Driving Healthcare Transformation Through Generative AI
Medications : Lisinopril 20mg daily , Atorvastatin 40mg daily , Metformin 1000mg BID , Aspirin 81mg daily
Social History : 20 pack year smoking history . Quit 5 years ago . No alcohol or recreational drug use .
Family History : Father with history of CAD , had MI at age 62 .
Review of Systems : Positive for chest pain , shortness of breath , diaphoresis , lightheadedness , and nausea . Negative for all other systems .
Vital Signs : BP 148 / 92 , HR 112 , RR 28 , Temp 98.5F , SpO2 92 % on room air
Physical Exam :
General : Appears uncomfortable , diaphoretic Cardiovascular : Tachycardia , regular rhythm . No murmurs . Pulmonary : Tachypneic . Lung sounds clear bilaterally . Extremities : No peripheral edema . Neurologic : Alert and oriented x3 .
This patient summary highlights the key details of Mr . Smith ' s history and examination pertinent to his chief complaint of chest pain and concern for acute coronary syndrome . The AI extracted relevant medical details and formatted them into an organized summary . The workflow routes this to a professional like physician ’ s assistant as a draft , who verifies it and approves it for the physician to look at before seeing a patient . This can help physicians quickly review the case before deciding on next steps in management and treatment . The AI can synthesize patient data into summaries like this , saving a physician ’ s time .
3.2 PERSONALIZED PATIENT INSTRUCTIONS
GenAI and medical LLMs hold significant promises for enhancing the quality and efficiency of healthcare through personalized patient instructions and advanced clinical notes . By integrating data from multiple sources like EHR ’ s , diagnostic tests , and physician notes , AI algorithms can create a nuanced patient profile . This allows the system to generate customized post-visit instructions that take into account individual conditions , history , and other contextual factors , like age and lifestyle . As new data becomes available , these instructions can be dynamically updated , ensuring they remain relevant and actionable .
Additionally , during a patient ' s visit , real-time data capture of vitals , form entries , and physician dictations enables the AI to generate advanced , structured clinical notes . These notes not only save time for healthcare professionals but also minimize human error , providing more accurate and insightful documentation for follow-up care . These auto-generated notes can include inferred insights , potential areas of concern , and recommended follow-up activities , thereby acting as a valuable tool for healthcare providers in decision-making .
96 March 2024