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Optimizando la Gestión de Licencias Médicas con Make y OpenAI
In the dizzying world of business management, automation is presented as an indispensable ally.
In particular, medical license processing has historically challenged human resources areas.
The management of medical licenses commonly falls in the human resources area of companies, a process that, until now, has been completely manual, subject to errors, and consuming a considerable number of weekly hours.
Each medical leave involves various tasks, such as extracting and validating data from documents, searching for the last three salary statements, and notifying the employee’s superior.
This article will explore how the Make and OpenAI platform can revolutionize medical leave management, transforming a manual and error-prone process into an efficient, accurate, and automated operation.
Benefits of Intelligent Automation of Health Processes
When we discuss automation in the health field, we are referring to simplifying tasks and optimizing the quality and efficiency of the processes.
Intelligent automation of healthcare processes provides several key benefits for small and medium-sized businesses:
- Greater Speed: Automation drastically reduces process execution times, allowing an agile response to medical licenses.
- Greater Reliability: With quality algorithms, automation minimizes human errors, guaranteeing precision in information processing.
- More Time for Value Tasks: By freeing healthcare professionals from routine tasks, they can focus on activities requiring their expertise and specialized attention.
- More Information: Artificial intelligence makes processing large amounts of data easier, providing a more complete view of relevant information.
- Cost Savings: Reducing resources associated with automated processes translates into tangible savings for the company.
Technological stack to use
Before diving into the automated process, it is important to know the tools and services that we are going to use:
- Make, our favorite process automation platform.
- Google Workspace, to manage the organization.
- Slack, Gmail and Google Task, as notification channels.
- Google Drive, as storage space for the different files involved.
- OpenAI, artificial intelligence to extract data from medical license files, using a wizard created with the GPT-4 model.
- Buk the Human Resources software that simplifies and centralizes all people management.
If you want me to share the prompt code created with GPT4 on OpenAI just let me know in the comments.
A Solution for Automated Medical Leave Management with Make:
Now, let’s delve into the specific solution we can create in Make for Medical Leave Management.
This innovative process simplifies every step, from receipt of medical leave to notification.
The process created is:
Here’s how it works:
- Valid if the individual is an employee of the company, performing a search in the Google Workspace instance.
- Verify the existence of a folder in Google Drive with the name corresponding to the current month number; Otherwise, proceed to create it.
- Upload the PDF file attached to the email to the current month’s folder in Google Drive.
- Transfer the file to the #OpenAI platform.
- Execute a request to the wizard designed in OpenAI, requesting the reading, interpretation, and return of specific data from the document.
- If the validation is successful, it creates a folder in Google Drive with the name composed of the date in Month-Year format and the employee’s RUT.
- Move the PDF file to the newly created folder.
- Record all the data extracted from the medical license PDF document in a Google spreadsheet.
- Notify the HR team via email notification and slack.
- Generates an alert to the Human Resources team with an expiration date of 2 hours from receipt of the document.
- Inform the employee via email and Slack that their medical leave has been received.
- Find the employee’s manager in Google Workspace. If available, it notifies you by email and Slack that your subordinate has sent a medical leave, recording the superior’s data in the Google spreadsheet.
- Search the Human Resources software, Buk, for the data associated with the employee.
- Locate the employee’s contract in Buk and store it in the folder created for said license in Google Drive.
- Look for the last three salary statements in the Human Resources software and save them in the folder designated for that license in Google Drive.
Thanks to this complete solution, the management of medical licenses
It becomes an agile, efficient, and error-free process.
Applying this automation not only optimizes time but also frees up human resources for higher-value tasks within the company.
How much does it cost to keep this system active?
In the image, you will see various colored balls, each called a module, representing an action within an application, such as uploading a file to Google Drive.
In the case of executing the entire process, 35 operations are consumed in the longest route.
It is essential to remember that Make’s charging unit is the number of operations, and the Core plan, valued at $9 per month, offers 10,000 operations.
Regarding the cost of OpenAI, processing 1 PDF, the estimated cost is 0.11 USD.
In this way, you could process approximately 385 medical licenses per month with this 9 USD plan
- Make Cost: 9 USD
- OpenAI Cost: 52 USD
- Support cost: 150 USD (3 hours per month)
Total cost 211 USD per month to process 385 licenses.
It is important to note that the total cost is an estimated value.
Evaluating the Value of Time Saved (VTG)
In analyzing the time spent processing medical leaves, let’s consider a scenario in which an employee spends approximately 9 minutes per leave.
If the license volume reaches 385, the required time will rise to 57.75 hours.
Suppose the value of one hour of work in human resources is $13.
Under these premises, the total cost would amount to 750.75 USD, without considering possible indirect costs that may arise.
Now, let’s evaluate the real impact of optimizing this process.
The Value of Time Saved (VTG), calculated as [(Unoptimized Cost - New Cost) / New Cost] x 100%, reveals that the efficiency obtained represents an impressive 256% 
This calculation highlights the importance of optimizing time spent and the significant reduction in associated costs.
It is crucial to remember that these numbers could even underestimate the true value, as some indirect costs have not been considered here.
Efficiency not only saves time but also resources! 
In conclusion:
In the dynamic scenario of medical leave management, automation with Make emerges as a game changer.
By freeing up resources, reducing processing times, and keeping all actors informed, this solution improves efficiency and increases the quality of processes.
