Modern healthcare is a treasure trove of data that’s ripe for analyzing. With a large enough sample size, valuable predictions can be made based on patterns observed in the past. This allows the healthcare staff to make the preparations needed to handle an unfortunate event before something goes awry by either minimizing the damage caused or avoiding it altogether.
To illustrate the importance of predictive analytics in healthcare, we are going to take a look at some of the common problems that occur in the industry and see how it impacts the bigger picture:
Avoiding appointment no-shows
Unannounced patient no-shows can not only skew a clinician’s entire schedule, but also create workflow inefficiencies and revenue loss. But thanks to modern predictive analytics, it’s possible to predict future business events like these by letting the clinicians know who is the most likely to not show up for an appointment. Based on this information, it’s possible to improve the odds of them showing up by following up with periodic reminders or even give them the possibility to pick another time and date that suits them better.
Predicting equipment maintenance
The rule of thumb is to make your best effort to identify any equipment issues before it needs to be taken offline for maintenance. As it goes without saying, it’s best to fix an equipment-related issue before the device stops functioning altogether to avoid having to call off appointments that are already scheduled. As you may already know, the modern hospital equipment comes with sensors you can hook up to software solutions that provide accurate readings. With the help of predictive analytics, you won’t have to resort to doing any kind of guesswork, since they will alert you when the time comes to apply maintenance fixes. Once this is done, you’ll be able to take that knowledge and pick the perfect timing for maintenance.
Managing resources and avoiding over-encumbrance
A care unit only has so much space, so much time, so much equipment, and other resources. Since emergencies are a standard occurrence in this industry, things can quickly get out of hand. While it’s impossible to predict when a disaster may strike, the next best thing is to anticipate these spikes and make the preparations necessary. For example, having a few spare beds available is a must if you don’t want to find yourself in a position where your hands are tied. The tricky part is knowing the right amount of resources to allocate which can be made simpler by utilizing predictive analytics. At the same time, it allows to always have enough staff available so over-encumbrance becomes less of an issue.
Bolstering cyber security
The sensitive nature of patient data goes without saying. Hence, protecting it by bolstering cyber security in healthcare remains one of the highest priorities to prevent it from getting into the wild and avoiding regulatory fines and loss of patient trust. As luck would have it, predictive analytics has the capacity to detect when something is out of the ordinary, which can make the difference between containing a breach or letting the hackers win. Remember that the latter tend to use the latest technology when making their moves, so it’s important to do the same if your objective is to level out the playing field.
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Conclusion
The examples listed above are only a small fraction of what predictive analytics can do for healthcare providers. As time goes on, adoption rates are projected to skyrocket, making it crucial to make the adjustments necessary today to adapt and beat out the competition.
