Remote health monitoring
This tutorial demonstrates how to interpret health reports to help keep track of your client's health and wellbeing
Open a health report
Select a client that you would like to view a health report for and click on "AI Patient Report". This will open the PDF report that the client receives via their client app. This report is updated daily based on information that the client logs in their app and analysis done by Velmio's machine learning algorithms, so you should encourage your client to complete their medical history questionnaire and log symptoms regularly to benefit from this feature.
Interpreting the symptom analysis
The symptom log classifies symptoms into the following categories: "Aches and pains", "Stress and anxiety", "Sleep issues and fatigue", and symptoms that do not fall into any of these categories. The gauge plots show how many symptoms the client logged in the past 7 days for the three named symptom categories. The text underneath each gauge plot indicates how this has changed compared to the previous 7 days, so that you can quickly spot any worrying trends. For example, "+1 this week" for "Aches and pains" means that the client logged one additional "Aches and pains" symptom this week compared to last week.
Interpreting the symptom analysis (continued)
The bar chart shows you the complete list of symptoms that the client has logged over the past month. The bars are in order of most frequently logged symptom to least frequently logged symptom, so that you can quickly see which symptoms are causing your client the most trouble. The colors correspond to the prevalence of the symptom in the general pregnant population. Darker colored bars mean that the corresponding symptom is more likely to occur at the client's current stage of pregnancy.
Interpreting the symptom trends
The symptom trend plot shows population data (not the client's own data) for the client's current week of pregnancy. In this example we can see that at the client's current week of pregnancy (week 5), symptoms such as nausea, tiredness and aching breasts are common. The plot also shows how likely these symptoms are to come or go in the following weeks of pregnancy. The client in this example is in the early stages of pregnancy, so their plot shows that these symptoms are likely to become more pronounced in the following weeks of pregnancy, during the first trimester.
Interpreting the risk factors
The client app provides a questionnaire about the client's medical history. Based on their responses, Velmio's machine learning algorithms calculate risk scores indicating the likelihood of various health issues occurring during the pregnancy. Machine learning predictions are probabilistic in nature, meaning that they are subject to random chance and a low risk does not guarantee that a health issue will not occur. If a client is concerned about being at higher risk for certain pregnancy complications, they may like to consult a medical professional about suitable preventative measures.
Interpreting the pregnancy history
The client is asked to provide information about any previous pregnancies in their medical history questionnaire. The pregnancy history chart shows an "x" next to any responses indicated by the client.
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