How AI Helps Healthcare Improve Post-Facility Care

Healthcare for any given person isn’t something that ends as soon as they leave the hospital. Bar certain conditions and emergencies, illnesses are things that a patient will need to manage and care not to exacerbate even after they receive proper treatment from a professional. Whether this need for vigilance in post-facility care is driven by chronic illnesses, recurring conditions, or management of symptoms, it is a requirement for effective treatment.

Unfortunately, if the conditions and symptoms experienced outside of the care facility aren’t given the same amount of care as the symptoms exhibited in the care facility, it’s only a matter of time before a flare up occurs and patients are put into even more dire straits than they were before. And this has never been more true now that a vast majority of patients are experiencing the harsh aftermaths of a COVID-19 infection. 

Lisa Romano, a nurse who was unfortunately afflicted with COVID-19 goes into great detail about just how true this is — describing the issues and symptoms she felt even after “recovering” and being sent home and even going on to explain the several necessities of proper recovery that many low-income families and patients unfortunately don’t have access to.

Of course, this isn’t a problem that is going unnoticed by the healthcare community. In fact, in response to this very apparent issue, healthcare AI is being deployed to great effect in conjunction with medical grade computers to ensure care after the hospital is just as effective as care received within the facility. Below are only some of the most recently discovered use cases that delve deeper into answering how AI helps healthcare improve post-facility care.

How AI Helps Healthcare With Social Determinants of Health

We’ve discussed before just how much social determinants of health can hold sway over the kinds of afflictions and care a patient receives. For example, 50% of low income families skip receiving care they need because of an inability to afford it. Many of these determinants such as income, access to resources, and more, are all facets that exist outside of the healthcare facility, making it difficult for providers to account for them when creating a treatment plan after discharge.

To address this gap in care, we’ve seen AI deployed to great effect. Facilities like Jvion have created Community Vulnerability Maps that show which communities are most at risk of developing severe cases of COVID-19 based on these social determinants we’ve discussed. Loading this data in conjunction with public census information into an AI program can allow for physicians to be notified of any patients who are at any immediate risk. With all this data in tow, allocation of resources and patient outreach efforts can be better optimized to help those who need care but don’t have access to it. 

How AI Helps Healthcare Identify Drivers of Disease

The entire appeal of AI in healthcare is its ability to create risk ratings for any given condition. Once a risk rating threshold has been breached, these AI programs can automatically alert care providers so that efforts can be taken to get ahead of worsened conditions. The same, of course, can be done for treating COVID patients and identifying risk factors that could make someone experience a much more fatal reaction to the illness.

AdventHealth, for example, recently switched to an EHR program that has been integrated with an AI platform called BERG. With this AI integration, EHRs are automatically scanned for conditions and variables — such as compromised immune systems and advanced age — that could make COVID much more deadly for a given patient. And when a patient has been identified as increased risk due to these drivers of disease, provider staff can be notified so that more resources can be allocated to their treatment and more outreach can be performed.

How AI Helps Healthcare Address Mental Hurdles

Not all drivers of disease and illness are strictly physical. Unfortunately in many cases, poor mental health and issues such as anxiety and depression can make it very difficult for patients to adhere to treatment plans after discharge. In more extreme cases, these conditions can even cause patients to harm themselves or willingly ignore treatment. These conditions are just as potent a driver of disease as anything treated within the care facility. And providers are starting to take notice as the number of patients experiencing stress, anxiety, depression, and substance abuse soar as a result of quarantine and isolation.

Thankfully, AI can also be integrated and used by behavioral health providers and help automatically notify them of at-risk patients in much the same way care providers are notified. These programs can even inform providers who don’t work with behavioral health what kinds of mental conditions can be predecessors to exacerbated conditions, treatment failures, and more. 

You Can Prepare for AI With Note Standardization

One of the most powerful things you can do to improve your odds of creating an effective AI program is optimizing your note taking practices and standardization. After all, an AI won’t do your facility a lick of good if the data it’s reading isn’t scannable by the program. So how can you facilitate standardizing your notes aside from simply educating your staff?

First and foremost, know that the main issue behind many instances of improperly formatted notes is burnout. Stressed out physicians and nurses who have to meticulously login again and again into EHRs that take two hours to log notes for every hour spent with a patient are more likely to make human errors. Streamlining the login process with medial cart computers that include RFID scanners can make the note-taking process much less monotonous and aid in alleviating this burnout that may cause note taking errors. These RFID scanners, outfitted onto even more portable and carry-able medical grade tablets can be used to scan patient bracelets and make logging and pulling up patient info just as easy.

Adopting a standardized way of logging notes such as SOAP notes format can also streamline the process of note logging while also providing a uniform note format that is much more easily scanned by AI and machine learning programs. 

Adapt to Overcome Hurdles Old and New

Healthcare was always known for its propensity to ebb and flow in response to new conditions and changes in care philosophy. While 2020 may have been a milestone shift in how we treat patients, it is by no means the first shift of its kind. Adapting care to meet the new demands hoisted upon us by COVID and the new demands we’ll be sure to shoulder in the future means constantly revisiting solutions such as AI, robotics, and more to see which innovations now have a place in the modern healthcare industry. And if you’re interested in learning how AI helps healthcare in the world of 2021 and beyond, you can learn more by contacting a professional from the Cybernet team.