How To Use AI For Patient Engagement

The increasing volume and complexity of healthcare data have resulted in an explosion of technological innovation in healthcare. Artificial Intelligence or AI has started becoming an integral part of the patient care continuum.

Not only is AI instrumental in improving the physician workflows and reducing instances of burnout, but the patients are also more open to integrating AI within their care plan.

Patients like the availability, time savings, and personalized insights that AI offers. According to a recent survey by Accenture, 47 percent of respondents were willing to rely on artificial intelligence when availing healthcare services. This reveals a shift towards a more personalized and data-driven approach to patient engagement.

Including Artificial Intelligence and Machine Learning in your patient engagement strategy can help you move ahead of your competitors. Here are compelling ways to use AI and ML for patient engagement within your healthcare organization.

1. Data-Driven Intelligent Decision Making

With greater adoption of EHRs and widespread use of healthcare and fitness wearables, the healthcare data being generated is piling up rapidly. Use of artificial intelligence and machine learning algorithms can help mine through the treasure trove of data to obtain actionable insights.

By taking advantage of data-driven decision making, healthcare professionals can make smarter decisions that are aimed at boosting the levels of patient engagement while improving the outcomes at the same time.

Jvion, a global leader in clinical artificial intelligence that enables healthcare organizations to avoid preventable harm and lower healthcare costs through its analytics platform leverages AI for improving patient outcomes.

It combines the clinical, behavioral, environmental and socio-economic data from a wide variety of clinical and non-clinical data points to build a personalized and holistic patient risk and intervention model.

By pinpointing the patient risk trajectories, physicians can plan the necessary interventions to reduce that risk. This helps in preventing harm, driving personalized care, improving overall outcomes and boosting the levels of patient engagement.

2. Healthcare Chatbots

AI-powered chatbots can help in solving the patient’s accessibility challenges and play a crucial role in driving a seamless and satisfactory patient experience. Chatbots can help put care related information in a patient’s hands more efficiently, improve workflow, facilitate self-service to boost patient engagement and drive better health outcomes.

A number of healthcare organizations have incorporated chatbots in their customer engagement strategies. From symptom checker bots in healthcare apps like Florence and Ada health to conversational bots for engaging with patients like Youper, Chatbots are making their presence felt in the healthcare space.

Conversational chatbots powered by artificial intelligence can also be integrated into the website of your medical practice for engaging with potential patients, capturing leads and scheduling future appointments.

Chatbots can also be a part of the care cycle and help with patient engagement during the treatment as well as post-treatment follow-ups.

Lifelink, a chatbot engagement platform specifically for the healthcare industry employs conversational AI to help patients better navigate their healthcare experience. It provides for seamless engagement across the care continuum for elevated patient experience.

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3. Early Physician Intervention with AI

The rate of collection of healthcare data far exceeds the human capabilities to process it. Faster processing of data from a range of clinical and non-clinical data points can significantly improve clinical efficiency and boost clinical decision making. To be clinically relevant, the data also needs to be dynamic and in alignment with the clinical workflow.

Use of Artificial Intelligence and analytics to process the clinical data can accelerate the care delivery and allow for interventions at an early stage, resulting in better outcomes and higher engagement.

Medical EarlySign, a company that leverages machine learning on existing medical records does just that.

It uses EHR and lab data to identify patients who are at elevated risk for developing serious health conditions. The crucial insights timely delivered when making patient care decisions provide opportunities for actionable interventions.

4. Efficient Workflow Management

Long delays and wait times hamper the patient experience and negatively impact patient engagement. Use of advanced analytics powered by artificial intelligence and machine learning can not only identify the areas where lags happen, but it can also provide intelligent data-driven solutions for managing the same.

By integrating artificial intelligence into your practice management strategy, the areas of bottlenecks which are also a detriment to patient satisfaction levels can be identified and rectified.

AI promises to improve the overall experience by anticipating patient needs in advance and accelerating the overall workflow of healthcare delivery.

5. Enhanced Provider-Patient Relationship

EHRs today have streamlined data sharing and storage making information easily accessible at a few taps into the system. However, data entry into the EHRs is a leading cause of physician burnout as well.

The benefit of AI isn’t limited to getting insights out of the EHR. It’s equally instrumental in getting data into the EHRs as well.

Use of natural language processing (NLP), a subset of AI that recognizes speech and text can be used for converting unstructured data in the form of physician notes or voice memos and converting it into structured data within the EHRs.

This frees up the physician’s time spent in doing manual data entry which can be more constructively utilized interacting with patients instead.

Increase in bedside time not only has a positive impact on the patient-provider relationship, but it also increases the levels of patient engagement and satisfaction.

Leveraging Artificial Intelligence for Patient Engagement

Recognizing AI’s potential in healthcare and understanding how it can be used to engage patients for your medical practice is only one half of the equation. There are a number of technical complexities that need to be addressed before you actually incorporate AI within your practice.

Since healthcare data is inherently sensitive in nature, stringent security measures need to be in place when training the algorithms. Any entity collecting, storing and transmitting PHI (patient health information) needs to comply with regulatory compliance norms such as HIPAA and HITECH laws.

Incorporating Artificial Intelligence to your patient engagement strategy requires inputs from the experts and Arkenea has you covered. With 9+ years of specialized expertise as a healthcare software development company and rich experience in developing AI/ML applications for a wide range of clients, we can help turn your idea into reality.

Get in touch with our team of consultants to discuss your ideas and our experts will get back to you with actionable insights to bring them to fruition.

Vinati Kamani
 

Dr Vinati Kamani writes about emerging technology and its application across industries for Arkenea. She is an avid reader and self proclaimed bibliophile. When Vinati is not at her desk penning down articles or reading up on the recent trends, she can be found travelling to remote places and soaking up different cultural experiences.