Identify clinic patients count
using AI
Below is a free classifier to identify clinic patients count. Just upload your image, and our AI will predict the number of patients expected in the clinic. - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("clinic-patients-count", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/clinic-patients-count/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_image_url"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_image_url"}' \
https://www.nyckel.com/v1/functions/clinic-patients-count/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict the number of patients expected in the clinic..
This pretrained image model uses a Nyckel-created dataset and has 8 labels, including 1-5, 101-200, 11-20, 201-500, 21-50, 501+, 51-100 and 6-10.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the number of patients expected in the clinic.).
Whether you're just curious or building clinic patients count detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify clinic patients count at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Patient Traffic Monitoring: This use case involves utilizing the false image classification function to track the number of patients entering a clinic within a specific time frame. By analyzing the counts, clinic managers can gain insights into peak hours and adjust staffing levels accordingly to improve patient service and reduce wait times.
- Capacity Management: The function can help clinics manage their capacity by identifying how many patients are currently present in the clinic. By integrating this information with appointment scheduling, clinics can optimize patient flow and ensure that they never exceed their capacity limits.
- Resource Allocation: By analyzing patient counts, clinic administrators can make informed decisions regarding resource allocation, such as medical staff and equipment. This helps ensure that the right resources are available when needed, enhancing the overall efficiency of clinic operations.
- Trend Analysis: The false image classification can be used to identify trends in patient visits over time. By analyzing historical data, clinics can better predict future patient volumes, allowing for proactive adjustments in scheduling and resource management.
- Marketing Strategy Optimization: Understanding patient volume patterns can inform marketing strategies, helping clinics to promote services more effectively. For example, targeted campaigns can be initiated during expected high traffic periods to encourage more visits and capitalize on peak times.
- Insurance and Billing Efficiency: Accurate tracking of patient counts can streamline insurance claim processes by ensuring billable visits are correctly recorded. This reduces errors in billing and helps in maintaining proper financial records, ultimately improving revenue cycle management.
- Safety and Compliance Monitoring: The function can be crucial for monitoring clinic compliance with health regulations, especially during health crises like pandemics. By keeping track of patient counts, clinics can ensure they adhere to safety protocols, such as social distancing measures or occupancy limits, thus safeguarding both patients and staff.