work management
Built upon the monday.com Work OS, monday work management is designed to help teams work without limits when it comes to project and task management . monday work management offers a user-friendly and intuitive interface with a range of customizable features that cater to the specific collaboration and communication needs of any team across industries.
Project portfolio management
With the new project portfolio solution you can and get a birds-eye view on project progress of multiple projects. The visibility can help you work through any project challenge with ease.
Gantt charts monday.com Gantt chart view allows you to create task dependencies and update task owners and due dates with ease.
Workflow builder Choose from 100s of pre-made automation recipes or create your own custom ones to create workflows that sync integrations, people, and more.
monday work management pricing:
More details on monday.com pricing.
Why customers love monday.com
Integrations: monday work management offers over 200+ integrations, including popular tools like Slack, Google Drive, Microsoft Teams, Zoom, and Salesforce.
It also has a robust API for custom integrations.
Asana is a popular project management tool designed to help teams collaborate and manage their tasks and projects more efficiently. Asana has gained a reputation as a reliable and easy-to-use tool for project management, and it is used by many businesses and organizations around the world.
Calendar view: See all of your most important deadlines in one dedicated calendar.
Agile framework: The software has a time boxed, iterative approach for product development workflows.
Change requests and case management : Enable efficient tracking of customer requests with priority and timeline.
Asana pricing: $10.99/user/month with annual plan. Free plan available. More details on Asana pricing and review here.
Integrations: Asana integrates with over 200 apps, including Slack, Google Drive, Microsoft Office 365, Dropbox, and Salesforce. It also offers a flexible API for custom integrations.
“Asana is excellent for managing a project involving multiple workers. It provides the team with increased visibility by displaying updates and working on a single thread. Asana is excellent for straightforward project management. Some advanced project management applications, such as Gantt charts, need paid subscriptions, however Asana excels at establishing and keeping classified task lists, as well as allocating tasks to specific individuals.” — G2 Review
Comparison: explore this Asana alternative .
Trello is a web-based project management tool that uses a board-based approach to help individuals and teams organize their tasks and projects. It is a popular and user-friendly tool that allows users to easily track their progress, collaborate with team members, and visualize their workflow simply and intuitively.
Kanban cards: Trello uses Kanban cards to help you track task and project progress.
Checklists: Break down cards into smaller tasks by adding checklists to them.
Activity feed: See all recent changes and activity on your project board, like card updates and comments.
Trello Pricing: $5/user/month with annual plan. Free plan available. More details on Trello pricing and review here .
Integrations: Trello offers numerous “Power-Ups” which are essentially integrations. These include popular tools like Slack, Google Drive, Jira, and Salesforce. It also has an API for custom integrations.
Reviews: “Trello is a great tool for the strategic management of projects. I like the fact that it is so simple to move tasks from one stage to another by just dragging and dropping them. The visual layout is my assistant in tracking everything at a high level on one screen. In the planning sessions, data presentation is simple as there are different ways of looking at it. It is comprehensible, hence the whole team can easily stay on the same page.” — G2 Review
Comparison: explore these Trello alternatives & our Trello vs. monday.com guide.
Smartsheet is a software as a service platform to plan, capture, track, automate, and report on work at scale. It offers a rich set of views, reports, workflows, and dashboards to adapt according to business needs.
Dynamic gantt charts: Smartsheet’s Gantt chart view allows users to visualize project timelines and dependencies, with the ability to easily adjust schedules and assignments.
Customizable forms: Smartsheet’s customizable forms feature enables users to gather and organize data, such as customer feedback or project requests.
Data integration: It seamlessly integrates with a wide range of third-party tools, such as Salesforce, Jira, and Microsoft Office.
Integrations: Smartsheet integrates with a wide range of business tools, including Microsoft 365, Google Workspace, Salesforce, Jira, and Slack. It also provides connectors for custom integrations.
Reviews: “Smartsheet exceeded my expectations when used for phase gate and readiness checklists, or when organizing projects and assigning stakeholders. It is far more featured out of the box than Excel, and has a place if a project management app like Asana is overkill.” — G2 Review
Smartsheet pricing: $7/user/month with annual plan. Free plan available. More details on Smartsheet pricing and review here.
Jira Software is a server-based customizable workflow management solution that organizes tasks and projects into a centralized platform. It provides businesses with an infrastructure for automated processes and increased productivity.
Configuration: This software is known to be highly configurable and customizable with granular control over security, privacy, and workflows.
Timeline view: Backed by Gantt Chart, timeline view lets users map dependencies and plans work effectively.
Cross-project syncing: You can mark tasks as a duplicate across projects to keep track of work between teams.
Jira pricing: $5/user/month with annual plan. Free plan available. More details on Jira review and pricing here.
Integrations: Jira offers a vast marketplace of apps and integrations, including connections with tools like Slack, GitHub, Bitbucket, and Zendesk. It also has a robust API for custom integrations.
Reviews: “Jira has been a game-changer for my team’s project management. The best thing about this is its issues and tracking features which helps us to deliver features on time and helps us stay ahead. Also, since it integrates with other tools, like Confluence, github etc, it helps us to track the changes and view the updates without even leaving the JIRA board.” — G2 Review
Comparison: explore these Jira alternatives
Wrike is a cloud-based project management software designed to help teams streamline their workflows and collaborate more effectively. It was founded in 2006 by Andrew Filev and has since grown into a popular tool used by businesses of all sizes to manage projects from start to finish.
Integrations: Wrike integrates with project management must-haves like Google Drive, Zendesk, WordPress, Zapier, Slack, and more.
Wrike Approvals: The software has a dedicated feature to make the project review process smoother.
Budget & expense management: Customize currency preferences and create default hourly rates and job roles for projects.
Wrike pricing: $9.80/user/month with annual plan. Free plan available. More details about Wrike pricing and review here.
Integrations: Wrike integrates with over 400 apps, including popular tools like Salesforce, Microsoft Teams, Google Drive, and Adobe Creative Cloud. It also offers an open API for custom integrations.
Reviews: “Wrike is a great tool for project managment! Wrike keeps me on track, organized, transparent, and saves me time. I use it to view my projects and tasks for the day, week, and upcoming months, and communicate internally with stakeholders, colleages and project managers.” — G2 Review
Basecamp is a project management tool that helps teams to stay organized, collaborate efficiently, and complete projects on time. It was first launched in 2004 by 37Signals, but later rebranded as Basecamp in 2014. Basecamp is a cloud-based project management software that can be accessed through a web browser, desktop, or mobile application.
Hill charts: Basecamp uses unique hill charts to give you an overview of project progress.
Automated check-ins: Create routine reminders for team members to provide project updates.
File storage: Keep project documents and assets organized in the same place you manage projects.
Pricing: $15/user/month with annual plan. Free plan unavailable. More info on Basecamp pricing here.
Integrations: Basecamp offers integrations with tools like Slack, Google Drive, and Zapier. While its native integrations are more limited compared to some competitors, it does offer an API for custom solutions.
Reviews: “Project management is a lot more effective now that I use Basecamp for business. The entire team is kept up to date on given tasks, due dates, and relevant developments in real time. Additionally, Basecamp provides a number of file organisation and sharing capabilities, which greatly facilitates teamwork.” — G2 Review
Comparison: explore these Basecamp alternatives
ClickUp is a cloud based collaboration and project management platform designed to streamline team workflows. It offers a wide range of features such as custom statuses, time tracking, and reporting.
Multitask toolbar: The multitask toolbar allows users to edit multiple tasks simultaneously, saving time and effort.
Custom fields: ClickUp’s custom fields enable it to tailor the platform to your specific workflow, making it more efficient and effective.
Mind Maps: ClickUp’s mind maps feature enables users to visually map out their ideas and workflows, making it easy to see how tasks relate to one another and streamline the project planning process.
Pricing: Free version is available for personal usage with limited features and paid plan starts from $7 per member per month
Integrations: ClickUp offers over 1,000+ integrations through native connections and its integration with Zapier. Popular integrations include Slack, Google Drive, GitHub, and Zoom.
Reviews: “ClickUp is easy to use. ClickUp has become a business tool that I cannot do without. I have both a business and a personal account because I adore this app. There are other task and project management choices available, however, none compare to ClickUp in terms of customization or functionality. They provide best customer service.” — G2 Review
Comparison: explore these ClickUp alternatives and our ClickUp vs monday.com comparison guide.
Zoho Projects is a cloud based project management software that helps organizations manage their resources. It comes with tools for task management, reporting, and issue tracking .
Blueprint: Zoho Projects’ Blueprint feature allows users to create project templates, making it easier to launch new projects with predefined workflows. Risk management: The Risk Management feature helps users identify and manage potential project risks, minimizing the chance of project delays or failures. Custom fields: With custom fields, users can track and report unique project metrics specific to their needs
Pricing: $4/user/month with annual plan. Free plan available.
Integrations: Zoho Projects integrates seamlessly with other Zoho apps and offers integrations with popular third-party tools like Google Apps, Microsoft Office 365, and Slack. It also provides an API for custom integrations.
Reviews: “I love the interface of Zoho, it is very intuitive and very easy to use. It has a lot of features task management, people management, time tracking. Also, there are collaboration features as well. The dashboard for every single project helps me a lot to keep track of everything. The integration with other apps is also very useful.” — G2 Review
Nifty Project Management is an online project management tool designed to improve team collaboration and project efficiency.
Task management: Team members can create, assign, and track tasks, set deadlines, and receive notifications.
Time tracking: A built-in time-tracking feature helps team members track their time spent on specific tasks.
Project roadmaps: Plot milestones and deadlines.
Pricing: $3.90/user/month with annual plan. Free plan available.
Integrations: Nifty offers integrations with tools like Slack, Google Drive, Zoom, and GitHub. It also integrates with Zapier, expanding its connectivity options.
Reviews: “Nifty is simple to use, but at the same time has a lot of features. It’s intuitive, so no need to get a whole training to be able to use it. In the company, we use it mainly for project management, task assignment, docs organization, and client communication. Even the less tech-savvy client is able to use it with no major issues.” — G2 Review
Todoist is a web-based project management and productivity platform designed to schedule and plan daily routine tasks. It lets users collect tasks, organize projects and plan their day.
Natural language input: Todoist allows users to add tasks quickly and easily using natural language, without the need for complicated forms or menus.
Smart schedule: Todoist’s smart schedule feature suggests the best date and time to schedule tasks based on their due dates, priorities, and other factors.
Customization: Todoist offers a range of customization options, including themes, filters, and labels, allowing users to personalize their task management experience.
Pricing: $4/user/month with annual plan. Free plan available.
Integrations: Todoist integrates with a variety of productivity tools including Google Calendar, Slack, Zapier, and Amazon Alexa. It also offers an API for custom integrations.
Reviews: “For me, Todoist is like a lifesaver. It was easy to set up and really helps in putting everything into clear to-do lists, even for big projects with my team. Plus, it makes changing deadlines very easy.” — G2 Review
Miro is a powerful project management and collaboration tool that helps teams work more efficiently and effectively. With its user-friendly interface and comprehensive features, Miro allows teams to plan, execute, and deliver projects.
Visual collaboration: Users can create boards, add sticky notes, and invite team members to work together from anywhere in the world.
Integrations: Miro integrates with a variety of popular tools like Trello, Slack, and Google Drive.
Templates: Miro provides a library of templates for different types of projects, including product roadmaps, user story maps, and agile boards.
Pricing: $8/user/month with annual plan. Free plan available.
Integrations: Miro offers integrations with over 100 tools including Slack, Microsoft Teams, Asana, Jira, and Google Drive. It also provides an API for custom integrations.
Reviews: “I absolutely love the overall interface of Miro. It’s clean, inviting, and aesthetically pleasing, making it a joy to use. The user-friendly design ensures that even complex tasks are easy to manage, which significantly enhances productivity and collaboration.” — G2 Review
Hive is a productivity platform designed for teams to collaborate and streamline their workflow. It offers real-time communication, task management, and analytics to ensure teams stay on track and meet their goals.
Action templates: Allows users to create and automate tasks, saving time and increasing efficiency.
Forms: Users can create custom forms to collect data and feedback from team members and stakeholders.
External actions: There is an option to integrate with external tools like Dropbox, Google Drive, and Salesforce.
Pricing: $5/user/month with annual plan. Free plan available.
Integrations: Hive integrates with over 1,000 apps through native integrations and Zapier. Popular integrations include Slack, Zoom, Google Drive, and Salesforce.
Reviews: “It is intuitive, easy to use, and what you can’t figure out? They have great customer support and the Hive Univeristy. We use it daily. Like in daily task lists, the templates are AWESOME!. They’ve made things so easy for us to set up programs, consistent habits, build projects, communicate, it’s amazing!” — G2 Review
Notion is a web based productivity and note-taking app comprising several organizational tools such as task management, to-do lists, project tracking and bookmarking.
Embeddable content: Notion allows users to embed various types of content such as Google Docs, Trello boards, and more, enhancing collaboration and accessibility.
Relational databases: With Notion’s relational database feature, users can connect different types of data across multiple pages and databases, making it easier to track information and analyze data.
Customizable templates: Notion provides a vast selection of customizable templates.
Pricing: $8/user/month with annual plan. Free plan available.
Integrations: Notion offers integrations with tools like Slack, Google Drive, and Trello. While its native integrations are more limited, it offers an API for custom integrations and connects with Zapier for expanded options.
Reviews: “Notion is incredible at giving you the flexibility you need to create whatever you need—from a simple notes library to reminders to project management. Over my two years of using it I’ve developed systems that help me in my day-to-day tasks both as a business owner, creative freelancer, and even a father of 4 kids. All of my notes and projects live inside Notion and I use it daily.” — G2 Review
Comparison: Explore these Notion alternatives
Airtable is a web-based low-code productivity platform to build collaborative apps. It’s designed for custom workflow creation, collaboration, and communication on shared development projects.
Custom extensions: With its Blocks SDK, users can create their own integrations, visualizations, and internal tools.
Linked records: Airtable’s Linked Records feature enables users to connect records from multiple tables within a single base, creating complex relational databases that are easy to manage.
Automations: With Airtable’s Automations, users can automate repetitive tasks and workflows without the need for complex coding or integration.
Pricing: $20/user/month with annual plan. Free plan available.
Integrations: Airtable offers a wide range of integrations including Slack, Google Drive, Jira, and Salesforce. It also provides an API and supports Zapier for additional integration options.
Reviews: “Airtable stands out because you can customize everything: fields, views, even templates. This flexibility lets you use it for anything from data visualization to project tracking. Plus, it’s super collaborative – assign tasks, leave comments, and see updates in real time. It’s our daily go-to task tracker, easy to use, and even connects with Google Sheets for extra power.” — G2 Review
Comparison: Explore these Airtable alternatives
Adobe Workfront is a comprehensive work management platform designed to help enterprise teams plan, manage, and execute complex projects. It offers robust features for project management, resource allocation, and collaboration, making it suitable for large organizations with diverse project needs.
Resource management: Optimize team workload and capacity planning.
Custom workflows: Create and automate workflows tailored to your organization’s needs.
Reporting and analytics: Generate detailed reports and dashboards for data-driven decision-making.
Pricing: Custom pricing based on organizational needs. Contact Adobe for a quote.
Integrations: Adobe Workfront offers extensive integrations with Adobe Creative Cloud apps and other popular tools like Slack, Microsoft Teams, and Salesforce. It also provides an API for custom integrations.
Reviews: “I find the assigning tasks and getting notifications helpful, this makes it easy to move through a project or initiative without needing to baby sit people. Having a request queue has been a hige help for my department as well, so we can field requested work coming in and prioritize it before its assigned.” — G2 Review
Comparison: Explore these Adobe Workfront Alternatives
Microsoft Project is a project management software developed and sold by Microsoft. It’s designed to assist project managers in developing plans, assigning resources, tracking progress, managing budgets, and analyzing workloads.
Gantt charts: Visualize project timelines and dependencies.
Resource management: Allocate and track resources across projects.
Integration with Office 365: Seamlessly work with other Microsoft tools
Pricing: Starts at $10/user/month for the cloud-based version. On-premises versions also available.
Integrations: Microsoft Project integrates seamlessly with other Microsoft 365 apps and offers some third-party integrations. It also provides an API for custom integrations, though its integration ecosystem is more limited compared to some competitors.
Reviews: “It’s easy to use, can be tempalitized, easy to export and maintain the critical paths. Also it’s integration with Microsoft and few Non Microsoft products. Its better product for sml to mid size organisations to manage the project and monitor it’s progress.” — G2 Review
ActiveCollab is a project management tool that focuses on simplicity and ease of use. It’s designed to help teams organize tasks, collaborate on projects, and track time and expenses.
Task management: Create, assign, and track tasks with ease.
Time tracking: Built-in timer for accurate project time tracking.
Invoicing: Generate invoices based on tracked time and expenses.
Pricing: Starts at $7.50/user/month when billed annually. Self-hosted option available.
Integrations: ActiveCollab offers integrations with tools like Slack, Zapier, and QuickBooks. While its native integrations are more limited, it does provide an API for custom solutions.
Reviews: “It’s an ideal tool for tracking project management activities during our weekly lineouts. In my sprint, I can see what everyone is working on, write notes, track time, and label it properly.” — G2 Review
Height is a modern project management and team collaboration tool that aims to combine the best features of task management, documentation, and communication platforms.
Flexible views: Switch between list, board, and calendar views.
Real-time collaboration: Edit and comment on tasks in real-time.
Customizable fields: Tailor task properties to fit your workflow.
Integrations: Height offers integrations with popular tools like Slack, GitHub, and Figma. As a newer platform, its integration ecosystem is still growing, but it does offer an API for custom integrations.
Reviews: “UI is very clear and looks great! Clean colors, visible tasks. I am using a well-known task tool and the interface is making the work unbearable. This tool seems so simple and efficient, I hope it lives up to its promise! I only just started using it, but I have a good feeling this is going to make managing projects easier.” — G2 Review
While the tools mentioned above are some of the most popular options, the project management software landscape is diverse. Here are some additional tools worth considering, each with its unique strengths:
Project management software transforms how teams work, boosting efficiency and driving success.
Here’s why companies rely on it:
By addressing these key areas, project management software helps companies stay organized, meet deadlines, and achieve their goals more effectively. It’s an essential tool for any business looking to optimize their operations and stay competitive in today’s fast-paced market.
As more teams work remotely and more businesses see the value of an organized work environment, it’s crucial that you identify which project planning software is right for you.
Do you want something focused on time and task management? Or monday work management, a complete, customizable, open platform that makes work…work.
While popularity varies by industry and team size, monday.com is one of the most widely adopted project management tools. Its versatility and user-friendly interface contribute to its widespread use.
Project managers use a variety of tools, with monday.com being a top choice. It offers comprehensive features for planning, tracking, and collaborating, making it suitable for diverse project management needs.
- Enhanced productivity - Centralized file and data access - Simple task management - Transparency between team members - Real-time goal setting and progress tracking - Accurate resource management - Better collaboration and communication
Google offers some basic project management features within Google Workspace, but it's not a dedicated project management solution. For more robust capabilities, many teams turn to specialized tools like monday.com, which also integrates well with Google's suite.
1. Portfolio management view so teams can stay on top of everything 2. Automations to automate aspects of team processes and get time back for the more important things 3. Dashboards to share valuable insight
monday.com is often recommended for beginners due to its intuitive interface and customizable features. It offers a gentle learning curve while providing powerful project management capabilities that grow with your needs.
User-friendliness is subjective, but monday.com consistently ranks high in this category. Its visual approach, drag-and-drop functionality, and customizable views make it accessible for users of all experience levels.
Account information.
8 Best Scrumban Project Management Software for 2024
Your email has been sent
Initially conceived to help teams transition from scrum to kanban, scrumban has evolved into a standalone hybrid project management methodology. As project requirements become more complex and fluid, scrumban’s hybrid approach offers a versatile solution for teams looking for more flexibility.
Here are eight of the best current project management software tools that support scrumban, compared against their key features, pricing and suitability for a variety of team needs.
Here’s a comparison of top scrumban project management tools that highlights their ratings, best uses, starting prices and key scrumban features.
Our rating: 4.6 out of 5
Jira by Atlassian is a leading project management tool designed specifically for software development teams practicing scrumban. It seamlessly combines the structured sprint planning of scrum with the continuous flow and visual task management of kanban . This allows software development teams to customize workflows, set WIP limits and manage tasks efficiently. Jira’s robust reporting tools provide critical insights into team performance, making it easier to track progress and make data-driven decisions.
I chose Jira because of its powerful features tailored for software development teams practicing scrumban. Its ability to manage both structured sprints and continuous task flows is essential for maintaining the dynamic nature of software projects.
The integration of kanplan, which blends kanban’s visual task management with scrum’s backlog grooming, offers a unique advantage in keeping projects organized and on track. Additionally, Jira’s extensive reporting tools, such as burndown and velocity charts, deliver valuable insights into team performance and project progress.
Pros | Cons |
---|---|
Our rating: 5 out of 5
ClickUp is a versatile project management tool with all sorts of features that support scrumban. It combines more than 15 views with structured sprint planning, which helps teams find that balance between flexible and organized project management. ClickUp’s highly customizable interface and vast feature set means its users can adapt their workflows to match all manner of project requirements, whether long or short-term.
I chose ClickUp because of its huge feature catalog. For instance, its multiple views offered me variety for visualizing my workflows effectively, with the option of using its Board view as a scrum board to maintain the structured nature I needed. It also offers a task management kanban template to quickly get off the ground with kanban. ClickUp also delivers customizable task statuses and automation capabilities that streamline task management and improve efficiency.
Miro is a dynamic collaboration tool whose versatile visual project management features align with scrumban practices. Pairing the strengths of kanban boards and scrum methodologies, Miro gives users an infinite digital canvas for teams to visually organize tasks, brainstorm ideas and track project progress. This flexibility makes it particularly useful for remote and distributed teams that most appreciate a customizable visual platform to manage their workflows.
Miro caught my eye because of its unique visual project management that flexibly converts sticky notes, shapes and text boxes into cards with information. This delivers an infinite canvas for expansive project mapping and brainstorming without space constraints, fostering creative and engaging collaboration. Plus, I found that Miro’s real-time collaboration and integration with tools like Jira and Slack made it more transparent and efficient for scrumban.
Our rating: 3.7 out of 5
Teamwork is a robust project management tool with support for scrumban through detailed resource management capabilities. It enables teams to plan, track and optimize resource allocation with features like task dependencies, time tracking and workload management. Teamwork is also capable of visualizing workflows through customizable kanban boards and advanced reporting tools, which come in handy when managing resources across multiple projects and ensuring transparency.
I chose Teamwork for its resource management as it best visualized resource allocation and tracked workloads, which helped me prevent bottlenecks in my project. With Teamwork, I can easily balance workloads, reassign tasks and monitor project progress to maintain a steady flow in scrumban projects.
Our rating: 3.9 out of 5
Asana is a versatile project management tool that aligns with scrumban by providing a flexible framework for managing tasks and workflows. The platform not only delivers both scrum and kanban software features but also moves seamlessly between these methodologies. Additionally, Asana has a customizable scrumban template and automation features that help teams optimize their workflows and improve productivity.
Asana made it onto my list because of its scrumban-focused customizable template. Many tools support scrumban by providing scrum and kanban capabilities and direction on how to combine them intuitively. However, Asana takes this a step further with a scrumban template that includes directions on how to organize the information. This provides consistency when transitioning from one methodology to the other.
monday.com is a project management platform whose scrumban qualities are supported by powerful automation and customization capabilities. It allows teams to efficiently manage their tasks, sprints and workflows with features like customizable kanban boards, sprint management and over 150 pre-built automations. Its interface is both attractive and highly customizable, enabling teams to adapt their workflows to all kinds of project needs.
monday’s advanced automation features simplified and streamlined my scrumban processes through the ability to automate repetitive tasks and reduce manual effort. Its customizable kanban boards and sprint management tools offer a structured yet adaptable framework for managing scrumban projects.
Wrike is a powerful project management tool that’s well-suited for complex projects thanks to flexible and robust features for both structured and adaptive workflows. It provides customizable online scrum boards, real-time collaboration tools and advanced reporting capabilities, allowing teams to manage complex projects with ease and transparency. Plus, Wrike’s focus on adaptability and continuous improvement makes it a great choice for teams needing to transition between different project methodologies seamlessly.
I found Wrike to be set up in the best way to handle complex projects. Its customizable online scrum boards allow teams to visualize their workflows and prioritize tasks, while the real-time collaboration tools enhance communication and teamwork. Additionally, Wrike’s automated reporting and analytics provide valuable insights into project performance, helping teams to continuously improve their processes and adapt to changing project requirements.
Rally, formerly CA Agile Central, is an enterprise-class agile development platform that’s well suited for large-scale scrumban implementations. The platform offers features such as customizable scrum and kanban boards, robust reporting tools and real-time visibility into work in progress. Its project tree and backlog management tools allow teams to visualize and manage tasks effectively.
I saw Rally as a straightforward pick for large-scale agile project management. The platform’s customizable boards and real-time reporting tools provide a clear view of project progress and resource allocation, while its integration with CI/CD pipeline tools like Jenkins and SonarQube makes it more valuable in software development environments.
Contact Broadcom sales for a quote as pricing information isn’t listed publicly.
Scrumban is considered to be a hybrid agile project management methodology, which means it’s designed to break down a project into smaller tasks. It combines the visual and flow-focused strengths of kanban with the structure of scrum to enhance scrum’s flexibility. Scrumban is ideal for a team that is looking to work in sprints to produce a large quantity of deliverables.
To choose the ideal scrumban project management software , you’ll need to determine if it meets the needs of your specific use case. You can assess this by answering questions such as:
You also need to evaluate scrumban-specific features to make sure the tool is a good fit for your project needs and enhances your workflow.
Look for software that provides kanban boards where you can visualize your work and track the progress of tasks through various stages such as “Backlog,” “In Progress” and “Done.” This feature is useful for understanding the flow of work and identifying bottlenecks in real time.
Choose tools that allow you to set limits on the number of tasks that can be in progress concurrently. This helps prevent overloading your team and ensures that members focus on completing tasks before taking on new ones. WIP limits ensure a manageable workflow and improve task completion rates.
Opt for software that supports a pull system where team members can pull tasks from the backlog as they have the capacity to work on them, rather than being assigned tasks in advance. With this, you can expect greater flexibility as it allows team members to work at their own pace, which reduces stress and improves productivity.
Select tools that facilitate regular retrospectives and process evaluations, as they help teams identify areas for improvement and make necessary adjustments to workflows. Continuous improvement is a cornerstone of scrumban, which aims to see processes evolve and improve over time.
Ensure the software is flexible enough to adjust processes and workflows as needed. This is particularly important in dynamic, fast-paced environments. The tool should support easy modifications to task priorities, workflow stages and project timelines to accommodate shifting project requirements.
The tools on this list were chosen by first assessing whether they offer agile features and then investigating their agile features to determine their approach to both scrum and kanban. I then tested the tools where possible to get a feel for the kanban and scrum features and compared them to the features they list on their web pages as well as the feedback these tools get from verified user reviews. From these, I was able to have a holistic picture of each tool and determine the strengths, weaknesses and differentiating qualities of each one.
Subscribe to Project Management Insider for best practices, reviews and resources. From project scheduling software to project planning apps, stay up to date with the latest in project management tools. Delivered Wednesdays
Get the web's best business technology news, tutorials, reviews, trends, and analysis—in your inbox. Let's start with the basics.
* - indicates required fields
Lost your password? Request a new password
Please enter your email adress. You will receive an email message with instructions on how to reset your password.
Check your email for a password reset link. If you didn't receive an email don't forgot to check your spam folder, otherwise contact support .
This will help us provide you with customized content.
You're all set.
Thanks for signing up! Keep an eye out for a confirmation email from our team. To ensure any newsletters you subscribed to hit your inbox, make sure to add [email protected] to your contacts list.
BMC Health Services Research volume 24 , Article number: 860 ( 2024 ) Cite this article
27 Accesses
Metrics details
Governments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient’s length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper.
We investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns.
The study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R 2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R 2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns.
Our study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.
Peer Review reports
Democratic governments worldwide are placing an increasing importance on transparency, as this leads to better governance, market efficiency, improvement, and acceptance of government policies. This is highlighted by reports from the Organization for Economic Co-operation and Development (OECD) an international organization whose mission it is to shape policies that foster prosperity, equality, opportunity and well-being for all [ 1 ]. Openness and transparency have been recognized as pillars for democracy, and also for fostering sustainable development goals [ 2 ], which is a major focus of the United Nations ( https://sustainabledevelopment.un.org/sdg16 ).
An important government function is to provide for the healthcare needs of its citizens. The U.S. spends about $3.6 trillion a year on healthcare, which represents 18% of its GDP [ 3 ]. Other developed nations spend around 10% of their GDP on healthcare. The percentage of GDP spent on healthcare is rising as populations age. Consequently, research on healthcare expenditure and patient outcomes is crucial to maintain viable national economies. It is advantageous for nations to combine investigations by the private sector, government sector, non-profit agencies, and universities to find the best solutions. A promising path is to make health data open, which allows investigators from all sectors to participate and contribute their expertise. Though there are obvious patient privacy concerns, open health data has been made available by organizations such as New York State Statewide Planning and Research Cooperative System (SPARCS) [ 4 ].
Once the data is made available, it needs to be suitably processed to extract meaning and insights that will help healthcare providers and patients. We favor the creation and use of an open-source analytics system so that the entire research community can benefit from the effort [ 5 , 6 , 7 ]. As a concrete demonstration of the utility of our system and approach, we revealed that there is a growing incidence of mental health issues amongst adolescents in specific counties in New York State [ 8 ]. This has resulted in targeted interventions to address these problems in these communities [ 8 ]. Knowing where the problems lie allows policymakers and funding agencies to direct resources where needed.
Healthcare in the U.S. is largely provided through private insurance companies and it is difficult for patients to reliably understand what their expected healthcare costs are [ 9 , 10 ]. It is ironic that consumers can readily find prices of electronics items, books, clothes etc. online, but cannot find information about healthcare as easily. The availability of healthcare information including costs, incidence of diseases, and the expected length of stay for different procedures will allow consumers and patients to make better and more informed choices. For instance, in the U.S., patients can budget pre-tax contributions to health savings accounts, or decide when to opt for an elective surgery based on the expected duration of that procedure.
To achieve this capability, it is essential to have the underlying data and models that interpret the data. Our goal in this paper is twofold: (a) to demonstrate how to design an analytics system that works with open health data and (b) to apply it to a problem of interest to both healthcare providers and patients. Significant advances have been made recently in the fields of data mining, machine-learning and artificial intelligence, with growing applications in healthcare [ 11 ]. To make our work concrete, we use our machine-learning system to predict the length of stay (LoS) in hospitals given the patient information in the open healthcare data released by New York State SPARCS [ 4 ].
The LoS is an important variable in determining healthcare costs, as costs directly increase for longer stays. The analysis by Jones [ 12 ] shows that the trends in LoS, hospital bed capacity and population growth have to be carefully analyzed for capacity planning and to ensure that adequate healthcare can be provided in the future. With certain health conditions such as cardiovascular disease, the hospital LoS is expected to increase due to the aging of the population in many countries worldwide [ 13 ]. During the COVID-19 pandemic, hospital bed capacity became a critical issue [ 14 ], and many regions in the world experienced a shortage of healthcare resources. Hence it is desirable to have models that can predict the LoS for a variety of diseases from available patient data.
The LoS is usually unknown at the time a patient is admitted. Hence, the objective of our research is to investigate whether we can predict the patient LoS from variables collected at the time of admission. By building a predictive model through machine learning techniques, we demonstrate that it is possible to predict the LoS from data that includes the Clinical Classifications Software (CCS) diagnosis code, severity of illness, and the need for surgery. We investigate several analytics techniques including feature selection, feature encoding, feature engineering, model selection, and model training in order to thoroughly explore the choices that affect eventual model performance. By using a linear regression model, we obtain an R 2 value of 0.42 when we predict the LoS from a set of 23 patient features. The success of our model will be beneficial to healthcare providers and policymakers for capacity planning purposes and to understand how to control healthcare costs. Patients and consumers can also use our model to estimate the LoS for procedures they are undergoing or for planning elective surgeries.
Stone et al. [ 15 ] present a survey of techniques used to predict the LoS, which include statistical and arithmetic methods, intelligent data mining approaches and operations-research based methods. Lequertier et al. [ 16 ] surveyed methods for LoS prediction.
The main gap in the literature is that most methods focus on analyzing trends in the LoS or predicting the LoS only for specific conditions or restrict their analysis to data from specific hospitals. For instance, Sridhar et al. [ 17 ] created a model to predict the LoS for joint replacements in rural hospitals in the state of Montana by using a training set with 127 patients and a test set with 31 patients. In contrast, we have developed our model to predict the LoS for 285 different CCS diagnosis codes, over a set of 2.3 million patients over all hospitals in New York state. The CCS diagnosis code refers to the code used by the Clinical Classifications Software system, which encompasses 285 possible diagnosis and procedure categories [ 18 ]. Since the CCS diagnosis codes are too numerous to list, we give a few examples that we analyzed, including but not limited to abdominal hernia, acute myocardial infarction, acute renal failure, behavioral disorders, bladder cancer, Hodgkins disease, multiple sclerosis, multiple myeloma, schizophrenia, septicemia, and varicose veins. To the best of our knowledge, we are not aware of models that predict the LoS on such a variety of diagnosis codes, with a patient sample greater than 2 million records, and with freely available open data. Hence, our investigation is unique from this point of view.
Sotodeh et al. [ 19 ] developed a Markov model to predict the LoS in intensive care unit patients. Ma et al. [ 20 ] used decision tree methods to predict LoS in 11,206 patients with respiratory disease.
Burn et. al. examined trends in the LoS for patients undergoing hip-replacement and knee-replacement in the U.K. [ 21 ]. Their study demonstrated a steady decline in the LoS from 1997–2012. The purpose of their study was to determine factors that contributed to this decline, and they identified improved surgical techniques such as fast-track arthroplasty. However, they did not develop any machine-learning models to predict the LoS.
Hachesu et al. examined the LoS for cardiac disease patients [ 22 ] and found that blood pressure is an important predictor of LoS. Garcia et al. determined factors influencing the LoS for undergoing treatment for hip fracture [ 23 ]. B. Vekaria et al. analyzed the variability of LoS for COVID-19 patients [ 24 ]. Arjannikov et al. [ 25 ] used positive-unlabeled learning to develop a predictive model for LoS.
Gupta et al. [ 26 ] conducted a meta-analysis of previously published papers on the role of nutrition on the LoS of cancer patients, and found that nutrition status is especially important in predicting LoS for gastronintestinal cancer. Similarly, Almashrafi et al. [ 27 ] performed a meta-analysis of existing literature on cardiac patients and reviewed factors affecting their LoS. However, they did not develop quantitative models in their work. Kalgotra et al. [ 28 ] use recurrent neural networks to build a prediction model for LoS.
Daghistani et al. [ 13 ] developed a machine learning model to predict length of stay for cardiac patients. They used a database of 16,414 patient records and predicted the length of stay into three classes, consisting of short LoS (< 3 days), intermediate LoS ( 3–5 days) and long LoS (> 5 days). They used detailed patient information, including blood test results, blood pressure, and patient history including smoking habits. Such detailed information is not available in the much larger SPARCS dataset that we utilized in our study.
Awad et al. [ 29 ] provide a comprehensive review of various techniques to predict the LoS. Though simple statistical methods have been used in the past, they make assumptions that the LoS is normally distributed, whereas the LoS has an exponential distribution [ 29 ]. Consequently, it is preferable to use techniques that do not make assumptions about the distribution of the data. Candidate techniques include regression, classification and regression trees, random forests, and neural networks. Rather than using statistical parametric techniques that fit parameters to specific statistical distributions, we favor data-driven techniques that apply machine-learning.
In 2020, during the height of the COVID-19 pandemic, the Lancet, a premier medical journal drew widespread rebuke [ 30 , 31 , 32 ] for publishing a paper based on questionable data. Many medical journals published expressions of concern [ 33 , 34 ]. The Lancet itself retracted the questionable paper [ 35 ], which is available at [ 36 ] with the stamp “retracted” placed on all pages. One possible solution to prevent such incidents from occurring is for top medical journals to require authors to make their data available for verification by the scientific community. Patient privacy concerns can be mitigated by de-identifying the records made available, as is already done by the New York State SPARCS effort [ 4 ]. Our methodology and analytics system design will become more relevant in the future, as there is a desire to prevent a repetition of the Lancet debacle. Even before the Lancet incident, there was declining trust amongst the public related to medicine and healthcare policy [ 37 ]. This situation continues today, with multiple factors at play, including biased news reporting in mainstream media [ 38 ]. A desirable solution is to make these fields more transparent, by releasing data to the public and explaining the various decisions in terms that the public can understand. The research in this paper demonstrates how such a solution can be developed.
We describe the following three requirements of an ideal system for processing open healthcare data
Utilize open-source platforms to permit easy replicability and reproducibility.
Create interpretable and explainable models.
Demonstrate an understanding of how the input features determine the outcomes of interest.
The first requirement captures the need for research to be easily reproduced by peers in the field. There is growing concern that scientific results are becoming hard for researchers to reproduce [ 39 , 40 , 41 ]. This undermines the validity of the research and ultimately hurts the fields. Baker termed this the “reproducibility crisis”, and performed an analysis of the top factors that lead to irreproducibility of research [ 39 ]. Two of the top factors consist of the unavailability of raw data and code.
The second requirement addresses the need for the machine-learning models to produce explanations of their results. Though deep-learning models are popular today, they have been criticized for functioning as black-boxes, and the precise working of the model is hard to discern. In the field of healthcare, it is more desirable to have models that can be explained easily [ 42 ]. Unless healthcare providers understand how a model works, they will be reluctant to apply it in their practice. For instance, Reyes et al. determined that interpretable Artificial Intelligence systems can be better verified, trusted, and adopted in radiology practice [ 43 ].
The third requirement shows that it is important for relevant patient features to be captured that can be related to the outcomes of interest, such as LoS, total cost, mortality rate etc. Furthermore, healthcare providers should be able to understand the influence of these features on the performance of the model [ 44 ]. This is especially critical when feature engineering methods are used to combine existing features and create new features.
In the subsequent sections, we present our design for a healthcare analytics system that satisfies these requirements. We apply this methodology to the specific problem of predicting the LoS.
We have designed the overall system architecture as shown in Fig. 1 . This system is built to handle any open data source. We have shown the New York SPARCS as one of the data sources for the sake of specificity. Our framework can be applied to data from multiple sources such as the Center for Medicare and Medicaid Services (CMS in the U.S.) as shown in our previous work [ 6 ]. We chose a Python-based framework that utilizes Pandas [ 45 ] and Scikit learn [ 46 ]. Python is currently the most popular programming language for engineering and system design applications [ 47 ].
Shows the system architecture. We use Python-based open-source tools such as Pandas and Scikit-Learn to implement the system
In Fig. 2 , we provide a detailed overview of the necessary processing stages. The specific algorithms used in each stage are described in the following sections.
Shows the processing stages in our analytics pipeline
Recent research has shown that it is highly desirable for machine learning models used in the healthcare domain to be explainable to healthcare providers and professionals [ 48 ]. Hence, we focused on the interpretability and explainability of input features in our dataset and the models we chose to explore. We restricted our investigation to models that are explainable, including regression models, multinomial logistic regression, random forests, and decision trees. We also developed separate models for newborns and non-newborns.
During our investigation, we utilized open-health data provided by the New York State SPARCS system. The data we accessed was from the year 2016, which was the most recent year available at the time. This data was provided in the form of a CSV file, containing 2,343,429 rows and 34 columns. Each row contains de-identified in-patient discharge information. The dataset columns contained various types of information. They included geographic descriptors related to the hospital where care was provided, demographic descriptors such as patient race, ethnicity, and age, medical descriptors such as the CCS diagnosis code, APR DRG code, severity of illness, and length of stay. Additionally, payment descriptors were present, which included information about the type of insurance, total charges, and total cost of the procedure.
Detailed descriptions of all the elements in the data can be found in [ 49 ]. The CCS diagnosis code has been described earlier. The term “DRG” stands for Diagnostic Related Group [ 49 ], which is used by the Center for Medicare and Medicaid services in the U.S. for reimbursement purposes [ 50 ].
The data includes all patients who underwent inpatient procedures at all New York State Hospitals [ 51 ]. The payment for the care can come from multiple sources: Department of Corrections, Federal/State/Local/Veterans Administration, Managed Care, Medicare, Medicaid, Miscellaneous, Private Health Insurance, and Self-Pay. The dataset sourced from the New York State SPARCS system, encompassing a wider patient population beyond Medicare/Medicaid, holds greater value compared to datasets exclusively composed of Medicare/Medicaid patients. For instance, Gilmore et al. analyzed only Medicare patients [ 52 ].
We examine the distribution of the LoS in the dataset, as shown in Fig. 3 . We note that the providers of the data have truncated the length of stay to 120 days. This explains the peak we see at the tail of the distribution.
Distribution of the length of stay in the dataset
We identified 36,280 samples, comprising 1.55% of the data where there were missing values. These were discarded for further analysis. We removed samples which have Type of Admission = ‘Unknown’ (0.02% samples). So, the final data set has 2,306,668 samples. ‘Payment Typology 2’, and ‘Payment Typology 3’, have missing values (> = 50% samples), which were replaced by a ‘None’ string.
We note that approximately 10% of the dataset consists of rows representing newborns. We treat this group as a separate category. We found that the ‘Birth Weight’ feature had a zero value for non-newborn samples. Accordingly, to better use the ‘Birth Weight’ feature, we partitioned the data into two classes: newborns and non-newborns. This results in two classes of models, one for newborns and the second for all other patients. We removed the ‘Birth Weight’ feature in the input for the non-newborn samples as its value was zero for those samples.
The column ‘Total Costs’ (and in a similar way, ‘Total Charges’) are usually proportional to the LoS, and it would not be fair to use these variables to predict the LoS. Hence, we removed this column. We found that the columns 'Discharge Year', 'Abortion Edit Indicator'' are redundant for LoS prediction models, and we removed them. We also removed the columns ‘CCS Diagnosis Description’, ‘CCS Procedure Description’, ‘APR DRG Description’, ‘APR MDC Description’, and ‘APR Severity of Illness Description’ as we were given their corresponding numerical codes as features.
Since the focus of this paper is on the prediction of the LoS, we analyzed the distribution of LoS values in the dataset.
We developed regression models using all the LoS values, from 1–120. We also developed classification models where we discretized the LoS into specific bins. Since the distribution of LoS values is not uniform, and is heavily clustered around smaller values, we discretized the LoS into a small number of bins, e.g. 6 to 8 bins.
We utilized 10% of the data as a holdout test-set, which was not seen during the training phase. For the remaining 90% of the data, we used tenfold cross-validation in order to train the model and determine the best parameters to use.
Many variables in the dataset are categorical, e.g., the variable “APR Severity of Illness Description” has the values in the set [Major, Minor, Moderate, Extreme]. We used distribution-dependent target encoding techniques and one-hot techniques to improve the model performance [ 53 ]. We replaced categorical data with the product of mean LoS and median LoS for a category value. The categorical feature can then better capture the dependence distribution of LoS with the value of the categorical feature.
For the linear regression model [ 54 ], we sampled a set of 6 categorical features, [‘Type of Admission’, ‘Patient Disposition’, ‘APR Severity of Illness Code’, ‘APR Medical Surgical Description’, ‘APR MDC Code’] which we target encoded with the mean of the LoS and the median of the LoS. We then one-hot encoded every feature (all features are categorical) and for each such one-hot encoded feature, created a new feature for each of the features in the sampled set, by replacing the ones in the one-hot encoded feature with the value of the corresponding feature in the sampled set. For example, we one-hot encoded ‘Operating Certificate Number’, and for samples where ‘Operating Certificate Number’ was 3, we created 6 features, each where samples having the value 3 were assigned the target encoded values of the sampled set features, and the other samples were assigned zero. We used such techniques to exploit the linear relation between LoS and each feature.
According to the sklearn documentation [ 55 ], a random forest regressor is “a meta estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting”. The random forest regressor leverages ensemble learning based on many randomized decision trees to make accurate and robust predictions for regression problems. The averaging of many trees protects against single trees overfitting the training data.
The random forest classifier is also an ensemble learning technique and uses many randomized decision trees to make predictions for classification problems. The 'wisdom of crowds' concept suggests that the decision made by a larger group of people is typically better than an individual. The random forest classifier uses this intuition, and allows each decision tree to make a prediction. Finally, the most popular predicted class is chosen as the overall classification.
For the Random Forest Regressor [ 56 , 57 ] and Random Forest Classifier [ 58 ], we only used a similar distribution dependent target encoding as a random forest classifier/ regressor is unsuitable for sparse one-hot encoded columns.
Multinomial logistic regression is a type of regression analysis that predicts the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables. It allows for more than two discrete outcomes, extending binomial logistic regression for binary classification to models with multiple class membership. For the multinomial logistic regression model [ 59 ], we used only one-hot encoding, and not target encoding, as the target value was categorical.
Finally, we experimented with combinations of target encoding and one-hot encoding. We can either use target encoding, or one-hot encoding, or both. When both encodings are employed, the dimensionality of the data increases to accommodate the one-hot encoded features. For each combination of encodings, we also experimented with different regression models including linear regression and random forest regression.
We experimented with different feature selection methods. Since the focus of our work is on developing interpretable and explainable models, we used SHAP analysis to determine relevant features.
We examine the importance of different features in the dataset. We used the SHAP value (Shapley Additive Explanations), a popular measure for feature importance [ 60 ]. Intuitively, the SHAP value measures the difference in model predictions when a feature is used versus omitted. It is captured by the following formula.
where \({{\varnothing }}_{i}\) is the SHAP value of feature \(i\) , \(p\) is the prediction by the model, n is the number of features and S is any set of features that does not include the feature \(i\) . The specific model we used for the prediction was the random forest regressor where we target-encoded all features with the product of the mean and the median of the LoS, since most of the features were categorical.
One approach to the problem is to bin the LoS into different classes, and train a classifier to predict which class an input sample falls in. We binned the LoS into roughly balanced classes as follows: 1 day, 2 days, 3 days, 4–6 days, > 6 days. This strategy is based on the distribution of the LoS as shown earlier in Figs. 3 and 4 .
A density plot of the distribution of the length of stay. The area under the curve is 1. We used a kernel density estimation with a Gaussian kernel [ 61 ] to generate the plot
We used three different classification models, comprising the following:
Multinomial Logistic Regression
Random Forest Classifier
CatBoost classifier [ 62 ].
We used a Multinomial Logistic Regression model [ 59 ] trained and tested using tenfold cross validation to classify the LoS into one of the bins. The multinomial logistic regression model is capable of providing explainable results, which is part of the requirements. We used the feature engineering techniques described in the previous section.
We used a Random Forest Classifier model trained and tested using tenfold cross validation to classify the LoS into one of the bins. We used a maximum depth of 10 so as to get explainable insights into the model.
Finally, we used a CatBoost Classifier model trained and tested using tenfold cross validation to classify the LoS into one of the bins.
We used three different regression models with the feature engineering techniques mentioned above ( Feature encoding section). These comprise:
Linear regression
Catboost regression
Random forest regression
The linear regression was implemented using the nn.Linear() function in the open source library PyTorch [ 63 ]. We used the ‘Adam’ optimization algorithm [ 64 ] in mini-batch settings to train the model weights for linear regression.
We investigated CatBoost regression in order to create models with minimal feature sets, whereby models with a low number of input features would provide adequate results. Accordingly, we trained a CatBoost Regressor [ 65 ] in order to determine the relationship between combinations of features and the prediction accuracy as determined by the R 2 correlation score.
The random forest regression was implemented using the function RandomForestRegressor() in scikit learn [ 55 ].
For the regression models, we used the following metrics to compare the model performance.
The R 2 score and the p -value. We use a significance level of α = 0.05 (5 %) for our statistical tests. If the p -value is small, i.e. less than α = 0.05, then the R 2 score is statistically significant.
For classifier models, we used the following metrics to compare the model performance.
True positive rate, false negative rate, and F1 score [ 66 ].
We computed the Brier score using Brier’s original calculation in his paper [ 67 ]. In this formulation, for R classes the Brier score B can vary between 0 and R, with 0 being the best score possible.
where \({\widehat{y}}_{i,c}\) is the class probability as per the model and \({I}_{i,c}=1\) if the i th sample belongs to class c and \({I}_{i,c}=0\) if it does not belong to class c .
We used the Delong test [ 68 ] to compare the AUC for different classifiers.
These metrics will allow other researchers to replicate our study and provide benchmarks for future improvements.
In this section we present the results of applying the techniques in the Methods section.
We provide descriptive statistics that help the reader understand the distributions of the variables of interest.
Table 1 summarizes basic statistical properties of the LoS variable.
Figure 5 shows the distribution of the LoS variable for newborns.
This figure depicts the distribution of the LoS variable for newborns
Table 2 shows the top 20 APR DRG descriptions based on their frequency of occurrence in the dataset.
Figure 6 shows the distribution of the LoS variable for the top 20 most frequently occurring APR DRG descriptions shown in Table 2 .
A 3-d plot showing the distribution of the LoS for the top-20 most frequently occuring APR DRG descriptions. The x-axis (horizontal) depicts the LoS, the y-axis shows the APR DRG codes and the z-axis shows the density or frequency of occurrence of the LoS
We experimented with different encoding schemes for the categorical variables and for each encoding we examined different regression techniques. Our results are shown in Table 3 . We experimented with the three encoding schemes shown in the first column. The last row in the table shows a combination of one-hot encoding and target encoding, where the number of columns in the dataset are increased to accommodate one-hot encoded feature values for categorical variables.
We obtained the SHAP plots using a Random Forest Regressor trained with target-encoded features.
Figures 7 and 8 show the SHAP values plots obtained for the features in the newborn partition of the dataset. We find that the features, “APR DRG Code”, “APR Severity of Illness Code”, “Patient Disposition”, “CCS Procedure Code”, are very useful in predicting the LoS. For instance, high feature values for “APR Severity of Illness Code”, which are encoded by red dots have higher SHAP values than the blue dots, which correspond to low feature values.
SHAP Value plot for newborns
1-D SHAP plot, in order of decreasing feature importance: top to bottom (for non-newborns)
A similar interpretation can be applied to the features in the non-newborn partition of the dataset. We note that “Operating Certificate Number” is among the top-10 most important features in both the newborn and non-newborn partitions. This finding is discussed in the Discussion section.
From Fig. 9 , we observe that as the severity of illness code increases from 1–4, there is a corresponding increase in the SHAP values.
A 2-D plot showing the relationship between SHAP values for one feature, “APR Severity of Illness Code”, and the feature values themselves (non-newborns)
To further understand the relationship between the APR Severity of Illness code and the LoS, we created the plot in Fig. 10 . This shows that the most frequently occurring APR Severity of Illness code is 1 (Minor), and that the most frequently occurring LoS is 2 days. We provide this 2-D projection of the overall distribution of the multi-dimensional data as a way of understanding the relationship between the input features and the target variable, LoS.
A density plot showing the relationship between APR Severity of Illness Code and the LoS. The color scale on the right determines the interpretation of colors in the plot. We used a kernel density estimation with a Gaussian kernel [ 61 ] to generate the plot
Similarly, Fig. 11 shows the relationship between the birth weight and the length of stay. The most common length of stay is two days.
A density plot showing the distribution of the birth weight values (in grams) versus the LoS. The colorbar on the right shows the interpretation of color values shown in the plot. We used a kernel density estimation with a Gaussian kernel [ 61 ] to generate the plot
We obtained a classification accuracy of 46.98% using Multinomial Logistic Regression with tenfold cross-validation in the 5-class classification task for non-newborn cases. The confusion matrix in Fig. 12 shows that the highest density of correctly classified samples is in or close to the diagonal region. The regions where out model fails occurs between adjacent classes as can be inferred from the given confusion matrix.
Confusion matrix for classification of non-newborns. The number inside each square along the diagonal represents the number of correctly classified samples. The color is coded so lighter colors represent lower numbers
For the newborn cases, we obtained a classification accuracy of 60.08% using Random Forest Classification model with tenfold cross-validation in the 5-class classification task. The confusion matrix in Fig. 13 shows that the majority of data samples lie in or close to the diagonal region. The regions where our model does not do well occurs between adjacent classes as can be inferred from the given confusion matrix,
Confusion matrix for classification of newborns. The number inside each square along the diagonal represents the number of correctly classified samples. The color is coded so lighter colors represent lower numbers
The density plot in Fig. 14 shows the relationship between the actual LoS and the predicted LoS. For a LoS of 2 days, the centroid of the predicted LoS cluster is between 2 and 3 days.
Shows the density plot of the predicted length of stay versus actual length of stay for the classifier model for non-newborns. We used a kernel density estimation with a Gaussian kernel [ 61 ] to generate the plot
A quantitative depiction of our model errors is shown in Fig. 15 . The values in Fig. 15 are interpreted as follows. Referring to the column for LoS = 2, the top row shows that 51% of the predicted LoS values for an actual stay of 2 days is also 2 days (zero error), and that 23% of the predicted values for LoS equal to 2 days have an error of 1 day and so on. The relatively high values in the top row indicates that the model is performing well, with an error of less than 1 day. There are relatively few instances of errors between 2 and 3 days (typically less than 10% of the values show up in this row). The only exception is for the class corresponding to LoS great than 8 days. The truncation of the data to produce this class results in larger model errors specifically for this class.
Shows the distribution of correctly predicted LoS values for each class used in our model. Along the columns, we depict the different classes used in the model, consisting of LoS equal to 1, 2, 3 …8, and more than 8. Each row depicts different errors made in the prediction. For instance, the top row depicts an error of less than or equal to one day between the actual LoS and the predicted Los. The second row from the top depicts an error which is greater than 1 and less than or equal 2 days. And so on for the other rows, for non-newborns
Figures 16 and 17 show the scatter plots for the linear regression models. The exact line represents a line with slope 1, and a perfect model would be one that produced all points lying on this line.
Scatter plot showing an instance of a linear regression fit to the data (newborns). The R 2 score is 0.82. The blue line represents an exact fit, where the predicted LoS equals the actual LoS (slope of the line is 1)
Scatter plot for linear regression. (non-newborns). The R 2 score is 0.42. The blue line represents an exact fit, where the predicted LoS equals the actual LoS (slope of the line is 1)
Figure 18 shows a density plot depicting the relationship between the predicted length of stay and the actual length of stay.
Shows the density plot of the predicted length of stay versus actual length of stay for the classifier model for non-newborns. We used a kernel density estimation with a Gaussian kernel [ 40 ] to generate the plot. The best fit regression line to our predictions is shown in green, whereas the blue line represents the ideal fit (line of slope 1, where actual LoS and predicted LoS are equal)
Most of the existing literature on LoS stay prediction is based on data for specific disease conditions such as cancer or cardiac disease. Hence, in order to understand which CCS diagnosis codes produce good model fits, we produced the plot in Fig. 19 .
This figure shows the three CCS diagnosis codes that produced the top three R 2 scores using linear regression. These are 101, 100 and 109. The three CCS Diagnosis codes that produced the lowest R 2 scores are 159, 657, and 659
We provide the following descriptions in Tables 4 and 5 for the 3 CCS Diagnosis Codes in Fig. 19 with the top R 2 Scores using linear regression.
Similarly, the following table shows the 3 CCS Diagnosis Codes in Fig. 19 for the lowest R 2 Scores using linear regression.
We trained a CatBoost Regressor [ 65 ] on the complete dataset in order to determine the relationship between combinations of features and the prediction accuracy as determined by the R 2 correlation score. This is shown in Fig. 20
The labels for each row on the left show combinations of different input features. A CatBoost regression model was developed using the selected combination of features. The R 2 correlation scores for each model is shown in the bar graph
We can infer from Fig. 20 that only four features (‘'APR MDC Code', 'APR Severity of Illness Code', 'APR DRG Code', 'Patient Disposition') are sufficient for the model to reach very close to its maximum performance. We obtain similar concurring results when using other regression models for the same experiment.
We used a random forest tree approach to generate the trees in Figs. 21 and 22 .
A random forest tree that represents a best-fit model to the data for newborns. With 4 levels of the decision tree, the R 2 score is 0.65
A random forest tree using only a tree of depth 3 that represents a best-fit model to the data for non-newborns. The R 2 score is 0.28. We can generate trees with greater depth that better fit the data, but we have shown only a depth of 3 for the sake of readability in the printed version of this paper. Otherwise, the tree would be too large to be legible on this page. The main point in this figure is to showcase the ease of interpretation of the working of the model through rules
We used tenfold cross validation to determine the regression scores. The results are summarized in Tables 6 and 7 .
We computed the multi-class classifier metrics for logistic regression, using one-hot encoding for non-newborns. The results are presented in Table 8 . The first row represents the accuracy of the classifier when Class 0 is compared against the rest of the classes. A similar interpretation applies to the other rows in the table, ie one-versus-rest. The macro average gives the balanced recall and precision, and the resulting F1 score. The weighted average gives a support (number of samples) weighted average of the individual class metric. The overall accuracy is computed by dividing the total number of accurate predictions, which is 49,686 out of a total number of 105,932 samples, which yields a value of 0.47.
For the category of non-newborns, Fig. 23 provides a graphical plot that visualizes the ROC curves for the different multiclass classifiers we developed.
This figure applies to data concerning non-newborns. We show the multiclass ROC curves for the performance of the catboost classifier for the different classes shown. The area under the ROC curve is 0.7844
In Table 9 we compare the performance of our multiclass classifier using logistic regression developed on 2016 SPARCS data against 2017 SPARCS data.
In order to compare the performance of the different classifiers, we computed the AUC measures reported in Table 10 . Figure 24 visualizes the data in Table 10 and Fig. 25 visualizes the data in Table 11 . In Tables 12 and 13 we report the results of computing the Delong test for non-newborns and newborns respectively. In Tables 14 and 15 we report the results of computing the Brier scores for non-new borns and newborns respectively.
A bar chart that depicts the data in Table 10 for non-newborns
A bar chart that depicts the data in Table 11
In Table 16 we present the parameter and hyperparameter values used in the different models.
Due to space restrictions, we show additional results in the Appendix/Supplementary Material. These results are in tabular form and describe the R 2 scores for different segmentations of the variables in the dataset, e.g. according to age group, severity of illness code, etc.
The most significant result we obtain is shown in Figs. 21 and 22 , which provides an interpretable working of the decision trees using random forest modeling. Figure 21 for newborns shows that the birth weight features prominently in the decision tree, occurring at the root node. Low birth weights are represented on the left side of the tree and are typically associated with longer hospital stays. Higher birth weights occur on the right side of the tree, and the node in the bottom row with 189,574 samples shows that the most frequently occurring predicted stay is 2.66 days. Figure 22 for non-newborns shows that the features of “APR DRG Code”, “APR Severity of Illness Code” and “Patient Disposition” are the most important top-level features to predict the LoS. This provides a relatively simple rule-based model, which can be easily interpreted by healthcare providers as well as patients. For instance, the right-most branch of the tree classifies the input data into a relatively high LoS (46 days) when the branch conditions APR DRG Code is greater than 813.55 and the APR Severity of Illness Code is less than 91.
The results in Fig. 19 and Table 4 show that if we restrict our model to specific CCS Diagnosis descriptions such as “coronary atherosclerosis and other heart disease”, we obtain a good R 2 Score of 0.62. The objective of our work is not to cherry-pick CCS Diagnosis codes that produce good results, but rather to develop a single model for the entire SPARCS dataset to obtain a birds-eye perspective. For future work, we can explicitly build separate models for each CCS Diagnosis code, and that could have relevance to specific medical specialties, such as cardiovascular care.
Similarly, the results in Fig. 19 and Table 5 show that there are CCS Diagnosis codes corresponding to schizophrenia and mood disorders that produce a poor model fit. Factors that contribute to this include the type of data in the SPARCS dataset, where information about patient vitals, medications, or a patient’s income level is not provided, and the inherent variability in treating schizophrenia and mood disorders. Baeza et al. [ 69 ] identified several variables that affect the LoS in psychiatric patients, which include psychiatric admissions in the previous years, psychiatric rating scale scores, history of attempted suicide, and not having sufficient income. Such variables are not provided in the SPARCS dataset. Hence a policy implication is to collect and make such data available, perhaps as a separate dataset focused on mental health issues, which have proven challenging to treat.
Figures 16 and 17 show that a better regression fit is obtained when a specific CCS Diagnosis code is used to build the model, such as “Newborn” in Fig. 16 . To put these results in context, we note that it is difficult to obtain a high R 2 value for healthcare datasets in general, and especially for large numbers of patient samples that span multiple hospitals. For instance, Bertsimas [ 70 ] reported an R 2 value of 0.2 and Kshirsagar [ 71 ] reported an R 2 value of 0.33 for similar types of prediction problems as studied in this paper.
Further details for a segmentation of R 2 scores by the different variable categories are shown in the Appendix/Supplementary Material section. For instance, the table corresponding to Age Groups shows that there is close agreement between the mean of the predicted LoS from our model and the actual LoS. Furthermore, the mean LoS increases steadily from 4.8 days for Age group 0–17 to 6.4 days for ages 70 or older. A discussion of these tables is outside the scope of this paper. However, they are being provided to help other researchers form hypotheses for further investigations or to find supporting evidence for ongoing research.
Table 3 shows that the best encoding scheme is to combine target encoding with one-hot encoding and then apply linear regression. This produces an R 2 score of 0.42 for the non-newborn data, which is the best fit we could obtain. This table also shows that significant improvements can be obtained by exploring the search space which consists of different strategies of feature encoding and regression methods. There is no theoretical framework which determines the optimum choice, and the best method is to conduct an experimental search. An important contribution of the current paper is to explore this search space so that other researchers can use and build upon our methodology.
The distribution of errors in Fig. 15 shows that the truncation we employed at a LoS of 8 days produces artifacts in the prediction model as all stays of greater than 8 days are lumped into one class. Nevertheless, the distribution of LoS values in Fig. 4 shows that a relatively small number of data samples have LoS greater than 8 days. In the future, we will investigate different truncation levels, and this is outside the scope of the current paper. By using our methodology, the truncation level can also be tuned by practitioners in the field, including hospital administrators and other researchers.
Our results in Fig. 7 show that certain features are not useful in predicting the LoS. The SHAP plot shows that features such as race, gender, and ethnicity are not useful in predicting the LoS. It would have been interesting if this were not the case, as that implies that there is systemic bias based on race, gender or ethnicity. For instance, a person with a given race may have a smaller LoS based on their demographic identity. This would be unacceptable in the medical field. It is satisfying to see that a large and detailed healthcare dataset does not show evidence of bias.
To place this finding in context, racial bias is an important area of research in the U.S., especially in fields such as criminology and access to financial services such as loans. In the U.S., it is well known that there is a disproportional imprisonment of black and Hispanic males [ 72 ]. Researchers working on criminal justice have determined that there is racial bias in the process of sentencing and granting parole, with blacks being adversely affected [ 73 ]. This bias is reinforced through any algorithms that are trained on the underlying data. There is evidence that banks discriminate against applicants for loans based on their race or gender [ 74 ].
This does not appear to be the case in our analysis of the SPARCS data. Though we did not specifically investigate the issue of racial bias in the LoS, the feature analysis we conducted automatically provides relevant answers. Other researchers including those in the U.K [ 21 ] have also determined that gender does not have an effect on LoS or costs. Hence the results in the current paper are consistent with the findings of other researchers in other countries working on entirely different datasets.
From Table 6 we see that in the case of data concerning non-newborns, the catboost regression performs the best, with an R 2 score of 0.432. The p -value is less than 0.01, indicating that the correlation between the actual and predicted values of LoS through catboost regression is statistically significant. Similarly, the p -values for linear regression and random forest regression indicate that these models produce predictions that are statistically significant, i.e. they did not occur by random chance.
From Table 7 that refers to data from newborns, the linear regression performs the best, with an R 2 score of 0.82. The p -value is less than 0.01, indicating that the correlation between the actual and predicted values of LoS through linear regression is statistically significant. Similarly, the p -values for random forest regression and catboost regression indicate that these models produce predictions that are statistically significant.
We examine the performance of classifiers on non-newborn data, as shown in Tables 10 and 12 . The Delong test conducted in Table 12 shows that there is a statistically significant difference between the AUCs of the pairwise comparisons of the models. Hence, we conclude that the catboost classifier performs the best with an average AUC of 0.7844. We also note that there is a marginal improvement in performance when we use the catboost classifier instead of the random forest classifier. Both the catboost classifier and the random forest classifier perform better than logistic regression. We conclude that the best performing model for non-newborns is the catboost classifier, followed by the random forest classifier, and then logistic regression.
In the case of newborn data, we examine the performance of the classifiers as shown in Tables 11 and 13 . From Table 13 , we note that the p -values in all the rows are less than 0.05, except for the binary class “one vs. rest for class 3”, random forests vs. catboost. Hence, for this particular comparison between the random forest classifier and the catboost classifier for “one vs. rest for class 3”, we cannot conclude that there is a statistically significant difference between the performance of these two classifiers. From Table 11 we observe that the AUCs of these two classifiers are very similar. We also note that only about 10% of the dataset consists of newborn cases.
From Table 14 we note that the Brier score for the catboost classifier is the lowest. A lower Brier score indicates better performance. According to the Brier scores for the non-newborn data, the catboost classifier performs the best, followed by the random forest classifier and then logistic regression. Table 15 shows that for newborns, the random forest classifier performs the best, followed by the catboost classifier and logistic regression. The performance of the random forest classifier and catboost classifier are very similar.
From a practical perspective, it may make sense to use a catboost classifier on both newborn and non-newborn data as it simplifies the processing pipeline. The ultimate decision rests with the administrators and implementers of these decision systems in the hospital environment.
Burn et al. observe [ 21 ] that though the U.S. has reported similar declines in LoS as in the U.K, the overall costs of joint replacement have risen. The U.K. government created policies to encourage the formation of specialist centers for joint replacement, which have resulted in reduction in the LoS as well as delivering cost reductions. The results and analysis presented in our current paper can help educate patients and healthcare consumers about trends in healthcare costs and how they can be reduced. An informed and educated electorate can press their elected representatives to make changes to the healthcare system to benefit the populace.
Hachesu et al. examined the LoS for cardiac disease patients [ 22 ] where they used data from around 5000 patients and considered 35 input variables to build a predictive model. They found that the LoS was longer in patients with high blood pressure. In contrast, our method uses data from 2.5 million patients and considers multiple disease conditions simultaneously. We also do not have access to patient vitals such as blood pressure measurements, due to the limitation of the existing New York State SPARCS data.
Garcia et al. [ 23 ] conducted a study of elderly patients (age greater than 60) to understand factors governing the LoS for hip fracture treatment. They used 660 patient records and determined that the most significant variable was the American Society of Anesthesiologists (ASA) classification system. The ASA score ranges from 1–5 and captures the anesthesiologist’s impression of a patient’s health and comorbidities at the time of surgery. Garcia et al. showed a monotonically increasing relationship between the ASA score and the LoS. However, they did not build a specific predictive model. Their work shows that it is possible to find single variables with significant information content in order to estimate the LoS. The New York SPARCS dataset that we used does not contain the ASA score. Hence a policy implication of our research is to alert the healthcare authorities include such variables such as the ASA score where relevant in the datasets released in the future. The additional storage required is very small (one additional byte per patient record).
Arjannikov et al. [ 25 ] developed predictive models by binarizing the data into two categories, e.g. LoS < = 2 days or LoS > 2 days. In our work, we did not employ such a discretization. In contrast, we used continuous regression techniques as well as classification into more than two bins. It is preferable to stay as close to the actual data as possible.
Almashrafi et al. [ 27 ] and Cots et al. [ 75 ] observed that larger hospitals tended to have longer LoS for patients undergoing cardiac surgery. Though we did not specifically examine cardiac surgery outcomes, our feature analysis indicated that the hospital operating certificate number had lower relevance than other features such as DRG codes. Nevertheless, the SHAP plots in Fig. 7 and Fig. 8 show that the hospital operating certificate number occurs within the top 10 features in order of SHAP values. We will investigate this relationship in more detail in future research, as it requires determining the size of the hospital from the operating certificate number and creating an appropriate machine-learning model. The Appendix contains results that show certain operating certificate numbers that produce a good model fit to the data.
A major focus of our research is on building interpretable and explainable models. Based on the principle of parsimony, it is preferable to utilize models which involve fewer features. This will provide simpler explanations to healthcare professionals as well as patients. We have shown through Fig. 20 that a model with five features performs just as well as a model with seven features. These features also make intuitive sense and the model’s operation can be understood by both patients and healthcare providers.
Patients in the U.S. increasingly have to pay for medical procedures out-of-pocket as insurance payments do not cover all the expenses, leading to unexpectedly large bills [ 76 ]. Many patients also do not possess health insurance in the U.S., with the consequence that they get charged the highest [ 77 ]. Kullgreen et.al. observe that patients in the U.S. need to be discerning healthcare consumers [ 78 ], as they can optimize the value they receive from out-of-pocket spending. In addition to estimating the cost of medical procedures, patients will also benefit from estimating the expected duration for a procedure such as joint replacement. This will allow them to budget adequate time for their medical procedures. Patients and consumers will benefit from obtaining estimates from an unbiased open data source such as New York State SPARCS and the use of our model.
Other researchers have developed specific LoS models for particular health conditions, such as cardiac disease [ 22 ], hip replacement [ 21 ], cancer [ 26 ], or COVID-19 [ 24 ]. In addition, researchers typically assume a prior statistical distribution for the outcomes, such a Weibull distribution [ 24 ]. However, we have not made any assumptions of specific prior statistical distributions, nor have we restricted our analysis to specific diseases. Consequently, our model and techniques should be more widely applicable, especially in the face of rapidly changing disease trajectories worldwide.
Our study is based exclusively on freely available open health data. Consequently, we cannot control the granularity of the data and must use the data as-is. We are unable to obtain more detailed patient information such as their physiological variables such as blood pressure, heartrate variability etc. at the time of admittance and during their stay. Hospitals, healthcare providers, and insurers have access to this data. However, there is no mandate for them to make this available to researchers outside their own organizations. Sometimes they sell de-identified data to interested parties such as pharmaceutical companies [ 79 ]. Due to the high costs involved in purchasing this data, researchers worldwide, especially in developing countries are at a disadvantage in developing AI algorithms for healthcare.
There is growing recognition that medical researchers need to standardize data formats and tools used for their analysis, and share them openly. One such effort is the organization for Observational Health Data Sciences and Informatics (OHDSI) as described in [ 80 ].
Twitter has demonstrated an interesting path forward, where a small percentage of its data was made available freely to all users for non-commercial purposes through an API [ 81 ]. Recently, Twitter has made a larger proportion of its data available to qualified academic researchers [ 82 ]. In the future, the profit motives of companies need to be balanced with considerations for the greater public good. An advantage of using the Twitter model is that it spurs more academic research and allows universities to train students and the workforce of the future on real-world and relevant datasets.
In the U.S., a new law went into effect in January 2021 requiring hospitals to make pricing data available publicly. The premise is that having this data would provide better transparency into the working of the healthcare system in the U.S. and lead to cost efficiencies. However, most hospitals are not in compliance with this law [ 83 ]. Concerted efforts by government officials as well as pressure by the public will be necessary to achieve compliance. If the eventual release of such data is not accompanied by a corresponding interest shown by academicians, healthcare researchers, policymakers, and the public it is likely that the very premise of the utility of this data will be called into question. Furthermore, merely dumping large quantities of data into the public domain is unlikely to benefit anyone. Hence research efforts such as the one presented in this paper will be valuable in demonstrating the utility of this data to all stakeholders.
Our machine-learning pipeline can easily be applied to new data that will be released periodically by New York SPARCS, and also to hospital pricing data [ 83 ]. Due to our open-source methodology, other researchers can easily extend our work and apply it to extract meaning from open health data. This improves reproducibility, which is an essential aspect of science. We will make our code available on Github to interested researchers for non-commercial purposes.
Our models are restricted to the data available through New York State SPARCS, which does not provide detailed information about patient vitals. More detailed physiological data is available through the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) framework [ 84 ], though for a smaller number of patients. We plan to extend our methodology to handle such data in the future. Another limitation of our study is that it does not account for patient co-morbidities. This arises from the de-identification process used to release the SPARCS data, where patient information is removed. Hence we are unable to analyze multiple hospital admissions for a given patient, possibly for different conditions. The main advantage of our approach is that it uses large-scale population data (2.3 million patients) but at a coarse level of granularity, where physiological data is not available. Nevertheless, our approach provides a high-level view of the operation of the healthcare system, which provides valuable insights.
There is growing interest in using data analytics to increase government transparency and inform policymaking. It is expected that the meaning and insights gained from such evidence-based analysis will translate to better policies and optimal usage of the available infrastructure. This requires cooperation between computer scientists, domain experts, and policy makers. Open healthcare data is especially valuable in this context due to its economic significance. This paper presents an open-source analytics system to conduct evidence-based analysis on openly available healthcare data.
The goal is to develop interpretable machine learning models that identify key drivers and make accurate predictions related to healthcare costs and utilization. Such models can provide actionable insights to guide healthcare administrators and policy makers. A specific illustration is provided via a robust machine learning pipeline that predicts hospital length of stay across 285 disease categories based on 2.3 million de-identified patient records. The length of stay is directly related to costs.
We focused on the interpretability and explainability of input features and the resulting models. Hence, we developed separate models for newborns and non-newborns, given differences in input features. The best performing model for non-newborn data was catboost regression, which used linear regression and achieved an R 2 score of 0.43. The best performing model for newborns and non-newborns respectively was linear regression, which achieved an R 2 score of 0.82. Key newborn predictors included birth weight, while non-newborn models relied heavily on the diagnostic related group classification. This demonstrates model interpretability, which is important for adoption. There is an opportunity to further improve performance for specific diseases. If we restrict our analysis to cardiovascular disease, we obtain an improved R 2 score of 0.62.
The presented approach has several desirable qualities. Firstly, transparency and reproducibility are enabled through the open-source methodology. Secondly, the model generalizability facilitates insights across numerous disease states. Thirdly, the technical framework can easily integrate new data while allowing modular extensions by the research community. Lastly, the evidence generated can readily inform multiple key stakeholders including healthcare administrators planning capacity, policy makers optimizing delivery, and patients making medical decisions.
Data is publicly available at the website mentioned in the paper, https://www.health.ny.gov/statistics/sparcs/
There is an “About Us” tab in the website which contains all the contact details. The authors have nothing to do with this website as it is maintained by New York State.
Gurría A. Openness and Transparency - Pillars for Democracy, Trust and Progress. OECD.org. Available: https://www.oecd.org/unitedstates/opennessandtransparency-pillarsfordemocracytrustandprogress.htm . Accessed 28 June 2024.
Jetzek T. The Sustainable Value of Open Government Data: Uncovering the Generative Mechanisms of Open Data through a Mixed Methods Approach. lCopenhagen Business School, Institut for IT-Ledelse Department of IT Management. 2015.
Move fast and heal things: How health care is turning into a consumer product. The Economist. 2022. https://www.economist.com/business/how-health-care-is-turning-into-a-consumer-product/21807114 . Accessed 28 June 2024.
New York State Department Of Health, Statewide Planning and Research Cooperative System (SPARCS). https://www.health.ny.gov/statistics/sparcs/ . Accessed 5 Oct 2022.
Rao AR, Chhabra A, Das R, Ruhil V. A framework for analyzing publicly available healthcare data. In 2015 17th International Conference on E-health Networking, Application & Services (IEEE HealthCom). 2015: IEEE, pp. 653–656.
Rao AR, Clarke D. A fully integrated open-source toolkit for mining healthcare big-data: architecture and applications. In IEEE International Conference on Healthcare Informatics ICHI, Chicago. 2016: IEEE, pp. 255–261.
Rao AR, Garai S, Dey S, Peng H. PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data. SN Computer Science. 2021;2(6):1–22.
Article Google Scholar
Rao AR, Rao S, Chhabra R. Rising mental health incidence among adolescents in Westchester, NY. Community Ment Health J. 2021:1–1.
Boylan J F. My $145,000 Surprise Medical Bill. New York Times. 2020. https://www.nytimes.com/2020/02/19/opinion/surprise-medical-bill.html . Accessed 28 June 2024.
Peterson K, Bykowicz J. Congress Debates Push to End Surprise Medical Billing. Wall Street J. 2020. https://www.wsj.com/articles/congress-debates-push-to-end-surprise-medical-billing-11589448603 . Accessed 28 June 2024.
Wang S, Zhang J, Fu Y, Li Y. ACM TIST Special Issue on Deep Learning for Spatio-Temporal Data: Part 1. 12th ed. NY: ACM New York; 2021. p. 1–3.
Google Scholar
Jones R. lining length of stay and future bed numbers. BJHCM. 2015;21(9):440–1.
Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Al-Thwayee A, Al-Mallah MH. Predictors of in-hospital length of stay among cardiac patients: a machine learning approach. Int J Cardiol. 2019;288:140–7.
Article PubMed Google Scholar
Sen-Crowe B, Sutherland M, McKenney M, Elkbuli A. A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic. J Surg Res. 2021;260:56–63.
Article CAS PubMed Google Scholar
Stone K, Zwiggelaar R, Jones P, Mac Parthaláin N. A systematic review of the prediction of hospital length of stay: Towards a unified framework. PLOS Digital Health. 2022;1(4):e0000017.
Article PubMed PubMed Central Google Scholar
Lequertier V, Wang T, Fondrevelle J, Augusto V, Duclos A. Hospital length of stay prediction methods: a systematic review. Med Care. 2021;59(10):929–38.
Sridhar S, Whitaker B, Mouat-Hunter A, McCrory B. Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital. PLoS ONE. 2022;17(11);e0277479.
Article CAS PubMed PubMed Central Google Scholar
CCS (Clinical Classifications Software) - Synopsis. https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/CCS/index.html . Accessed 13 Jan 2022.
Sotoodeh M, Ho JC. Improving length of stay prediction using a hidden Markov model. AMIA Summits on Translational Science Proceedings. 2019;2019:425.
PubMed Central Google Scholar
Ma F, Yu L, Ye L, Yao DD, Zhuang W. Length-of-stay prediction for pediatric patients with respiratory diseases using decision tree methods. IEEE J Biomed Health Inform. 2020;24(9):2651–62.
Burn E, et al. Trends and determinants of length of stay and hospital reimbursement following knee and hip replacement: evidence from linked primary care and NHS hospital records from 1997 to 2014. BMJ Open. 2018;8(1);e019146.
Hachesu PR, Ahmadi M, Alizadeh S, Sadoughi F. Use of data mining techniques to determine and predict length of stay of cardiac patients. Healthcare informatics research. 2013;19(2):121–9.
Garcia AE, et al. Patient variables which may predict length of stay and hospital costs in elderly patients with hip fracture. J Orthop Trauma. 2012;26(11):620–3.
Vekaria B, et al. Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning. BMC Infect Dis. 2021;21(1):1–15.
Arjannikov T, Tzanetakis G. An empirical investigation of PU learning for predicting length of stay. In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI). 2021: IEEE, pp. 41–47.
Gupta D, Vashi PG, Lammersfeld CA, Braun DP. Role of nutritional status in predicting the length of stay in cancer: a systematic review of the epidemiological literature. Ann Nutr Metab. 2011;59(2–4):96–106.
Almashrafi A, Elmontsri M, Aylin P. Systematic review of factors influencing length of stay in ICU after adult cardiac surgery. BMC Health Serv Res. 2016;16(1):318.
Kalgotra P, Sharda R. When will I get out of the hospital? Modeling Length of Stay using Comorbidity Networks. J Manag Inf Syst. 2021;38(4):1150–84.
Awad A, Bader-El-Den M, McNicholas J. Patient length of stay and mortality prediction: a survey. Health Serv Manage Res. 2017;30(2):105–20.
Editorial-Board. The Lancet, HCL and Trump. Wall Street J. 2020. https://www.wsj.com/articles/the-lancet-hcl-and-trump-11591226880 . Accessed 28 June 2024.
Servick K, Enserink M. A mysterious company’s coronavirus papers in top medical journals may be unraveling. Science. 2020. https://www.science.org/content/article/mysterious-company-s-coronavirus-papers-top-medical-journals-may-be-unraveling . Accessed 28 June 2024.
Gabler E, Rabin RC. The Doctor Behind the Disputed Covid Data. New York Times. 2020. https://www.nytimes.com/2020/07/27/science/coronavirus-retracted-studies-data.html . Accessed 28 June 2024.
Lancet-Editors. Expression of concern: Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis. 2020;395:10240. https://www.science.org/content/article/mysterious-company-s-coronavirus-papers-topmedical-journals-may-be-unraveling . Accessed 28 June 2024.
Editorial-Board. Expression of Concern: Mehra MR et al. Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19. N Engl J Med. 2020. https://www.nejm.org/doi/full/10.1056/NEJMoa2007621 . Accessed 28 June 2024.
Hopkins JS, Gold R. Authors Retract Studies That Found Risks of Using Antimalaria Drugs Against Covid-19. Wall Street J. 2020. https://www.wsj.com/articles/authors-retract-study-that-found-risks-of-using-antimalaria-drug-against-covid-19-11591299329 . Accessed 28 June 2024.
https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(20)31180-6.pdf . Accessed 9 Jan 2022.
Wolfensberger M, Wrigley A. Trust in Medicine. Cambridge University Press. 2019. ISBN-13: 978-1108487191.
Bhattacharya J, Nicholson T. A Deceptive Covid Study, Unmasked. Wall Street J. 2022. https://www.wsj.com/articles/deceptive-covid-study-unmasked-abc-misleading-omicron-north-carolina-students-duke-mask-test-to-stay-11641933613 . Accessed 28 June 2024.
Baker M. 1,500 scientists lift the lid on reproducibility. Nature. 2016;533(7604):452–4.
Begley CG, Ioannidis JP. Reproducibility in science: improving the standard for basic and preclinical research. Circ Res. 2015;116(1):116–26.
Eisner D. Reproducibility of science: Fraud, impact factors and carelessness. J Mol Cell Cardiol. 2018;114:364–8.
Wang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept “black box” medicine? Am College Phys. 2020;172:59–60.
Reyes M, et al. On the interpretability of artificial intelligence in radiology: challenges and opportunities. Radiol Art Intell. 2020;2(3):e190043.
Savadjiev P, et al. Demystification of AI-driven medical image interpretation: past, present and future. Eur Radiol. 2019;29(3):1616–24.
McKinney W. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O’Reilly Media, Inc. 2012.
Pedregosa F, et al. Scikit-learn: Machine learning in Python. J Machine Learn Res. 2011;12:2825–30.
Cass S. The top programming languages: Our latest rankings put Python on top-again-[Careers]. IEEE Spectr. 2020;57(8):22–22.
Tjoa E, Guan C. A survey on explainable artificial intelligence (xai): Toward medical xai," IEEE Transactions on Neural Networks and Learning Systems. 2020.
https://www.health.ny.gov/statistics/sparcs/docs/sparcs_data_dictionary.xlsx . Accessed 28 June 2024.
Design and development of the Diagnosis Related Group (DRG). https://www.cms.gov/icd10m/version37-fullcode-cms/fullcode_cms/Design_and_development_of_the_Diagnosis_Related_Group_(DRGs).pdf . Accessed 5 Oct 2022.
ARTICLE 28, Hospitals, Public Health (PBH) CHAPTER 45. 2023. Available: https://www.nysenate.gov/legislation/laws/PBH/A28 . Accessed 28 June 2024.
Gilmore‐Bykovskyi A, et al. Disparities in 30‐day readmission rates among Medicare enrollees with dementia. J Am Geriatr Soc. 2023.
Rodríguez P, Bautista MA, Gonzalez J, Escalera S. Beyond one-hot encoding: Lower dimensional target embedding. Image Vis Comput. 2018;75:21–31.
Montgomery DC, Peck EA, Vining GG. Introduction to linear regression analysis. 6th ed. John Wiley & Sons; 2021. ISBN-13 978-1119578727.
Random forest regressor in sklearn. Available: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html . Accessed 28 June 2024.
Breiman L. Random forests. Mach Learn. 2001;45:5–32.
Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43(6):1947–58.
Liaw A, Wiener M. Classification and regression by randomForest. R news. 2002;2(3):18–22.
Böhning D. Multinomial logistic regression algorithm. Ann Inst Stat Math. 1992;44(1):197–200.
Vaid A, et al. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. J Med Internet Res. 2020;22(11);e24018.
Density Estimation. https://scikit-learn.org/stable/modules/density.html . Accessed 5 Oct 2022.
CatBoost, a high-performance open source library for gradient boosting on decision trees. Available: https://catboost.ai/ and https://catboost.ai/en/docs/concepts/python-usages-examples . Accessed 28 June 2024.
PyTorch documentation for torch.nn, the basic building blocks for graphs. Available: https://pytorch.org/docs/stable/nn.html . Accessed 28 June 2024.
Kingma DP, Ba J. Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980. 2014.
Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. CatBoost: unbiased boosting with categorical features," arXiv preprint arXiv:1706.09516. 2017.
Tharwat A. Classification assessment methods. Applied computing and informatics. 2020;17(1):168–92.
Brier GW. Verification of forecasts expressed in terms of probability. Mon Weather Rev. 1950;78(1):1–3.
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988:837–45.
Baeza FL, da Rocha NS, Fleck MP. Predictors of length of stay in an acute psychiatric inpatient facility in a general hospital: a prospective study. Brazilian Journal of Psychiatry. 2017;40:89–96.
Bertsimas D, et al. Algorithmic prediction of health-care costs. Oper Res. 2008;56(6):1382–92.
Kshirsagar R. Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans," presented at the AAAI 2021 Conference. 2021.
Ulmer J, Painter-Davis N, Tinik L. Disproportional imprisonment of Black and Hispanic males: Sentencing discretion, processing outcomes, and policy structures. Justice Q. 2016;33(4):642–81.
Angwin J, J. Larso J, Mattu S, Kirchner L. Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica (2016). Google Scholar. 2016;23.
Steil JP, Albright L, Rugh JS, Massey DS. The social structure of mortgage discrimination. Hous Stud. 2018;33(5):759–76.
Cots F, Mercadé L, Castells X, Salvador X. Relationship between hospital structural level and length of stay outliers: Implications for hospital payment systems. Health Policy. 2004;68(2):159–68.
Evans M, McGinty T. Hospital Prices Are Arbitrary. Just Look at the Kingsburys’ $100,000 Bill. Wall Street J. 2021. https://www.wsj.com/articles/hospital-prices-arbitrary-healthcare-medical-bills-insurance-11635428943 . Accessed 28 June 2024.
Evans M. Hospitals Often Charge Uninsured People the Highest Prices, New Data Show. Wall Street J. 2021. https://www.wsj.com/articles/hospitals-often-charge-uninsured-people-the-highest-prices-new-data-show-11625584448 . Accessed 28 June 2024.
Kullgren JT, et al. A survey of Americans with high-deductible health plans identifies opportunities to enhance consumer behaviors. Health Aff. 2019;38(3):416–24.
Wetsman N. Hospitals are selling treasure troves of medical data — what could go wrong? The Verge. 2021. Available: https://www.theverge.com/2021/6/23/22547397/medical-records-health-data-hospitals-research . Accessed 28 June 2024.
Hripcsak G, et al. Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers. Stud Health Technol Inform. 2015;216:574–8.
PubMed PubMed Central Google Scholar
Gabarron E, Dorronzoro E, Rivera-Romero O, Wynn R. Diabetes on Twitter: a sentiment analysis. J Diabetes Sci Technol. 2019;13(3):439–44.
Statt N. Twitter is opening up its full tweet archive to academic researchers for free. The Verge. 2021. Available: https://www.theverge.com/2021/1/26/22250203/twitter-academic-research-public-tweet-archive-free-access . Accessed 28 June 2024.
Evans M, Mathews AW, McGinty T. Hospitals Still Not Fully Complying With Federal Price-Disclosure Rules. Wall Street J. 2021. https://www.wsj.com/articles/hospital-price-public-biden-11640882507 .
Johnson AE, et al. MIMIC-III, a freely accessible critical care database. Scientific data. 2016;3(1):1–9.
Download references
We are grateful to the New York State SPARCS program for making the data available freely to the public. We greatly appreciate the feedback provided by the anonymous reviewers which helped in improving the quality of this manuscript.
No external funding was available for this research.
Authors and affiliations.
Indian Institute of Technology, Delhi, India
Raunak Jain, Mrityunjai Singh & Rahul Garg
Fairleigh Dickinson University, Teaneck, NJ, USA
A. Ravishankar Rao
You can also search for this author in PubMed Google Scholar
Raunak Jain, Mrityunjai Singh, A. Ravishankar Rao, and Rahul Garg contributed equally to all stages of preparation of the manuscript.
Correspondence to A. Ravishankar Rao .
Ethics approval and consent to participate.
Not applicable as no human subjects were used in our study.
Not applicable.
The authors declare no competing interests.
Publisher’s note.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary material 1., rights and permissions.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Reprints and permissions
Cite this article.
Jain, R., Singh, M., Rao, A.R. et al. Predicting hospital length of stay using machine learning on a large open health dataset. BMC Health Serv Res 24 , 860 (2024). https://doi.org/10.1186/s12913-024-11238-y
Download citation
Received : 19 June 2023
Accepted : 24 June 2024
Published : 29 July 2024
DOI : https://doi.org/10.1186/s12913-024-11238-y
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
ISSN: 1472-6963
Call for papers.
The 33rd EEE/ACM International Conference on Program Comprehension (ICPC) is the premier venue for work in the area of software program comprehension. It encompasses human activities for comprehending the software as well as methodologies and technologies for supporting such comprehension.
The research track provides a quality forum for researchers and practitioners from academia, industry, and government to present and discuss new results in program comprehension. Topics of interest for all tracks include but are not limited to:
A subset of the Research Track papers accepted for presentation at ICPC 2025 will be invited to be revised and extended for consideration in a thematic special issue of the Springer Empirical Software Engineering Journal (EMSE) . The best papers of the research track will be awarded with an ACM SIGSOFT Distinguished Paper Award at ICPC. In addition, ICPC 2025 will feature ICPC Honorable Mentions, special awards that will be assigned, on the basis of the program committee reports, to the papers that have applied extremely novel and/or outstanding research methods to the problem of interest.
Submissions must conform to the IEEE conference proceedings template, specified in the IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt type, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf options).
All submissions must not exceed 10 pages for the main text, inclusive of all figures, tables, appendices, etc. Two more pages containing only references are permitted. All submissions must be in PDF. Accepted papers will be allowed one extra page for the main text of the camera-ready version.
Submissions to the Technical Track that meet the above requirements can be made via the Research Track submission site by the submission deadline. Any submission that does not comply with these requirements may be desk rejected without further review.
Submission site: TBD
We encourage the authors to upload their paper info early (and can submit the PDF later) to properly enter conflicts for double-anonymous reviewing. It is the sole responsibility of the authors to ensure that the formatting guidelines, double anonymous guidelines, and any other submission guidelines are met at the time of paper submission.
Submissions from the ICPC 2025 General Chair, Program Chairs, their current students and current postdocs are not allowed to any ICPC 2025 track.
The research track of ICPC 2025 is striving to abide by Open Science policies. In summary, the steering principle is that all research results should be accessible to the public, if possible, and that empirical studies should be reproducible. In particular, we actively support the adoption of open data and open source principles and encourage all contributing authors to disclose (anonymized and curated) data to increase reproducibility and replicability. Note that sharing research data is not mandatory for submission or acceptance. However, sharing is expected to be the default, and non-sharing needs to be justified.
Upon submission to the research track, authors are asked: - to make their data available to the program committee (via upload of supplemental material or a link to an anonymous repository) – and provide instructions on how to access this data in the paper; or - to include in the paper an explanation as to why this is not possible or desirable; and - to indicate if they intend to make their data publicly available upon acceptance. - Supplementary material can be uploaded via the ICPC submission site or anonymously linked from the paper submission. Authors are asked to carefully review any supplementary material to ensure it conforms to the double-anonymous policy (described above). For example, code and data repositories may be exported to remove version control history, scrubbed of names in comments and metadata, and anonymously uploaded to a sharing site to support review. One resource that may be helpful in accomplishing this task is this blog post .
Research papers will be reviewed by at least three members of the Program Committee. The reviewers will remain anonymous and signing of reviews will not be permitted in the review.
Submissions will be evaluated based on the following criteria:
Reviewers will carefully consider all of these criteria during the review process, and authors should take great care in clearly addressing them all. The paper should clearly explain the claimed contributions, and how they are sound, significant, novel, and verifiable, as described above.
ICPC 2025 will offer an author response period. In this period the authors will have the opportunity to inspect the reviews, and answer specific questions raised by the program committee. This period is scheduled after all reviews have been completed, and serves to inform the subsequent decision-making process. Authors will be able to see the full reviews, including the reviewer scores as part of the author response process.
Authors can withdraw their paper at any moment until the final decision has been made, through the paper submission system. Resubmitting the paper to another venue before the final decision has been made without withdrawing from ICPC 2025 first is considered a violation of the concurrent submission policy, and will lead to automatic rejection from ICPC 2025 as well as any other venue adhering to this policy.
Upon notification of acceptance, all authors of accepted papers will receive further instructions for preparing the camera-ready versions of their submissions. If a submission is accepted, at least one author of the paper is required to register for ICPC 2025, attend the conference and present the paper in person . All accepted papers will be published in the conference electronic proceedings. The presentation is expected to be delivered in person, unless this is impossible due to travel limitations (related to, e.g., health, visa, or COVID-19 prevention). Details about the presentations will follow the notifications. The official publication date is the date the proceedings are made available in the ACM or IEEE Digital Libraries. This date may be up to two weeks prior to the first day of ICPC 2025. The official publication date affects the deadline for any patent filings related to published work. Purchases of additional pages in the proceedings is not allowed.
Wed 6 Nov 2024new Abstract Submission |
Sat 9 Nov 2024new Paper Submission |
Tue 17 Dec 2024new Author Notification |
Sat 21 Dec 2024new Author Response Deadline |
Sun 12 Jan 2025new Final Author Notification |
Wed 5 Feb 2025new Camera Ready |
Vrije universiteit brussel.
University of british columbia (ubc).
IMAGES
VIDEO
COMMENTS
research assistant. Zotero is a free, easy-to-use tool to help you collect, organize, annotate, cite, and share research. Download. Available for Mac, Windows, Linux, and iOS. Just need to create a quick bibliography? Try ZoteroBib.
Step 1: find. I used to find new papers by aimlessly scrolling through science Twitter. But because I often got distracted by irrelevant tweets, that wasn't very efficient. I also signed up for ...
Research management can be a complex and challenging process. Some tools address the various challenges that arise when referencing and managing papers. Zotero. Coined as a personal research assistant, Zotero is a tool that brings efficiency to the research process. Zotero helps researchers collect, organize, annotate, and share research easily.
Essential Software for Researchers. #1. Google Scholar: Best for Scholarly Literature Search and Keeping Up-to-date with Research in Your Field. Credits: Wikipedia. Summary. One of the top academic search engines. Enables users to keep up-to-date with the latest research in their respective fields. Provides citation data for each article ...
7. Google Docs. GoogleDocs is a powerful collaborative writing software that saves in real-time. GoogleDocs is ideal for collaborative research organizations. Teams can collaborate on one document that updates in real time, providing a secure and functional space for collaboration.
It's best to organize your research papers chronologically. If you want to do all this at once, I suggest using a reference manager like Zotero or Mendeley (more on reference managers later). File renaming. Make sure you rename your files on your computer according to your own renaming strategy.
Zotero: Collect, manage, and cite your research sources. Lets you organize data into collections and search through them by adding tags to every source. This is a computer program, but there's a browser extension that helps you send data to it. Google Scholar: A simple way to search for scholarly literature on any subject. Diigo: Collect, share ...
1. Mendeley Reference Manager: End-to-End Reference Manager. Mendeley Reference Manager lets you manage your references quickly and effectively while collaborating with other researchers online. Aside from automatically generating bibliographies, this is one of the best referencing software that lets you organize, store, share, and cite references and other research data.
Mendeley is a research paper helper. It helps researchers organize and share research papers and find data. It lets users store, note, and cite references, access cross-publisher articles, and import desktop documents in real-time. Features. Up to 1 GB of free online storage, with options to upgrade. Reporting/Analytics; Activity Dashboard
There are several online tools for researchers to manage and organize their work, including keeping track of task completion, setting deadlines, and just having everything in one place. Four tools for researchers that must be there in your productivity kit are Trello, GanttPRO, Evernote, and My Research Projects.
LITERATURE REVIEW SOFTWARE FOR BETTER RESEARCH. Used by 250,000+ researchers, students and professionals across 150 ... "This tool really helped me to create good bibtex references for my research papers" ... "Litmaps is extremely helpful with my research. It helps me organize each one of my projects and see how they relate to each other ...
6. Scrivener. Scrivener is another great tool for research writing and keeping your notes organized. Used by researchers, screenwriters, novelists, non-fiction writers, students, journalists, academics, lawyers, translators, and more, Scrivener is a tool made for long writing projects.
Trello: Streamline individual and collaborative projects. Researchers need to keep track of various activities to optimize their productivity. A useful app for researchers, Trello is a user-friendly app wherein one can create work boards for different projects and populate them with tasks. The user can assign deadlines and keep updating ongoing ...
5. Mendeley. License: Free. Mendeley Desktop is free academic software (Windows, Mac, Linux) for organizing and sharing research papers and generating bibliographies with 1GB of free online storage to automatically back up and synchronize your library across desktop, web, and mobile. 6.
1. Google Scholar. Google Scholar is perhaps the most popular tool for finding scholarly literature on a plethora of topics. The search engine makes it simple for anyone to explore academic papers, theses, case law, books, etc. On the search results page, you can view the author name, journal title, and total citations, which can help you gauge ...
EndNote is a reference manager and literature review tool widely used in academia for managing research papers and systematic reviews. It allows users to organize references, create bibliographies, and collaborate with colleagues. EndNote offers a subscription-based model, making it a robust choice for in-depth research and systematic reviews.
Mendeley is a free reference manager and academic social network that can help you organize your research, collaborate with others online, and discover the latest research. The program automatically generate bibliographies, imports papers from other research software, helps you find papers based on what you are reading, and enables you to ...
These brief, peer-reviewed articles complement full research papers and are an easy way to receive proper credit and recognition for the work you have done. Research elements are research outputs that have come about as a result of following the research cycle - this includes things like data, methods and protocols, software, hardware and more.
Amy Nichol Smith spent more than 20 years working as a journalist for TV and newspapers before transitioning to software and hardware product reviews for consumers and small businesses.
Data-backed business trends, research insights, and industry analyses for business builders, delivered weekly. ... Best Review Management Software 1. HubSpot. HubSpot provides a suite of tools — marketing, sales, customer service, and customer relationship management (CRM) — to help you attract visitors, convert leads, and close customers. ...
The 33rd IEEE/ACM International Conference on Program Comprehension (ICPC 2025) is the premier venue for work in the area of program comprehension. It encompasses both human activities for comprehending the software and technologies for supporting such comprehension. ICPC 2025 promises to provide a quality forum for researchers and practitioners from academia and industry to present and to ...
Trello is a web-based project management tool that uses a board-based approach to help individuals and teams organize their tasks and projects. It is a popular and user-friendly tool that allows users to easily track their progress, collaborate with team members, and visualize their workflow simply and intuitively.
Jira: Best for software development teams. Image: Jira Software. Our rating: 4.6 out of 5. Jira by Atlassian is a leading project management tool designed specifically for software development ...
Stone et al. [] present a survey of techniques used to predict the LoS, which include statistical and arithmetic methods, intelligent data mining approaches and operations-research based methods.Lequertier et al. [] surveyed methods for LoS prediction.The main gap in the literature is that most methods focus on analyzing trends in the LoS or predicting the LoS only for specific conditions or ...
A subset of the Research Track papers accepted for presentation at ICPC 2025 will be invited to be revised and extended for consideration in a thematic special issue of the Springer Empirical Software Engineering Journal (EMSE). The best papers of the research track will be awarded with an ACM SIGSOFT Distinguished Paper Award at ICPC. In ...