Using AI to Improve Patient Care
AI can be used to improve patient care and outcomes by handling administrative tasks, giving healthcare administrators and doctors more time to engage with their patients and make important healthcare decisions. Here are some of the healthcare AI technology examples of improving patient care through administrative efficiency:
- Triage - Using AI technology for symptom triage can help create consistency, improve patient flow, and quickly and accurately stratify risks based on symptoms, vital signs, and medical history. Emergency room tests show AI triage systems helping put patients on appropriate care paths for their concerns more quickly, decreasing patient wait times and improving care outcomes.
- Scribes - Doctors also report success using AI scribe technology during appointments, to transcribe and summarize the questions and answers discussed between provider and patient. In tests, doctors agree this AI technology helps them focus more energy on patient care and making medical decisions instead of managing documentation behind a keyboard.
- Personal Assistants - At the patient level, an AI healthcare personal assistant can help everyday users find answers about their medical coverage and get healthcare help more quickly. The KindHealth AI app quickly analyzes coverage information, matching users to the health benefits packages that best fit their needs, then acting as a healthcare concierge, answers questions about plan information, finds in-network providers, and tracks medical spending.
Using AI for Health Diagnosis and Treatment Planning
Another area where AI is playing a crucial role in modernizing healthcare is diagnosis and treatment. Machine learning algorithms can analyze large volumes of medical data, including medical images and lab work, patient records, and scientific research documents, to assist doctors in making accurate diagnoses and create more personalized and preventative treatment plans. Here are some examples:
- Wearables - From the health apps on a smart phone or watch to other wearable devices and sensors from a doctor, AI technology can analyze data on patients' vital signs and health history in real-time. This information can be used by healthcare providers to closely monitor a patient’s health remotely and intervene if necessary. This real-time data can also be paired with predictive analytics to help identify patients who are at high risk of developing certain conditions, allowing for early intervention and disease prevention.
- Preventative Care - AI technology can not only uncover patterns for early disease detection and diagnosis, but it can also be used to actively identify and inform patients about the preventative care options covered as part of their health insurance plan. Studies show knowledge about preventative care solutions is an important factor in utilization.
With a health benefits assistant like KindHealth AI, patients can simply ask AI which preventative care services are covered by their health plan, what requirements may be–such as whether they need a referral to see a specialist–along with in-network provider recommendations, effectively using AI to reduce the knowledge gap. - Personalized Treatment - Tailoring medical treatment plans to specific patient needs is another promising use for AI healthcare technology. AI can quickly analyze massive data sets and comb available treatment material, matching it with a patient’s personal data, clinical information, and healthcare history, even down to the genetic and molecular level, to create truly personalized treatment plans for those with complex symptoms, serious diseases, and chronic conditions.
Using AI for Patient Engagement and Benefits Support
AI-powered chatbots and virtual assistants are also enhancing patient engagement points and improving benefits support by providing reliable and accessible information, scheduling appointments, and answering basic medical questions. This enables patients to have more control over their health and access care resources conveniently, and alleviates healthcare administration burdens for employee benefits program managers and insurance carriers such as:
- Customer Service Triage - Surveys indicate powerful shifts in productivity and effectiveness for customer support teams using traditional and generative AI. But where companies should invest depends on where they’re starting from. More complex cases are detailed below.
In simplest form, using AI to generate answers for the bulk of basic customer service questions allows call center representatives more time to focus on bigger, complex issues. If a customer needs healthcare help, an AI assistant like KindHealth AI analyzes insurance carrier plan data instantly to provide answers on coverage questions and calculate procedure cost estimates, so customers don’t have to pick up the phone. - Benefits Management - The same goes for everyday businesses supporting employees with benefits administration and management. Allowing an AI assistant to support human resources and benefits management teams by handling employee questions about health benefits offers more time to focus on business-critical tasks like attracting and retaining employees, ensuring compliance, and preparing total compensation plans.
- Open Enrollment Support - With businesses reporting spending up to 8 hours per week on benefits management during open enrollment periods specifically, AI technology can offer health benefits managers an added layer of organizational and administrative support. Uploading healthcare plan information into KindHealth AI takes the pressure of educating and helping employees choose the right benefits package off human resources teams.
Using AI for Healthcare Facility Management and Operations
AI technology is also utilized in hospital facility management to streamline operations and enhance efficiency. Through data analysis, AI systems can predict maintenance needs for medical equipment, optimize staffing and resource allocation, and improve energy management processes. AI algorithms can also help monitor and regulate building temperature, air quality, and lighting to ensure a comfortable environment for patients and staff.
More complex external customer support and satisfaction use cases for AI in healthcare include automating patient onboarding, post-procedure care information, and referrals, and streamlining prior authorizations and claims management.
AI can also enhance internal operational support with document management and administrative tasks, creating and administering clinician training and ongoing education programs, identifying prescription trials, grants, and research opportunities, and creating reports for financial requests or philanthropic contributions.
Summary of Top Healthcare AI Technology Use Cases
Overall, AI technology will continue transforming healthcare and improving diagnosis accuracy, treatment effectiveness, patient care, and medical administration and operations for years to come, and KindHealth AI is helping businesses and insurance carriers streamline their health benefits experiences for employees and customers today. See our AI in action and take a look at the top 25 healthcare AI use cases we’re excited to follow below:
- Predictive analysis for patient health outcomes.
- Early disease detection and diagnosis.
- Improving medical imaging analysis.
- Personalized treatment plans for patients.
- Drug discovery and development.
- Identifying patients at risk of readmission.
- Automated appointment scheduling and reminders.
- Automated medical coding and billing.
- Virtual nursing assistants for patient monitoring.
- AI-powered chatbots for patient communication.
- Robotics-assisted surgery.
- Improving clinical trial efficiency.
- Virtual healthcare assistants for symptom triage.
- Patient risk assessment and stratification.
- AI-based decision support tools for clinicians.
- Automated detection of adverse drug reactions.
- Improving patient experience through AI-powered tools.
- Automating administrative tasks, such as inventory management.
- Personalized nutrition and wellness recommendations.
- Improving medication adherence.
- Real-time analysis of vital signs and patient data.
- AI-based tools for mental health diagnosis and treatment.
- Analysis of electronic health records (EHRs) for population health management.
- Improving supply chain management for medical supplies.
- Predictive maintenance of medical equipment.
