Artificial Intelligence

Improve Patient Care with Artificial Intelligence

By understanding how machine learning benefits patient care and outcomes, we can get a feel for what is happening on the ground and how it affects real lives.

Michael DeWitt
Jul 8, 2024
6 min read

A 2019 Emerg survey called 'Machine Learning in Healthcare' asked over 50 healthcare executives where they believe AI in healthcare will be by 2025. Over 50% said AI would be ubiquitous in healthcare.

ReportLinker forecast predicts that AI in healthcare will grow from $2.1 billion in 2018 to $36.1 billion by 2025. The future of AI in healthcare is bright. In this article, we will look at how that growth benefits hospitals and the healthcare industry. 

Predicting Patient Outcomes

MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) is trying to help doctors make more efficient decisions. Making real-time treatment decisions can be challenging with a barrage of data from multiple patients and hospitals and often in inconsistent formats.

AI is excellent at taking voluminous amounts of data, finding patterns, and providing a digestible result. ICU Intervene is an AI-based approach created by CSAIL. It takes enormous amounts of data from previous ICU cases and makes suggestions for treatment. Additional notes are present in the use cases fed to ICU Intervene, which makes ICU Intervene suggestions that much more valuable.

"The goal is to leverage data from medical records to improve health care and predict actionable interventions."

"The system could potentially be an aid for doctors in the ICU, which is a high-stress, high-demand environment," says Ph.D. student Harini Suresh, lead author on a paper about ICU Intervene, to MIT News. "The goal is to leverage data from medical records to improve health care and predict actionable interventions."

2018 study published in Nature Digital Medicine showed that an advanced algorithm could predict readmissions, in-hospital deaths, and extended hospital stays more accurately than previous approaches. The algorithm used de-identified health records from 216,000 adult patient hospitalizations. From this data, its model was able to determine patterns leading to its final results.

Applying AI to medical records is only possible because so many medical records have been digitized in recent years. According to HealthIT, by 2016, 96% of US hospitals were using certified EHR (electronic health records) to HealthIT. Additionally, computing power has dramatically increased, allowing AI to be applied to real-world problems rather than theoretical ones (i.e., AI 1.0).

Machine Learning (ML) Use Cases

We will look at several companies to get a broader view of how machine learning is used by actual companies in healthcare.

Smart Records

Supporting EMR systems can be costly. ML-based enterprise software products can trim costs by eliminating the tedious, laborious process of cross-record analysis and replacing it with automated, real-time analysis. Such solutions also significantly reduce human work from hours to mere seconds.

Predicting Illnesses and Treatment

Enterprise-grade machine learning means taking a company's unstructured data from multiple sources, formats, and infrastructures and transforming it into data that AI models can use. The result is new insights that were not previously available to the client.

Such software is ultimately a patient prediction platform. It helps doctors understand who will get sick, what they will come down with, and how much treatment will cost.

Enhancing health information management and exchange of health information

More excellent health can be achieved through better health information management. By analyzing health information, companies can make operational improvements, optimize revenue, and create better patient outcomes.

Immuno-oncology research with help from IBM Watson

Pfizer and IBM's Watson created a medical research collaboration in 2016. The partnership utilizes Watson's machine learning capabilities and Pfizer's medical scientist to accelerate research in immuno-oncology.

Virtual Nursing

The nursing field is facing a shortage of nurses, and the prognosis doesn't look good anytime soon. The Bureau of Labor Statistics Employment Projections 2016-2026 shows that nursing (RNs) is expected to be the fastest-growing field through 2026. The Bureau projects that 203,700 new RNs will be needed each year.

AI may provide some relief in the form of automated tasks. These are routine tasks performed by nurses that aren't high-value. AI will augment other tasks, freeing up some time for nurses to focus on more critical areas.

Augmentation

Nurses perform routine tasks by taking patient vitals and asking questions to see how a patient is doing. After the patient session, all information must be recorded and stored. In some hospitals, notes can be recorded in real-time as they are being taken. This saves some time when having to transfer notes into another system separately.

Virtual avatars exist that can communicate empathically and naturally with customers/patients. Their clientele includes insurers, pharmacies, and various other companies that use avatars to answer employee questions. These avatars are virtual nurses.

Patients at home can bring up an app on their smartphone. The avatar appears and helps patients take their blood pressure and weight. It also asks a few questions about how the patient is doing. Patients feel comfortable using the technology because the avatar is interactive and empathetic. All the information collected by the avatar is available to a healthcare provider. Depending on the patient session results, the avatar may tell the patient to call a real nurse.

Utilizing avatars allows nurses to save time collecting vitals and asking various questions. However, an avatar cannot make a diagnosis. Analyzing patient data and question responses in real-time can determine if further assistance is needed (e.g., calling a nurse).

Image Analysis

Google AI provides an excellent example of how image analysis or computer vision is being used in healthcare. Google has focused its healthcare machine-learning efforts on ophthalmology and digital pathology by working with doctors and clinicians.

Diagnosing diabetic eye disease

Google developed a dataset of 128,000 eye images. These images trained a deep neural network to detect referable diabetic retinopathy. They then tested their algorithm's performance on two separate clinical validation sets. Combined, these datasets totaled approximately 12,000 images.

The result is that Google's models performed as well as ophthalmologists. As more images are fed to the models, and they can improve their results through further training, this kind of machine learning will augment the detection of referable diabetic retinopathy.

Assisting pathologists in detecting cancer—Google's next focus area was analyzing pathology slides to detect breast cancer in lymph node biopsies. Analyzing pathology slides is hugely time-consuming and requires years of training, expertise, and experience. Google's machine-learning pathology efforts are meant to augment the expertise of physicians rather than be used as a final diagnosis.

Precision Medicine

A recent Chil Mark Research report titled 'Precision Medicine and Health IT: New Data, New Challenges' concludes that the full potential of precision medicine won't be possible without utilizing advanced machine learning and deep learning technologies.

The full potential of precision medicine is impossible without utilizing advanced machine learning and deep learning technologies.

The report's authors recognize that the volume of medical data produced will only increase as more computers, devices, and methods of gathering and accumulating data continue to grow, outpacing what clinicians and researchers can keep up with and extract value from. Machine learning can analyze such volumes of data and provide results in real-time.

The above use cases show how machine learning is utilized today in healthcare. The overarching theme is that AI is an augmented technology, not a technology meant to replace healthcare professionals.

AI is an augmented, not a technology meant to replace healthcare professionals.

Healthcare.ai — Bringing AI to Healthcare

Some neutral organizations lead the most significant industry efforts. Healthcare.ai has taken on that role for AI in healthcare. It's an excellent place to learn about advancements in the healthcare field driven by AI.

Healthcare.ai has some practical uses for software developers. However, for those looking to apply AI in healthcare, the site might not be the best place, as you will need to know how to use programming languages such as Python or R.

Will The Government Slowdown ML in Healthcare?

Ensuring that AI software is safe to use in healthcare falls under the realm of the FDA. It might seem odd that the FDA is approving AI software, but this is nothing new to the FDA. It has been approving AI software in the healthcare industry since 2017. It approved the Apple Watch ECG in September 2018, which became available in the Apple Watch Series 4.

AI algorithms are constantly evolving due to their learning nature. This presents a problem for the FDA. To approve an algorithm, the algorithm must stop growing. Once approved, the software can be published.

The FDA is still designing a framework for approving AI software. It recognizes that an approval process will slow the design and evolution of AI. For now, it is looking at a middle ground. After initial approval, allow AI software to evolve to a certain extent with well-known safeguards.

Machine learning has existed for decades. However, it sat in the shadows until advances in computing power and the growth of digital data made the results we see today possible. AI is no longer a term followed by "what-if."

Machine learning excels at real-time data analysis and precise pathology identification. After initial training, machine learning models have proven that they can produce practical results.

For the foreseeable future, doctors, clinicians, and nurses will not be replaced by AI. Instead, AI will help augment these professionals in their everyday work of saving lives.

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