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How Responsible Healthcare Data Management Saves Lives
Healthcare has made enormous strides in recent years, thanks to technological breakthroughs. Advanced treatment methods and detection tools allow doctors and nurses to provide better care faster, but medicine is still far from perfect. Big data can help improve it.
Business analytics and similar practices could make healthcare more accurate, more affordable and more effective. Though the methodology is still in its initial stages, big data examples in healthcare demonstrate significant promise.
1. Improved Screening
Intelligent computing systems are often superior to humans in the realm of data analysis. These systems can analyze immense quantities of information in short amounts of time, offering efficient and thorough insights and predictions. This functionality makes big data ideal for healthcare, as it would improve the screening process.
Data analysis systems have the potential to identify health risks before illnesses develop. The vast majority of health factors occur outside of a clinical setting, making them difficult to examine or measure with a traditional screening process. Big data analytics can consider these factors and produce a more robust picture of an individual's health.
2. Enhanced Treatment
The potential of data analytics in healthcare doesn't end at diagnoses. It can provide solutions to more appropriate treatment as well. Some hospitals and research centers have begun using big data systems to determine the best ways to treat patients.
These systems analyze patient health records as well as commonalities across those with similar conditions to produce an optimized treatment plan. Insights allow doctors to tailor an individualized approach to serve each patient best.
3. Optimized EMS
Just as big data improves companies' productivity levels, business analytics in healthcare can optimize hospital operations, such as EMS. Real-time data analytics can improve the efficacy of EMS by determining and then measuring key performance indicators.
AI systems can make connections between seemingly unrelated factors that human analysts might miss. By gathering and analyzing information throughout the emergency response process, these programs could highlight potential areas of improvement.
Using these insights, EMS teams can adjust their operations to reduce response times significantly, getting patients the help they need quicker.
4. Efficient Alert Systems
Many people live with health conditions that may not require constant attention but could worsen at any time, putting them at risk. Data gathered from a variety of health devices provides sufferers with an early warning system. Wearables can monitor their bodies and alert both them and their care providers if there's a potential issue.
Similarly, devices like the Asthmapolis inhaler can track both risks in individuals and broader health trends. With the widespread use of these technologies, healthcare professionals can administer care quickly as well as catch growing issues before they become epidemics.
5. Intelligent Scheduling Solutions
Though they may not seem like a critical healthcare concern, staffing and scheduling significantly affect patient care. Being understaffed can lead to chaos, with patients in need having to wait inordinately long before receiving help. Overstaffing can crowd workspaces and drive up costs.
A few hospitals in France are attempting to solve this problem with AI, using predictive analytics to determine staffing needs. By predicting admission rates, these systems can inform managerial staff if they need to schedule fewer or more workers.
Integrating Big Data in Healthcare
Many of these examples of big data in healthcare are still in a trial period. The technology and how organizations use it may not be perfect yet, but the preliminary results are promising. As more hospitals adopt these systems, their function will improve.
Carefully integrating big data analytics into healthcare systems can save hospitals money and, more importantly, save patients' lives. If healthcare professionals are responsible in how they manage information, it could lead to a safer, more efficient field of medicine.