Public perception of the use of clinical decision support tools within the NHS for rare disease case finding

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A public perception survey (n = 254), promoted primarily through social media, was used to gauge public sentiment towards the use of clinical decision support tools, specifically using a rare disease case finding software (MendelScan) as an example. This research aimed to build upon previous findings regarding public opinion on health data use. The sample was broadly representative of the UK population, although with a higher proportion of females and individuals with undergraduate or postgraduate education. Respondents positively endorsed the use of health data to improve healthcare and the use of technology to identify rare disease patients both by the NHS in general (92.8%) and their own General Practitioner (GP) or hospital (88.8%). More than 90% of respondents anticipated feeling grateful, and nearly 85% expected to feel comforted, if they were contacted by their GP or hospital about a potential rare disease, even if they had not been asked to explicitly consent in advance. Analysis of responses to open questions supported these findings, with respondents predicting that they would feel relieved, hopeful and grateful if technologies were used on their electronic health records to identify rare diseases. While respondents indicated likely permission for such data use and no anticipated opt out, they strongly desired to be informed about the use of their health data. Concerns primarily revolved around the accuracy of the technology, the knowledge that a medical device created by a private company had access to their health data, and the privacy and security of their health data. Anxieties were expressed about the NHS’s capacity to implement such technology due to perceived inadequacies in its current infrastructure. The findings of this survey demonstrate there's a clear willingness among respondents to allow their data to be used for improving healthcare and identifying rare diseases, particularly when accompanied by transparent communication and the option to opt out. Addressing specific concerns regarding technology use - particularly related to accuracy, privacy, the emotional impact of a potential diagnosis and NHS infrastructure readiness - highlights areas for ongoing research and development to foster public trust and effective implementation. Health sciences/Health care/Diagnosis Health sciences/Health care/Health policy Health sciences/Health care/Health services Health sciences/Health care/Medical ethics Physical sciences/Engineering/Biomedical engineering Physical sciences/Mathematics and computing/Software Rare diseases Diagnostic odyssey Early diagnosis Decision support technology Public perception and opinion Figures Figure 1 Figure 2 Background Rare diseases, though individually uncommon, collectively affect a significant portion of the population, impacting approximately 1 in 17 people in their lifetime [1]. These conditions are often characterised by complex, multisystemic and progressive symptoms, making diagnosis a challenging process. Clinicians must integrate diverse information against a rapidly evolving scientific understanding of over 7,000 identified rare diseases [2]. A rare disease is typically defined as a condition affecting fewer than 1 in 2,000 people in the European Union [3] and fewer than 200,000 people in the United States [4]. Difficult-to-diagnose diseases refer to conditions where standard diagnostic procedures fail to identify a cause, often leading to prolonged diagnostic uncertainty due to overlapping symptoms or limited understanding within the medical community [5]. The road to diagnosis is often long for people with a rare disease, taking approximately 4.7 years [6] and involving consultations with more than five physicians. During this period, an estimated 41% of people receive at least one misdiagnosis [7]. This diagnostic uncertainty can lead to depression, anxiety and feelings of hopelessness which impact all aspects of patients' lives including their emotional, inter-personal and occupational functioning [8, 9]. Financially, receiving a late diagnosis is costly, with undiagnosed rare disease patients costing the healthcare system, on average, up to twice that of non-rare disease patients. In the UK, figures estimate this to be £13,064 compared to £5,910 per patient over a 10-year period, equating to an additional £340m spent each year for the National Health Service (NHS) [10]. Conversely, early rare disease diagnosis is life-changing for patients as it opens up new options for treatments, clinical trials, care and support, all of which can improve patients’ health and wellbeing by reducing the psychological and financial burden of diagnostic uncertainty and minimising the likelihood of their symptoms worsening. In the UK, it is standard practice for routine encounters with healthcare professionals to be documented on a patient’s electronic health records (EHRs). Recent advancements in data modelling have created new opportunities to apply technology to patient data to identify patterns and trends that are challenging for individual clinicians to discern, especially in the current climate of pressured healthcare systems where only around half (52%) of patients now regularly see the same General Practitioner (GP) [11]. Within this context, AI-powered tools hold significant promise for enhancing diagnostic efficiency. One such tool, MendelScan, is a MHRA-registered Class 1 Medical Device developed by the London-based MedTech company Mendelian. It functions as a clinical decision support tool focused on shortening the diagnostic odyssey of rare and difficult-to-diagnose diseases. MendelScan uses “case finding” algorithms to interrogate EHRs and identify those that contain patterns of specific rare diseases. Patients that may meet the criteria for a suspected disease are flagged and a report is sent to their healthcare provider for further review and a potentially expedited diagnosis. MendelScan is deployed in primary care, a key bottleneck where rare disease cases are often missed or misidentified. Mendelian reports that MendelScan has been used to deploy approximately 40 validated rare disease case finding algorithms in various NHS primary care pilots. They also report that a single algorithm is currently being applied to over 10 million patient records, a sixth of the UK population. The MendelScan technology was awarded as one of the winners of the Artificial Intelligence in Health and Care Award in 2023, leading to funding from the UK’s Department of Health and Social Care for a significant research project to evaluate its potential impact within the NHS. The research presented in this manuscript was conducted as part of this AI Award Project. A substantial body of evidence indicates that public sentiment towards the sharing of healthcare data for clinical care and secondary purposes (e.g., research and planning) is generally supportive, provided that appropriate safeguards, transparency, and clear communication are in place. Studies in the UK and internationally consistently show public willingness to share health data when convinced of the benefits to patient care and medical advancement, and when issues of privacy, security, and control are adequately addressed [12]. For instance, research on patient attitudes towards data sharing in the context of rare diseases specifically highlights the importance of transparent data governance and patient involvement [13]. Despite this general support, there remains a degree of public distrust, particularly concerning the use of healthcare data for purposes beyond direct patient care. A notable example in the UK was a government initiative to create a centralised pseudonymised database of coded patient healthcare data from primary care records for research, which led to a significant increase in patients choosing to "opt out" due to concerns about openness and transparency of data use [14]. This survey aimed to build on findings from previous published surveys gauging public opinion of the use of health data for research in the UK [12, 13, 15, 16, 17]. Methods Survey The public perception survey was iteratively co-designed through a collaboration between Mendelian, Health Innovation East and the Mendelian Patient and Public Involvement (PPI) group. This collaborative approach aimed to ensure that the survey was accessible to a wide audience, easily understandable and gathering valuable public insights. The data has been collected anonymously using the Zoho© survey platform. A copy of the survey questions can be found in Additional File 1. To maximise participation and comfort, respondents were given the option "prefer not to say" for all requested demographic information. The survey first assessed respondents' familiarity with rare and difficult-to-diagnose diseases and their general opinions on health data sharing. Subsequently, it delved into views on the use of technology for identifying people with possible rare and difficult-to-diagnose diseases, both by the NHS broadly and specifically within their own GP practice or hospital. Respondents were also asked about their anticipated emotional reaction to being identified with a potential rare disease. Finally, the survey gathered specific views on Mendelian’s MendelScan software, as a case study for understanding perceptions of such technological tools in healthcare. The study was approved by the internal ethical review at Health Innovation East. All data collection and usage adhered to strict data governance, complying with GDPR law in the UK. All survey respondents provided informed consent. Recruitment The survey was open for responses from September 2023 to January 2024 and was promoted virtually through social media including LinkedIn and Facebook, the National Health Innovation Network newsletter and the Health Innovation East newsletter. Community groups and stakeholders with an interest in rare diseases were asked to promote the survey within their own networks. The inclusion criteria targeted people over 18 years of age who were able to consent. The survey was directed at the UK population, but we cannot exclude the possibility that some participants may have been from other countries. Data analysis Closed questions Statistical analysis was conducted with R 4.3.2 data analysis and graphical software. Due to the categorical nature of the data non-parametric inferential statistics were used. Descriptive statistics were presented as frequencies and reported as percentages to account for the variable sample size for each question. The relationship between variables was evaluated using Spearman’s Rank. Spearman’s Rank correlation assesses associations between variables, and whether this difference could have occurred by chance. However, it is important to note that it does not indicate causality even if a relationship is found. Between group differences were determined using the Kruskal-Wallis H test with post-hoc analysis using the Dunn’s test where significant main effects were found, and the paired Wilcoxon Signed Rank test was used for within group comparisons. The significance threshold for rejecting the null hypothesis is set at p < 0.05 and statistics reported to two decimal places for all. Open questions Survey responses to the three open questions were imported into NVivo software to facilitate thematic analysis. The three open questions invited respondents to share, in their own words, emotions, concerns or any other comments relating to the MendelScan software specifically (Additional File 1). An initial set of codes was developed inductively through familiarisation with the respondents’ full text responses, and deductively, informed by the quantitative analysis of the closed questions. Codes were then grouped semantically into overarching themes. Sample A total of 254 responses were received, of which 190 surveys were completed in full and 64 were partially completed. Overall completion rate for incomplete questionnaires ranged from 12% to 61%. For each individual question completion rate declined with position in the survey (range = 0% - 87.5% complete), with those nearer the end having lower completion rates. Questions about the MendelScan software, which were the last questions in the survey, were not completed in any of the partially completed questionnaires. Respondent demographics compared to national demographic information for the UK population as a whole obtained from the Office of National Statistics are presented in Table 1. There was no significant difference between the demographics of those who completed the survey compared to those who did not, consequently, all available data was included in the analysis. Overall, the demographic profile of survey respondents was similar to the UK population as a whole, however as a group they were more likely to be female (respondents = 72.8%, UK population = 51%) and to be educated to undergraduate level or higher (respondents = 51.6%. UK population = 33.8%). The majority of respondents became aware of the survey as a result of the social media campaign (74.2%) with only 12.1% being affiliated with either Mendelian (1.1%) or a rare disease group (11.0%). Respondents were evenly split between those who felt they knew more than most people about rare diseases (55.7%) or difficult-to-diagnose diseases (56.5%), and those who didn’t. <> Table 1: Distribution (%) of respondents Distribution (%) in general population + Likelihood of opting out of the use of their health data for research and planning Age X 2 (8, N=254) = 10.86, p = ns 18-24 8.7 7.9% 2 [Unlikely] 25-34 8.7 17.5% 2 [Unlikely] 35-44 14.2 17.3% 1 [Very unlikely] 45-54 15.4 16.2% 2 [Unlikely] 55-64 19.7 16.7% 2 [Unlikely] 65-74 22.4 12.5% 2 [Unlikely] 75-84 7.9 8.7% 1 [Very unlikely] 84+ 0.8 3.2% 4 [Likely] Prefer not to say 2.4 Not available 5 [Very likely] Gender X 2 (8, N=254) = 10.37, p = 0.015 Female 72.8% 51%* 2 [Unlikely] Male 20.5% 49%* 1 [Very unlikely] Identify as a gender other than their sex registered at birth 3.1% 0.5% 2 [Likely] Prefer not to say 3.5% Data not available 4 [Likely]* Ethnicity X 2 (4, N=254) = 30.27, p < 0.001 White 87.8% 81.7% 2 [Unlikely] Asian/Asian British 2.0% 9.3% 3 [Neither unlikely nor likely] Black/Black British/African/Caribbean 4.3% 4.0% 1 [Very unlikely] Mixed/Multiple ethnic groups 1.6% 2.9% 1.5 [Unlikely] Prefer not to say 4.3% Data not available 4 [Likely]* Education X 2 (6, N=254) = 11.36, p = ns No formal education 3.9% 18.2%* 2 [Unlikely] Secondary (GCSE or equivalent) 9.4% 23%* 2 [Unlikely] A-levels (Highers or equivalent) 15.4% 16.9% 1.5 [Unlikely] Vocational 15.7% 5.3%* 1 [Very unlikely] Undergraduate 17.7% 33.8% (data reported as a combined figure)* 2 [Unlikely] Postgraduate/professional 33.9% 1 [Very unlikely] Prefer not to say 3.9% Data not available 3.5 [Likely] Sexuality X 2 (4, N=254) = 153.14, p = ns Heterosexual (Straight) 76.4% 93.5% 2 [Unlikely] Homosexual (Gay or lesbian) 8.3% 1.8% 1 [Very unlikely] Bisexual 4.7% 1.5% 2 [Unlikely] Other 2.4% 0.6% 3 [Neither unlikely nor likely] Prefer not to say 8.3% Data not available 2.5 [Neither unlikely nor likely] + Statistics for general population taken from Office of National Statistics; *statistically significant difference between respondents and UK population, p<0.05 Table 1 Legend: Demographic characteristics of all respondents who completed the Public Perception Survey, situated against national demographic statistics for the UK. Details included about the likelihood of respondents “opting-out” of their data being included for use in rare disease case finding broken down by demographic characteristic. Results Acceptability of the use of health data for research and planning Across all respondents, the majority positively endorsed the use of health data to improve healthcare (83.5%) and the use of technology to identify rare disease patients both by the NHS as a whole (92.8%) and their own GP practice or hospital (88.8%). When considering the inclusion of their own health data for research and planning, most respondents anticipated giving permission to both their GP practice or hospital (82.2%) and the NHS as a whole (81.1%) with only a minority saying they would opt out of the use of their data (12.2% [own GP practice or hospital], 15.3% [NHS]). When considering the use of technology to identify undiagnosed rare disease patients the majority of respondents wished to be informed whether this was being done by the NHS (88.5%) or their own GP practice or hospital (84.5%) (Figure 1). <> Factors which influenced responding Responding was influenced by respondents’ own self-declared knowledge of rare or difficult-to-diagnose diseases. Respondents who felt they knew more about rare diseases also felt they knew more about difficult-to-diagnose diseases as reflected by the non-significant difference between responses on these two items of the survey (Z = 0.59, p = 0.553). Respondents with higher self-declared knowledge of either rare diseases (RD) or difficult-to-diagnose diseases (DDD) gave a more positive endorsement for the use of technology to identify rare disease patients by the NHS (RD: r(188) = 0.16, p < 0.028; DDD: r(188) = 0.17, p < 0.018 ) and by their own GP practice or hospital (RD: r(188) = 0.15, p < 0.04, DDD: r(188) = 0.14, p <=0.05) than those without. Self-declared knowledge of rare diseases or difficult-to-diagnose diseases was not related to their endorsement of the sharing of health data to improve healthcare by either the NHS or their own GP practice or hospital. Opinions on using technology to identify rare diseases did not differ depending on whether it was the NHS as a whole or their own GP practice or hospital who were using their health data (Z = 1.56, p = 0.12), (see Figure 1). Similarly, on the whole demographic factors did not influence respondents’ anticipated likelihood of opting out of the use of their health data. Analysis using the Kruskall-Wallis test found no significant difference across age groups, (X 2 (8, N=254) = 10.86, p = 0.21), nor educational levels (X 2 (6, N=254) = 11.36, p = 0.078). A significant group difference for gender (X 2 (8, N=254) = 10.37, p = 0.015) was driven by a greater likelihood of opting out by those who preferred not to provide their gender (Median = 2 [“Likely”]; all other groups median = 5 [“Very unlikely”], p < 0.05). Similarly, a significant group difference for ethnicity (X 2 (4, N=254) = 30.27, p < 0.001 ) was driven by a greater likelihood of opting out by those who preferred not to provide their ethnicity (Median = 2 [“likely”]; all other groups median = 4 [“Unlikely”] or 5[“Very unlikely”], p < 0.05). (see Table 1 for statistics). Feelings about being contacted about a potential rare disease More than half of respondents (57.4%) anticipated being worried if they were contacted about a potential rare disease diagnosis by their GP practice or hospital. However, only a minority (22.6%) anticipated that they would be concerned that their electronic health records had been evaluated for a rare disease without their consent with most respondents anticipating feeling grateful (90.2%) and comforted (84.3%). Patients preferred remote forms of communication (83.1%) that did not require in-person visits to the GP practice or hospital to notify them that a rare disease case finding software was being applied to their health records. However, there was a clear preference for direct contact e.g. text, email or letter, rather than indirect contact e.g. inclusion on a website where the emphasis is on the patient to check (87.83%). Views on the use of specific technology e.g., MendelScan The use of technology in general [median response: ”Strongly agree”] and MendelScan specifically [median response: ”Agree”], was positively endorsed by respondents regardless of whom it was being used by. Overall, respondents positively endorse the use of MendelScan by both the NHS (77.4%) and by their own GP practice or hospital (76.9%) with only 12.6% of respondents indicated that they would opt out of the use of data for research and planning purpose knowing that MendelScan was used. Importantly, 88.4% indicated that they would like to be informed by their GP practice or hospital that their health data was being scanned. However, public sentiment was more positive towards technology as a whole than towards MendelScan specifically (NHS: Z = 5.73, p < 0.001; GP practice or hospital: Z = 5.99, p < 0.001), with respondents more likely to opt out (Z = 1.94, p = 0.05) and less likely to give permission (Z = 2.26, p = 0.024) to the use of MendelScan on their health data than technology in general. Based on the percentage of positive endorsements (either Likely or Very likely), worries about the use of MendelScan were ranked as: How accurate the technology is (56.3%) A medical device developed by a private company has access to my health data (46.8%) The privacy and security of my health data (45.8%) If I don’t have a rare disease, yet MendelScan incorrectly identified me as possibly having one, I may end up having unnecessary tests or referrals (42.1%) The emotional distress or disruption to my life if I was identified as possibly having an undiagnosed rare disease (37.4%) On average 25% of respondents on each item (ranging from 23.2% - 29.5%) had not yet formed clear opinions about their worries around the use of MendelScan (Table 2 and Figure 2). Table 2: Worries about the use of MendelScan If MendelScan was used on your health data, how likely is it that you would feel worried about the following: Very unlikely Unlikely Neither likely nor unlikely Likely Very likely Not sure The privacy and security of my health data 7.89% 20.53% 23.16% 24.21% 21.58% 2.63% How accurate the technology is 3.16% 14.21% 24.21% 35.79% 20.53% 2.11% A medical device developed by a private company has access to my health data 6.32% 14.74% 29.47% 27.37% 19.47% 2.63% The emotional distress or disruption to my life if I was identified as possibly having an undiagnosed rare disease 12.63% 22.63% 26.32% 25.79% 11.58% 1.05% If I don’t have a rare disease, yet MendelScan incorrectly identified me as possibly having one, I may end up having unnecessary tests or referrals 6.84% 23.16% 24.74% 24.21% 17.89% 3.16% <> Qualitative Analysis Of the 254 questionnaires that were at least partially complete, 53 respondents completed the open question “If you think you may feel anything other than the emotions listed above, please feel free to share your thoughts here”, 49 respondents answered the questions “If you have any other concerns about MendelScan, please feel free to share them with us”, and 43 respondents answered the question “Do you have any other comments, positive or negative, on technologies like MendelScan that you would like to share?” A preliminary analysis suggested that respondents tended to use these questions to share personal experiences and emotions, often linked to their diagnosis journey rather than answer the questions posed. Therefore, responses to all three questions were pooled and coded together to reflect respondents’ thoughts, concerns and comments about the use of rare disease case finding software, such as MendelScan. Three key themes were identified within the data: Acceptability of the technology. Consistent with the quantitative analysis, the majority of respondents positively endorsed the use of rare disease case finding software. Drawing upon their own experiences and diagnostic journeys, many expressed optimism that such technology would reduce the diagnostic odyssey for others. Some respondents shared feelings of unease at the prospect that a private company may benefit financially from access to NHS health data, and that the NHS would be charged to use the technology. Others proposed that endorsement should be contingent upon robust evidence of the efficacy and cost effectiveness of the technology. There was also some scepticism about the ability of the NHS to fully utilise the technology due to inaccuracies in the current electronic notes systems and a lack of knowledge and competency held by NHS staff. Concerns about the use of the technology. Respondents shared worries about the increased burden on the NHS infrastructure and on patients directly as a result of using rare disease case finding software. Noting the current pressures, long waiting lists and limited availability of some services, there were concerns that reliable pathways would not be in place to respond to ‘flags’ in a timely fashion and apprehension about the increased psychological burden patients would experience if they could not access the relevant ‘post-flag’ exploratory investigations quickly. Some respondents felt that patients may worry unnecessarily about their health just by the knowledge that a rare disease case finding software was being applied to the health records and suggested only notifying people of its use if they were ‘flagged’. Although, for some a lack of confidence in the NHS to protect their personal data and distrust in the intentions and accountability of private companies meant they saw a priori consent as essential. There were also concerns that the software would be used inappropriately by GPs to restrict access to expensive exploratory tests for patients who had not been flagged as they may be deemed low risk of having a rare disease. Anxieties about privacy and anonymity were also shared. Anticipated emotions in response to the technology. Many respondents anticipated feeling relieved, hopeful and grateful at the prospect of rare disease case finding software being used within the NHS. They expressed hope that it would end their own, and others, diagnostic odyssey and saw the use of the technology as an action in the respondent’s best interest. Others however, anticipated feeling shocked and surprised if they were notified that their records had been flagged. They predicted that they would worry about what the ‘post-flag’ pathway would look like and expressed the need for timely support and information sharing to reduce the psychological burden in that period. Details of the themes and subordinate themes are presented in Table 3 along with illustrative quotes. Table 3: Themes and subordinate themes emerging from the open questions on the Public Perception Survey. Theme Subtheme Description Illustrative quote Acceptability of the technology How well it works (9) The majority of respondents positively endorsed the use of technology generally, seeing it as an anticipated and welcome step. Respondents tended to use their own diagnosis journey to frame their opinions, highlighting difficulties accessing services within the NHS as justification for the need to introduce new technologies with the hope that others did not need to have the same experiences. “I have a rare disease and the diagnostic process and the time in limbo where no one could tell me what was wrong was utter hell. Anything ethical to make this easier for other patients needs to be done, my diagnostic odyssey was horrifying.” (Respondent 123) “Having been passed around the NHS for more than 6 years before a rare disease was finally identified by chance, I am all for faster diagnosis for others.” (Respondent 144) Commercial companies profiting (22) Respondents described wanting to see evidence of the efficacy and cost effectiveness of the technology before fully endorsing its use within the NHS. Some concerns were expressed that the NHS would be charged to access rare disease case finding software and that a private company would be profiting from access to health data. “Privatisation of the health service does not have a good history especially in relation to IT initiatives. I would want to know this was a validated and reliable programme that was cost effective”. (Respondent 130) “In the ideal world the use of AI should be positive in helping identify the many people who are diagnosed too late BUT the data of NHS patients is extremely valuable to private companies who can use it to refine their products and subsequently make millions or billions from it. This is a major concern.” (Respondent 131) “I would like to think that the access to data should be set against the costs of using the technology.” (Respondent 4) NHS unequipped to use it (37 There was scepticism about the ability of the NHS to utilise the technology to its full benefit due to inaccuracies in the current electronic notes systems and a lack of knowledge and competency held by NHS staff. “ The concept is overly reliant on GPs/clinicians accurately recording their patient’s health data 100% of the time, which is extremely flawed given the reality .” (Respondent 220) Concerns about the use of technology Inadequacy of current NHS infrastructure (19) Worries about the capacity of existing NHS services to respond to “flags” in a timely manner were raised and the potential for additional psychological distress as a consequence discussed. “Like with newborn screening - there would need to be a clear, and timely pathway to investigate potential diagnoses and exclude 'false positive' results. There is a lot of potential for increasing worry without subsequent reassurance without these pathways being established.” (Respondent 57) Fears were expressed that clinicians would use the rare disease case finding software to guide their decision-making over who did, and did not, receive further exploratory tests for unexplained symptoms. “ Doctors will use this technology to gatekeep diagnoses and treatment from patients who’s rare disease isn’t flagged by the technology. ” (Respondent 220) A lack of confidence in the ability of the NHS to protect personal data and maintain privacy and anonymity was shared. “The NHS treats data about patients as if they own it. Unfortunately, therefore the NHS can’t be trusted to provide patient data to companies in a responsible manner (i.e. with specific consent of the data owner), so it would be difficult for any company to gain my trust on use of my data for providing enhanced diagnostics until the NHS gets its act together on protection of personal data.” (Respondent 24) Potential to cause harm (13) Respondents felt that knowing a rare disease case finding software was being applied may trigger unnecessary health worries for some people and highlighted the need for careful and considered language choices in all patient facing correspondence. “People are individuals. Sticking an algorithm onto people’s health has huge implications. Nocebo language can impact greatly to people’s perspective on their health. You can seriously reduce someone’s mental and physical wellbeing depending upon the language used daily. More needs to be considered before systems such as this can be used.” (Respondent 237) Knowing that the rare disease case finding software was owned by a private company made some respondents fear they would lose control of their personal data worrying that their data may be sold on without their knowledge or used by a third party. “It being a private company rather than academic or NHS project makes me concerned about the ethics and potential for abuse/misuse/reselling of health data.” (Respondent 123) “Protections against selling on the data or using it for health screenings / insurance determination are vital.” (Respondent 148) Some respondents considered that obtaining consent to deploy rare disease case finding software may cause unnecessary worry for the majority of people who were not “flagged” whereas others unequivocally felt that a priori consent should always be obtained. “For me, even to be told retrospectively about it being used would be okay but I understand how others may see it differently.” (Respondent 226) “It’s MY health, and MY data. If the NHS wants to make use for research I want to know what research and by who, otherwise it is NOT informed consent. If the NHS wants to use my data to look for health issues I might have, I want to know and have the option of consenting or not.” (Respondent 24) Anticipated emotions in response to the technology Negative (7) Some anticipated feeling shocked, surprised and worried about what would happen next if they were contacted and highlighted the need for support and information to be made available quickly afterwards. “if contacted with a possibility then you would feel quite anxious and want to know what the next steps were, how soon, and any possible outcomes. I would want the process to happen quickly as uncertainty is quite draining, I would also want to know what steps of self-care might be necessary.” (Respondent 166) Positive (11) Respondents described feeling like the technology was acting in their own and others best interests to reduce the diagnostic odyssey experienced by many. They anticipated feeling relieved, hopeful and grateful. “its similar to when a bank contacts you or blocks a payment, I'd be glad they are looking out for you, especially if there is some possibility of improving life quality”. (Respondent 252) Discussion The online survey, completed by a sample broadly representative of NHS users (including individuals with and without personal connections to the rare disease community), found strong positive endorsement for using rare disease case finding technology within the NHS. There was, however, a clear mandate calling for transparency around its use and for patients to be actively informed. Sentiment did not change based on whether the technology was used by the NHS overall, patients’ GPs or their own hospital, nor was it particularly influenced by demographic factors. Qualitative analysis of the free response questions identified views on the technology’s acceptability and concerns, along with the anticipated emotional response. In line with the quantitative data, respondents generally positively endorsed rare disease case finding, expecting to feel relieved, hopeful and grateful if this technology was used on their health data. However, worries about the readiness of the NHS to implement such technology were shared, along with concerns about the potential impact on existing NHS infrastructures due to the increased demand for services. Respondents also expressed discomfort about private companies potentially profiting at the expense of the NHS from the use of this technology. They also described their distrust in the NHS ability to safeguard personal data and fears that clinicians might use the technology to justify denying people access to future explorative tests. The results of the study clearly demonstrate that people support the sharing of health data for the purpose of identifying undiagnosed rare diseases, which is consistent with the findings from recent research by the Health Foundation [ 18 ] and NHS England [ 19 ] which have also shown similar positive attitudes toward the use of digital technologies and data sharing in general. Given the complex public perception of rare diseases and the frequent stigmatisation of affected individuals and their families, it could not be assumed that general public support for data sharing would automatically extend to the specific context of rare disease case finding. The current study demonstrates that it does, at least within this representative sample. Furthermore, concerns that were raised in the current study about the appropriate use and effective safeguarding of data by the NHS, along with worries about the potential for third parties to profit from NHS data are all consistent with issues identified in other research [ 19 , 20 ]. Did the survey reasonably capture the relevant views of the public on this issue? People who completed the survey were demographically similar to the UK population as a whole, although there was an over representation of female participants and those educated to undergraduate level or beyond. It is a widely observed phenomenon that women and more highly educated people are more likely to respond to surveys [ 21 ]; acknowledging this overrepresentation is crucial for the generalisability of these findings. This is especially important given that rare diseases are associated with medical, financial and psychological challenges that can, in themselves, limit people’s educational potential [ 22 ]. The survey achieved an even representation of views from people who did and did not have prior familiarity with rare or difficult-to-diagnose diseases. This even split underscores the significance of validating positive endorsement beyond the immediate rare disease community. While support from within this community was anticipated, securing acceptance among the general population is equally crucial for ensuring widespread adoption and successful implementation of such technology within the healthcare system. Which features influenced people's attitudes and why? The study observed that people with prior knowledge of rare or difficult-to-diagnose diseases were more likely to support health data sharing, but only when they knew the data would be specifically used to identify rare diseases. This finding aligns with established literature emphasising the importance of perceived direct benefit as key drivers of public willingness to share sensitive health data [ 12 ]. Previous research consistently demonstrates that transparency about how data will be used and the value it generates is paramount for fostering public acceptance [ 23 ]. These findings specifically highlight that individuals become more amenable when the data purpose directly addresses a significant challenge, such as shortening the diagnostic odyssey for rare diseases. This isn't merely about abstract societal good; it's about connecting data use to a tangible, life-changing personal impact. For those already familiar with the complexities of rare conditions, this specific benefit resonated profoundly, amplifying their support for health data sharing. This suggests that communication strategies for new health technologies should articulate the specific, relatable outcomes for patients and their families. Findings highlight a complex interplay between demographic factors and willingness to share health data for research and planning. While age and education level showed no significant impact on opt out rates, the choice to disclose one's gender and ethnicity emerged as influential factors. This suggests that a general reluctance to share personal information, rather than gender or ethnicity specifically, is a key predictor of opting out. This aligns with the notion that individuals who are hesitant to provide even basic demographic details might be understandably more cautious about sharing their sensitive health data. Overall, these findings underscore the importance of addressing potential concerns when seeking to engage individuals in health data sharing initiatives. Building trust and transparency, particularly among groups that may be more hesitant, will be crucial for ensuring equitable participation and maximising the potential benefits of such endeavours. What learnings are there for data sharing and consent? Results from the survey indicate that respondents overwhelmingly endorse the use of their health data for the purpose of identifying patients with potentially undiagnosed rare diseases. The findings that respondents' opinions on the use of their health data for rare disease identification did not differ based on whether it was the NHS, their GP, or their hospital using the data contrast with some previous findings. In Jones et al. (2022) [ 15 ], for example, there was a clear preference for data being used by one's own GP compared to other entities. The current study suggests a lack of such distinction, implying a broader acceptance of data use regardless of the specific entity within the NHS. This could indicate a growing trust in the NHS as a whole to handle health data responsibly, or a decreased concern about data sharing within the healthcare system for specific high value applications, in this case when a clear and compelling benefit like rare disease diagnosis is presented. Respondents wished to be informed if their healthcare data was included in such a project and anticipated providing consent (if asked) and did not envisage pursuing options to “opt out”. However, the qualitative data presented a more mixed picture regarding the need to ask for explicit consent. Some respondents felt strongly that consent was an essential pre-requisite whilst others felt that asking permission in advance would add, unnecessarily, to the worry of those who had included their health data. This tension between the desire for information and the potential for "consent fatigue" or undue distress aligns with broader discussions in public health about dynamic consent models [ 24 , 25 ]. This survey results are also consistent with the work by Atkins et al. (2021) [ 26 ], highlighting low public awareness of the national opt out scheme in place since 2018 and general confusion regarding the routine use of data within the NHS. This underscores a persistent gap in public understanding of current data governance mechanisms. Overall, respondents anticipated feeling hopeful, grateful and relieved that tools like MendelScan were being used but concerns were shared about the security of the data and the ability and intention of both the NHS and private companies to maintain anonymity and confidentiality of patients’ healthcare data. These anxieties are widely reported in the literature on health data sharing, where trust in data custodians and the perceived trustworthiness of commercial involvement are consistently identified as critical factors influencing public acceptance [ 27 ]. There was also consideration of the psychological impact for patients who were “flagged” during the screening and scepticism about the ability of the current NHS infrastructure to manage the additional investigations required for those patients identified, in a timely manner. These infrastructure concerns add a unique dimension to the public perception of case finding in healthcare, extending beyond data governance to the practical realities of implementation within a stretched health system. What are the strengths and limitations of this study? Strengths of this study include its collaborative design, the continuation of existing research and the provision of actionable insights. The involvement of a Patient and Public Involvement (PPI) group in co-designing the survey ensured its accessibility and relevance to the target audience and likely contributed to increased response rates and data quality. The study builds upon and offers nuanced comparisons with prior work, contributing to a broader understanding of public perceptions of health data usage within the context of the NHS. At the same time, it focuses on rare diseases, an often-overlooked area of healthcare. The findings provide valuable information for healthcare organisations and MedTech companies highlighting the importance of transparency, targeted communication, and addressing concerns around privacy and data security to foster public trust. It is acknowledged however, that there was an under-representation of participants from certain communities in this study which is a limitation of the work and likely to impact generalisability. Future work to understand the barriers to participation would help ensure that all communities are enabled to share their perspective. Conclusion The survey results offer several crucial insights into public perception and acceptance of health data usage for rare disease identification. First and foremost, there is a clear and strong willingness among respondents to allow their health data to be used for improving healthcare and identifying rare diseases. This positive sentiment is particularly evident when accompanied by transparent communication about data use and the option to opt out. This positive endorsement is further amplified among individuals with greater knowledge of rare and difficult-to-diagnose conditions, underscoring the importance of public education and awareness campaigns in fostering acceptance for such initiatives. The study revealed a lack of distinction in respondents' opinions based on whether the data was used by the NHS centrally or their local healthcare providers, suggesting a growing trust in the healthcare system as a whole to manage data responsibly for public good. However, the consistent desire to be informed about data usage, regardless of the entity involved, highlights the importance of maintaining open communication channels, ensuring patient autonomy and raising awareness of existing data use policies and national opt out scheme. The specific concerns raised regarding the use of technology, exemplified by MendelScan, primarily revolved around accuracy, privacy, potential for unnecessary tests, and the emotional impact of a possible rare disease diagnosis. This emphasises the need for continued research and development to enhance the accuracy and reliability of such tools, coupled with robust data protection measures and clear pathways to address the emotional and medical / practical implications of a potential diagnosis. While acknowledging certain concerns that must be actively addressed through responsible implementation and ongoing dialogue, the findings suggest broad public support for health data initiatives aimed at rare disease identification when implemented transparently and responsibly. This willingness presents a significant opportunity to leverage technology to improve patient outcomes in this challenging area of healthcare. Declarations Competing Interests PF, EM, JS, MH are all employees of Mendelian. JF, SR are employees of Health Innovation East who were commissioned by Mendelian to conduct the survey and complete the analysis. SM worked as an associate for Health Innovation East to complete the analysis and write-up. All the remaining authors declare no conflict of interest. Author Contribution SM – data analysis, interpretation, manuscript drafting, reviewing and approval of manuscript.JF – oversight of the data analysis and interpretation, reviewing and approval of manuscript.SR - oversight of the data analysis and interpretation, reviewing and approval of manuscript.PF - oversight of the data analysis and interpretation, reviewing and approval of manuscript.EM - reviewing and approval of manuscript.JS - reviewing and approval of manuscript.HM - reviewing and approval of manuscript. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Department of Health and Social Care. The UK Rare Diseases Framework. (UK Government, London, 2021). Griggs, R.C. et al. Clinical research for rare disease: opportunities, challenges, and solutions. Mol. Genet. Metab. 96 , 20–26 (2009). Regulation (EC) No 141/2000 of the European Parliament and of the Council on orphan medicinal products of 16 December 1999. Official Journal of the European Communities L 18 , 22 January 2000. National Organization for Rare Disorders. Rare disease day: frequently asked questions. NORD https://rarediseases.org/wp-content/uploads/2019/01/RDD-FAQ-2019.pdf (2019). Undiagnosed Diseases Network Foundation. What is an undiagnosed disease. UDNF https://udnf.org/what-is-an-undiagnosed-disease/ (2025). Faye, F. et al. Time to diagnosis and determinants of diagnostic delays of people living with a rare disease: results of a Rare Barometer retrospective patient survey. Eur J Hum Genet. 32 , 1116–1126 (2024). EURORDIS-Rare Diseases Europe. The voice of 12,000 patients: Experiences and expectations of rare disease patients on diagnosis and care in Europe . (EURORDIS-Rare Diseases Europe, 2009). Willmen, T. et al. Rare diseases: why is a rapid referral to an expert center so important. BMC Health Serv Res. 23 , 904 (2023). Zhang, Z. Diagnosing rare diseases and mental well-being: a family's story. Orphanet J Rare Dis. 18 , 45 (2023). Department of Health and Social Care. England Rare Disease Action Plan 2022 . (UK Government, London, 2022). Hull, S.A., Williams, C., Schofield, P., Boomla, K., Ashworth, M. Measuring continuity of care in general practice: a comparison of two methods using routinely collected data. Br. J. Gen. Pract. 72 , e773–e779 (2022). Baines, R. et al. Patient and public willingness to share personal health data for third-party or secondary uses: systematic review. J. Med. Internet Res. 26 , e50421 (2024). Courbier, S., Dimond, R., Bros-Facer, V. Share and protect our health data: an evidence based approach to rare disease patients' perspectives on data sharing and data protection - quantitative survey and recommendations. Orphanet J. Rare Dis. 14 , 175 (2019). Tazare, J. et al. NHS national data opt-outs: trends and potential consequences for health data research. BJGP Open. 8 , BJGPO.2024.0020 (2024). Jones, L.A. et al. Public opinion on sharing data from health services for clinical and research purposes without explicit consent: an anonymous online survey in the UK. BMJ Open. 12 , e057579 (2022). Stockdale, J., Cassell, J., Ford, E. "Giving something back": a systematic review and ethical enquiry into public views on the use of patient data for research in the United Kingdom and the Republic of Ireland. Wellcome Open Res. 3 , 6 (2019). Healthwatch. How do people feel about their data being shared by the NHS. Healthwatch https://www.healthwatch.co.uk/news/2018-05-17/how-do-people-feel-about-their-data-being-shared-nhs (2018). Thornton, N., Horton, T., Hardie, T., Coxon, C. Exploring public attitudes towards the use of digital health technologies and data. The Health Foundation https://www.health.org.uk/reports-and-analysis/briefings/exploring-public-attitudes-towards-the-use-of-digital-health (2023). NHS England Digital. Public attitudes to data in the NHS and social care. NHS England Digital https://digital.nhs.uk/data-and-information/keeping-data-safe-and-benefitting-the-public/public-attitudes-to-data-in-the-nhs-and-social-care (2024). NHS Transformation Directorate. A guide to effective NHS data partnerships. The Centre for Improving Data Collaboration https://transform.england.nhs.uk/key-tools-and-info/centre-improving-data-collaboration/guide-to-effective-nhs-data-partnerships/ (2023). Green, K.E. Sociodemographic factors and mail survey response. Psychol. Mark. 13 , 171–184 (1996). Verger, S., Negre, F., Rosselló, M.R., Paz-Lourido, B. Inclusion and equity in educational services for children with rare diseases: challenges and opportunities. Child. Youth Serv. Rev. 119 , 105518 (2020). Yusuf, Z.K. et al. Building and sustaining public trust in health data sharing for musculoskeletal research: semistructured interview and focus group study. J. Med. Internet Res. 26 , e53024 (2024). Kaye, J. et al. Dynamic consent: a patient interface for twenty-first century research networks. Eur. J. Hum. Genet. 23 , 141–146 (2015). Teare, H.J.A., Prictor, M., Kaye, J. Reflections on dynamic consent in biomedical research: the story so far. Eur. J. Hum. Genet. 29 , 649–656 (2021). Atkin, C. et al. Perceptions of anonymised data use and awareness of the NHS data opt-out amongst patients, carers and healthcare staff. Res. Involv. Engagem. 7 , 40 (2021). Ghafur, S. et al. Public perceptions on data sharing: key insights from the UK and the USA. Lancet Digit. Health 2 , e444–e446 (2020). Additional Declarations Competing interest reported. PF, EM, JS, MH are all employees of Mendelian. JF, SR are employees of Health Innovation East who were commissioned by Mendelian to conduct the survey and complete the analysis. SM worked as an associate for Health Innovation East to complete the analysis and write-up. All the remaining authors declare no conflict of interest. Supplementary Files Additionalfile1SurveyQuestions.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":33600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eViews on granting access to health data and the wish to be informed. Sentiment did not change depending on if it was the NHS or respondents’ own GP practice/hospital who were using the health data. a)\u003c/em\u003e \u003cem\u003eThe likelihood of opting out did not differ (Z = 1.32, p = 0.186). b) the likelihood of granting permission did not differ (Z = 0.842 p = 0.40), c) the desire to be notified did not differ (Z = 0.19, p = 0.85).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6304847/v1/b2ba10678559ba08069c2425.png"},{"id":91815392,"identity":"afffe504-8137-4870-b607-21104d0ba6b2","added_by":"auto","created_at":"2025-09-22 06:24:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31646,"visible":true,"origin":"","legend":"\u003cp\u003eWorries about the use of MendelScan. Likelihood respondents would be worried about each item. Recorded in percentage\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6304847/v1/8074d19a108bf5ca60b3da51.png"},{"id":91816446,"identity":"9ea2be7c-13cc-4135-a660-7d14225d2ab9","added_by":"auto","created_at":"2025-09-22 06:40:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1238246,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6304847/v1/3d21dc2d-e4d6-4684-9cbf-c25bc805d571.pdf"},{"id":91815396,"identity":"a1a4426d-9ab8-440a-a6de-08ac2fa36962","added_by":"auto","created_at":"2025-09-22 06:24:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":96295,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1SurveyQuestions.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6304847/v1/fa715b2a8a3c64914b5cd107.pdf"}],"financialInterests":"Competing interest reported. PF, EM, JS, MH are all employees of Mendelian. \nJF, SR are employees of Health Innovation East who were commissioned by Mendelian to conduct the survey and complete the analysis. \nSM worked as an associate for Health Innovation East to complete the analysis and write-up. \nAll the remaining authors declare no conflict of interest.","formattedTitle":"Public perception of the use of clinical decision support tools within the NHS for rare disease case finding","fulltext":[{"header":"Background","content":"\u003cp\u003eRare diseases, though individually uncommon, collectively affect a significant portion of the population, impacting approximately 1 in 17 people in their lifetime [1]. These conditions are often characterised by complex, multisystemic and progressive symptoms, making diagnosis a challenging process. Clinicians must integrate diverse information against a rapidly evolving scientific understanding of over 7,000 identified rare diseases [2]. A rare disease is typically defined as a condition affecting fewer than 1 in 2,000 people in the European Union [3] \u0026nbsp;and fewer than 200,000 people in the United States [4]. Difficult-to-diagnose diseases refer to conditions where standard diagnostic procedures fail to identify a cause, often leading to prolonged diagnostic uncertainty due to overlapping symptoms or limited understanding within the medical community [5].\u003c/p\u003e\n\u003cp\u003eThe road to diagnosis is often long for people with a rare disease, taking approximately 4.7 years [6] and involving consultations with more than five physicians. During this period, an estimated 41% of people receive at least one misdiagnosis [7]. This diagnostic uncertainty can lead to depression, anxiety and feelings of hopelessness which impact all aspects of patients\u0026apos; lives including their emotional, inter-personal and occupational functioning [8, 9]. Financially, receiving a late diagnosis is costly, with undiagnosed rare disease patients costing the healthcare system, on average, up to twice that of non-rare disease patients. In the UK, figures estimate this to be \u0026pound;13,064 compared to \u0026pound;5,910 per patient over a 10-year period, equating to an additional \u0026pound;340m spent each year\u003csup\u003e\u0026nbsp;\u003c/sup\u003efor the National Health Service (NHS) [10]. Conversely, early rare disease diagnosis is life-changing for patients as it opens up new options for treatments, clinical trials, care and support, all of which can improve patients\u0026rsquo; health and wellbeing by reducing the psychological and financial burden of diagnostic uncertainty and minimising the likelihood of their symptoms worsening.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the UK, it is standard practice for routine encounters with healthcare professionals to be documented on a patient\u0026rsquo;s electronic health records (EHRs). Recent advancements in data modelling have created new opportunities to apply technology to patient data to identify patterns and trends that are challenging for individual clinicians to discern, especially in the current climate of pressured healthcare systems where only around half (52%) of patients now regularly see the same General Practitioner (GP) [11]. Within this context, AI-powered tools hold significant promise for enhancing diagnostic efficiency. One such tool, MendelScan, is a MHRA-registered Class 1 Medical Device developed by the London-based MedTech company Mendelian. It functions \u0026nbsp;as a clinical decision support tool focused on shortening the diagnostic odyssey of rare and difficult-to-diagnose diseases. MendelScan uses \u0026ldquo;case finding\u0026rdquo; algorithms to interrogate EHRs and identify those that contain patterns of specific rare diseases. Patients that may meet the criteria for a suspected disease are flagged and a report is sent to their healthcare provider for further review and a potentially expedited diagnosis. MendelScan is deployed in primary care, a key bottleneck where rare disease cases are often missed or misidentified. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMendelian reports that MendelScan has been used to deploy approximately 40 validated rare disease case finding algorithms in various NHS primary care pilots. They also report that a single algorithm is currently being applied to over 10 million patient records, a sixth of the UK population. The MendelScan technology was awarded as one of the winners of the Artificial Intelligence in Health and Care Award in 2023, leading to funding from the UK\u0026rsquo;s Department of Health and Social Care for a significant research project to evaluate its potential impact within the NHS. The research presented in this manuscript was conducted as part of this AI Award Project.\u003c/p\u003e\n\u003cp\u003eA substantial body of evidence indicates that public sentiment towards the sharing of healthcare data for clinical care and secondary purposes (e.g., research and planning) is generally supportive, provided that appropriate safeguards, transparency, and clear communication are in place. Studies in the UK and internationally consistently show public willingness to share health data when convinced of the benefits to patient care and medical advancement, and when issues of privacy, security, and control are adequately addressed [12]. For instance, research on patient attitudes towards data sharing in the context of rare diseases specifically highlights the importance of transparent data governance and patient involvement [13]. Despite this general support, there remains a degree of public distrust, particularly concerning the use of healthcare data for purposes beyond direct patient care. A notable example in the UK was a government initiative to create a centralised pseudonymised database of coded patient healthcare data from primary care records for research, which led to a significant increase in patients choosing to \u0026quot;opt out\u0026quot; due to concerns about openness and transparency of data use [14]. This survey aimed to build on findings from previous published surveys gauging public opinion of the use of health data for research in the UK [12, 13, 15, 16, 17].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSurvey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe public perception survey was iteratively co-designed through a collaboration between Mendelian, Health Innovation East and the Mendelian Patient and Public Involvement (PPI) group. This collaborative approach aimed to ensure that the survey was accessible to a wide audience, easily understandable and gathering valuable public insights. \u0026nbsp;The data has been collected anonymously using the Zoho\u0026copy; survey platform. A copy of the survey questions can be found in Additional File 1. To maximise participation and comfort, respondents were given the option \u0026quot;prefer not to say\u0026quot; for all requested demographic information. The survey first assessed respondents\u0026apos; \u0026nbsp;familiarity with rare and difficult-to-diagnose diseases and their general opinions on health data sharing. Subsequently, it delved into views on the use of technology for identifying people with possible rare and difficult-to-diagnose diseases, both by the NHS broadly and specifically within their own GP practice or hospital. Respondents were also asked about their anticipated emotional reaction to being identified with a potential rare disease. Finally, the survey gathered specific views on Mendelian\u0026rsquo;s MendelScan software, as a case study for understanding perceptions of such technological tools in healthcare. The study was approved by the internal ethical review at Health Innovation East. All data collection and usage adhered to strict data governance, complying with GDPR law in the UK. All survey respondents provided informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecruitment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey was open for responses from September 2023 to January 2024 and was promoted virtually through social media including LinkedIn and Facebook, the National Health Innovation Network newsletter and the Health Innovation East newsletter. Community groups and stakeholders with an interest in rare diseases were asked to promote the survey within their own networks. The inclusion criteria targeted people over 18 years of age who were able to consent. The survey was directed at the UK population, but we cannot exclude the possibility that some participants may have been from other countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e \u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClosed questions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted with R 4.3.2 data analysis and graphical software. Due to the categorical nature of the data non-parametric inferential statistics were used. Descriptive statistics were presented as frequencies and reported as percentages to account for the variable sample size for each question. The relationship between variables was evaluated using Spearman\u0026rsquo;s Rank. Spearman\u0026rsquo;s Rank correlation assesses associations between variables, and whether this difference could have occurred by chance. However, it is important to note that it does not indicate causality even if a relationship is found. Between group differences were determined using the Kruskal-Wallis H test with post-hoc analysis using the Dunn\u0026rsquo;s test where significant main effects were found, and the paired Wilcoxon Signed Rank test was used for within group comparisons. The significance threshold for rejecting the null hypothesis is set at p \u0026lt; 0.05 and statistics reported to two decimal places for all.\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOpen questions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvey responses to the three open questions were imported into NVivo software to facilitate thematic analysis. The three open questions invited respondents to share, in their own words, emotions, concerns or any other comments relating to the MendelScan software specifically (Additional File 1). An initial set of codes was developed inductively through familiarisation with the respondents\u0026rsquo; full text responses, and deductively, informed by the quantitative analysis of the closed questions. Codes were then grouped semantically into overarching themes.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 254 responses were received, of which 190 surveys were completed in full and 64 were partially completed. Overall completion rate for incomplete questionnaires ranged from 12% to 61%. For each individual question completion rate declined with position in the survey (range = 0% - 87.5% complete), with those nearer the end having lower completion rates. Questions about the MendelScan software, which were the last questions in the survey, were not completed in any of the partially completed questionnaires.\u003c/p\u003e\n\u003cp\u003eRespondent demographics compared to national demographic information for the UK population as a whole obtained from the Office of National Statistics are presented in Table 1. There was no significant difference between the demographics of those who completed the survey compared to those who did not, consequently, all available data was included in the analysis. Overall, the demographic profile of survey respondents was similar to the UK population as a whole, however as a group they were more likely to be female (respondents = 72.8%, UK population = 51%) and to be educated to undergraduate level or higher (respondents = 51.6%. UK population = 33.8%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe majority of respondents became aware of the survey as a result of the social media campaign (74.2%) with only 12.1% being affiliated with either Mendelian (1.1%) or a rare disease group (11.0%).\u003c/p\u003e\n\u003cp\u003eRespondents were evenly split between those who felt they knew more than most people about rare diseases (55.7%) or difficult-to-diagnose diseases (56.5%), and those who didn\u0026rsquo;t.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt;INSERT TABLE 1 HERE\u0026gt;\u0026gt;\u003c/p\u003e\n\u003cp\u003eTable 1:\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"907\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDistribution (%) of respondents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eDistribution (%) in general population\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003eLikelihood of opting out of the use of their health data for research and planning\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csup\u003e2\u003c/sup\u003e (8, N=254) = 10.86, p = ns\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003e18-24\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003e25-34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e17.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003e35-44\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e17.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e1 \u003cem\u003e[Very unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003e45-54\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e16.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003e55-64\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003e65-74\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e12.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003e75-84\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e1 \u003cem\u003e[Very unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003e84+\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e3.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e4 \u003cem\u003e[Likely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003ePrefer not to say\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e5 \u003cem\u003e[Very likely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csup\u003e2\u003c/sup\u003e (8, N=254) = 10.37, p = 0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e72.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e51%*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e20.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e49%*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e1 \u003cem\u003e[Very unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eIdentify as a gender other than their sex registered at birth\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Likely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003ePrefer not to say\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eData not available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e4 \u003cem\u003e[Likely]*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csup\u003e2\u003c/sup\u003e (4, N=254) = 30.27, p \u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eWhite\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e87.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e81.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 [Unlikely]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eAsian/Asian British\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e3 \u003cem\u003e[Neither unlikely nor likely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eBlack/Black British/African/Caribbean\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e1 \u003cem\u003e[Very unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eMixed/Multiple ethnic groups\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e1.5 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003ePrefer not to say\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eData not available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e4 \u003cem\u003e[Likely]*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csup\u003e2\u003c/sup\u003e (6, N=254) = 11.36, p = ns\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eNo formal education\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e18.2%*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eSecondary (GCSE or equivalent)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e23%*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 [Unlikely]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eA-levels (Highers or equivalent)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e15.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e16.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e1.5 [Unlikely]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eVocational\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e15.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e5.3%*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e1 [Very unlikely]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eUndergraduate\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e17.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e33.8% (data reported as a combined figure)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 [Unlikely]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003ePostgraduate/professional\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e33.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e1 \u003cem\u003e[Very unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003ePrefer not to say\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eData not available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e3.5 \u003cem\u003e[Likely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSexuality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csup\u003e2\u003c/sup\u003e (4, N=254) = 153.14, p = ns\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eHeterosexual (Straight)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e76.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e93.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eHomosexual (Gay or lesbian)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e1 \u003cem\u003e[Very unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eBisexual\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2 \u003cem\u003e[Unlikely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003eOther\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e3 \u003cem\u003e[Neither unlikely nor likely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cem\u003ePrefer not to say\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eData not available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 322px;\"\u003e\n \u003cp\u003e2.5 \u003cem\u003e[Neither unlikely nor likely]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eStatistics for general population taken from Office of National Statistics; *statistically significant difference between respondents and UK population, p\u0026lt;0.05 \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1 Legend: Demographic characteristics of all respondents who completed the Public Perception Survey, situated against national demographic statistics for the UK. Details included about the likelihood of respondents \u0026ldquo;opting-out\u0026rdquo; of their data being included for use in rare disease case finding broken down by demographic characteristic. \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAcceptability of the use of health data for research and planning\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all respondents, the majority positively endorsed the use of health data to improve healthcare (83.5%) and the use of technology to identify rare disease patients both by the NHS as a whole (92.8%) and their own GP practice or hospital (88.8%). When considering the inclusion of their own health data for research and planning, most respondents anticipated giving permission to both their GP practice or hospital (82.2%) and the NHS as a whole (81.1%) with only a minority saying they would opt out of the use of their data (12.2% [own GP practice or hospital], 15.3% [NHS]).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWhen considering the use of technology to identify undiagnosed rare disease patients the majority of respondents wished to be informed whether this was being done by the NHS (88.5%) or their own GP practice or hospital (84.5%) (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt;INSERT FIGURE 1 HERE\u0026gt;\u0026gt;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors which influenced responding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResponding was influenced by respondents\u0026rsquo; own self-declared knowledge of rare or difficult-to-diagnose diseases. Respondents who felt they knew more about rare diseases also felt they knew more about difficult-to-diagnose diseases as reflected by the non-significant difference between responses on these two items of the survey (Z = 0.59, p = 0.553).\u003c/p\u003e\n\u003cp\u003eRespondents with higher self-declared knowledge of either rare diseases (RD) or difficult-to-diagnose diseases (DDD) gave a more positive endorsement for the use of technology to identify rare disease patients by the NHS (RD: r(188) = 0.16, p \u0026lt; 0.028; DDD: r(188) = 0.17, p \u0026lt; 0.018 ) and by their own GP practice or hospital (RD: r(188) = 0.15, p \u0026lt; 0.04, DDD: r(188) = 0.14, p \u0026lt;=0.05) than those without. Self-declared knowledge of rare diseases or difficult-to-diagnose diseases was not related to their endorsement of the sharing of health data to improve healthcare by either the NHS or their own GP practice or hospital.\u003c/p\u003e\n\u003cp\u003eOpinions on using technology to identify rare diseases did not differ depending on whether it was the NHS as a whole or their own GP practice or hospital who were using their health data (Z = 1.56, p = 0.12), (see Figure 1).\u003c/p\u003e\n\u003cp\u003eSimilarly, on the whole demographic factors did not influence respondents\u0026rsquo; anticipated likelihood of opting out of the use of their health data. Analysis using the Kruskall-Wallis test found no significant difference across age groups, (X\u003csup\u003e2\u003c/sup\u003e (8, N=254) = 10.86, p = 0.21), nor educational levels (X\u003csup\u003e2\u003c/sup\u003e (6, N=254) = 11.36, p = 0.078). A significant group difference for gender (X\u003csup\u003e2\u003c/sup\u003e (8, N=254) = 10.37, p = 0.015) was driven by a greater likelihood of opting out by those who preferred not to provide their gender (Median = 2 [\u0026ldquo;Likely\u0026rdquo;]; all other groups median = 5 [\u0026ldquo;Very unlikely\u0026rdquo;], p \u0026lt; 0.05). Similarly, a significant group difference for ethnicity (X\u003csup\u003e2\u003c/sup\u003e (4, N=254) = 30.27, p \u0026lt; 0.001 ) was driven by a greater likelihood of opting out by those who preferred not to provide their ethnicity (Median = 2 [\u0026ldquo;likely\u0026rdquo;]; all other groups median = 4 [\u0026ldquo;Unlikely\u0026rdquo;] or 5[\u0026ldquo;Very unlikely\u0026rdquo;], p \u0026lt; 0.05). (see Table 1 for statistics).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeelings about being contacted about a potential rare disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMore than half of respondents (57.4%) anticipated being worried if they were contacted about a potential rare disease diagnosis by their GP practice or hospital. However, only a minority (22.6%) anticipated that they would be concerned that their electronic health records had been evaluated for a rare disease without their consent with most respondents anticipating feeling grateful (90.2%) and comforted (84.3%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients preferred remote forms of communication (83.1%) that did not require in-person visits to the GP practice or hospital to notify them that a rare disease case finding software was being applied to their health records. However, there was a clear preference for direct contact e.g. text, email or letter, rather than indirect contact e.g. inclusion on a website where the emphasis is on the patient to check (87.83%). \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eViews on the use of specific technology e.g., MendelScan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of technology in general [median response: \u0026rdquo;Strongly agree\u0026rdquo;] and MendelScan specifically [median response: \u0026rdquo;Agree\u0026rdquo;], was positively endorsed by respondents regardless of whom it was being used by.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, respondents positively endorse the use of MendelScan by both the NHS (77.4%) and by their own GP practice or hospital (76.9%) with only 12.6% of respondents indicated that they would opt out of the use of data for research and planning purpose knowing that MendelScan was used. Importantly, 88.4% indicated that they would like to be informed by their GP practice or hospital that their health data was being scanned.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, public sentiment was more positive towards technology as a whole than towards MendelScan specifically (NHS: Z = 5.73, p \u0026lt; 0.001; GP practice or hospital: Z = 5.99, p \u0026lt; 0.001), with respondents more likely to opt out (Z = 1.94, p = 0.05) and less likely to give permission (Z = 2.26, p = 0.024) to the use of MendelScan on their health data than technology in general.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the percentage of positive endorsements (either Likely or Very likely), worries about the use of MendelScan were ranked as:\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eHow accurate the technology is (56.3%)\u003c/li\u003e\n \u003cli\u003eA medical device developed by a private company has access to my health data (46.8%)\u003c/li\u003e\n \u003cli\u003eThe privacy and security of my health data (45.8%)\u003c/li\u003e\n \u003cli\u003eIf I don\u0026rsquo;t have a rare disease, yet MendelScan incorrectly identified me as possibly having one, I may end up having unnecessary tests or referrals (42.1%)\u003c/li\u003e\n \u003cli\u003eThe emotional distress or disruption to my life if I was identified as possibly having an undiagnosed rare disease (37.4%)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOn average 25% of respondents on each item (ranging from 23.2% - 29.5%) had not yet formed clear opinions about their worries around the use of MendelScan (Table 2 and Figure 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Worries about the use of MendelScan\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIf MendelScan was used on your health data, how likely is it that you would feel worried about the following:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVery unlikely\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUnlikely\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNeither likely nor unlikely\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLikely\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVery likely\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNot sure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe privacy and security of my health data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e20.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e23.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e24.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e21.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHow accurate the technology is\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e3.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e14.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e24.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e35.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e20.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eA medical device developed by a private company has access to my health data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e14.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e29.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e27.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e19.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe emotional distress or disruption to my life if I was identified as possibly having an undiagnosed rare disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e12.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e22.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e26.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e25.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e11.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIf I don\u0026rsquo;t have a rare disease, yet MendelScan incorrectly identified me as possibly having one, I may end up having unnecessary tests or referrals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e23.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e24.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e24.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e17.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e3.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026lt;\u0026lt;INSERT FIGURE 2 HERE\u0026gt;\u0026gt;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 254 questionnaires that were at least partially complete, 53 respondents completed the open question \u0026ldquo;If you think you may feel anything other than the emotions listed above, please feel free to share your thoughts here\u0026rdquo;, 49 respondents answered the questions \u0026ldquo;If you have any other concerns about MendelScan, please feel free to share them with us\u0026rdquo;, and 43 respondents answered the question \u0026ldquo;Do you have any other comments, positive or negative, on technologies like MendelScan that you would like to share?\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eA preliminary analysis suggested that respondents tended to use these questions to share personal experiences and emotions, often linked to their diagnosis journey rather than answer the questions posed. Therefore, responses to all three questions were pooled and coded together to reflect respondents\u0026rsquo; thoughts, concerns and comments about the use of rare disease case finding software, such as MendelScan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree key themes were identified within the data:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eAcceptability of the technology.\u0026nbsp;\u003c/strong\u003eConsistent with the quantitative analysis, the majority of respondents positively endorsed the use of rare disease case finding software. Drawing upon their own experiences and diagnostic journeys, many expressed optimism that such technology would reduce the diagnostic odyssey for others.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSome respondents shared feelings of unease at the prospect that a private company may benefit\u0026nbsp;financially\u0026nbsp;from access to NHS health data, and that the NHS would be charged to use the technology. Others proposed that endorsement should be contingent upon robust evidence of the efficacy and cost effectiveness of the technology. There was also some scepticism about the ability of the NHS to fully utilise the technology due to inaccuracies in the current electronic notes systems and a lack of knowledge and competency held by NHS staff.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConcerns about the use of the technology.\u0026nbsp;\u003c/strong\u003eRespondents shared worries about the increased burden on the NHS infrastructure and on patients directly as a result of using rare disease case finding software. Noting the current pressures, long waiting lists and limited availability of some services, there were concerns that reliable pathways would not be in place to respond to \u0026lsquo;flags\u0026rsquo; in a timely fashion and apprehension about the increased psychological burden patients would experience if they could not access the relevant \u0026lsquo;post-flag\u0026rsquo; exploratory investigations quickly. Some respondents felt that patients may worry unnecessarily about their health just by the knowledge that a rare disease case finding software was being applied to the health records and suggested only notifying people of its use if they were \u0026lsquo;flagged\u0026rsquo;. Although, for some a lack of confidence in the NHS to protect their personal data and distrust in the intentions and accountability of private companies meant they saw a priori consent as essential. There were also concerns that the software would be used inappropriately by GPs to restrict access to expensive exploratory tests for patients who had not been flagged as they may be deemed low risk of having a rare disease. Anxieties about privacy and anonymity were also shared.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAnticipated emotions in response to the technology.\u0026nbsp;\u003c/strong\u003eMany respondents anticipated feeling relieved, hopeful and grateful at the prospect of rare disease case finding software being used within the NHS. They expressed hope that it would end their own, and others, diagnostic odyssey and saw the use of the technology as an action in the respondent\u0026rsquo;s best interest. Others however, anticipated feeling shocked and surprised if they were notified that their records had been flagged. They predicted that they would worry about what the \u0026lsquo;post-flag\u0026rsquo; pathway would look like and expressed the need for timely support and information sharing to reduce the psychological burden in that period. \u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDetails of the themes and subordinate themes are presented in Table 3 along with illustrative quotes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Themes and subordinate themes emerging from the open questions on the Public Perception Survey.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"930\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eSubtheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003eIllustrative quote\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAcceptability of the technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eHow well it works (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eThe majority of respondents positively endorsed the use of technology generally, seeing it as an anticipated and welcome step. Respondents tended to use their own diagnosis journey to frame their opinions, highlighting difficulties accessing services within the NHS as justification for the need to introduce new technologies with the hope that others did not need to have the same experiences.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;I have a rare disease and the diagnostic process and the time in limbo where no one could tell me what was wrong was utter hell. Anything ethical to make this easier for other patients needs to be done, my diagnostic odyssey was horrifying.\u0026rdquo; (Respondent 123)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;Having been passed around the NHS for more than 6 years before a rare disease was finally identified by chance, I am all for faster diagnosis for others.\u0026rdquo; (Respondent 144)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eCommercial companies profiting (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eRespondents described wanting to see evidence of the efficacy and cost effectiveness of the technology before fully endorsing its use within the NHS. Some concerns were expressed that the NHS would be charged to access rare disease case finding software and that a private company would be profiting from access to health data.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;Privatisation of the health service does not have a good history especially in relation to IT initiatives. I would want to know this was a validated and reliable programme that was cost effective\u0026rdquo;. (Respondent 130)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;In the ideal world the use of AI should be positive in helping identify the many people who are diagnosed too late BUT the data of NHS patients is extremely valuable to private companies who can use it to refine their products and subsequently make millions or billions from it. This is a major concern.\u0026rdquo; (Respondent 131)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;I would like to think that the access to data should be set against the costs of using the technology.\u0026rdquo; (Respondent 4)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eNHS unequipped to use it (37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eThere\u0026nbsp;was scepticism about the ability of the NHS to utilise the technology to its full benefit due to inaccuracies in the current electronic notes systems and a lack of knowledge and competency held by NHS staff.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u0026ldquo;\u003cem\u003eThe concept is overly reliant on GPs/clinicians accurately recording their patient\u0026rsquo;s health data 100% of the time, which is extremely flawed given the reality\u003c/em\u003e.\u0026rdquo; (Respondent 220)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eConcerns about the use of technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eInadequacy of current NHS infrastructure (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eWorries about the capacity of existing NHS services to respond to \u0026ldquo;flags\u0026rdquo; in a timely manner were raised and the potential for additional psychological distress as a consequence discussed.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;Like with newborn screening - there would need to be a clear, and timely pathway to investigate potential diagnoses and exclude \u0026apos;false positive\u0026apos; results. There is a lot of potential for increasing worry without subsequent reassurance without these pathways being established.\u0026rdquo;\u003c/em\u003e (Respondent 57)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eFears were expressed that clinicians would use the rare disease case finding software to guide their decision-making over who did, and did not, receive further exploratory tests for unexplained symptoms.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u0026ldquo;\u003cem\u003eDoctors will use this technology to gatekeep diagnoses and treatment from patients who\u0026rsquo;s rare disease isn\u0026rsquo;t flagged by the technology.\u003c/em\u003e\u0026rdquo; (Respondent 220)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eA lack of confidence in the ability of the NHS to protect personal data and maintain privacy and anonymity was shared.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;The NHS treats data about patients as if they own it. Unfortunately, therefore the NHS can\u0026rsquo;t be trusted to provide patient data to companies in a responsible manner (i.e. with specific consent of the data owner), so it would be difficult for any company to gain my trust on use of my data for providing enhanced diagnostics until the NHS gets its act together on protection of personal data.\u0026rdquo; (Respondent 24)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003ePotential to cause harm (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eRespondents felt that knowing a rare disease case finding software was being applied may trigger unnecessary health worries for some people and highlighted the need for careful and considered language choices in all patient facing correspondence.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;People are individuals. Sticking an algorithm onto people\u0026rsquo;s health has huge implications. Nocebo language can impact greatly to people\u0026rsquo;s perspective on their health. You can seriously reduce someone\u0026rsquo;s mental and physical wellbeing depending upon the language used daily. More needs to be considered before systems such as this can be used.\u0026rdquo; (Respondent 237)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eKnowing that the rare disease case finding software was owned by a private company made some respondents fear they would lose control of their personal data worrying that their data may be sold on without their knowledge or used by a third party.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;It being a private company rather than academic or NHS project makes me concerned about the ethics and potential for abuse/misuse/reselling of health data.\u0026rdquo; (Respondent 123)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;Protections against selling on the data or using it for health screenings / insurance determination are vital.\u0026rdquo; (Respondent 148)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eSome respondents considered that obtaining consent to deploy rare disease case finding software may cause unnecessary worry for the majority of people who were not \u0026ldquo;flagged\u0026rdquo; whereas others unequivocally felt that \u003cem\u003ea priori\u003c/em\u003e consent should always be obtained.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;For me, even to be told retrospectively about it being used would be okay but I understand how others may see it differently.\u0026rdquo; (Respondent 226)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;It\u0026rsquo;s MY health, and MY data. If the NHS wants to make use for research I want to know what research and by who, otherwise it is NOT informed consent. If the NHS wants to use my data to look for health issues I might have, I want to know and have the option of consenting or not.\u0026rdquo; (Respondent 24)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAnticipated emotions in response to the technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eNegative (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eSome anticipated feeling shocked, surprised and worried about what would happen next if they were contacted and highlighted the need for support and information to be made available quickly afterwards.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;if contacted with a possibility then you would feel quite anxious and want to know what the next steps were, how soon, and any possible outcomes. I would want the process to happen quickly as uncertainty is quite draining, I would also want to know what steps of self-care might be necessary.\u0026rdquo; (Respondent 166)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003ePositive (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eRespondents described feeling like the technology was acting in their own and others best interests to reduce the diagnostic odyssey experienced by many. They anticipated feeling relieved, hopeful and grateful.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;its similar to when a bank contacts you or blocks a payment, I\u0026apos;d be glad they are looking out for you, especially if there is some possibility of improving life quality\u0026rdquo;. (Respondent 252)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe online survey, completed by a sample broadly representative of NHS users (including individuals with and without personal connections to the rare disease community), found strong positive endorsement for using rare disease case finding technology within the NHS. There was, however, a clear mandate calling for transparency around its use and for patients to be actively informed. Sentiment did not change based on whether the technology was used by the NHS overall, patients\u0026rsquo; GPs or their own hospital, nor was it particularly influenced by demographic factors. Qualitative analysis of the free response questions identified views on the technology\u0026rsquo;s acceptability and concerns, along with the anticipated emotional response. In line with the quantitative data, respondents generally positively endorsed rare disease case finding, expecting to feel relieved, hopeful and grateful if this technology was used on their health data. However, worries about the readiness of the NHS to implement such technology were shared, along with concerns about the potential impact on existing NHS infrastructures due to the increased demand for services. Respondents also expressed discomfort about private companies potentially profiting at the expense of the NHS from the use of this technology. They also described their distrust in the NHS ability to safeguard personal data and fears that clinicians might use the technology to justify denying people access to future explorative tests.\u003c/p\u003e\u003cp\u003eThe results of the study clearly demonstrate that people support the sharing of health data for the purpose of identifying undiagnosed rare diseases, which is consistent with the findings from recent research by the Health Foundation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and NHS England [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] which have also shown similar positive attitudes toward the use of digital technologies and data sharing in general. Given the complex public perception of rare diseases and the frequent stigmatisation of affected individuals and their families, it could not be assumed that general public support for data sharing would automatically extend to the specific context of rare disease case finding. The current study demonstrates that it does, at least within this representative sample. Furthermore, concerns that were raised in the current study about the appropriate use and effective safeguarding of data by the NHS, along with worries about the potential for third parties to profit from NHS data are all consistent with issues identified in other research [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eDid the survey reasonably capture the relevant views of the public on this issue?\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePeople who completed the survey were demographically similar to the UK population as a whole, although there was an over representation of female participants and those educated to undergraduate level or beyond. It is a widely observed phenomenon that women and more highly educated people are more likely to respond to surveys [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; acknowledging this overrepresentation is crucial for the generalisability of these findings. This is especially important given that rare diseases are associated with medical, financial and psychological challenges that can, in themselves, limit people\u0026rsquo;s educational potential [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The survey achieved an even representation of views from people who did and did not have prior familiarity with rare or difficult-to-diagnose diseases. This even split underscores the significance of validating positive endorsement beyond the immediate rare disease community. While support from within this community was anticipated, securing acceptance among the general population is equally crucial for ensuring widespread adoption and successful implementation of such technology within the healthcare system.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhich features influenced people's attitudes and why?\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study observed that people with prior knowledge of rare or difficult-to-diagnose diseases were more likely to support health data sharing, but only when they knew the data would be specifically used to identify rare diseases. This finding aligns with established literature emphasising the importance of perceived direct benefit as key drivers of public willingness to share sensitive health data [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous research consistently demonstrates that transparency about how data will be used and the value it generates is paramount for fostering public acceptance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings specifically highlight that individuals become more amenable when the data purpose directly addresses a significant challenge, such as shortening the diagnostic odyssey for rare diseases. This isn't merely about abstract societal good; it's about connecting data use to a tangible, life-changing personal impact. For those already familiar with the complexities of rare conditions, this specific benefit resonated profoundly, amplifying their support for health data sharing. This suggests that communication strategies for new health technologies should articulate the specific, relatable outcomes for patients and their families.\u003c/p\u003e\u003cp\u003eFindings highlight a complex interplay between demographic factors and willingness to share health data for research and planning. While age and education level showed no significant impact on opt out rates, the choice to disclose one's gender and ethnicity emerged as influential factors. This suggests that a general reluctance to share personal information, rather than gender or ethnicity specifically, is a key predictor of opting out. This aligns with the notion that individuals who are hesitant to provide even basic demographic details might be understandably more cautious about sharing their sensitive health data. Overall, these findings underscore the importance of addressing potential concerns when seeking to engage individuals in health data sharing initiatives. Building trust and transparency, particularly among groups that may be more hesitant, will be crucial for ensuring equitable participation and maximising the potential benefits of such endeavours.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhat learnings are there for data sharing and consent?\u003c/b\u003e\u003c/p\u003e\u003cp\u003eResults from the survey indicate that respondents overwhelmingly endorse the use of their health data for the purpose of identifying patients with potentially undiagnosed rare diseases. The findings that respondents' opinions on the use of their health data for rare disease identification did not differ based on whether it was the NHS, their GP, or their hospital using the data contrast with some previous findings. In Jones et al. (2022) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], for example, there was a clear preference for data being used by one's own GP compared to other entities. The current study suggests a lack of such distinction, implying a broader acceptance of data use regardless of the specific entity within the NHS. This could indicate a growing trust in the NHS as a whole to handle health data responsibly, or a decreased concern about data sharing within the healthcare system for specific high value applications, in this case when a clear and compelling benefit like rare disease diagnosis is presented.\u003c/p\u003e\u003cp\u003e Respondents wished to be informed if their healthcare data was included in such a project and anticipated providing consent (if asked) and did not envisage pursuing options to \u0026ldquo;opt out\u0026rdquo;. However, the qualitative data presented a more mixed picture regarding the need to ask for explicit consent. Some respondents felt strongly that consent was an essential pre-requisite whilst others felt that asking permission in advance would add, unnecessarily, to the worry of those who had included their health data. This tension between the desire for information and the potential for \"consent fatigue\" or undue distress aligns with broader discussions in public health about dynamic consent models [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This survey results are also consistent with the work by Atkins et al. (2021) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], highlighting low public awareness of the national opt out scheme in place since 2018 and general confusion regarding the routine use of data within the NHS. This underscores a persistent gap in public understanding of current data governance mechanisms.\u003c/p\u003e\u003cp\u003eOverall, respondents anticipated feeling hopeful, grateful and relieved that tools like MendelScan were being used but concerns were shared about the security of the data and the ability and intention of both the NHS and private companies to maintain anonymity and confidentiality of patients\u0026rsquo; healthcare data. These anxieties are widely reported in the literature on health data sharing, where trust in data custodians and the perceived trustworthiness of commercial involvement are consistently identified as critical factors influencing public acceptance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. There was also consideration of the psychological impact for patients who were \u0026ldquo;flagged\u0026rdquo; during the screening and scepticism about the ability of the current NHS infrastructure to manage the additional investigations required for those patients identified, in a timely manner. These infrastructure concerns add a unique dimension to the public perception of case finding in healthcare, extending beyond data governance to the practical realities of implementation within a stretched health system.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhat are the strengths and limitations of this study?\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStrengths of this study include its collaborative design, the continuation of existing research and the provision of actionable insights. The involvement of a Patient and Public Involvement (PPI) group in co-designing the survey ensured its accessibility and relevance to the target audience and likely contributed to increased response rates and data quality. The study builds upon and offers nuanced comparisons with prior work, contributing to a broader understanding of public perceptions of health data usage within the context of the NHS. At the same time, it focuses on rare diseases, an often-overlooked area of healthcare. The findings provide valuable information for healthcare organisations and MedTech companies highlighting the importance of transparency, targeted communication, and addressing concerns around privacy and data security to foster public trust.\u003c/p\u003e\u003cp\u003eIt is acknowledged however, that there was an under-representation of participants from certain communities in this study which is a limitation of the work and likely to impact generalisability. Future work to understand the barriers to participation would help ensure that all communities are enabled to share their perspective.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe survey results offer several crucial insights into public perception and acceptance of health data usage for rare disease identification. First and foremost, there is a clear and strong willingness among respondents to allow their health data to be used for improving healthcare and identifying rare diseases. This positive sentiment is particularly evident when accompanied by transparent communication about data use and the option to opt out. This positive endorsement is further amplified among individuals with greater knowledge of rare and difficult-to-diagnose conditions, underscoring the importance of public education and awareness campaigns in fostering acceptance for such initiatives.\u003c/p\u003e\u003cp\u003eThe study revealed a lack of distinction in respondents' opinions based on whether the data was used by the NHS centrally or their local healthcare providers, suggesting a growing trust in the healthcare system as a whole to manage data responsibly for public good. However, the consistent desire to be informed about data usage, regardless of the entity involved, highlights the importance of maintaining open communication channels, ensuring patient autonomy and raising awareness of existing data use policies and national opt out scheme.\u003c/p\u003e\u003cp\u003eThe specific concerns raised regarding the use of technology, exemplified by MendelScan, primarily revolved around accuracy, privacy, potential for unnecessary tests, and the emotional impact of a possible rare disease diagnosis. This emphasises the need for continued research and development to enhance the accuracy and reliability of such tools, coupled with robust data protection measures and clear pathways to address the emotional and medical / practical implications of a potential diagnosis.\u003c/p\u003e\u003cp\u003eWhile acknowledging certain concerns that must be actively addressed through responsible implementation and ongoing dialogue, the findings suggest broad public support for health data initiatives aimed at rare disease identification when implemented transparently and responsibly. This willingness presents a significant opportunity to leverage technology to improve patient outcomes in this challenging area of healthcare.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePF, EM, JS, MH are all employees of Mendelian. JF, SR are employees of Health Innovation East who were commissioned by Mendelian to conduct the survey and complete the analysis. SM worked as an associate for Health Innovation East to complete the analysis and write-up. All the remaining authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eSM \u0026ndash; data analysis, interpretation, manuscript drafting, reviewing and approval of manuscript.JF \u0026ndash; oversight of the data analysis and interpretation, reviewing and approval of manuscript.SR - oversight of the data analysis and interpretation, reviewing and approval of manuscript.PF - oversight of the data analysis and interpretation, reviewing and approval of manuscript.EM - reviewing and approval of manuscript.JS - reviewing and approval of manuscript.HM - reviewing and approval of manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDepartment of Health and Social Care. \u003cem\u003eThe UK Rare Diseases Framework.\u003c/em\u003e (UK Government, London, 2021).\u003c/li\u003e\n \u003cli\u003eGriggs, R.C. et al. Clinical research for rare disease: opportunities, challenges, and solutions. \u003cem\u003eMol. Genet. Metab.\u003c/em\u003e\u003cstrong\u003e96\u003c/strong\u003e, 20\u0026ndash;26 (2009).\u003c/li\u003e\n \u003cli\u003eRegulation (EC) No 141/2000 of the European Parliament and of the Council on orphan medicinal products of 16 December 1999. \u003cem\u003eOfficial Journal of the European Communities\u003c/em\u003e\u003cstrong\u003eL 18\u003c/strong\u003e, 22 January 2000.\u003c/li\u003e\n \u003cli\u003eNational Organization for Rare Disorders. Rare disease day: frequently asked questions. \u003cem\u003eNORD\u003c/em\u003e https://rarediseases.org/wp-content/uploads/2019/01/RDD-FAQ-2019.pdf (2019).\u003c/li\u003e\n \u003cli\u003eUndiagnosed Diseases Network Foundation. What is an undiagnosed disease. \u003cem\u003eUDNF\u003c/em\u003e https://udnf.org/what-is-an-undiagnosed-disease/ (2025).\u003c/li\u003e\n \u003cli\u003eFaye, F. et al. 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Mark.\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 171\u0026ndash;184 (1996).\u003c/li\u003e\n \u003cli\u003eVerger, S., Negre, F., Rossell\u0026oacute;, M.R., Paz-Lourido, B. Inclusion and equity in educational services for children with rare diseases: challenges and opportunities. \u003cem\u003eChild. Youth Serv. Rev.\u003c/em\u003e\u003cstrong\u003e119\u003c/strong\u003e, 105518 (2020).\u003c/li\u003e\n \u003cli\u003eYusuf, Z.K. et al. Building and sustaining public trust in health data sharing for musculoskeletal research: semistructured interview and focus group study. \u003cem\u003eJ. Med. Internet Res.\u003c/em\u003e\u003cstrong\u003e26\u003c/strong\u003e, e53024 (2024).\u003c/li\u003e\n \u003cli\u003eKaye, J. et al. Dynamic consent: a patient interface for twenty-first century research networks. \u003cem\u003eEur. J. Hum. Genet.\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e, 141\u0026ndash;146 (2015).\u003c/li\u003e\n \u003cli\u003eTeare, H.J.A., Prictor, M., Kaye, J. Reflections on dynamic consent in biomedical research: the story so far. \u003cem\u003eEur. J. Hum. Genet.\u003c/em\u003e\u003cstrong\u003e29\u003c/strong\u003e, 649\u0026ndash;656 (2021).\u003c/li\u003e\n \u003cli\u003eAtkin, C. et al. Perceptions of anonymised data use and awareness of the NHS data opt-out amongst patients, carers and healthcare staff. \u003cem\u003eRes. Involv. Engagem.\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 40 (2021).\u003c/li\u003e\n \u003cli\u003eGhafur, S. et al. Public perceptions on data sharing: key insights from the UK and the USA. \u003cem\u003eLancet Digit. Health\u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e, e444\u0026ndash;e446 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rare diseases, Diagnostic odyssey, Early diagnosis, Decision support technology, Public perception and opinion","lastPublishedDoi":"10.21203/rs.3.rs-6304847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6304847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated public acceptability of using technology on patient health data within the National Health Service (NHS) to shorten the diagnostic odyssey of patients with rare and difficult-to-diagnose diseases. A public perception survey (n\u0026thinsp;=\u0026thinsp;254), promoted primarily through social media, was used to gauge public sentiment towards the use of clinical decision support tools, specifically using a rare disease case finding software (MendelScan) as an example. This research aimed to build upon previous findings regarding public opinion on health data use.\u003c/p\u003e\u003cp\u003eThe sample was broadly representative of the UK population, although with a higher proportion of females and individuals with undergraduate or postgraduate education. Respondents positively endorsed the use of health data to improve healthcare and the use of technology to identify rare disease patients both by the NHS in general (92.8%) and their own General Practitioner (GP) or hospital (88.8%). More than 90% of respondents anticipated feeling grateful, and nearly 85% expected to feel comforted, if they were contacted by their GP or hospital about a potential rare disease, even if they had not been asked to explicitly consent in advance. Analysis of responses to open questions supported these findings, with respondents predicting that they would feel relieved, hopeful and grateful if technologies were used on their electronic health records to identify rare diseases. While respondents indicated likely permission for such data use and no anticipated opt out, they strongly desired to be informed about the use of their health data. Concerns primarily revolved around the accuracy of the technology, the knowledge that a medical device created by a private company had access to their health data, and the privacy and security of their health data. Anxieties were expressed about the NHS\u0026rsquo;s capacity to implement such technology due to perceived inadequacies in its current infrastructure.\u003c/p\u003e\u003cp\u003eThe findings of this survey demonstrate there's a clear willingness among respondents to allow their data to be used for improving healthcare and identifying rare diseases, particularly when accompanied by transparent communication and the option to opt out. Addressing specific concerns regarding technology use - particularly related to accuracy, privacy, the emotional impact of a potential diagnosis and NHS infrastructure readiness - highlights areas for ongoing research and development to foster public trust and effective implementation.\u003c/p\u003e","manuscriptTitle":"Public perception of the use of clinical decision support tools within the NHS for rare disease case finding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 06:24:45","doi":"10.21203/rs.3.rs-6304847/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"daad4555-0658-410c-ae6e-454e6545d1fd","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54525130,"name":"Health sciences/Health care/Diagnosis"},{"id":54525131,"name":"Health sciences/Health care/Health policy"},{"id":54525132,"name":"Health sciences/Health care/Health services"},{"id":54525133,"name":"Health sciences/Health care/Medical ethics"},{"id":54525134,"name":"Physical sciences/Engineering/Biomedical engineering"},{"id":54525135,"name":"Physical sciences/Mathematics and computing/Software"}],"tags":[],"updatedAt":"2025-09-22T06:24:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 06:24:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6304847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6304847","identity":"rs-6304847","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}