Healthcare is a very competitive market, and its success revolves around the well-being of its customers (Read: Patients). It is all about striking the right chords with the patients to make them feel more valued through a personalized and enhanced service experience. To achieve this and cater more efficiently to the increasing population of internet-savvy patients who are far more aware of their own health than before, the healthcare industry today has been striving to leverage the phenomenal power of Artificial Intelligence (AI).
The COVID-19 pandemic impacted several industries, including Healthcare, in the way businesses operate and customers buy products and services. The increasing need to enhance customer experience is fueling the demand for recommendation engines that provide relevant insights to both businesses and customers. According to a recent Grand View Research report, the global recommendation engine market size was valued at USD 1.77 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 33.0% from 2021 to 2028.
The healthcare sector is not an exception to this new technology trend. Using AI algorithms capable of learning and drawing inferences from complex data, the healthcare sector has also started delivering highly personalized services to patients through recommendation engines.
Let’s explore how the big data revolution and AI have been enabling healthcare organizations to understand their patients better, present them with relevant information, and enhance their overall experience.
AI-Powered Recommendation Engines: Transforming the Healthcare Industry
AI has been helping healthcare providers deliver a better service experience by empowering themselves as well as the patients with accurate information.
The healthcare industry faces various challenges such as the inability to deliver good and timely patient care, very high treatment costs, patient attrition, and less engagement. Adopting AI-driven Health Recommender Systems (HRS), widely known as Patient Recommendation Engines, helps deliver appropriate healthcare information to both providers and patients based on the personal health records of patients. Considering the preferable choices and actionable insights offered by recommendation engines, patients and providers can make informed decisions for better care and smoother operations.
5 Benefits of AI-Powered Recommendation Engines in Healthcare
Healthcare providers maintain Electronic Health Records (EHR) that show patients’ medical history. Analyzing the data of these extensive records using traditional processes may be quite a task. AI-enabled HRSs cut down workload for healthcare professionals, allowing them to focus more on other care delivery that requires personal attention and cannot be streamlined with AI. Recommendation Engines help healthcare providers and patients in the following ways:
1. Establish a Long-term Provider-Patient Relationship
2. Make Accurate Healthcare Decisions
3. Get Comprehensive Patient Summaries
4. Predict Patient No-shows
5. Use Patient Feedback to Deliver Quality Care
Challenges in Implementing AI for Patient Recommendation Services
1. Large Volume of Data in Diverse Formats
2. Shortage of AI Expertise
3. Regulatory Compliance in Using AI
Prerequisites for AI-enabled Patient Recommendation Services
1. Robust Data Platform:
In order to streamline business processes to current market requirements, it is imperative for healthcare providers to build a robust data platform. It is impractical to extract actionable insights from patient records without a proper data management system built.
Considering the big data revolution, it is sensible to establish a robust data platform, enabling AI-driven solutions to ease the process. Healthcare professionals can draw intelligent recommendations and structure their services to patient expectations only when these modern technologies are leveraged.
2. Historical Data:
No amount of assistance or recommendations can be delivered without patients’ health records. There needs to be a systematic way of gathering, storing, and analyzing this patient data for actionable insights. Without historical data, delivering the right patient recommendations is not possible.
3. Centralized Platforms:
Shuffling between multiple systems to understand patient health records can be time-consuming, may hinder productivity, and lead to inaccuracy. It is very convenient to have a single centralized dashboard where physicians can see and analyze patient data in a single ( 360-degree) view whenever and wherever they want to.
The Road Ahead
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.