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 JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
Despite the increasing availability of mobile health services, clinical engagement remains minimal.
This study aims to identify and weight barriers to and drivers of health app use among health care professionals (HCPs) from the United Kingdom.
A discrete choice experiment was conducted with 222 HCPs using a web-based survey between March 2019 and June 2019. Participants were recruited to take part via social media and asked to choose their preferred option of 2 hypothetical health apps to prescribe to a hypothetical patient or to prescribe neither. Choices were characterized by differing levels of patient age, cost, published evidence bases, whether they had a National Health Service (NHS) stamp of approval, personal familiarity with the technology, and whether they were recommended by a fellow HCP. The results were analyzed using a mixed logit model, with subgroup analyses to account for heterogeneity.
We received 230 responses, a total of 96.5% (n=222/230) of respondents understood the survey task and passed the test of rationality. The median age was between 36 and 45 years, and 62.6% (n=139/222) of the health care providers responding to the survey had previously recommended the use of health apps to patients. Health apps were most likely to be prescribed to patients if they had an NHS stamp of approval or if they were recommended by another HCP (both
An NHS stamp of approval, published studies, and recommendations from fellow HCPs are significant facilitators of digital prescribing, whereas increasing costs and patient age are significant barriers to engagement. These findings suggest that demonstrating assurances of health apps and supporting both the dissemination and peer-to-peer recommendation of evidence-based technologies are critical if the NHS is to achieve its long-term digital transformation ambitions.
Increasing public expectations of National Health Service (NHS) care, a rapidly aging population, and an ever-increasing prevalence of long-term and comorbid conditions mean that health care systems are working harder than ever. Adapting the way patients and health care professionals (HCPs) communicate and collaborate in promoting health and well-being is, therefore, essential if the future expectations of high-quality patient-centric care are to be realized.
New digital technologies, which are generally low cost and widely accessible to 95% of adults in the United Kingdom who own a smartphone [
From 2024, patients in England will have the
Being the gatekeepers of health services, understanding the perspectives of HCPs is crucial to achieving the long-term plans of the NHS for digital transformation, and the effective delivery and uptake of safe and high-quality digital solutions, which have proven to be clinically effective. However, to date, a paucity of evidence regarding HCP preferences for digital health solutions limits our ability to realize any potential such technologies may deliver.
On the basis of the combined results of a qualitative pilot study [
The study did not require ethical approval as the form of the study was opinion seeking for the purpose of market research, with the subject matter limited to topics that are strictly within the professional competence of the participants. In addition, no vulnerable groups were included, the data were completely anonymous, there was no risk of disclosure, and the data collected were not sensitive in nature. We also submitted an inquiry to the NHS Health Research Authority, which confirmed that ethical approval was not required.
The data that support the findings of this study are available from the authors upon reasonable request.
We followed methodological guidelines from the International Society for Pharmacoeconomics and Outcomes Research [
A DCE, designed to elicit the preferences of HCPs for digital health prescribing, and including the attributes identified during the literature review, face-to-face discussions, and ranking exercise [
All respondents were HCPs working in the United Kingdom. We included qualified nursing and medical staff of all grades (nurse, GP, secondary care physician, and allied health professional), from primary, secondary, and tertiary care settings. We stated clearly in the description of the survey and advertisements that the survey was only to be answered by HCPs based in the United Kingdom; however, we also included a screening question as the first question of the survey, where respondents were asked to provide their job title. Responses from those who were not HCPs were removed at this point. Each respondent received 16 discrete choice tasks plus a test of rationality to gauge their understanding of the survey. In the test of rationality, one option was clearly superior to the alternative in every domain, including a lower price, a greater number of studies, and an NHS stamp of approval. If respondents failed the test of rationality, by selecting the inferior option, their responses were excluded from the formal analysis. Each question asked respondents to choose between 2 digital health technologies, each characterized by different levels of the attributes included (
Attributes and levels of the discrete choice experiment.
Attribute | Levels |
Number of studies concerning safety and effectiveness | 0, 1, 2, or 3 |
Does the app have an NHSa stamp of approval? | No or yes |
Cost of the technology to the NHS, £ | 0, 5, 25, or 75 |
You have personally used the app yourself? | No or yes |
Age of the patient (years) | 18, 35, 55, or 75 |
The app has been recommended by other HCPsb | No or yes |
aNHS: National Health Service.
bHCPs: health care professionals.
We used a mixed logit model to estimate preferences for the different levels of attributes associated with digital health technologies, thereby determining which increased or decreased utility and subsequently increased/decreased the likelihood of recommending these technologies to patients. Dummy coding was used for all categorical variables, with the number of published studies, patient age, and the cost of the app coded as linear continuous variables. We first estimated the main effects model and then estimated the effects for subgroups based on factors such as years of experience as an HCP, current digital engagement, and clinical role (eg, GP, secondary care physician, and allied health professional). WTP analyses were performed to determine how HCPs were willing to trade-off one attribute for another. CIs for WTP estimates were estimated via joint distributed bootstrapping. All analyses were performed using Stata 14 (StataCorp LP) and deemed statistically significant at the 5% level (ie,
Between March 2019 and June 2019, 250 responses were received, of which 20 were excluded because of not being from HCPs. A further 8 respondents were excluded because of failing the test of rationality, suggesting an understanding rate of 96.5% (n=222/230), and a complete dataset of 222 respondents.
Characteristics of health care professionals completing the discrete choice experiment (N=222).
Characteristic | Value, n (%) | |
|
||
|
<26 | 5 (2.3) |
|
26-35 | 34 (15.3) |
|
36-45 | 74 (33.3) |
|
46-55 | 76 (34.2) |
|
56-65 | 27 (12.2) |
|
66-75 | 5 (2.3) |
|
>75 | 1 (0.5) |
|
||
|
Yes | 169 (76.1) |
|
No | 53 (23.9) |
|
||
|
Yes | 139 (62.6) |
|
No | 83 (37.4) |
|
||
|
Allied health professional | 86 (38.7) |
|
Community caregiver | 5 (2.3) |
|
Dentist | 3 (1.4) |
|
General practitioner | 32 (14.4) |
|
Nurse | 27 (12.2) |
|
Pharmacist | 1 (0.5) |
|
Secondary care physician | 40 (18) |
|
Other | 28 (12.6) |
In the discrete choice analysis, all attributes were statistically different from 0, suggesting importance with respect to digital health prescribing and the decision to prescribe to patients.
Having a stamp of approval from the NHS was the most important factor in encouraging mobile health (mHealth) prescribing (β=2.36, 95% CI 2.08-2.64), followed by a recommendation from a fellow HCP (β=1.28, 95% CI 1.07-1.49) and having used health apps personally (β=1.04, 95% CI 0.83 to 1.26). Although having published studies to demonstrate safety and effectiveness was important (β=.55, 95% CI 0.44-0.67), it would take 5 published studies to be as convincing as an NHS stamp of approval and 3 to be as convincing as a recommendation from a fellow HCP. Patient age (β=−0.02, 95% CI −0.01 to −0.02) and the cost of the app (β=−0.02, 95% CI −0.02 to −0.02) were both statistically significantly associated with a reduced likelihood of prescribing digital health technologies, suggesting that as patient age (per year) and cost (per £1) increase, prescribing of digital health technologies can be expected to fall. Finally, the opt-out option was also statistically significant; regardless of a high number of clinical studies, recommendations by HCPs, or having a stamp of approval, 8.1% (n=18/222) of respondents chose to prescribe neither app, suggesting a reluctance to utilize health apps among a considerable number of respondents.
Preferences for digital health technologies as reported by health care professionals.
Attribute | Coefficient (β), mean (SD) | 95% CI |
NHSa stamp of approval (yes) | 2.36b (1.51) | 2.09-2.64 |
Health app recommended by HCPc (yes) | 1.28b (0.76) | 1.07-1.49 |
Used health apps personally (yes) | 1.04b (0.82) | 0.83-1.26 |
Published study (per additional study) | .555b (0.29) | 0.44-0.67 |
Patient age (per additional year) | −0.018b (−0.01) | −0.02 to −0.01 |
Cost (per additional £1) | −0.019b (−0.02) | −0.02 to −0.02 |
Alternative specific constant | 1.25b (1.58) | 0.84-1.65 |
Observations | 10,656 (N/Ad) | N/A |
Log likelihood | −1226.3 (N/A) | N/A |
aNHS: National Health Service.
bSignificant at 5% level. The table represents beta coefficients and CIs from mixed logit regression. The regression coefficients for each attribute level represent the mean part-worth utility of that attribute level in the respondent sample. A positive value denotes utility/satisfaction, and a negative value denotes disutility/dissatisfaction.
cHCP: health care professional.
dNot applicable.
Increasing patient age had a stronger negative impact on digital prescribing among allied health professionals when compared with secondary care physicians and GPs, whereas among nurses, patient age did not impact digital health prescribing behavior at all. Similarly, a recommendation to use digital health prescribing from another HCP was shown to be highly influential in promoting digital prescribing among nurses and secondary care physicians, but significantly less so among GPs and allied health professionals (
Having an NHS stamp of approval was the most influential factor in promoting digital prescribing across all subgroups. Having published studies to demonstrate safety and clinical effectiveness was also important to respondents, and exceptionally so among younger professionals and those unfamiliar with prescribing digital health technologies to patients. However, such studies were less important to allied health professionals who believe that cost is the most important factor in the decision to prescribe or not to prescribe health apps to patients.
Finally, reluctance to use health apps varied considerably among the HCP groups under analysis. Older clinicians (aged >46 years), nurses, allied health professionals, and those who do not currently use health apps personally, all things being equal, are far more likely to opt out of prescribing digital health technologies to patients. Conversely, the most digitally enabled groups were secondary care physicians, GPs, and those who use digital health technology to manage their own health and well-being (
Relative attribute importancea.
Group | Value, n | Characteristics of health apps | ||||||
|
|
Published study | NHSb stamp of approval | Cost | Used health apps personally | Patient age | Recommended by another HCPc | |
All | 222 | 7 | 10 | 6 | 4.4 | 4.3 | 5.4 | |
|
||||||||
|
Yes | 169 | 7.2 | 10 | 5.8 | 4.8 | 4.6 | 5.5 |
|
No | 53 | 7.5 | 10 | 3.9 | 1.6 | 3.1 | 3.9 |
|
||||||||
|
Yes | 139 | 6.2 | 10 | 6.3 | 4.5 | 4.3 | 4.8 |
|
No | 83 | 8.8 | 10 | 6 | 3.5 | 3.3 | 5.1 |
|
||||||||
|
>46 | 113 | 5.8 | 10 | 7.1 | 4 | 4.1 | 5.2 |
|
<46 | 109 | 9.1 | 10 | 5.4 | 4.6 | 5.3 | 5.4 |
|
||||||||
|
General practitioner | 32 | 6.8 | 10 | 5.6 | 2.2 | 3.6 | 3 |
|
Allied health professional | 86 | 5.1 | 10 | 6.2 | 4.3 | 6.3 | 4.7 |
|
Secondary care physician | 40 | 6.7 | 10 | 2.6 | 6.3 | 5.8 | 7.4 |
|
Nurse | 27 | 8.3 | 10 | 0.5 | 3.3 | 0.4 | 6.6 |
aStandardized relative attribute importance (RAI) for each attribute was calculated across the subgroups to allow for across subgroups comparisons. First, an RAI was calculated for each attribute by taking the difference between the most and least preferred levels. The RAI was then standardized across subgroups by dividing it by the RAI of the most important attribute across the subgroups (NHS stamp of approval) and multiplying it by 10. The resulting number indicates the relative importance of each attribute across the subgroups (where a higher number indicates a relatively more important attribute).
bNHS: National Health Service.
cHCP: health care professional.
HCPs expressed a WTP of £124.61 (US $152.02) for a digital health technology with an NHS stamp of approval; however, this varied from £119.14 (US $145.35) among those already familiar with prescribing digital health technology to patients, up to a maximum of £1616 (US $1971.45) among nurses, as shown in
Willingness-to-pay by health care professional subgroup.
Group | Value, n | Willingness-to-pay according to characteristics of health apps, £ (US $) | ||||
|
|
Published study | NHSa stamp of approval | Used health apps personally | Recommended by another HCPb | |
All | 222 | 29.20 (35.62) | 124.61 (152.02) | 54.81 (66.87) | 67.30 (82.10) | |
|
||||||
|
Yes | 169 | 31.11 (37.95) | 129.44 (157.91) | 62.11 (75.77) | 71.28 (86.96) |
|
No | 53 | 48.56 (59.24) | 194.38 (237.14) | 31.19 (38.05) | 76.44 (93.25) |
|
||||||
|
Yes | 139 | 24.48 (29.86) | 119.14 (145.35) | 53.43 (65.18) | 57.57 (70.23) |
|
No | 83 | 36.89 (45.00) | 125.42 (153.01) | 43.37 (52.91) | 64.11 (78.21) |
|
||||||
|
>46 | 113 | 20.25 (24.70) | 105.00 (128.10) | 41.75 (50.93) | 54.21 (66.13) |
|
<46 | 109 | 42.35 (51.67) | 140.00 (170.79) | 64.59 (78.80) | 76.29 (93.07) |
|
||||||
|
General practitioner | 32 | 30.28 (36.94) | 134.44 (164.01) | 29.56 (36.06) | 39.84 (48.60) |
|
Allied health professional | 86 | 20.79 (25.36) | 121.38 (148.08) | 52.79 (64.40) | 57.38 (70.00) |
|
Secondary care physician | 40 | 63.50 (77.47) | 284.75 (347.38) | 178.13 (217.31) | 209.75 (255.89) |
|
Nurse | 27 | 449.50 (548.37) | 1616.00 (1971.45) | 526.00 (641.70) | 1061.50 (1294.99) |
aNHS: National Health Service.
bHCP: health care professional.
In this first-of-its-kind study, examining the barriers and drivers of digital health prescribing among a broad sample of HCPs from the United Kingdom, we found that the factors most influential in digital prescribing behaviors are an NHS stamp of approval, a published evidence base, cost of the technology, and recommendation by other HCPs. Respondents expressed a WTP of more than £100 for technologies with a stamp of approval from the NHS and were willing to pay approximately an extra £30 for every additional published study. The strength of this preference varied significantly among our heterogenous cohort, influenced by clinical role, age, and current level of digital literacy. This suggests that a one-size-fits-all approach to increasing digital health adaptation is unlikely to be successful in increasing the uptake of digital health in routine practice.
A recommendation from a HCP can go a long way in encouraging patients to use digital health technology. Although previous research suggests there is an appetite for digital health technologies among HCPs [
The latter is of interest, given the strong tendency of our sample to reduce digital prescribing as patient age increased, in line with a recent study conducted among chronic obstructive pulmonary disorder specialists in Canada [
We also found that the age of a HCP may also predict digital engagement, with those over the age of 46 years considerably more likely to opt out of providing digital health technology, all things being equal, than any other group. A mixed methods cross-sectional study conducted in the Czech Republic observed a similar pattern [
A 2019 study of Irish chronic obstructive pulmonary disorder specialists found a need for a strong evidence before considering the adaptation digital health technology in clinical practice, with published studies seen as a surrogate for safety [
Our pilot study suggested that HCPs value an NHS stamp of approval above all else [
Our study has several strengths. First, to the best of our collective knowledge, the combination of qualitative pilot testing followed up by the quantitative assessment of relative preferences using the DCE methodology make this research the first-of-its-kind when considering attitudes toward digital health. Second, the low rate of failures during the rationality test suggests that participant understanding of the survey was very high, with just 3.5% failing to progress. This lends support for the responses received being the true beliefs of the HCPs involved, rather than stochastic variation, which can be expected in the event of misunderstanding the survey. Finally, although our participants could be considered an anomalous group of highly digitally engaged HCPs, our subgroup analyses, which adjusted for digital familiarity and personal perceptions around the use of digital technologies, found no significant differences between those who personally use health apps and those who do not, and more importantly, those who currently recommend health apps to patients and those who do not. Therefore, the findings of this study can be considered representative of underlying preferences as vignettes from all levels of digital engagement were included.
The findings of our study should also be viewed in the context of several limitations. First, although we rigorously followed methodological guidelines for eliciting preferences, our study was not large enough to capture all relevant attributes that factor into the decision to provide or not to provide health apps to patients. For example, a systematic review conducted in 2018 highlighted that excessive data creation and burden in the analysis would be a significant deterrent to GPs recommending health apps to patients [
This is the first study to quantitatively determine factors associated with health app prescribing among HCPs in the United Kingdom. The findings suggest that having an NHS stamp of approval, published studies, and recommendations to use digital health technology from fellow HCPs are the greatest facilitators of digital prescribing among HCPs, whereas increasing costs and patient age are significant barriers to engagement. These findings suggest that demonstrating assurances and supporting both the dissemination and peer-to-peer recommendation of evidence-based technologies that meet health challenges are critical if the NHS is to achieve its digital transformation ambitions.
Search terms for literature review.
Ranking exercise to determine most important characteristics of health-apps to health care professionals.
Sample survey instrument.
Variation in health care professional preferences for digital health prescribing, subgrouped by age and clinical role.
Variation in health care professional preferences for digital health prescribing, subgrouped by digital familiarity and literacy.
discrete choice experiment
electronic prescription service
health care professional
mobile health
National Health Service
National Institute for Health and Care Excellence
Organisation for the Review of Care and Health Applications
randomized controlled trial
willingness-to-pay
The authors would like to thank the many HCPs who gave their time to answer the survey. This research was funded by ORCHA Health Limited.
SL devised the study and acted as a guarantor for the paper, and SL and LA collected the data. SL and TA planned statistical analyses, and SL performed all statistical analyses. SL, LA, and TA wrote the first draft of the manuscript and revised and approved the final manuscript as submitted. All authors helped draft the manuscript and approved the final submitted version.
SL is a paid contractor, and both TA and LA are paid directors of ORCHA Health Limited, a company specializing in the review of digital health technologies. There are no other financial or other interests to declare.