Sold by: Mphasis
Deployed on AWS
Ensemble Machine Learning algorithm-based solution which predicts the probability of patient no-shows for outpatient medical appointments.
Overview
Patient no-shows for scheduled appointments is a significant challenge for healthcare providers. Patient no-shows leads to inefficient utilization of scarce healthcare resources and service quality risks. This solution predicts the likelihood of patient no-shows, based on patients’ appointment and medical information. The solution assists health care providers to make informed patient scheduling and reminder decisions.
Highlights
- Medical Appointment No- Show Predictor uses patients’ appointment and medical information to predict the likelihood of no-shows.
- This solution, developed on cutting-edge machine learning algorithms, can be leveraged by healthcare institutions to pursue targeted interventions and improve patient turnout.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any 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
This is version 2.4.
Additional details
Inputs
- Summary
- Supported content types: 'csv' file only
- Mandatory fields: Patient_Id, Gender, Age, Schedule_Weekday, Appointment_Weekday, Lead_Time, Prior_Appointments, Prior_No_Show_Percent, Appointment_Notification, Beneficiary_Govt_Medical_Scheme, Hypertension, Diabetes, Alcoholism, Differently Abled.
- Input MIME type
- text/csv, text/plain, application/zip
https://github.com/Mphasis-ML-Marketplace/Medical-Appointment-No-Show-Predictor/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Medical-Appointment-No-Show-Predictor/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 |
|---|---|---|---|
Patient_Id | Numeric or alpha-numeric value to uniquely identify a patient. For example, ‘P01’. | Type: FreeText | Yes |
Gender | ‘Male’ or ‘Female’ | Type: Categorical
Allowed values: Male, Female | Yes |
Age | Numeric value
For example, a child of 10 months has age ‘0’ | Type: Integer | Yes |
Schedule_Weekday | Numeric value for the day on which appointment is confirmed. For example, ‘1’: Monday, ‘2’: Tuesday, ’3’: Wednesday, ’4’: Thursday, ‘5’: Friday, ’6’: Saturday, ‘7’: Sunday. | Type: Integer | Yes |
Appointment_Weekday | Numeric value for the day on which appointment is scheduled. For example, ‘1’: Monday, ‘2’: Tuesday,’3’: Wednesday,’4’: Thursday, ‘5’: Friday,’6’: Saturday, ‘7’: Sunday | Type: Integer | Yes |
Prior_Appointments | Number of prior appointments for the patient | Type: Integer | Yes |
Lead_Time | Number of days between the date of appointment booking/confirmation and the date of scheduled appointment. For example, If confirmation date is July 1 and appointment date is July 6, then Lead_Time value is ‘5’. | Type: Integer | Yes |
Prior_No_Show_Percent | This is the ratio of prior no-shows to the total appointments.
For example, if a patient did not show up for 10 appointments from a total of 100 appointments, the Prior_No_Show_Percent is ‘0.1’. | Type: Continuous | Yes |
Appointment_Notification | If the patient has received an appointment confirmation notification, the value is ‘Y’ Else, the value is ‘N’ | Type: Categorical
Allowed values: Y, N | Yes |
Beneficiary_Govt_Medical_Scheme | If the patient is a beneficiary of any Government sponsored healthcare scheme, the value is ‘Y’ Else, the value is ‘N’. | Type: Categorical
Allowed values: Y, N | Yes |
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