Deployed on AWS
The solution uses supervised machine learning model to predict the number of days that an in-patient remains in hospital.
Overview
Introducing the Length of Hospital Stay Prediction model – a cutting-edge solution designed to enhance healthcare operations and patient care by leveraging advanced analytics and machine learning algorithms. This innovative product accurately estimates the duration of a patient's hospitalization by analyzing comprehensive patient data from Electronic Medical Records. With this valuable information, healthcare professionals can optimize resource allocation, streamline discharge planning, and improve patient flow for a seamless healthcare experience.
Highlights
- Length of hospital stay prediction product empowers healthcare providers with the ability to optimize resource allocation within their facility. This includes efficient management of bed availability, staff scheduling, and other critical resources required for patient care. By improving resource utilization, healthcare facilities can enhance operational efficiency, reduce costs, and ultimately improve patient satisfaction.
- With this model healthcare teams can engage in effective discharge planning. Proactively coordinating care, arranging follow-up appointments, and preparing necessary medications and supplies ensures a smooth transition for the patient. This results in timely discharges, better patient outcomes, and freed-up hospital resources for new admissions. Experience the difference our Length of Hospital Stay Prediction product can make in your healthcare facility today.
- 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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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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $100.00 |
ml.t2.xlarge Inference (Real-Time) Recommended | Model inference on the ml.t2.xlarge 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.
Legal
Vendor terms and conditions
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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
The input is a json document with key and values from the EMR records for a patient. Mandatory attributes required for prediction: Facility Id, Age Group, Gender, Race, Type of Admission, CCS Diagnosis Code, CCS Procedure Code, APR DRG Code, APR MDC Code, APR Severity of Illness Code, APR Risk of Mortality, APR Medical Surgical Description, Payment Typology 1, Total Charges, Total Costs
Mandatory attributes not used for prediction: Health Service Area, Hospital County, OCN, Zip Code
- Limitations for input type
- Not used for prediction: Ethnicity, Disposition, Discharge Year, CCS Diagnosis Description, CCS Procedure Description, APR DRG Description, APR MDC Description, APR Severity of Illness Description, Payment Typology 2, Payment Typology 3, Birth Weight, Abortion Edit Indicator, Emergency Department
- Input MIME type
- application/json
https://github.com/HDTS-user/IHP-Length-of-hospital-stay-prediction/tree/main/input
https://github.com/HDTS-user/IHP-Length-of-hospital-stay-prediction/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 |
|---|---|---|---|
Health Service Area | A description of the Health Service Area (HSA) in which the hospital is located. | Type: FreeText | Yes |
Hospital Country | A description of the county in which the hospital is located. Blank for abortion records.
| Type: FreeText | Yes |
Operating Certificate Number | The facility Operating Certificate Number as assigned by NYS Department of Health. Blank for abortion records.
| Type: FreeText | Yes |
Facility Id | Permanent Facility Identifier. Blank for abortion records. | Type: Integer | Yes |
Facility Name | The name of the facility where services were performed based on the Permanent Facility Identifier (PFI) | Type: FreeText | Yes |
Age Group | Age in years at time of discharge. | Type: Categorical
Allowed values: Grouped into the following age groups: 0 to 17, 18 to 29, 30 to 49, 50 to 69, and 70 or Older. | Yes |
Zip code - 3 digits | The first three digits of the patient's zip code.Â
| Type: FreeText | Yes |
Gender | Patient gender | Type: Categorical
Allowed values: (M) Male, (F) Female, (U) Unknown. | Yes |
Race | Patient race. | Type: Categorical
Allowed values: Black/African American, Multi, Other Race, Unknown, White. Other Race includes Native Americans and Asian/Pacific Islander. | Yes |
Ethnicity | The ethnicity of the patient | Type: Categorical
Allowed values: Spanish/Hispanic Origin, Not of Spanish/Hispanic Origin, Multi, Unknown | Yes |
Resources
Support
Vendor support
Business hours email support marketplaceSupp@harman.comÂ
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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