Potential Uses of AI in Primary Care
| Category | Definition | Example |
|---|---|---|
| Self-care, illness prevention, and wellness | Tools that support people in living healthier lives | A machine learning algorithm analyzes vital sign data from a patient's smartwatch in real time, documenting trends in their primary care electronic medical record (EMR). They receive personalized reminders to exercise, eat well, and get enough sleep. Their physician is alerted when trends show a decline in heart health. |
| Triage and early diagnosis | Tools that help triage patients and identify the need for additional health resources | A machine learning-based symptom checker informs a patient with a gradual development of severe foot pain to book an appointment with their primary care provider as early as possible. |
| Diagnostics | Tools that assist providers with point-of-care diagnosis | A primary care provider uploads a cell phone photo taken of a patient's retina to an app that uses deep learning to predict the risk of complications from diabetes. He refers the patient to an ophthalmologist.19 |
| Clinical decision support | Tools that structure relevant information to help physicians determine treatment course or need for referral to specialist or acute care services | An EMR-integrated machine learning algorithm predicts which patients are at high risk for becoming infected with HIV within a 3-year timeframe. Risk profiles can help primary care providers who would most benefit from pre-exposure prophylaxis medications.20 |
| Care delivery | Tools that support direct interactions between patients and providers | A natural language processing tool automatically converts the conversation between a patient and provider into chart notes, orders laboratory tests, and writes referrals to specialists during a clinic visit. This tool can also reach out to patients in advance of the appointment to gather necessary information.21 |
| Chronic care management | Tools that help patients and providers manage chronic diseases like diabetes or heart disease | A patient with diabetes has a blood glucose monitor that syncs with an AI-based app on their phone. The algorithm learns the patient's dietary and insulin delivery schedule over time. It begins to send helpful reminders to eat, check blood glucose, and inject insulin. The app is integrated with the patient's primary care EMR. It notifies the provider when the patient's insulin needs appear to change significantly. |
| Population health management | Tools that analyze large datasets to identify trends in population health to inform shifts in clinical programs and intervention targeting | A deep learning algorithm analyzes a clinic's raw EMR data. It identifies the patients at the highest risk for hospital admission within the next 30 days. Providers in the clinic schedule appointments with these patients to discuss their health and preventative interventions.14 |
| Health care operations | Tools that decrease time spent on routine administrative tasks that occur in the background of patient care | A classical machine learning algorithm learns that times and days of the week where appointments are in highest demand, and helps clinic clerical staff optimize the staffing schedule |