With the increasing rates of COVID-19 hospitalizations, healthcare providers must make critical decisions concerning medical resource allocation to patients who need them the most. While practitioners can speculate about a person’s risk of immediate hospitalization based on their co-morbidities, experts and researchers are making efforts to improve the process with a predictive analysis strategy using machine learning technology.
The use of machine learning development solutions can help healthcare decision-makers make wide-ranging population health-level decisions on the population they are serving. With the various types of COVID virus emerging, and with the relaxations of regulations, the need for care has quite changed. Hospitals are being overburdened and restricted for resources to meet the requirements.
Machine Learning Helps Predict Healthcare Resource Utilization
Whether it be country, state, or some other region, can you predict how much healthcare resources you will need in the coming week or a couple of weeks? You will need to analyze volumes of clinical data to come to a decision, however, it may be not as accurate as offered by machine learning solutions.
By assessing social determinants of health data and including patient information into algorithms, machine learning can help in detecting patients who will need critical medical resources the most, like ventilators due to COVID-19 hospitalization.
Moreover, policymakers can utilize the algorithms to inform national and state public health decisions regarding wearing of masks, and reopening based on the health data of the population and community spread.
Machine learning development solutions help healthcare decision-makers to leverage large volumes of data for better COVID-19 decision-making for population health.
Therefore, healthcare authorities are turning to Machine Learning and Artificial Intelligence services to make the best use of these technologies to make well-informed decisions.
Major Challenges of Using AI to Make Medical Decisions
While AI can be a great asset in medical decision-making, especially when it comes to dealing with tons of data, it comes with a set of challenges.
As healthcare organizations are investing in AI, ML, and IoT development services to develop health IT infrastructure, they are collecting heaps of health data on a daily basis. However, the large volumes of data that are collected aren’t always available for analytical decision-making. They are fragmented across various health systems and solutions or stored within the health information infrastructure. But, they might not be readily available for actionable decision-making leveraging machine learning solutions.
This issue can be overcome by using specialized analytics to put data into an examinable format.
Furthermore, ML algorithms must be effectively trained using sundry data to serve the patient population well and prevent algorithm bias. Also, before deploying the technology into medical practice, make sure that the technology is working optimally and equitably. The AI and ML solutions must deliver fair and equitable care by eliminating certain disparities in performance across gender in the rural-urban population.
Ongoing testing of the algorithm and supporting the quality and diversity of data is required to ensure its proper training.
Final Thought
Working with machine learning models can help healthcare decision-makers make data-driven decisions about population health. With access to meaningful data and insights, they can predict the risk of hospitalization, make better decisions, and improve the performance of the services and workers.
If you want to implement this model, you can partner with a company that offers top-class ML and Artificial Intelligence services.
