The Doctor Will See Yourself Now: Review and Discussion of a

Mass-market Self-service Technology for Medical Advice

Joseph Ollier

and Tobias Kowatsch

2,3 b

Center for Digital Health Interventions, Chair of Technology Marketing, ETH Zurich, Zurich, Switzerland

Center for Digital Health Interventions, Chair of Information Management, ETH Zurich, Zurich, Switzerland

Center for Digital Health Interventions, Institute of Technology Management, St. Gallen, Switzerland

Keywords: Self-service Technology, Digital Health, Data Privacy.

Abstract: Leveraging new technological tools in medical service delivery has been shown as important factor adding

scalability and/or value to patient care. However, as of yet, relatively little research has focused on the

implementation of mass-market digital health products to address population needs. The current paper

examines one such tool; a browser-optimized smartphone app developed by a major Swiss health insurance,

offering validated medical information for patients to identify the optimal care path of action (i.e. self-care,

pharmacy visit, general practitioner visit, hospital visit). Summary statistics of usage data from 149 922 users

over 6 months are outlined, overviewing; (i) key usage cases for the service over time, (ii) for whom the app

was used, (iii) dropout rates and potential design pitfalls. Possible themes are identified such as the importance

of additional information regarding privacy or service/usage experience information, and some considerations

for both the research, design and implementation communities.

1 INTRODUCTION

Never before in human history has so much

information been available at a few taps of the finger

(Acquisti, Brandimarte, & Loewenstein, 2015), yet

finding trustworthy and legitimate sources of medical

information remains a challenge (Soldaini & Goharian,

2017). At one end of the spectrum, digital platforms

have enabled the recycling of long disputed

information to new audiences, for example, the rise of

“anti-vaxxer” campaigns (Kennedy, 2019), whereas at

the other end of the spectrum, digital artefacts have

enabled health information to connect with new

audiences in a meaningful and engaging manner

(Barello et al., 2015). Somewhere between these two

extremes resides the average individual, searching for

information online and making health decisions for

themselves or their family. With over 1 billion health

related searches globally on Google, equating to 70

000 queries a minute, the consumer desire for health

information is clear (M. Murphy, 2019).

For policy makers and firms alike, addressing this

desire for health information is both an opportunity

and a challenge. Opportunities lie in reducing the

https://orcid.org/0000-0001-8603-0793

https://orcid.org/0000-0001-5939-4145

burden for healthcare systems; for example within

Switzerland, despite rising hospital admissions, only

2.4% of the national healthcare budget (equating to

19 CHF per month) is spent on preventative measures

such as the dissemination of healthcare information

(FSO, 2019). For firms, creating trustworthy health

services through self-service technologies can

address this state of consumer confusion; forging a

meaningful connection with patients/customers and

delivering medical services that add value (Sweeney,

Danaher, & McColl-Kennedy, 2015; W.-T. Wang,

Cheng, & Huang, 2013). For all parties, addressing

these needs solves the long-standing issue of

individuals entering the health system at the wrong

point in time (Mayer, Villaire, & Connell, 2005);

either too early (before adequate self-care steps have

been taken) or too late (when the danger of serious

complications has increased).

In seeking to address these challenges a number

of mass-market digital tools have been developed by

organizations, offering information on medical

symptoms, check-ups, diagnosis or other information

(Lupton, 2016), such as Ada, a digital app health

companion (Ada, 2020) or the Health A-Z website

798

Ollier, J. and Kowatsch, T.

The Doctor Will See Yourself Now: Review and Discussion of a Mass-market Self-service Technology for Medical Advice.

DOI: 10.5220/0009373607980807

In Proceedings of the 13th Inter national Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 798-807

ISBN: 978-989-758-398-8; ISSN: 2184-4305

(NHS, 2020). However, as of yet, consumer reception

to such medical self-service technologies is relatively

little understood due to a lack of usage data released

by the implementers of such services to the general

public. The current paper therefore overviews some

first insights based on six months usage data with one

such tool developed and offered starting 2018 by a

major health insurer in Switzerland.

The remainder of this paper is structured as

follows: first, we review the conceptual background

of digital health information tools, including the

justification for both policy makers and firms that

enact them. Next, we overview the self-service

technology in use (a smartphone-optimized browser

app), and then we examine the highlighted results

from available usage data. Finally, findings are

reviewed in light of relevant academic work and

useful future research streams are addressed.

2 CONCEPTUAL BACKGROUND

In making health choices, it has long been known that

individuals face difficulties in making suitable deci-

sions within a complex healthcare system (Paasche-

Orlow, Parker, Gazmararian, Nielsen-Bohlman, &

Rudd, 2005). Individuals require easily available,

accurate and timely information (Hibbard & Peters,

2003), however the abundance of information does not

always translate into more informed choices. Visits to

the emergency department for minor complaints

(Mayer et al., 2005; S. M. Murphy & Neven, 2014;

Rieffe, Oosterveld, Wijkel, & Wiefferink, 1999) or

unnecessary general practitioner visits when a

pharmacy could provide better self-care information

and medication (Hassell, Rogers, & Noyce, 2000)

have long been established as contributing to

increased healthcare system costs (Hewner, Sullivan,

& Yu, 2018; Mayer et al., 2005; Rieffe et al., 1999).

Free healthcare services providing health

information have been touted as potentially useful

solutions, preventing individuals entering the

healthcare system at the wrong entry point (Hwang,

Liao, Griffin, & Foley, 2012). This has included

phone consultations (Hallfors, Saku, Makinen, &

Madanat, 2018), and more recently the utilization of

digital platforms (Bahadori, Teymourzadeh, &

Mousavi, 2018) such as email and smartphone

communications (Chua et al., 2017). These free

services have the added benefit of being a potentially

useful way to reach non-insured and/or low income

groups which may benefit in particular with a trusted

health information source (Hwang et al., 2012).

For patients, use of digital tools have been shown

to lead to higher health literacy (Xie, 2011) and by

consequence, less unnecessary emergency room or

general practitioner visits, which contribute to reducing

overcrowding in healthcare systems (FSO, 2019).

From a business perspective, for private enterprises

creating digital tools, services research has shown the

ability to work well with healthcare resources enables

a “co-creation” of value, with positive downstream

consequences for both customer and firm (McColl-

Kennedy, Vargo, Dagger, Sweeney, & van Kasteren,

2012; Sweeney et al., 2015). However, to date,

relatively little commercial data has been available

examining consumer reception to the introduction to

such digital products at a mass-market / population

level. Yet this is vital, for policy makers, practitioners

and academics to push the development of such digital

tools to their next steps of development.

3 METHOD

The current paper outlines data from a browser-based

smartphone app created by a major Swiss health

insurance firm, available on any device without

requiring the app’s download. The app was developed

based on a verified medical framework created

independently by doctors and adapted by the

insurance firm into a dialogue/survey like format.

Individuals could input their main and secondary

complaints and answer a variety of questions to

receive medical advice. The medical advice consisted

of a recommendation of a course of action, rather than

a medical diagnosis per se, as the apps purpose was

to direct individuals to the correct healthcare system

entry point for further evaluation. Upon completion

of the dialogue, individuals would be recommended

to; (i) take self-care steps, (ii) visit a pharmacy, (iii)

visit their general practitioner, (iv) visit emergency

department, for example. See figures 1-4 for

screenshots of the tool in English.

In total, 149 222 app uses were recorded during 6

months of field use in German-speaking Switzerland,

from the product launch in September 2018 to the end

of the data collection period in February 2019. The

tool was not widely marketed or available to the

general public, but current customers of the health

insurance firm were emailed a link stating that the

product was live and that they could access and use

the tool. A selection of data was made available by

our partner company for the purposes of

disseminating knowledge of healthcare innovations

in the field. Data made available includes; (i) for

whom the tool was used (age and gender), (ii) the

primary symptom (main usage reason), (iii) changes

The Doctor Will See Yourself Now: Review and Discussion of a Mass-market Self-service Technology for Medical Advice

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Figure 1: Dialogue start.

Figure 3: Self-care recommendation.

Figure 2: Dialogue symptom selection.

Figure 4: Doctor-visit recommendation.

Table 1: Buttons leading out of optimal usage path.

Use Button Description

Terms and conditions as PDF

Receive terms and conditions as a PDF document

Info Icons

Explain in more detail (e.g. about symptoms, the meaning of

statements)

Emergency

Emergency contact numbers given (911 equivalent)

Frequently asked questions

FAQ

Further information about the app, service information and

data and security

Contact center

Contact numbers for call center staff

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in disease ranking, showing most common symptoms

selected between 1-3 (first three months) and 4-6

months (latter three months) respectively, (iv) users

continuing/drop outs (per section of the app, where

section consists of a completed dialogue section

around symptoms and/or a separate page of

information) and buttons leading out of the optimal

usage path (i.e. clicking to view FAQs, or further

information buttons etc.) as summarized in table 1.

4 RESULTS

Figure 5 shows a breakdown of the use cases by age

and gender, whereby these figures reflect the intended

recipient of the medical information. Overall cases

were recorded for all age groups, however,

individuals aged 15+ reflect the bulk of intended

recipients of information. This is potentially as users

still exhibit a lack of trust and/or preference for

human support for cases of the very young or elderly.

Figure 6 outlines primary symptoms selected by

users. As likely to be expected, the primary use case

for the app is for common acute illnesses. “Other

complaints” (not included in figure) include

conditions such as insect stings/bites, heart

palpitations, joint problems etc. which occur less

frequently but require more specialist knowledge.

Table 2 further elucidates symptom choices by

showing the ranking of main symptom selected in

November 2018, in the first 3 months, as well as in

February 2019, in the latter 3 months. Rank 1

indicates the most selected symptom, with

descending scores equating to decreased frequency of

selection. Symptom changes over time outlined in the

table indicate that there could be some seasonality in

symptom prevalence; for example, with vomit/nausea

and fever changing dramatically in ranking. This

could also point to the usefulness of digital tools in

predicting population level healthcare trends, for

example, the spread of illnesses as outlined in the

discussion later.

Figure 5: For whom the tool was used (by age and gender, total N=149 222).

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Figure 6: Primary symptoms selected (total N=149 222, other symptoms N=99 382).

Figure 7: App dialogue section / page visited.

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Table 2: Symptom ranking for 3 months usage (1 is highest rank).

Symptom

Ranking between Sept 2018 to

Nov 2018

Ranking between Dec 2018 to

Feb 2019

Ranking

change

Stomach pain 1 1 0

Headache 3 2 +1

Cold/flu 6 3 +3

Fever 14 4 +10

Rash 2 5 -3

Vomit/Nausea 18 6 +12

Back pain 5 12 -7

Figure 8: Buttons out of the optimal usage path.

Figure 7 shows the number of remaining

users/dropouts through each page/section of the app.

As can be evidenced, once users have begun the main

dialogue (i.e. talking about the affected person), the

majority continue through to the results page.

However, a large number of individuals drop out

between the start page and the affected person page.

This is possibly due to the request of the customer

insurance number, and subsequent fears around data

privacy, as explored further in the discussion.

Figure 8 outlines buttons clicked upon by the user,

removing them from the “optimal” usage path (i.e. the

most direct path to results, buttons that mayrequire

navigating back to main usage path). Buttons clicked

include; requiring terms and conditions as a PDF,

information icons (explaining medical

symptoms/jargon), frequently asked questions, and

contact center (i.e. how to contact a human for help).

Interestingly, there were no uses of the emergency

contact button; perhaps as individuals feared

requesting an ambulance by using the button, or

perhaps as users would prefer to use more traditional

channels in case of emergency. As also overviewed in

the discussion, these results hint at the importance of

including sufficient information within the main app

usage path, so as to not derail customers from the

intended usage/service experience.

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Figure 9: Buttons out of the optimal usage path

Finally figure 9 highlights the number of users

returning to use the medical tool again, with the

majority of users using the app only once. Figures

show that some individuals did return to use the app

again over the six-month period; 21% of users

(n=31 337) used the app two or more times, 8% three

or more times (n=11 938) and so on.

5 DISCUSSION AND FUTURE

RESEARCH

Trust in novel digital medical platforms and medical

services has been identified as of importance

previously (Mackert, Mabry-Flynn, Champlin,

Donovan, & Pounders, 2016; Moreira & Silva, 2015),

and for digital platforms more generally, trusting

preferences have been linked to both consumer and

platform characteristics (Komiak & Benbasat, 2006;

Metzger, 2006, 2007; W. Q. Wang & Benbasat,

2007). Usage statistics indicate that the app was

primarily used for older teenagers and adults, with

relatively fewer cases for more at-risk groups (i.e. the

young and elderly) where consequences of

misdiagnosis are more severe. In addition, primary

symptoms selected in the app were for common

complaints which typically consumers already have

an adequate level of health knowledge about (i.e.

cold, fever, rash). Thusly it appears that although

consumers welcome using these apps, they still

exhibit a degree of caution and mistrust about relying

on them for more serious medical queries, as also

further evidenced by no use of the emergency button

within the app. It would therefore be interesting to

examine how consumers react to using such digital

platforms, when the intended medical enquiry is more

severe, and something for future researchers to

investigate.

The number of app dialogue sections/pages

visited and the dropout rate is also interesting

considering heightened privacy concerns amongst the

general populace after recent well publicized scandals

(Isaak & Hanna, 2018; Zhang et al., 2018). Despite

attracting a large number of customers to the app

launch site, many dropped out when asked to provide

their customer insurance number and date of birth for

validation purposes, even though they were informed

no health data would be saved by the firm. Previous

research has identified the timing of consent is of

importance in disclosure decisions (Anderson &

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Agarwal, 2011), and one explanation therefore may

be that individuals were not significantly engaged

with the service offering before being asked to

provide information and thusly many left the app.

Alternatively, as user “emotions and resistances” may

stand in the way of successful digital health tools

(Lupton, 2013), it may be that individuals simply did

not believe the company when they stated no health

data would be saved yet also asked health insurance

customer number. As privacy disclosures are both a

legal requirement, and evidently of importance for

users, future research may wish to consider

alternative methods of framing privacy related

information which connects with individuals on a

meaningful level (Zhang et al., 2018).

In addition to privacy related information, further

service/usage experience information may also be of

benefit, as evidenced from the utilization of buttons

out of the main interaction path. Users selected to

receive further information related to privacy (e.g.

terms and conditions) or service matters (e.g.

frequently asked questions) indicating that service

roles were unclear (Solomon, Surprenant, Czepiel, &

Gutman, 1985). Expectation setting has been known

as importance in service evaluations (Ofir &

Simonson, 2007), and including extra information

within the main section of the app (particularly if it is

a dialogue based format as the current service) may

serve to “onboard” individuals to the service

experience (Rawson, Duncan, & Jones, 2013). This

has been found as particularly important in extant

research where service literacy is important

(Voorhees et al., 2017), which is likely the case with

novel digital products such as those employed in

healthcare.

The potential of use of such apps for health care

providers is also outlined when examining symptom

changes from 3 months and 6 months which show

changes in the ranking of symptoms, for example,

fever changing from ranked 14

to 4

. Previously

tracking symptom web searches and social media

posts has been used to predict influenza spread at both

population and sub-population levels (Santos &

Matos, 2014; Volkova, Ayton, Porterfield, & Corley,

2017). Thusly one could suppose that providing the

provision of these new tools was widespread enough,

and relevant permissions were given to healthcare

bodies to access the data, they may provide a highly

accurate data source for making predictions. This

may resolve some of the issues around inaccurate

information used for predictions, for example with

Google Flu (Kandula & Shaman, 2019). Future

research should also consider how such tools can be

monitored in terms of whether they successfully

prevent unnecessary emergency room or general

practitioner visits (Bahadori et al., 2018), or

successfully refer individuals to the correct healthcare

entry point.

Finally, repeat usage numbers show that there

exists some interest amongst the populace in

exploring digital healthcare tools. The current self-

service technology utilized a dialogue-type

interaction, guiding individuals through a linear path

to their results, in a dyadic manager mimicking

regular clinician-patient communication (McColl-

Kennedy et al., 2012; Sweeney et al., 2015). Virtual

agents such as text-based chatbots have been widely

applied to medical contexts to act as digital coaches

(Kowatsch et al., 2017), for example, the cognitive-

behavioral therapy chatbot “WoeBot” (Fitzpatrick,

Darcy, & Vierhile, 2017). It would be interesting to

discern whether making such self-services more

anthropomorphized would bring any benefits in terms

of repeat usage and minimizing drop outs in future

research, as anthropomorphism has been touted as a

key satisfaction driving mechanism for “service

robots” placed in the frontline (Wirtz et al., 2018).

For future practitioners seeking to implement

such digital tools, the authors would firstly note that

the number of total uses indicates a good appetite

amongst consumers to try such tools. Important

however, as noted earlier in the discussion, is to find

ways to successfully onboard users to the service

experience through use of supplementary

information. In particular, privacy concerns seem to

dominate, and a key recommendation for

practitioners wishing to roll out such digital self-

service technologies is to find ways to reduce user

privacy concern. This could be through the use of

additional privacy assurance disclosures, for

example, or not requesting customer details (e.g.

interaction.

6 SUMMARY AND NEXT STEPS

In summary results show that such digital self-service

technologies hold great potential with large numbers

of customers visiting the app site within a relatively

short time frame. However, the results also show that

trust in such digital services is not entirely established

yet, as evidenced by the large number of drop outs

after requesting the potentially sensitive customer

insurance number. In addition, the use cases for the

product seem to be for relatively benign complaints,

rarely for at-risk groups (young, elderly) and never in

the case of emergency. Future research should

The Doctor Will See Yourself Now: Review and Discussion of a Mass-market Self-service Technology for Medical Advice

805

examine how information relating to privacy and

service/usage experience can be made more

meaningful and/or clear to users, and whether this is

evidenced in changing use cases and usage patterns.

REFERENCES

Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015).

Privacy and human behavior in the age of information.

Science, 347(6221), 509-514. doi:10.1126/science.

aaa1465

Ada. (2020). Ada: Your Digital Health Companion.

Retrieved from https://ada.com/

Anderson, C. L., & Agarwal, R. (2011). The Digitization of

Healthcare: Boundary Risks, Emotion, and Consumer

Willingness to Disclose Personal Health Information.

Information Systems Research, 22(3), 469-490.

doi:10.1287/isre.1100.0335

Bahadori, M., Teymourzadeh, E., & Mousavi, S. M. (2018).

eHealth solutions and nonurgent visits in emergency

departments. Technol Health Care, 26(3), 571-572.

doi:10.3233/THC-181290

Barello, S., Triberti, S., Graffigna, G., Libreri, C., Serino,

S., Hibbard, J., & Riva, G. (2015). eHealth for Patient

Engagement: A Systematic Review. Front Psychol, 6,

2013. doi:10.3389/fpsyg.2015.02013

Chua, M. E., Saunders, M. A., Bowlin, P. R., Ming, J. M.,

Lopes, R. I., Farhat, W. A., & Dos Santos, J. (2017).

Impact of smartphone digital photography, email, and

media communication on emergency room visits post-

hypospadias repair. Can Urol Assoc J, 11(3-4), E134-

E137. doi:10.5489/cuaj.4170

Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017).

Delivering Cognitive Behavior Therapy to Young

Adults With Symptoms of Depression and Anxiety

Using a Fully Automated Conversational Agent

(Woebot): A Randomized Controlled Trial. JMIR Ment

Health, 4(2), e19. doi:10.2196/mental.7785

FSO. (2019). Health Pocket Statistics 2018. Switzerland:

Federal Statistics Office Retrieved from

https://www.bfs.admin.ch/bfs/en/home/statistics/health

/health-system.assetdetail.7347549.html

Hallfors, E., Saku, S. A., Makinen, T. J., & Madanat, R.

(2018). A Consultation Phone Service for Patients With

Total Joint Arthroplasty May Reduce Unnecessary

Emergency Department Visits. J Arthroplasty, 33(3),

650-654. doi:10.1016/j.arth.2017.10.040

Hassell, K., Rogers, A., & Noyce, P. (2000). Community

pharmacy as a primary health and self-care resource: a

framework for understanding pharmacy utilization.

Health Soc Care Community, 8(1), 40-49.

doi:10.1046/j.1365-2524.2000.00222.x

Hewner, S., Sullivan, S. S., & Yu, G. (2018). Reducing

Emergency Room Visits and In-Hospitalizations by

Implementing Best Practice for Transitional Care Using

Innovative Technology and Big Data. Worldviews Evid

Based Nurs, 15(3), 170-177. doi:10.1111/wvn.12286

Hibbard, J. H., & Peters, E. (2003). Supporting informed

consumer health care decisions: data presentation

approaches that facilitate the use of information in

choice. Annu Rev Public Health, 24, 413-433.

doi:10.1146/annurev.publhealth.24.100901.141005

Hwang, W., Liao, K., Griffin, L., & Foley, K. L. (2012). Do

free clinics reduce unnecessary emergency department

visits? The Virginian experience. J Health Care Poor

Underserved, 23(3), 1189-1204. doi:10.1353/hpu.

2012.0121

Isaak, J., & Hanna, M. J. (2018). User Data Privacy:

Facebook, Cambridge Analytica, and Privacy

Protection. Computer, 51(8), 56-59. doi:10.1109/mc.

2018.3191268

Kandula, S., & Shaman, J. (2019). Reappraising the utility

of Google Flu Trends. PLoS Comput Biol, 15(8),

e1007258. doi:10.1371/journal.pcbi.1007258

Kennedy, J. (2019). Populist politics and vaccine hesitancy

in Western Europe: an analysis of national-level data.

Eur J Public Health, 29(3), 512-516.

doi:10.1093/eurpub/ckz004

Komiak, & Benbasat. (2006). The Effects of

Personalization and Familiarity on Trust and Adoption

of Recommendation Agents. Mis Quarterly, 30(4).

doi:10.2307/25148760

Kowatsch, T., Nißen, M., Shih, C.-H. I., Rüegger, D.,

Volland, D., Filler, A., . . . Büchter, D. (2017). Text-

based Healthcare Chatbots Supporting Patient and

Health Professional Teams: Preliminary Results of a

Randomized Controlled Trial on Childhood Obesity.

Paper presented at the Persuasive Embodied Agents for

Behavior Change (PEACH2017) Workshop at the 17th

International Conference on Intelligent Virtual Agents.

Lupton, D. (2013). The digitally engaged patient: Self-

monitoring and self-care in the digital health era. Social

Theory & Health, 11(3), 256-270. doi:10.1057/

sth.2013.10

Lupton, D. (2016). Digital health technologies and digital

data: new ways of monitoring, measuring and

commodifying human bodies. Research handbook on

digital transformations, 85.

Mackert, M., Mabry-Flynn, A., Champlin, S., Donovan, E.

E., & Pounders, K. (2016). Health Literacy and Health

Information Technology Adoption: The Potential for a

New Digital Divide. J Med Internet Res, 18(10), e264.

doi:10.2196/jmir.6349

Mayer, G. G., Villaire, M., & Connell, J. (2005). Ten

recommendations for reducing unnecessary emergency

department visits. J Nurs Adm, 35(10), 428-430.

doi:10.1097/00005110-200510000-00003

McColl-Kennedy, J. R., Vargo, S. L., Dagger, T. S.,

Sweeney, J. C., & van Kasteren, Y. (2012). Health Care

Customer Value Cocreation Practice Styles. Journal of

Service Research, 15(4), 370-389. doi:10.1177/

1094670512442806

Metzger, M. J. (2006). Effects of Site, Vendor, and

Consumer Characteristics on Web Site Trust and

Disclosure. Communication Research, 33(3), 155-179.

doi:10.1177/0093650206287076

Scale-IT-up 2020 - Workshop on Best Practices for Scaling-Up Digital Innovations in Healthcare

806

Metzger, M. J. (2007). Communication Privacy

Management in Electronic Commerce. Journal of

Computer-Mediated Communication, 12(2), 335-361.

doi:10.1111/j.1083-6101.2007.00328.x

Moreira, A. C., & Silva, P. M. (2015). The trust-

commitment challenge in service quality-loyalty

relationships. Int J Health Care Qual Assur, 28(3), 253-

266. doi:10.1108/IJHCQA-02-2014-0017

Murphy, M. (2019). Dr Google will see you now: Search

giant wants to cash in on your medical queries.

Retrieved from https://www.telegraph.co.uk/techno

logy/2019/03/10/google-sifting-one-billion-health-

questions-day/

Murphy, S. M., & Neven, D. (2014). Cost-effective:

emergency department care coordination with a

regional hospital information system. J Emerg Med,

47(2), 223-231. doi:10.1016/j.jemermed.2013.11.073

NHS. (2020). Health A-Z. Retrieved from

https://www.nhs.uk/conditions/

Ofir, C., & Simonson, I. (2007). The effect of stating

expectations on customer satisfaction and shopping

experience. Journal of Marketing Research, 44(1), 164-

174. doi:DOI 10.1509/jmkr.44.1.164

Paasche-Orlow, M. K., Parker, R. M., Gazmararian, J. A.,

Nielsen-Bohlman, L. T., & Rudd, R. R. (2005). The

prevalence of limited health literacy. J Gen Intern Med,

20(2), 175-184. doi:10.1111/j.1525-1497.2005.

40245.x

Rawson, A., Duncan, E., & Jones, C. (2013). The truth

about customer experience. Harvard Business Review,

91(9), 90-98.

Rieffe, C., Oosterveld, P., Wijkel, D., & Wiefferink, C.

(1999). Reasons why patients bypass their GP to visit a

hospital emergency department. Accident and

Emergency Nursing, 7(4), 217-225. doi:10.1016/s0965-

2302(99)80054-x

Santos, J. C., & Matos, S. (2014). Analysing Twitter and

web queries for flu trend prediction. Theor Biol Med

Model, 11 Suppl 1, S6. doi:10.1186/1742-4682-11-S1-

Soldaini, L., & Goharian, N. (2017). Learning to rank for

consumer health search: a semantic approach. Paper

presented at the European conference on information

retrieval.

Solomon, M. R., Surprenant, C., Czepiel, J. A., & Gutman,

E. G. (1985). A Role Theory Perspective on Dyadic

Interactions: The Service Encounter. Journal of

Marketing, 49(1). doi:10.2307/1251180

Sweeney, J. C., Danaher, T. S., & McColl-Kennedy, J. R.

(2015). Customer Effort in Value Cocreation Activities:

Improving Quality of Life and Behavioral Intentions of

Health Care Customers. Journal of Service Research,

18(3), 318-335. doi:10.1177/1094670515572128

Volkova, S., Ayton, E., Porterfield, K., & Corley, C. D.

(2017). Forecasting influenza-like illness dynamics for

military populations using neural networks and social

media. PLoS One, 12(12), e0188941.

doi:10.1371/journal.pone.0188941

Voorhees, C. M., Fombelle, P. W., Gregoire, Y., Bone, S.,

Gustafsson, A., Sousa, R., & Walkowiak, T. (2017).

Service encounters, experiences and the customer

journey: Defining the field and a call to expand our lens.

Journal of Business Research, 79, 269-280.

doi:10.1016/j.jbusres.2017.04.014

Wang, W.-T., Cheng, S.-Y., & Huang, L.-Y. (2013).

Technology-Based Service Encounters Using Self-

Service Technologies in the Healthcare Industry.

International Journal of Human-Computer Interaction,

29(3), 139-155. doi:10.1080/10447318.2012.695728

Wang, W. Q., & Benbasat, I. (2007). Recommendation

agents for electronic commerce: Effects of explanation

facilities on trusting beliefs. Journal of Management

Information Systems, 23(4), 217-246.

doi:10.2753/Mis0742-122230410

Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V.

N., Paluch, S., & Martins, A. (2018). Brave new world:

service robots in the frontline. Journal of Service

Management, 29(5), 907-931. doi:10.1108/josm-04-

2018-0119

Xie, B. (2011). Effects of an eHealth literacy intervention

for older adults. J Med Internet Res, 13(4), e90.

doi:10.2196/jmir.1880

Zhang, X., Liu, S., Chen, X., Wang, L., Gao, B., & Zhu, Q.

(2018). Health information privacy concerns,

antecedents, and information disclosure intention in

online health communities. Information &

Management, 55(4), 482-493. doi:10.1016/j.im.2017.

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