Patient data is very important for healthcare professionals because it adds value to life more than to business. And data analytics is important, whether it is using data to detect anomalies in a tumor report or review the spread of disease. Data analytics solutions play a vital role in the everyday healthcare of patients.
The healthcare industry is one of the most expensive areas on a global scale. Healthcare expenses will be around $28 billion by 2025 according to an estimate. Data analytics is here to help hospitals and healthcare systems cut down costs while improving outcomes.
Here is an article on how data analytics is helpful in the healthcare industry:
Uses of Healthcare Analytics
1) Predicting Hospital Visits
Covid-19 has shown that both health of people and the economy of a country can turn upside down any day. And the pandemic has revealed the limited healthcare resources and its consequences. The shortage of hospital beds during the pandemic was a global issue. Predicting hospital bed usage is an important concern. It helps hospitals effectively plan in-patient care.
Healthcare professionals in hospitals used predictive analytics solutions to predict hospital admissions and emergency room visits. The solutions employed time series analysis algorithms to predict the admission rates based on factors such as heat waves or flu season. Leveraging healthcare data to gain actionable insights helps to realize great benefits. Predicting the patient flow at the emergency rooms and becoming proactive helps the hospitals to provide better care.
2) Reviewing Co-Morbidities with Healthcare Analytics
Co-morbidities and drug side effects are one of the important aspects that patients forget to note during their treatment. Healthcare professionals need to be aware of the patient’s co-morbidities while prescribing medications. Co-morbidities are a significant concern, especially in the case of type 2 diabetes.
Data analytics solutions enable better co-morbidities assessment and prediction. The doctors can determine which cases are likely to result in sepsis due to long hospital stays or develop other complications. The analytics solutions provide a co-morbidities risk assessment to doctors, so they change the treatment to slow down disease progression.
3) Ensuring Regular Health Checkups
Self-medication and failure to go to the doctor are primary reasons for adverse medical conditions. Such outcomes could be avoided when patients don’t medicate on their own and when they make it to their doctors’ appointments. AI-based healthcare analytics solutions are used to track the patient no-show rates and follow up with the patients.