AI the nurse’s little helper

Technologic trends are driving clinicians into an ever-faster cycle of adaptation and continuation of integration of information. Nursing informatics specialists have to stay a head of the wave by researching, reviewing, and validating technology, as well as supporting the changes in clinical practice. A tenet of nursing informatics practice is incorporating new technologies, such as artificial intelligence (AI) and chatbots.

AI is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. The incorporation of artificial intelligence into one’s clinical practice, even as a clinical innovator, may seem daunting, out of reach, or even like it is pushing the limits of one’s scope and practice, and yet informatics is leading the global ecosystem of healthcare.

There are many disciplines in nursing, and while AI can be used for numerous applications, the implementation of AI with a focus on risk re-admission programs in discharge planning for case managers, hospitals, primary health insurance companies, travel insurance companies, and initial triage for clinicians, offers significant potential benefits. Such framework requires further review.

In the United States, the Affordable Care Act (ACA) established healthcare security and the hospital Readmissions Reduction Program, which required a reduction in payments to hospitals with excess re-admissions. Thus, the ability of hospitals to identify re-admission risk by identifying patients that would benefit most from care transition interventions, as well as risk-standardized re-admission rates for purposes of hospital comparison, is very important. Hospitals, insurance companies, and patients have a vested interest in post-30-day hospital discharge planning due to the reduction in insurance company payments, reduced payment for subsequent re-admissions, and repeat hospital stays for patients, due to risk of re-exposure to hospital-acquired infections.

The use of artificial intelligence to predict which patients are most at risk of being re-admitted, using formulas to calculate re-admission adjustment factors and re-admission payment adjustment amounts, can assist and guide clinical staff. Clinicians need to understand the benefits of putting artificial intelligence into practice, which include improving patient outcomes by identifying patients at high risk of unplanned re-admission as a component of discharge planning strategies, in addition to pharmacy reviews aimed at preventing returns to the hospital. The use of machine learning is being used by Health Catalyst to provide real-time data to clinicians to make “meaningful clinical predictions.”

Clinical innovators can remain ahead of the curve in implementing new tools into practice by building mobile health applications (mhealth) into health chatbots for discharge planning or initial triage. The clinician can design protocols for AI and mhealth-focused tools for patient-centered care in relation to chronic conditions. A health chatbot allows clinicians to focus on the user experience for both clinicians and patients.

For example, a case manager can design his or her own follow-up health chatbot to streamline the experience to focus on reducing asthmatic and/or cardiovascular patient hospital re-admissions. A nursing informatics specialists can also help design and implement initial triage procedures.

The world is on the go, and that includes patients. Whether you are a business or casual traveler, trying to remain healthy while on the road can be a challenge. According to a 2016 report by the National Travel and Tourism Office, the rate of Americans traveling abroad has increased by 8.2 percent. Cost factors relating to international travel, including travel health insurance and the time to research insurance coverage to ensure you have adequate insurance to cover healthcare emergencies and chronic conditions, are not often discussed.

The increase in global travel means clinicians have to take a step back to understand global health trends in order to triage patients accordingly and review new exposures to Zika, Ebola, and Dengue by reviewing the CDC’s current outbreak list. New AI tools, such as Alexa, are being used more and more often in homes to gather information, and this data can be used as an initial triage for patients.

The thought of AI and chatbots could seem out of reach for the average clinician, but the same principles of care apply to the new tools, for example, designing a health chatbot that is language-specific for triage and discharge, as well as audio and visual-specific items that meet the Americans with Disabilities Act and cultural guidelines.