AI Recommendation Engine Benefits for Healthcare | Kanini

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

Using AI-driven patient recommendation systems, healthcare providers today are able to connect with their patients better and the patients in turn also feel more cared for. This has been helping healthcare providers in building long-term service relationships and goodwill.

2. Make Accurate Healthcare Decisions

AI-enabled patient recommendation engines make it possible to intelligently filter data and use the relevant information to analyze patient symptoms, assist in precise diagnosis, suggest personalized treatment plans to patients, and predict future health risks for them.

3. Get Comprehensive Patient Summaries

AI recommendation platforms offer a single view of the patient with all relevant data. This eliminates the tedious process of collecting and storing information, and analyzing it, leaving no scope for any human errors while making decisions about patient treatment plans.

4. Predict Patient No-shows

Machine learning recommendation systems analyze patients’ behavior historically and predict patient no-shows which enable providers to utilize their time efficiently. AI-enabled HRSs help allot these slots to other patients, ensuring smooth business operations. Likewise, considering patients’ requirements, these ai recommendation engines offer information about top relevant healthcare providers and their available time slots.

5. Use Patient Feedback to Deliver Quality Care

Patients may give relevant feedback when they are not satisfied with the service delivery. In these scenarios, recommendation platforms enable healthcare providers to identify past complaints, be prepared and structure their services accordingly.

Challenges in Implementing AI for Patient Recommendation Services

Leveraging AI for patient recommendations comes with a set of challenges. It may not always be easy for all healthcare providers to integrate AI-enabled solutions into their business operations. Some of these limitations are:

1

Large Volume of Data in Diverse Formats

Healthcare is a data-heavy industry. Analyzing patients’ historical medical data from EHRs and EMRs and deriving insights out of such data, leveraging AI, help providers deliver appropriate patient care and optimize it without delays. However, the large volume of data, in unusable or incomplete formats, can often hamper or undermine the usefulness of AI. To overcome this challenge, healthcare organizations should establish a proper data management strategy and a robust data platform.
2

Shortage of AI Expertise

One of the biggest challenges the healthcare industry is facing today is the shortage of AI skillsets and talent. This is leaving healthcare businesses impaired to realize the immediate value of AI. On top of it, they lack the ability to upskill their personnel and get them familiarized with these advanced technologies. In order to tap the full potential of AI, healthcare companies will need to find ways to bridge the AI skills gap.
3

Regulatory Compliance in Using AI

Healthcare is a highly regulated business. It should be overly cautious when complying with specific regulations such as HIPAA and GDPR. With strong data privacy concerns and regulations in healthcare, it is a challenging task to collect healthcare data and use it to train AI models as swiftly or efficiently as in other industries. Furthermore, the regulatory approval process for any new technology takes time as it goes through extensive scrutiny. Innovations can take years to navigate the approval process. Hence, healthcare organizations must adhere to the relevant legal requirements such as patient privacy, transparency, risk mitigation practices, and audits to ease AI implementation.

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

According to a report published by Grand View Research, the global artificial intelligence market size was valued at USD 93.5 billion in 2021 and is projected to expand at a compound annual growth rate (CAGR) of 38.1% from 2022 to 2030. Healthcare is certainly a leading contributor to this skyrocketed growth in AI adoption. Implementation of AI-enabled solutions is a natural progression in this age of digitization and a smart way of evolving into a modern, advanced, and intelligent healthcare setup. Healthcare providers must deploy these technologies to support their interpretations and offer accurate and prompt treatment recommendations to patients, delivering exceptional healthcare.

KANINI helps implement AI solutions and builds robust data platforms that meet modern healthcare requirements and enable enhanced service delivery. Contact us to understand more about how KANINI leverages AI to enhance patient recommendation services.

Author

Anand Subramaniam

Anand Subramaniam leads Data Analytics & AI practice at Kanini and is passionate about the data science domain and has championed data analytics practices across startups to Enterprises in various verticals. He is a thought leader, start-up mentor, and data architect. He brings forth over 2 decades of techno-functional leadership in envisaging, planning, and building high-performance state-of-the-art technology teams.

Author

Anand Subramaniam

Anand Subramaniam leads Data Practice at Kanini and is passionate about the data science domain and has championed data analytics practices across startups to Enterprises in various verticals. He is a thought leader, start-up mentor, and data architect. He brings forth over 2 decades of techno-functional leadership in envisaging, planning, and building high-performance state-of-the-art technology teams.

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