One of the challenges of digitalizing our lives is the sheer amount of information that it produces. In 2016, IBM stated that 90 percent of data in the world today had been created in the previous two-year period, and that rate continues to compound. It’s easy to look at Big Data and be overwhelmed by the sheer amount, but it’s equally important to break this impressive forest down into its individual trees. Over the past twenty years, technology and its role in healthcare have increased by leaps and bounds. Nowhere is this more important than understanding the role that analytics plays in medical devices.
The Major Advantages Medical Device Analytics Provide
Data in other areas can undoubtedly improve the quality of life, making actions more efficient, productive or pleasant. When it comes to healthcare, however, utilizing data takes on a literally lifesaving role. Proper analysis of data from medical devices can produce results in four areas:
Descriptive data records what happened. Depending on the medical device being used, this can record relatively simple events (e.g., a seizure) or complex events with multiple coordinating causes, such as a stroke.
Diagnostic data identifies why the event occurred. Baseline information is recorded, including blood pressure, heart rate, brain activity, fluid output and blood oxygenation.
Predictive data uses the composite picture of descriptive and diagnostic data to identify risk factors in patients. For example, certain combinations of electrical activity changes in the brain and a decrease in blood pressure could indicate that a stroke is imminent.
Prescriptive data utilizes information that’s been previously gathered in similar events to recommend medications or treatments that have worked in the past. This isn’t limited to major events (e.g., a heart attack), but can also offer tremendous value when addressing risk factors leading up to the heart attack.
How Medical Device Analytics Bolster Healthcare
Effective data utilization in a healthcare setting can provide advantages in five critical areas. Some of these make hospitals run more efficiently, driving down costs, while others directly contribute to lowered mortality rates and increased chances of patient recovery.
Tracking device performance and health is the most foundational application of medical device analytics. As technology evolves and devices become more capable of running self-diagnostics and proactively reporting when maintenance is required or batteries need to be replaced, that equipment becomes more reliable.
Optimizing equipment use rates is the next level of application. As devices are interconnected across a hospital network, administrators can monitor which units have an excess of equipment and which floors are running short, ensuring that the maximum utility is obtained from devices the healthcare facility already has.
More accurate documentation might seem like a small detail when cast on the backdrop of an emergency situation. However, being able to see trends and record the effects of various medications and interventions is a cornerstone of effective patient care. Effectively leveraging medical device analytics can emphasize precise record keeping.
Recognizing patient decline early is a crucial aspect of patient care. While those in critical care are continuously monitored, more stable patients are checked every 4 to 6 hours. Using predictive data to identify risk factors and prevent health deterioration would allow a higher standard of care to be applied across the entire patient population.
Reducing alarm fatigue has consistently been identified as a top priority by healthcare professionals. Current device design with numerous signals can create a distraction in a single room; when a nurse has to listen to numerous audio signals across an entire floor, it’s easy to miss the more important ones. Designing devices to prioritize certain factors over others will directly contribute to patient recovery and lives saved.
Effective Analytics Occurs at all Stages of Medical Device Use
To gather and use this data effectively, several major stages must be incorporated, beginning with device design. Engineers work closely with healthcare providers to identify what information would be useful for them, how it should be collected, and how it can be displayed to provide the most actionable data. Identifying how that data should be obtained is also critical, so a hospital’s network design becomes a cornerstone of effective medical analytics. Finally, the raw data must be compiled, sorted and interpreted into useful reports, which falls within the realm of data science. Effective medical device analytics truly is a team effort from start to finish.