Showing Up at the Right Time
Let's say you're a hospital administrator. You want to help your patients get seen at the right time. How would you do that? Scheduling appointments isn't easy, especially if the hospital is dealing with a high volume of patients. With AI and machine learning, hospitals can now schedule more effectively by predicting when a person will show up for an appointment (based on historical data). For example, an artificial intelligence scheduling system could accomplish the following process:
A patient's needs are identified using data on past appointments (e.g., diagnostic and treatment history, discharge summaries). This information is analyzed by a predictive model that estimates the likelihood of same-day or next-day return hospital visits for a patient.
The system then suggests the best time to schedule an appointment, factoring in factors like urgency (is this an emergency visit?) and availability (do staff members have overlapping shifts?). By using these AI scheduling systems, hospitals can improve their patients' experience by making sure they get seen at the right time.
The system also provides an optimal appointment time to the patient, so they can choose a convenient time for their visit. Using AI/ML scheduling systems will help you improve your hospital's structure and thus improve patients' experience of care.
Making Sure Patients Are Processed Correctly
Hospitals are dealing with an influx of patients. How can healthcare workers handle so many patients? Artificial intelligence and machine learning can help hospitals process patients more efficiently by recommending the best diagnostic tests for each patient.
Depending on a patient's condition, AI/ML systems recommend specific diagnostic tests that doctors can run to look for certain conditions. The system may also provide further information about those conditions, such as possible treatment options or suggested follow-up appointments. By providing this information, AI/ML systems make it easier for doctors to identify the correct course of treatment.
AI/ML can be used to provide healthcare workers with tailored diagnostic information that will ultimately benefit patients.
Supporting Care Through the Entire Treatment Process
Outcome-based measures like complications, readmissions, and mortality are of high importance in healthcare. To improve these metrics, artificial intelligence systems can help with outcomes reporting, follow-ups, discharge instructions, and other aspects of patient care. For example, AI/ML systems can be used in:
Outcome Reporting: Hospitals can use AI to report the outcomes for patients. By analyzing data, these systems can provide more detailed information on a patient's condition. For example, they generate personalized reports that doctors and nurses can use to ensure appropriate follow-up care and interventions.
Discharge Instructions: If a patient is going home with discharge instructions, an artificial intelligence system can read those instructions to the patient. The AI system can also gather updated personal information from patients (e.g., new medications or allergies). This way, if a patient has any complications in the future, they can pass that information along to doctors right away.
Follow-up Care: AI can facilitate follow-ups for patients by identifying important data that needs to be reported to physicians. This way, if patients have any urgent problems, doctors will be notified right away.
Patient Education: AI systems can provide up-to-date information about patients' conditions. For example, if a patient has diabetes, the system can tell them what foods to eat and how often to check their blood sugar levels.
Using artificial intelligence for outcomes reporting, follow-ups, discharge instructions, and other aspects of patient care is an effective way to improve outcomes, reduce complications, provide better follow-up care, and save time monitoring patients.