How to Personalize Care across Four Generations of Patients

Over the next five years, hospitals and health systems will need to make greater use of artificial intelligence (AI) and patient data to glean valuable insights that can help them differentiate their services, according to AHA’s recently released Futurescan 2023. In order to tailor and enhance treatment delivery, health care organisations have gotten more sophisticated in how they gather, analyse, and use patient-experience data. As providers react to increased competition from retail health care disruptors who have established long-term connections with clients and possess in-depth knowledge of their specific preferences, these qualities will become even more crucial in the years to come. According to the AHA's recently released Futurescan 2023, hospitals and health systems will need to leverage artificial intelligence (AI) and patient data more frequently over the next five years in order to gain useful insights that can help them differentiate their services. Futurescan 2023, created by the American College of Healthcare Executives and the AHA's Society for Health Care Strategy & Market Development. Personalized medicine, also known as precision medicine, is an approach to healthcare that tailors treatment to the individual patient based on their unique characteristics, such as their genetics, lifestyle, and environment. Personalizing patient care can help to improve outcomes, reduce side effects, and increase patient satisfaction. 

Here are some ways to personalize patient care:

• Conducting comprehensive assessments: A thorough assessment of the patient's medical history, physical examination, and laboratory tests can help to identify the patient's unique characteristics and risk factors.
• Using genetic testing: Genetic testing can help to identify genetic variations that may influence the patient's response to treatment. Genetic testing can also help to identify patients at risk for certain conditions.
• Using patient-reported outcomes (PRO): PRO measures provide insight into how a patient experiences a condition, and can help to identify treatment goals and preferences.
• Using predictive modeling: Predictive modeling uses patient data to create a computer-generated model that can predict a patient's response to treatment.
• Using patient engagement and education: Engaging patients in their care and educating them about their condition, treatment options, and potential side effects can help to increase patient satisfaction and adherence to treatment.
• Using telemedicine: Telemedicine can help to increase access to care, particularly for patients in remote or underserved areas.
• Using data analytics: collecting, analyzing and using patient data can help to identify patterns and trends, and can assist in making more informed decisions about patient care.

Personalized medicine is still an evolving field and there is ongoing research being conducted to improve the understanding and implementation of this approach.