Artificial intelligence, data visualization, business intelligence, predictive analytics, machine learning – you’ve likely heard these words but been unsure as to their actual applications. These buzzwords are associated with big data, and they apply to healthcare more than you think.
Big data is a term generally used to describe the large amounts of data that an organization has access to that can potentially be put to use. Big data might also be referred to as data analytics. Big data can be used to find patterns in an organization and make predictions for the future.
Hospitals, specifically, are collecting huge amounts of data at all times. Data amassed from smart phones, payer records, patient portals, research studies, public records, and electronic medical records are all accessible to hospitals. Unfortunately, with such a diversity in format, type, and context, merging, dissecting, and processing this data in order to form conclusions or predictions can be nearly impossible. The challenge for all industries, specifically healthcare, is to take big data and break it down into useful information. In other words, hospitals need to make big data small again to make it useful.
Even though, healthcare tends to lag behind other industries when it comes to adopting new technology, many hospitals are utilizing innovative technologies to convert their big data into actionable information. At August Health in Virginia, for example, combining EHR data with geospatial mapping tools provides a uniquely useful visual exploration of how multi-drug resistant organisms (MDROs) spread between patients. Artificial intelligence (AI) is also making waves in healthcare. A study published in Nature shared data about an AI system “capable of surpassing human experts in breast cancer prediction.”
Radiology, a field that formerly has been viewed as just yielding a set of images, is using big data to find patterns and connections across different sets of images. With more and more historical data, radiologists can make predictions of future anatomical changes in a patient. At Emory’s Center for Biomedical Imaging Statistics, researchers are working on creating analytical tools that search for and find biosignatures in brain scans that identify abnormalities such as mental illnesses, addiction, and cognitive issues.
Clean Hands – Safe Hands is dissecting big data to make it actionable for customers. This starts with real-time feedback. Facilities don’t have to wait for a report or for the numbers to be crunched. While reports are sent and the data is aggregated and stored, real-time alerts can notify leaders to problems before they happen. The Real-Time Intervention Blueprint™, for example, alerts unit leaders when a room is getting very little activity or maybe a lot of activity, but very poor hand hygiene. Leaders can use this actionable data to quickly determine which patients aren’t being visited regularly or which providers aren’t washing their hands when they are supposed to.
Additionally, Clean Hands – Safe Hands pulls data across many different sources, including the hospital’s clinical surveillance system. With integrations with systems like Theradoc, room modes can be automatically updated so patients receive the right care. Patients in isolation or with C. diff have different protocols than regular patients even down to how providers sanitize, so ensuring that the room is properly marked is important. Big data makes this easy.
Actionable big data can transform staff workflows as well. Clinical intervention data is invaluable to hospitals and allows leadership to make informed decisions around staffing. With an IoT system like Clean Hands – Safe Hands, clinical intervention data is both accessible and digestible. The system can be used to measure nurse rounding and pinpoint workflow inefficiencies. In one hospital, a team was dedicated to the care of central lines. Their supply carts were too big to fit inside rooms, so they would stay parked in the hallway. Clinicians would go in and out of the room every time they needed supplies and they usually did not clean their hands. The staff were doing what was practical, but this presented a significant risk to the organization because they could be transferring infections to the cart and to other patients. The Clean Hands – Safe Hands system alerted the hospital to this inefficient and potentially dangerous situation. The hospital changed their policies to provide supplies next to each room’s bed instead.
Big data is going to continue to grow, and hospitals are going to need the infrastructure to make that data actionable. Data can be powerful, but only if it’s broken down into useful pieces. To learn more about equipping your hospital with the technology needed to process your data and provide real-time feedback, contact Clean Hands – Safe Hands.