Estimating the number of usability problems affecting medical devices: modelling the discovery matrix
Background. Usability testing of medical devices are mandatory for market access. The testings’ goal is to identify usability problems that could cause harm to the user or limit the device’s effectiveness. In practice, human factor engineers study participants under actual conditions of use and list the problems encountered. This results in a binary discovery matrix in which each row corresponds to a participant, and each column corresponds to a usability problem. One of the main challenges in usability testing is estimating the total number of problems, in order to assess the completeness of the discovery process. Today’s margin-based methods fit the column sums to a binomial model of problem detection. However, the discovery matrix actually observed is truncated because of undiscovered problems, which corresponds to fitting the marginal sums without the zeros. Margin-based methods fail to overcome the bias related to truncation of the matrix. The objective of the present study was to develop and test a matrix-based method for estimating the total number of usability problems.
Methods. The matrix-based model was based on the full discovery matrix (including unobserved columns) and not solely on a summary of the data (e.g. the margins). This model also circumvents a drawback of margin-based methods by simultaneously estimating the model’s parameters and the total number of problems. Furthermore, the matrix-based method takes account of a heterogeneous probability of detection, which reflects a real-life setting. As suggested in the usability literature, we assumed that the probability of detection had a logit-normal distribution.
Results. We assessed the matrix-based method’s performance in a range of settings reflecting real-life usability testing and with heterogeneous probabilities of problem detection. In our simulations, the matrix-based method improved the estimation of the number of problems (in terms of bias, consistency, and coverage probability) in a wide range of settings. We also applied our method to five real datasets from usability testing.
Conclusions. Estimation models (and particularly matrix-based models) are of value in estimating and monitoring the detection process during usability testing. Matrix-based models have a solid mathematical grounding and, with a view to facilitating the decision-making process for both regulators and device manufacturers, should be incorporated into current standards.
Figure 1
Figure 2
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
On 18 Sep, 2020
On 29 Jul, 2020
On 28 Jul, 2020
Posted 09 Jul, 2020
On 10 Jul, 2020
On 09 Jul, 2020
On 09 Jul, 2020
On 08 Jul, 2020
On 25 Jun, 2020
Received 24 Jun, 2020
On 22 Jun, 2020
Received 20 Jun, 2020
On 19 May, 2020
On 21 Apr, 2020
Invitations sent on 21 Apr, 2020
On 21 Apr, 2020
Received 21 Apr, 2020
On 20 Apr, 2020
On 16 Apr, 2020
On 03 Mar, 2020
Received 18 Feb, 2020
On 17 Feb, 2020
Received 29 Jan, 2020
On 25 Jan, 2020
Received 25 Jan, 2020
On 23 Jan, 2020
Invitations sent on 22 Jan, 2020
On 03 Nov, 2019
On 30 Oct, 2019
On 29 Oct, 2019
On 29 Oct, 2019
Estimating the number of usability problems affecting medical devices: modelling the discovery matrix
On 18 Sep, 2020
On 29 Jul, 2020
On 28 Jul, 2020
Posted 09 Jul, 2020
On 10 Jul, 2020
On 09 Jul, 2020
On 09 Jul, 2020
On 08 Jul, 2020
On 25 Jun, 2020
Received 24 Jun, 2020
On 22 Jun, 2020
Received 20 Jun, 2020
On 19 May, 2020
On 21 Apr, 2020
Invitations sent on 21 Apr, 2020
On 21 Apr, 2020
Received 21 Apr, 2020
On 20 Apr, 2020
On 16 Apr, 2020
On 03 Mar, 2020
Received 18 Feb, 2020
On 17 Feb, 2020
Received 29 Jan, 2020
On 25 Jan, 2020
Received 25 Jan, 2020
On 23 Jan, 2020
Invitations sent on 22 Jan, 2020
On 03 Nov, 2019
On 30 Oct, 2019
On 29 Oct, 2019
On 29 Oct, 2019
Background. Usability testing of medical devices are mandatory for market access. The testings’ goal is to identify usability problems that could cause harm to the user or limit the device’s effectiveness. In practice, human factor engineers study participants under actual conditions of use and list the problems encountered. This results in a binary discovery matrix in which each row corresponds to a participant, and each column corresponds to a usability problem. One of the main challenges in usability testing is estimating the total number of problems, in order to assess the completeness of the discovery process. Today’s margin-based methods fit the column sums to a binomial model of problem detection. However, the discovery matrix actually observed is truncated because of undiscovered problems, which corresponds to fitting the marginal sums without the zeros. Margin-based methods fail to overcome the bias related to truncation of the matrix. The objective of the present study was to develop and test a matrix-based method for estimating the total number of usability problems.
Methods. The matrix-based model was based on the full discovery matrix (including unobserved columns) and not solely on a summary of the data (e.g. the margins). This model also circumvents a drawback of margin-based methods by simultaneously estimating the model’s parameters and the total number of problems. Furthermore, the matrix-based method takes account of a heterogeneous probability of detection, which reflects a real-life setting. As suggested in the usability literature, we assumed that the probability of detection had a logit-normal distribution.
Results. We assessed the matrix-based method’s performance in a range of settings reflecting real-life usability testing and with heterogeneous probabilities of problem detection. In our simulations, the matrix-based method improved the estimation of the number of problems (in terms of bias, consistency, and coverage probability) in a wide range of settings. We also applied our method to five real datasets from usability testing.
Conclusions. Estimation models (and particularly matrix-based models) are of value in estimating and monitoring the detection process during usability testing. Matrix-based models have a solid mathematical grounding and, with a view to facilitating the decision-making process for both regulators and device manufacturers, should be incorporated into current standards.
Figure 1
Figure 2
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.