Humans beings possess the ability to observe and learn from experience. Advanced technologies of the twenty-first century such as Artificial Intelligence (AI) and Machine Learning (ML) can learn from the past tasks on a day-to-day basis through constant learning. It is the ability to learn with ‘data’ that separates ML, which forms a part of AI, from ordinary machines. ML is so applicable today that we are using it for basic operations such as online retail or travel.
For physicians across the healthcare spectrum, it is easier to gain insights, both direct and inductive, from small data using computational tools like excel, graphs, charts, and others.
But, what happens when they are dealing with Big data? Have we even imagined the possibilities with ML in the healthcare space?
With technologies like Hadoop, it is easier to perform calculations in Big data to obtain exact insights. Inductive insights, however, are beyond conventional capacity to infer from massive amounts of data. Since traditional analytics are not enough for capturing the full value of Big data and that is exactly when ML comes in.
Unlike traditional analysis, ML thrives on growing datasets; something which healthcare isn’t running short of. So now, more than ever, developments are taking place in methodologies to identify and fine-tune machine learning algorithms and models which can deliver accurate predictions. Why?
It is better to prevent the disease with early intervention rather than go for a treatment after diagnosis.
Prediction capabilities of ML can tremendously help physicians make more informed decisions based on statistical evidence. Assessing readmission risk of patients, especially that of those who are chronically ill will significantly increase their chances of getting better care management and post-discharge support. Besides, lowering the rate of readmission through the application of ML will make physicians concentrate their efforts on preemptive care.
Why is ML assessment of readmission risk of patients better? What are the various outcomes that physicians can derive from ML assessment?
- Hospital admissions can be streamlined based on types of admission and predicting the length of stays
- Providers and payers can learn about the revenue to be earned based on the cost of treatment
- Providers can gain insights on actual vs. predicted admissions based on disease and illness
- Care teams can be better assisted with decisions about what condition a patient might have, or what treatment might work the best
- Caregivers can deliver more precise and timely care, meanwhile each patient gets a personalized treatment plan
- Physicians and care teams can learn about combinations of drugs that should not be taken together
- In medical diagnostics, physicians can detect serious disorders through image analytics i.e. classifying imagery, such as mole scans, to identify a disease
Preventing bottlenecks in care continuum by analyzing patient flow across network
Based on historical claims data and reimbursement analysis, ML can help in forecasting the revenue of a network.
Optimizing workflow with ML by using historical data for staffing can severely reduce costs while ensuring that the right clinician is at the right time and place.
Moreover, ML can help with efficient use of hospital resources through preventing bottlenecks in urgent care by analyzing patient flow during peak times. It can also help routing the patients to the best practices for treatments without having to leave the network.
Moving from a hypothesis-based approach to a data-first approach
As health care has evolved to gain the ability to access large volumes of data with agility and ready access, the same principle if and when applied to ML can open up healthcare for a variety of innovations. Instead of limiting themselves to working with “sample” sets of data, physicians today are empowered by Big data, which has enabled them to leverage massive sets of data without restriction.
With ML and healthcare data combined on a unified platform, physicians can rely on the data itself (in all of its granularity, nuance, and detail) rather than relying on representative data samples. That is why it called a “data first” approach and many organizations have made the switch from a hypothesis-based approach.
A legion of industries have benefited from ML by achieving an unprecedented level of efficiency in overcoming data complexity. Thus, we have the solution that could potentially solve the most glaring challenge that complex datasets poses to healthcare. Then why not take a shot at it?
Machine Learning: The future of health IT that can make healthcare simple again
As sciences and industry are on the cusp a data revolution, it has given rise to completely new data formats and databases of unprecedented scale. We are at a stage where we have just begun exploring the possibilities of ML in healthcare and already we find that it augments our ability to identify and capture value in data.
Big data and ML are two very distinct concepts, however as both are finding their way into healthcare, it is crucial to know how they are intricately tied together. Big Data’s entry in healthcare has given rise to a massive opportunity for Big Data and ML to come together and develop techniques to handle modern data types. ML has the capability to draw on statistical and computational intelligence for navigating vast amounts of information with minimal or no human supervision, while Big Data, well, it just keeps on growing. Thus, while Big Data and ML are not directly related, coming together of these two can do real wonders.
A technology that has the power to give everyone more control and precision over health and care is eagerly waiting to be explored, the question is, is healthcare ready?
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