Think for a moment about how different the medical landscape would be today without technology – patients could still be waiting for a doctor in a patient waiting room perhaps driving for over an hour in rural America instead of dialing in via a secure telemedicine link to diagnose a raspy cough, raging rash or sudden change in heart rhythm.
While some might argue that artificial intelligence (AI) and data collection threaten personal privacy and negatively impact patient-doctor relationships, technology enables paramount medical research and collaborative patient care. Many believe it is time to hit the pause button, or at least slow down the spread of technology in medical settings, but a brief look at some ways hospitals use AI to enhance patient care defies that logic.
Researchers believe a cure for most cancers are within reach – partially due to data collection and AI. Beyond finding cures for congenital and preventable diseases, hospitals use AI to boost revenue potential, shape patient experiences and overcome challenges associated with staff shortages.
Speaking to HealthITAnalytics‘s Jessica Kent about the role of predictive analytics in medicine, Sarah Osborne (FSA, FCA, MAAA) explained, “By training an algorithm to identify severe medical conditions in health care data, the actuary is able to swiftly uncover data with major implications for patient costs and health outcomes.”
It is important to remember that AI is not intended to replace human decision-making. It should be thought of as a tool that empowers providers, hospital executives and other healthcare leadership to make well-informed decisions about service delivery that ultimately benefits the patient. For example, cost savings for an organization should also result in cost-sharing and collection policies that reduce financial burdens for client-patients, especially people who face economic challenges due to age, health and other factors.
Hospitals use AI and machine learning techniques to guide safety decisions as well as financial decisions. Armed with the knowledge that the more robust data collection is, the more accurate AI output is, but hospital leadership continues to work to enhance safety. By carefully monitoring device-driven data, and utilizing algorithms that identify errors and anomalies, hospital administrators and patient-facing decision-makers can improve processes across all touchpoints.
As an example, consider how dramatically diabetes management has evolved over the past four decades. We have gone from virtually zero self-monitoring and management to a world with insulin pumps, preloaded insulin pins, and fast-acting insulin to simplify inpatient and outpatient care.
Recent technological advancements and AI are improving daily life and reducing comorbidities for Type I diabetics, according to the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer director Deepak Vashishth. Data-driven decisions help medical providers develop personalized testing and treatment plans based on age, disease progression, and other factors including, “how often sensor, insulin pump, and infusion set faults occur.”
Along with enhancing in-patient diagnostics and treatment, analytics informs cleaning and sterilization protocol, patient education campaigns, specimen collection, medication administration, and risk prevention plans.
Using AI to improve healthcare also enables innovation and curating collaborative relationships. Some hospital emergency departments are utilizing AI to help patients decide if an urgent care center would be a more appropriate and cost-effective option for treatment before they are added to the waitlist. This approach better serves ED visitors as well as patients directed toward a local walk-in clinic by reducing wait time for service and improving the quality of care. And, in terms of cost control, studies show that patient-consumers typically pay 10 times more for an ED visit than they would at an urgent care center.
There may soon be a shift toward AI-driven x-ray interpretations as screening tools for major thorax diseases in clinical and hospital settings, as technological advances deliver improved performance and accuracy. Another area that machine learning is helping hospitals improve patient experience and time management is with automated alerts that nudge staff members to take action necessary to eliminate bottlenecks and barriers to an efficient, speedy discharge. Medical software that looks for barriers to discharge, such as certain tests and other criteria may send a text or email to the floor nurse to remind staff to take immediate action.
These gentle reminders reduce errors, streamline discharge procedures and help prevent burnout associated with stress and pressure to perform when bed space is limited and length of stay averages are growing.
Through an early warning system, such as data-driven hospital software, frontline staff and administrators gain better insight into the obstacles preventing cost-effective continuity of care and heightened patient satisfaction that drive better experiences and outcomes.
Today, more than ever before, artificial intelligence to improve healthcare plays an integral part in the diagnosis and treatment of disease, as well as research and development. But, medicine is about more than treating disease, it is about treating people who suffer from medical conditions. Organizations that embrace big data position show they care about the people who need their services and those who dedicate themselves to service within the hospital walls – their staff, their patients, their community and investors.
If you’re curious how your hospital could be improving on security measures related to AI Systems or areas of expertise which include Food Services, Environmental Services, Laundry & Linens, and other healthcare services, contact Soriant. As a national healthcare consulting company, Soriant is dedicated to helping healthcare systems across the country improve the patient experience while maximizing savings.
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