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Interoperability +2. Data Standardization +3. Predictive Analytics +4. Clinical Decision Support +5. Data Quality + +# Prior Authorization Medical Necessity Requirements by Policy +1. Policy Understanding +2. Claims Processing +3. Policy Compliance +4. Fraud Detection +5. Policy Optimization + +# CCD Summarization +1. Data Extraction +2. Data Standardization +3. Summarization +4. Data Visualization +5. Longitudinal Analysis + +# 🩺 AI Applications in Healthcare + +## 🏥 Integration in Health Care + +AI can be used in multiple ways in healthcare integration, especially with HL7v2, v3, v4 for ADT, SIU, ORM, CCDA, and FHIR. + +### 🔁 Interoperability +- 🧩 Understanding and mapping different versions of HL7 messages. +- 🔄 Seamless data exchange between disparate systems. + +### 📊 Data Standardization +- 🔄 Transforming data in different standards to a common format. +- ✔️ Enabling more effective data usage across different healthcare systems. + +### 📈 Predictive Analytics +- 🔮 Predicting patient outcomes based on data from different health care standards. +- 🎯 Enabling better patient care. + +### 🩺 Clinical Decision Support +- 🧠 Providing clinical decision support by analyzing data from different healthcare standards. + +### ✅ Data Quality +- 🔍 Detecting and correcting errors in different healthcare standards. +- ⬆️ Improving the quality of healthcare data. + +## 📄 Prior Authorization Medical Necessity Requirements by Policy + +AI can help in several ways in the area of Prior Authorization Medical Necessity Requirements by policy. + +### 📚 Policy Understanding +- 📖 Understanding the nuances of different policies. +- 💡 Determining the medical necessity requirements. + +### 📝 Claims Processing +- 💼 Processing claims more efficiently. +- 📄 Understanding the medical necessity requirements. + +### ✅ Policy Compliance +- ☑️ Ensuring all medical procedures comply with the necessary policies. + +### ⚠️ Fraud Detection +- 🕵️ Detecting any fraudulent activities. +- 🔍 Comparing the claims with the medical necessity requirements. + +### 🔧 Policy Optimization +- 📈 Suggesting improvements to policies based on analysis of past claims and medical necessity requirements. + +## 📋 CCD Summarization + +AI can play a crucial role in CCD summarization, creating longitudinal and easy-to-understand clinical summaries. + +### 📑 Data Extraction +- 🔎 Extracting relevant information from CCD for summarization. + +### 📊 Data Standardization +- 🔄 Standardizing the extracted information for easier understanding. + +### 🖊️ Summarization +- 📝 Summarizing the CCD in a way that's easy to understand for both clinicians and patients. + +### 📊 Data Visualization +- 🎨 Creating visual summaries of CCD for easier comprehension. + +### 🗓️ Longitudinal Analysis +- 📈 Creating longitudinal summaries of patient health data. +- 🕰️ Monitoring patient progress over time. + +# 🧓 Medicare and Medicaid Innovations +- 📚 Understanding different requirements and regulations. +- 💼 Processing claims more efficiently. +- ⚠️ Detecting fraud. +- 🩺 Providing decision support to clinicians. + diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..672d729bcff65b1afcd340b302b2bc902e5039fe --- /dev/null +++ b/app.py @@ -0,0 +1,78 @@ +import os +import random +import gradio as gr + +#links +Article = "" +Article = Article + " Gallery UI documentation: https://gradio.app/docs/" +Article = Article + " Unsplash free image source requires 5MP or larger images: https://unsplash.com/" +Article = Article + " Github should also be a worthy alternative with free repos. " +Article = Article + " Raw git content can be accessed by URL like so: https://raw.github.com/AaronCWacker/Yggdrasil/images/" +Article = Article + " Originals go here: https://github.com/AaronCWacker/Yggdrasil/tree/main/images" +# Aaron_Wacker_health_and_medical_icon_set_on_white_background_bba24b60-9fcf-411b-9c00-dd1ba1e3553c.png + +def fake_gan(): + base_dir = os.getcwd() # Get the current base directory + jpg_files = [file for file in os.listdir(base_dir) if file.lower().endswith((".jpg", ".jpeg"))] # List all files ending with ".jpg" or ".jpeg" + images = [(random.choice(jpg_files), os.path.splitext(file)[0]) for file in jpg_files] + return images + +def fake_gan_old3(): + base_dir = os.getcwd() # Get the current base directory + png_files = [file for file in os.listdir(base_dir) if file.endswith(".jpg")] # List all files ending with ".png" + images = [(random.choice(png_files), os.path.splitext(file)[0]) for file in png_files] + return images + +def fake_gan_old2(): + base_dir = os.getcwd() # Get the current base directory + png_files = [file for file in os.listdir(base_dir) if file.endswith(".png")] # List all files ending with ".png" + + images = [(random.choice(png_files), f"label {i}" if i != 0 else "label" * 50) for i in range(10)] + + return images + + +def fake_gan_old(): + images = [ + (random.choice( + [ + "Aaron_Wacker_health_and_medical_iccon_set_on_white_background_0384ab0e-c970-4b30-8ed5-070505799cc2.png", + "Aaron_Wacker_health_and_medical_iccon_set_on_white_background_2e8c1e07-ab53-4ad3-92f5-064d978cf7f9.png", + "Aaron_Wacker_health_and_medical_iccon_set_on_white_background_e9d86d2e-502c-4e86-951a-b8650d4bb435.png", + "Aaron_Wacker_health_and_medical_icon_set_on_white_background_117cea12-a29f-4fde-9382-59cb3954907c.png", + "Aaron_Wacker_health_and_medical_icon_set_on_white_background_5560d026-d6fe-4dc2-bef7-46362f261cf1.png", + "Aaron_Wacker_health_and_medical_icon_set_on_white_background_5aafc47e-a0f7-452d-944c-405f1fe07c66.png", + "Aaron_Wacker_health_and_medical_icon_set_on_white_background_bba24b60-9fcf-411b-9c00-dd1ba1e3553c.png", + "Aaron_Wacker_multiple_graph_and_chart_UI_multiple_data_visualiz_3c08106c-51fd-4efe-be1a-7565ae73841e.png", + "Aaron_Wacker_multiple_graph_and_chart_UI_multiple_data_visualiz_56084dcc-99ba-4bbd-8192-166e3321b9b8.png", + "Aaron_Wacker_multiple_graph_and_chart_UI_multiple_data_visualiz_a4d1e2f4-cf56-4ac2-ab89-ef60c0576672.png", + "Aaron_Wacker_multiple_graph_and_chart_UI_multiple_data_visualiz_144e27a1-3f78-43cb-bdec-c4507a9c8a30.png", + "Aaron_Wacker_clean_minimalist_GUI_pack_c85a49b0-d3f8-4e65-9f55-8bc507650231.png", + ] + ), f"label {i}" if i != 0 else "label" * 50) + for i in range(10) + ] + return images + + +with gr.Blocks() as demo: + with gr.Column(variant="panel"): + with gr.Row(variant="compact"): + text = gr.Textbox( + label="Health and Medical Icon Sets", + show_label=False, + max_lines=1, + placeholder="Enter your prompt", + ).style( + container=False, + ) + btn = gr.Button("Generate image").style(full_width=False) + + gallery = gr.Gallery( + label="Generated images", show_label=False, elem_id="gallery" + ).style(grid=[2], height="auto") + + btn.click(fake_gan, None, gallery) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file