic Diseases (HCD):
Personalization and the CA-Patient Relationship
A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH
(Dr. sc. ETH Zurich)
presented by THERESA SCHACHNER
MSc, Universität Wien
born on 13.08.1990 citizen of Austria
accepted on the recommendation of Examiner: Prof. Dr. Florian von Wangenheim
Co-Examiner: Tobias Kowatsch, PhD
2021
I Summary
This dissertation addresses the personalization of conversational agent (CA)-patient interactions in a health care for chronic diseases (HCD) context. CAs have found ample use in health care [1]. Here, chronic diseases are an increasingly growing societal and economic burden [2]. They require challenging disease management that often spans over a prolonged period and is cost-and labor intensive [3,4]. In this context, the interaction between health care professionals and chronically ill patients plays a fundamental role; it is long-known to constitute a key factor for patient satisfaction, treatment adherence, and associated subsequent treatment success [5–7]. However, it has been unknown how interaction styles can be implemented into CA-patient interactions and what their effect on health care related interaction outcomes would be. Against this background, personalization offers a promising point of view as it caters to the paradigmatic shift of physician- focused to patient-focused care at the turn of the century [4]. The development and implementation of personalized health care CAs for chronic disease management has the potential to reduce the economic and personal burden of human health care professionals.
The goal of this dissertation is as following: (i) to develop and validate a process of inducing different interaction styles in a chronic health care CA-patient interaction, (ii) to investigate the personalization of health care CAs in the context of HCD, and (iii) to offer tangible insights to health care professionals on personalized CAs for implementation in a chronic disease management context. This dissertation consists of four papers, whereas Paper 1 describes the development and validation of a novel approach to induce different interaction styles in a CA-patient interaction, Paper 2 investigates interaction style preferences of chronically ill patients as well as implements and experimentally tests the moderators of the preferences of chronically ill patients for the interactions styles that were developed in Paper 1, Paper 3 describes the development and evaluation of a novel intervention design and a multi-stakeholder CA, and Paper 4 presents a systematic literature review on the current state of Artificial Intelligence (AI)-based CAs in HCD.
II Zusammenfassung
Diese Dissertation befasst sich mit der Personalisierung von Conversational Agent (CA)-Patienten-Interaktionen im Kontext des Gesundheitsmanagement bei chronischen Krankheiten. CAs haben im Gesundheitsmanagement weitreichende Verwendung gefunden [1]. Chronische Krankheiten stellen hier eine zunehmend wachsende gesellschaftliche und wirtschaftliche Belastung dar [2]. Sie erfordern ein anspruchsvolles Krankheitsmanagement, das sich oft über einen längeren Zeitraum erstreckt und kosten- und arbeitsintensiv ist [3,4]. In diesem Zusammenhang spielt die Interaktion zwischen medizinischem Fachpersonal und chronisch kranken Patienten eine fundamentale Rolle; es ist seit langem bekannt, dass sie einen Schlüsselfaktor für die Patientenzufriedenheit, die Behandlungsadhärenz und den damit verbundenen späteren Behandlungserfolg darstellt [5–7]. Bisher war jedoch nicht bekannt, wie Interaktionsstile in CA-Patienten-Interaktionen implementiert werden können und welchen Einfluss sie auf die gesundheitsbezogenen Interaktionsergebnisse haben. Vor diesem Hintergrund bietet die Personalisierung einen vielversprechenden Gesichtspunkt, da sie dem paradigmatischen Wechsel von einer arzt- zu einer patientenfokussierten Versorgung zur Jahrhundertwende Rechnung trägt [4]. Die Entwicklung und Implementierung von personalisierten CAs für das Management chronischer Krankheiten hat das Potenzial, die wirtschaftliche und persönliche Belastung der Menschen im Gesundheitswesen zu reduzieren.
Das Ziel dieser Dissertation ist wie folgt: (i) die Entwicklung und Validierung eines Verfahrens zur Induktion verschiedener Interaktionsstile in eine Interaktion zwischen chronisch kranken Patienten und CAs, (ii) die Untersuchung der Personalisierung von CAs im Gesundheitswesen im Kontext von HCD und (iii) die Bereitstellung konkreter Erkenntnisse für Fachkräfte im Gesundheitswesen über personalisierte CAs zur Implementierung im Kontext des Managements chronischer Krankheiten. Diese Dissertation besteht aus vier Beiträgen, wobei Artikel 1 die Entwicklung und Validierung eines neuartigen Ansatzes zur Induktion verschiedener Interaktionsstile in einer CA-Patienten-Interaktion beschreibt, Artikel 2 die Interaktionsstil-Präferenzen chronisch kranker Patienten untersucht sowie die Moderatoren der Präferenzen chronisch kranker Patienten für die in
III Artikel 1 entwickelten Interaktionsstile implementiert und experimentell testet, Artikel 3 die Entwicklung und Evaluation eines neuartigen Interventionsdesigns und einer Multi-Stakeholder-CA beschreibt und Artikel 4 eine systematische Literaturübersicht über den aktuellen Stand der auf Künstlicher Intelligenz (KI) basierenden CAs in der HCD darstellt.
IV Acknowledgments
I mainly want to thank my supervisor Prof. Dr. Florian von Wangenheim for his ongoing support, guidance, feedback, and trust in me and my co-examiner Dr.
Tobias Kowatsch, whose energy and enthusiasm for the topic of conversational agents and health care are truly contagious.
I also want to thank the co-authors of my papers for the good teamwork and constant effort.
I finally want to thank my family who continuously encourages and motivates me to achieve the best I can.
V Summary ... I Zusammenfassung ... II Acknowledgments ... IV Table of content ... V
Introduction ... 1
Paper Overview ... 5
Paper Summaries ... 6
Summary of Paper 1 ... 6
Summary of Paper 2 ... 7
Summary of Paper 3 ... 9
Summary of Paper 4 ... 11
Discussion and Contributions ... 12
Outlook ... 15
Abbreviations ... 17
References ... 18
Appendix A: Full papers ... 22
Paper 1 ... 23
Paper 2 ... 55
Paper 3 ... 95
Paper 4 ... 142
Appendix B: Curriculum Vitae ... 159
Introduction
Conversational agents (CAs) or chatbots are computer systems that imitate natural conversation with human users through images and written or spoken language [1].
In health care, CAs have demonstrated multiple benefits for disease diagnosis, monitoring, or treatment support in the last two decades [9–12]. They are used as digital interventions to deliver cost-efficient and scalable medical support solutions that can be delivered at any time and any place via web-based solutions or mobile apps [13–15]. CAs have found ample health care applications for a variety of diseases such as breast cancer [16], asthma [17], Alzheimer [18], or general assistance for health coaching to promote a healthy lifestyle [19].
Besides the medical importance of all these applications, one of the most urgent and immediate health care challenges of the 21st century in health care is the rise of chronic diseases [4]. Chronic diseases are one of the leading drivers for reduced quality of life and increased economic health care expenses through repeated hospitalization, disability, and treatment expenditures [2]. In the United States alone, they affected over 50% of adults in 2016 and accounted for 86% of health care spending [4]. Hvidberg et al [20] and others defined chronic conditions as ailments that are anticipated to last at least 12 or more months, lead to functional limitations, and require continuous medical support [21,22]. Such health care for chronic diseases (HCD) entails fundamentally different prevention, treatment, and management approaches than acute conditions, which are episodic, allow for general solutions, and can be treated within health care sites [4]. In contrast, chronic conditions require challenging lifestyle and behavioral changes, frequent self-care, and ongoing and personalized treatment that go beyond traditional health care sites and reach personal settings [4,23,24]. Related care often spans over a prolonged period, aims at generally improving health-related quality of life through controlling of symptoms or prolonging life expectation through secondary prevention, and requires personalized disease management that functions long term [3,4]. Here, CAs can offer a viable and cost-efficient solution for supporting and extending cost- and time-expensive human care as they allow for ubiquitous, scalable, and digital assistance outside the clinical setting [1,25].
Against this background and as known from human-to-human settings, the
interaction style between patients and health care professionals is an imperative part of successful and long-term collaboration [5]. It is recognized as critical success factor for patients’ satisfaction and treatment related behavior, treatment adherence, and subsequent treatment outcome [6,7]. However, it remains so far effectively unknown (i) how different interaction styles can be implemented into CA- patient interactions to achieve personalization of said CA-patient interaction and (ii) what their effect on patient preferences for several interaction related outcomes such as willingness to change behavior would be.
Personalization offers a promising point of view in this context; the paradigmatic shift from physician-focused care towards patient-centered care in the last decades [4] puts an increasing weight on recognizing the personalized needs and requirements of empowered patients [8]. Personalization describes the “process of using a customer’s information to deliver a targeted solution to that customer” [26, p1344] and consists of three core stages: (1) learning about the customer’s likings, mainly driven by collecting and analyzing personal data, (2) matching the gained insights with the company’s product offering, and (3) learning about the success of the hereby achieved personalization by evaluating the customers’ satisfaction with the offered products [26]. In addition to understanding personalization as a process with various stages, Fan & Poole [27] propose an interdisciplinary framework of the three most fundamental components for personalization. These components describe various dimensions of a system or object providing personalization to a user or a group of users. The first basic component represents the part of the system that provides personalization, i.e. what is personalized (e.g. content, interface, or functionality [27]), the second basic component is the target of personalization, i.e.
for whom the personalization is provided, and the third basic component is the source of personalization, i.e. who directs the personalization (e.g. the user or an automatic system).
Besides such an interdisciplinary framework, there is little knowledge about how personalization of conversational agents is constituted in a digital health care context where most personalized applications to date still lack theoretical and evidence-based foundations [28]. Addressing this gap, this dissertation does not only aim at answering the above-mentioned questions relating to interaction styles between patients and CAs, but further contributes to a better understanding of
personalization of CAs in a digital health care context. I focus on the first stage of the overarching personalization framework by [26], i.e. learning about the customer, here: the chronic patient, and aim at understanding how preferences of interaction styles play out between patients and a digital CA. Beside the focus on the first step of personalization as described by [26], the papers of this dissertation can be further classified among the basic components for personalization of the framework by [27].
Figure 1 depicts this categorization and illustrates the localization of the papers within the framework.
Figure 1: Personalization framework with localization of dissertation papers.
The main goal of this dissertation is to address the above mentioned questions around CA-patient interactions, i.e. (i) how different interaction styles can be implemented into CA-patient interactions to achieve personalization of said CA- patient interaction and (ii) what their effect on patient preferences for several interaction related outcomes such as willingness to change behavior would be, and to contribute to a better understanding of personalization of health care related CAs.
In detail, I outline the development and subsequent experimental testing of a novel procedure to induce two different interaction styles (the paternalistic and
deliberative interaction style [5]) as personalization factor into a health care related CA (Paper 1). With this, I address the first basic personalization component by [27], i.e. what to personalize. In Paper 2, I investigate the preferences of chronically ill patients on their interaction with a CA. This paper consists of two studies, whereas the first study outlines baseline differences in interaction preferences of chronically ill patients regarding their interaction with a CA, and the second study implements the validated interaction styles of Paper 1, which are created with our newly developed approach, into a real-life CA-patient interaction for investigating preferences by patients for and moderates of these preferences of two CA-patient interaction styles. By focusing on chronically ill patients, this paper addresses the second basic personalization component by [27], i.e., for whom to personalize. In Paper 3, I investigate a variation of the second basic personalization component by outlining the development and experimental testing of a novel intervention design for bringing together chronic patients and CAs in a multi-stakeholder setup. With Paper 4 finally, I address the third basic personalization component by [27], i.e. who directs the personalization. I conduct an in-depth analysis of the current state of conversational artificial intelligence (AI)-based CAs for health care for chronic diseases (HCD), hereby outlining the state of the art of automatic systems that can be used for e.g. directing personalization.
In addition to the scientific findings of this dissertation, I further intend to support practitioners in the health care context. For them, this dissertation can provide guidance on how to react to the needs of an increasing number of patients with chronic diseases with the help of scalable, digital, and personalized technological solutions in the form of CAs.
In the next section, I will give an overview over the papers of this dissertation.
Paper Overview
The following Table 1 gives an overview over the papers in this thesis. The table further provides information on my contribution in each of the papers, their publication status including their dois, and relevance for this dissertation. In the next section, I will provide summaries of all included papers.
Table 1: Overview and characteristics of papersa
Paper 1 Paper 2 Paper 3 Paper 4
Title Deliberative and Paternalistic Interaction Styles for Conversational Agents in Digital Health: Procedure and Validation Through a Web- Based Experiment
Personalization of Conversational Agent- Patient Interaction Styles for Chronic Disease Management:
Results from Two Studies with COPD Patients
Conversational Agents as Mediating Social Actors in Chronic Disease
Management Involving Health Care Professionals, Patients, and Family Members: Intervention Design and Results of a Multi-site, Single-arm Feasibility Study
AI-based
Conversational Agents for Chronic Conditions:
A Systematic Literature Review
Author(s) Theresa Schachner*, Christoph Gross*, Andrea Hasl, Tobias Kowatsch, Florian von Wangenheim
Christoph Gross*, Theresa Schachner*, Andrea Hasl, Dario Kohlbrenner, Christian Clarenbach, Tobias Kowatsch, Florian von Wangenheim
Tobias Kowatsch, Theresa Schachner, Samira Harperink, Filipe Barata, Ullrich Dittler, Grace Xiao, Catherine Stanger, Florian von Wangenheim, Elgar Fleisch, Helmut Oswald &
Alexander Moeller
Theresa Schachner, Roman Keller, Florian von Wangenheim
Contributions by Theresa Schachner
Study conception and design, data collection, data analysis, first draft, revisions, and final draft
Study conception and design, data collection, data analysis, first draft, revisions, and final draft
First draft, revisions, and
final draft Study conception and design incl. search strategy, data screening, data extraction, data analysis, first draft, revisions, and final draft
Publication
status Accepted in JMIR in 2020, forthcoming in 2021
Submitted and in
review in JMIR, 2020 Submitted and in re-
review in JMIR, 2020 Published in JMIR, 2020
Link / doi https://preprints.jmi r.org/preprint/2291 9/accepted doi:
https://doi.org/10.2 196/preprints.22919
https://preprints.jmir.
org/preprint/26643 doi:
https://doi.org/10.219 6/preprints.26643
https://preprints.jmir.org/
preprint/25060 doi: :
https://doi.org/10.2196/p reprints.25060
https://www.jmir.org/
2020/9/e20701/pdf doi:
doi:10.2196/20701 Relevance for
dissertation Included Included Included Included
Paper Summaries
Summary of Paper 1 – “Deliberative and Paternalistic Interaction Styles for Conversational Agents in Digital Health: Procedure and Validation Through a Web-Based Experiment”
This paper describes the development of a novel approach to reproducibly induce a deliberative and a paternalistic interaction style into the interaction between CAs and humans in a digital health care context with a focus on chronic conditions. It further tests and validates this novel approach in a chronic health care setting.
This paper investigates the first basic component of personalization [27], i.e. the personalization aspect by focusing on the interaction styles between CAs and patients. So far, it has been unknown how such personalized interaction styles can be developed and subsequently employed to CA-patient interactions and whether these styles are recognizable by human users.
To close this gap, we developed a novel and systematic approach that allows inducing a paternalistic and a deliberative interaction style into a CA-patient interaction. We tested the developed interaction styles in a between-subject web- based experiment (n=88) to assess whether humans can correctly identify and label the according interaction style. The resulting approach is based on the Roter Interaction Analysis System (RIAS) [34]. The results of the web-based experiment showed that the relation between the randomly assigned CA type and the correct identification of CA type by the participants was significant. This indicates that participants could correctly identify and label the induced interaction styles. This has implications for future applications of health care CAs; instead of providing one generic CA to all patients, future health care CAs could be applying a deliberative or a paternalistic interaction style, which could lead to an improved CA-patient interaction quality and subsequent improvements of patient satisfaction, treatment adherence, and treatment outcome.
Future research should implement and test the developed interaction styles in real- life CA- patient settings as well as investigate moderators for personal interaction style preferences in addition to compare results between medical conditions.
Summary of Paper 2 – “Personalization of Conversational Agent-Patient Interaction Styles for Chronic Disease Management: Results from Two Studies with COPD Patients”
This paper focuses on preferences for CA-delivered interaction styles by chronically ill patients and comprises of two consecutive studies. Patients of both studies suffer from Chronic obstructive pulmonary disease (COPD), a lung disease whose main characteristics are long-term breathing problems and poor airflow [35].
This paper investigates the second basic component of personalization [27], i.e. the personalization target in the form of chronically ill patients. It is long known that different patients prefer different interaction styles with their medical counterpart [36,37], and that a correctly adapted and personalized interaction style plays an essential role for patient satisfaction, treatment adherence, and final treatment outcome. However, there lacks information on the preferences for and effects of personalized interaction styles between CAs and patients.
With two consecutive studies, this paper thus investigates (i) whether COPD patients express any personal preferences for any of the four prevalent interaction styles – paternalistic, informative, interpretive, or deliberative interaction style [5] – for their interaction with a CA (study 1), and (ii) which aspects influence such potential personal preferences (study 2).
The first study (n=117) revealed a preference for two interaction styles (deliberative [50 out of 117] and informative [34 out of 117]) across demographic characteristics as well as a preference for the paternalistic interaction style for patients with more severe COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) level 3/4). The second study (n=123) was based on the newly developed and validated CA-patient interaction styles that are described in Paper 1 of this dissertation and thus presents a follow up on this first paper. The results of this second study showed that age and personal disease experience moderate personal preferences for CA-patient interaction styles of COPD patients. In detail, we found that younger (older) patients report better interaction-related outcomes when interacting with a deliberative (paternalistic) CA, and that COPD patients with
longer (shorter) personal disease experience report better interaction-related outcomes when interacting with a deliberative (paternalistic) CA. We did not find evidence that gender, disease level, or disease literacy moderate preferences for any CA interaction style. However, irrespective of the CA type, disease literacy positively predicted both investigated dimensions of working alliance (goal agreement and attachment bond) [38].
The results of this paper indicate that (i) chronically ill COPD patients express personal preferences for their interaction with a CA, and that (ii) age and personal disease experience moderate such interaction style preferences. These insights have implications for the development of personalized health care CAs. By deploying such personalized CAs, improved interaction related outcomes between patients and CAs could be achieved. In the long term, this could support increased adherence to health-promoting behavior support provided by CAs. Overall, these achievements could support chronic disease management and could further strengthen the utilization of CAs therein as scalable, digital, ubiquitous, and cost- efficient personalized tools.
We specifically advise future research to extend the investigation of personal preferences and their moderators for CA-patient interaction styles with a greater variety of interaction styles, different diseases, and by extending the underlying technology to implement Artificial Intelligence (AI)-based architecture to allow for an automatic, dynamic, and even more personalized CA-patient interaction.
Summary of Paper 3 – “Conversational Agents as Mediating Social Actors in Chronic Disease Management Involving Health Care Professionals, Patients, and Family Members: Intervention Design and Results of a Multi-site, Single-arm Feasibility Study”
This paper investigates the role of CAs in a health care related multi-stakeholder environment. It investigates the second basic component of personalization [27], i.e.
the personalization target by focusing on trustful personalized collaborations between patients, health care professionals, and family members for successful management of chronic diseases.
In detail, this paper describes the design and evaluation of the CA MAX, which offers a novel asthma intervention for 10-15-year old asthma patients. The CA is designed to increase their cognitive skills (i.e. knowledge about asthma) and behavioral skills (i.e. inhalation technique). On an intervention level, MAX offers a new and personalized intervention approach where asthma patients can choose individually when to interact with the CA for completing their tasks instead of having to follow a pre-defined rigid intervention regime [e.g., 39,40].
The results of the multi-site feasibility study (n=49) show an overall positive evaluation of the personalized CA, a strong patient-CA working alliance, and high acceptance by all involved stakeholders. It could further lead to an improvement of the targeted cognitive and behavioral skills of the patients. In addition, the overall completion rate of the intervention was high with 75.5%.
The results of this paper indicate that personalized CAs are accepted as mediating social actors in a multi-stakeholder environment including health care professionals, chronic patients, and family members. They further show potential to improve health-relevant outcomes in chronic disease management by increasing e.g. treatment related behavioral skills of patients. The results of this study have implications for the development and integration of digital health care CAs in complex multi-stakeholder environments. A digital CA like MAX can act as orchestrator in such situations and hereby supports the health care professional in charge with treatment related tasks. Instead of replacing the human professionals, such a CA can expand their capabilities by being available at and time and in any
place.
Future work could comprise the development of a real-life product of the CA MAX, which could be offered to a wider range of users. In addition, the implementation of different interaction styles as presented in Paper 1 and Paper 2 is advised to further improve CA-patient interactions.
Summary of Paper 4 – “AI based Conversational Agents for Chronic Conditions – A Systematic Literature Review”
This systematic literature review gives an overview over existing Artificial Intelligence (AI)-based CAs that are applied for chronic conditions. This paper hereby adds to the inquiry of the third basic component of personalization [27], i.e.
the source or who directs the personalization (here: an automatic system).
There is a rising number of CAs equipped with AI architecture, which are implemented and used in a variety of sectors [10,41,42]. These CAs allow for a more authentic and personalized interaction with their users than traditional rule based CAs [13]. Within health care, they are increasingly used for applications such as education, diagnosis, and support [9–12]. A growing body of literature demonstrates their benefits and investigates their application [16, 19, 43-45].
However, so far, there was no systematic investigation of their usage for chronic conditions.
With this systematic literature review, we closed this gap by reviewing the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. We conducted a systematic literature review accessing seven data bases and several Google alerts that used variations of the search terms, and screened reference lists for other relevant articles. Our search retrieved 2062 individual articles, out of which 10 met the inclusion criteria after abstract and full-text screening.
The results of this review show that the literature on AI-based CAs for chronic conditions is in a nascent stage and currently mostly consists of quasi-experimental studies. Most existing CAs are in a prototype stage, utilize natural language processing, and allow for multimodal user interaction.
We advise future research to engage in evidence- based evaluation of the CAs and the underlying AI architecture. We further advise the comparison of several CAs within and between different chronic health conditions.
Discussion and Contributions
This dissertation focuses on the personalization of CAs in a health care context, with a special emphasis on chronic diseases and the interaction styles between CAs and patients. I presented four distinct scientific papers that each address a basic component of personalization as outlined by [27] and which can be localized within the first step of a hypothetical personalization process as outlined by [26].
The research of this dissertation makes several contributions to the areas of CAs in digital health care, CA-patient interactions, and personalization, which I will discuss in this section.
First, this dissertation adds to the growing field of scientific literature of CAs in digital health care. Here, CAs get increasingly used for various applications such as providing information to patients [16], helping with self-anamnesis [44], or assisting coaching for a healthy lifestyle [19]. In addition, they have potential to prove useful for disease management, which is especially important for chronically ill patients who require ongoing and often time-intensive medical support [46–48].
Paper 1 contributes to the existing literature on CAs in digital health care by describing the development and validation of a completely novel approach for transferring patient-physician interactions to patient-CA interactions. It hereby contributes to the current scientific understanding of capabilities of CAs in their role as supporting or even partially replacing human health care professionals, whose time is limited and expensive, in a context of chronic disease management [46].
Paper 2 extends the findings of Paper 1 and the associated scientific understanding of the capabilities and roles of CAs in digital health care by describing the results of two studies about personal preferences of chronically ill patients regarding their interaction style with CAs. Paper 3 contributes to the field of digital health care CAs by presenting a novel design of a multi-stakeholder CA that manages to combine human health care professionals, patients, and patients’ family members in the context of a chronic disease management intervention.
In general, scientific literature on CAs in health care is evolving. For example, several systematic literature reviews have outlined and discussed applications of CAs for
health care in general [9,48] or for mental health applications [49]. However, so far there was surprisingly little systematic information available on CAs in health care that are based on AI architecture, which allows for a more natural and thus realistic interaction between CAs and patients due to such CAs’ ability to imitate natural written and spoken language [50,51]. Paper 4 of this dissertation addresses this gap by providing a systematic literature review on the state of the art of AI-based CAs for health care for chronic diseases. The results show that the field is nascent, but advancing, and primarily comprises of prototype applications, which are scattered across chronic conditions and often lack evidence-based evaluation.
Second, this dissertation contributes to the scientific understanding and knowledge of CA-patient interactions, an area with scarce prior scientific work. This area derives insights from patient-physician interactions in the medical field. Here, the importance of patient-physician interactions is widely recognized and known as key success factor for various aspects such as treatment adherence, patient satisfaction, and long-term treatment outcome [3,46]. However, there has been practically no knowledge on how to create, implement, and subsequently evaluate any patient-CA interaction styles. I address this gap and contribute to the scientific knowledge with Paper 2 and Paper 3. Paper 2 describes a novel way of developing and implementing two major interaction styles – the deliberative and paternalistic interaction style [5]
– into a CA-patient interaction. Besides developing and describing the approach, this paper also validates it with a web-based experiment. Paper 3 extends the insights of this Paper 2 by implementing the developed approach in a CA-patient interaction with COPD patients. It first outlines the existence of baseline differences in CA- patient interaction preferences as observed in a paper-based study. It then shows with a web-based experiment with COPD patients that these patients exhibit personal preferences for one of the two prompted interaction styles and that age and personal disease experience moderate these preferences.
Third, this dissertation contributes to the literature of personalization, especially related to CAs and HCD. In detail, I localize my work within the first step of a hypothetical personalization process as described by [26], i.e. learning about the
customer of personalization, and further address each of the three basic components of personalization as described by [27]. In detail, Paper 1 contributes to the current scientific knowledge about the first basic component, the aspect of personalization [27], by providing novel insights into the personalization of the patient-CA interaction in the context of digital health with a focus on chronic diseases. It shows that it is possible to develop a systematic approach to induce different interaction styles into a CA-patient interaction, which lays the basis for creating and evaluating personalized CA-patient interactions. Paper 2 adds to these insights by contributing to the scientific knowledge about the second main component of personalization, the target audience [27]. It focuses specifically on patients suffering from COPD, one of the most prevalent chronic disease worldwide [35]. This paper shows that baseline differences in this target audience toward preferred personal interaction styles with CAs exist, and further details that COPD patients’ preferences between two specific interaction styles – the deliberative and paternalistic interaction style [5] – are moderated by their age and personal experience with their disease. Paper 3 also contributes to the knowledge about the second basic component for personalization in a CA digital health care context but addresses a different aspect thereof. It focuses not only on a target group that is characterized by patients with a specific chronic disease, here: asthma, but also addresses a wider group of personalization targets, i.e. a multi-stakeholder compound of patients, their family members, and health care professionals. This paper describes a novel type of a health care intervention for this target audience and includes several aspects of personalization, such as choice of gender of the used CA or overall intervention length. Finally, Paper 4 contributes to the scientific knowledge about the third main component of personalization, the source [27]. It addresses AI-based CAs for chronic health care, a nascent and evolving form of health care CAs. Such CAs can be a powerful source of automating personalization when further developed and systematically evolved.
Outlook
Besides adding novel insights and contributing to the current scientific knowledge, the research papers presented in this dissertation lay ground for a variety of future research opportunities. Detailed advise for future research is outlined in each individual paper (see Appendix A). This section aims at giving an outline of the most prevalent options for continuing the research of this dissertation.
First, Paper 1 – presenting a novel and systematic approach for developing and inducing two specific interaction styles between patients and CAs – could be further extended by developing and validating additional interaction styles, e.g. an informative interaction style as described by [5]. This could allow insights into a more fine-grained adaption to personal needs of patients for their interaction style preferences with a digital CA. In the long run, having a wider variety of interaction styles available could also allow a dynamic adaption to each patient over time, hereby even stronger contributing to e.g. an increased long-term adherence to CA induced medical treatment.
Second, the research presented in Paper 2 – already constituting an extension of the research of Paper 1 – could be extended in the following ways: (i) investigating a long-time interaction between patients and the CA for understanding potential changes of personal interaction style preferences over time, (ii) investigating different (chronic) diseases in order to evaluate disease type as potential moderator for interaction style preferences, (iii) introducing AI as underlying CA architecture to provide a more natural personal interaction between CAs and patients.
Third, the work of Paper 3 – outlining a novel CA driven intervention design in a multi-stakeholder environment – could be extended by introducing different interaction styles as presented in Paper 1 and Paper 2. This would allow for investigating them in a more complex setting and with a different patient target group (COPD in Paper 1 and Paper 2, asthma in Paper 3). In addition, the research of this paper could be extended by working with different age groups and different
(chronic) diseases. Comparable to the future research opportunities of Paper 2, this paper could also benefit from introducing AI into the underlying CA architecture.
When it comes to the aspect of personalization, the here presented research could be further developed along two main axes: (1) extending work along the personalization process as described by [26]. i.e. adding research on stage two and three of the process, and (2) diversifying and expanding work on each main basic personalization component as described by [27].
As for the personalization process by [26], the integration and evaluation of different interaction styles in the CA MAX, which is currently closest to becoming a real-life industry product, for addressing the second and third step of the personalization process by [26] is suggested. In this context, it could be further explored which other health care related products besides the CA MAX could benefit from personalization based on different interaction styles.
As for the main basic personalization components by [27], the above-mentioned suggestions for future research based on Paper 1-3 can be mapped to extending work here as well. For example, developing and investigating additional CA-patient interaction styles adds to deepening knowledge on the aspect of the personalization (i.e., the first main component of personalization [27]). By expanding the presented research in this dissertation to a wider variety of (chronic) diseases, the target of the personalization (i.e., the second main component of personalization [27]) is further examined. And lastly, implementing and investigating the effects of AI as underlying architecture in health care CAs offering different interaction styles to patients further expands knowledge on the source of personalization (i.e., the third main component of personalization [27]).
Abbreviations
AI = Artificial Intelligence CA = Conversational Agent
COPD = Chronic Obstructive Pulmonary Disease
GOLD = Global Initiative for Chronic Obstructive Lung Disease HCD = Health care For Chronic Diseases
JMIR = Journal Of Medical Internet Research
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Appendix A: Full papers
Paper 1
This paper can be published and reproduced under the terms of Creative Commons Attribution license 4.0 in the context of this dissertation.
Deliberative and paternalistic interaction styles for conversational agents in digital health: Procedure
and validation through an online experiment
Theresa Schachner, Christoph Gross, Andrea Hasl, Tobias Kowatsch, Florian von Wangenheim
Submitted to: Journal of Medical Internet Research on: July 27, 2020
Disclaimer: © The authors. All rights reserved. This is a privileged document currently under peer-review/community review. Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review purposes only. While the final peer-reviewed paper may be licensed under a CC BY license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.
Table of Contents
Original Manuscript ... 5 Supplementary Files ... 23 Figures ... 24
Figure 3 ... 25
Figure 4 ... 26
Figure 5 ... 27
Figure 1 ... 28
Figure 2 ... 29 Multimedia Appendixes ... 30
Multimedia Appendix 2 ... 31
Multimedia Appendix 3 ... 31
Multimedia Appendix 4 ... 31
Multimedia Appendix 1 ... 31
Deliberative and paternalistic interaction styles for conversational agents in digital health: Procedure and validation through an online experiment
Theresa Schachner1* BA, BSc, MSc; Christoph Gross1* BSc, MA; Andrea Hasl2 BSc, Mag.; Tobias Kowatsch3 Prof., Dr.; Florian von Wangenheim1 Prof., Dr.
1Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich Zurich CH
2Department of Educational Sciences, University of Potsdam Potsdam DE
3Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland St. Gallen CH
*these authors contributed equally
Corresponding Author:
Christoph Gross BSc, MA
Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich WEV J 409, Weinbergstr. 56/58
Zurich CH
Abstract
Background: In recent years, the number of people suffering from chronic conditions that require ongoing medical support reaching into the everyday lives of patients is constantly increasing. Global health systems are, however, not adequately equipped for this extraordinarily time-consuming and cost-intensive development. Here, conversational agents (CAs) can offer easily scalable and ubiquitous support. However, different aspects have not yet been sufficiently investigated to fully exploit their potential. One such trait is the interaction style between patients and CAs. In human-to-human settings, the interaction style is an imperative part of the relationship between patients and physicians. The patient-physician interaction is recognized as critical success factor for patient's satisfaction, treatment adherence, and subsequent treatment outcome. However, it remains so far effectively unknown how different interaction styles can be implemented into CA relationships and if these are recognizable by users.
Objective: The objective of this paper is to develop an operationalization scheme to induce two specific interaction styles into CA-patient dialogues and subsequently test and validate them in a chronic healthcare context.
Methods: Based on the Roter Interaction Analysis System (RIAS) and iterative evaluations by scientific experts and medical healthcare professionals, we identified 15 communication components that characterize the two operationalized interaction styles, i.e., the deliberative and paternalistic interaction style. These communication components were used to develop two CA variations, each representing one of the two interaction styles. We assessed them in an online between-subject experiment. Here, participants were asked to put themselves in the position of a patient suffering from chronic obstructive pulmonary disease (COPD). Participants were randomly assigned to interact with one of the two CAs and subsequently asked to identify the respective interaction style. A chi-square test was used to assess the correct identification of the CA-patient interaction style.
Results: 88 individuals (48% female, mean age= 31.5 years) fulfilled the inclusion criteria and participated in the online experiment. Participants in both the paternalistic and deliberative condition correctly identified the underlying interaction style of the CAs in more than 80% of the assessments (X2 (1, 88) = 38.23, P = .000; r? = .68). The validation of the operationalization scheme was hence successful.
Conclusions: We developed an operationalization scheme tailored for a medical context to induce a paternalistic, respectively, deliberative interaction style into a written interaction between a patient and a CA. We successfully tested and validated the operationalization scheme in an online experiment with 88 participants. Future research should implement and test the operationalization scheme with actual chronic patients and compare results between medical conditions. The operationalization scheme can further be used as a starting point to develop dynamic CAs that adapt their interaction style to their users.
(JMIR Preprints 27/07/2020:22919)
DOI: https://doi.org/10.2196/preprints.22919
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Original Manuscript
Deliberative and paternalistic interaction styles for conversational agents in digital health: Procedure and validation through an online experiment
Theresa Schachner*1, Christoph Gross*1, Andrea Hasl2,3, Tobias Kowatsch1,4+, Florian von Wangenheim1+
*Equal contribution, +shared last authorship
1 Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
2 Department of Educational Sciences, University of Potsdam, Potsdam, Germany
3 International Max Planck Research School on the Life Course (LIFE), Berlin, Germany
4 Center for Digital Health Interventions, Institute of Technology Management, University of St.
Gallen, St. Gallen, Switzerland Contact Person
Christoph Gross
WEV J 409, Weinbergstr. 56/58, 8092 Zurich, Switzerland Email: christophgross@ethz.ch
Abstract
Background: In recent years, the number of people suffering from chronic conditions that require ongoing medical support reaching into the everyday lives of patients is constantly increasing. Global health systems are, however, not adequately equipped for this extraordinarily time-consuming and cost-intensive development. Here, conversational agents (CAs) can offer easily scalable and ubiquitous support. However, different aspects have not yet been sufficiently investigated to exploit their potential fully. One such trait is the interaction style between patients and CAs. In human-to-human settings, the interaction style is an imperative part of the interaction between patients and physicians. The patient-physician interaction is recognized as a critical success factor for patient's satisfaction, treatment adherence, and subsequent treatment outcome. However, it remains so far effectively unknown how different interaction styles can be implemented into CA interactions and if these are recognizable by users.
Objective: The objective of this paper is to develop an approach to reproducibly induce two specific interaction styles into CA-patient dialogues and subsequently test and validate them in a chronic healthcare context.
Methods: Based on the Roter Interaction Analysis System (RIAS) and iterative evaluations by scientific experts and medical healthcare professionals, we identified 10 communication components that characterize the two developed interaction styles, i.e., the deliberative and paternalistic interaction style. These communication components were used to develop two CA variations, each representing one of the two interaction styles. We assessed them in an online between-subject experiment. Here, participants were asked to put themselves in the position of a patient suffering from chronic obstructive pulmonary disease (COPD). Participants were randomly assigned to interact with one of the two CAs and subsequently asked to identify the respective interaction style. A chi-square test was used to assess the correct identification of
