Healthcare Data Science Use Case #4: Healthcare Predictive Analytics
Predictive Analytics in healthcare is one of the most widely discussed topics in health analytics. A predictive model utilizes historical data to learn from it, discover patterns, and make accurate predictions.
It discovers correlations and associations between symptoms, habits, and diseases and makes meaningful predictions.
Healthcare predictive analytics is helping to improve patient care, chronic disease management, and the efficiency of supply chains and pharmaceutical logistics.
Population health management is becoming a hot topic in predictive analytics. It is a data-driven method of disease prevention that focuses on diseases that are prevalent in society.
With the help of data science, hospitals can predict patient health deterioration and provide preventive measures and early treatment, reducing the risk of further deterioration of patient health.
Predictive analytics in healthcare is also helpful in tracking the logistic supply of hospitals and pharmaceutical departments.
Healthcare Data Science Use Case #5: Patient Health Monitoring
Data science is critical in IoT. (Internet of Things). These IoT devices, which are present as wearable devices, track the users' heartbeat, temperature, and other medical parameters. Data science in healthcare is used to analyze the data that is collected.
Doctors can use analytical tools to monitor a patient's circadian cycle, blood pressure, and calorie intake. A doctor can monitor a patient's health using home devices and wearable monitoring sensors. Several systems for chronically ill patients track their movements, monitor their physical parameters, and analyze the patterns in the data.
It uses real-time analytics to predict whether the patient will have a problem based on their current condition. Furthermore, it assists doctors in making the necessary decisions to help patients in distress.
Healthcare Data Science Use Case #6: Disease Monitoring and Prevention
Data science is critical in monitoring patients' health and notifying requisite steps to be taken to prevent possible diseases from occurring. Data Scientists use powerful healthcare predictive analytical tools to identify chronic diseases early on.
In many extreme situations, diseases are not detected at an early stage due to their ineligibility. This has a negative impact on not only the patient's health but also the economic costs. As a result, data science healthcare plays a significant role in optimizing financial spending on healthcare.
In several cases, AI has played a significant role in identifying diseases at an initial stage. Researchers at Brazil's University of Campinas have created an AI platform to detect the Zika virus using metabolic markers. Machine learning is being used by several other companies, including IQuity, to detect autoimmune diseases.
Healthcare Data Science Use Case #7: Offering Virtual Assistance
Data scientists have created an extensive virtual platform that assists patients with the help of disease predictive data modeling.
Patients can use these platforms to enter their symptoms and receive information and insight about the various potential diseases based on their confidence rate. Data science applications in healthcare help patients suffering from psychological issues such as depression, anxiety, and neurodegenerative diseases such as Alzheimer's use virtual applications to assist them in their daily tasks.
Ada, a Berlin-based startup that predicts diseases based on the user's symptoms, is a popular example of a virtual assistant. And Woebot, a Stanford University-developed chatbot that provides therapy treatments to patients suffering from depression.
Healthcare Data Science Use Case #8: Preventing Mistakes and Streamlining the Process of Medical Records Management
With the recent increase in health-related issues, more medical establishments have recognized the importance of building a system for proper medical record management.
Several organizations provide medical record management training. The only thing that matters is that the training you receive comes from an authentic and well-known organization and will assist you and your staff in performing your roles effectively. Training programs typically last six weeks. You must be extremely careful to ensure that all the information and protocols you've learned have been implemented correctly in your hospital or clinic.
When you have an electronic health record, you will better understand your patient's needs. Patients' current health status, drug prescriptions, and up-to-date information can be centralized. When medical records are in electronic form, it is also easier for other doctors and nurses to review them. When medical records are in electronic form, it is also easier for other doctors and nurses to check them.
Applying data science in healthcare will aid you in your record-keeping duties by organizing your medical records. Any of them can be downloaded from the Internet and installed on your computer or laptop. This is one of the most basic methods for avoiding medical record mismanagement. Such programs have various characteristics, so compare them carefully to choose the most appropriate software for your health institute.
Healthcare Data Science Use Case #9: Automatic Disease Detection via Wearable
The amount of data generated by the human body each day is two terabytes. We can now collect most of it thanks to technological advances, including information about heart rate, sleep patterns, blood glucose levels, stress levels, and even brain activity. With such a wealth of health data at their disposal, scientists are pushing the limits of health monitoring.
More common conditions, such as heart or respiratory diseases, can be detected and tracked using machine learning algorithms. Technology can detect the smallest changes in a patient's health indicators and predict potential disorders by gathering and monitoring heart rate and breathing patterns. While 600,000 people in the United States die from sudden cardiac arrest each year, having the ability to predict the issue and send out timely alerts could save countless lives.