JOURNAL OF MEDICAL INTERNET RESEARCH
Jurkeviciute et al
Original Paper
Identifying the Value of an eHealth Intervention Aimed at Cognitive
Impairments: Observational Study in Different Contexts and
Service Models
Monika Jurkeviciute1, MSc; Lex van Velsen2, PhD; Henrik Eriksson1, PhD; Svante Lifvergren1,3, MD, PhD; Pietro
Davide Trimarchi4, PhD; Ulla Andin3, MD, PhD; Johan Svensson3,5, MD, PhD
1
Centre for Healthcare Improvement, Chalmers University of Technology, Gothenburg, Sweden
2
eHealth Group, Roessingh Research and Development, Enschede, Netherlands
3
Skaraborg Hospital Group, Lidköping, Sweden
4
IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
5
Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
Corresponding Author:
Monika Jurkeviciute, MSc
Centre for Healthcare Improvement
Chalmers University of Technology
Vera Sandbergs Allé 8
Gothenburg, 41296
Sweden
Phone: 46 766061558
Email: monika.jurkeviciute@chalmers.se
Abstract
Background: Value is one of the central concepts in health care, but it is vague within the field of summative eHealth evaluations.
Moreover, the role of context in explaining the value is underexplored, and there is no explicit framework guiding the evaluation
of the value of eHealth interventions. Hence, different studies conceptualize and operationalize value in different ways, ranging
from measuring outcomes such as clinical efficacy or behavior change of patients or professionals to measuring the perceptions
of various stakeholders or in economic terms.
Objective: The objective of our study is to identify contextual factors that determine similarities and differences in the value
of an eHealth intervention between two contexts. We also aim to reflect on and contribute to the discussion about the specification,
assessment, and relativity of the “value” concept in the evaluation of eHealth interventions.
Methods: The study concerned a 6-month eHealth intervention targeted at elderly patients (n=107) diagnosed with cognitive
impairment in Italy and Sweden. The intervention introduced a case manager role and an eHealth platform to provide remote
monitoring and coaching services to the patients. A model for evaluating the value of eHealth interventions was designed as
monetary and nonmonetary benefits and sacrifices, based on the value conceptualizations in eHealth and marketing literature.
The data was collected using the Mini–Mental State Examination (MMSE), the clock drawing test, and the 5-level EQ-5D
(EQ-5D-5L). Semistructured interviews were conducted with patients and health care professionals. Monetary data was collected
from the health care and technology providers.
Results: The value of an eHealth intervention applied to similar types of populations but differed in different contexts. In
Sweden, patients improved cognitive performance (MMSE mean 0.85, SD 1.62, P<.001), reduced anxiety (EQ-5D-5L mean
0.16, SD 0.54, P=.046), perceived their health better (EQ-5D-5L VAS scale mean 2.6, SD 9.7, P=.035), and both patients and
health care professionals were satisfied with the care. However, the Swedish service model demonstrated an increased cost, higher
workload for health care professionals, and the intervention was not cost-efficient. In Italy, the patients were satisfied with the
care received, and the health care professionals felt empowered and had an acceptable workload. Moreover, the intervention was
cost-effective. However, clinical efficacy and quality of life improvements have not been observed. We identified 6 factors that
influence the value of eHealth intervention in a particular context: (1) service delivery design of the intervention (process of
delivery), (2) organizational setup of the intervention (ie, organizational structure and professionals involved), (3) cost of different
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Jurkeviciute et al
treatments, (4) hourly rates of staff for delivering the intervention, (5) lifestyle habits of the population (eg, how physically active
they were in their daily life and if they were living alone or with family), and (6) local preferences on the quality of patient care.
Conclusions: Value in the assessments of eHealth interventions need to be considered beyond economic terms, perceptions, or
behavior changes. To obtain a holistic view of the value created, it needs to be operationalized into monetary and nonmonetary
outcomes, categorizing these into benefits and sacrifices.
(J Med Internet Res 2020;22(10):e17720) doi: 10.2196/17720
KEYWORDS
eHealth value; evaluation of value; eHealth intervention; cognitive impairment; role of context; cost benefit
Introduction
The Concept of Value in eHealth
The concept of value has taken a central role in health care,
including eHealth development and evaluation. This trend has
been ongoing in health care since 2006, when Porter and
Teisberg introduced the value-based health care concept to
improve and innovate healthcare [1], resulting in increased
interest in the value concept. For example, some argue that there
has been a shift in the health care discourse, from improvement
of quality to improvement of value [2]. Moreover, many
improvements using the value concept have been reported in
recent years [3]. However, the concept of value is somewhat
unclear within the context of summative eHealth evaluation.
Subsequently, it has been approached in different ways in
various studies. Sometimes, value has been investigated as
positive outcomes such as clinical efficacy or behavior change
of patients or professionals [4,5]. Others have aimed to identify
value through the perceptions of various stakeholders [6,7]. In
order to arrange reimbursement for an eHealth service and to
support decisions about investments in technology development
or implementation, the value of eHealth has also been interpreted
in economic terms (ie, whether health outcomes justify the costs)
[8]. While different evaluation frameworks have addressed some
aspects of value [9], there is no framework that explicitly guides
the evaluation of value in eHealth interventions. The immature
conceptual and methodological base of value in eHealth can
create confusion due to the large number of studies that are hard
to compare, learn from, and transfer from one context to another.
To explore the conceptualizations of value beyond the area of
eHealth, inspiration could be taken from other disciplines, such
as the marketing of products and services. One way to approach
value can be as benefits and sacrifices, which measure service
quality in relation to cost [10] and can reflect both monetary
and nonmonetary outcomes [11]. In addition, value occurs from
the interaction between a subject (eg, a patient) and an object
(an eHealth intervention) and is relative. Relativity means that
value is comparative (signifying that the value of one eHealth
intervention can be compared to the other), personal (what is
valuable for one end-user or stakeholder is not necessarily
valuable for the other), and situational (ie, value depends on the
context of use) [10].
Assuming that value is relative, one can question the usefulness
of evaluating a certain eHealth intervention in a specific context.
How do the results translate from one context to the next when
the concept of value, costs, and benefits are potentially different?
Systematic reviews that have analyzed eHealth intervention
studies in dementia care [12] and eHealth evaluation frameworks
[13] revealed that the role of context has been neglected. The
current discourse treats the context as the circumstances under
which an intervention is effective or not [14]. However, there
are no studies that have explicitly investigated how the context
influences the value of the eHealth intervention. Knowledge of
these contextual factors can help to translate the interventions
into practice or new settings [15-17].
To sum up, one way of conceptualizing the assessment of value
in eHealth interventions could be to combine the
benefit-and-sacrifice approach from marketing [8] with the
value-based health care logic that assesses patient outcomes
against cost [1]. In this view, benefits could refer to financial
earnings or savings as monetary benefits, and eHealth service
quality or utility as nonmonetary benefits [18]. Sacrifices relate
to the financial investment and expenditure as monetary
sacrifices, and social disadvantages (ie, what it takes to provide
the service physically or emotionally) as nonmonetary sacrifices.
In addition, it might also be fruitful to add an emphasis on the
context, which has been lacking in eHealth studies [12,13,17].
The proposed structure of assessing the value of an eHealth
intervention is depicted in Textbox 1.
Textbox 1. Structure of the value assessment of an eHealth intervention.
Benefits
•
Monetary
•
Nonmonetary
Sacrifices
•
Monetary
•
Nonmonetary
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In this study, we identified the value of a nonpharmacological
eHealth intervention, which combined an integrated care model
with eHealth and targeted to treat cognitive impairment in
elderly populations in Italy and Sweden.
The 2 objectives that guided this study were (1) to identify the
contextual factors that determined the similarities and
differences in the value of an eHealth intervention between the
2 contexts, and (2) to reflect on and contribute to the discussion
about the specification, assessment, and relativity of the “value”
concept in the evaluation of eHealth interventions.
Context
This study was a part of the European Union–funded project
“Digital Environment for Cognitive Inclusion” (DECI). The
entire DECI project was performed over 3 years and focused
on the development of digital solutions to improve the care of
elderly individuals with mild cognitive impairment (MCI) and
mild dementia (MD).
In our study, we examined the implementation of a 6-month
eHealth intervention among patients recruited in Sweden and
Italy. The inclusion criteria for participants were ≥ 60 years of
age, a diagnosis of MCI or a diagnosis of dementia according
to DSM-5 criteria, a clinical dementia rating (CDR) of ≤ 1,
living at home, and the ability to provide informed consent or
the availability of a proxy for informed consent. The exclusion
Jurkeviciute et al
criteria were living in a care institution, previous or present
major psychiatric illness (eg, schizophrenia, bipolar disorder,
or recurrent major depression), previous or present major
neurological illness other than MCI or MD (eg, stroke, multiple
sclerosis, brain tumor, traumatic brain injury), the presence of
other serious comorbidities (eg, severe chronic obstructive
pulmonary disease, severe heart disease, or severe chronic
kidney failure), a history of drug or alcohol abuse, severe
sensory impairments (mainly visual and auditory), a history of
intellectual disability or other developmental diseases, and a
life expectancy of less than 1 year (as judged by a clinician).
The specific procedures for the inclusion of patients in Italy and
Sweden differed. After inclusion, the 6-month eHealth
intervention was initiated.
Clinical efficacy variables and quality of life were assessed at
baseline and at the end of the 6-month intervention. Further
data in terms of monetary and nonmonetary outcomes were
obtained by performing semistructured interviews 6 months
after the eHealth intervention and by collecting information
from the health care providers after the intervention had been
completed.
The DECI Intervention
The DECI intervention consisted of a set of integrated eHealth
applications and a service model, as depicted in Figure 1.
Figure 1. Components of the Digital Environment for Cognitive Inclusion (DECI) intervention.
The digital component of the intervention was centered around
a web-based portal that disclosed a set of several services via
single sign-on. A digital platform that served as the data
repository for a patient allowed for the following main
functionalities: messaging among the key actors for a patient’s
care (the patient, professional caregivers, family members, and
informal caregivers), sending data to and retrieving data from
a patient’s electronic medical record (EMR) and other digital
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applications, and the provision and processing of
patient-centered surveys. A web-based service provided the
Otago fall prevention program [19] (used for improving physical
health [20]) through video instruction, accompanied by written
instructions and a voice-over that pronounced these written
instructions. Patients could indicate the difficulty they had with
exercises; based upon this feedback, the service decided whether
(1) they could continue to the next level of the Otago program
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after completing a week, (2) they would remain at the same
level, or (3) they would go down a level. Figure 2 depicts a
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screenshot of an exercise in the Otago program.
Figure 2. Screenshot of the Otago exercise program.
Smartbrain [21], a web-based collection of exercises aimed at
cognitive stimulation normally provided to patients with MCI,
Alzheimer disease, Parkinson disease, and similar conditions,
offered patients different difficulty levels. In addition, a physical
activity dashboard supported by the ADAMO care watch
(Caretek s.r.l) and that included pedometer functionality [22]
allowed care professionals to inspect a patient’s physical activity
(in terms of daily steps and active hours) and provided the
patient with daily feedback about his or her physical activity
levels.
The patients received a tablet for accessing digital services.
Next, the case manager role was introduced as the organizational
element of the intervention. The case manager was responsible
for coordinating care and introducing the technology to the
patients.
The Italian Context of the DECI Intervention
The Italian National Health System provides free health care
for patients throughout the country. It is funded by taxes and
charges for some services. The DECI study was carried out in
the Milan area (Lombardy region) at the Istituto Palazzolo (IP),
Fondazione Don Gnocchi (FDG). All services are integrated to
offer a care program that is shared between the elderly patients
and the caregivers, and to provide follow-up along all the clinical
pathways. The DECI project mainly targeted patients enrolled
at the Memory Clinic of the Istituto Palazzolo.
Patients eligible for inclusion were identified using the IP-FDG
Memory Clinic database and were then contacted to receive
information on the study and a proposal of participation. Then,
an inclusion visit was arranged for written informed consent,
clinical anamnesis, and a neuropsychological and functional
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examination of patients that accepted participation and met the
inclusion criteria. Afterward, patients received training and
delivery of the study materials. If needed, a follow-up visit was
arranged. Lastly, clinical and satisfaction assessments were
performed at the exit visit.
The Italian DECI team consisted of 1 senior physician
specialized in geriatrics and dementia, 1 social worker (case
manager), 2 neuropsychologists, and 1 engineer. A
physiotherapist from the rehabilitation services gave advice but
did not participate actively in visits and follow-up. The patients
received a 10.1-inch Samsung Android tablet with a 4G SIM
card to guarantee connectivity. The case manager and one of
the neuropsychologists provided instructions and training for
the patient regarding the DECI platform, usually during an
individualized 90-minute meeting at the memory clinic. Related
information was also available through the case manager by
phone or through the message system of the DECI platform.
The patients used the tablet for physical and cognitive training
during the 6-month intervention. An online help desk by phone
was established during working hours (Monday to Friday).
Inclusion visits and follow-up were carried out at the memory
clinic.
The Swedish Context of the DECI Intervention
Health care in Sweden is tax-funded and provided by the
communities (elderly care) and the counties (specialized and
primary care). The DECI study was carried out in the Skaraborg
region, which has a permanent interorganizational network for
integrated care that delivers mobile, coordinated person-centered
care for patients with various chronic diseases. DECI patients
receive mobile, networked care managed by the Swedish DECI
team.
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The study purpose was described in advertisements in local
media and leaflets that were distributed in local care units. Then,
patients who contacted the Swedish DECI team were invited
for screening. Patients who met the inclusion criteria and agreed
to participate were visited at home for written informed consent,
clinical anamnesis, and neuropsychological and functional
examination; inclusion and exclusion criteria were checked
further. During subsequent visits, patients received training and
delivery of the material. Lastly, clinical and satisfaction
assessments were performed at the exit visit at the patient’s
home.
The DECI team consisted of 1 senior physician specialized in
geriatrics from Skaraborg Hospital Group and 1 experienced
nurse from one of the communities (case manager). An
occupational therapist and a physiotherapist from 2 other
communities in Skaraborg gave advice but did not participate
in the home visits. All patients received a 10.1-inch Samsung
Android tablet with a 4G SIM card to guarantee connectivity.
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The patients were assessed using memory tests. The case
manager and the geriatrician provided instructions and training
for the patient regarding the DECI platform, usually during a
1-hour long meeting at the patient’s home. Information was
also given during subsequent visits at the patients’ home by the
case manager, in some cases with the geriatrician also present.
The patient used the tablet for physical and cognitive training
during the 6-month intervention. An online help desk by phone
was established during working hours (Monday to Friday).
Follow-up was carried out at the patients’ homes.
Methods
Value Specification
The evaluation conducted in this study was based on Textbox
1. In order to meet the requirements and aims of the DECI
project, the model presented in Textbox 1 was populated with
the variables collected during the project. The specification of
value for the DECI intervention is depicted in Textbox 2.
Textbox 2. Value specification in the Digital Environment for Cognitive Inclusion (DECI) intervention.
Benefits
•
•
Monetary
•
Income
•
Prevented cost of treatment
Nonmonetary
•
Clinical efficacy
•
Quality of life
•
Patient satisfaction
•
Job satisfaction
Sacrifices
•
•
Monetary
•
Investment
•
Operating expenses
•
Cost of spent time
Nonmonetary
•
Patient safety
•
Workload
The monetary benefits were operationalized as income and the
prevented cost of treatment. The nonmonetary benefits were
expressed as clinical efficacy, quality of life, patient satisfaction,
and job satisfaction. The monetary sacrifices were
operationalized as investment, operating expenses, and the cost
of spent time. The nonmonetary sacrifices were expressed as
patient safety and workload.
Data Collection and Analysis
Data to assess the monetary and nonmonetary outcomes of
DECI (Textbox 2) were collected from the Italian and Swedish
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health care providers involved in the project. Details of the
collection and analysis of data are described below.
Monetary Benefits
Income
Income data was related to the annual state reimbursement for
the treatment of MCI for a single patient. The total yearly
income was calculated by multiplying the projected yearly
population in the treatment by the yearly state reimbursement
per patient.
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Prevented Cost of Treatment
Prevented cost included the postponement of care and the
prevented cost of treatment of falls. Postponement of care was
based on the delayed conversion from MCI to MD. Data was
collected using the Clinical Dementia Rating Scale (CDR) [23]
at baseline and at follow-up after 6 months. The annual cost of
treatment of MD was collected from the Italian and Swedish
health care providers involved in the project. In the analysis of
data, the conversion rate (changes in CDR after 6 months) was
multiplied by the annual targeted population and the cost of
MD treatment.
Data regarding falls were theorized based on the assumptions
extracted from the relevant literature, since such data was not
collected in the DECI study. Elderly people (from 65 years of
age and older) fall 0.33 times a year [24], which can create care
needs such as visits to a general practitioner (GP; 9%),
emergency visits (5%) [25], or treatments for fracture (3%,
based on the professional judgment of health care professionals
in the DECI study). Previous studies involving the Otago
physical activity coaching program have demonstrated that it
helps to prevent falls by 68% [26]. The cost information for GP
visits, emergency visits, and treatments for fracture was
collected from the Italian and Swedish health care providers
involved in the project. During the data analysis, a preventable
number of falls was calculated by multiplying 0.33 by 68% and
by a targeted population size. Then, a number of prevented GP
visits, emergency visits, and fractures were calculated by
multiplying the number of falls by 9%, 5%, and 3%,
respectively. To calculate the cost, the results were multiplied
by the costs for a single GP visit, emergency visit, and fracture
treatment. The results were summed up to calculate the total
preventable costs due to falls.
Nonmonetary Benefits
Clinical Efficacy
The Mini–Mental State Examination (MMSE) [27] and the
clock drawing test (CDT) [28] were used to assess the cognitive
performance of the patients. The instruments were administered
at baseline (T0) and at follow-up after 6 months of intervention
(T1). Within-group differences were calculated by comparing
values at T1 with those at T0 using the Wilcoxon test.
Between-group differences were analyzed by comparing the
changes from baseline (T1–T0) using the Mann-Whitney U test.
The statistical analysis was performed using SPSS for Windows
(version 24; IBM Corp). A P value of <.05 was considered
statistically significant.
Quality of Life
The EQ-5D-5L [29] questionnaire was used to estimate quality
of life at baseline and at follow-up after 6 months. Within-group
differences were calculated using the Wilcoxon test, and
between-group differences were analyzed by comparing the
changes from baseline using the Mann-Whitney U test.
Patient Satisfaction
Data were collected using semistructured interviews with
patients in Sweden and Italy after 6 months of having used the
DECI services services (the interview protocol can be found in
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Multimedia Appendix 1). The sampling was purposeful [30] in
order to be careful not to create too big of a cognitive burden
during the assessment activities. Thematic analysis [31] of the
data helped to identify the perceived benefits by the patients
and the necessary sacrifices in order to deliver it.
Job Satisfaction
Data were collected using in-depth semistructured interviews
with the health care professionals providing the DECI services
in Italy and Sweden services (the interview protocol can be
found in Multimedia Appendix 2). Thematic analysis [31] of
the data identified the perceived benefits by the health care
professionals and the necessary sacrifices in order to deliver it.
Monetary Sacrifices
Cost of Spent Time
The spent time included the direct provision of the DECI
services and participation in the multi-disciplinary meetings
(those hours did not overlap). The hours spent by healthcare
professionals were collected from the Swedish and Italian
healthcare providers. The cost was then calculated by
multiplying the number of hours by the hourly tariff. The total
cost of spent time was a sum of costs of the different
professional categories.
Investment
Investment related to the one-time cost of starting to use the
DECI technologies. Investment data were collected via email
from the healthcare providers.
Operating Expenses
Operating expenses included the annual cost of hardware
(tablets), servers, the usage fees of DECI technologies,
maintenance, and a help-desk function. Data were collected
from the healthcare and technology providers using an Excel
file containing various categories of the operating expenses.
The usage fees of the DECI technology were stable, except for
the fee for the ADAMO activity monitoring device (a
wristwatch) that depended on the number of users.
Nonmonetary Sacrifices
Patient Safety
Data were collected using semistructured interviews with
patients and health care professionals. The data were
thematically analyzed in order to identify safety-related issues.
Workload
Data were collected through semistructured interviews with
health care professionals. The data were thematically analyzed
in order to identify the workload-related issues.
Monetary Benefit/Sacrifice Ratio
The ratio was calculated by dividing the sum of monetary
benefits
by
the
sum
of
monetary
sacrifices
(Ratio=Benefits/Sacrifices). The ratio was calculated for the
scenarios at year 1, year 2, and year 3 of using the DECI
services.
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Ethics
The DECI study was approved by the Ethical Committee of the
Fondazione Don Carlo Gnocchi and the Regional Ethical
Committee of Gothenburg.
Jurkeviciute et al
Results
Demographic Characteristics
Table 1 depicts the demographic characteristics of the patients
involved in the study.
Table 1. Demographic characteristics of patients in Italy (n=53) and Sweden (n=54) at baseline.
Characteristics
Patients in Italy (n=53)
Patients in Sweden (n=54)
P valuea
Age in years, mean (SD)
77.6 (5.3)
74.8 (5.9)
<.001
Gender, n (%)
.66
Female
27 (51)
30 (56)
Male
26 (49)
24 (44)
Diagnosis, n (%)
MCIb
39 (74)
49 (91)
MDc
14 (26)
5 (9)
9.2 (4.3)
11.6 (2.9)
<.001
MMSE (range 0-30), mean (SD)
26.6 (2.9)
28.2 (1.4)
<.001
CDTe (range 0-5), mean (SD)
3.30 (1.38)
4.81 (0.52)
<.001
Education years, mean (SD)
d
a
.007
Differences between groups were examined using the Mann-Whitney U test for continuous variables and using chi-square tests for categorical variables.
b
MCI: mild cognitive impairment.
c
MD: mild dementia.
d
MMSE: Mini–Mental State Examination.
e
CDT: clock drawing test.
Monetary Benefits
costs) was zero, the Swedish preventable costs consisted of the
prevention of falls only.
Prevented Cost of Treatment
Income
The results in Italy showed a 10% lower conversion rate from
MCI to MD compared to the conversion rates of patients in
regular care. In the Swedish patients, who were younger than
the Italian patients and had a less marked decrease in cognitive
function at baseline (higher MMSE and CDT scores; Table 1),
the results showed a 2% higher conversion rate from MCI to
MD compared to the patients in regular care, thus bringing no
preventable costs from the postponed care. Since the
postponement of care (which is the element of the preventable
The Italian and Swedish health care systems reimburse the cost
of time when providing the eHealth-supported care. Therefore,
the cost of time spent on providing the DECI treatment (a
category in monetary sacrifices) was considered as income for
year 1, year 2, and year 3.
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Summary of Monetary Benefits
Table 2 shows a summary of the monetary benefits per patient
in Italy and Sweden for 3 years.
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Table 2. Monetary benefits, in euros (a currency exchange rate of EUR €1=US $1.18 is applicable).
Monetary benefits in Italy and Sweden
Year 1 (€)
Year 2 (€)
Year 3 (€)
Total
191.696
287.544
335.468
Per patient
1916
1916
1916
Total
6155
9232
10.770
Per patient
12
12
12
Total
67.938
101.906
118.891
Per patient
679
679
679
Total
826.882
1.240.324
1.447.044
Per patient
1653
1653
1653
Preventable costs - Italy
Preventable costs - Sweden
Income - Italy
Income - Sweden
The large differences in monetary values between Italy and
Sweden occurred due to the different targeted population sizes.
In Italy, it was 100 patients in year 1, 150 patients in year 2,
and 175 patients in year 3. In Sweden, it was 500 patients in
year 1, 750 patients in year 2, and 875 patients in year 3.
Nonmonetary Benefits
Clinical Efficacy
The mean (SD) changes in MMSE and CDT scores in Italy were
-0.14 (2.86) and 0.23 (1.52), respectively; in Sweden, these
changes were 0.85 (1.62) and -0.11 (0.57), respectively. In Italy,
both MMSE and CDT scores at the 6-month follow-up were
similar to those at baseline (P=.35 and P=.34, respectively). In
Sweden, MMSE scores were higher (better) at the 6-month
follow-up compared to those at baseline (P<.001), whereas CDT
scores were unchanged (P=.18). When comparing the results
in Italy versus those in Sweden, the changes in MMSE scores
were significantly greater in the Swedish cohort (P=.004),
whereas there were no differences in CDT scores (P=.15). Thus,
as determined by MMSE scores, cognitive performance was
improved in the Swedish study population but not in the Italian
study population.
Quality of Life
In Italy, there was no difference between the 6-month follow-up
and baseline in any of the EQ-5D-5L subscales (mobility, P=.41;
self-care, P=.41; activity, P=.58; pain, P=.16; anxiety, P=.59)
and the EQ-5D-5L visual analog scale (VAS), P=.53. In Sweden,
there were improvements in the mean (SD) changes in the
EQ-5D-5L subscale anxiety and the EQ-5D-5L VAS scale,
which were 0.16 (0.54) and 2.6 (9.7), respectively. The 6-month
follow-up values in these variables were also significantly
different from those at baseline (P=.046 and P=.035,
respectively), confirming beneficial effects in these variables
in the Swedish cohort. Other EQ-5D-5L subscales were
unchanged in Sweden: mobility, P=.20; self-care, P=.16;
activity, P=.20; and pain, P=.26. However, when comparing
the results in Italy versus those in Sweden, there were no
between-group differences. Therefore, although some
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improvement was observed in Sweden, there was no difference
in the effect on quality of life between the countries.
Patient Satisfaction
In Italy, 10 patients were interviewed. The patients perceived
the DECI service as simple and as containing engaging
exercises, which helped the patients become more physically
and cognitively active. The patients were willing to try more
advanced exercises matching their physical condition, and it
was deemed that a customized exercise program could increase
the motivation of the patients. Some patients struggled to
navigate the technologies and sought help from health care
professionals or family members. Like in Sweden, the help-desk
function could help to reduce the load on clinicians for solving
technical questions or problems. The activity monitoring watch
was appreciated by the patients, but its design could be improved
in order to meet the aesthetic standards of the elderly.
In Sweden, 10 patients were interviewed. The DECI service
was appreciated by the patients due to the clinician-monitored
exercise possibilities at home and multiple home visits by health
care professionals. The patients expressed a willingness to
continue using the physical and cognitive activity programs.
Previous information technology (IT) experience was
determined to be helpful in navigating the tablet. Less
experienced patients relied on family members for help, while
others called health care professionals. Therefore, an IT help
desk that helps to solve tablet-related issues is a necessary
element of the service. The activity monitoring watch did not
meet patients’ expectations without a pedometer and a display.
Job Satisfaction
In Italy, 4 health care professionals were interviewed. Their
occupations were a geriatrician, a social worker, a
neuropsychologist, and a clinical neuropsychologist. The health
care professionals felt enabled to build relationships with
patients through a dedicated case manager. The physical and
cognitive activity coaching programs gave the professionals
tools that could not be found in usual care practices. The
exercises motivated and engaged the patients, which positively
contributed to the job satisfaction of the professionals. The
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ability to remotely monitor patients’ performances and
adherence to the tools was seen as an additional value. The
professionals also utilized the messaging and data sharing
functions in the DECI platform, which facilitated the
interdisciplinary work.
In Sweden, 2 health care professionals were interviewed. Their
occupations were a geriatrician and a nurse. The professionals
were satisfied that the DECI services helped to form positive
relationships with the patients while visiting them in their home
environments. The case manager’s role was perceived as
rewarding. Professionals could deepen their knowledge of the
patients when observing physical status in their usual
environment. The professionals felt empowered since they could
offer digital tools to the patients with beneficial, customized
exercises for different muscle groups and cognitive conditions.
However, the messaging function in the DECI platform was
perceived as having low value, since the mobile team was mostly
on the road and used mobile phones for communication.
Jurkeviciute et al
Monetary Sacrifices
Investment
In Italy, the one-time investment concerned the cost of staff for
server preparation in the hospital. In Sweden, the investment
concerned the cost of staff for server preparation and installation
of the DECI technologies. Additionally, investments in Sweden
also entailed one-time costs for purchasing tablets, including
4G-sim cards for training for patients (n=52) and members of
the DECI-team (n=2).
Operating Expenses
In both Italy and Sweden, the highest usage fee concerned the
ADAMO activity monitoring device. The annual operating costs
in Italy include purchasing the tablets (a stable number of tablets
purchased for patients every year, depreciation in 12 months),
server hosting, the configuration of the tablets, the personnel
cost of maintenance and the help desk, licenses, and 4G
connectivity. In Sweden, it consists of the server hosting,
first-line help desk, and management costs. Operating expenses
are depicted in Table 3.
Table 3. Monetary sacrifices, in euros (a currency exchange rate of EUR €1=US $1.18 is applicable).
Monetary sacrifices in Italy and Sweden
Year 1 (€)
Year 2 (€)
Year 3 (€)
Total
71.161
106.743
124.533
Per patient
711
711
711
Total
693.773
969.160
1.130.687
Per patient
1387
1387
1387
Total
140
0
0
Per patient
1.4
0
0
Total
18.636
18.636
18.636
Per patient
37
24
21
Total
126.746
171.896
194.471
Per patient
1267
1145
1111
Total
348.015
499.965
575.940
Per patient
696
666
658
Cost of spent time - Italy
Cost of spent time - Sweden
Investment - Italy
Investment - Sweden
Operating expenses - Italy
Operating expenses - Sweden
Cost of Spent Time
The cost was calculated based on the hours spent on the DECI
service provision and the multi-disciplinary meetings (Table
3). The following professional categories were involved in Italy:
physician, nurse practitioner, physiotherapist, technician, case
manager (social worker), and psychologist. In Sweden, the cost
was calculated for a geriatrician, an occupational therapist, a
physiotherapist, and a nurse practitioner.
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Table 3 shows a summary of the monetary sacrifices per patient
in Italy and Sweden for 3 years. The large differences in
monetary values between Italy and Sweden occurred due to the
different targeted population sizes and care models.
Nonmonetary Sacrifices
Patient Safety
In Italy, the ADAMO activity monitoring device caused an
allergic reaction in 1 patient, due to sensitivity to nickel and
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plastic. The health care professionals in Italy (n=4, 1 geriatrician,
1 social worker, 1 neuropsychologist, and 1 clinical
neuropsychologist) and Sweden (n=2, 1 geriatrician and 1 nurse)
noted that the physical activity training program Otago could
be less safe for older people if used without supervision. Hence,
the level of exercise difficulty is of high importance.
Workload
In Italy, the 4 health care professionals dedicated, on average,
29.3 hours a week to engage with the existing patients at the
memory clinic that took part in the DECI study (the result is
based on their self-reported data). A substantial amount of this
time was dedicated to digital data entry for the DECI study.
Jurkeviciute et al
This time was used for patient inclusion, training, phone calls,
solving technical issues, and digital data entry for the study.
In Sweden, the 2 health care professionals spent, on average,
52.5 hours per week, thus reporting overtime. This time was
used for the full-scale dementia examination, training, phone
calls, home visits to the patients, solving technical issues, and
digital data entry for the study. The result is based on their
self-reported data.
Monetary Benefit/Sacrifice Ratio
Table 4 depicts a summary of monetary benefits and sacrifices,
and provides a calculation of the benefit/sacrifice ratio in Italy
and Sweden for 3 years.
Table 4. Summary of the DECI scenario in monetary value, in euros (a currency exchange rate of EUR €1=US $1.18 is applicable).
Benefits and sacrifices in Italy and Sweden
Year 1
Year 2
Year 3
Total
259.633
389.450
454.359
Per patient
2596
2596
2596
Total
833.037
1.249.555
1.457.815
Per patient
1666
1666
1666
Total
198.047
278.639
319.004
Per patient
1980
1857
1822
Total
1.060.424
1.487.761
1.725.263
Per patient
2120
1983
1971
Benefit/sacrifice ratio - Italy
1.31
1.39
1.42
Benefit/ sacrifice ratio - Sweden
0.78
0.84
0.84
Total monetary benefits – Italy (€)
Total monetary benefits – Sweden (€)
Total monetary sacrifices – Italy (€)
Total monetary sacrifices – Sweden (€)
The benefit/sacrifice ratio showed that the Italian intervention
could bring positive monetary value from the first year onward.
In Sweden, the intervention did not bring monetary value during
the first 3 years. However, the gap between monetary benefits
and sacrifices reduces with the growing number of patients.
Discussion
Principal Findings
This study was guided by 2 objectives: (1) to identify the
contextual factors that determine the similarities and differences
in the value of an eHealth intervention between 2 contexts, and
(2) to reflect on and contribute to the discussion about the
specification, assessment, and the relativity of the “value”
concept in evaluating eHealth interventions. This study was
based on the implementation of an eHealth platform for remote
home monitoring of physical and cognitive activity for people
suffering from cognitive impairment in Italy and Sweden.
The findings of this study show that there is a differing value
derived from the implementation of the same eHealth technology
to similar types of populations in different contexts.
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We translated value into benefits and sacrifices and assessed
these for the intervention in 2 countries. In Sweden, the
identified benefits of the eHealth intervention included improved
cognitive performance assessed by the MMSE, reduced patient
anxiety assessed by the EQ-5D-5L, better perceived health
estimated using the EQ-5D-5L VAS scale, and satisfaction with
the care received. However, these benefits can require sacrifices,
such as an increased cost and higher workload for health care
professionals. With the service model of Sweden (home visits),
a lower number of patients could be visited per day.
Additionally, the relatively high hourly rates of the staff
increased the cost of the intervention. In Italy, the identified
benefits included patient satisfaction with the care received,
empowered health care professionals, and acceptable workloads.
Moreover, for the Italian patients, who were older and had a
more marked decrease in cognitive function (lower MMSE and
CDT scores) at baseline than the Swedish patients, the
intervention could bring positive monetary value from the first
year onward. This was the result of a higher preventable cost
of treatment and state reimbursement, in comparison to the cost
that was based on relatively lower hourly staff rates and the
service model that reduced the time spent per patient (the
intervention was implemented on a sample of existing patients
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of the clinic and the visits were performed at the clinic).
However, the clinical efficacy and quality-of-life improvements
have not been observed over the course of the 6-month
intervention in Italy.
In this study, the monetary side of value was influenced by
factors such as (1) the service delivery design of the intervention
(process of delivery), (2) the organizational setup of the
intervention (ie, organizational structure and professionals
involved), (3) the cost of different treatments, and (4) the hourly
rates of staff for delivering the intervention. These factors
affected the cost-effectiveness through the expenses incurred
(including potentially preventable costs due to the intervention)
and necessary investments. The nonmonetary side of the value
of the intervention was also influenced by the service delivery
design and organizational setup of the intervention, as well as
by a fifth factor: (5) the lifestyle habits of the population (eg,
Jurkeviciute et al
how physically active they were in their daily lives and if they
were living alone or with family). Finally, the value of eHealth
should be seen against the sixth factor: (6) local preferences on
the quality of patient care. This study showed that even the
non–cost-efficient intervention can be viewed as valuable locally
and deemed worthy of implementation. In such a case, local
preferences on the quality of patient care can be a decisive
factor. Particular positive nonmonetary outcomes might be
valued highly enough to proceed with adopting the
eHealth-supported service model. Moreover, it should not be
neglected that the service delivery design and organizational
set-up of the intervention can be adjusted to make it more
cost-efficient.
A summary of the contextual factors affecting the value of
eHealth intervention is provided in Textbox 3.
Textbox 3. Conceptual model for eHealth value specification and the influencing contextual factors.
Contextual factors (benefits)
•
•
Monetary
•
Service delivery design of an intervention (process)
•
Organizational setup of an intervention (structure)
•
Cost of different treatments
•
Hourly rates of staff for delivering an intervention
Nonmonetary
•
Service delivery design of an intervention (process)
•
Organizational setup of an intervention (structure)
•
Lifestyle habits of the population
•
Local preferences on the quality of patient care
Contextual factors (sacrifices)
•
•
Monetary
•
Service delivery design of an intervention (process)
•
Organizational setup of an intervention (structure)
•
Cost of different treatments
•
Hourly rates of staff for delivering an intervention
Nonmonetary
•
Service delivery design of an intervention (process)
•
Organizational setup of an intervention (structure)
Limitations
The identified contextual factors affecting the value of eHealth
interventions could be limited because the study was based on
a summative eHealth evaluation conducted in 2 countries. To
study 2 countries in-depth demanded time and effort, and
including more countries was not feasible. Other contextual
setups could enrich the list of the factors and need to be
investigated further. Also, the study was constrained by a
6-month follow-up time for the patients. A longer follow-up
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time could enrich the contextual factors and the proposed
eHealth value conceptualization by revealing long-term effects.
Comparison with Prior Work
The conceptual model proposed for assessing the value of
eHealth interventions (Textbox 3) was built on prominent
eHealth evaluation frameworks [32,33], in addition to previous
conceptualizations of value in the eHealth [4-8] and marketing
literature [10]. We argue that value needs to be operationalized
in both monetary and nonmonetary outcomes, and our model
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JOURNAL OF MEDICAL INTERNET RESEARCH
suggests categorizing them into benefits and sacrifices. In
practice, an evaluation study on value needs to adapt the model
to its needs by populating the model with the themes of
evaluation. It is important not to overlook the nonmonetary
aspects that can reveal a broader and more accurate view of the
value created (in contrast to the cost-versus-outcomes view
[1,8,34,35]). We propose that all the parts of the model
(monetary and nonmonetary benefits/sacrifices) need to be
assessed in order to obtain a holistic view of the value created.
Regarding the conceptualization of value for eHealth
interventions, our study showed that an overly limited view on
value is obtained if assessing it as only positive outcomes, such
as behavior change or clinical efficacy [4,5], perceptions
regarding the added value by various stakeholders [6,7], or
economic outcomes [8]. The view of value as clinical efficacy
[4] is sometimes not possible when studies have a shorter
follow-up time. Furthermore, this study showed that economic
outcomes might be an overly limited measure of value, when
the non–cost-efficient intervention can be viewed as valuable
locally for other positive outcomes and deemed worthy for
implementation in practice despite the increased cost. We
suggest the following conceptualization of value for eHealth
interventions:
Jurkeviciute et al
Value is a holistic view of the created monetary and
nonmonetary benefits of eHealth that require
monetary and nonmonetary sacrifices in a particular
context.
Finally, we argue that more studies need to apply a holistic view
to the summative assessments of value, rather than focusing on
defining value for the stakeholders during the design process
[36]. This holistic value assessment could help to clarify which
adjustments could be made to reduce sacrifices and maximize
benefits. Moreover, such assessments (especially across multiple
contexts) could improve the transferability of eHealth
interventions.
Conclusions
This study offers a step toward better conceptualization of the
value concept within the eHealth context. We argue that it
should be interpreted as both monetary and nonmonetary
benefits and sacrifices achieved in a context. Next, we argue
that without considering value beyond economic terms or
assessing it as merely perceptions, a full picture of the value
created might be missed. We offer a model that provides some
conceptual considerations for the assessment of value of eHealth.
Applied within a summative evaluation in this cross-context
study, it can be a useful starting point for future research.
Acknowledgments
The collection of data used in this study was financed by the European Union’s Horizon 2020 research and innovation program
under grant agreement no. 643588. The funding organization had no role in designing the study, collecting and analyzing the
data, or in preparing this manuscript.
Conflicts of Interest
LvV works at one of the enterprises that supplied an eHealth solution to the DECI project and the current study.
Multimedia Appendix 1
Patient interview protocol.
[DOCX File , 17 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Healthcare professional interview protocol.
[DOCX File , 17 KB-Multimedia Appendix 2]
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Abbreviations
CDR: clinical dementia rating
CDT: clock drawing test
DECI: Digital Environment for Cognitive Inclusion
FDG: Fondazione Don Gnocchi
GP: general practitioner
IP: Istituto Palazzolo
IT: information technology
MCI: mild cognitive impairment
MD: mild dementia
MMSE: Mini–Mental State Examination
Edited by G Eysenbach; submitted 07.01.20; peer-reviewed by E Aramaki, F Adebesin; comments to author 10.03.20; revised version
received 17.05.20; accepted 14.06.20; published 08.10.20
Please cite as:
Jurkeviciute M, van Velsen L, Eriksson H, Lifvergren S, Trimarchi PD, Andin U, Svensson J
Identifying the Value of an eHealth Intervention Aimed at Cognitive Impairments: Observational Study in Different Contexts and
Service Models
J Med Internet Res 2020;22(10):e17720
URL: http://www.jmir.org/2020/10/e17720/
doi: 10.2196/17720
PMID: 33064089
©Monika Jurkeviciute, Lex van Velsen, Henrik Eriksson, Svante Lifvergren, Pietro Davide Trimarchi, Ulla Andin, Johan Svensson.
Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 11.10.2020. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal
of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on
http://www.jmir.org/, as well as this copyright and license information must be included.
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J Med Internet Res 2020 | vol. 22 | iss. 10 | e17720 | p. 14
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