# Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation
## 5 Medical Consultation Flows
### Selecting an Already Defined Case Type
The graphic in Figure 6 uses a logarithmic scale to highlight the difference of two orders of magnitude between the execution times of both learning methods. The efficiency results reveal that the Bayesian learning method is difficult to apply due to high execution times, even when processing small volumes of data. Execution time results also reaffirm the benefits of the instance-based learning method, which significantly outperforms the Bayesian approach in terms of efficiency. ### Testing the Applicability of the Instance-Based Learning Approach

In order to test the applicability of the proposed approach, a prototype was developed and deployed on Google Compute Engine, the Infrastructure as a Service component of the Google Cloud Platform. The prototype was evaluated by advanced medical students in their last year of training at Universidad de la Republica, Uruguay. #### 7.4.1 Comparison with Praxis

Praxis reports the average time required to write a CT starting from an empty CKB [15]. In order to compare the proposed approach with the original implementation of Praxis, the average time to write a CT using the prototype was measured. Figure 7 illustrates both average writing times starting from an empty CKB, by considering the medical attendance of the first 50 patients. Figure 6: Average execution time comparison: instance-based learning vs. Bayesian learning method. Figure 7: Average time of 50 medical students to write the notes of a case type (continuous line). Average time according to Praxis reports (dotted line). Both evaluations start with an empty CKB. Although Praxis presents shorter registering times for the first two medical consultations, more than 50 evaluations are needed to achieve a convergence point. The proposed approach significantly reduces registration times from the third case registered onwards, converging quickly to less than three minutes of writing consultations. The proposed learning method demanded 210 min to register 50 consultations (i.e., 4.2 min per consultation), while using Praxis requires 519 min (10.4 min per consultation). The overall time reduction factor is 2.5\(\times\). #### 7.4.2 Improvement Using a Pre-Loaded CBK

The time needed to register a medical consultation can be reduced by using previously registered information. The average time to record a CT was measured in a context in which medical students could use a pre-loaded CKB. Figure 8 shows the average time to write a CT taking advantage of a pre-loaded CKB containing typical workday scenarios. As a relevant result, the use of a pre-loaded CKB implied a reduction of up to five minutes for recording the notes of the first six medical consultations. Furthermore, a pre-loaded CKB also accelerated the convergence to three minutes of writing consultations. Regarding the scalability of the incremental processing of new case types, results suggest a convergence towards a short writing time for medical consultations, even when processing large volumes of data. #### 7.4.3 Survey about the Proposed Approach

More than 50 medical students from different editions of the Medical Informatics course were surveyed after using the prototype of the proposed approach. The advanced medical students have tested the prototype during course editions from 2016 to 2020. Figure 9 summarizes the best features identified by students. Results show that 43% of the surveyed students mentioned that the learning curve was steep before they could benefit from the proposed learning method. As a relevant result, more than 73% of the students considered the prototype as an appropriate tool for medical practice, especially at medical consultations. Moreover, 62% of the students were able to speed up writing time during medical consultations. Figure 8: Average time of 50 medical students starting with an empty CKB (continuous line). Average time of 50 medical students taking advantage of a pre-loaded CKB (dotted line).