With everything from cancer diagnosis research to robotic surgery, it's no wonder that machine learning has started playing a huge role in the healthcare industry. This sector has long been a proponent of early adoption of technology, and it's usually benefitted from those advancements.
Everything from new procedures to handling data has been affected by machine learning, and it's only made the processes stronger in the process. But what else can healthcare's ML connection accomplish? What else does it stand to gain?
We're here to tell you all about it. Read on to learn everything you need to know.
With machine learning, practices can develop software that standardizes and optimizes the way patient data and records are kept. Not only can this help keep data secure, but it can help cut down on time and costs for electronic medical records.
When it comes to the human component of record keeping, there is a lot of room for error and even security problems. An effective ML model can help combat this problem.
ML models can also help doctors make more informed and accurate diagnoses. Radiologists can use these models to help differentiate between things like tumors and healthy cells and are sometimes capable of doing an even better job than a human might.
For most diseases, the earlier you catch it the better. ML models can help do exactly that, even with the most dangerous ones. With that early detection comes a higher chance of successful treatment, and a higher chance at detecting any possible worsening of the disease.
Some models can even help make treatment decisions, and also help with surgical planning when it's needed.
What diseases can these models help detect? For at-risk patients, the most common ones are diabetes, liver and kidney diseases, and even cancer.
ML models are also making a huge impact in the drug development ring.
Pfizer even partnered with IBM Watson to implement an ML model to research how the body's immune system fights cancer. It does this mainly through identifying new drug targets, patient selection strategies, and also combination therapies for research in immuno-oncology.
The goal is to learn more about treating cancer, and for Pfizer to accelerate the timeframe of their development programs.
This can make a huge difference in the overall process because it helps to speed up the development while lowering the cost. That money saved means more accessibility for patients that need it.
With models like Somatix on the market, patients can experience close monitoring when they're sick, and they can also learn adaptability for specific habits they have. The purpose of this ML model is to track a patient's behavior and routine and then point it out to them if there are problems.
From there, it's up to the patient to learn to change the behavior, but it's a great way to bring awareness to certain things they might not have otherwise known about.
Likewise, practices could use their ML model for similar purposes — whether those behaviors are electronic or physical. This is also called data tracking, and it can help practices become more efficient and safe over the long run.
It's no secret that clinical trials are lengthy and expensive. This is necessary and beneficial most of the time, but sometimes treatments need to be released as soon as possible (like with the COVID-19 vaccines).
Implementing ML models for things like this can help not only speed the process up but keep the cost relatively low — at least when it's compared with a longer, in-person trial. These models can be used to gather data points, keep track of and analyze ongoing data from trial participants, and even help select the best sample for the trial.
Further, it's very likely to reduce the risk of errors.
Arguably the most prevalent use for machine learning right now, these models can be used to help predict when an epidemic or pandemic might be brewing. They work to analyze satellite data, news, social media reports, and even videos that help to predict whether or not the disease might grow out of control.
While we may not have been prepared ahead of time (much to the dismay of many experts), we're more prepared for the future.
The adoption of machine learning in the healthcare industry offers significant promise, but it is not without its challenges, some of which can pose substantial obstacles:
Provider Mistrust: One of the foremost challenges lies in the inherent mistrust that some healthcare providers harbor towards machine learning technologies. Convincing these stakeholders to embrace machine learning can be an arduous task. The skepticism often stems from concerns about the reliability and safety of automated systems. Overcoming provider resistance requires not only demonstrating the efficacy and safety of machine learning but also addressing fears about the impact on established workflows. In short, find a partner and platform that you can trust.
System Updates and Learning Curve: Even when healthcare professionals are willing to embrace machine learning, the prospect of updating existing systems or adapting to new technology can be daunting. Many healthcare facilities operate with legacy systems that have served them reliably for years. Implementing machine learning solutions may require significant changes to these systems, which can lead to resistance due to the perceived learning curve and disruption to daily operations. To learn more about how Bitstrapped tackles this challenge, check out our Data Strategy Accelerator and Data Roadmap Development Offering.
Shortage of Qualified Professionals: Building and deploying effective machine learning models in healthcare requires a specialized skill set. While the number of professionals in the field is growing, there is still a shortage of qualified engineers and data modelers with the expertise to develop high-quality machine learning models. This scarcity can hinder the rapid adoption of machine learning solutions in healthcare, as finding and retaining skilled professionals remains a challenge.
Data Privacy and Security: Healthcare is an industry marked by stringent data privacy regulations and the need for robust security measures. Machine learning applications in healthcare must adhere to strict data protection standards, which can add complexity to model development and deployment. Ensuring patient data confidentiality and safeguarding against security breaches are paramount concerns.
Interoperability: Healthcare institutions often use diverse and fragmented information systems that do not seamlessly integrate with one another. Achieving interoperability and data sharing between these systems to facilitate machine learning implementation can be a significant challenge. Overcoming data silos and achieving a unified data ecosystem is an ongoing effort.
Ethical Considerations: The use of machine learning in healthcare raises ethical concerns regarding bias, fairness, and accountability. Ensuring that machine learning models do not perpetuate existing healthcare disparities and that their decision-making processes are transparent and explainable is a critical ethical imperative.
Addressing these challenges requires a multifaceted approach, encompassing education and training, regulatory compliance, technological innovation, and collaboration among professionals and stakeholders. While the road ahead may be marked by obstacles, the potential benefits of harnessing machine learning for improved healthcare outcomes make these challenges worth tackling, especially when you are working with an experienced partner.
To learn more about Bitstrapped's work in the healthcare industry, click here.
With so many technological advancements made over the last few decades, it's easy to see that the ML connection to healthcare has the potential to help move the industry forward for many years to come. From automation to accuracy, ML in the healthcare industry is a necessity for a lot of different reasons.
It's also the perfect addition to any practice — big or small — and BitStrapped can help you get started. Our team of experts is built to help you create any model you need. All you have to do is contact us to book a free 30-minute discovery call without team today or ask questions about how machine learning can help your healthcare business.
Also see our healthcare case studies: Click here.