Assessment of and Intention to Use Electronic Health/Patient Records in European Countries

Last registered on June 24, 2024


Trial Information

General Information

Assessment of and Intention to Use Electronic Health/Patient Records in European Countries
Initial registration date
June 03, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 24, 2024, 11:53 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.



Primary Investigator

Munich School of Politics & Public Policy; TUM School of Management

Other Primary Investigator(s)

PI Affiliation
Munich School of Politics & Public Policy; Technical University of Munich
PI Affiliation
Munich School of Politics & Public Policy; Technical University of Munich

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
This study seeks to advance our understanding of the general public's overall assessment of — and specifically acceptance of and intention to use – electronic health records (hereafter EHR) in Germany, Italy and the UK, as well as possibly the Netherlands (i.e., what in German is mostly known as the "elektronische Patientenakte", in Italian as the "Fascicolo Sanitario Elettronico" and in English is also known as an electronic medical record). The introduction of this digital health technology holds enormous promise for improvements in individual medical care and population health, including possibly significant cost savings, but take-up of EHRs and similar digital health technologies has been much weaker than public health and medical experts had hoped – in Europe (see, e.g. Albrecht 2016; Albrecht et al 2017; Dahlhausen et al 2022) and beyond (e.g., Dhagarra et al 2020).

We examine patients/citizens' attitudes towards EHRs through the use of an online survey experiment with three alternative information treatments. The survey context allows us to control for (or examine as correlates or in heterogeneity analyses) a variety of pre-treatment factors (health status; experience with, and general attitudes towards, digital goods and services; trust in science, scientists and public authorities; political-ideological preferences; and various demographics) that might be drivers of preferences and acceptance, as well as post-treatment possible broader effects on health policy preferences (including regarding the desirability of various digital health tools (such as digital therapeutics) and interest in using specific features or types of digital health technologies.
External Link(s)

Registration Citation

Büthe, Tim, Janina Steinert and Henrike Sternberg. 2024. "Assessment of and Intention to Use Electronic Health/Patient Records in European Countries." AEA RCT Registry. June 24.
Experimental Details


Preliminary research, including several pilot exploratory studies in Germany pre- and post-pandemic, suggested that there is widespread awareness of some of the possible downsides and risks (esp. concerning privacy and cybersecurity) but rather limited awareness of the benefits and promise of electronic health records. We therefore provide participants with information about some of those benefits as experimental treatments. Specifically, we split our sample into 3 treatment groups and 1 control group. The control group gets just enough basic information to allow the participants assigned to this group to answer questions about electronic health records even if they have never heard or thought about EHRs previously. Each treatment combines the first couple of sentences from the control group's introductory text with a 3-7 line vignette (preceded by a bold heading), which highlights one of the following uses/benefits of having such electronic records:

(i) improved medical treatment in case of accident/emergency
(ii) improved medical care, lower risks/fewer errors for the survey participant
(iii) early detection of population health risks; advances in medical research

The three treatments were selected based on theoretical reasoning leading us to want to examine systematically the distinction between private interests (urgently in treatments i; more broadly in treatment ii) and the public interest (treatments iii), as well as findings from the March 2023 pilot study that none of these benefits are considered widely known (i.e., they should be usable as information treatments). To the extent that the vignettes provide new information to the survey subjects, they also allow us to test more broadly to what extent individual assessments of, e.g., the cost-benefit balance of a digital health technology solutions, is responsive to factual information.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
To gauge the effect of the treatments on respondents' preferences for getting an EHR, our primary outcome is the answer to the question: "If you were offered to have an electronic health or patient record (EHR) set up for you with excellent data security and privacy protection features (minimal effort required; all costs paid by your health insurance), would you agree to have an EHR set up for you?" (6 response options, from "definitely no" to "definitely yes").

To gauge the effect of the treatments on respondents' intended use of an EHR, our primary outcome will be the answer to the question: "If an electronic patient record were set up for you (by default), how frequently would you expect to make use it, for instance, by having your doctor(s) consult information in the record and enter information about your visits, having other health service providers (such as a physical therapist) review or add information, or having your pharmacist record the prescriptions filled for you? (7 response options, from "virtually never" to "virtually always"; mid-point anchor-label "every other time"). For both of these outcome variables, if the information treatments work as expected, we would expect higher support for getting an EHR set up and a higher expected level of intended usage among the treated. Since we would generally assume respondents to be more self-interested than altruistic, this effect should be strongest for the first treatment (immediacy of the personal/private medical care benefit), a bit weaker for the more diffuse personal/private medical care benefit highlighted in the second treatment, and weakest for the third, public interest-focused treatment.

We also ask respondents to tell us their overall assessment. An additional outcome therefore consists of the answers to the question: "On balance, do you think the benefits/upside potential exceed the costs/downside risks? Please consider pros and cons both for you personally and for society (if any)." (horizontal slider from –10 (labeled "downside risks exceed potential benefits") to +10 ("potential benefits exceed down") with the midpoint at 0 labeled "potential benefits approximately equal downside risks"). If the treatments work as expected, the average score on this overall assessment should be higher for those in the treated group (with similar differences among the treatments as for the primary outcome).
Primary Outcomes (explanation)
Getting an electronic patient record set up is one thing; using it is another. Asking separately about willingness to accept an EHR (having it set up for you at no cost) and about intended use is intended to prompt respondents to recognize and think about the difference (if they perceive any).

Secondary Outcomes

Secondary Outcomes (end points)
Several additional (supplemental) outcomes are possible:
(1) We ask respondents to elaborate on their reasoning in a write-in field, preceded by the open-ended question/request: "In a few sentences, please explain your overall assessment of electronic patient records, including pros and cons:" This question is designed, above all, to allow us to gain supplemental insights into the causal mechanisms underpinning different responses to the overall assessment. In addition, however, replies may yield additional, supplemental outcome measures. For this purpose, we will look for patterns that, if the treatments work as expected, should indicate greater weight being put on benefits or "pros" among individuals in the treatment groups than in the control group.
(2) in the current survey, the information treatment-based experiment is combined with a further experiment that examines respondents' willingness to opt in or out of up to 13 optional EHR components/functions, some of which are closely related to the treatments in this acceptance & use-intent experiment. The primary purpose of that experiment is to ascertain to what extent citizens perceive a difference between an EHR system where the default is to have all the functions turned off and patients have the opportunity to opt in (=turn the functions on), versus an EHR system where the default is to have all the functions turned on and patients have the opportunity to opt out (=turn the functions off). Irrespective of the opt-in/opt-out difference, we would generally expect the stated intent to opt in to be higher in the system where the functions are by default turned off (or the stated intent to opt out to be lower where they are by default turn on). We should expect the clearest differences where the functions are clearly related to the treatments. For instance, one such function is to allow emergency medical personnel in case of an accident or medical emergency to access the information in the person's EHR. We would expect respondents' propensity to turn this function on to be higher among those who received the first treatment.
(3) Further secondary outcomes will be the amount of time spent on the response to the overall pros-and-cons question and/or the willingness to elaborate on the respondent's rationale for that overall assessment.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Poor information as an explanation for low levels of technology acceptance is generally frowned upon in much of the social science literature, often to the point of calling into question whether citizens/users' acceptance or refusal/rejection, concerns or skepticisms vis-a-vis technological changes is at all based on a rational assessment of the cost/downside risks and the (expected) benefits. Systematic analyses, however, are comparatively rare, and seem particularly warranted when the information environment is so skewed in favor of downside risks.

Our simple individual-level RCT is designed to test three information treatments (which pilots had shown to be little known among the general population), with random assignment to the treatment arms to allow for causal identification. The experiment is conducted among samples recruited by the survey research firm Respondi/Bilendi such that the sample for each country is representative of the country’s population with respect to gender, age, education, and subnational region.

Just below the information treatment vignette (for respondents in any of the three treatment groups), but before they click "next" to reach the first screen with outcome variables, respondents are asked how well-known they believe the stated benefit of EHRs is among their family members and friends. This question is intended to nudge respondents to engage the substantive contents of the information treatment and seeks to gauge to what extent respondents believe to have already known the information in the vignette, which presumably is inversely correlated with the extent to which the vignette provides new information and thus may be expected to "work" as an information treatment. Answers may therefore also be used in conditional estimates of the treatment effect.

A number of variables drawn from other parts of the survey (such as general attitudes toward technological change or importance of medical care due to, e.g., chronic illness; see abstract, above) will allow us to examine other possible alternative explanations or conditional effects in heterogeneity analyses or robustness checks.
Experimental Design Details
Not available
Randomization Method
Random assignment to one of the 3 treatment groups or the control group, separately within each country, using the Randomizer function of survey software Qualtrics
Randomization Unit
Individual respondent (each respondent answers for him/herself)
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
4000 individuals per country (DE, IT, UK; 1000 for NL)
Sample size: planned number of observations
4000 individuals per country (DE, IT, UK; 1000 for NL)
Sample size (or number of clusters) by treatment arms
ca. 1000 individuals per treatment arm within each of DE, IT, UK (ca. 250 for NL)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We performed power calculations for a standardized effect size (standardized so that the effect size is expressed in terms of a normal distribution with mean 0 and standard deviation 1). Using the Optimal Design software, we calculated that, with 1,000 respondents per treatment arm, we will be able to detect a standardized effect size of at least 0.18 with 80% statistical power and an alpha of 0.05.

Institutional Review Boards (IRBs)

IRB Name
IRB of the German Association for Experimental Economic Research (GfeW)
IRB Approval Date
IRB Approval Number