Three Ways Real-Time Data Changes the Delivery of Healthcare

The following is a guest article by Michael Sanky, Global Industry Lead, Healthcare & Life Sciences at Databricks.

Across industries like retail, media, and financial services, real-time data can improve the customer experience, boost sales, and help achieve faster solutions to problems. In healthcare, access to real-time data not only has a profound impact on improving patient care, but it also saves lives through early detection and by getting patients the care they need quickly.

This McKinsey report on transforming healthcare with data and artificial intelligence (AI) paints an important picture of the impact that new technology can have on our current healthcare landscape for healthcare providers. On a day-to-day basis, it can improve productivity and efficiency of care delivery. For healthcare professionals, it can save hours during the day on admin-related tasks and create space for them to use their time where it adds the most value: responding to patient needs.

While AI and real-time data solutions can deliver drastically improved healthcare solutions across an array of use cases, here are the top three most impactful in my mind right now.

Interventional care

Sepsis is a leading cause of death in the US, accounting for half of all hospital deaths. Early sepsis prediction is critical, however, it’s challenging given that some of its signs and symptoms are similar to those of other, less critical conditions. The value of early intervention in cases of sepsis cannot be overstated – if you catch it early enough ahead of a certain level of patient deterioration, you can change the outcome. Real-time data and AI can help us track the patient status and lower the time to diagnosis of both sepsis and other critical healthcare issues. One report shows an AI algorithm’s potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%.

To illustrate the power of intervention care, one good example comes to mind. Over the past few years, the Medical University of South Carolina (MUSC) used data analytics and machine learning (ML) modeling to capture live streaming data from electronic health records (EHRs) and real-time telemetry instruments in the ICU. The team then used these records to build better classifiers for patient deterioration due to sepsis and later used that same data program to assess Covid-19 patients at risk of decline. MUSC is just one of many hospitals taking steps to improve its intervention care models through the help of unified data analytics and AI platforms.

Resource optimization

50% of global healthcare leaders call their organization’s data analytics and predictive modeling usage “poor” or “fair,” according to recent HIMSS research. Physicians have ways to improve data usage, especially when it comes to resource optimization — including moving staff around efficiently and identifying where a patient can receive care the fastest.

One hospital system we work with has done incredible work on the resource optimization front through the use of real-time data and AI tools. Its national emergency score of wait times billboards helps patients see where they can get ER care the fastest and which hospitals are full. The hope is to one day be able to triage ambulances and use the real-time data to help these ambulances reach the right hospitals for specific patient needs faster. Consider operating room (OR) capacities — if an ambulance is headed to a hospital where the OR is already full, gathering that data and showcasing it across geographies and hospital systems can get the patient redirected to a location where a surgeon is available to meet their needs faster.

Of course, the work done on the resource optimization front has been under a magnifying glass during the pandemic and as hospital systems reached (and are still reaching, during the Omicron surge) bed capacity. Ensuring seamless integration of technology and data that informs resource optimization into existing processes will remain critical as more hospital systems and healthcare providers work to continuously improve patient care.

Digital engagement

Think of how many Apple watches, Fitbits, and other step-counting, heart rate monitoring wearable devices you see on people at the gym or just while walking down the street. We’re accustomed to the basic real-time features and data that devices like the Apple Watch offers, but we can only expect more advances on the wearable front that help us better manage our health.

Rumors of health updates for the Apple Watch 8 just surfaced last month, and they include a thermometer to help with fertility planning and blood-pressure monitoring, which would work by “measuring the speed of the wave a heartbeat sends through a person’s arteries using sensors.” “Credible rumors” also suggest features like non-invasive blood glucose monitoring and sleep apnea detection could also be on the way. One company called Livongo already produces glucose monitors that stream real-time data into a mobile app to provide personalized behavioral recommendations.

While the efficacy of the new Apple Watch features remains to be seen, the progress on the wearable front has been notable and indicates a move toward consumer ownership of personal healthcare data and increased insights through real-time updates.

Progress in the real-time data and technology space is exciting across many industries, but nowhere does it seem more impactful than in the healthcare space. With faster access to a more comprehensive set of health data, we have the power to save lives, optimize resources to improve patient experience, and put everyone in the driver’s seat when it comes to maintaining personal health.

   

Categories