- 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
It’s Time to Move On
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
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
• Explores no-show causes/factors
• Learns from past data
• Mines data to identify patterns
• Analyzes patient sentiments
• Enables clinic/objective-specific customization
• Brings single view of patient/patient 360
Choosing the Right AI Technique
- 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.
- 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
What’s in it for You? It’s All Gain, No Pain.
- 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
• A Robust Data Platform
• Data Integration Hub
• 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
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.