Addressing the Aftermath of a Pandemic With Predictive Analytics in Healthcare

Appointment numbers are set to rise as patients return to facilities to receive treatment for long ignored conditions. Learn how you can prepare with predictive analytics.

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. 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. 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.

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.

The Must-Have Predictive Analytics Tools

Population Health Management

Better than simply understanding a single patient is preemptively understanding the population your facility treats as a whole. 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.

Patient Monitoring Hardware

As telehealth continues to explode in popularity, 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.

Those interested in gathering more data from patients remotely can easily begin building out the network to do so by investing in patient wearables, tracking hardware, and HIPAA compliant remote communications.

Secure Data Storage

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.

Fortunately, a workstation’s protection 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. M

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.

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. Any decision-maker looking to implement predictive care in their own facility 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.