While the entire world has faced a staggering blow as a result of the COVID pandemic, none have felt the brunt of that impact more than the healthcare sector. Closed businesses have begun to experience soft re-openings, employees are seeing some more of their hours return, but healthcare has been experiencing a consistent and unrelenting attack on its resources, morale, and profit for several months now. Unfortunately, with hospitals now opening back up for non-essential services and elective procedures again, it doesn’t look like they’ll be out of the forest for a while longer as there’s a new host of issues to deal with as a result. 

As old patients who were previously staying home to keep themselves and their loved ones free of infection trickle back into care facilities, they’re going to need care for conditions and illnesses they haven’t been able to treat for months. Thankfully, hospitals have already done a wonderful job employing medical computer systems in order to address scheduling and patient cancellations and delays; however, several of these incoming patients have ignored treating their chronic conditions and will be seeking care for exacerbated symptoms that will require intense, involved, costly treatment. 

While this can bring in a little more revenue for beleaguered facilities by tackling these treatments, they’ll need to prepare for this overflow of sicker patients with their likely very limited resources. In their efforts to do so, many hospitals and acute care facilities have turned to predictive analytics in healthcare.

Predictive Analytics Defined

Predictive analytics in healthcare refers to the practice of using health and patient data to predict future developments in a particular patient’s condition and using that to inform decision making on their treatments. If employed correctly, predictive analytics in healthcare can draw on several sources of information in order to enhance patient care. Patient health history, socioeconomic factors, data on other patients with similar conditions or health histories- these are all pieces of information that can be poured through in order to enhance patient care by getting ahead of future health issues. 

Sounds like a no-brainer, right? Of course being able to treat conditions before they arise would be a boon to patient outcomes. So, what makes this particular piece of healthcare innovation tech more relevant today?

How is Predictive Analytics in Healthcare Addressing the Aftermath?

We’re hardly in the aftermath of the current pandemic, however, even after COVID is dealt with, the healthcare sector’s issues aren’t simply going to end. Once patients deem it safe to come back to their physicians and receive treatment for their long-standing ailments, facilities will begin facing an onslaught of work in a time where their resources are at their lowest.

What predictive analytics in healthcare can do is help facilities prepare for this inevitable surge by giving them insight into the conditions they’ll need to treat, and the resources needed to treat them. 

Let’s say, for example, a facility knows that their served patient population has a higher rate of diabetes. Using predictive analytics tools, that hospital could use established information on their patients, such as the data stored in their EHRs as well as the large pool of information they have on diabetic patients and how their symptoms worsen, to predict the kinds of treatments they’ll need to deliver. By knowing to some extent what to expect as far as worsened symptoms and their treatments, that facility can begin to prepare by bringing on the proper staff, bolstering their necessary medical equipment stocks, and so on. 

A proactive approach such as this can be invaluable for many facilities, especially those that know what ailments commonly plague their patient-base. Getting started, however, requires the right predictive analytics tools.

The Must-Have Predictive Analytics Tools

As far as building out the data gathering infrastructures needed to get started in predictive analytics in healthcare, there are a few strong first steps your facility can take. Below are only a few popular data-powered health solutions that have been leveraged towards predictive care. 

Population Health Management

Better than simply understanding a single patient is preemptively understanding the population your facility treats as a whole. This is the philosophy behind population health management. By building out information on a population such as its residents’ socioeconomic factors, common illnesses, and even findings made by other clinicians who treat the same population, a facility can be better informed on what treatments their future patients will require.  

Building out this network can be started by first reaching out to your state’s HIE representative for health data exchange providers and clinical collaboration system recommendations. If you’re interested in population health management and how to get started in that area, there are also a few other best practices you can invest in to get the ball rolling.

Patient Monitoring Hardware

The more patient data you have available, the more accurate your predictive analytics can be. To that end, remote monitoring programs, in much the same way as population health management, can help deepen the pool of knowledge your team has access to by giving patients devices capable of tracking important vitals and symptoms. 

As telehealth continues to explode in popularity and healthcare becomes more open to innovative new technology, several providers have begun building out the modes of communication necessary to gather patient data remotely. Through self-use tools capable of tracking vitals and symptoms such as oximeters, blood pressure cuffs, and more, patients can gather and record their own symptoms and share with physicians how they’ve gotten better or worsened daily.

Using this information as a guide-post, nurses and doctors can begin to predict with more accuracy how other patients with similar conditions, lifestyles, and socio-economic conditions will develop or adapt to similar forms of treatment.

Remote patient monitoring remains more promising than ever, especially according to a recent study by Sony’s Wearable Platform Division. In it, they emphasize that patients are very receptive to being administered monitoring hardware, with 75% saying they would happily use them to gauge, track, and treat their chronic conditions. Those interested in gathering more data from patients remotely can easily begin building out the network to do so by investing in 3 key pieces of hardware and software

Secure Data Storage

Predictive analytics tools aren’t strictly limited to data gathering hardware/software. Special attention needs to be paid in how that data is stored, processed, and protected. Cybersecurity threats have shown no signs of slowing, in fact, they’ve only increased in frequency as criminals sense facilities are at their most vulnerable with the pandemic taking the lion’s share of their attention. 

Naturally, efforts to gather the amount of valuable, highly targeted information necessary to implement predictive analytics in healthcare need to be matched by healthcare industry cybersecurity efforts to keep it safe. As far as these efforts go, hospital workstations are often where the bulk of a facility’s patient information will be stored, and thus, require primary attention placed on their protection from cyberattacks. 

Fortunately, a workstation’s protection from attack can be addressed in a number of ways, predominantly, through the addition of identity authentication hardware such as CaC readers or biometric scanners. RFID tablets, for example, can be customized with scanners capable of scanning staff ID badges, ensuring only qualified personnel are accessing patient information. Medical device computers and desktop workstations can also be outfitted with these same capabilities if they’re sourced from the right manufacturer.

As far as the software side of protection goes, providers can also look into imprivata single sign on solutions capable of confirming a staff member’s login credentials through an off-site server. In addition to the hardware we mentioned earlier, a solution such as an SSO program can provide a much needed extra layer of defense. 

Predictive Analytics in Healthcare Require Data and a Lot of It

Without data, prediction simply becomes guessing. In healthcare, even looking to the future and preemptively treating conditions before they become apparent requires an incredible amount of information and patient data. Fortunately, predictive analytics tools that make gathering this data simple surely do exist. Any decision-maker looking to implement predictive care in their own facility, especially in order to prepare for the incoming surge of patients post-COVID, needs to ensure these data gathering protocols are in place. For more information on what those protocols look like, contact an expert from Cybernet today.