SUMMARY
Coming up with the correct diagnosis and anticipating the progression of a certain disease per patient profile could make a difference. On a daily basis, physicians have to make critical diagnosis decisions under time and workload pressure. The decision itself is influenced, among others, by the patient profile, patient-doctor relationship, test availability/hospital resources or experience of physicians. Using predictive analytics, the efficiency of the diagnosis process will improve, positively impacting the level of assistance and monitoring of the patient.
In this sense, an early disease diagnosis and progression prediction will also help physicians to anticipate the potential evolution of the disease, allowing them to take early actions to mitigate the expected effect on patients. It will also help in limiting the most invasive medical tests to the right patients. This increases the diagnostic accuracy and reduces the number of unnecessary invasive tests, benefitting the patients and ensuring a better allocation of resources.
Knowing in advance the potential number of patients that will require certain services or potential changes between outpatients and in patients, will also help hospitals and care centers to allocate resources more efficiently; from personal resources to facility management and medicines and equipment procurement.
This paper explains the approach of the TAZI Disease Diagnosis and Progression Solution. The solution is based on TAZI’s patented Continuous and Explainable, No-Code, Automated Machine Learning platform. How continuous learning explores the patient patterns and trends will be discussed. Moreover, how the physicians will approach the risk of the potential diseases and how could improve the services provided are to be explained. Conclusively, the customization level of this solution will be explained and potential improvement in resources allocation will be discussed.
