Big data analytics can help optimize healthcare operations by uncovering relevant patterns, offering supply chain analysis, and enabling performance monitoring. It can reduce labor costs and improve customer service.
Big data can also analyze patient behavior, identify new treatment methods, and speed up drug development. It can even detect patient health trends, helping prevent human errors and cut medical costs.
In This Article
Identifying At-Risk Patients
Data analytics enables healthcare professionals to identify patients who may be at risk. It can be done through genetic testing, imaging studies, claims-based data, and more.
For instance, Tempus has created a library of molecular and clinical data that helps doctors understand the unique context of each patient’s cancer case. It allows them to create targeted treatment plans for each patient.
We can identify patients susceptible to health risks and provide preventive care using predictive analytics. It can reduce hospital admissions and overall costs. Healthcare organizations can monitor patients’ behavior and sentiment to ensure they follow their treatment plan. This information is then used to target educational campaigns.
Preventing Human Errors
The healthcare industry generates vast data that needs to be captured, stored, and analyzed for the best results. Healthcare data analytics software can help eliminate human errors that may lead to misdiagnoses, improper medication dosages, or medical malpractice.
Data analytics can also monitor patient behavior and sentiment. This information can help promote positive habits, identify common issues, and enhance treatment methods.
Predictive analytics can help identify growth opportunities for a healthcare organization. This technology uses real-time and historical data to forecast trends and patterns. It can help them align their efforts with a desired outcome, maximize outreach efforts, and make more informed decisions about their budgets and resources. It can also prevent fraud and abuse that would otherwise cost hospitals a lot of money.
Identifying New Treatment Methods
The healthcare industry generates vast data that can be leveraged for procedural, technical, and medical improvements. Emerging ML or AI-based strategies like natural language processing are refining this data and making it more useful in analysis.
Using data analytics, healthcare professionals can determine the most effective treatment methods for each patient, improving the quality of care.
Data analytics is also helping reduce prescription errors by analyzing the results of previous treatments and alerting doctors when something seems wrong. It’s also allowing researchers to predict outbreaks of diseases and respond quickly. It’s even helping to reduce hospital costs by enabling healthcare organizations to forecast their supply needs more accurately. It saves money that can be used to treat more patients.
Monitoring Patient Health
Data analytics allows healthcare institutions to monitor patients’ real-time behavior and sentiments. Using this tool can help encourage better patient habits and detect potential problems early on, preventing them from becoming dangerous or life-threatening.
Predictive analytics is also used to reduce hospital readmission rates and prevent chronic illnesses by analyzing patients’ lab test results, insurance case histories, personal health records, wearable technology data, and other sources of information. This data is analyzed to identify those at risk of developing diseases and provide them with the right treatments to prevent their illness.
For example, one woman visited the ER on more than 900 occasions for three years, so doctors used data analytics to analyze her medical history and quickly identify the cause of her symptoms. It prevented unnecessary hospital visits and improved her chances of survival.
Identifying Patients at Risk for Cancer
Data analytics can improve patient outcomes and operational efficiency in a healthcare system. Precisely, this is accomplished by applying predictive models to medical, insurance, or consumer data. These models help healthcare professionals identify and implement outreach initiatives that align with a desired outcome and forecast potential growth opportunities.
It is possible to identify patients at risk of developing future health problems through predictive analytics. It can help to reduce hospital admissions and provide preventative care. For example, a recent white paper by Intel explains how four hospitals in the Paris area were able to predict Covid-19 patient numbers using predictive analytics. It enabled the hospitals to allocate resources and staff better, reducing the number of patients that required a hospital stay.
