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Access at: www.CFRjournal.com
Telehealth is a multiform term embracing the applications of telematics
to medicine, in order to enable diagnosis and/or treatment remotely
through a set of communication tools, including phones, smartphones
and mobile wireless devices, with or without a video connection.1
Until a few years ago, digital applications in medicine were restricted
to the use of data obtained from electronic health records (EHR), but,
in more recent times, the technological context has notably expanded:
the number of existing internet-connected mobile devices has roughly
doubled every five years. This phenomenon will probably lead to the
simultaneous operability of around 50 billion devices by 2020.2
Sensors
Sensors are tools that are capable of detecting, recording and
responding to specific inputs coming from a physical setting (e.g. a
patient’s vital signs) and are increasingly embedded in smartphones
and other mobile devices. Recording and quantifying biological
variables by means of sensors is generating large digital datasets
that are suitable for transmission in real-time to healthcare and
non-healthcare professionals. Computer applications arising from
these phenomena are potentially numberless and will probably drive
changes in both doctor–patient relationships and healthcare economic
scenarios. Several insurance companies have already introduced
better money premiums for customers who demonstrate regular use
of smartphone applications aimed at illness prevention.1
Some issues that will need to be addressed in the near future
concern patient privacy and data safety.3 As the practice of selling
personal data to third parties for commercial purposes has come
to light, increased attention has focused on data security of digital
platforms and mobile devices.4,5 Several reports published recently
have revealed a concerning lack of details regarding the way that
personal data is managed by telehealth application developers.5 The
Global Privacy Enforcement Network has disclosed that around 60% of
the applications they evaluated exhibited criticisms regarding privacy
issues, as they did not properly inform users how their personal data
would be used and the number of personal questions asked was
considered inappropriate.6
Heart Failure Epidemiology
Heart failure (HF) is a common clinical syndrome associated with
high morbidity and mortality. It is a major public health problem, with
a prevalence of over 5.8 million people affected in the US, and over
26 million people worldwide.7 In the US and in Europe, HF prevalence
ranges from 1.1% to 2.2 % in the general population. Most of the HF
burden is situated in people aged over 65 years, who account for more
than 80 % of deaths and prevalent cases in the US and in Europe.8,9
The lifetime probability of developing HF is believed to be one in five.
Notwithstanding the historical equation that attributes HF genesis to
a reduced left ventricular ejection fraction (LVEF), it has been shown
that, in real medical practice, HF with preserved LVEF is more prevalent
than HF with reduced LVEF in patients over 60 years of age (median
prevalence 4.9 % and 3.3 %, respectively)10. Despite recent advances
in the diagnosis and treatment of HF with reduced LVEF, management
of HF with preserved LVEF is debated, and both types of HF still carry
substantial morbidity and mortality, with 5-year mortality rates that are in
some cases comparable to those of some cancers with a poor prognosis.
In addition, HF is a leading cause of hospitalisation and hospital
readmission worldwide. Data from the ARNO Observatory have shown
Abstract
The use of telemonitoring and telemedicine is a relatively new but quickly developing area in medicine. As new digital tools and applications
are being created and used to manage medical conditions such as heart failure, many implications require close consideration and further
study, including the effectiveness and safety of these telemonitoring tools in diagnosing, treating and managing heart failure compared
to traditional face-to-face doctor–patient interaction. When compared to multidisciplinary intervention programs which are frequently
hindered by economic, geographic and bureaucratic barriers, non-invasive remote monitoring could be a solution to support and promote
the care of patients over time. Therefore it is crucial to identify the most relevant biological parameters to monitor, which heart failure
sub-populations may gain real benefits from telehealth interventions and in which specific healthcare subsets these interventions should
be implemented in order to maximise value.
Keywords
Telehealth, telemedicine, heart failure management, remote patient monitoring, digital medical tools, telemonitoring.
Disclosure: The authors have no conflicts of interest to declare.
Received: 28 March 2017 Accepted: 4 July 2017 Citation: Cardiac Failure Review 2017;3(2):116–21. DOI: 10.15420/cfr.2017:6:2
Correspondence: Gian Franco Gensini, Director, Digital SIT (Italian Telemedicine Society), Via Teodoro Valfrè, 11, Rome, Italy. E: gfgensini@gmail.com
Value of Telemonitoring and Telemedicine in Heart Failure Management
Gian Franco Gensini,1 Camilla Alderighi,2 Raffaele Rasoini,2 Marco Mazzanti3 and Giancarlo Casolo4
1. Digital SIT (Italian Telemedicine Society); 2. Fiorentino Institute of Care and Assistance (IFCA), Florence, Italy;
3. International Research Framework on Artificial Intelligence inCardiology, Royal Brompton Hospital and Harefield
NHS Foundation Trust, London, UK; 4. Cardiology Unit, New Versilia Hospital, Lido di Camaiore (LU), Italy
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CARDIAC FAILURE REVIEW
a hospitalisation rate in HF of 2.8 patients per 1000,11 which represents
1–4% of all the hospitalisations in the US and Europe.12 Moreover, 30-day
readmission rates of HF patients range from 19% to 25% and have been
reported to be up to 50 % at 1 year,13 even if discrepancies between
actual causes of HF admissions (frequently attributable to comorbidities)
and hospital diagnoses from clinical records (usually assigned to
decompensated HF) have increased the possibility of an overestimation
of HF-related hospital readmission rates.14 Nonetheless, one of the
most challenging issues for the healthcare systems nowadays is finding
innovative ways to reduce the high hospital admission and readmission
rates of patients with HF.13
Purposes and Goals
Some studies have shown that some interventions aimed at improving
the management of patients with HF after hospital discharge, in
particular, periodic monitoring of symptoms/signs and reviews of
pharmacological therapy, are related to a significant decrease in hospital
readmission rates.15,16 However, the heavy economic costs related to the
systematic organisation of patient follow-ups after hospital discharge
have pushed the development of remote monitoring systems for the
continuous control of clinical variables, such as blood pressure, oxygen
saturation, heart rate, electrocardiogram and intracardiac/pulmonary
pressure. The implementation of these monitoring tools has been
hypothesised to augment medical control over the unstable syndrome
of HF in order to prevent decompensations and to concurrently gain
time and resources when compared to traditional care.
Artificial Intelligence as a Clinical Support Tool
for HF Care
A development in computer science that could be applied in future
HF management is artificial intelligence (AI). In cardiology, AI is being
investigated in the application of domains that span from clinical decision
support systems to imaging interpretation. Some machine learning (ML)
techniques allow computers, whether “trained” with wide datasets that
have been previously correctly classified and labelled by doctors, to
“learn” and develop autonomous (and sometimes inscrutable) rules in
order to apply the learned classifications to new inputs as far as these
new inputs are similar enough to those included in the training datasets.
This process is mainly focused on the development of automated
decision support systems aimed at diagnostic or predictive prognostic
purposes. However, an appropriate classification of telemedical systems
based on ML techniques is lacking and profiles of patients who could
benefit most from ML-based telemedicine solutions are unknown and
need to be adequately investigated.17
Prevention and treatment of disease exacerbations and promotion of
patient self-empowerment are the main objectives of telemedicine
in HF. Individual characteristics of patients with HF obtained from
the analysis of a large number of EHR may allow the identification of
those patients at higher risk of negative outcomes who could most
likely benefit from individualised medical treatments. For example, the
Seattle Heart Failure Model is an ML-based framework for calculating
mortality risk in HF that examines multiple clinical features obtained
from EHR to predict HF prognosis and incorporates the potential
impact of HF therapies on patient outcomes.18 The Seattle Heart Failure
Model was developed at the Mayo Clinic, where an ML risk prediction
model was trained with routinely collected clinical data obtained from
EHR. This decision support system showed a potential usefulness
in the identification of patients with HF at higher risk of negative
outcomes, but presented barriers to implementation (it was time
consuming, expensive, required doctor familiarity with computers and
did not account for clinical variables that could not be included as part
of the collected data).18 Proper management of follow-up in HF patients
is considered critical to reduce common causes of re-hospitalisation,
that can lead to worse outcomes and increasing costs to patients and
society.19 In this setting, ML techniques could be potentially valuable in
remote monitoring of high-risk HF patients.
Results of Clinical Trials of Telemedicine in HF
The 2016 European Society of Cardiology Guidelines for the diagnosis
and treatment of acute and chronic HF recommend for the first time
“remote patient monitoring” of HF patients with a recommendation
of grade IIb, Level of Evidence B.20 In HF patients, telemonitoring is
mainly focused on predicting acute decompensation episodes that
are usually associated with fluid congestion and require optimisation
of therapy. Clinical practice guidelines on chronic HF recommend
daily weight measurements and include a warning alert when an
increased weight of more than 2 lbs in a day is observed.20 However,
even if body weight trend is rightly considered a critical element to
predict decompensations, sensitivity and specificity of body weight
variability alone as a proxy of total body water has revealed to be an
inaccurate predictor of HF decompensations.21 Other variables have
been explored in the Multisensor Monitoring in Congestive Heart
Failure (MUSIC) and Sensitivity of the InSync Sentry OptiVol Feature for
the Prediction of Heart Failure (SENSE-HF) trials.22,23 In the MUSIC study,
a multisensor, non-invasive external device was used to measure
and remotely transmit bio-impedance, heart rate, respiratory rate and
volume, physical activity duration and intensity, and body posture.
Investigators used a development cohort to identify a single or a
multiparameter reliable algorithm based on three main components:
fluid index, breath index, and personalisation parameters. Use of all
three parameters yielded a sensitivity of 65% and a specificity of 90%
in predicting acute HF decompensations. The failure rate of the device
used in MUSIC was shown to be approximately 45 %, reflecting the
need for further improvements.22
In the SENSE-HF study, performed on patients with chronic systolic HF
who had been implanted with cardiac implantable electronic devices
(CIED), an intrathoracic impedence-derived fluid index (intrathoracic
impedence was measured between the lead and the pace maker’s
case) consistently showed low sensitivity and low positive predictive
value for hospitalisation prediction.23 Other studies have assessed
the effectiveness of remote monitoring through CIED (cardiac
resynchronisation therapy with or without defibrillator function) in
reducing clinical decompensations, overall mortality or hospitalisations
in HF patients.
In the Evolution of Management Strategies of Heart Failure Patients
with Implantable Defibrillators (EVOLVO) study, 200 patients with
chronic systolic HF and a mean age of 66 years were randomised to
remote monitoring (through CIED) of intra-thoracic impedance, atrial
arrhythmias and ICD-shocks versus usual care (scheduled visits every
4 months). A significant reduction of emergency visits in the remote
monitoring group was observed when compared to usual care.24
More recently, in the Implant-Based Multiparameter Telemonitoring of
Patients with Heart Failure (IN-TIME) trial, 716 HF patients with a mean
age 65 years and a mean LVEF of 26 %, who had been previously
implanted with CIED, were randomly assigned to a telemonitoring
strategy or a control “standard care” group: in the active arm patient
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data were transmitted and reviewed both by the study investigators
and by a central monitoring unit (composed of trained study nurses and
supporting physicians). A clinical response (standardised telephone
call or additional clinical care) was undertaken at the discretion of
investigators. After 1 year, a modest benefit was observed in a clinical
composite score (all causes of death, overnight hospital admission for
HF, change in New York Heart Association (NYHA) class and change in
patient global self-assessment).25
In the Optimization of Heart Failure Management using OptiVol Fluid
Status Monitoring and CareLink (OptiLink HF) study, conducted in ICD
carriers with severe systolic HF randomised to have fluid status alerts
or usual care, no significant effect was detected in the composite
endpoint of all-cause of death and cardiovascular hospitalisations.26
Some authors have speculated that alerts may even be responsible
for a delay in the detection of clinical deterioration, with a consequent
postponement of appropriate treatment.
In the multicentric Remote Management of Heart Failure Using
Implantable Electronic Devices (REM-HF) study, which enrolled patients
with a mean age of 70 years who had been previously implanted
with CIED, no significant difference was detected between the CIED
remote monitoring group (using weekly downloads) and the usual care
group with respect to the primary endpoint of death for any cause or
unplanned hospitalisation for cardiovascular reasons. A concern in
this study has been raised by the report that approximately 70% of
the patients in the intervention group underwent additional actions
that were driven by the results of remote monitoring. This result,
whether interpreted in light of the observed lack of effect on outcomes,
highlights the potential risks of medicalisation and overtreatment that
may arise from inappropriate use of remote monitoring strategies.27
Aside from CIED, the basic concept of care that is extended beyond
traditional healthcare settings is also well captured by the phone call
monitoring strategies wherein patient compliance, symptoms, vital
signs, and weight are followed remotely.28–30 The Randomised Trial of
Telephone Intervention in Chronic Heart Failure (DIAL) study was one
of the first trials investigating structured telephone support (STS)
in 1,518 HF patients randomised to an STS intervention group or to a
control “usual care” group.15 In the intervention group, dedicated nurses
phoned patients every 14 days and adjusted the frequency accordingly
thereafter for a year. Predetermined standardised questions were
used to assess dyspnea/fatigue, daily weight monitoring, oedema
progression, dietary/drug compliance and physical activity. Nurses
were only allowed to change the diuretic dose and recommend a
non-scheduled medical consultation. Nurses used a computer-aided
software system to keep a log of conversations and receive reminders
for phone calls. All study subjects were followed at the study centres
on a 3-month basis irrespective of unscheduled visits and phone
calls. Most of these patients had systolic dysfunction and NYHA class
II-III symptoms. Overall, the intervention group had fewer hospital
readmissions both in the short term and even at 1–3 years after
stopping intervention. Mortality was similar in both groups. At the end
of the study the intervention group had a better quality of life score
than the usual care group.
Similarly, in a meta-analysis, Inglis et al. reviewed 16 studies investigating
STS interventions and detected a non-significant trend towards
improved mortality with STS versus usual care (RR 0.88 [95 % CI
0.76–1.01], p=0.08), but a significant 23% reduction of HF hospitalisations
(RR 0.77 [95 % CI 0.68–0.87]).31 Of the 16 studies considered, six
reported improved quality of life with STS in both overall and physical
scores on the Minnesota Living with Heart Failure Questionnaire and
on the Kansas City Cardiomyopathy Questionnaire.
The Telemonitoring to Improve Heart Failure Outcomes (Tele-HF) study
randomised 1,653 subjects within 30 days of an HF hospitalisation to
a telephone-based interactive voice response system or usual care.
The voice response system included a series of questions related
to general health and HF symptoms, with patients entering their
responses using their telephone keypad.32
The Trans-European Network – Home-Care Management System
(TEN-HMS) study attempted to identify whether home telemonitoring
was able to improve outcomes compared with nurse telephone
support and usual care.33 Home telemonitoring consisted of twice-daily
patient self-measurement of weight, blood pressure, heart rate, and
heart rhythm with automated devices linked to a cardiology centre.
The structured telephone support consisted of specialist nurses who
were available to patients by telephone. Primary care physicians
delivered usual care. The primary endpoint was days lost for death
or hospitalsation with nurse telephone support (NTS) versus home
telemonitoring (HTM) at 240 days. At the end of the study, the number
of admissions and mortality were similar among patients randomly
assigned to nurse telephone support or home telemonitoring. Patients
randomly assigned to receive usual care had higher 1-year mortality
than patients assigned to receive NTS or HTM, but with a weakly
meaningful difference (p=0.032).
A smaller study by Goldberg et al.34 reported a 10.4% absolute and
56.2% relative reduction in mortality in a monitoring system using only
symptoms and weight monitoring.
Another large telemonitoring study which evaluated feasibility and
perception of the Telemedical Interventional Monitoring in Heart Failure
(TIM-HF) trial35 used Bluetooth technology to transmit weight, blood
pressure, heart rhythm, and a self-assessed health status over a mobile
telephone connection. Apart from structured monthly phone calls,
physician-led medical support was available 24 hours a day, 7 days a
week. Intervention was provided based on set standards on an ongoing
basis. A total of 710 patients were randomised to the monitoring
system or to usual care. Compliance in the intervention arm was high:
81% had at least 70% daily data transmission. However, follow up at
26 months showed no difference in overall mortality, cardiovascular
mortality, or hospitalisations.35 A pre-specified subgroup analysis for
the TIM-HF trial pointed out that specific characteristics of patients
(i.e. a depression model of Patient Health Questionnaire [PHQ-9]<10 or a
prior HF decompensation or an ICD implantation), could be associated
with better outcomes in mortality (only the subgroup with PHQ-9<10)
and numbers of days lost due to hospitalisation for HF or death.36
Findings from two Cochrane meta-analyses including studies up to
201537,38 have shown that, compared with usual care, STS can reduce
all-cause mortality at a follow-up of 6–12 months, and can reduce
HF-related hospitalisations. The recent Better Effectiveness After
Transition – Heart Failure (BEAT-HF) study,39 one of the largest trials in
telemonitoring in HF, also needs to be mentioned. This is a multicentre
randomised controlled trial conducted at six academic medical centres
in California, which compared usual care with a telehealth-based
care transition intervention for older patients (n=1457, median age
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73, 664 [46.2 %] female, 316 [46.2 %] African-American) discharged
home after in-hospital treatment for decompensated HF.
Patients assigned to the telemonitoring intervention group were
scheduled to receive nine telephone coaching calls over a 6-month
period, generally from the same nurse, who had access to patient
medical histories and medication records. All telephone calls covered
content reinforcing the pre-discharge education materials. Patients were
asked to use the telemonitoring equipment daily to transmit their weight,
blood pressure, heart rate, and responses to three questions about
symptoms, which were sent via cellular bandwidth to a secure server
and accessed daily by the telephone call centre nurses. Readings that
exceeded predetermined thresholds triggered nurses to telephone the
patient so that they could investigate potential causes. When symptoms
were of concern, patients were encouraged to contact their health call
centre. Nurses also called patients who had stopped transmitting data
to determine why and to encourage them to resume daily monitoring.
Only 61.4% (439 of 715) and 55.4% (396 of 715) of patients randomised
to the intervention were more than 50% adherent to telephone calls and
telemonitoring. This study, characterised by very poor adherence, found
that a combination of remote patient monitoring with care transition
management did not reduce all cause readmission at 180 days after
hospitalisation for HF when compared to usual care. Hospitalisations
in the first 30 days and 180-day mortality were also not reduced with
telemonitoring intervention.
Few studies have assessed the effectiveness of remote monitoring to
promote cardiac exercise training in stable HF patients, the so-called
“telerehabilitation”. In patients with stable HF, exercise training can
improve life quality, symptoms, exercise capacity and hospitalisations.
According to the 2016 ESC HF guidelines, all stable HF patients should
undergo exercise training (class I level A).20 However, a gap has
been identified between this recommendation and a lack of specific
instructions about physical training. In this context, telerehabilitation
has been advocated by some authors as a way to improve adherence
and a practical way to promote regular exercise training in stable HF
patients.40 One randomised trial on telerehabilitation in HF patients
showed that an 8-week home-based telemonitored rehabilitation
program based on walking training resulted as effective as an
outpatient-based standard cardiac rehabilitation program and provided
similar improvements in life quality.41 Another randomised trial, which
included patients with CIED, compared an 8-week home-based
telerehabilitation program to usual care (which did not include specific
exercise programs except for lifestyle advice). This study showed better
life quality and better 6-walk test distances in the telerehabilitation
group, but results could have been affected by disparities in the extent
of intervention between the groups.42
In summary, randomised clinical trials about telehealth interventions
in HF have disclosed conflicting results regarding the ability of
these interventions to reduce mortality and hospitalisation rates.
Trials comparing remote telehealth interventions to usual care are
nonetheless hardly comparable because of differences in the remote
interaction processes, choice of monitoring systems and measured
variables.43 Even in the most recent trials, little information is available
on which specific therapeutic interventions have been adopted in
response to abnormal changes of vital parameters and which measures
have been taken to check whether patients were able to understand
and follow the instructions received. Therefore, a large heterogeneity
exists among current studies designs and outcomes because of
the use of different monitoring techniques and differences among
the clinical profile of the patients studied. For example, of the four
different non-invasive remote monitoring strategies employed (STS,
telemonitoring, videophone and interactive voice response device),
only STS and telemonitoring have demonstrated in a few studies a
reduction in all-cause mortality and HF-related hospitalisation.37,38
Moreover, although several clinical trials and two meta-analyses
have demonstrated a benefit with the above strategies in mortality
reduction and in HF-related hospitalisations, the impact of STS and
telemonitoring in HF is not univocally considered to be cost-effective.
Nevertheless, when compared to the uncommon chance of access to
multidisciplinary intervention programs, that is frequently hindered by
economic, geographic and bureaucratic barriers, non-invasive remote
monitoring may be a solution to support and promote the care of HF
patients over time, especially during the tricky early discharge phase
after a hospitalisation. In view of the above-reported complex and
heterogeneous literature, it is crucial to identify the most relevant
biological parameters to monitor, which HF sub-populations may
gain real benefits from telehealth interventions and in which specific
healthcare subsets these interventions should be implemented in
order to maximise their value. For example, a meta-regression analysis
on the effectiveness of telehealth programs in patients with chronic
HF showed significantly greater effectiveness in reducing mortality
and hospitalisations in HF patients at higher risk.44 Another meta-
analysis related the lowest mortality index for telehealth programs in
HF with the promptness of feedback actions (interventions performed
within 1 day of a change in the patient’s vital signs). Moreover, the
complex literature on telehealth also seems marked by methodological
issues, like publication bias and poor recruitment in clinical trials.45
For example, in the TELE-HF study, 14 % of patients assigned to
telemonitoring never used the system and by the final week of the
study period, only 55% of the patients were still using the system at
least three times a week.33 As an appropriate adherence to a given
intervention can contribute to an adequate external validity of the
studies, improvement of adherence represents a key element of the
future research on telehealth.
In the end, it has been hypothesised that a “judicious and flexible use”
of technology could exist in daily clinical practice, but it might not have
been intercepted by too strict and inflexible study protocols that are
not able yet to fit real world settings.45
Barriers to Implementation
The clear-cut reimbursement restriction of telehealth services is one
of the biggest hurdles to their dissemination. In the US, while some
insurance programs related to Medicaid – each one with remarkable
restrictions – reimburse telehealth services in 48 states, Medicare
limits reimbursements to those areas where an inadequate supply of
healthcare services has been clearly established. It has been estimated
that Medicare paid around five million dollars for telehealth services in
2012, which is less than 0.001% of its expenditure.1
The second barrier to telehealth dissemination concerns the
replacement of traditional face-to-face evaluations with digital ones,
highlighting some of the critical issues related to the quality of doctor–
patient relationship, to the potential incompleteness of “touch-free”
virtual objective examinations and, in general, to the care process
itself. Moreover, the fragmentation of care that would probably be
delivered by heterogeneous and non-interconnected professionals
may result in patients receiving different and possibly conflicting
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recommendations for identical clinical pictures. With regard to legal
issues, physicians who operate in the context of telehealth are not yet
requested any specific accreditation: in some countries, as in the US,
however, doctors need to provide verifiable references to be allowed
to practice telehealth, but difficulties can arise in practicing outside the
state where a physician obtained their license.1
Costs and Sustainability
Telemedicine is believed to have the potential to improve costs related to
healthcare.1 Direct-to-consumer telehealth, such as patient–physician
meetings via videoconference, may become an efficient way to deliver
care as it could reduce costs to both the patient (e.g. travel expenses,
work loss, etc.) and healthcare systems. Nonetheless, the scientific
literature lacks studies in good methodological quality about the
comprehensive economic evaluations of telehealth services. A recent
review on the cost/effectiveness of telemedicine use in chronic HF
concluded that, without full economic analyses, the cost-effectiveness
of telehealth interventions in chronic HF remains very difficult to be
reliably determined.46 Otherwise, a recent sensitivity analysis showed
that cost savings of telehealth programs are most sensitive to patient
risk (i.e. more cost-effective in higher risk patients).47 This further
underlines the importance of an adequate risk stratification of patients
included in clinical studies on telehealth.
Moreover, concerns have been raised about some of the potential
unintended consequences of telemedicine medical encounters.
Despite their hypothesised efficiency, virtual medical visits may
paradoxically have physicians schedule more future virtual visits than
they would in traditional face-to-face encounters, with a consequential
unexpected increase in healthcare costs.48
A recent study analysed commercial claims data on 300,000 patients
to explore patterns of spending for acute respiratory illnesses. The
study concluded that direct-to-consumer telehealth may increase
access to care by making it more accessible and convenient for
some patients, but at the same time it may also increase utilisation
and healthcare expenditure.49 In the above study, costs were lower
for patients who underwent direct-to-consumer telehealth visits but
increased overall because of a noticeable rise in the number of new
utilisations. The authors estimated that only 12% of direct-to-consumer
telehealth visits replaced visits to other providers, but 88% were new
utilisations.49 Despite the above concerns, no sufficient and reliable
evidence is available about cost-effectiveness of telehealth services,
and therefore no informed decision at a policy level about delivery of
such services will be well-grounded until evidence becomes available.
Patient Participation
A recent policy statement of the American Heart Association on
telemonitoring-based management of HF has suggested that effective
programs need timely data, appropriate staff, and a feedback loop
to patients with sufficient empowerment to understand and follow
the proposed interventions.50 Participation of patients to the HF care
process is a basic need for the success of any management program
and particularly for a telemonitoring-based approach. Self-management
support may be a key to the implementation of telehealth models
and requires the active participation of patients. For example, in a
qualitative study led with interviews, it was observed that non-video
telehealth technologies fostered the sharing of personal information
and a non-judgemental attitude in patients, but each contact between
a telehealth professional and a patient required a skilful negotiation
of the relationship to engage the patient as an “expert of their own
illness”.51 In addition, it has been pointed out recently that HF self-
management may be associated with reduced hospital admissions
only in a subgroup of patients with HF (i.e. patients under 65 years of
age), whereas in other subgroups (patients with moderate or severe
depression), involvement in self-management may be even associated
with a reduced survival rate.45 Again, careful stratification of patients
enrolled in clinical studies seems to be a pivotal pre-requirement for
a valuable application of telehealth to different healthcare contexts.
Need for a New Approach
In recent times, technological developments have expanded to the
medical sector, with the ambitious objective to gain a dominant
role in the future of healthcare improvements. Some authors,52 in
the wake of evidence-based medicine, but also according to ethical
primum non nocere and economic issues, have highlighted that
new technologies, such as telehealth models, should be evaluated
in methodologically sound and reproducible studies and compared
to usual care before being approved and implemented in medical
practice. Nonetheless, even this may turn out to be an insufficient
approach. Indeed, Greenhalgh et al,52 by recalling the principle of
the philosopher Heidegger that technology has its maximum value
when it helps achieve “what matters to us”, have underlined that the
use of technological tools in healthcare must be only considered in
the precise context of the physical, material and symbolic spaces
in which they are applied and perfectly embedded in the social and
cultural contexts in which they must operate. This perspective could
overcome the old dichotomy between “high tech” and “high touch”
and potentially lead to the development of technologies that are
natural extensions of both the patient’s and doctor’s intents and are
not felt by users as obligations or as a waste of time.
Based on results of a qualitative study performed on 40 people
with comorbidities aged 60 to 98 years, the ARCHIE framework52
has suggested requirements that any new technology applied to
healthcare should meet before implementation. In particular, telehealth
products should be “anchored in what matters to users; realistic about
the natural history of illness, continuously co-created (developing
and adapting solutions in an ongoing way with those who are using
them), underpinned by strong human relationships and embedded in
social networks; integrated using the principles of computer-supported
cooperative work (maximising mutual awareness and mobilising
knowledge and expertise across the network)”.52
Conclusion
The essential premise for any technological solution applied to health is
the real (not theoretical or experimental) fulfilment of individual needs
for whom that product had been conceived. This implies a shift from
standard blinded “one size fits all” models to open personalised ones.
We believe that such perspective represents a necessary starting point
for future research on telehealth that is focused on a real supporting
role for suffering people. n
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