As the volume of available data generated daily increases, the responsibility to manage and deliver value-based care by healthcare providers has become essential. Medical professionals require access to relevant patient records on a timely basis. Due to innovations in machine learning (ML) and access to technology within the health industry, the appropriate design and development of decision support systems are quickly becoming vital. The lh-toolkit web components have made tremendous progress in the last 3 years, with nearly all components available for an electronic health record (EHR) platform. The project will develop web components and implement a computerized physician order entry (CPOE) system that is human-centered, saves clinician time, and interfaces with AI/ML models.
Clinicians spend a large amount of their time maintaining patient records and making decisions based on these records. Recent studies suggest more than 52% of visit time is spent on documentation. The quantity of information that is required by a clinician to be read and understood is getting untenable and each decision made in this environment can lead to harmful patient outcomes. Therefore, to deliver precise knowledge that enhances patients’ health clinical decision support is necessary. The prime purpose of a clinical decision support system (CDSS) is to enhance the decision-making approach through evidence-based practices. Each characteristic of an individual patient is linked to the computerized medical knowledge database that produces a patient-specific recommendation which is then presented to the physician to make an improved decision.
The traditional clinical decision support tools are often integrated into electronic health records and patient health records to streamline the workflows. This also allows the system to take advantage of existing patient data sets to provide a flexible and focused medical summary. Although these systems curb preventable mistakes many organizations are experiencing significant issues when it comes to creating a user-friendly interactive application with effective protocols. Poorly integrated CDSS can lead to the generation of unwanted alerts and continuous monitoring of critical alarms has resulted in burnouts and fatigue amongst the nurses and administrative staff.
The deliverables of the project are as follows:
Develop web components to build a robust CPOE system
Assemble the components in a SPA app for ordering drugs and tests.
Develop web components that allow clinicians to select relevant EHR fields to build ML classification models.
Integrate outputs of these models into the SPA for appropriate drug and test orders.
Hello Sir! My name is Ayushman Tripathi I came across the “Computerized Physician Order Entry System with Integrated Artificial Intelligence Models”, project, and I find it really fascinating. I believe my skills and experience would be a great fit for this project. I am really interested in contributing to it, and I’ve relevant skills for contributing to it as I’ve experience in full stack web development (React JS, Node JS, MongoDB, PostgreSQL, REST API, Django, AWS, Python).
I’ve worked on 10+ full stack projects & 4 internships in the past 1.5 years, and I have also completed the App Academy Open Software Engineering Bootcamp (500+ hours of full-stack online curriculum), where I learned a variety of skills. Additionally, I have worked on various frontend and backend projects, and I’m also preparing for the AWS Certified Developer Associate certification.
I’m willing to learn new skills while contributing to the project. I’ll put my best efforts into successfully contributing to the project. Could you please give me some advice so that I can contribute to this
Hello Sir, my name is Rakesh Roushan. I have experience working on projects related to MERN stack development, Machine Learning, Deep Learning Models, and AI model integration with Web Applications. I am interested in contributing to the “Computerized Physician Order Entry System with Integrated Artificial Intelligence Models” project.
My college mini project, which focused on medical transcription to reduce documentation work, I feel this is directly related to the goals of the project. Additionally, I have completed two internships in the domains of Machine Learning model development and Backend Developer.
I am eager to learn new skills while contributing to the project and am committed to putting my best efforts into my contributions. Could you please provide some guidance on how I can get started and make meaningful contributions to the project?
I Am the flutter And Web Devolper i will make Couples of Web Project I Recently Work on React And Tailwind css . I saw Your Project In GSOC 2023 And I want to be part Of the Community So can i Join The Community
Good afternoon sir,
Kindly allow me to introduce myself and extend my sincere greetings. I am Aryan Gupta, a 2nd year student studying Computational Natural Sciences at IIIT-H (Bachelors in CSE & masters in Natural sciences). Upon reading the about the CPOE project requirements and going through the GitHub codebase provided, I find myself to be interested to participate. I have extensive experience in working on full stack development(MERN, HTML, CSS, FastAPI .etc) as well as implementing AI models in python via sklearn. I am extremely motivated and willing to contribute to this project as it resonates with my interests and future research opportunities.
After throughly going through the code base provided, I’ve tried my best to look for a viable solution for one of the issues.
I would love to contribute to this as well as similar projects & learn the necessary skills. Kindly please provide me with some guidance, resources and definitive steps I should take in order to partake.