According to a study by SCI Solutions, patient no-shows and missed appointments cost the US healthcare system more than $150 billion a year and individual physicians an average of $200 per unused time slot. That’s huge, isn’t it?! A patient not canceling the medical appointment in time for another patient to be scheduled for that same time slot results in massive losses — decreased revenues, lost time, wasted healthcare resources, and compromised quality of patient care delivery.
Imagine, as a healthcare service provider, you schedule an appointment and wait to meet your patient — you review the patient’s file, line up your staff, and make all the necessary arrangements, only to learn much later that there is no sign of the patient anywhere. Patient no-shows can get frustrating.
But, there can be several reasons why patients miss their appointments, such as —
- Forgetting about the appointment
- Logistics problems
- Limited knowledge of own health or ignorance
- Fear and anxiety
- Financial constraints
- Lack of an appointment cancellation/rescheduling system
These reasons for patient no-shows can be managed efficiently by leveraging advanced AI technologies. This article throws light on how to predict patient no-shows efficiently and accurately, in time, with an AI-driven model that can help you stay ahead in this competitive healthcare market.
It’s Time to Move On
Stats by Becker’s ASC Review reveal that one physician’s practice with multiple physicians can have 14,000-time slots go unfilled each year. Healthcare providers have been trying out different ways to address the problem of patient no-shows—sending out text reminders, making phone calls before the appointment, booking same-day appointments, and much more. Some of these manual measures may bridge the patient-provider gap to a certain extent, but these surely fail to tackle the problem of patient no-shows head-on in the long run.
So, what is the new-age fix to the age-old problem of patient no-shows in healthcare?
It is Predictive Analytics, driven by Artificial Intelligence.
AI-Driven Predictive Model: No More No-Shows
You can now say no to missed appointments — thanks to the AI model that forecasts a patient no-show by evaluating the patient 360 data — patient’s profile, past visits to physicians, clinician’s notes, EHR, EMR, etc. Data is at the core of this intelligent system.
Based on the above analysis, AI helps in predicting the following —
- Probability of a patient no-show for a particular service registered
- Probability of provider notional loss in the event of a patient no-show
- Probability of patient churn
Not just that! Based on provider notes, profile, their revenue cycle management, an AI-driven predictive analytics platform can predict the probability of physician no-shows too.
Here’s how the AI model works —
• Explores no-show causes/factors
The AI model explores factors that cause patient no-shows and uses the logistic regression algorithm or a similar predictive model to forecast a patient’s probability of no-shows based on the patient’s age, gender, location, socio-economic status, past appointment attendance records, patient sentiments, etc.
• Learns from past data
Machine Learning uses existing EHR data to learn and train the system for more efficient and accurate predictions in the future.
• Mines data to identify patterns
The AI system uses algorithms to uncover patterns in large datasets to predict appointment outcomes.
• Analyzes patient sentiments
The Predictive Analytics AI model can mine and analyze patient sentiments or opinions that they may have shared across online and offline platforms to derive a patient no-show probability score.
• Enables clinic/objective-specific customization
The model can be customized to deliver clinic-specific requirements and work in line with the business objective of the healthcare organization.
• Brings single view of patient/patient 360
A solid digital and data platform can offer you a 360-degree view of your patient which is invaluable in offering patient-centric service.
Struggling to reduce patient no-shows and manage them efficiently? AI can help you
Choosing the Right AI Technique
Various AI techniques can be applied for different outcomes depending on the type and volume of data. For example,
- Unsupervised learning algorithms like K-means clustering, K-prototype, DBSCAN, and CBLOF can be applied to understand different clusters of patients based on their transactions, visit data, EHR and EMR data, and past no-show data.
- Supervised learning algorithms like Logistic Regression, Decision Trees, Random Forest, and Support Vector Models can be used to predict the “No-show probability of a patient”.
- Supervised learning algorithms like Linear Regression, Decision Tree Regressor, Random Forest Regressor, and Support Vector Regressor, can be used to predict the “Notional Revenue loss that a hospital will incur ” due to patient no-shows.
- Neural Network algorithms like Recurrent Neural Network, Long Short-Term Method, Gated Recurrent Units, Pre-Trained Transformer models like BERT, and Hugging Face can be used to understand patient call data, unstructured text in EHR, and doctor’s notes.
Applying Natural Language Processing (NLP) to the above data can help –
- Predict patient sentiment
- Predict medical codes and terminologies from EHR and doctor’s notes
- Predict patient no-show from a previous conversation or email
- Predict the patient risk score
A Case in Point: AI-Predictive Model Use Case
King Faisal Specialist Hospital and Research Centre Organization (KFSHRC) in Riyadh was constantly battling the problem of patient no-shows. Despite taking extensive measures to improve patient attendance, its no-show rate continued to remain at 18%. This was after it was brought down from the initial 49% by improving communication through telephone reminders, SMS, and other such interventions. But there was a cost associated with such interventions that needed to be weighed against long-term sustainability. It was time for decisions.
The hospital board decided to bring in a prediction system that would use machine learning to predict no-show probability based on the data in the electronic medical reports (EMR). This was found to be a good solution for long-term sustenance. Their model was specifically targeted towards reducing the no-show rates among high-risk patients and yielded a reasonably good level of accuracy. What they found was that a patient’s history of no-shows was the best predictor of future no-shows. Many such inferences were derived with the AI model and the course of actions planned that helped KFSHRC save on the cost of under-utilized expensive resources and deliver quality patient care.
Source: NCBI
The Global Healthcare Predictive Analytics Market size is expected to reach $7.8 billion by 2025 according to Report Linker.
What’s in it for You? It’s All Gain, No Pain.
There are numerous benefits of using an Artificial Intelligence-powered patient no-show predictor for your healthcare business. To list out a few –
- Gives you accurate outcomes
- Makes way for a data-driven culture in your organization
- Predicts patient no-shows in real-time
- Readies all the data for better predictions on ‘patient churn’ and ‘patient segmentation’ along with patient no-show
- Guides you in making informed scheduling decisions or ‘strategic scheduling’
- Allows you to send reminders and secure responses on Mobile/Apps/Portal from the patients in advance
- Enables the operations team to work towards identifying a better time slot that works out both for the patient and the physician
- Helps you explore percentage overbooking (POB) and the basic appointment scheduled interval size (BASIS) strategies to schedule unused visit slots
- Minimizes operational disruptions and improves operational efficiencies
- Improves resource utilization and prevents revenue losses
- Empowers you to deliver optimum patient care through a single view of the patient/patient 360
- Brings you a single view of the patient that enriches patient intelligence and patient analytics, and propels you towards a patient-centric journey
- Provides defined clinic-specific outcomes with its highly customizable predictor system.
All You Need is a Few Things in Place to Get Started
Before you adopt a Predictive Analytics AI model for patient no-shows, there are a few prerequisites that you must ensure are in place. These include —
• A Robust Data Platform
Building a robust data platform with good quality and clean data that is well-segregated is the first requirement. Without a streamlined data management system, drawing actionable insights on patient no-shows would be a challenge.
• Data Integration Hub
To ensure your data is easily accessible for efficient analysis, you must consider a Data Integration Hub that allows access to data from multiple disparate databases. An API-enabled Data Integration Hub can drive data to be available in target systems in real-time or near real-time.
• Data Lake
A centralized data storage system or data lake for securely storing and processing all your raw data can make further analysis for patient no-shows far more structured and accurate.
We Will Help You Get There
In this age of Big Data, not using data to your advantage may not be the best thing to do. Data-driven decisions are meaningful and yield definite results in the long run. AI is the ultimate driving force for actionable data-led insights and has been a game changer in how healthcare predicts patient no-shows today. The advantages of using a predictive model are innumerable and once you have a platform in place, trust us, you will probably wonder how you ever functioned without one. Manual processes can be cumbersome and inadequate to address the patient no-show problem at the grassroots level, ultimately impacting your service standards.
With patient centricity as the end objective, an AI-driven patient no-show predictor will allow you to adopt a more holistic approach towards your end healthcare consumers/patients as you curb patient no-shows, utilize resources intelligently, cut down revenue losses, and deliver quality healthcare to patients who are in urgent need of an appointment through smart appointment management.
If you are a part of the healthcare industry and would like to know more about how AI technology can be the best choice for your business, reach out to us. Our experts know what your healthcare business needs and can recommend advanced ML solutions that are easy to embrace and quick to deliver positive results. Click here to get in touch with us.
Author
Anand Subramaniam
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.
