Big Data Analytics Reduces Infections in Hospitals

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According to the CDC, more than 2 million patients contract a hospital-related infection annually. The most common Healthcare Acquired Infections (HAIs) are pneumonia, sepsis, urinary tract infection and surgical site infection. HAIs are extremely dangerous to patients as their existing conditions may have left them with compromised immune systems. HAI detection can become difficult as symptoms often present that are similar to other common ailments. Treatment is often further complicated by drug-resistant strains and/or the patient’s already compromised health.

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Big Data Analytics Reduces Infections in Hospitals
There are numerous conditions contributing to an increased risk of contracting an HAI including longer hospital stays, surgical procedures, inadequate hygiene protocols and overuse of antibiotics. In response, hospitals are combining traditional infection control practices with advanced data technologies to combat this pervasive problem.

The challenge with sepsis is early indicators are similar to many common ailments -fever, chills, respiratory difficulties -thus sepsis may not be diagnosed early. To diagnose sepsis, physicians must obtain historical, clinical, and laboratory findings indicative of infection and organ dysfunction. According to the AAFP -in septic shock, the initiation of antibiotic therapy within one hour increases survival; with each hour antibiotic therapy is delayed, survival decreases by about 8%.

Wearables can help detect early indicators of sepsis. This is especially important when the patient is discharged from the hospital. While convalescing at home has many benefits, many patients won’t recognize the early indicators of septic onset. The wearables can transmit real-time vitals to caregivers allowing for early intervention. Early intervention is crucial because as septic progresses organ failure is likely, which is why there is such a high mortality rate from septic shock.

Hospitals are now able to correlate real-time patient data from electronic health records with data indicating emerging environmental conditions to identify who is at risk and what is the best approach to mitigating the risk of infection. This information is added to the knowledge base of where and when an outbreak occurs and which care providers and other patients interacted with the sepsis patient to develop actionable insights into containing the outbreak.

Of all HAIs, sepsis is the most life-threatening. By identifying patients at risk through population health analysis, care providers can then determine appropriate preventative care procedures to reduce the risk of sepsis. This is coupled with real-time monitoring of high-risk patients through wearables and aggregating patient data through EHRs and other test results from various departments within the hospital for greater care outcomes.

Sepsis is preventable. And Big Data can help…

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