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Evaluating remote healthcare practices: Experiences and recommendations of healthcare professionals on smart applications

Digital Health

March 2025
11

DOI:10.1177/20552076251315786

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CC BY-NC 4.0

Authors:

Fadime Baştürk

Yozgat Bozok Üniversitesi

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Objective With the digitalization of objects and spaces, healthcare services are being reshaped globally, creating many potential applications. This study aimed to determine the application potential of remote healthcare services (RHS) in a hospital by considering the experiences, interests, and suggestions of health professionals, and examples of useful applications that can be used, developed, or invented for healthcare systems. Methods A semi-structured, face-to-face interview survey was conducted with 176 healthcare professionals working at Bozok University. Results Branches with the highest practice experience were internal medicine, cardiac, pediatric, infection, and orthopedics. Experienced participants rated the usability of “Consultation,” “Support,” and “Monitoring” applications higher than other apps, and indicated that they would prefer to use them for themselves (η²≤0.12). Requirements adequacy was lower for older adults, internal/surgical branches, and physicians than for other groups (η² ≥ 0.05). Application categories showed a significant relationship (0.4 ≤ r ≤ 0.8, p < 0.05). Several variables significantly explained the models (p < 0.001): application-usability (64%), user-demand (64%), and requirements-adequacy (25%). Professionals’ demand (r = 0.83) was more strongly correlated to usability than patient demand (r = 0.63). Health professionals (N = 105) from 17 branches provided 57 available, 51 developable, and 19 innovative application recommendations. These were coded according to application type, critical features, presence, integration status, and usefulness. Conclusion RHS’ application potential in a hospital was revealed considering demographic factors and application categories based on health professionals’ experiences, practical interests, and suggestions, with a strong, comprehensive, and up-to-date methodology. The findings have the potential for international application and can contribute to implementing useful and developing original applications.

Participant flowchart.

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Applicability categories.

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Application feature; code sub-code model.

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Diagnosis and monitoring applications; code subcode model.

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Biometric measurement applications; hierarchical code sub-code model.

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Evaluating remote healthcare practices:

Experiences and recommendations of healthcare

professionals on smart applications

Fadime Baştürk

, Arif Osman Tokat

, Osman Öztürk

,Çiğdem Kader

and Levent Işikay

Abstract

Objective: With the digitalization of objects and spaces, healthcare services are being reshaped globally, creating many

potential applications. This study aimed to determine the application potential of remote healthcare services (RHS) in a hos-

pital by considering the experiences, interests, and suggestions of health professionals, and examples of useful applications

that can be used, developed, or invented for healthcare systems.

Methods: A semi-structured, face-to-face interview survey was conducted with 176 healthcare professionals working at

Bozok University.

Results: Branches with the highest practice experience were internal medicine, cardiac, pediatric, infection, and orthopedics.

Experienced participants rated the usability of “Consultation,”“Support,”and “Monitoring”applications higher than other

apps, and indicated that they would prefer to use them for themselves (η²≤0.12). Requirements adequacy was lower for

older adults, internal/surgical branches, and physicians than for other groups (η²≥0.05). Application categories showed

a signiﬁcant relationship (0.4 ≤r≤0.8, p < 0.05). Several variables signiﬁcantly explained the models (p < 0.001): applica-

tion-usability (64%), user-demand (64%), and requirements-adequacy (25%). Professionals’demand (r =0.83) was more

strongly correlated to usability than patient demand (r =0.63). Health professionals (N =105) from 17 branches provided

57 available, 51 developable, and 19 innovative application recommendations. These were coded according to application

type, critical features, presence, integration status, and usefulness.

Conclusion: RHS’application potential in a hospital was revealed considering demographic factors and application categor-

ies based on health professionals’experiences, practical interests, and suggestions, with a strong, comprehensive, and up-

to-date methodology. The ﬁndings have the potential for international application and can contribute to implementing useful

and developing original applications.

Keywords

Remote healthcare, medical informatics applications, digital technology, smart materials, healthcare management

Submission date: 4 June 2024; Acceptance date: 8 January 2025

Patient Rights Department, Health Practice and Research Center, Yozgat

Bozok University, Yozgat, Turkey

Department of Thoracic Surgery, Faculty of Medicine, Yozgat Bozok

University, Yozgat, Turkey

Child Health and Diseases, Faculty of Medicine, Yozgat Bozok University,

Yozgat, Turkey

Department of Internal Medical Sciences, Faculty of Medicine, Yozgat

Bozok University, Yozgat, Turkey

Department of Surgical Medical Sciences, Faculty of Medicine, Yozgat

Bozok University, Yozgat, Turkey

Corresponding author:

Fadime Baştürk, Patient Rights Department, Health Practice and Research

Centre, Yozgat Bozok University, Yozgat, Turkey.

Email: f.arslanbasturk@gmail.com

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial

4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work

without further permission provided the original work is attributed as speciﬁed on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/

open-access-at-sage).

Original Research Article

DIGITAL HEALTH

Volume 11: 1–29

Article reuse guidelines:

sagepub.com/journals-permissions

DOI: 10.1177/20552076251315786

journals.sagepub.com/home/dhj

Background

Currently, smart technologies are developing at an unprece-

dented pace. Smart environments and objects are being

designed, and complex interventions in biological structures

are being performed remotely by leveraging the sensitive

sensors of smart devices.

The use of new technologies in

healthcare and medicine has created tremendous application

potential, especially given the digital transformations neces-

sitated by the COVID-19 pandemic.

2,3

For instance, applica-

tions that make impossible procedures possible, such as

remote robotic surgeries, have become widespread.

Moreover, important factors that advantage users are

ﬁnding solutions to the obstacles arising from spatial, tem-

poral, and individual limits, facilitating, accelerating, and

simplifying the work procedures by transferring some of

them to technology,

thus preventing human-caused

errors.

Therefore, the concept of digital healthcare plays

an important role in providing more up-to-date, efﬁcient,

and quality healthcare services while beneﬁting individuals.

As digital health reforms progress globally, the situation

in hospitals—the cornerstones of the healthcare system—is

becoming particularly intriguing. In the developing version

of remote healthcare services (RHS), with the transforma-

tive effect of new health technologies, hospitals are

moving from being places of observation and intervention

to remote control centers. In this framework, institutions

should not only overcome their deﬁciencies but also

promptly take advantage of innovation opportunities.

Hospitals should structure their information systems, and

establish their application laboratories and markets.

In its traditional sense, RHS is generally used by private

healthcare providers, and is a format that includes only

patient and physician meetings. There is also conceptual

confusion among researchers and users about whether the

terms tele, e, mobile, online, virtual, smart, web-based

and digital health are within the scope of the RHS.

Therefore, traditional deﬁnitions must be updated within

the framework of technological development before design-

ing a study on the subject.

However, the transition to new practices depends on

established orders and standards.

For applications that

are in use and can be improved, studies have evaluated

certain types of RHS.

9,10

Furthermore, the applicability of

RHS has been investigated using a mixed methodology spe-

ciﬁc to certain health branches and generally for commonly

used applications.

Additionally, commonly used applica-

tions that healthcare professionals recommend to their

patients have been addressed in structured quantitative

studies. However, these designs provide a limited under-

standing of the basic and indispensable issues in developing

or adapting Internet-based RHS interventions.

Studies have also highlighted the potential for digital

health security and artiﬁcial intelligence (AI)-driven appli-

cations. For example, AI-driven applications in preventive

health can be considered an alternative treatment approach

for infectious diseases.

Alternately, secure, interactive

multimedia applications by enhancing remote media areas

with heterogeneous image sources; can also be used to

provide healthcare support in non-hospital settings.

The majority of future innovation recommendations are

based on literature reviews or qualitative studies with small

groups of participants.

7,15,16

Furthermore, e-health applica-

tions are often designed by researchers without consulting

the primary users. These studies do not consider the

needs of end users but require intense effort, resulting in

poor usability, frustration, and limited acceptance.

12,17

Therefore, more studies are required on both the multifa-

ceted evaluation of the usability of applications and devel-

oping new applications.

18–22

Physicians’adoption of

telemedicine can be explained by behavioral usage inten-

tion, actual usage, satisfaction, attitude, continuous usage

and recommendation intention variables.

Also a deeper

exploration of user needs can provide smarter healthcare

solutions that can better meet practical requirements.

Addressing these gaps, this study evaluates the applica-

tion potential of RHS in a hospital, considering application

type, healthcare branch, and professionals. It aims to answer

the question, “I

f experiences, practical interests, and recom-

mendations of health professionals improve RHS applic-

ability, what applications can be used, developed, or

invented considering these factors?”In particular, the fol-

lowing speciﬁc questions are answered:

Qualitative questions: 1. What are the types of RHS and

applicability categories? 2. Is there another app profes-

sionals would like to use in their work? 3. What applica-

tions are health professionals interested in or would like

to see developed?

Quantitative questions: 1. In which branches and to what

extent are the RHS types used? 2. Is there a relationship

between applicability categories and the usability of RHS

types? 3. Does RHS applicability differ among different

demographic groups? 4. How do usefulness, adequacy,

experience, demand, education, and age play a role in

RHS applicability categories and app-type usability?

Common inference questions: (1) What are the coded

recommendations based on application type and other

emerging themes? (2) What are the common inferences

from the quantitative and qualitative questions? (3) What

are the RHS application potentials for the hospital regarding

existing, developable, or innovative applications?

Overall, this study has important strengths as the use

of both qualitative and quantitative methods provides a

well-rounded understanding of the topic. For new

research topics, exploratory and explanatory data are

required. While qualitative data can provide sufﬁcient

insight, quantitative data can provide easy, effective,

and powerful measurements. Description and generaliza-

tion become easier as the scope and diversity of partici-

pants increase.

2DIGITAL HEALTH

Moreover, this study addresses practical concerns and

suggestions from healthcare professionals, making the ﬁnd-

ings highly relevant for improving RHS. Additionally,

including healthcare professionals from various branches

ensures a comprehensive view of the current state and

needs of RHS.

Methods

Research design

This study employed a cross-sectional, descriptive, and ana-

lytical design. The big picture of the study problem was

explained using quantitative data, important codes and

themes were explored using qualitative data. In addition

the quantitative data were complemented and explained

with qualitative data. A complex mixed-methods design

was used instead of a single method design.

Speciﬁcally, the study was conducted in three stages: an

exploratory sequential design, followed by an explanatory

sequential design, and ﬁnally, a concurrent design.

This

study utilized the six basic criteria determined by Hirose

and Creswell for mixed-method studies.

Scheme 1 pre-

sents the research design procedure and describes the data

collection, analysis methods, and associated outputs. The

research design and reporting were in accordance with the

standards speciﬁed in “Mixed Methods Reporting in

Rehabilitation & Health Sciences.”

Researcher Background and Contributions to the

Project:Data collection, analysis, and reporting were con-

ducted by researchers who have been working in manage-

ment and professional healthcare in various institutions

for a long time. Therefore, observations and expert evalua-

tions were included.

Environment and scope

The research was conducted in a university hospital in

Turkey between September 25, 2022, and March 25,

2023. Turkey’s health institutions have their own informa-

tion systems. Additionally, Turkey’s personal health record

application, “E-Nabız,”is used under the control of the

Ministry of Health.

Generally, health professionals with a medical background

work in at least one different health institution before being

appointed to an academic position at a university hospital.

Other employees may transfer from private hospitals or differ-

ent health institutions. Transfers from private hospitals are

common in the hospital where the study was conducted.

Permission and ethical approval to conduct the study

was received from the Hospital Management and

University Ethics Commission (September 21, 2022, No.:

36/18). The purpose of the research and conﬁdentiality of

the data were explained to the participants, and informed

verbal and written consent was obtained.

Participants

The study population comprised 301 healthcare profes-

sionals actively working in 21 branches of a university hos-

pital clinic. To reach a statistically signiﬁcant sample size,

the sample size was calculated as 170 people with a conﬁ-

dence interval of 95% and a margin of error of 5%.

Interviews were conducted with 191 people, comprising

managers, assistant managers, supervisors, and unit

employees, who agreed to participate in the interview vol-

untarily with the guidance of the unit manager.

Subsequently, 15 participants who left 5% or more of the

survey items blank, double-ticked, or had inconsistencies

in their answers were excluded. Furthermore, 105 partici-

pants from 17 branches made RHS application recommen-

dations. At least one person from each branch participated

in the study. In qualitative research, a large amount of infor-

mation can be obtained with a small sample size in studies

conducted with participants who are experts with high

knowledge power.

Participants presented examples of

RHS relevant to their ﬁeld and examples of other ﬁeld

applications that make their ﬁeld possible. This is signiﬁ-

cant when considering the interdependence among

branches in providing health services.

Healthcare professionals have a very heterogeneous spe-

cialization structure. This study ensured participation from

every active branch in the hospital, especially by unit heads

or their assistants. Furthermore, non-physician healthcare

professionals can work in mixed units comprising more

than one branch. Therefore, participants who did not

work in a speciﬁc branch were included in the mixed

branch.

29,30

Subgroup comparisons were made if the

number of participants was at least 10 or if statistical signiﬁ-

cance was achieved according to the analysis. Ultimately,

data obtained from 176 participants grouped into 15

branches were analyzed (Figure 1).

Data collection

Pilot study. In the ﬁrst phase of this study, aimed at deter-

mining the RHS types and applicability categories, trial

interviews were conducted with participants representing

internal, surgical and mixed specialties. Based on this,

eight themes of RHS categories were determined, two

themes of “user demand,”and three themes of “adequacy

of requirements.”Subsequently, adjustments were made

to the interview protocol based on expert experience and

information provided by the participants. The semistruc-

tured interview form was revised, unnecessary expressions

were removed, and new ones were added or changed

(Appendix 1). Detailed information is given in Scheme 1.

Instrumentation

Remote Health Services and Application Categories. Many

current deﬁnitions of health have emerged with the

Baştürk et al. 3

combination of technological and health terms, such as tele,

electronic (e), mobile, online, virtual, smart, web-based,

and digital health/medicine. These refer to a broad or

narrow scope of services depending on the meaning of

the added word and technological features. For example,

e-health is the new name for health in the technology age

and is deﬁned as the provision of health services electronic-

ally in a web-based environment.

New health technolo-

gies are changing and improving how health services and

many traditional practices are implemented. For instance,

AI, augmented reality, sensor sensing, blockchain,

Internet-of-Things (IoT), metaverse, and other technologies

are making it more realistic to consider health in new con-

texts and have also developed tools to increase physical

independence.

16,33

The traditional name for RHS is telehealth. Telehealth,

in addition to tele, includes medicine, consultancy, monitor-

ing, and several other aspects. Here, diverse deﬁnitions are

created by adding words. The most important differentiator

in telehealth is physical distance. Many applications, such

as monitoring with connectivity technologies, robotic

surgery, drug treatment, implants, diagnosis, and care, can

be performed over long distances.

Therefore, RHS can

be deﬁned as remote delivery of healthcare services

through digital technologies such as wearable smart

devices, application software, information communication,

and connectivity technologies. Besides distance, RHS

covers other new deﬁnitions and key areas of healthcare

such as electronic or operational.

The application categories were determined according to

the general service areas of health services provided in the

hospital based on the literature and extant practices.

32–34

They were revised in the ﬁrst and second phases, and at

the end of the study.

Applicability of Remote Health Services. In prior studies, to

evaluate the usability of an application, participants who had

experienced an application were usually asked about state-

ments that measure the usefulness, practicality, reliability,

user satisfaction, and other aspects of the application.

However, three main themes have been highlighted in the

research implications: “usability”

1,11,35–37

;“adequacy”

—

technical adequacy,

1,36–40

worker count adequacy,

worker

qualiﬁcation adequacy,

28,35,40–42

reliability,

20,43

and ﬁnancial

and legal adequacy; and “demand”

20,44,45

—profession,

patient,

supplier, and politician demand. The current study

investigated the usability of applications that employees not

only experience but are also interested in. Therefore, the

usability of each RHS type with speciﬁc characteristics was

investigated. To determine the application experience, partici-

pants were asked to indicate their previous use of the applica-

tion types via yes or no options. During the trial phase, the

reliability, ﬁnancial adequacy, legal adequacy, and supplier

demand categories were eliminated. Healthcare professionals

perceive these issues as technical aspects under the control of

institutional owners, experts, or management. The applicabil-

ity categories included in the research questionnaire are

shown in Figure 2.

Implementation. The interview method was used to collect

data. During the interview, participants were asked to evalu-

ate the RHS categories speciﬁcally for their branches. The

responses were transcribed verbatim using a voice recording

or note-taking application with the participant’s permission.

The content was reviewed, coded, and anonymized.

Data analysis

Principal component analysis (PCA) was conducted to deter-

mine constructs in the applicability questionnaire: “app use-

fulness,”“user demand,”and “adequacy of requirements.”

IBM SPSS Program Statistics 29 (IMB Corp., NY, USA)

was used for the quantitative analysis. Compliance with a

normal distribution was examined using the Kolmogorov–

Figure 1. Participant ﬂowchart.

4DIGITAL HEALTH

Smirnov test. Additionally, skewness and kurtosis values

were examined. If these values are between ±2, continuous

scores show normal distribution.

Descriptive statistics are

provided for the sociodemographic and applicability categor-

ies. A one-way analysis of variance (ANOVA) was used to

compare independent groups. Signiﬁcant results in the

ANOVA test were examined using the multiple comparison

tests to determine which group the difference was in.

Pearson’s correlation (r) was used to measure the linear rela-

tionship between the applicability categories. Subsequently,

multivariate linear regression was performed to measure the

role of usefulness, adequacy, experience, demand, education,

and age in the applicability categories of RHS and app-type

usability. The basic assumptions required for statistical com-

parison methods were tested. The statistical signiﬁcance level

was set at 5%. Content and thematic analyses were applied to

the sample application ideas in MAXQDA 2022. The data

were presented with weighted code models and crosstabs

using the frequency distribution of coded documents or sec-

tions. Key ﬁndings from the qualitative and quantitative ana-

lyses were combined into one table to illustrate the common

conclusions. Thus, stronger ﬁndings can be achieved by

adding the insights developed from integrating qualitative

and quantitative data.

Reliability and validity tests

Cronbach’s alpha and PCA were calculated for each con-

struct in the questionnaire.

Usefulness: Cronbach’s alpha: 0.925; PCA: eight com-

ponents (“physical examination,”“measurement,”“evalu-

ation,”“treatment/care,”“operation,”“monitoring,”and

“counseling”) explaining 65.9% of the variance.

User Demand: Cronbach’s alpha: 0.822; PCA: two com-

ponents (“own-demand”and “patient demand”) explaining

84.9% of the variance.

Adequacy of requirements: Cronbach’s alpha: 0.924;

PCA: three components (“technical adequacy,”“worker

count adequacy,”and “worker qualiﬁcation adequacy”)

explaining 91.8% of the variance.

To ensure the reliability of the qualitative analysis,

data, analyst, and method triangulation was applied,

and application categories determined from the literature

were used.

Results

Evaluation of the applicability of remote

health applications

Participant proﬁle. A total of 53% (94 people) and 47% (81

people) of the participants were in the 20–30 years and 30–

67 age groups, respectively. Furthermore, 39% (69 people)

had an undergraduate education, while 42.6% (75 people)

had a postgraduate education. Finally, 48.3% (85 people)

were nurses, 40.3% were physicians (71 people), and

11.4% (20 people) were other health workers (Table 1).

Regarding working experience in professional branches,

Figure 2. Applicability categories.

Baştürk et al. 5

95 (54%) participants had been working for 5 years or less,

61 (34.7%) for 5–15 years, and 20 (11.4%) for more than 15

years. The distribution of participants by branch was 10 at

the lowest and 15 at the highest.

Determining sample application usage experience according to

branch. A total of 175 (99.4%) participants reported

having used RHS applications before. Of these, 110

(62.5%) had used any RHS application category

through an informal or ofﬁcial digital application. The

branches with the highest practice experience were

internal medicine (13 people, 92.9%), cardiac (12

people, 80%), and pediatrics (8 people, 80%).

Participants mostly used consultation (cardiac: 11

people; surgery/child/infection: 9 people; total: 89

people, 51.4%), support (internal medicine: 9 people;

orthopedics: 5 people; total: 37 people, 21.6%), and mon-

itoring applications (internal medicine: 8 people; pediat-

rics: 6 people; orthopedics: 5 people; total: 33 people,

19.2%) (Table 2).

Pearson correlation analysis: identifying relationships affecting

applicability. Pearson’s correlation analysis was used to

determine the relationships between “adequacy”and

“demand”with the usability of examination, measurement,

evaluation, treatment/care, operation, monitoring, counsel-

ing, and support applications (Table 3).

Table 1: Participant Proﬁle

Demographic Factor Count %

Branch Emergency 10 5.7%

Anesthesiology 12 6.8%

General Surgery 10 5.7%

Ophthalmology 12 6.8%

Gynecology/Obstetrics 13 7.4%

Urology 11 6.2%

Orthopedics 11 6.2%

İnfectious diseases 12 6.8%

Physiotheraphy 10 5.7%

Internal medicine 14 8.0%

Neurology 15 8.5%

Internal Other 11 6.2%

Pediatry-S 10 5.7%

Pulmonology-S 10 5.7%

Cardiology-S 15 8.5%

Total 176

Working Year in

Branch

Under 1 Year 21 11.9%

Between 1-5 Years 74 42.0%

Between 5-10 Years 38 21.6%

Between 10-15 Years 23 13.1%

Above 15 20 11.4%

Total 176

Age Under 25 26 14.9%

26-30 68 38.9%

31-35 35 20.0%

36-40 18 10.3%

Above 41 28 16.0%

Total 175

(continued)

Table 1: Continued.

Demographic Factor Count %

Education Primary education 0 0.0%

High school 20 11.4%

Associate Degree 12 6.8%

Licence 69 39.2%

Master's Degree 46 26.1%

Doctorate 29 16.5%

Total 176

Profession Physician 71 40.3%

Nurse 85 48.3%

Other 20 11.4%

Total 176

Note: Mixed (…S) Both internal and surgical branches.

6DIGITAL HEALTH

Table 2. Sample application usage experience according to branch.

Branch and application type

Application →

Physical Examination Measurement Evaluation Treatment / Care Operation Monitoring Counseling Support

Experienced

participant

Total

participants

Branch ↓NN%N%

Surgical Emergency 1 1 1 2 0 1 5 3 6 60.0 10 5.7

Anesthesiology 1 1 2 0 2 2 6 1 7 58.3 12 6.8

General Surgery 0 3 3 0 0 1 2 3 4 40.0 10 5.7

Ophthalmology 1 1 2 2 0 1 5 4 7 58.3 12 6.8

Gynecology/Obstetrics 1 0 1 0 0 1 2 1 6 46.2 13 7.4

Urology 0 1 0 0 0 1 7 1 6 54.5 11 6.3

Orthopedics 0 1 1 4 4 5 7 5 8 72.7 11 6.3

İnternal İnfectious diseases 1 1 2 1 0 1 9 2 9 75.0 12 6.8

Physiotherapy 0 0 0 1 0 0 7 1 7 70.0 10 5.7

Internal medicine 1 4 3 2 3 8 9 9 13 92.9 14 8.0

Neurology 1 1 0 3 1 2 8 2 10 66.7 15 8.5

Internal Other 0 1 1 1 0 1 1 1 3 27.3 11 6.3

Mixed Pediatry-S 1 4 4 4 2 6 9 2 8 80.0 10 5.7

Pulmonology-S 1 1 1 0 0 1 1 1 4 40.0 10 5.7

Cardiology-S 5 1 1 3 1 2 11 1 12 80.0 15 8.5

Total N 14 21 22 23 13 33 89 37 110 175 176 100

% 8.0 12.1 12.8 13.4 7.6 19.2 51.4 21.6 62.5 99.4 100

Note: Mixed (…S) Both internal and surgical branches.

Baştürk et al. 7

Table 3. Relationships between usability of application types, adequacy of requirements and user demand.

Physical

examination Measurement Evaluation

Treatment /

Care Operation Monitoring Counseling Support

Patient

demand

Own

demand

Adequacy of

requirements

Pearson

.424** .364** .283** .316** .329** .405** .358** .313** .407** .354**

Physical examination Pearson

1 .608** .517** .618** .586** .546** .560** .540** .444** .581**

Measurement Pearson

1 .764** .786** .676** .653** .527** .567** .495** .694**

Evaluation Pearson

1 .748** .649** .613** .464** .542** .504** .670**

Treatment / Care Pearson

1 .663** .609** .555** .550** .604** .731**

Operation Pearson

1 .704** .482** .560** .499** .680**

Monitoring Pearson

1 .659** .622** .549** .719**

Counseling Pearson

1 .639** .522** .656**

Support Pearson

1 .522** .689**

Patient demand Pearson

1 .698**

p< 0.01. Signiﬁcance levels are two-way.

8DIGITAL HEALTH

“Care usability”and “own-demand”exhibited a highly

positive (r =0.731) and signiﬁcant (p < 0.05) relationship,

with 53.4% of the variation within the “care usability”vari-

able being explained by the “own-demand”variable.

Participants reported wanting to use care applications that

they found useful in their work. In general, all variables

exhibited a moderate (r =0.40–0.59) to strong (r =0.60–

0.79) signiﬁcant (p < 0.05) relationship. The relationships

between the “adequacy of requirements”variable, and

measurement, operation, examination, consultation,

support, and “own-demand”variables were weak (r =

0.20–0.39) but signiﬁcant (p < 0.05).

One-way ANOVA: identifying demographic group differences

regarding RHS applicability. A one-way ANOVA was con-

ducted to determine demographic group differences

regarding RHS applicability. Signiﬁcant differences were

found between the groups, especially regarding the

adequacy of requirements (Table 4).

The mean scores related to the adequacy of requirements

showed a signiﬁcant difference by age group (F[4–170] =

3.33, p < 0.05). The multiple comparison test revealed that

the perceptions of the adequacy of requirements among

those in the under-25-years age group (M =2.64, SD =

0.513) were signiﬁcantly (η²=0.07) higher than among

those in the 36–40-years (M =1.88, SD =0.786) and 41

+-years age groups (M =1.96, SD =0.820). The results

were similar for all three subcategories of the adequacy of

requirements.

Furthermore, comparing the applicability means of the

education groups revealed differences between the groups

in “count adequacy “(F[4–171] =2.509, p < 0.05) and

Table 4. Identifying Demographic Group Differences Regarding RHS Applicability

Groups 1. Group 2. Group 3. Group 4. Group 5. Group ANOVA

Age 25 –26–30 31–35 36–40 41 +df =4,170

Measures M SD M SD M SD M SD M SD F p η²

Technical 2,58 0,516 2,3 0,893 2,21 0,856 1,82 0,74 2,01 0,838 2,912 ,023 ,064

Count A. 2,66 0,547 2,31 0,859 2,33 0,93 1,92 0,865 1,97 0,916 3,060 ,018 ,067

Gualiﬁcation 2,67 0,559 2,2 0,883 2,26 0,992 1,89 0,81 1,88 1,021 3,359 ,011 ,073

Adequacy 2,64 0,513 2,27 0,852 2,27 0,908 1,88 0,786 1,96 0,82 3,331 ,012 ,073

Education High Associate Licence Master Doctorate df =4,170

Measures M SD M SD M SD M SD M SD F p η²

Count A. 2,41 0,913 2,32 0,602 2,44 0,839 2,2 0,869 1,88 0,881 2,509 ,044 ,055

Care Use 2,65 1,018 3,33 0,811 2,75 1,108 3,32 1,192 3,18 1,148 2,781 ,028 ,061

Profession Physician Nurse Other df =2,173

Measures M SD M SD M SD F p η²

Technical 2,01 0,803 2,39 0,816 2,36 0,864 4,536 ,012 ,050

Count A. 1,97 0,828 2,49 0,809 2,46 0,937 8,020 <,001 ,085

Gualiﬁcation 1,97 0,909 2,35 0,854 2,46 0,997 4,554 ,012 ,050

Adequacy 1,98 0,808 2,41 0,784 2,42 0,92 6,053 ,003 ,065

Support 3,53 1,164 3,4 0,97 2,76 1,108 4,162 ,017 ,046

Branch Surgical İnternal Mixed df =2,173

(continued)

Baştürk et al. 9

“care usability”(F[4–171] =2.781, p < 0.05). The percep-

tions of the “doctorate”group regarding “count adequacy”

(M =1.88, SD =0.881) were moderately (η²=0.055) lower

than those of the “high school”(M =2.41, SD =0.913) and

“license”groups (M =2.44, SD =0.839). Furthermore, the

perceptions of the “master”group regarding “care usabil-

ity”(M =3.32, SD =1.192) were signiﬁcantly (η²=0.061)

higher than those of the “high school”(M =2.65, SD =

1.018) and “license”groups (M =2.75, SD =1.108).

Next, comparing the applicability means of the profes-

sion groups revealed signiﬁcant differences in “adequacy”

(F[2–173] =6.053, p < 0.05) and “support usability”(F[2–

173] =4.162, p < 0.05). The perceptions of the “physician”

group regarding “adequacy”(M =1.98, SD =0.808) were

signiﬁcantly (η²=0.065) lower than those of the “other”

(M =2.42, SD =0.920) and “nurse”groups (M =2.41,

SD =0.784). The results for the “adequacy”subcategories

were similar. However, the perceptions of the “other”

group regarding “support usability”(M =2.76, SD =

1.108) were moderately (η²=0.046) lower than those of

the “physician”(M =3.53, SD =1.164) and “nurse”

groups (M =3.40, SD =0.970).

Subsequently, comparing applicability means of experi-

ence groups revealed signiﬁcant differences in “own-

demand”(F[2–173] =3.533, p < 0.05), “monitoring usabil-

ity”(F[2–173] =4.186, p < 0.05), “consultation usability”

(F[2–173] =11.412, p < 0.05), and “support usability”

(F[2–173] =5.508, p < 0.05). The perceptions of the “two

app+”group regarding “own-demand”(M =3.35, SD =

0.834) were moderately (η²=0.039) higher than those of

the “none”(M =2.92, SD =0.959) and “one app”groups

(M =3.03, SD =0.964). Moreover, the perception of “con-

sultation usability”of the “none”group (M =2.93, SD =

1.187) was signiﬁcantly (η²=0.117) lower than those of

the “two app+”(M =3.78, SD =0.850) and “one app”

groups (M =3.55, SD =0.997). Furthermore, the percep-

tion of “support usability”of the “two app+”group (M =

3.75, SD =0.899) was signiﬁcantly (η²=0.060) higher

than those of the “none”(M =3.13, SD =1.081) and “one

app”groups (M =3.28, SD =1.194). similarly, the “two

app+”group’s perceptions of “monitoring usability”(M

=3.47, SD =0.821) were moderately (η²=0.046) higher

than those of the “none”(M =2.96, SD =1.083) and “one

app”groups (M =3.08, SD =1.156).

Finally, comparing the applicability means of the branch

groups revealed a signiﬁcant difference in “adequacy”

(F[

2–173

]=4.512, p < 0.05). the perceptions of the

“mixed”group regarding “Adequacy”(M =2.68, SD =

0.930) were moderately (η²=0.050) higher than those of

the “surgical”(M =2.13, SD =0.766) and “internal”

groups (M =2.21, SD =0.840). the results for the

“adequacy”subcategories were similar.

Table 4. Continued.

Groups 1. Group 2. Group 3. Group 4. Group 5. Group ANOVA

Age 25 –26–30 31–35 36–40 41 +df =4,170

Measures M SD M SD M SD F p η²

Technical 2,13 0,75 2,21 0,861 2,65 0,952 3,931 ,021 ,043

Count 2,18 0,788 2,22 0,895 2,74 0,937 4,475 ,013 ,049

Gualiﬁcation 2,09 0,838 2,2 0,946 2,66 0,976 4,024 ,020 ,044

Adequacy 2,13 0,766 2,21 0,84 2,68 0,93 4,512 ,012 ,050

Experience None One App Two App+df =2,173

Measures M SD M SD M SD F p η²

Own D. 2,92 0,959 3,03 0,964 3,35 0,834 3,533 ,031 ,039

Monitoring_U 2,96 1,083 3,08 1,156 3,47 0,821 4,186 ,017 ,046

Consultation_U 2,93 1,187 3,55 ,997 3,78 ,850 11,412 <,001 ,117

Support_U 3,13 1,081 3,28 1,194 3,75 ,899 5,508 ,005 ,060

*P< 0,05, η²; Estimated eta-squared.

10 DIGITAL HEALTH

Multiple linear regression analysis: the role of usefulness,

adequacy, experience, demand, education, and age on RHS

applicability categories and app type usability. The role of

the applicability variables was evaluated together with the

impact of demographic changes. For each applicability cat-

egory, a model was created that included age, education,

and experience variables along with other applicability cat-

egories. The most appropriate model was determined by

removing variables that did not signiﬁcantly contribute to

the model. All applicability variables together signiﬁcantly

affected the model variables. Among the demographic vari-

ables, only education contributed signiﬁcantly to the

adequacy model (Table 5).

Among the effects of the model variables on “useful-

ness,”the “demand”(β=0.749, p < 0.001) and “adequacy”

(β=0.106, p < 0.05) variables signiﬁcantly explained

63.8% of the model (R

=0.638, F =152.478, p < 0.001).

Next, regarding the effects on “demand,”the “usefulness”

(β=0.751, p < 0.001) and “adequacy”(β=0.101, p <

0.05) variables signiﬁcantly explained 63.7% of the

model (R

=0.637, F =151.814, p < 0.001). regarding the

effects on “adequacy,”the “demand”(β=0.244, p <

0.05), “usefulness”(β=0.248, p < 0.05), and “education”

(β=−0.236, p < 0.001) variables signiﬁcantly explained

24.6% of the model (R

=0.246, F =18.722, p < 0.001).

While a one-unit increase in the “education”variable

Table 5. The Role of Usefulness, Adequacy, Experience, Demand, Education, and Age on RHS Applicability Categories and App Type

Usability

Models Variables B SE βt p Tolerance VIF

1. Usefulness (Constant) ,587 ,155 3,797 <,001

Demand ,769 ,052 ,749 14,916 <,001 ,830 1,205

Adequacy ,113 ,054 ,106 2,118 ,036 ,830 1,205

Summary R =,799 R

=,638 AR

=,634 F =152,478 df: 2,173 P<,001 DW: 2,027

2. Adequacy BSE βt p Tolerance VIF

(Constant) 1,552 ,275 5,638 <,001

Demand ,235 ,105 ,244 2,239 ,026 ,370 2,700

Usefulness ,232 ,102 ,248 2,280 ,024 ,371 2,695

Education −,168 ,048 −,236 −3,540 <,001 ,986 1,014

Summary R =496 R

=,246 AR

=,233 F =18,722 df: 3,172 P<,001 DW: 2,055

3. Demand BSE βt p Tolerance VIF

(Constant) ,435 ,153 2,832 ,005

Usefulness ,732 ,049 ,751 14,916 <,001 ,827 1,209

Adequacy ,104 ,052 ,101 1,998 ,047 ,827 1,209

Summary R =,798 R

=,637 AR

=,633 F =151,814 df: 2,173 P<,001 DW: 2,031

4. App Type Demand BSE βt p Tolerance VIF

(Constant) ,428 ,135 3,159 ,002

Care ,301 ,043 ,393 7,036 <,001 ,570 1,754

Monitoring ,193 ,052 ,233 3,721 <,001 ,454 2,202

(continued)

Baştürk et al. 11

reduced the “adequacy”variable by 0.24 units, a one-unit

increase in the “demand”variable increased the “adequacy”

variable by 0.24 units. Likewise, a one-unit increase in the

“usefulness”variable increased the “adequacy”variable by

0.25 units.

Finally, examining the effects of model variables on

“app type demand,”“care usability”(β=0.393, p <

0.001), “monitoring usability”(β=0.233, p < 0.001), “con-

sultation usability”(β=0.157, p < 0.05), and “support

usability”(β=0.215, p < 0.001) variables signiﬁcantly

explained 70.2% of the model (R

=0.702, F =98.821, p

< 0.001). Furthermore, examining the effects on “app type

adequacy,”“examination usability”(β=0.289, p < 0.001)

and “monitoring usability”(β=0.247, p < 0.05) variables

signiﬁcantly explained 22.3% of the model (R

=0.223,

F=24.851, p < 0.001).

Example application ideas

The responses received from 105 participants in 17

branches were categorized by content analysis using

MAXQDA 2022. Speciﬁcally, the application features,

RHS type, integration into the RHS system, and applicabil-

ity were examined. The number of coded sections (CS) was

calculated.

Features speciﬁed in applications. The features emphasized

as necessary or useful for the implementation of the appli-

cation were coded. The number of coded sections was cal-

culated. The main features required to use the application

were identiﬁed as data storage and transmission (CS =

70), followed by recognition-detection (CS =55), wearable

portable device (CS =55), multi-connection-integration

(CS =49), planning-setting (CS =47), warning-command

(CS =42), notiﬁcation (CS =40), meeting (CS =31),

digital identity creation-veriﬁcation (CS =27), display

(CS =25), practicability (CS =24), reporting (CS =23),

reminding (CS =19), digital assistant (CS =19), and loca-

tion determination (CS =18) (Figure 3). The application

suggestions are given in Appendix 2.

Application recommendations speciﬁc to health service type.

Sample application ideas were coded according to RHS

type. Although physical examination, evaluation, and mon-

itoring are determined to be different service types in the lit-

erature, because there is no detailed content about these

groups, the categories have been combined as diagnosis-

monitoring types. Application recommendations can be

categorized into many types of services by their content.

In this study, coding was performed to cover all of applica-

tion type; however, when selecting the examples given for

the service type, the main emphasis in the application pro-

posal was considered.

Diagnosis and monitoring applications. In this application

group, scanning, monitoring, and evaluation recommenda-

tions were made for the branch, disease, and health status.

The most frequently emphasized codes in this category

were follow-up and monitoring (CS =51), followed by

physical examination (CS =13), symptom scanning (CS =

10), emergency assessment (CS =7), mobility assessment

(CS =7), and branch-speciﬁc evaluation

(Figure 4).

Some application suggestions are given in Appendix 3.

Biometric measurement applications. In these applica-

tions, vital values (CS =27), blood sugar (CS =11), blood

pressure (CS =11), imaging (CS =11), mobility (CS =6),

and electrocardiography were the most frequently used

Table 5. Continued.

Models Variables B SE βt p Tolerance VIF

Consultation ,126 ,049 ,157 2,552 ,012 ,469 2,131

Support ,171 ,047 ,215 3,626 <,001 ,505 1,980

Summary R =,838 R

=,702 AR

=,695 F =98,821 df: 4,168 P<,001 DW: 1,971

5. App Type Adequacy BSE βt p Tolerance VIF

(Constant) 1,025 ,186 5,525 <,001

Examination ,220 ,061 ,289 3,619 <,001 ,702 1,424

Monitoring ,197 ,064 ,247 3,094 ,002 ,702 1,424

Summary R =,472 R

=,223 AR

=,214 F =24,851 df: 2,173 P<,001 DW: 1,954

F: ANOVA F value showing the signiﬁcance of the model; DW: Autocorrelation between variables Durbin Watson; VIF: Variance Inﬂation Factor;

12 DIGITAL HEALTH

Figure 3. Application feature; code sub-code model.

Figure 4. Diagnosis and monitoring applications; code subcode model.

Baştürk et al. 13

codes (Figure 5). Some application suggestions are given in

Appendix 4.

Care and treatment applications. In these applications, the

medication (CS =12) and exercise (CS =11) codes were

most frequently emphasized (Figure 6). Thus, the recom-

mended practices should ensure the effectiveness, efﬁ-

ciency, and controllability of treatment, especially for

patients who are sent home. Some application suggestions

are given in Appendix 5.

Operation applications. The most frequently used codes in

these applications were remote adjustment or use of devices/

equipment by an expert user (CS =12), emergency interven-

tion (CS =8), and drug administration (CS=7) (Figure 7).

Some application suggestions are given in Appendix 6.

Figure 5. Biometric measurement applications; hierarchical code sub-code model.

Figure 6. Care and treatment applications; code sub-code model.

14 DIGITAL HEALTH

Counseling and support applications. Among these appli-

cations, the most common code was counseling practice

between the patient and healthcare provider (CS =23), fol-

lowed by suggestions regarding remote health environ-

ments (CS =27) (Figure 8). Some application suggestions

are given in Appendix 7.

Integration status of the application into the hospital remote

health system. Recommended applications were classiﬁed

according to their development status, and their distribution

according to branch was provided by creating the

MAXQDA 2022 Cross Table (Table 6).

The majority of recommendations were regarding the

integration of existing applications into the system (N =

57, 44.88%). This was followed by recommendations for

developing and integrating existing applications (N =51,

40.16%). While internal branches mostly recommended

integrating existing applications by improving them

Figure 7. Operation applications; code sub-code model.

Figure 8. Counseling and support applications; code sub-code model.

Baştürk et al. 15

(internal: 24-surgical: 17), surgical branches mostly sug-

gested integrating new applications (surgery: 13, internal:

4). Some application suggestions are given in Appendix 8.

Sample applications according

to their application potential

The potential for using applications for each application

recommendation was graded according to expression

style, emotion analysis, and development status. The use

of positive expressions about the application, such as beau-

tiful, useful, good, and excellent, in the text content and

application suggestion made by more than one participant

were considered in the rating (Table 7).

The data in Table 7 were obtained using the analysis

made with the MAXQDA 2022 Interactive Quote Matrix.

Many RHS applications with high application potential

exist for all branches. The majority of the recommendations

related to integrating existing applications into the corpor-

ate system either directly or by improving them.

Qualitative-Quantitative integrated ﬁndings

on RHS applicability

Different data types and analyses yielded ﬁndings that

support, explain, or extend each other (Table 8).

“Experience”(N =110) and “quan-usefulness”(M =

3.10) categories had high values in the same application

types: “counseling”(N =89/M =3.40), “support”(N =37/

Table 6. Distribution of application development status based on branch.

Coded sections were counted once per document.

Table 7. Distribution of Application Potential and Integration Status Based on Main Branch

Main Branch

Participant Application Potential Application Integration Status

Total Very High High Middle Low Total Available New By İmproving

Mixed 16 12 11 6 1 30 12 3 15

İnternal 42 42 14 5 2 63 29 4 30

Surgical 47 33 15 9 3 60 25 14 21

Total 105 87 40 20 6 153 66 21 66

The number of applications was determined by the sum of the same applications suggested by different participants or different applications suggested by the

same participants.

16 DIGITAL HEALTH

Table 8. Qualitative-quantitative integrated ﬁndings on RHS applicability.

Analysis Data

Applicability

categories

Physical

examination Measurement Evaluation

Treatment /

Care Operation Monitoring Counseling Support

General or

total (T)

Regression

App

Adequacy

β=.289 β=.247 R2=.223

One-Way Anova

Demographic G/

Usefulness

Differences

Experience 3&1|2* 1&2|3* 3&1|2* p> 0.05

Education 4&1|3* p> 0.05

Profession 3&1|2* p> 0.05

*p< 0.05, **p< 0.01 level. η²: estimated eta-squared. Groups differences (GD: 1., 2., 3., 4. and 5.) were identiﬁed by “&;and, |;or”signs.

1See Table 1 for more details.

See Figures for more details.

See Appendices for more details.

See Table 3 for more details.

See Table 4 for more details.

See Table 5 for more details.

See Table 4 for more details.

18 DIGITAL HEALTH

Discussion

Overall, the results of this study reveal that the majority of

healthcare professionals used at least one application from

each type of RHS either informally or formally. The most

experienced branches are internal medicine, cardiac, pediat-

rics, infection, and orthopedics. Internal (surgical) branches

come to the fore in terms of application experience (appli-

cation recommendations) and current (new) applications.

Illustrating the constant pursuit of progress in healthcare,

the growth curve of robotic surgery points to innovation.

While application recommendations are mostly for

support, measurement, and evaluation applications, appli-

cations which employees have more experience of using,

such as “Consulting,”“Support,”and “Monitoring,”are

perceived to be more usable. Unlike the current study, a

study conducted with physiotherapists revealed that the

applications were mostly used in monitoring, treatment,

and evaluation.

In another study, doctors recommended

support applications such as receiving test results, schedul-

ing appointments, and medication reminders to their

patients; however, they were reluctant to recommend appli-

cations such as test result evaluation, video conferencing,

and remote monitoring of vital parameters.

42,49

n this study, professionals ﬁnd applications that they

actually used to be useful. Furthermore, professionals want

to use more technical applications if the necessary conditions

are met. Similar results were reported by a study conducted in

Korea: Those with experience in distance therapies exhibited

a signiﬁcantly higher willingness to continue participation

throughout the institutionalization process.

Early adopters

of telemedicine applications may inﬂuence others to try

them. A strong recommendation intention indicates a posi-

tive experience and successful adoption. Recommendation

intention emerges as an important outcome variable inﬂu-

enced by a number of psychological and motivational

factors, such as “performance expectancy”and “effort

expectancy.”

Medical practitioners’awareness and posi-

tive attitudes play an important role in the effective function-

ing of RHS systems.

An interesting ﬁnding of this study is

that professionals’self-use demand exhibits a stronger asso-

ciation with application usability than patients’demand. The

relationship of education and experience with applicability,

which is frequently emphasized in the literature; must be

examined from different perspectives. As such, rigorous,

in-depth research should be conducted on professional regu-

lations for RHS content creators, RHS operators, RHS engi-

neers, RHS safety experts, virtual health assistants, hologram

doctors, and so on, as a professional function or as new

professions.

The applicability categories in this study exhibit moder-

ate to high correlations. All three applicability categories

signiﬁcantly explain each other in the multiple regression

models. Educational status, a demographic factor, is also

included in the “Adequacy of Requirements”model. The

adequacy of requirements for applicability is lower for

older adults than for the youth, for internal/surgical

branches than for mixed branches, and for physicians than

for other professionals. Furthermore, those with higher edu-

cation perceive “staff number”as low score and “care

usability”as high score . Thus, technical and human

resources must be carefully managed for RHS applicability.

Insufﬁcient integration resulting in unnecessary repetitions

increases workload and affects user demand.

Healthcare

managers should also consider demographic inﬂuences

when making assignments. For example, because they

interact with technology more, the younger generation

may be more aware of the beneﬁts of technology or new

applications than the older generation.

User participation in health technology design is very

important for application usability. The development of

RHS systems requires a multidisciplinary approach with

patient and healthcare professional participation.

By con-

sidering user suggestions during the design process, user-

friendly applications can be developed as technical solu-

tions can be incorporated in advance.

For instance, a

study evaluating access to remote treatment revealed that,

while the majority of individuals have access to digital

devices, the system used for remote therapy does not

meet the minimum requirements.

For remote monitoring

application in chronic patients, important features such as

data storage and transmission, user interface, and alarm

have been successful. Furthermore, the system design

allows the incorporation of data sources other than

medical data, such as temperature, humidity, or pollution

level. These data can improve predictions and may be espe-

cially important for chronic patients with respiratory pro-

blems.

In another study, healthcare providers indicated

that adopting, scaling, and sustaining technology-enhanced

nutrition care models beneﬁts patients, clinicians, and

healthcare overall.

Potential barriers to RHS adoption include resistance to

change, resistance to use, perceived risk, status quo bias,

work-related issues, quality of care concerns, and organiza-

tional challenges.

System complexity, lack of user train-

ing, lack of system integration, security and privacy,

inadequate technical support, and inﬂexibility of systems

in use have been cited by healthcare professionals.

Resistance from healthcare providers may arise from lack

of training, limited clinical knowledge, low levels of staff

participation, reduced productivity, lack of required rou-

tines, communication problems, unplanned and ineffective

implementation, limited resources leading to infrastructure

deﬁciencies, technology usability challenges, design and

software barriers, integration issues with other information

technology systems, fears related to system effectiveness or

performance risk, and doubts about the clinical and cost-

effectiveness of the system.

The metaverse could offers a tremendous opportunity for

RHS applicability. The metaverse comprises various virtual

Baştürk et al. 19

components that represent embodiments of their physical

counterparts, such as digital avatars and three-dimensional

virtual environments. Technologies that enable the meta-

verse include augmented reality, AI, distributed computing,

digital twins, and telecommunications.

These components

can enhance the quality of remote interaction.

Lack of demand for use of RHS may be due to privacy

and data security concerns.

To address security concerns;

Real-world evaluations such as “wireless hacking test”are

carried out in environments where IoT devices are used in

RHS applications; safety precautions must be taken.

practically ensure the scalability and security of RHS appli-

cations; security models suggested for smart application

design can be used.

Studies have suggested security solu-

tions such as rigorous authentication processes, data

encryption, resistance to attacks, and continuous monitor-

ing, emphasizing the need to support patient autonomy,

ensure conﬁdentiality of data, and maintain equal access

to healthcare in the context of IoT communication security.

Measures against the ever-evolving threats in smart health-

care environments must be incorporated into application

design.

These privacy-enhancing strategies will align

RHS applications with global data protection standards in

ensuring the security of patient data.

Overall, combining current study ﬁndings with those

from the literature, the critical features needed in an RHS

application are illustrated in Figure 9. Although the features

vary depending on the type of service, most features are

indispensable in a digital application owing to the connec-

tion between the services. Another crucial inference is that

design requirements demonstrate the importance of the

digital health engineering ﬁeld.

The strategic gaps in smart healthcare applications are

“legal regulations,”“economic factors,”“user behavior

habits,”“politics and culture,”and “environment and tech-

nology.”

Studies have emphasized the integration of pol-

itical, technological,

organizational,

and cultural

aspects, as well as the development of new applications

in these directions to increase the socioeconomic beneﬁts

in RHS delivery. The current study’s proposed future

version of RHS includes features such as multi-faceted

translation, multiple and ﬂexible integration capabilities,

personalization, alternative options, sensor detection, and

advanced interaction. These features can signiﬁcantly

increase the applicability of the RHS from a socio-

demographic perspective.

In summary, the extant application features experienced

related to a RHS operate within a limited framework.

Furthermore, the system is not useful enough, applications

are not integrated and used ofﬁcially, and application fea-

tures need to be signiﬁcantly improved. Several applica-

tions with high application potential exist for every

branch and service type, and are even used in some institu-

tions. Application recommendations are mostly related to

integrating existing applications into the corporate system

either directly or through development. Nonetheless, there

Figure 9. Critical application feature.

20 DIGITAL HEALTH

are interesting suggestions for future innovation. To move

forward in this regard, the necessary conditions and user

demand must be met. Table 9 presents meta-inferences

and sample applications regarding application recommen-

dations based on RHS application types.

Physical examination

The application type wherein usability is most dependent on

adequacy is physical examination. However, compared with

other applications, a lower demand is required for its usabil-

ity. The use of physical examination was mostly recom-

mended in the outcome evaluation phase or for greenﬁeld

patients (Appendix 3). Remote examination can be used

effectively in a system that enables support, measurement,

monitoring, and evaluation applications. For example, appli-

cations that recommend examination or treatment decisions

can be developed by adequately detecting the patient’s con-

dition. These decisions can be veriﬁed by remote examin-

ation. Furthermore, more effective results can be obtained

when using applications with advanced technology.

Studies have exhibited the potential of new technology to

remotely enable individuals to experience the sensations of

touch, sight, and sound, which are indispensable for physical

examination.

Another study comparing simultaneous

remote and in-person musculoskeletal examination using

augmented reality, tactile sensors, and three-dimensional

imaging found that the evaluations matched.

In another

study comparing remote and face-to-face application of

heart and lung auscultation, remote application was found

to be an acceptable alternative to face-to-face application.

Measurement

Compared with consulting practice, the usability of meas-

urement application depends more on technical adequacy

and health professionals’own demand. Concerns about

the reliability of applications may impact professionals’

demand for RHS use. A study on biometric measurement

and monitoring using a smartwatch found that patients

were satisﬁed with the application; however, technical pro-

blems reduced usability.

In the current study, suggestions were reported for mea-

surements such as sound, hearing, touch, vision, rhythm/

motion, pressure, and temperature using sensors. Other

studies have shown that remote measurements such as

height/weight,

auditory brainstem response testing,

heart rate,

or blood pressure,

among others, are feasible.

Evaluation

The evaluation application is the least related application to

“adequacy”and second least related application to

“demand.”However, it can be considered the third most

useful application. This study suggests that, medical

device data should be transferred to the RHS information

system for better evaluation. Here, health status-speciﬁc

screening and assessment applications can be used.

Additionally, applications that evaluate functions, such as

sleep, nutrition, movement, respiration, circulation, excre-

tion, and emotionality, can be integrated into the system.

Innovative suggestions for malignancy prediction using

noninvasive chips or digital twins have also been presented.

Some applications on evaluation have been reported in

the literature. For instance, patients at risk of mortality

can be identiﬁed by the remote assessment of heart failure

symptoms.

Another study found that “prehospital assess-

ment using commercial mobile phones with ﬁfth-generation

wireless communication technology is feasible and reliable

during ambulance transport in urban areas.”

Additionally,

the telemedical approach can signiﬁcantly reduce the wait

time for specialist evaluation in a real-world setting.

Treatment/care

Compared with other applications, less adequacy and more

demands are needed for usability of treatment/care applica-

tion. Improved versions of applications exist, such as medi-

cation use, mobilization, diabetes, circulation, nutrition,

wound care, excretion, stress, sleep, and respiratory man-

agement, which are recommended to be used within the

scope of remote care-treatment. Many applications are actu-

ally used unofﬁcially. Health professionals believe that their

use will be beneﬁcial. Moreover, those with higher educa-

tion perceived “Care Usability”as high.

A scoping review of distance physiotherapy found

remote therapy to be safe, feasible, and acceptable to

patients, and more cost-effective than face-to-face

therapy.

Another study found a positive usability evalu-

ation of a digital self-management app for depression.

Furthermore, the use of the remote health program was

associated with signiﬁcant clinical improvement in

anxiety and depression.

Additionally, studies have con-

cluded that remote health intervention programs can be

applicable, safe, and effective in the rehabilitation process

of neurological diseases.

Operation

Operation is the fourth application that the participants per-

ceived as related to the “demand”and “adequacy”categories.

This application is the least experienced but the second most

usable application. In applications involving direct intervention,

security or uncertainty may affect applicability more than other

factors. For example, contemporary robotic surgery systems

face challenges such as security, privacy, reliability, latency,

and costly impacts of blockchain-based storage.

Developments such as augmented reality, AI, machine

learning, integration of imaging and visualization technolo-

gies, improved precision and dexterity of robotic arms,

Baştürk et al. 21

Table 9. Remote health application: examples of useful RHS recommendations—existing, developable, or innovative applications.

Application Deﬁnition Resource Available Must be developed Future innovations

Physical

Examination

Physical examination is usually performed remotely with the

patient and doctor meeting simultaneously. The ability to

perform ﬁxed/moving images, tissue density/saturation,

voice and speech analyses, and tactile analyses via sensors

during the examination will increase the usability of these

applications.

7,15,16,35,57,58

1. Pre or post-examination

interview.

1. İnterview supported by symptom checker

app or wearable devices

1. Simultaneous analysis of image, tissue

density/fullness, sound, and speech

Using remote sensors.

2. Examination in an environment

supported by metaverse whith 3D

visualizations

Measurement It involves remote measurement of a person’s measurable

physical and behavioral values using a digital device and

application. Measurements such as image, vital, O2, EEG,

EMG, ECG, blood, urine, sugar, color, and mobility

measurements should be given as examples of these

applications.

9,35,76,81

1. Temperature, pulse, blood or

pressure measurements

1. Function measurements such as respiration

and excretion

2. Spermiogram

3. Tumor markers

1. Glasses measuring intraocular

pressure,

2. Delivering a digital tablet through

the urethra to diagnose kidney stones.

Evaluation It includes the remote identiﬁcation and evaluation of the

individual’s health-speciﬁc conditions using tools such as

digital devices/applications, artiﬁcial intelligence,

algorithms, etc. Evaluation types such as disease risk,

complications, health status, screening, prediction,

prioritization/triage, and wound evaluation should be given

as examples of evaluation applications.

11,33,36,38,81

. 1. Health status-speciﬁc

screening, and evaluation

applications

2. Evaluation of sleep,

nutrition, movement,

respiration, circulation,

excretion or emotionality.

1. Transferring medical device data to the RHS

information system.

1. Malignancy prediction using a

noninvasive chip or digital twin.

2. Real-time hologram doctor review

of critical health results via an app that

continuously monitors and reports

biometric data.

Treatment / Care It includes remote management and implementation of care

and treatment services using digital devices and

applications. The prominent application features of these

services are disease-speciﬁc and personalized reminders,

alerts, task lists, adjustments made according to

measurement and/or monitoring results, treatment

compliance, progress, and side effect checks. Practices such

as nutrition, sleep, exercise, medication, wound care, and

stress management should be given as examples of these

applications.

11,12,15,38,83

. 1. Apps to manage personal

care functions such as diet,

sleep, exercise, stress

1. Medication use control and management.

2. Treatment applications for elderly and

chronic patients, powered by sensors, voice

digital assistants, user-friendly interfaces

and translation features.

1. Prescribing personalized treatments for

speciﬁc patient care such as drug use,

wound care, diabetic patient care

2. Applying treatment practices with

digital nurse support

Operation It involves carrying out a transaction in person remotely, using

a digital device and applications. Applications such as

robotics, surgery, interventional procedures, drug

administration, environment setting, and device setting

should be given as examples of these applications.

7,15,16,36,84

1. Adjustment of environment,

device or drug

1. Emergency intervention apps for elderly

falls, heart attacks, e.g.,

2. Robotic surgery

1. Operations platforms, powered by

Metaverse, for example, personalized

simulations on digital twin/avatar

2. Neuron implants that will enable

remote intervention and control

movement

Monitoring It involves remote monitoring of an individual’s health-speciﬁc

conditions using tools such as digital devices/applications,

artiﬁcial intelligence, algorithms, etc. For example, features

such as remote monitoring of health ﬁndings, warning

when there is a deviation in values, and remote patient

visits should be given.

9–11,15,38,74

. 1. Health status-speciﬁc

monitoring applications

1. Patient wristband application with biometric

measurement, tracking, and analysis

features.

1. Monitoring the sedated patient’s level of

consciousness.

2. Continuous monitoring of physical,

mental, and emotional health status

via a personalized chip.

3. Virtual visits

(continued)

22 DIGITAL HEALTH

Table 9. Continued.

Application Deﬁnition Resource Available Must be developed Future innovations

Counseling It is the simultaneous or asynchronous meeting with relevant

people via a digital application when needed regarding a

certain subject. There are ways of applying it between

individuals or groups, formally or informally, related to

health issues. It can be done between the same or different

levels of expertise based on patient, branch, profession, or

institution.

3,15,42,77,78

1. Interview between experts

with a formal app

1. Formal consultation app, involving experts or

institutions at various levels.

1. International consultant group

application consisting of different

interest groups.

2. Consultation platforms involving

virtual experts and expert’s avatar

Support It is the implementation of activities that assist in the provision

of health services using digital tools. Applications such as

recording, decision support, personal digital assistant,

information, training, and meeting applications should be

given as examples of support applications.

7,16,42,43,78,82,85

. 1. Patient admission, navigation,

decision support,

information, training, etc.

1. Audio, speech, sign, text, etc. transformation.

2. Voice reporting application

3. Current content production and/or

supervision by health professionals

1. Informational applications in digital

games.

2. Digital identity application that

includes personal health information,

with features such as identiﬁcation,

veriﬁcation, authorization, censorship,

etc.

3. Virtual health Assistant special for

professionals and patients

General RHS

system

RHS can be deﬁned as remote delivery of healthcare services

through digital technologies such as wearable smart

devices, application software, information communication,

and connectivity technologies.

For RHS, an application platform is required that includes

smart systems, equipment, environment, connectivity,

digital users, data management, security, translation,

telecommunications, sensors and analytics.

33,35,40,41,44,57,66,69,77,81,85

1. Promotion, compliance

training, etc. Activities for

using the existing system.

Currently generally for

individual use

1. İntegrated data, devices, system,

organization and applications. 2. Mobile

phone as a medical device and mobile

version of apps.

Ofﬁcial use of institutional regional

integration

1. Application environments (cabin) at

speciﬁc locations, supported by the

virtual universe, connected to RHS

centers.

2. Critical features in RHS system and

applications design (see Figure 9)

Multiple and Global integrations.

Coded documents N 57 51 19

Baştürk et al. 23

robotic design allowing ﬂexible surgery, tactile feedback,

and sensory enhancement, are increasing the potential for

robotic surgery application.

Despite reservations regarding using RHS in surgical

branches, important suggestions have been made for opera-

tive patient groups related to planning, operation simula-

tion, robotic surgery, three-dimensional imaging, or a

common platform, in addition to pre- or post-procedure

follow-ups. Considering these suggestions, a common plat-

form can be created, supported by AI and augmented and

virtual reality technologies, wherein surgeries performed

throughout Turkey can be monitored or relevant experts

can be involved when necessary. The technology to simul-

taneously connect multiple consoles to perform a single

procedure is especially beneﬁcial for patients with

complex medical conditions that require a multi-specialty

approach. Experts from different disciplines can connect

from different locations and collaborate on the same

patient in real time.

The application system may have a

feature that allows operation planning and simulation on a

person-by-person basis. Such an application not only sup-

ports surgeons but also helps assistants obtain experience

from cases they have not encountered before. AI technol-

ogy can help create visual animations and aid surgeons in

understanding what they cannot see.

Additionally, deci-

sion support applications can be used during the operation,

providing both visual and informational support on a trans-

action basis.

Monitoring

In terms of usability, the second most dependent application

on “Adequacy”and “Demand”is “Monitoring.”This appli-

cation is also the third most experienced and most usable

application. A study on monitoring, diagnosis, and treat-

ment applications revealed that wireless technology is

mostly used in the monitoring area.

Based on the results

of the current study, the monitoring application must be

strengthened with supporting applications. For instance,

the “patient wristband application integrated with the

RHS system, with biometric measurement, tracking and

analysis features”suggested in this study may be an inter-

esting application.

Similar to the results of this study, other studies have

also recommended using health condition-speciﬁc applica-

tions. For instance, in a study conducted with heart failure

patients, the monitoring application was found to be

effective.

Similar results have been obtained for patients

with chronic

9,81

and psychotic issues,

Parkinson’s,

and so on.

Counseling

The usability of the consulting application is higher than

that of other applications when sufﬁcient conditions and

patient demand are provided. Participants suggested that

the most used consultancy application should be done

through the formal application and one with more technical

features. Previous studies support this conclusion. For

example, remote consultation has been shown to be import-

ant and reduces the number of patient visits, especially

when isolating those with infectious diseases. However,

the current RHS system must be improved with support

applications.

40,82

Another study found that chatbots devel-

oped for medication counseling supported healthcare pro-

fessionals.

Currently, counseling is generally performed

via one-on-one meetings. In informal applications, it can

also be done through a portal with certain groups of consul-

tants or counselee. Consultation groups practice (consultant

groups) is being carried out informally among health pro-

fessionals usually using WhatsApp. Participants stated

that exchanging ideas on a subject is useful and frequently

used in groups comprising professionals or experts from

different branches. The application is especially effective

in using multiple minds and reaching a common conclusion

in evaluating critical situations. The ofﬁcial implementation

system should be developed considering these situations.

As an application feature, transaction-based communication

styles should be created.

Support

An application whose applicability is secondarily depend-

ent on patient demand is the support application. This

application is the second most experienced and most

usable application; compared with other applications,

less adequacy and more demands are needed for its

usability. Notably, most application recommendations

have been made for this application. This application

type is widely discussed, especially in digital health lit-

erature. Nonetheless, it has a wide application and devel-

opment potential. For instance, a study evaluating the

support practices used by the Turkish Ministry of

Health found that the most used E-Nabızservicewas

ﬁnding out the examination results.

Similar applications

were recommended to patients by doctors.

In this study,

application recommendations were made for decision

support, education, information, guidance, preventive

health, and patient acceptance. Thus, there is a wide-

ranging potential for information applications. Scholars

can examine digital content production on a selected

topic, such as exercise, patient information, and obtaining

consent. Content does exist on most subjects; however,

this the content should be produced by professionals, or

at least revised and updated. This also reveals “digital

health information”content production as a new profes-

sional function. Thus, virtual health assistant application

based on AI

can be more effective than existing

applications.

24 DIGITAL HEALTH

The most striking code in support applications is RHS

environments. In the literature, location designs such as

smart hospital, house, city, building, apartment, outdoor,

or mobile health-based design have been mentioned for

RHS.

33,79,86

Considering the recommendations made in

this study, RHS environments can be in certain locations

such as a health institution, home, or in the city/intercity.

Certain applications such as measurement, interview, mon-

itoring, treatment, or operation must be possible in the

environment. For this, application features must be

designed very well. Especially for treatment and operation

applications, adjustments such as device, medication, and

environment settings should be made remotely. The recom-

mendations in this study support and expand the literature

on future innovation.

7,15,16

Based on these recommendations, voice recognition can

be especially used in support applications for various tasks

and processes, such as note taking, reporting, transaction

recording, and commanding. This technology is used in

smart hospitals, including electronic medical document

transcription, pathological voice recognition, and medical

process optimization through human–medical equipment

interaction.

Furthermore, remote support and inspection

applications with sound, image, sign, and text features

can be designed with a feature that enables communication

functions, such as messaging when necessary, and performs

different converting different functions, such as converting

voice to text.

Integration

Integration should include user, data, device, application,

and system. The participants emphasized that, the applica-

tions can be integrated with E-Nabız or hospital information

systems, all necessary data can be transferred to the system,

and access to the system can be provided through a

common application. In this context, the management of

big data is crucial to ensure health data standardization,

quality, and integrity. To ensure this, a general health data-

base should be developed at the national level to which

other applications and standardized health institution data-

bases can be connected. Moreover, applications should be

accessible within the framework of the needs and authority

of the institution and application users, and digital health

identities should be capable of being integrated into differ-

ent health systems.

Furthermore, the integration of frequently used personal

devices, such as smart watches and mobile phones, into the

general system is among the notable suggestions. It is stated

by the participants that, the applications used must have a

mobile version and be installed on the mobile phones of

users such as patients or healthcare professionals.

However, paradoxically, mobile phones, one of the

wearable-portable devices that are indispensable for RHS

use, do not have a speciﬁc standard as an RHS device

and are often used informally. This issue must be addressed.

An in-depth examination of the integration of both existing

applications and digital technologies that will enable the use

of these applications into the RHS system is needed.

Limitations

This study has some limitations. First, it was conducted on a

sample from a university hospital within the Turkish health-

care system. Nonetheless, besides minor institutional differ-

ences, the results provide useful information suitable for a

regional or international audience as health services are uni-

versal, and especially standardized in hospital settings.

Additionally, the contributions of academic health profes-

sionals who have worked in various institutions are

valuable.

Second, this study considered only some feasibility

factors owing to the nature of the study. Other dimensions,

such as social, economic, cultural, politics, law, and secur-

ity, are important in terms of feasibility and each may

require detailed investigations. Some of these dimensions

were added in the initial versions of this study’s question-

naire; however, based on the expert evaluation, it was

decided that the dimensions would be a separate study

subject owing to their scope and sensitivity. For example,

ﬁnances are under the control of the institutional owner

and management, especially in hospitals. As such, this

factor should be examined more comprehensively by

healthcare service managers, providers, and ﬁnanciers.

Furthermore, during the trial phase, healthcare profes-

sionals perceived these issues as technical aspects and

described them as subjects they had secondary knowledge

about. Similarly, the demand factor was limited to the

primary users of healthcare services. Politicians and sup-

plier companies have quite important roles or expectations

in the use of RHS. Finally, the security dimension should be

studied in more detail to reveal the different risks of each

type of application. An up-to-date measurement tool can

be developed based on the type of application by also bene-

ﬁting from the features outlined here regarding the func-

tionality of the applications.

Conclusion

This study provides strong inferences regarding the charac-

teristics of RHS applications. Thus, it contributes unique

insights regarding the potential for RHS implementation.

The results reveal that the adequacy of requirements,

patient–professional demand, and usability categories are

important for RHS applicability. Professionals ﬁnd the

applications they have experienced useful and want to use

other applications as well if the necessary conditions are

met. Interestingly, patient–professional demand has a

greater impact on the usability of applications compared

with the adequacy of requirements. Additionally, the

Baştürk et al. 25

professional’s own-use demand for RHS usability is more

effective than the patient’s demand. Notably, our results

are obtained both from quantitative and qualitative data

which consider the needs and interests of professionals.

Consequently, our conclusions regarding the application

features are robust and valuable. Based on the results, we

suggest that, for a hospital of the future, RHS application

should have at least these critical features. For instance,

as deﬁned in the speciﬁcations, virtual assistants and

avatars should be speciﬁc to patients and professionals.

Furthermore, we provide interesting examples of existing,

improvable, or innovative application suggestions that can

be integrated into the RHS system, such as meta-inferences

based on application type, which are derived from combin-

ing professional recommendations.

Moreover, education and experience signiﬁcantly affect

applicability. This result, which is frequently emphasized in

other studies, draws attention to a missing issue in terms of

adaptation to the digital age: For RHS applicability, profes-

sions or professional functions should be updated according

to the newly emerging application potential. Furthermore,

differences between demographic groups are mostly

related to the adequacy of requirements. Hence, healthcare

managers should consider demographic factors, such as

age, education, profession and experience, when planning

resources and staff. Similarly, application developers

should consider demographic and applicability categories

along with the features that an RHS application should

have. For example, patient–professional application users

should have usage options that consider their age, educa-

tion, profession, health status, and so on. Given the substan-

tial demand and necessary conditions, tremendous potential

exists for developing and inventing usable applications.

List of abbreviations

AI artiﬁcial intelligence

ANOVA one-way analysis of variance

CS coded sections

IoT internet of things

PCA principal component analysis

RHS remote healthcare services.

Acknowledgements:We would like to thank the hospital’s head

nursing department for contributing to the implementation of the

surveys during the data collection process. We would also like

to thank the healthcare professionals who participated in the

study and shared their opinions, ideas, and experiences. We

would like to thank Editage (www.editage.com) for English

language editing. We would also like to thank the reviewers and

editorial team for their comments that contributed to the quality

and expressiveness of the study.

Contributorship: F.B.: conception or design of the work (65%),

data collection (65%), data analysis and interpretation (65%),

drafting the article (65%), and critical revision of the article

(65%). A.O.T.: conception or design of the work (20%), data

collection (20%), data analysis and interpretation (20%), drafting

the article (20%), and critical revision of the article (20%). O.Ö.:

conception or design of the work (5%), data collection (5%),

data analysis and interpretation (5%), drafting the article (5%),

and critical revision of the article (5%). Ç.K.: conception or

design of the work (5%), data collection (5%), data analysis and

interpretation (5%), drafting the article (5%), and critical

revision of the article (5%). L.I.: conception or design of the

work (5%), data collection (5%), data analysis and interpretation

(5%), drafting the article (5%), and critical revision of the article

(5%). Final approval of the version to be submitted: all named

authors have read and approved the ﬁnal version of the

manuscript.

Availability of data and materials: The datasets generated and/

or analyzed during the current study are not publicly available due

to limitations of ethical approval involving the participants’data

and anonymity but are available from the corresponding author

on reasonable request.

Consent for publication: The purpose of the research and

conﬁdentiality of the data obtained were explained to the

participants, and their informed verbal and written consent was

obtained.

Declaration of conﬂicting interests: The authors declared no

potential conﬂicts of interest with respect to the research,

authorship, and/or publication of this article.

Ethics approval and consent to participate: The Yozgat Bozok

University Ethics Committee approved the research on 21

September 2022, with reference number 36/18. The purpose of

the research and conﬁdentiality of the data obtained were

explained to the participants, and their informed verbal and

written consent was obtained.

Funding: The authors disclosed receipt of the following ﬁnancial

support for the research, authorship, and/or publication of this

article: The study was supported by Yozgat Bozok University

Scientiﬁc Research Projects Commission under the code

THD-2023-1093.

ORCID iD: Fadime Bastürk https://orcid.org/0000-0003-2928-

824X

Supplemental material: Supplemental material for this article is

available online.

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ResearchGate has not been able to resolve any citations for this publication.

Facilitators and barriers in acceptance of telemedicine among healthcare providers in Pakistan: A cross-sectional survey

Article

Full-text available

Jul 2024

Objectives This study aimed to assess healthcare professionals’ awareness and acceptance of telemedicine residing in Pakistan. Materials and Methods A cross-sectional study was conducted across Pakistan from November 2021 to March 2022. Data entry was done through a self-administered questionnaire. In this survey, the non-probability purposive sampling method was adopted. Medical doctors from all specialties that fall into the age category of 20–60 years were included in this study. Statistical Package for Social Sciences (SPSS) version 22.0 was used for univariate data analysis. Results Among the 860 healthcare professionals who received the survey, 198 responded (23.0% response rate). The mean ± SD age of the participants was 31.16 ± 8.56. The results were found to be statistically significant for knowledge about telemedicine among healthcare professionals workplace, that is, healthcare professionals working in the private sector indicated a high level of awareness regarding telemedicine as compared to those in the public sector ( p = 0.03). A significant difference ( p-value ⩽ 0.05) in knowledge was seen among doctors working in the public sector and those working in a private setup. Results indicate significant positive associations between telemedicine utilization and perceived benefits over disadvantages ( β = 1.03, p = 0.017), reliance among healthcare professionals (β = 1.22, p = 0.008), and enhancement in healthcare quality ( β = 1.42, p = 0.001). Moreover, familiarity with telemedicine correlates strongly with its usage ( β = 2.56, p < 0.000). Conclusion Overall, healthcare professionals exhibited satisfactory knowledge regarding telemedicine and showed an accepting attitude toward telemedicine. Our study recommends enhancing the importance of Telemedicine training at the Healthcare professionals’ level to produce quality service delivery in healthcare organizations.

Analyzing the Shortcomings in Smart Healthcare for Remote Home Care—A Case Study of the Taiwan Market

Article

Full-text available

Jun 2024
Int J Environ Res Publ Health

In Taiwan, remote healthcare initially focused on telemedicine, with systematic development starting in 2007 through the “Remote Care Pilot Program” and subsequent initiatives. Significant advancements came with the “Remote Health Care Services Development Plan” in 2010, which integratedArtificial intelligence and Information and communications technologies, enhancing smart healthcare in home care. This study investigated strategic gaps in smart healthcare applications for remote home care using a mixed-methods approach, particularly the Analytic Hierarchy Process (AHP). This study identified and ranked strategic gaps, including “legal regulations”, “economic factors”, “user behavior habits”, “policy and culture”, and “environment and technology”. Findings, based on in-depth interviews with 6 experts and 16 AHP questionnaire samples, highlight “legal regulations” and “user behavior habits” as critical areas needing attention. Addressing these gaps can improve user acceptance and the effectiveness of smart healthcare applications, providing valuable insights for future research and practice in making remote home care more comprehensive and efficient.

Design, implementation and usability analysis of patient empowerment in ADLIFE project via patient reported outcome measures and shared decision making

Article

Full-text available

Jun 2024
BMC MED INFORM DECIS

Introduction This paper outlines the design, implementation, and usability study results of the patient empowerment process for chronic disease management, using Patient Reported Outcome Measurements and Shared Decision-Making Processes. Background The ADLIFE project aims to develop innovative, digital health solutions to support personalized, integrated care for patients with severe long-term conditions such as Chronic Obstructive Pulmonary Disease, and/or Chronic Heart Failure. Successful long-term management of patients with chronic conditions requires active patient self-management and a proactive involvement of patients in their healthcare and treatment. This calls for a patient-provider partnership within an integrated system of collaborative care, supporting self-management, shared-decision making, collection of patient reported outcome measures, education, and follow-up. Methods ADLIFE follows an outcome-based and patient-centered approach where PROMs represent an especially valuable tool to evaluate the outcomes of the care delivered. We have selected 11 standardized PROMs for evaluating the most recent patients’ clinical context, enabling the decision-making process, and personalized care planning. The ADLIFE project implements the "SHARE approach’ for enabling shared decision-making via two digital platforms for healthcare professionals and patients. We have successfully integrated PROMs and shared decision-making processes into our digital toolbox, based on an international interoperability standard, namely HL7 FHIR. A usability study was conducted with 3 clinical sites with 20 users in total to gather feedback and to subsequently prioritize updates to the ADLIFE toolbox. Results User satisfaction is measured in the QUIS7 questionnaire on a 9-point scale in the following aspects: overall reaction, screen, terminology and tool feedback, learning, multimedia, training material and system capabilities. With all the average scores above 6 in all categories, most respondents have a positive reaction to the ADLIFE PEP platform and find it easy to use. We have identified shortcomings and have prioritized updates to the platform before clinical pilot studies are initiated. Conclusions Having finalized design, implementation, and pre-deployment usability studies, and updated the tool based on further feedback, our patient empowerment mechanisms enabled via PROMs and shared decision-making processes are ready to be piloted in clinal settings. Clinical studies will be conducted based at six healthcare settings across Spain, UK, Germany, Denmark, and Israel.

Digitally managing depression: A fully remote randomised attention-placebo controlled trial

Article

Full-text available

Jun 2024

Background Depression is a common and disabling condition. Digital apps may augment or facilitate care, particularly in under-served populations. We tested the efficacy of juli, a digital self-management app for depression in a fully remote randomised controlled trial. Methods A pragmatic randomised controlled trial that included participants aged > 18 who self-identified as having depression and scored > 5 on the Patient Health Questionnaire-8. Participants were randomly assigned (1:1) to receive juli for 8 weeks or a limited attention-placebo control app. Our primary outcome was the difference in Patient Health Questionnaire-8 scores at 8 weeks. Secondary outcomes were remission, minimal clinically important difference, worsening of depression, and health-related quality of life. Analyses were per-protocol (primary), and modified and full intention-to-treat (secondary). The trial was registered at ISRCTN (ISRCTN12329547). Results Between May 2021 and January 2023, we randomised 908 participants. 662 completed the week 2 outcome assessment and were included in the modified intention-to-treat analysis, and 456 completed the week 8 outcome assessments (per-protocol). In the per-protocol analysis, the juli group had a greater reduction in Patient Health Questionnaire-8 score (10.78, standard deviation 6.26) than the control group (11.88, standard deviation 5.73) by week 8 (baseline adjusted β-coefficient −0.94, 95% CI: −1.87 to −0.22, p = 0.045). Achieving remission and a minimal clinically important difference was more likely in the juli group at 8 weeks (adjusted odds ratios 2.22, 95% CI: 1.45–3.39, p < 0.001 and 1.56, 95% CI: 1.08–2.27, p = 0.018, respectively). There were no between-group differences in health-related quality of life or worsening of depression. Modified and full intention-to-treat analyses found similar results, but the primary outcome was non-significant. Conclusion The use of juli for 8 weeks resulted in a small reduction in symptoms of depression compared with an attention-placebo control. The juli app is a digital self-management tool that could increase the accessibility of evidence-based depression treatments.

Evaluating the feasibility of a tele-diagnostic auditory brainstem response service in a rural context

Article

Full-text available

May 2024

Background There is a noticeable gap in access to audiology services in South Africa, and the gap is intensified in rural areas. Often, primary healthcare (PHC) facilities have an unequal ratio of audiologists to patients in need. Telehealth can expand the range of hearing healthcare services. Objectives This study aimed to determine whether, for infants, tele-diagnostic Auditory Brainstem Response (ABR) assessment results conducted within a mobile clinic van are comparable to face-to-face diagnostic ABR results in rural Winterveldt, Pretoria North, South Africa. Method The study utilised a quantitative, prospective cross-sectional comparative within-subject design. Each participant received both face-to-face and mobile tele-diagnostic ABR tests, which were then compared to evaluate the feasibility of mobile tele-diagnostic ABR testing. The Student’s t-test was used to determine whether there was a difference between face-to-face and tele-diagnostic tests, and Bland -Altman plots were used to assess the level of agreement between the ABR testing results. Results There was a strong correlation (p < 0.001) between face-to-face and mobile tele-diagnostic ABR test results for both neurological and audiological ABR tests. The study found that there was no statistical significance between face-to-face and tele-diagnostic ABR measures; additionally, the results were within clinically acceptable and normative measures. Conclusion Tele-diagnostic ABR offered within a mobile clinic van is feasible as it produces similar and clinically acceptable results when compared to the traditional assessment method. Contribution This feasibility study is a positive indicator that tele-diagnostic ABR testing through a mobile clinic van may be considered to accelerate the delivery of hearing healthcare services to the infant population in rural communities.

Factors Shaping Physicians’ Adoption of Telemedicine: A Systematic Review, Proposed Framework, and Future Research Agenda

Article

Oct 2024

Telemedicine offers transformative solutions for healthcare delivery, particularly in bridging geographical barriers and enhancing patient care. Nevertheless, its adoption by physicians is intricate, driven by diverse factors that require systematic understanding. This systematic review aims to identify and classify the factors influencing physicians’ adoption of telemedicine. From a collection of 2098 articles sourced from Web of Science and Scopus databases, 59 studies met the predetermined criteria and were meticulously assessed. The salient factors from these chosen papers were segmented into distinct categories: psychological and behavioral factors, technological factors, social and cultural factors, security and privacy factors, conditional factors, quality factors, healthcare-related factors, and inhibiting factors. Building on this classification, we proposed a comprehensive framework for telemedicine adoption tailored to physicians. This framework endeavors to act as a scaffold for subsequent empirical investigations. The taxonomy of the factors also aided in offering numerous research agendas to pave the way for more exhaustive studies into telemedicine adoption among medical professionals. The insights procured serve not only to deepen theoretical comprehension but also to present pragmatic guidelines for practitioners, service providers, application developers, and decision-makers.

Effectiveness of remote neuropsychological interventions: A systematic review

Article

Jul 2024

Objective: Remote healthcare services is an upgrowing dynamic field that has been used to reduce potential disease spread and prevent overloading of the healthcare system during COVID-19 pandemic. The need for online interventions during the pandemic required immediate response with sometimes inadequate preparation. The aim of the present study is to investigate the effectiveness of remote healthcare services in the field of neuropsychological interventions. Methods: A systematic literature search was conducted in the electronic databases of PubMed, PsychINFO and Google Scholar. The main search terms were "remote neuropsychological intervention or training." The included articles were RCT studies published in English, examining the effectiveness of remote healthcare services in neuropsychological interventions for adults with neurological disease diagnoses. Studies involving psychiatric disorders were excluded. Two reviewers assessed the quality of the studies and risk of bias using the PEDro Scale. Results: A total of 10 studies with 2.221 participants were included. All studies concluded that remote healthcare intervention programs can be feasible, safe and effective in the rehabilitation process of neurological diseases. Discussion: The present review demonstrated that the domains of neuropsychology have opportunities to forge ahead beyond traditional settings and have the ability to adapt to constantly changing environmental conditions with a view to providing patient care. Health policy plans should therefore be reformulated to include these needs in accordance with the social and cultural context of implementation.

Patient Experience and Feasibility of a Remote Monitoring System in Parkinson's Disease

Article

Jul 2024

Background Remote monitoring systems have the potential to measure symptoms and treatment effects in people with Parkinson's disease (PwP) in the home environment. However, information about user experience and long‐term compliance of such systems in a large group of PwP with relatively severe PD symptoms is lacking. Objective The aim was to gain insight into user experience and long‐term compliance of a smartwatch (to be worn 24/7) and an online dashboard to report falls and receive feedback of data. Methods We analyzed the data of the “Bringing Parkinson Care Back Home” study, a 1‐year observational cohort study in 200 PwP with a fall history. User experience, compliance, and reasons for noncompliance were described. Multiple Cox regression models were used to identify determinants of 1‐year compliance. Results We included 200 PwP (mean age: 69 years, 37% women), of whom 116 (58%) completed the 1‐year study. The main reasons for dropping out of the study were technical problems (61 of 118 reasons). Median wear time of the smartwatch was 17.5 h/day. The online dashboard was used by 77% of participants to report falls. Smartphone possession, shorter disease duration, more severe motor symptoms, and less‐severe freezing and balance problems, but not age and gender, were associated with a higher likelihood of 1‐year compliance. Conclusions The 1‐year compliance with this specific smartwatch was moderate, and the user experience was generally good, except battery life and data transfer. Future studies can build on these findings by incorporating a smartwatch that is less prone to technical issues.

Prehospital neurologic assessment using mobile phones: Comparison between neurologists and emergency physicians

Article