The Real-World Benefits of Machine Learning in Healthcare

8/16/2019

The Real-World Benefits of Machine Learning in Healthcare

The Real-World Bene ts of Machine Learning in Healthcare usm systems Aug 16 ¡ 7 min read

It is safe to say that there are many manual processes in medicine. While in training, I wrote lab values, diagnoses and other chart notes on paper. I know this is one area where technology can help improve my workflow and I hope this will also improve patient care. Since then, advances in electronic medical records have been great, but the information they provide is no better than the old paper charts they replaced. If technology is there to improve care in the future, then the need to improve the power of electronic information analytics and machine learning to provide physicians.

Using these types of sophisticated analytics, we can better inform physicians during patient care. It is common and easy to get blood pressure and other vital signs when I see my patient. Past 50 blood pressure readings, laboratory test results, race, sex, family history, socioeconomic status, and latest clinical trial data.

We need to provide more information to physicians so that they can make better decisions about patient diagnoses and treatment options, but understand the possible outcomes and costs for everyone. The value of machine learning in health care is its ability to process large datasets beyond human capacity, and then reliably transform the analysis of that data into clinical insights that can help clinicians in planning and care, ultimately leading to better outcomes, lower costs of care and increased patient satisfaction.

Applied Machine Learning in Healthcare Machine learning has recently made headlines in medicine. Google has developed a machine-learning algorithm to help detect cancer tumors on mammograms. Stanford uses a deep learning algorithm to detect skin cancer. A recent JAMA article reported the https://medium.com/@usmsystems23/the-real-world-benefits-of-machine-learning-in-healthcare-b310b6656e95

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8/16/2019

The Real-World Benefits of Machine Learning in Healthcare

results of a deep machine learning algorithm that can diagnose diabetic retinopathy in retinal images. It is clear that machine learning is another arrow in clinical decision making.

However, machine learning is better for some processes than others. Algorithms can provide an immediate advantage to departments with reproducible or standardized processes. Also, those with a large image dataset such as radiology, cardiology, and pathology are strong candidates. Machine learning can be trained to look at images, detect abnormalities, and suggest areas that need attention, thereby improving the accuracy of these procedures. Long-term, machine learning can benefit a family practitioner or intern at the bedside. Machine learning provides an objective view of improving efficiency, reliability, and accuracy.

At Health Catalyst, we use the proprietary platform to help doctors loop back in realtime to analyze data and assist in clinical decision making. At the same time, a physician sees the patient and enters the EMR with symptoms, data, and test results, a backstage machine that looks at everything about that patient, and prompts the physician with useful information to diagnose, direct the test, or suggest preventive screening. In the long run, capabilities reach all aspects of medicine as we get more useful, betterintegrated data. We are able to include large data sets that can be analyzed and compared in real-time to provide all kinds of information to the provider and the patient.

The ethics of using algorithms in healthcare It has been predicted that the best brain learning tool in health care is the doctor's brain.

Is there a tendency for doctors to view machine learning as an unwanted second opinion? At one point, automakers feared that robotics would eliminate their jobs.

Similarly, there may be clinicians who fear that machine learning is just the beginning of an obsolete process. But this is the art of medicine. Patients always need a caring and compassionate relationship with people who provide human touch and care. Other technologies in the future of machine learning, or medicine, will not eliminate this but will become tools used by physicians to improve ongoing care.

The focus should be on how machine learning can be used to increase patient care. For example, if I am testing a patient for cancer, I want the highest-quality biopsy results I can get. A machine learning algorithm that reviews the pathology slides and assists the https://medium.com/@usmsystems23/the-real-world-benefits-of-machine-learning-in-healthcare-b310b6656e95

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The Real-World Benefits of Machine Learning in Healthcare

pathologist with diagnosis is valuable. If I can get results with the same level of accuracy, eventually, it will improve patient care and satisfaction (I am writing this because my own mother is anxiously waiting for her test results).

Healthcare machine learning needs to be viewed as a real-world tool that can be implemented today, from the concept of the future. For machine learning to have a role in health care, we must take a growing approach. We must find specific use cases where machine learning capabilities provide value from a specific technology application (e.g., Google and Stanford). It is a step-by-step way to incorporate more analytics, machine learning, and predictive algorithms into everyday clinical practice.

Initially, our goals should match our capabilities. It is understandable to many people to train a machine-learning algorithm to detect skin cancer from skin cancer images. If we know that radiologists are being replaced by algorithms, people are hesitant to understand. It should bridge over time. Radiologists will never be obsolete, but future radiologists will monitor and review machine readings first. They use machine learning as a collaborative partner to help them identify specific partner areas, illuminate the sound, and focus on areas of high potential.

How to reach the threshold required to trust machine learning?

Medicine has a method to investigate and prove that treatments are safe and effective. It takes a long trial and error process and decisions on evidence. Machine learning requires similar processes as we look to ensure its safety and efficiency. We need to understand the ethics of handing over a portion of what we do. “What If� scenario on the probability of machine learning A few months ago, I gave a presentation on the future of analytics and its potential impact on clinical care. In my slides, I have shown the predictive algorithms that a hyper EMR runs when a doctor examines his patient. The pop-up box displays real-time diagnostics, pathology results and treatment options, as well as the potential impact of each patient and the cost to this patient.

The patient may be absent in this case, which was made after a daddy passed away several years ago from prostate cancer. I chose this scenario to show possible results if machine learning was available at the time. https://medium.com/@usmsystems23/the-real-world-benefits-of-machine-learning-in-healthcare-b310b6656e95

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The Real-World Benefits of Machine Learning in Healthcare

In my father’s original case, his doctor initially gave him two years to live. It is based on a combination of the doctor’s experience with such patients and the treatment options available at the time. What is unknown to him is that he is going to take an active role in overseeing my father’s care. I have done research on clinical trials and new therapeutic options. I studied side effects and trial outcomes. Do treatments make people live longer? If so, is it a few weeks, a few months, or more?

Nanny is a great oncologist, but he has taken care of thousands of patients with various types of cancer. He will never be able to put in the time and effort needed to learn all the new drugs and therapeutic options that come with all these cancers. Many times, I have presented treatment options and clinical trials unknown to my father’s doctor.

With my years of training and expertise, I can pull out literature and recommend the best options for daddy. In other words, I have the human algorithm, the therapist’s brain, the paths they have, and most importantly, the motivation and time to work with the dad’s therapist to develop the right plan, which ultimately extends the father’s life to nine years.

Running in the wake of the analytics platform and machine learning, the human algorithm — no additional layer of backup physician — is required. The analytics engine has more data than any person can process. It has a library of patients like my dad, with his diagnosis and tissue type. As long as they are effective, mortality rates, side effects, and cost-effective treatment options are available. Regardless of all the efforts of the human caregiver, an analytics platform can do endless work behind the scenes and provide the clinician with conclusive information in real-time.

Data Drive Machine Learning As more data is available, we have better information to provide to patients. Predictive algorithms and machine learning give attendees a better model of mortality that doctors can use to educate patients.

But machine learning requires some data to create an efficient algorithm. Most of the machine learning comes from organizations with large datasets. Health Catalyst is developing Collective Analytics for Excellence (CAFÉ)), built on a nationally-recognized repository of health care data from enterprise data warehouses (EDWs) and third-party https://medium.com/@usmsystems23/the-real-world-benefits-of-machine-learning-in-healthcare-b310b6656e95

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The Real-World Benefits of Machine Learning in Healthcare

data sources. It produces comparative effectiveness, research, and specialized, powerful machine learning algorithms. CAFÉ collaborates with our health care system partners, large and small.

Another possibility for small firms is the ability to merge their data with larger systems.

At some point, we can see regional data hubs with datasets customized for geographical, environmental and socioeconomic factors, giving access to more data for healthcare systems of all sizes.

As large datasets begin to implement machine learning, we will improve care for each area in more specific ways. Considering rare diseases with low data volumes, it is possible to merge regional data into national sets to measure the volume required for machine learning.

Move machine learning from theoretical to clinical reality We have already seen applications of machine learning in healthcare, which are developing a new field of medicine. It’s exciting to think about where it can go.

Someday, it is common to have embedded machine learning expertise, which not only happens with patients in real-time, but also what happens with similar patients in multiple health care systems, what applicable clinical trials are, and the cost of effectiveness and new treatment options. It may seem futuristic, but there is now an analytics engine that can provide this information during care.

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