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Practice managers are familiar with the complexities of healthcare IT in large practices. The right enterprise EHR system should offer efficiency, scalability, revenue opportunities, and a positive patient experience while meeting the needs for healthcare compliance and security. That’s a tall order, and 60% of practices say outdated IT systems limit their performance.

Is your current IT setup helping or hindering you? It’s time to conduct an assessment of your systems and determine how to leverage your IT as a tool for growth. 

Why Large Medical Practices Face Unique IT Challenges

Managing large medical practices means juggling multiple tasks simultaneously. Clinical workflow optimization is always the top priority for practice administrators. But large practices face additional challenges:

Multi-location complexity: Practices with multiple offices must manage numerous appointment calendars and staffing schedules, while also overseeing operations at all sites to ensure they are fully stocked with the appropriate supplies.

Diverse specialties and workflows: Multi-specialty practices must ensure each provider has the necessary equipment and nursing support, and that patients receive clear, timely, and accurate communication about appointments and procedures.

High patient volume: Large practices see dozens or hundreds of patients each week, with a high demand for telehealth and remote care tech. Each appointment entails patient notes, claims to submit, referrals to generate, and follow-up care to schedule, all of which are critical to delivering quality care.

With digitalization on rising, we have entered the age of AI & Machine learning that provides data-driven decisions, high-end technologies, and increased inefficiency in the healthcare software industry

The rise in adoption of such technologies has helped segments and industries to generate more data and organizations that make use of such systems have helped them stay ahead. Although AI and ML have taken a stride in various industries, the healthcare industry has seen massive disruption.

According to an Accenture report, the healthcare artificial intelligence (AI) market may hit $6.6 billion in the next three years.

The highly coveted machine learning has helped healthcare providers to collect data quickly, manage patient outcomes and use monitoring devices to make data-driven decisions. Furthermore, it has cut the costs of operations and physical places drastically.

In a nutshell, Machine learning has made it easier for both the healthcare provider and the patients to save costs, efforts, and resources. The best part is that healthcare technology has expanded to almost every field along with the ease of accessibility.

In this article, we have shared more about the incorporation of machine learning in healthcare services and how healthcare software development companies like IBM are adopting AI and ML to generate quick data and make decisions.

What Is Machine Learning?

Machine Learning is a field that focuses on creating algorithms that enable computers to learn from data sets without any explicit programming or human oversight.

Machine learning applications are derived using various algorithms that are designed to learn from the data independently without any human intervention.

Machine learning has been a major game-changer in the healthcare industry by utilizing the critical components of algorithms: representation, evaluation, and optimization.

How has Machine Learning has affected the healthcare industry?

Machine learning has had a significant impact on the healthcare industry. It has led to advancements in healthcare application, data analytics, and diagnosis.

Machine learning in healthcare provides various neural networks that work together to increase the treatment efficiency and provide comprehensive data on the patient’s condition including their X-rays, CT scans, various tests, and screenings.

Since the introduction of machine learning, it has helped doctors drastically reduce the amount of time taken for diagnosis and treatment decisions.

Previously, it was challenging for healthcare providers to analyze and manage a large amount of data. However, healthcare software applications have made it possible to incorporate Machine learning into their system to boost efficiency.

Various healthcare software development companies are now creating customized solutions for healthcare organizations and enterprises to manage large staff, provide lab reports on the go, diagnose diseases and faster drug discovery.

Machine learning and deep learning algorithms detect the anomalies in images and it can also be helpful in providing vital stats such as a patient’s previous medical records, real-time data, and roster management for the healthcare staff.

What Are the Precise Applications for Machine Learning in Healthcare and Medicine?

Machine learning and AI have now become the core of various healthcare software applications. Healthcare software companies are focusing on using machine learning in a way that makes the operation efficient without making the processes like sign up and collecting data less daunting.

One of the best examples of Machine learning in the healthcare industry is the use of bot and voice assistants.

An AI-based nurse assistant can come in real handy if the patient needs quick assistance in the absence of a healthcare provider. Similarly, data scientists and data engineers are using neural networks to find the solutions such as health monitoring, telemedicine, and even cure the disease.

Machine learning mostly uses neural networks to establish relations between various processes that can be utilized in the diagnosis of disease or even its prevention.

For example, CNN (convolutional neural networks) performs image identification, recognition, and image detection to link them with each other.

The machine learning ability and its sight have even surpassed the microscopic view of the sample. And, this can be done at even a faster rate. Thus, more hospitals and diagnostics labs are now using these automated technologies to reduce the human workforce and improve efficiency.

The only catch is that machine learning, AI, or deep learning works on the pattern or is linked through the neural network. This poses a little challenge in finding the origin of rare diseases and the links of the diseases.

Best Real-world applications of Machine learning in Healthcare

AI has already deep-rooted into healthcare and it is evident from the fact that most healthcare providers have switched to remote monitoring and healthcare services.

Technology-enabled healthcare has made smart devices one of the most coveted things. With more functionalities added each day, AI and machine learning are in charge of processing thousands of different data points, predicting the outcomes, and analyzing the risks with accuracy.

Here is how machine learning is has aided healthcare technology and driven digital transformation:

Diabetes Prediction & Treatment

Diabetes is a leading chronic disease that affects more than 30 million people in the United States. The number is expected to increase further in the next few years.

Machine learning algorithms like KNN, Decision Tree, and Naive Bayes are the next big thing that could help in diabetes prediction and keep a check on Type 1 and Type 2 diabetes.

Some healthcare custom software development companies use big data and analytics to predict the blood glucose level in the body. Let’s say, a blood monitoring system delivers the data through cloud computing methods.

The healthcare provider then accesses the information transmitted from the cloud to learn about the patient’s habits using the machine algorithms.

Disease diagnosis and identification

One of the various applications of Machine learning is the early diagnosis of diseases. A number of genetic rare diseases or cancer are heard to be detected at an early stage.

A few years back, there was almost no way to find the origin or the whereabouts of some diseases, but machine learning has now made management easier at the initial stage.

Tech-enterprises like IBM Watson and Microsoft have developed systems that combine cognitive computing with genome-based tumor sequencing and predict the nature of the disease at the very initial stage.

Image analysis and scanning

Medical software development companies are working with data engineers and healthcare providers in boosting healthcare analytics.

The experts are creating various solutions for the imaging analysis methods such as CT, MRI, PET, ultrasound, and X-ray.

Philips AI has created an AI-based solution that has made ultrasound more efficient. This has helped the sonographers to detect the abnormalities that a naked eye would fail to do.

MRI or magnetic resonance imaging uses magnetic fields and radio waves to produce images of internal body parts. MRI is mostly used for detecting soft tissue conditions like aneurysms.

Now, AI and Machine learning has drastically reduced the workload of radiologists and doctors who spent hours detecting the abnormalities in tissue and human error is still a possibility.

Health records improvement

Machine learning and Natural language processing are now utilized by data engineers to enhance Electronic Health Records.

Various medical development software companies are now utilizing these systems together to record the patient’s health assessment, documents, gauge patient satisfaction, and use voice recognizing systems to transform speech to text.

The cloud-based medical records provide easy accessibility, reduced dependency on traditional systems, and a secured environment.

Conclusion:

Machine Learning is one of the hottest topics in healthcare today. It’s used to understand health data, predict disease trends, and create personalized treatments based on patient data.

Healthcare software companies are creating solutions that can also assist doctors with diagnoses and treatment recommendations by identifying patterns in the medical records of patients.

As AI technology continues to evolve, so will its ability to improve health care for everyone.