Background: Use of synchronous digital health technologies for care delivery to children with special health care needs (having a
chronic physical, behavioral, developmental, or emotional condition in combination with high resource use) and their families at home has shown promise for improving outcomes and increasing access to care for this medically fragile and resource-intensive population. However, a comprehensive description of the various models of synchronous home digital health interventions does not exist, nor has the impact of such interventions been summarized to date.
Objective: We aim to describe the various models of synchronous home digital health that have been used in pediatric populations with special health care needs, their outcomes, and implementation barriers.
Methods: A systematic scoping review of the literature was conducted, guided by the Arksey and O’Malley Scoping Review Framework. MEDLINE, CINAHL, and EMBASE databases were searched from inception to June 2018, and the reference lists of the included systematic reviews and high-impact journals were hand-searched.
Results: A total of 38 articles were included in this review. Interventional articles are described as feasibility studies, studies that aim to provide direct care to children with special health care needs, and studies that aim to support family members to deliver care to children with special health care needs. End-user involvement in the design and implementation of studies is evaluated using a human-centered design framework, and factors affecting the implementation of digital health programs are discussed in relation to technological, human, and systems factors.
Conclusions: The use of digital health to care for children with special health care needs presents an opportunity to leverage the capacity of technology to connect patients and their families to much-needed care from expert health care providers while avoiding the expenses and potential harms of the hospital-based care system. Strategies to scale and spread pilot studies, such as involving end users in the co-design techniques, are needed to optimize digital health programs for children with special health care needs.
Advances in neonatal and pediatric care for complex medical conditions have contributed to the increased survival of children who live with chronic health care needs . Although definitions of this group vary, children with special health care needs are generally considered to be those with or at risk for chronic physical, developmental, behavioral, or emotional conditions, often requiring substantial use of health and social services [ , ]. In the United States, the prevalence of children with special health care needs is estimated to be 19.8% of the pediatric population [ ]. Canadian provincial administrative data report a similar prevalence rate of 17.5% [ ].
Children with special health care needs often require care from specialists, typically located in urban tertiary centers . In between specialist visits, children with special health care needs frequently experience the need for urgent care, often delivered by health care providers unfamiliar with their complex histories, intersecting conditions, and intricate care regimens [ ]. This scenario often leads to extemporized courses of clinical management as well as recurrent emergency department visits and hospital admissions [ ]. Such unpredictability confers vulnerability for children with special health care needs in terms of exposure to medical errors and other nosocomial harms such as infection [ ].
Although children with special health care needs comprise less than 20% of the pediatric population in the United States, they account for 41% of total pediatric health expenditures . Substantial time and resources are also contributed by families who care for children with special health care needs, estimated at 1.5 billion hours of care in the United States in 2015 [ ]. Were these care hours provided by health care aides, the cost would approximate to US $35.7 billion or US $6400 per child [ ]. Foregone income due to caregiving responsibilities in the home, as well as out-of-pocket expenses for parent and family members, add to the cost burden. Losses in parental earnings are estimated at US $3200 per child per year, and annual out-of-pocket expenses have been documented at over US $1000 per year in 20%-25% of children with special health care needs families [ ].
Recent attention has been given to synchronous digital health technologies, designed to increase access for patients and families to clinical teams in real time from their homes. Synchronous digital health technologies refer to the use of audio, video, and health information interfaces to facilitate the provision of health care remotely, in real time . Both randomized and nonrandomized studies of digital health interventions in children with special health care needs to date have shown improved clinical, economic, and quality of life outcomes [ - ]. Synchronous digital health technologies have also been documented to improve parental caregiver outcomes such as quality of life, psychological health, satisfaction with care, and social support. One systematic review reported that 62 of 65 studies (95%) of synchronous digital health technologies observed significant improvements in these outcomes for caregivers of children and adults with chronic and degenerative diseases [ ].
A national survey in the United States documented 51 digital health programs providing care to pediatric populations , supporting the momentum for such programs. At this time, the number of existing digital health pediatrics programs in Canada is unknown. Although the evidence base in support of the effectiveness of pediatric synchronous digital health interventions is growing [ , - ], a comprehensive description of the ways in which synchronous home digital health solutions are used to care for children with special health care needs and support for their families is not yet documented.
Purpose and Objectives
The purpose of this review is to summarize the current body of literature in order to describe how synchronous digital health technologies are used in the care of children with special health care needs and their families and to provide practical information for health care decision makers, considering digital health program implementation or expansion.
Scoping Review Phases
A scoping review was undertaken to allow for examination of the breadth of research activity on the design of digital health interventions for children with special health care needs, implementation, uptake, and evaluation of these programs as well as health care provider and family involvement in digital health solutions. Levac and colleagues’  revision of Arksey and O’Malley’s [ ] original methodology was used to conduct this work in five phases: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; and (5) collating, summarizing, and reporting the results.
The search strategy was designed to capture a wide breadth of literature related to the research question, irrespective of study design. We included any type of article, book, dissertation, or report describing the use of synchronous digital health technologies to provide direct care to children with special health care needs or aimed at parents or caregivers with the intention of affecting outcomes in children. With the assistance of a librarian, a comprehensive search of the MEDLINE, CINAHL, and EMBASE databases was conducted by the first author (MB). Subject headings and keywords were used to locate articles describing the use of digital health in home settings for pediatric populations. The indexes of four key journals were also hand-searched for relevant articles. The initial literature search was run on June 30, 2018, with no date, age, or geographical limits set in order to increase the breadth of results. During the screening and data extraction phases, reference lists of highly relevant studies and reviews were scanned, and additional studies were screened for inclusion.
Inclusion and Exclusion Criteria and the Review Process
Inclusion was based on four criteria: (1) the population of interest was children (<18 years) or children’s caregivers; (2) the population met the definition of children with special health care needs articulated by Newachek et al , ie, having a chronic physical, behavioral, developmental, or emotional condition in combination with high resource use; (3) care for the child was ongoing and occurring in the home setting; and (4) care for the child was delivered by synchronous digital health. All studies included at least one synchronous intervention element (eg, real-time phone call or video visit.). However, included studies could feature multifaceted interventions that included nonsynchronous components as well. Papers were excluded if they were not published in English, no full text was available, or if they were published prior to 2008 in order to ensure that the interventions described were relevant to stakeholders today. In accordance with scoping review methodology [ , ], no quality assessments were completed on the selected articles.
Screening and Data Extraction
A two-stage screening process using screening forms developed by the team was employed for this review. Prior to screening, a validation test of the title and abstract screening tool was first completed by two authors (MB and NC). Validation screening resulted in 90% agreement, with conflicts resolved through discussion and consensus between authors. After refinement of the screening tool, title and abstract screening was completed by one author (MB). Prior to full-text screening, all authors met to arrive at a consensus on the inclusion criteria. Test screening of three articles per author was performed, and discrepancies were resolved via email communication. Each author was then assigned articles to screen and extract data from using a standardized survey template. Authors were in frequent communication during the screening process, and weekly emails with updates, group questions, and discrepancies were circulated to ensure consistency.
Our interest in providing decision makers with relevant information related to digital health program implementation or expansion prompted us to extract and analyze practical considerations of these applications. To this end, we analyzed digital health intervention characteristics, end-user involvement (patients, families, and health care providers) in digital health intervention design, and barriers to implementation. Data extracted from relevant articles were downloaded into Excel (Microsoft Corporation, Redmond, Washington) files and reviewed by research team members. We used two frameworks to guide analysis: Data from feasibility studies are presented using a framework by Bowen and colleagues , and end-user involvement in co-design and implementation was evaluated using the Human-Centered Design framework from IDEO [ ]. The two frameworks are described briefly below.
Our use of the term “feasibility” is broad in nature, in keeping with work by Bowen and colleagues , suggesting that feasibility trials encompass any study that assists investigators to prepare for a full-scale trial of intervention effectiveness. Using this definition, feasibility outcomes may be grouped into eight general areas of focus, which include acceptability (intervention recipient feedback), demand (intervention use), implementation (success of intervention deployment), practicality (interference with resource use), adaptation (necessary modifications), integration (fit of intervention to context), expansion (intervention applications to new context), and limited-efficacy testing (preliminary outcomes) [ ].
We sought out information from all papers related to the inclusion of end users in digital health intervention design and implementation using the IDEO Framework as a guide to this data extraction. Consisting of a six-stage, iterative cycle, the IDEO Framework aims to increase the relevance and appropriateness of interventions . End users are included in the stages of observation (understanding the end user), ideation (brainstorming ideas), prototyping (creating rough intervention mock-ups), user feedback (soliciting input from end-users), iteration (intervention refinement), and implementation (deployment into practice) [ ]. In the health care sector, the IDEO Human-Centered Design framework has been used to generate solutions such as helping patients remember to take their prescription medications and communicating messages of support to women recovering from surgical procedures [ ]. Finally, consideration was given to issues of digital health implementation in relation to technological, human, and system-level factors.
Numbers, Sources, and Types of Papers
Results of the screening process and overall yield of papers are presented in. Of the 38 papers included in the review, as shown in , 50% originated in the United States—an expected result, given the size and population base. Eleven articles originated in Australia, where the use of digital health may represent a solution to timely care delivery for the country’s large rural and remote population.
depicts the variation in study design, as reported by the authors. The majority of the papers reported on evaluation of digital health initiatives through feasibility studies (n=12), program evaluations (n=8), randomized controlled (n=6), nonrandomized controlled trial (n=3), mixed methods (n=1), and cohort studies (n=1).
|Country of origin||Number of papers||References|
|United States||19||[- , , - ]|
|Australia||11||[, , - ]|
|United Kingdom||3||[- ]|
|Research method||Number of papers||References|
|Feasibility studies (n=12)||12||[, , , - , , , , - ]|
|Program evaluation (n=8)||8||[, , , , , , , ]|
|Randomized controlled trial (n=6)||6||[, , , , , ]|
|Nonrandomized controlled trial (n=3)||3||[, , ]|
|Discussion paper (n=2)||2||[, ]|
|Review (n=2)||2||[, ]|
|Cost minimization analysis (n=1)||1|||
|Mixed methods (n=1)||1|||
Studies Reporting on Digital Health Interventions
A major focus of this review was on empirical studies that evaluated the use of digital health in caring for children and families. A large number of the empirical studies included were feasibility trials, leading us to report these separately from full-scale studies. Here, we first describe feasibility trials and then studies that used digital health interventions to provide direct care to children with special health care needs (such as employing video consultations for physical assessments), followed by interventions aimed at supporting families to care for children at home. Where possible, we have included information on published statistical results; however, many studies were performed with small samples, and therefore, the results were not analyzed for statistical significance.
provides details of the feasibility studies using digital health interventions. Based on Bowen and colleagues’ [ ] definition of feasibility studies, we identified 12 articles that reported feasibility-related outcomes. Of note, five of these studies were conducted with hematology/oncology/palliative care populations, whereas the remaining interventions targeted diverse disease groups. One intervention used telephone calls and a blog for communication [ ], another used “Skype” and “WhatsApp” for video chats and text messaging [ ], and all other studies utilized video formats with either embedded audio or separate telephone audio. There was a wide range of uses for digital health, including assessing acute clinical issues, providing routine care and follow-up, facilitating case conferences, providing psychosocial support, delivering therapy, and monitoring progress and adherence.
Among the included studies, six of the eight dimensions of feasibility were measured, and these outcomes are reported in. Ten studies looked at acceptability, with seven studies measuring family-reported acceptability [ , - , , , ], and five studies measuring health care provider acceptability [ - , , ]. Overall, most families and health care providers reported high satisfaction with digital health interventions and found the equipment to be easy to use. The demand for digital health was reported in seven studies by describing the number and length of calls made over the study period [ , , , , , , ]. Two of these articles also measured changes in demand over time, with both studies observing an increase in the utilization of digital health over the study period [ , ]. A total of seven studies reported implementation and integration issues in the form of technical difficulties [ , - , , , ]. These technical problems were both human related (eg, confusion with using equipment) and technology related (eg, firewall settings, poor internet coverage in remote areas, and bandwidth limitations). In terms of practicality, three studies conducted cost analyses [ , ], and two studies found that patient and staff availability, workloads, and scheduling influenced how the intervention was implemented [ , ].
Four studies conducted limited-efficacy testing of their interventions [, , , ]. Gur and colleagues [ ] piloted the use of text messaging and video chats with individuals with cystic fibrosis, but found no statistically significant differences in measured outcomes between the control and intervention groups. The remaining three studies did not have control groups but reported benefits of improved child functional outcomes [ ], reduced parental anxiety (median State and Trait Anxiety Inventory score reduction: 6 points; P<.05) [ ], and prevention of health care visits/admissions [ ]. Among all the feasibility studies identified, none adapted a previously established program or reported on outcomes related to the expansion of an already successful intervention. Additionally, four studies led to future publications describing larger-scale interventions [ , , , ].
|Study identifiers: first author (year), country (sample size) [reference}||Study purpose: objectives, uses||Intervention characteristics: technology used, diagnosis of sample, health care providers||Feasibility outcomes: acceptability, adaptation, demand, integration, implementation, expansion, practicality, limited-efficacy testing|
|Ludikhuize (2016), Netherlands (n=21) |
|Katalinic (2013), Australia (n=14) |
|Bradford (2010), Australia (n=2) |
|Bensink (2009), Australia (n=11) |
|Bensink (2008), Australia (n=8) |
|Gur (2017), Israel (n=18) |
|Casavant (2014), US (n=14) |
|Jury (2014), Australia (n=not reported) |
|Constantinescu (2012), Australia (n=17) |
|Miyahara (2009), New Zealand (n=7) |
|Cady (2008), US (n=5) |
|McCrossan (2008), UK (n=5) |
aAPN: advanced practice nurse.
bSW: social worker.
cCF: cystic fibrosis.
Interventions to Provide Direct Care via Digital Health
Ten articles representing seven studies described the use of digital health with children with special health care needs for the purposes of providing direct patient care or replacing in-person assessments (). Of these, six articles (four studies) examined digital health interventions for children with medical complexities [ , , , , , ], two articles (one study) focused on palliative care [ , ], one article focused on asthma [ ], and one article focused on children with congenital heart disease [ ]. Telephone was an interventional component in all studies; the next most commonly employed technologies were video [ , , ] and email [ ]. The makeup of digital health teams varied between studies: Some interventions were delivered by a single group of practitioners such as registered nurses [ , ] or advanced practice nurses [ , , , ], while others involved a multidisciplinary team [ , , ]. One study did not specify the profession of the consultant involved in the intervention [ ].
Studies that examined children with special health care needs–related outcomes had mixed results, while studies that examined family-related outcomes reported mainly positive results. Positive outcomes for children with special health care needs were constituted by parent-reported decreases in hospitalizations and quicker recovery from illness , reductions in unplanned hospitalizations (year 1 mean number of unplanned hospitalizations per child: 1.7; year 2 mean number of unplanned hospitalizations per child: 0.8; P<.007) [ ], reduced health care resource use (37% lower in the video conferencing group compared to the control groups; P<.05) [ ], and improved asthma severity scores [ ]. In contrast, two studies found no change in emergency department visits (18.4% enrolled patients presented to the emergency department per month in 2003 and 15.0% per month in 2006; P=.41) or hospital admissions (8.0% of enrolled patients hospitalized per month in 2003 and 7.3% hospitalized per month in 2006; P=.67) [ ], and no significant differences in health-related quality of life as measured by the PedsQL based on analysis of variance scores (F=0.90; P=.41) [ ] for children with special health care needs. Family members reported overall high satisfaction scores with digital health interventions, for example, average scores reported were 8.3/10 [ ], and 9.3/10 [ ]. Parents participating in the intervention arm of a digital health study rated their satisfaction with their child’s personal doctor (P=.001) and level of care coordination (P=.03) as significantly better than control groups based on the Consumer Assessment of Healthcare Providers and Systems Clinician and Group survey [ ], and in an additional study, parents perceived availability of digital health to be “very important” in assisting them in managing their child’s condition at home [ ]. However, using descriptive analysis, Bradford and colleagues [ ] found no change in caregiver quality of life in parents of children receiving palliative care via digital health.
|Study identifiers: first author (year), country (sample size) [reference}||Study purpose: objectives||Intervention components: technology used, patient population, intervention, health care providers||Reported or perceived outcomes|
|Graham (2017), US (n=320) ||Describe the utilization and satisfaction of a program with 24/7 family-driven access to health care teams with the aim of providing comprehensive, individually tailored care to children with CRIa|
|Cady (2014), US (n=27) |
and Cady (2009), US (n=43) [ ]
|Describe the attributes and effects of an APN-administered care coordination program for children with medical complexities and their families|
|Looman (2015), US, (n=148)  and|
Looman (2018), US (n=163) [ ]
|Examine the effects of adding a high-intensity, APN-delivered digital health care coordination intervention within an existing medical home model|
|Sutton (2008), Australia (n=220) ||Determine if continuous mobile phone access to EDh RNs can increase families\' capacities to manage care of child at home and decrease ED visits and ED length of stay|
|Bradford (2014), Australia (n=not reported)  and|
Bradford (2012), Australia (n=14) [ ]
|Measure the effects of a home digital health program for pediatric palliative care consultations on caregiver quality of life. Compare in-person with video palliative care consultations|
|Nelson (2009), US, (n=not reported) ||Describe a severity-based nurse-administered asthma management protocol administered to children/families at home via telephone|
(2012), UK (n=83) [ ]
|Evaluate a digital health intervention for clinical utility and intervention quality, and determine impacts on health care resource use|
aCRI:. chronic respiratory insufficiency
bAPN: advanced practice nurse.
cSW: social worker.
dSO: system outcomes.
eFO: family outcomes.
fRN: registered nurse.
gPO: patient outcomes.
hED: emergency department.
iHPO: health care provider outcomes.
Interventions to Teach and Support Parents and Families
Seven papers described digital health interventions intended to train or provide support to parents of children with special health care needs (). Four of these papers involved parents of children with autism spectrum disorder [ , , , ], two papers were focused on asthma [ , ], and one was focused on a mental health issue [ ]. In four studies, behavior consultants or therapists used video to train parents of children with autism spectrum disorders to use autism specific interventions including applied behavioral analysis [ , , , ]. Reported outcomes of these interventions include reduction in problem behavior [ , ] and gains in communication skills for children [ ]. For example, Lindgren and colleagues [ ] found a mean reduction in problem behavior of over 90% for children with autism treated by specialists in their homes (mean reduction: 95.76%), by telehealth in a clinic setting (mean reduction: 91.00%), and via telehealth in their homes (mean reduction: 97.27%). Between-group differences based on analysis of variance scores were significant (P=.07).
Two papers used telephone consultation to support and train parents of children with asthma [, ], with mixed outcomes reported. Neither study reported any benefit in patient outcomes: Gustafson and colleagues [ ] found no difference in medication adherence (P=.76) or number of symptom-free days for children (P>.99), while Garbutt and colleagues [ ] found no improvements in either children’s quality of life as measured by the Pediatric Asthma Quality of Life Questionnaire (between group difference: –0.17; 95% CI −0.47 to 0.12) or number of urgent events per year (between group difference: 1.15; 95% CI 0.82-1.61). However, at the family level, they reported that parental quality of life (measured using the Pediatric Asthma Caregiver’s Quality of Life Questionnaire) improved with an asthma coaching program (between-group difference 0.38; CI 0.14-0.63).
Kierfeld and colleagues  used a telephone intervention with minimal therapist contact to train parents of children with externalizing problem behaviors. Results included improvements in the treatment group in problem behaviors, as measured by analysis of variance (F1,44=21.14, P<.001, ddiff=1.22), parenting strategies (F1,43=9.43, P=.002, ddiff=0.92), and parenting-related strains (F1,43=12.28, P<.001, ddiff=1.03) [ ].
|Study identifiers: first author (year), country (sample size) [reference}||Study purpose: objectives||Intervention components: technology used, patient population, intervention, health care providers||Reported or perceived outcomes|
|Lindgren (2016), US (n=107) ||Determine whether challenging behavior in children with autism can be treated successfully at lower cost by using telehealth to train parents to implement applied behavior analysis|
|Suess (2014), US, (n=parents of 3 children) ||Evaluate the fidelity with which parents of children with autism spectrum disorders implemented treatment procedures and the types of fidelity errors they made during coached and independent trials|
|Vismara (2013), US (n=8 families) ||Teach parents to implement autism-specific interventions|
|Baharav (2010), US (n=2) ||Assess the use of technology and telepractice as a tool for coaching parents of children with autism spectrum disorders.|
|Gustafson (2012), US (n=301 parent-child dyads) ||Support and train parents and improve asthma control and medication adherence.|
|Garbutt (2010), US (n=362) ||Coach parents and children with asthma to improve disease-related quality of life and reduce incidence of asthma episodes requiring urgent care.|
|Kierfeld (2013), Germany (n=48 families) ||Support and train parents of children with externalizing problem behavior to administer interventions with minimal therapist contact|
aPO: patient outcomes.
bSO: system outcomes.
cFO: family outcomes.
dRN: registered nurse.
Family and Health Care Provider Involvement in Design of Digital Health Interventions
Across the body of included literature, there were few studies that explicitly included families and health care providers (intervention end-users) in the design and implementation of digital health interventions. However, a few key examples showcased end-user involvement, most commonly, in the early stages of intervention design such as the observation or ideation phases, as well as by garnering user feedback.
In one study by Miyahara and colleagues , the researchers actively involved families in the development, testing, and refinement of the intervention (feedback and iteration). An iterative process of two-way communication between the researchers and participants was used to evaluate and refine the intervention (a set of digital versatile discs, a workbook, and a website) throughout the study [ ]. Authors reported that the impacts of end-user involvement increased participation in interventional components as well as the development of educational materials that were acceptable and useful to parents. Cady and colleagues [ ] conducted a survey prior to initiating a videoconferencing intervention to find out what types of technologies were available to families (observation). Results of the survey supported that most families already had adequate home technology to support videoconferencing; however, apparent survey response bias led the researches to caution of a potential “digital divide” in access to technology between Caucasian and minority populations [ ]. Finally, Sutton’s group [ ] engaged in a formal parent survey and the collection of anecdotal feedback from parents, health care providers, and subspecialty staff about the current care model, which spurred the development of the intervention (observation). Researchers then developed a study advisory group, consisting of key stakeholders such as parents and a variety of health care providers (ideation). Although the exact responsibilities of the advisory group are unclear, the inclusion of an end-user advisory group can lend valuable insights into intervention content and structure, making interventions more user-friendly and feasible to implement [ ].
Factors Affecting Implementation of Digital Health Technologies
In addition to implementation challenges reported in the feasibility studies section, we also examined included studies for factors that may impact implementation. These factors, which we categorized as technological, human, or system, stem from family and health care provider perceptions as well as lessons learned by the researchers.
Many studies reported encountering technical issues, which affected the implementation and acceptance of digital health interventions if the quality of videoconferencing or health care provider workflow patterns are disrupted [, ]. For example, a barrier to videoconferencing was the limited availability of devices and broadband internet [ ]. To overcome barriers to access, some interventions supplied equipment or internet services to families in varying capacities such as webcams, software packages, and computers on loan from the study with prepaid wireless connections [ , , , , , , ]. These practical considerations are vital to acknowledge and plan for prior to digital health intervention deployment.
In general, patients, families, and providers were satisfied with digital health interventions and were open to learning how to use new technologies if they thought it would save them time . However, digital health was not always appropriate, depending on the clinical use case. For example, Constaninescu [ ] reported that therapists had difficulty engaging with younger children with hearing loss during videoconferencing appointments. Additional human factor barriers noted by Edirippulige and colleagues [ ] were that social workers preferred in-person appointments to facilitate a personal connection with patients, and Seuss’ team [ ] hypothesized that some parents may require face-to-face demonstrations of clinical skills for optimal treatment fidelity. With regard to human-technology interfaces, Casavant and colleagues [ ] reported that the availability of real-time visual images was an important factor in decision making for health care providers treating children on home ventilator support, and a lack of visuals was cited as a concern for health care providers in two phone-only interventions [ , ]. Additionally, some studies cited barriers of scheduling, time constraints, and workload for both patients/families and health care providers [ , , , , ]. Family commitment (ie, history of good attendance in clinic) and health care provider engagement were crucial for successful implementation of the digital health interventions, with health care provider engagement being facilitated by strong leadership and rapid resolution of problems [ , ].
Several studies reported system factor barriers to digital health related to funding, such as difficulties in obtaining consent to bill and restrictions on who could be reimbursed for delivering digital health interventions [, ]. Additionally, connectivity issues [ , , , , ] and device interoperability between systems [ , ] were additional barriers. System factors that facilitated implementation include detailed planning, high-level support, standardization and education, and adequate administrative support [ , ].
In this scoping review, we sought to synthesize the current available evidence on the use of digital health to care for children with special health care needs and their families. Our results draw attention to gaps evident in the knowledge base in this area, including the few full-scale randomized trials testing such interventions, and the dearth of literature discussing the involvement of end-users in intervention design and implementation. Despite national studies such as the SPROUT survey in the United States reporting 22 dedicated pediatric digital health programs, and an additional 29 programs providing digital health to mixed adult and pediatric populations , published research on such programs remains scarce.
Practical Considerations for Implementing Digital Health Technologies
This scoping review of the literature has demonstrated that digital health technologies have the potential to provide high-quality, effective interventions for children with special health care needs and their families in the convenience of their homes. Recent advances and widespread use of technology (eg, smartphones and tablets) have created an international landscape ready for implementation of digital health interventions. However, despite the pervasiveness of user-friendly technology, barriers to implementation continue to exist. Health care providers and health care administrators should consider the following implications when thinking about how to successfully implement digital health interventions.
Many of the included studies report the use of a digital health center or related infrastructure support, which may come with benefits such as having digital health–trained health care providers, dedicated technical support, and digital health–focused resources. Jury et al  reported the use of a website that contains staff and patient resources with how-to guides and troubleshooting material. However, other studies have demonstrated the effectiveness of interventions delivered by independent health care providers. For example, studies by Vismara and colleagues [ ] and Baharav and Reiser [ ] have shown therapeutic outcomes associated with interventions delivered by health care providers from their office computers. Although many studies reported technical issues such as connectivity or interoperability conflicts, it was often unclear whether dedicated ongoing technical support was available. When considering implementing digital health solutions, it is important to be aware of the type of infrastructure available, how technical support will be provided, and what effect program implementation will have on health care provider workflows. Explicit reporting of these vital factors in published journal articles or reports may assist in moving the field of digital health forward and achieving optimal digital health intervention integration into health systems.
In addition, some health care providers and administrators may be able to capitalize on available funding for the implementation of digital health interventions , which can assist in rapidly implementing or scaling a digital health program. To increase the uptake of digital health, decision makers should consider that funding must be available not only to set up infrastructure, but also to inform health care providers and families of digital health intervention availability on an ongoing basis, and to assist in day-to-day operational management of the program. For example, Jury et al [ ] reported using a program manager and telehealth “champions” to facilitate implementation, promoting digital health to families, and referring general practitioners. The demand created by these promotional strategies may well neutralize the added costs of personnel involved in the digital health program in for-profit situations.
Finally, care equity deserves special consideration when implementing digital health interventions. For example, in rural and remote areas, poor internet connectivity may prove to be a significant challenge for digital health programs to overcome [, ]. One method that was used when bandwidth was insufficient for high-quality video was to utilize the Internet for video, while using the phone line for audio [ , , ]. Using this strategy, fluctuations in picture quality were mitigated by clear and reliable audio components, and the call was not entirely interrupted. An additional care equity point to consider when implementing digital health programs is families’ access to devices that are required for using digital health. Although some studies in this review excluded participants who did not have access to the required devices or sufficient internet speeds, others provided hardware or financial support to install high-speed internet. By excluding those who do not have access to devices or adequate internet, health care systems may be further marginalizing underresourced populations and exacerbate the “digital divide.” Crucial to the successful implementation of digital health interventions is finding solutions to mitigate barriers to access. Modern technology options such as tablets are cost-efficient and easy to use, albeit reliant on Web-based software. Conversely, videoconferencing units that utilize phone lines are more expensive and require more technical support but may be more suitable for remote regions. Regardless of the types of devices and connection used, having requisite supports in place to rapidly overcome technical and user-related barriers in the provision of digital health is essential for intervention uptake.
A promising area of results of this review is the use of digital health to teach and support parents to deliver care to children with special health care needs. Across a multitude of clinical specialties, chronic disease self-management is heralded as promoting improved patient engagement and collaborative care . For children with chronic conditions, self-management necessitates the involvement of parents or other caregivers to deliver requisite proactive planning, disease surveillance, and health maintenance. Lozano and Houtrow [ ] highlight the need for children and youth with chronic conditions to participate in shared care management where possible while also allowing appropriate amounts of autonomy. The positive impacts of parental training noted in this review, particularly in studies examining the parental delivery of autism therapies, could have important implications for improving clinical outcomes and conserving health care resources.
Co-Design of Digital Health Interventions
Literature in the field of intervention co-design reports that the concerns of health care practitioners and patients are often fundamentally different and that aligning program goals is a prerequisite for the successful implementation of patient-centered digital health services . No studies included in this review made explicit use of co-design principles in intervention development using an established framework or theory, although a small number did incorporate end-user feedback at various stages. Few of the feasibility studies identified moved on to larger trials, supporting that uptake and integration of digital health interventions into usual clinical workflows remains problematic. Mounting evidence suggests that patient-orientated research—the inclusion of end-users in co-design and coproduction of interventions—assists in the generation of ideas and products that are feasible, appropriate, and of value to end-users [ , ]. Interventions designed to meet the requirements of end-users are associated with improved intervention acceptance, reduced user errors, and an enhanced reputation [ ]. Evidence from other populations validates these points. For example, a co-design study of a flexible hip protector garment for older adults in care facilities resulted in high levels of interest from residents and support from site managers [ ]. In another study, a codeveloped tool designed to improve the communication about heart failure trajectory and palliative care resulted in nurses reporting increased knowledge, improved confidence, and enhanced skills in end-of-life conversations [ ]. Future work in digital health for children with special health care needs should incorporate co-design principles into the development of digital health interventions in order to increase user acceptance and intervention integration.
Limitations of this Review
Although we attempted to be comprehensive in our search, missed studies may have limited the scope of this review. To be as comprehensive as possible, we followed a rigorous process using a predefined scoping methodology framework and assistance from an experienced librarian to develop our search strategy. We hand-searched reference lists of included articles and relevant journal databases to enhance the breadth of our search. However, we suspect that some organizations using digital health to care for children with special health care needs may be doing so without publishing their results. We did not contact experts in the field to inquire about known ongoing projects in this capacity; therefore, there is the possibility of some projects were missed.
Our team used an ongoing communication strategy, validation screening, and predefined study inclusion criteria and data extraction forms, contributing to the rigor of our data collection and extraction processes. However, due to time and resource constraints, we did not double screen the included studies. Thus, the potential for inappropriately including or excluding studies exists.
Additionally, we classified studies by methodology to the best of our ability, taking cues from authors’ own descriptions or stated study type. However, some studies had methodologies that were ambiguous or not well detailed, leading to difficulty in classifying them. We suggest that authors publishing future work on digital health intervention implementation use clear language and reference a well-developed model for intervention stage such as the NIH Stage Model for Behavioral Intervention Development .
Finally, as per scoping review methodology, no quality appraisal was conducted on the included studies. The intent of our review was a broad overview of the literature; thus, omitting a quality appraisal was appropriate, as we did not wish to exclude smaller or less rigorously conducted studies. However, because of this, we would caution readers who are intending to use the evidence from this review to conduct their own quality appraisal of individual studies. Although we have preidentified articles for a variety of children with special health care needs, the utilization of high-quality evidence in practice is of equal importance.
The use of digital health to care for children with special health care needs presents an opportunity to leverage the capacity of technology to connect patients and their families to much-needed care from expert health care providers while avoiding the expenses and potential harms of the hospital-based care system. This review has summarized the use of digital health in providing care at home to children with special health care needs and their families while also highlighting challenges within the field. To move work in this important area forward, we strongly recommend the use of co-design and coproduction principles to involve end-users in meaningful ways in the design and implementation of digital health interventions. Additionally, much of the work in this area starts and ends with pilot and feasibility studies. Researchers should consider and integrate lessons learned from feasibility studies into large-scale interventions to operationalize programs with proven feasibility to better serve children with special health care needs and their families.
Conflicts of Interest
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|APN: advanced practice nurse|
|CF: cystic fibrosis|
|CRI: . chronic respiratory insufficiency|
|ED: emergency department|
|FO: family outcomes|
|HPO: health care provider outcomes|
|PO: patient outcomes|
|RN: registered nurse|
|SO: system outcomes|
|SW: social worker|
Edited by O Perski; submitted 19.06.19; peer-reviewed by T Risling, S Toukhy; comments to author 16.08.19; revised version received 24.08.19; accepted 19.09.19; published 21.11.19Copyright
©Marissa Bird, Lin Li, Carley Ouellette, Kylie Hopkins, Michael H McGillion, Nancy Carter. Originally published in JMIR Pediatrics and Parenting (http://pediatrics.jmir.org), 21.11.2019.
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