Traditional ways of creating patient engagement in healthcare via voice, SMS, emails, and in-person patient conversations were quite helpful in the past, but with today’s technological revolution, the healthcare industry needs something more – which is AI. The present and the future of patient engagement is Artificial Intelligence.
Overcoming Data Challenges with AI
- Disparate data repositories – Patient data is scattered everywhere making it challenging for providers to get a “Single View of the Patient Data“.
- Huge volumes of data from disparate data sources need to be integrated, cleansed, transformed and stored quickly to help with actionable insights.
- The lack of data-driven culture impedes healthcare providers from intelligence about their patients, preventing them from providing relevant and timely services.
Data Analytics & AI in Patient Engagement
- A robust and modernized data platform that allows data ingestion, consolidation, transformation, analytics, and usage. Out of these, usage is vital to attain value from the Big Data that is generated.
- Artificial intelligence that helps achieve the results below-
- AI can help in screening large volumes of consolidated data and help automate workflows like patient appointment booking, payment collection, predicting no-shows, recommending patient services, etc.
- Conversational AI can help triage patient emergency calls and advise them with the right set of actions and steps.
- AI can help providers in pre-empting the probability of patient no-shows, saving millions of dollars
- AI can back up a live patient counselor by providing relevant intelligence during a call with a patient
- AI can help support a patient during times when patient counselors are not available.
The global patient engagement solutions market size was estimated at USD 15.1 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 21.4% from 2021 to 2028.
– Grand View Research
- Patient Conversations
- Patient Recommendations
- Patient Sentiments
- Patient Payments
Tap the full potential of AI to redefine your healthcare service delivery
Top 5 AI Use Cases for Driving Patient Engagement
Patient engagement is at the heart of patient experience. Today, patients have multiple options available to them. If they are not happy with one healthcare provider, they will go to the other. More so, they are willing to rely on AI for utilizing healthcare services. Realizing this fact, healthcare organizations are embarking on taking a personalized and data-driven approach to patient engagement. They are shifting from fee-for-service care to patient-centered value-based care driven by AI-enabled tools that enable healthcare providers and staff to deliver exceptional patient care. Here are the top five ways to use AI for patient engagement:
1. Healthcare Virtual Assistants (Chatbots)
By 2023, 20% of all patient interactions will involve some form of AI enablement within clinical or nonclinical processes, up from less than 4% today.
2. AI-powered Patient Self-Service Portal
- Alerting the patient on the next arriving appointment.
- Recommending the best and most suitable, also the nearest, doctors to patients for consultation, and automatically presenting them with the choice of time slots.
- Giving patients an estimate of the cost involved for the consultation.
- Helping patients to be ready with payer information to submit any claims to the payer.
- Suggesting personalized patient care plan.
3. 360-degree View of the Patient
- Enables the provider to track the patient history across all the services they have solicited and serve their needs better.
- Allows the provider to get additional patient intelligence in terms of improving future patient engagements.
- Helps the provider to use patient feedback to proactively address issues for other patients.
- Improves operational efficiency within the provider organization.
A Patient 360 view can be achieved by implementing a data platform that is capable of –
- Integrating data from various data sources
- Providing data integrity to remove redundancies
- Building a data pipeline that can handle large volume, high velocity, and high variety of data
- Allowing faster analytics
4. Risk Assessments for Preventive Care
5. Healthcare Workforce Optimization
Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams.