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Healthcare Data Challenges! Can Machine Learning Eliminate Them? – ResolveDatas
Healthcare Data Challenges! Can Machine Learning Eliminate Them?

Healthcare Data Challenges! Can Machine Learning Eliminate Them?

McKinsey’s study shows that AI ML use cases in healthcare and pharma can revolutionize the industry to help them make better decisions. It will also improve the efficiency of research and clinical trials, optimize innovation, and offer new tools for consumers, regulators, and physicians.

In simple terms, the more insights that AI and ML bring to the table on medicine, the faster the industry grows. This article will talk more about the future of machine learning in healthcare.

Introduction to Machine Learning

Machine Learning is the most common form of Artificial Intelligence. It processes and locates patterns in large sets of data to help with decision-making. Machine learning applications have algorithms and a collection of details to perform a certain set of tasks. The algorithms are designed to gather insights from data independently.

These algorithms can eventually improve prediction accuracy without the need for programming.

So, machine learning has
three main components-

These algorithms can eventually improve prediction accuracy without the need for programming

Here are the three main areas where AI and ML used in healthcare is making an impact in today’s world.

Record Keeping

Machine learning in healthcare can help in streaming recording keeping and this includes Electronic Health Records. AI ML use cases in healthcare can improve EHR management. This, in turn, can improve patient care, optimize operations, and reduce administrative and healthcare costs.

For instance, language processing. This helps physicians to grab and record clinical notes by eliminating natural processes.

Disease Identification and Diagnosis

Machine learning can detect patterns related to a disease and health condition simply by analyzing patient data and healthcare records.

Recent changes in machine learning increase healthcare access in all developing countries and introduce the diagnosis and treatment of cancer. According to an article published by AMA Journal of Ethics, AI and ML in healthcare now help in diagnosing skin cancer more accurately than a certified dermatologist. This study focuses on the additional benefits of machine learning, including diagnostics efficiency and speed.

Drug Discovery and Manufacturing

The use of AI and ML in preliminary drug discovery has different uses, from initial drug compound screening to predicted success on the basis of biological factors. It also includes Research and Development technologies, such as next-gen sequencing.

Precision medicine, involving identifying methods for multifactorial disease, appears to be the frontier in this case. Most of the research consists of unsupervised learning and a large part of it is still confined to identifying the data patterns without predictions.

Epidemic Outbreak Prediction

AI and ML technology in healthcare are being used to monitor and predict epidemic outbreaks across the world, based on details collected from historical information over the internet, satellites, real-time social media updates, and various other sources.

The best example of AI and ML technology being used today is the opioid epidemic. Artificial neural networks and support vector machines have been used for predicting malaria outbreaks. ​

What are the Problems with Machine Learning in Healthcare Today?

Machine learning in healthcare can only be secured if we are able to overcome the challenges. One of the main disadvantages of applying Machine Learning to medicine and healthcare is the lack of proven cases. Medicine needs a robust involvement of human intuition.

For instance, low blood pressure and high fever can be caused due to a wide range of health problems. AI and MI excel in industries that are largely dependent on quantitative information. Making the correct diagnoses involves science, as well as art. This is the reason, even though algorithms are excellent when it comes to diagnosing malignant tumors, it can be difficult to diagnose a complicated condition from the common cold. To differentiate this, algorithms and experts both need experience when it comes to using Artificial Intelligence in healthcare.

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Why Is It Not Easy to Implement AI ML Use Cases in Healthcare?

There are many roadblocks to the implementing machine learning in healthcare. Let’s take a look at them.

  • Data governance is a major issue that has to be addressed. Medical data is personal and isn’t easy to access. Therefore, it can be assumed that a majority of the people are wary of releasing details due to privacy concerns.
  • A more transparent algorithm is required to cater to the strict regulations of drug development. People should also be able to get a glimpse through the black box to understand the reasoning behind machine outcomes.
  • Encouraging a data-centric view and breaking down data silos across different sectors is of great importance. It will help in shifting the mindset of the industry towards embracing and looking for value in incremental changes over a longer period. Medicine and pharma companies have been reluctant to make changes that support the initiative unless there is a significant or immediate monetary value.
  • Recruiting data science talent in the medicine and pharma industry and building strong skills is a prime necessity.
  • Streamlining electronic records that at present are still fragmented and messy across databases will be an important step in ramping up customized treatment solutions.

How to Solve the Problems and How can Machine Learning Benefit Healthcare?

Artificial Intelligence and Machine Learning will play a significant role in healthcare. Many tech giants are already using industry-specific solutions. There are various ways for an organization to harness Machine Learning technology in healthcare and pharma. Let’s take a look at them.

  • The clinical staff is busy. For instance, the intensive care unit has multiple patients in serious condition under their watch. So, restricted cognition and mobility during long-term treatment might affect the overall recovery of patients. It is crucial to monitor their activities. For improving results, Stanford University researchers and Intermountain LDS Hospitals have used depth sensors that are equipped with Machine Learning algorithms in the room of the patient for tracking their mobility. The technology has been accurate in identifying movements 87% of the time. Eventually, researchers are looking to offer alerts to the ICU staff whenever patients are in trouble.
  • One of the primary challenges of drug development is to conduct successful clinical trials. Considering its progress, it can take more than a decade to bring a potentially life-saving and new drug to a market, as per a report in Trends in Pharmacological Sciences. Also, it might cost between $1.5 billion and $2 billion. About half of the time is spent on clinical trials, much of which fail. Using artificial technology, researchers can recognize the patients eligible for the experiments.

But using machine learning in healthcare comes with many challenges. Long-grained institutional practices and various cultures in organizations can’t be optimized simply by slapping an algorithm. Legacy Electronic Medical System and EHR running on-premises do not play well with other ones used by other organizations.

Organizations have to take into account the government regulations that keep changing. So making sense of large volumes being generated that are primarily unstructured is not an easy thing. This is the reason data scientists who have trained in the recent techniques and technologies are in great demand in the healthcare industry.

We have just scratched the surface on the possible impact of Artificial Intelligence and Machine Learning in healthcare. However, one thing is pretty clear that AI and ML use cases in healthcare industry are important for its future.

With an increase in the demand for the best healthcare facilities, organizations need to hire professionals with deep knowledge of growing technologies like AI and ML. ResolveData is one of them.

How can ResolveData Help?

ResolveData actualizes data and helps in achieving the best results for your organization. Technologies like AI and ML have the potential to create a great impact when it comes to improving healthcare facilities. However, the challenges emerge when organizations have to realize their potential. This is when ResolveData comes to your aid.

It aims to remove the challenges and bridge the gap between what AI promises and practices in healthcare. Thus, you can expect to achieve the best result. ResolveData aims to assist the healthcare industry with the right set of data-driven foresight and insight. It leads to better patient care and a higher return on investment.

ResolveData specializes in Data Security, Artificial Intelligence, Machine Learning, Data Management, and Analytics. It provides its services to hospitals, diagnostics, pharma, and equipment manufacturers. We transform data and drive outcomes for you.

So, if you need assistance with ML for your healthcare organization, you just have to schedule a meeting with ResolveData experts. Together with you, the experts will create a robust plan for accomplishing your goals.

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