Critical Health Care Prioritization Using a No-code AI Recommender System

Summary

At medical institutions, there are times when the patient flow is overwhelming. Even the best and renowned medical institutions fail to cope with the surge in patient flow. In such cases, prioritization in real-time becomes crucial to save numerous lives. Caboom recommendation platform can help hospitals to prioritize critical patients in real-time and manage the patient flow.

Scenario 

With COVID-19 on the rise, we hear news about the overwhelming outflow of  COVID patients, hospitals running out of isolation beds, and ICU packed with patients everyday.  Even the major health institutions  fail to manage these kinds of unprecedented surge. The countries with a utopian healthcare system suffer more. The delay in decision making and last minute prioritization have caused recorded deaths. However, the problem of prioritization can be solved by leveraging the technology today. Caboom would be the right tool to recommend priority to critical care patients based on their medical attributes. Making a fast and efficient decision on how to utilize the limited medical resources can work as an elixir to save many lives.

How to prioritize patients based on their health conditions?

A majority of the medical institution uses a First come first serve basis to treat the patient. The system starts to break down when there is an overwhelming number of patients and the patient needs to be prioritized. It is a tedious process to classify patient conditions and demands doctors and hospital management teams’ time and attention. They have to go through a number of  medical reports before coming to a decision, which makes the service expensive and less efficient. 

Caboom based Critical Patient Ranking

Building AI models is about working on heavy math, finding patterns hidden in data, working with all the caveats, and power technology to meticulously combine everything to your use-cases. At Caboom, this complex and time-consuming task is automated with just a few clicks. At Caboom, we let any user, technical or non-technical, build a recommendation model in just a few clicks without worrying about the technical aspects of the process. 

You only need to have data that is rich in incorporating the pattern. To help you understand about your data, Caboom comes with a feature to quantify how rich your data is. You will only work in collecting rich data, the rest of the work is automated for you.

Traditional way and the Caboom way

Traditional way of doing


Before the doctor has to go through every report to make decisions which is inefficient when there is an abundant patient flow in. To tackle the problem efficiently, we suggest implementing an AI-based recommender system to rank critical patients. There are two ways to achieve this. The first method is to hire a team of AI experts and invest a lot of time and resources to have a viable recommendation system.

Simplified by Caboom


The other problem with this traditional method is that it requires a lot of testing. Using the traditional approach means having an in-house AI expert, dealing with the mathematics and code. It also costs a lot of time to have a finished product. It does not make economic sense to have a whole team unless you have enough data to train and test.

The second method is a simpler one using Caboom. Caboom has given a competitive edge by using Artificial intelligence to classify the distribution of the disease and rank according to it. This can make the decision-making process of hospitals cost-effective, quick, and efficient. It does not require an in house team of experts and also tracks the richness of the data.

With Caboom platform, the value addition for the hospital are:

  1. Caboom automatically handles larger attributes of patients, analyses them with statistical graphs, and makes the overall data more visualized and explanatory.
  2. Critical and in need patients get prioritization.
  3. It helps to reduce administrative errors.
  4. It reduces the cost as it does not require an in-house data science team.
  5. It is not technology-heavy and also does not require strong mathematics to build the AI system.
  6. It democratizes AI, as anyone with a different field and background can Caboom within a few clicks.



The  traditional way in the diagram shows what would be the process for developing an AI-based Recommendation system for critical HealthCare. The process for developing an AI-based system by traditional way starts from the data in the database to ranking the critical patient which takes at least 11 weeks. Whereas, Caboom can enable the solution in less than a week.

Caboom with few clicks trains the best multi-class classifier and recommendation model automatically so that patients with higher severity get predicted. With sample data, ideas about patient prioritization are validated. So the patients with critical conditions can be benefited from healthcare services first.

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