It’s a Tuesday night. A nurse in the emergency department (ED) receives an alert on her smartphone: the ED will be overcrowded after 1.5 hours. The alert also gives suggestions, such as the number of beds that will be filled or what type of care will be required. The nurse uses this information to communicate with transport, radiology, and lab teams to make the necessary preparations. As a result, when patients start to pour in, the ED is in a better position to treat them.
This is an example of real-time healthcare analytics in action. The nurse uses an app that leverages historical data trends of the ED to notify her about expected outcomes.
However, things don’t always sail this smoothly in the healthcare industry. Due to data silos, information isn’t readily available. Real-time healthcare analytics helps make processes more connected by ingesting large amounts of aggregated data and providing useful insights. These insights, in turn, improve operational efficiency and patient care at every level.
Analyzes EHR Data To Improve Patient Care
An electronic health record (EHR) is a digital record of patient information stored in a database. This includes medical history, prescriptions, lab results, diagnoses, and treatments. EHRs might collect and show data, but they don’t have the ability to analyze that data in real time.
Real-time analytics provides updates to healthcare providers every second. Medical professionals can not only review medical information but also receive suggestions and recommendations based on this information. Your real-time system is designed to ingest all the relevant data points from the EHR (e.g., progress and nursing notes) and identify patterns that can help with diagnoses. It can detect minor changes in a patient’s condition as they happen and highlight individuals who need to be prioritized because of their deteriorating condition.
Every year, 270,000 people die due to sepsis. Detecting sepsis in the beginning stages is crucial, but it can get tricky due to the similarities between the symptoms of sepsis and other diseases. The combination of AI and real-time analytics can be effective in the early detection of this condition, increasing it by up to 32%, according to one report.
The Medical University of South Carolina (MUSC) is a healthcare institution working on this combination. The hospital management uses real-time data from EHRs and machine learning algorithms for creating classifiers (a deep-learning algorithm that can categorize data into relevant categories) to identify when a patient’s health worsens due to sepsis.
Encourages People To Look After Their Health
Over the last few years, smartwatches and fitness trackers from the likes of Apple, Samsung, Fitbit, and others have enabled many people to monitor their own health and adopt healthier habits. They help people walk more by tracking their daily step count via in-app challenges, calculate the calories they lose during workouts and sports activities, and monitor their daily caloric intake. These wearables collect data from their sensors and use real-time analytics to provide useful insights.
While these devices are far from replacements for a doctor visit, they might alert the user to potential health risks. If someone notices their heart rate is often too high/too low, that is an incentive to visit their physician and check if everything is okay.
Apple Watch tracks your heart rate and alerts the user if it’s more than 120 or less than 40 while the user is in a resting state. Recently, Apple Watch helped a 12-year-old girl, Imani Miles, to go to a hospital on time. Miles continuously received alarms from her watch, which alerted her mother about her unusually high heart rate. Miles was taken to a healthcare facility where doctors found her suffering from a rare condition in children: a neuroendocrine tumor on her appendix. According to Miles’ mother, her situation could have been worse without the watch warning them.
Manages Disease Spread
Real-time analytics can help you identify trends in the spread of an illness, allowing healthcare institutions, doctors, and individuals to manage better.
After COVID-19 wreaked havoc across the world in 2020, real-time analytics was used to identify the growing disease. Healthcare organizations used a set of data management techniques to learn how fast the virus was spreading in real time and how it mutated under various conditions.
For example, the EU launched a software in 2020, InferRead, that collected image data from a CT scanner to analyze whether lungs were damaged due to a COVID infection. This analysis was generated within a few seconds, allowing a doctor to study it and diagnose the patient quickly.
Real-time analytics can also help to manage resources in the case of an outbreak. In the US, the Kinetica Active Analytics Platform was used to create a real-time analytics program for aggregating and tracking data. The purpose of this program was to aid emergency responders by collecting information on test kit quantities, PPE availability, and hospital capacity. This allowed decision-makers to determine whether they could redirect patients to a hospital with capacity or set up alternative triage centers. Similarly, these insights also helped to distribute PPE to the locations where it was needed most, especially when a shortage made it harder to access.
Optimizes Hospital Staff Allocation
Staffing is one of the biggest challenges for healthcare organizations. Sometimes, the patient inflow and urgent tasks at hand are minimal while too many nurses are present. Other times, there aren’t enough nurses to cater to the hospital’s needs.
Real-time healthcare analytics can help predict staffing scenarios and requirements based on the historical data of that hospital. By going through a hospital’s historical information and examining how nurses and other staff operated at different moments, real-time analytics produces recommendations for each hour and also factors in unexpected scenarios. This can improve patient care by ensuring patients receive an adequate level of care without the hospital lacking any resources.
Intel published a paper that discusses the use of real-time analytics by four hospitals that collect data from a wide range of sources to generate predictions on hospital admissions. The system used time series analysis — a type of statistical technique — to identify key patterns from the hospital’s admission records and determine when and how many patients will enter its premises every hour.
Data also help healthcare institutions increase their employees’ job satisfaction and minimize turnover rates. For example, they can find what percentage of staff is experienced and willing to work emergency shifts or overtime when incentivized. Similarly, they can pinpoint individuals that have been overburdened and either distribute their load to other employees or hire more workers.
Improve Patient Care With Striim
If you run a healthcare organization and are looking to make the most of real-time healthcare analytics, you need a reliable tool that can ingest and analyze large amounts of data daily.
Striim drives decision-making for your departments based on data generated in your sources in real time. For instance, you can dispatch emergency services when an alert notifies you about a drop in a patient’s vitals at an outpatient clinic. Learn about more use cases and how Striim can modernize your healthcare institution by requesting a demo today.