# LabConcept--A New Mobile Healthcare Platform for Standardizing Patient Results in Telemedicine
## 3 Appling the LabConcept Telemedicine Platform
### Accessing the Results  
The stored results can be accessed in the \"view_result.fig\" GUI, which assigns the corresponding domain to the selected dropdown menu option. The table is inserted in a figure in order for its structure to be easily modified when selecting the domain, depending on the number of columns and the table header. Figure 6 displays the interface for the \"View results\" option.  
When the domain is selected, the command \"set(table,'Data'/data)\" is used to change the \"uitable\" properties and \"set(table,'ColumnName','[Date (MM/DD/YYYY)','<p1>','<p2>',...,'<pns>')]\" to modify the columns in the table according to the correct blood results domain, where \"<p1>\", <p2>\" to \"<pno\" are the names of the parameters from each database. In the top left text box, the name of the patient is extracted from the name of the database, which has the format \"<first_name>_<last_name>_<domain>_mat\" and is displayed in the text box.  
When the results database is displayed, the user can select a row from the table and select the \"Prediag\" function in order for the results to be automatically analyzed.  
The platform contains predefined rules for analyzing the values of the parameters and if the statements are true, a message box will appear.  
An improper and uncertain result can affect the judgement when medical decisions are made. To overcome this situation, the predictive value theory, also known as Bayes' theorem, is used to describe the relationship between the posttest and pretest probability of disease or no disease based on the sensitivity and specificity of the test, where P(D) is the probability of disease before the test results are known, P(D\(\mid\)T) is the probability of disease after the test results are known, P(\(\overline{\text{D}}\)) is the probability of disease not present, P(T\(\mid\)D) is the probability that the test is positive when the disease is present (true positive rate) and P(T\(\mid\)\(\overline{\text{D}}\)) is the probability that the test is positive when the disease is not present (false positive rate) [24]:  
\[\text{P(D\mid T)}=\frac{\text{P(T\mid\text{D})\cdot\text{P(D)}}}{\text{P(T\mid \text{D})\cdot\text{P(D)}+\text{P(T\mid\overline{\text{D}})\cdot\text{P( \overline{\text{D}})}}}} \tag{4}\]  
In Figure 6, the first row was selected. The measured pH was compared with the normal pH, which has a normal range of 7.35-7.45. In this case, the pH is above 7.45, which indicates an alkalosis. If it would have been under 7.35, it would have been an acidosis. The second parameter analyzed is the partial pressure of carbon dioxide (pCO2) and it can  
Figure 6: Accessing the stored results: (a) View results by domain and date; (b) Prediag function of the stored results.  
be observed that its value is 20 mmHg, which is under the normal range of 35-45 mmHg, indicating a respiratory disorder.  
The third parameter involved in the pre-diagnostic mechanism regarding the blood gases is the bicarbonate (HCO\({}_{3}\)), which has the result of 22 mmol/L, being at the lower limit of the normal range of 22-29 mmol/L [25]. The algorithm uses \"case\" statements and if the set of results correspond to a specific case, a message box is displayed in which is indicated a non-compensated respiratory alkalosis with hypoxemia.  
For each blood assay domain there are predefined specific rules and messages, depending of the results analyzed using the \"Prediag\" function. If the \"View\" button is selected, the \"Result chart\" GUI is executed.