Predictive Analytics model to forecast the likelihood of patients being tested positive for COVID-19, as well as the assessment of risk for Covid-19 patients based on past medical records.
As the global healthcare machinery is crumbling under the increased demands for Covid-19 testing and treatment, the number of crises for hospitals and health care organizations are spiking as well. However, Data scientists believe that through a predictive analytics model possible outcomes for each patient can be forecasted based on their past medical records, at both the testing and treatment phases. A data-based Covid-19 Risk Assessment Plan can enable the hospitals to be prepared and agile in resource management while treating Covid-19 patients.
Governments and healthcare workers are still battling the first wave of the Covid-19 infections; and predictions for a potential second wave have already been made. Assessment of a people’s health risks will be of prime importance in care-giving and treatment planning.
Data scientists are capable of developing a specific analytics model that feeds on patient data from the hospital or clinic’s electronic medical records; it ingests the data using statistical algorithms and calculates health risk possibilities in patients.
Key findings from those insights can reveal high, medium, and low-risk patterns. For example, the likelihood of some patients testing negative for Covid-19 who were given a certain flu vaccine/drug in the past can be noted. Additionally, it can also forecast the requirement of hospitalization/critical care for certain Covid-19 patients based on the severity of other health conditions they might have.
Findings of predictive analytics studies can report probable risk factors based on gender, age, geography, and socio-economic conditions as well. It can also highlight and correlate the intake of treatment drugs and its impact on various subsets of patients.
Based on these insights, Hospitals can preemptively notify the high-risk patients to come in for check-ups, if they haven’t already. This will expedite the process of identifying, isolating, and treating Covid-19 patients effectively. For the patients who have tested positive for Covid-19, Hospitals can make use of the knowledge from the drug/treatment correlation insights in order to provide precision medicine to each patient. For example, say there are three drugs for Covid-19 treatment-A, B and C. Predictive analytics insights based on past patient records suggest that a combination of drugs A and C has helped 7 out of 10 male patients of advanced age with no underlying health conditions to recover quickly. For the next patient of the same subset (male patients of advanced age with no underlying health conditions), a precise combination of medicines could be administered by healthcare workers based on the insights.
Continuous data-driven resource management and implementation of precision medicine by health care institutions can not only reduce the health risks of Covid-19 but also counter the progression of the disease.
Moreover, while improving the efficiency of testing and treatment, healthcare institutions will also be able to optimally utilize hospital beds infrastructure, protective equipment, and healthcare personnel.
To discuss a data-based Covid-19 Risk Assessment Plan for your clinic, hospital, or healthcare center, contact the team of data scientists at Enquete Group today.
Read more blogs
- Can AI and Predictive Analytics be Game-Changers in Optimizing Marketing Campaigns?
- Role of predictive analytics to forecast patient census & capacity of healthcare systems
- Role of predictive analytics in social media to make more informed business decisions
- How Predictive models can help in forecasting COVID-19 patient’s risks & outcomes based on past medical records?
- Predictive Analytics: Future of Agricultural Industry