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How Data Analytics is Lowering Healthcare Costs
Across industries, big data analytics has changed how we manage, analyze, and leverage data. Healthcare is one of the most visible industries where data analytics has a significant impact.
Fremont, CA: When used correctly, data analytics in healthcare can lower treatment costs, provide a comprehensive understanding of patients and the conditions that affect or have the potential to affect them, and improve the overall quality of life.
Healthcare data analytics combines historical and real-time data to forecast trends, reveal actionable insights, achieve medical breakthroughs, and drive long-term growth.
When data analytics and visualization are used correctly, they can increase patient access to services, resulting in lower costs, more revenue, and higher patient satisfaction. In this section, we will look at various methods for lowering healthcare costs through data analytics tools and solutions.
Electronic health records (EHRs): One of the most significant advantages of data analytics in healthcare is digitizing medical records, resulting in substantial savings. EHRs generate a large amount of data due to the wealth of clinical information. EHR data includes administrative and diagnostic patient information updated in real-time for each encounter. EHR, in particular, provides data on procedures, demographics, length of stay, and fees.
Operating Room Demand is Forecasted: Operating rooms are expensive to build, manage, and staff. As a result, optimizing operating room use without jeopardizing patients' health is in every hospital's best interest. To achieve this goal, several healthcare providers and administrators are using data analytics to understand better the relationships between the numerous operating room variables that wreak havoc on effective scheduling. These variables include the availability of the surgeon, the working hours, and the functionality and availability of the equipment.
Optimizes Staffing: In many healthcare facilities, on-the-fly scheduling is done without considering other factors, which can sometimes result in a staff shortage that affects patient care. Because labor costs account for half of a hospital's budget, accurate staffing is critical. The administration is now aided by data analytics.
Prevents 30-day Hospital Readmissions: Unnecessary readmissions are common in the United States healthcare system. They also impose an unnecessary financial burden on hospitals with limited resources. Reduced readmissions promise to save hospitals money. In addition, data analytics tools can identify patients who have specific symptoms or diseases that cause them to be readmitted.
Prevents No-show Appointments: When patients fail to show up for scheduled appointments, the unexpected gaps in a practitioner's daily calendars can have financial consequences and disrupt the workflow. Using data analytics to identify patients who are likely to miss appointments without notice can reduce revenue loss significantly, allow medical professionals to offer open slots to other patients, and improve customer experience.