Deployed on AWS
The solution uses the supervised machine learning to predict if a patient will show up for a medical appointment or not.
Overview
Medical appointment no-shows are a significant issue in the healthcare system. When a patient is not able to keep his/her appointment and fails to notify the clinic, the clinic doesn’t have the opportunity to book another patient in the same slot. This may mean the loss of an opportunity for another patient to get served. The medical appointment no-shows prediction leverages data analytics and patient behaviour insights to predict if the patient is at higher risk of missing their scheduled medical appointments. By identifying these potential no-shows in advance, healthcare providers can proactively take measures such as sending reminders or adjusting their schedules to reduce disruptions, optimize resource allocation, and improve patient attendance rates.
Highlights
- This solution uses the data sourced from Electronic Medical Records (EMR) to leverage various features related to demographics, health, and behavioral aspects of the patient to predict the risk of no-shows. The algorithm has been tuned to be robust to specific features to allow it to generalize over a large number of scenarios.
- This solution can be leveraged in various settings of healthcare providers like clinics, hospital outpatient departments, dialysis centers etc to facilitate better management of their limited resources.
- Need more machine learning, deep learning, NLP and Quantum Computing solutions. Reach out to us at Harman DTS.
Details
Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $100.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $5.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $100.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $100.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $100.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $100.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $100.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $100.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $100.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $100.00 |
Vendor refund policy
We do not provide any usage related refunds at this time.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug fixes and feature updates
Additional details
Inputs
- Summary
Model input is a json object with specific patient and appointment attributes.
- Input MIME type
- application/json
https://github.com/HDTS-user/medical-appointments-no-show/tree/main/input
https://github.com/HDTS-user/medical-appointments-no-show/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Gender | M: Male, F: Female | Type: Categorical
Allowed values: M, F | Yes |
ScheduledDay | The appointment scheduling time stamp | Type: FreeText
Limitations: Should be in linux time stamp format | Yes |
AppointmentDay | The date for which the appointment has been scheduled | Type: FreeText
Limitations: Should be in linux time stamp format | Yes |
Age | Age of patient in years | Type: Integer
Minimum: 0
Maximum: 100 | Yes |
Hypertension | Does patient has hypertension? Yes - 1, No - 0 | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Diabetes | Does patient has a history of diabetes? Yes - 1, No - 0 | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Alcoholism | Does patient has a history of alcoholism? Yes - 1, No - 0 | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Handicap | Does patient has any handicap? Yes - 1, No - 0 | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
SMS_received | If a reminder message has been received by patient or not? Yes - 1, No - 0 | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Prev_Missed | Has patient missed an previous appointment? Yes - 1, No - 0 | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Resources
Support
Vendor support
Business hours email support marketplaceSupp@harman.comÂ
AWS infrastructure support
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