Moving into the Healthcare Machine Learning Age
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Healthcare has been plagued by its own ways of doing things and the fact that change has been hard to implement. But, times call for changes, improvements and advancements that many other businesses have already put into practice. One of those advancements that is proving its weight in gold is machine learning. This is in the same category as artificial intelligence but healthcare machine learning is unique to the needs of the industry and hard to employ because of the diversity with data in the industry.

At one point or another we all seek out medical help, yet there have been many complaints that the healthcare system is broken. It is really hard for anyone to explain or justify that the costs have continually increased, yet the improvements to outcomes don’t seem to be growing upwards in the same manner. Not always to these things correlate with one another, however, the frustration is a real issue, and the want for better care is a realistic requirement from the consumers. This is exactly why healthcare machine learning is a sort or reset button on many aspects of the industry and a tool that may just help break the old mold.

Increasing the Efficiency of the System

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One area that can seriously benefit from the information gained from healthcare machine learning is the efficiency level within an organization. Processes and procedures are based on understanding of the needs of the organization. This understanding comes from data gathered from those actions and the results. The results of internal processes, such as patient treatments or personnel schedules, or external processes, such as billing or report issuance, begin to show patterns of efficiency and effectiveness. Some patterns are easy to pick out without much data, but most trends, either positive or negative, require an in-depth recognition that comes from analysis of data.

Traditionally, this has been a pain-staking long process that has improved over the years as organizations have implemented healthcare software that can detect many of these patterns. However, timeliness is a huge factor in eliminating inefficiencies, and even the best software cannot always keep up with or full comprehend all these trends and what they might mean for the organization. The change to healthcare machine learning means a change toward faster and more accurate understanding of all that is occurring in the organization. What makes machine learning different from the current healthcare software currently being utilized is that machine learning takes the need for a programmer out of the picture and replaces it with its own continual learning and improving methods. Thus, there is a removal of human error and biases, and steps up the rate at which actionable data is produced for a more real-time movement toward efficiency.

Saving Money for the Consumer and the Service Provider

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As stated before, it doesn’t take a rocket scientist to see that the cost of healthcare has increase significantly and recurrently. Without a doubt, there have been many scientific and medical enhancements that warrant such cost increases. You never hear about exploratory surgeries these days, or the need to perform things like lobotomies or shock therapies. However, the costs related to hospitals stays, imaging scans and other procedures seems to be greatly exaggerated. Some costs are absolutely unavoidable simply because they are related to receiving a service and for the many people that are involved to make it all happen. Other costs are just estimated from the organization because they have not been able to accurately track a patient-by-patient cost.

Every business has some leeway built into their billing and procedures, but the healthcare industry comes under a lot of scrutiny not only from patients but also insurance companies, employers that are paying for health coverage and the government, which has been trying to get control of the cost hikes for some time now. The help of a healthcare machine learning system would help to find where waste is happening within the organization, provide better health data for more accurate care and provide more information to both patient and professional to enable better long-term outcomes. When better care is provided, especially directly to the patient, the less they may have to utilize the healthcare system as a whole, which translates into a cost savings across the board.

Saving Lives is the True Priority

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You can talk all you want about being more efficient and saving money, but more than anything what most patients and family members would prefer is the saving of a life. Data-driven decisions, especially in real-time, can be the difference between a life saved or lost. Healthcare machine learning takes into consideration the patient’s health history, family health history, current health conditions, previous patients with similar conditions and symptoms, as well as information gathered from external devices, such as FitBit, and compiles a profile that helps a healthcare professional understand the exact needs of the patient right in front of them. This kind of precision or individual care not only takes some of the guess work out of the healthcare process, but works towards helping a patient to live a better life, recover more quickly and hopefully not to be another statistic.

It does go without saying that at some point in time we will all pass away, but healthcare machine learning is working to eliminate the number of cases where avoidable errors are made, lessen the risk that patients are subjected to and to help the patient to adhere more strictly to things like medication schedules, follow-up appointments, and mandates that will help in recovery.

The healthcare system isn’t perfect, and they are a little behind the times, but they are making up for it with the push toward technology and implementing healthcare machine learning, which will help in so many facets of the care structure. Machine learning is in its infancy and there is much more to come, but even today we are all benefitting from things like higher efficiencies, lower costs and better healthcare.