Machine Learning in Healthcare: Examples for IT Teams

Healthcare is accelerating; that includes IT systems. See how machine learning is improving the patient experience – and helping healthcare IT teams keep up.

Machine learning in healthcare is emerging as an indispensable technology. It supports patients and clinicians to offset industry challenges and create a more unified system to improve clinical processes. Before it was difficult for healthcare professionals to gather and inspect massive volumes of data. With limited technologies or tools available, it just wasn’t possible.

Now with Machine Learning (a subset of Artificial Intelligence), it’s been relatively easy. Big data technologies are evolved enough for wide-scale adoption. Machine learning aids in data-driven decision making, identification of key trends, and driving research efficiency. When it comes to healthcare, there are many ways in which machine learning techniques can be implemented. Use cases include precise disease prediction, diagnosis, and treatments, improving the overall operations of healthcare.

Let’s explore how machine learning is improving the patient experience – and helping healthcare IT teams keep up.

Is Machine Learning Improving Overall Healthcare Experience?

These days, machine learning plays a vital role in various health-related realms, including the development of current medical procedures. It’s also finding new ways of improving healthcare systems. These range from case management of prevalent chronic diseases to leveraging patient health data.

Here are some Machine learning in healthcare examples:

Infectious disease modeling/prediction

Machine Learning in healthcare provides new confidence to diagnose, prevent and control the spread of infectious diseases. It also aids scientists in quicker clinical trials, identification, and development of effective drug discovery for tackling novel diseases.

Automated record keeping & data collection

Machine Learning in Healthcare ensures the most accurate, automated and complete clinical data collection. Because precise patient data is crucial, machine learning has enough capacity to store, process, and analyze vast volumes of patient data. Records are kept within a single facility that a professional needs to visit and request the medical record.

Behavioral diagnostics

The behavioral diagnostic process would benefit greatly from data-driven machine learning tools that can enhance accuracy and specificity. It also improves the healthcare context and addresses current inequities and biases, streamlines pathways to care, and increases capacity and accessibility.

Meta analysis of imaging

Machine Learning has shown great promise in the field of clinical imaging. It helps clinical professionals evaluate the diagnostic accuracy in detecting tumor metastasis using medical radiology imaging to optimize current imaging processes.

Risks Involved From Using Machine Learning in Healthcare

We’ve discussed so many benefits and a few examples of machine learning in healthcare. However, due to the volume of sensitive data it handles, there are concerns about data privacy and security. One bad incident can result in the risk of losing crucial clinical data that helps to improve your business. So it is vitally important to secure the clinical system and professionals from the risks of a cyber breach.

Here are some important risks that are involved with machine learning:

Privacy concerns

Artificial intelligence (AI) has made the focus on users and systems more accurate. There have been cases where hackers have scraped sensitive clinical data through blind spots in the clinical database. In fact, the only objective of many hackers is to steal patient data by luring them, and they sell this data for a huge unethical profit.

Ethical concerns

There are a lot of questions related to ethical concerns around data usage in healthcare. The information in these systems includes more than just people’s identification and address; it also has private health records. Many professionals and people worry about the ethics behind creating such powerful and potential technologies that house this critical information. Healthcare professionals and AI experts need to address these issues to ensure that artificial intelligence isn’t doing more harm than good.

Bias in ML

Data bias in machine learning is a sort of error in which some elements of a dataset are more heavily weighted than others. A biased dataset will not accurately depict the use case, resulting in skewed outcomes, low accuracy levels, and analytical errors.

Human + AI: Streamlining Healthcare of Tomorrow

machine learning in healthcare example

Machine Learning has the potential to transform the whole healthcare system. Therefore, it can accelerate productivity and the efficiency of care delivery and let healthcare systems provide higher-quality care to more people. Machine Learning in healthcare can support healthcare practitioners by enabling them to spend more time in direct patient care. This in turn reduces burnout around paperwork and manual processes around data storage.

Building on automation, Machine Learning has the potential to revolutionize healthcare and help address major structural and transformational challenges, minimize human errors, and manage the huge volumes of clinical data for building proactive and focused healthcare management. However, avoid vulnerabilities and regulatory issues by addressing any blind spots in your clinical system,

Find Flow in Healthcare Operations with AIOps

The massive challenge ahead for Machine Learning in healthcare is not whether the technologies will be efficient enough, but rather ensuring their adoption and streamlining the processes in daily clinical practice.

Windward has extensive experience in AIOps (Artificial Intelligence for IT Operations) Implementation in healthcare environments.

From our proven expertise, we support your implementation and streamline the processes of your healthcare investment. We’d love to talk about your needs.

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