Mapping the use of computational modelling and simulation in clinics: A survey

Abstract

In silico medicine describes the application of computational modelling and simulation (CM&S) to the study, diagnosis, treatment or prevention of a disease. Tremendous research advances have been achieved to facilitate the use of CM&S in clinical applications. Nevertheless, the uptake of CM&S in clinical practice is not always timely and accurately reflected in the literature. A clear view on the current awareness, actual usage and opinions from the clinicians is needed to identify barriers and opportunities for the future of in silico medicine. The aim of this study was capturing the state of CM&S in clinics by means of a survey toward the clinical community. Responses were collected online using the Virtual Physiological Human institute communication channels, engagement with clinical societies, hospitals and individual contacts, between 2020 and 2021. Statistical analyses were done with R. Participants (n = 163) responded from all over the world. Clinicians were mostly aged between 35 and 64 years-old, with heterogeneous levels of experience and areas of expertise (i.e., 48% cardiology, 13% musculoskeletal, 8% general surgery, 5% paediatrics). The CM&S terms "Personalised medicine" and "Patient-specific modelling" were the most well-known within the respondents. "In silico clinical trials" and "Digital Twin" were the least known. The familiarity with different methods depended on the medical specialty. CM&S was used in clinics mostly to plan interventions. To date, the usage frequency is still scarce. A well-recognized benefit associated to CM&S is the increased trust in planning procedures. Overall, the recorded level of trust for CM&S is high and not proportional to awareness level. The main barriers appear to be access to computing resources, perception that CM&S is slow. Importantly, clinicians see a role for CM&S expertise in their team in the future. This survey offers a snapshot of the current situation of CM&S in clinics. Although the sample size and representativity could be increased, the results provide the community with actionable data to build a responsible strategy for accelerating a positive uptake of in silico medicine. New iterations and follow-up activities will track the evolution of responses over time and contribute to strengthen the engagement with the medical community.

Keywords: clinicians; communities; computer modelling; in silico medicine; simulations; translation; trust in technology.

Figure 1

Awareness and familiarity with in silico terms and methodologies. (A) Level of awareness in in silico terms. Respondents who answered very and extremely aware (resp. Not at all aware and slightly aware) were grouped. (B) Level of familiarity with computational methods related to in silico technologies. Respondents who answered very and extremely familiar (resp. Not at all aware and slightly familiar) were grouped. The number of responses (n) amounting to 100% of the responses is reported on the right of each bar.

Figure 2

CM&S and technical expertise in clinical teams (A) Ranking of CM&S related backgrounds from the most represented to the less in the clinicians’ teams (survey question #2) (B) Number of respondents who declares having team members dedicated to CM&S and percentage of them being based in clinical premises.

Figure 3

Usage of CM&S and related methods in the clinics. (A) percentage of clinicians who have already used CM&S for planning procedures (question #10). (B) Frequency of usage of CM&S among clinicians who have already used it to plan procedure. (C) Fields in which CM&S was used to plan intervention, expressed in percentage of n = 122 responses. (question #11) (D) Purpose of application of CM&S by clinicians ranked based on number of responses (question #9) (E) Ranking of CM&S related methods from the less to the most applied to clinical practice by respondents (question #4). (F) Association between the medical field in which respondents have applied CM&S (questions #11) and CM&S method that have been applied by the respondents. Test of independence: Fisher Exact test, the independence is rejected for p-values < 0,05, indicated with (*).

Figure 4

(A) Overall trust level distribution for in silico technologies, n = 147 (B) Comparison of trust between CM&S users and nonusers. The grouping between user and nonuser was done according to the responses to question #4 of the survey (see Method). The non-parametric Wilcoxon test indicates that there is no significant difference of trust between users and nonusers. (n = 147) (C) Distribution of trust, grouped by level of awareness in “in silico medicine” concepts, n = 147 (D) Comparison of agreement with statement on CM&S accuracy between users and nonusers showed that the level of agreement was dependent on the group. P-value is displayed for the Cochran-Armitage test of association to compare ordinal variable between two groups, N = 156 (E) Level of agreement with various statements related to CM&S (N = 156–158 depending on the statement). The difference of answers between the user groups were evaluated with Cochrane Armitage test: * and ** indicate p-values < 0.05 and <0.005 respectively. Detailed p-values in Supplementary Figure S2. (F) Percentage of respondents seeing a role for expertise in CM&S in their team in the next 5 years (G) Association between the usage of CM&S to plan intervention and the presence of dedicated members in the team. (H) Type of evidence requested by clinicians to trust CM&S, ranked by number of votes (multiple choice possible).