Usually doctors write lab values, diagnoses and other chart notes on paper.This is an area in which technology could help improve the workflow and also improve patient care. Since last few years the advancements in electronic medical records have been remarkable, but the information they provide is not much better than the old paper charts they replaced. If technology is to improve care in the future, then the electronic information provided to doctors needs to be enhanced by the power of analytics and machine learning.
Using these types of advanced analytics, we can provide better information to doctors at the point of patient care. Having easy access to the blood pressure and other vital signs when they see patient is routine and expected. Imagine how much more useful it would be if a doctor is also shown his patient’s risk for stoke, coronary artery disease, and kidney failure based on the last 50 blood pressure readings, lab test results, race, gender, family history, socioeconomic status, and latest clinical trial data.
We need to advance more information to clinicians so they can make better decisions about patient diagnoses and treatment options, while understanding the possible outcomes and cost for each one. The value of machine learning in healthcare is its ability to process huge datasets beyond the scope of human capability, and then reliably convert analysis of that data into clinical insights that aid physicians in planning and providing care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction.
Machine learning in medicine has recently made headlines. Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithm to identify skin cancer. It was reported recently that the results of a deep machine-learning algorithm that was able to diagnose diabetic retinopathy in retinal images. It’s clear that machine learning puts another arrow in the quiver of clinical decision making.
Still, machine learning lends itself to some processes better than others. Algorithms can provide immediate benefit to disciplines with processes that are reproducible or standardized. Also, those with large image datasets, such as radiology, cardiology, and pathology, are strong candidates. Machine learning can be trained to look at images, identify abnormalities, and point to areas that need attention, thus improving the accuracy of all these processes. Long term, machine learning will benefit the family practitioner or internist at the bedside. Machine learning can offer an objective opinion to improve efficiency, reliability, and accuracy. We use a proprietary platform to analyze data, and loop it back in real time to physicians to aid in clinical decision making. At the same time a physician sees a patient and enters symptoms, data, and test results into the EMR, there’s machine learning behind the scenes looking at everything about that patient, and prompting the doctor with useful information for making a diagnosis, ordering a test, or suggesting a preventive screening. Long term, the capabilities will reach into all aspects of medicine as we get more usable, better integrated data. We’ll be able to incorporate bigger sets of data that can be analyzed and compared in real time to provide all kinds of information to the provider and patient.
It’s been said before that the best machine learning tool in healthcare is the doctor’s brain. Could there be a tendency for physicians to view machine learning as an unwanted second opinion? At one point, autoworkers feared that robotics would eliminate their jobs. Similarly, there may be physicians who fear that machine learning is the beginning of a process that could render them obsolete. But it’s the art of medicine that can never be replaced. Patients will always need the human touch, and the caring and compassionate relationship with the people who deliver care. Neither machine learning, nor any other future technologies in medicine, will eliminate this, but will become tools that clinicians use to improve ongoing care.
The focus should be on how to use machine learning to augment patient care. For example, if testing is being done for a patient for cancer, then the highest-quality biopsy results once can possibly get will be required. A machine learning algorithm that can review the pathology slides and assist the pathologist with a diagnosis, is valuable. If we can get the results in a fraction of the time with an identical degree of accuracy, then, ultimately, this is going to improve patient care and satisfaction.
Healthcare needs to move from thinking of machine learning as a futuristic concept to seeing it as a real-world tool that can be deployed today. If machine learning is to have a role in healthcare, then we must take an incremental approach. We must find specific use cases in which machine learning’s capabilities provide value from a specific technological application (e.g., Google and Stanford). This will be a step-by-step pathway to incorporating more analytics, machine learning, and predictive algorithms into everyday clinical practice.
Initially, our goals need to match our capabilities. Training a machine learning algorithm to identify skin cancer from a large set of skin cancer images is something that most people understand. If we were to learn that radiologists are being replaced by algorithms, then people would be understandably hesitant. This must be bridged over time. Radiologists won’t ever become obsolete, but radiologists of the future will supervise and review readings that have been initially read by a machine. They will employ machine learning like a collaborative partner that identifies specific areas of focus, illuminates noise, and helps focus on high probability areas of concern.
How do we reach the threshold needed to trust machine learning? Medicine has a method for investigating and proving that treatments are safe and effective. It’s a long process of trial and error—and basing decisions on evidence. We need these same processes in place as we look at machine learning to ensure its safety and efficacy. We need to understand the ethics involved in handing over part of what we do to a machine.
With an analytics platform and machine learning running in the background, the human algorithm—the extra layer of a back-up physician—wouldn’t be necessary. The analytics engine would have infinitely more data than any one person could ever process. It would have a library of patients, with his diagnosis and tissue type. It would have treatment options available with predictions of how long they would be effective, mortality rates, side effects, and cost. Regardless of all the effort by a human caregiver, an analytics platform could put in infinitely more work behind the scenes and deliver decisive information to the physician in real time. As more data is available, we have better information to provide patients. Predictive algorithms and machine learning can give us a better predictive model of mortality that doctors can use to educate patients.
But machine learning needs a certain amount of data to generate an effective algorithm. Much of machine learning will initially come from organizations with big datasets. Another possibility for smaller entities will be their ability to merge their data with larger systems. At some point, we may see regional data hubs with datasets customized for geographical, environmental, and socioeconomic factors, that give healthcare systems of all sizes access to more data.
As larger datasets begin to run machine learning, we can improve care in more specific ways for each region. And considering rare diseases with low data volumes, it should be possible to merge regional data into national sets to scale the volume needed for machine learning. We already see applications of machine learning in healthcare that are advancing medicine into a new realm. It’s exciting to think about where it can go. Someday, it will be commonplace to have embedded machine learning expertise that analyzes not only what’s going on with patients in real time, but also what’s going on with similar patients in multiple healthcare systems, what applicable clinical trials are underway, and the efficacy and cost of new treatment options. It may sound futuristic, but the analytics engine that can present all this information at the point of care is available now.
Now it is believed that machine learning is the life-saving technology that will transform healthcare. This technology challenges the traditional, reactive approach to healthcare. In fact, it’s the exact opposite: predictive, proactive, and preventative—life-saving qualities that make it a critically essential capability in every health system. People ask whether this is just a technology fad or whether it will provide true value in healthcare?
Well the fact is that the introduction and widespread use of machine learning in healthcare will be one of the most important, life-saving technologies ever introduced. The opportunities are virtually limitless for the technology to improve and accelerate clinical, workflow, and financial outcomes. The following are just a few examples:
1. Reduce re admissions- Machine learning can reduce re admissions in a targeted, efficient, and patient-centered manner. Clinicians can receive daily guidance as to which patients are most likely to be readmitted and how they might be able to reduce that risk.
2. Prevent hospital acquired infections (HAIs)- Health systems can reduce HAIs, such as central-line associated bloodstream infections (CLABSIs)—40 percent of CLABSI patients die—by predicting which patients with a central line will develop a CLABSI. Clinicians can monitor high-risk patients and intervene to reduce that risk by focusing on patient-specific risk factors.
3. Reduce hospital Length-of-Stay (LOS)- Health systems can reduce LOS and improve other outcomes like patient satisfaction by identifying patients that are likely to have an increased LOS and then ensure that best practices are followed.
4. Predict chronic disease- Machine learning can help hospital systems identify patients with undiagnosed or misdiagnosed chronic disease, predict the likelihood that patients will develop chronic disease, and present patient-specific prevention interventions.
5. Reduce 1-year mortality- Health systems can reduce 1-year mortality rates by predicting the likelihood of death within one year of discharge and then match patients with appropriate interventions, care providers, and support.
6. Predict propensity-to-pay- Health systems can determine who needs reminders, who needs financial assistance, and how the likelihood of payment changes over time and after particular events.
Predict no-shows. Health systems can create accurate predictive models to assess, with each scheduled appointment, the risk of a no-show, ultimately improving patient care and the efficient use of resources.
I hope now we have a good idea regarding the benefits of Machine Learning in Healthcare. In the next post we'll see some popular Machine Learning Applications and Use Cases in our Daily Life. So till we meet again keep exploring and learning about this great technology.
Using these types of advanced analytics, we can provide better information to doctors at the point of patient care. Having easy access to the blood pressure and other vital signs when they see patient is routine and expected. Imagine how much more useful it would be if a doctor is also shown his patient’s risk for stoke, coronary artery disease, and kidney failure based on the last 50 blood pressure readings, lab test results, race, gender, family history, socioeconomic status, and latest clinical trial data.
We need to advance more information to clinicians so they can make better decisions about patient diagnoses and treatment options, while understanding the possible outcomes and cost for each one. The value of machine learning in healthcare is its ability to process huge datasets beyond the scope of human capability, and then reliably convert analysis of that data into clinical insights that aid physicians in planning and providing care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction.
Machine learning in medicine has recently made headlines. Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithm to identify skin cancer. It was reported recently that the results of a deep machine-learning algorithm that was able to diagnose diabetic retinopathy in retinal images. It’s clear that machine learning puts another arrow in the quiver of clinical decision making.
Still, machine learning lends itself to some processes better than others. Algorithms can provide immediate benefit to disciplines with processes that are reproducible or standardized. Also, those with large image datasets, such as radiology, cardiology, and pathology, are strong candidates. Machine learning can be trained to look at images, identify abnormalities, and point to areas that need attention, thus improving the accuracy of all these processes. Long term, machine learning will benefit the family practitioner or internist at the bedside. Machine learning can offer an objective opinion to improve efficiency, reliability, and accuracy. We use a proprietary platform to analyze data, and loop it back in real time to physicians to aid in clinical decision making. At the same time a physician sees a patient and enters symptoms, data, and test results into the EMR, there’s machine learning behind the scenes looking at everything about that patient, and prompting the doctor with useful information for making a diagnosis, ordering a test, or suggesting a preventive screening. Long term, the capabilities will reach into all aspects of medicine as we get more usable, better integrated data. We’ll be able to incorporate bigger sets of data that can be analyzed and compared in real time to provide all kinds of information to the provider and patient.
It’s been said before that the best machine learning tool in healthcare is the doctor’s brain. Could there be a tendency for physicians to view machine learning as an unwanted second opinion? At one point, autoworkers feared that robotics would eliminate their jobs. Similarly, there may be physicians who fear that machine learning is the beginning of a process that could render them obsolete. But it’s the art of medicine that can never be replaced. Patients will always need the human touch, and the caring and compassionate relationship with the people who deliver care. Neither machine learning, nor any other future technologies in medicine, will eliminate this, but will become tools that clinicians use to improve ongoing care.
The focus should be on how to use machine learning to augment patient care. For example, if testing is being done for a patient for cancer, then the highest-quality biopsy results once can possibly get will be required. A machine learning algorithm that can review the pathology slides and assist the pathologist with a diagnosis, is valuable. If we can get the results in a fraction of the time with an identical degree of accuracy, then, ultimately, this is going to improve patient care and satisfaction.
Healthcare needs to move from thinking of machine learning as a futuristic concept to seeing it as a real-world tool that can be deployed today. If machine learning is to have a role in healthcare, then we must take an incremental approach. We must find specific use cases in which machine learning’s capabilities provide value from a specific technological application (e.g., Google and Stanford). This will be a step-by-step pathway to incorporating more analytics, machine learning, and predictive algorithms into everyday clinical practice.
Initially, our goals need to match our capabilities. Training a machine learning algorithm to identify skin cancer from a large set of skin cancer images is something that most people understand. If we were to learn that radiologists are being replaced by algorithms, then people would be understandably hesitant. This must be bridged over time. Radiologists won’t ever become obsolete, but radiologists of the future will supervise and review readings that have been initially read by a machine. They will employ machine learning like a collaborative partner that identifies specific areas of focus, illuminates noise, and helps focus on high probability areas of concern.
How do we reach the threshold needed to trust machine learning? Medicine has a method for investigating and proving that treatments are safe and effective. It’s a long process of trial and error—and basing decisions on evidence. We need these same processes in place as we look at machine learning to ensure its safety and efficacy. We need to understand the ethics involved in handing over part of what we do to a machine.
With an analytics platform and machine learning running in the background, the human algorithm—the extra layer of a back-up physician—wouldn’t be necessary. The analytics engine would have infinitely more data than any one person could ever process. It would have a library of patients, with his diagnosis and tissue type. It would have treatment options available with predictions of how long they would be effective, mortality rates, side effects, and cost. Regardless of all the effort by a human caregiver, an analytics platform could put in infinitely more work behind the scenes and deliver decisive information to the physician in real time. As more data is available, we have better information to provide patients. Predictive algorithms and machine learning can give us a better predictive model of mortality that doctors can use to educate patients.
But machine learning needs a certain amount of data to generate an effective algorithm. Much of machine learning will initially come from organizations with big datasets. Another possibility for smaller entities will be their ability to merge their data with larger systems. At some point, we may see regional data hubs with datasets customized for geographical, environmental, and socioeconomic factors, that give healthcare systems of all sizes access to more data.
As larger datasets begin to run machine learning, we can improve care in more specific ways for each region. And considering rare diseases with low data volumes, it should be possible to merge regional data into national sets to scale the volume needed for machine learning. We already see applications of machine learning in healthcare that are advancing medicine into a new realm. It’s exciting to think about where it can go. Someday, it will be commonplace to have embedded machine learning expertise that analyzes not only what’s going on with patients in real time, but also what’s going on with similar patients in multiple healthcare systems, what applicable clinical trials are underway, and the efficacy and cost of new treatment options. It may sound futuristic, but the analytics engine that can present all this information at the point of care is available now.
Now it is believed that machine learning is the life-saving technology that will transform healthcare. This technology challenges the traditional, reactive approach to healthcare. In fact, it’s the exact opposite: predictive, proactive, and preventative—life-saving qualities that make it a critically essential capability in every health system. People ask whether this is just a technology fad or whether it will provide true value in healthcare?
Well the fact is that the introduction and widespread use of machine learning in healthcare will be one of the most important, life-saving technologies ever introduced. The opportunities are virtually limitless for the technology to improve and accelerate clinical, workflow, and financial outcomes. The following are just a few examples:
1. Reduce re admissions- Machine learning can reduce re admissions in a targeted, efficient, and patient-centered manner. Clinicians can receive daily guidance as to which patients are most likely to be readmitted and how they might be able to reduce that risk.
2. Prevent hospital acquired infections (HAIs)- Health systems can reduce HAIs, such as central-line associated bloodstream infections (CLABSIs)—40 percent of CLABSI patients die—by predicting which patients with a central line will develop a CLABSI. Clinicians can monitor high-risk patients and intervene to reduce that risk by focusing on patient-specific risk factors.
3. Reduce hospital Length-of-Stay (LOS)- Health systems can reduce LOS and improve other outcomes like patient satisfaction by identifying patients that are likely to have an increased LOS and then ensure that best practices are followed.
4. Predict chronic disease- Machine learning can help hospital systems identify patients with undiagnosed or misdiagnosed chronic disease, predict the likelihood that patients will develop chronic disease, and present patient-specific prevention interventions.
5. Reduce 1-year mortality- Health systems can reduce 1-year mortality rates by predicting the likelihood of death within one year of discharge and then match patients with appropriate interventions, care providers, and support.
6. Predict propensity-to-pay- Health systems can determine who needs reminders, who needs financial assistance, and how the likelihood of payment changes over time and after particular events.
Predict no-shows. Health systems can create accurate predictive models to assess, with each scheduled appointment, the risk of a no-show, ultimately improving patient care and the efficient use of resources.
I hope now we have a good idea regarding the benefits of Machine Learning in Healthcare. In the next post we'll see some popular Machine Learning Applications and Use Cases in our Daily Life. So till we meet again keep exploring and learning about this great technology.
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