Predictive Analytics In Healthcare

Predictive Analytics in Healthcare 3

How It’s Making Treatment Decisions Faster and More Efficient

What if medical professionals could predict, with a high degree of accuracy, which patients were going to develop serious health problems in the near future? Well, that’s now possible thanks to predictive analytics in healthcare—or what some are calling Big Data in medicine.

As these analytical systems become more and more ubiquitous in hospitals and other healthcare facilities, doctors are increasingly able to make treatment decisions faster, more efficiently, and with an unparalleled level of precision. Here’s how it works…

Predictive analytics in health care is a broad term used to describe any type of analysis that uses data to predict future events. The goal of these analyses is to help doctors make better decisions about patient care.

This includes predicting which patients will develop certain diseases, how long they will live after being diagnosed, and whether they will need additional treatment. In this post, we will explore predictive analytics in healthcare, what it is, and how it can be used to improve patient outcomes.

1) What predictive analytics is and how it works

Predictive Analytics In Healthcare
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Predictive analytics can be defined as “the process of using data mining and modeling techniques to make predictions about future events.” In other words, predictive analytics uses historical data to try and identify patterns that can be used to predict future events.

There are many different types of predictive analytics, but they all have one goal in common: to help decision-makers anticipate future problems and take action to avoid them.

Predictive analytics models are used in healthcare to help doctors, nurses, and other medical professionals make treatment decisions faster and more efficiently.

2) How predictive analytics is being used to improve healthcare outcomes

How predictive analytics is being used to improve healthcare outcomes
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In medicine, predictive analytics is used to forecast disease outbreaks or the likelihood of an epidemic spreading from one group to another by looking at information like the patient’s age, gender, eating habits, and immunization history.

Predictive analytics has a wide range of potential applications in healthcare.

Some of the most common uses include:

  • Identifying patients who are at risk for developing certain diseases
  • Predicting how long a patient will live after being diagnosed with a disease
  • Determining which patients are most likely to need additional treatment
  • Forecasting demand for certain types of medical procedures

Predictive analytics has the potential to improve patient outcomes by helping doctors make better-informed decisions about treatment. In addition, predictive analytics can also help healthcare organizations improve their overall efficiency.

For example, clinicians can use predictive analytics to better anticipate which patients will need to be readmitted. Given this data, clinicians may be able to better prepare for the patient’s next scheduled appointment. This information can then be used to target resources and interventions toward those patients, which can reduce the number of readmissions and save healthcare organizations money.

3) Value of predictive analytics in healthcare

Value of predictive analytics in healthcare
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In predictive analytics in healthcare, the value of looking forward can trump what we have learned in the past. Let’s look at examples of where this might work.

Healthcare predictive analytics: Intensive care unit

A group of healthcare researchers explored whether the daily mortality risk could be predicted for individual patients admitted to the intensive care unit (ICU).

  1. What these researchers found was that there is a predictive relationship between respiratory rate and in-hospital mortality. The study showed that for every one-point increase in respiratory rate, there was a corresponding 0.12% increase in in-hospital mortality.
  2. This predictive relationship allowed the researchers to develop a predictive model that could be used to estimate the risk of mortality for individual patients. The model showed that, on average, patients with a respiratory rate of 30 breaths per minute had a mortality risk of 8.5%.
  3. This predictive model can be used to help guide clinical decision-making. For example, if a patient’s respiratory rate is higher than predicted, this may be an indication that the patient is at increased risk for mortality and may need closer monitoring or different treatment.

Healthcare predictive analytics: Diabetes

As predictive analytics in healthcare becomes more commonplace, its potential to improve outcomes for patients with diabetes is becoming increasingly clear.

Whether a loved one with diabetes is making good decisions about their food choices or not, data mining can provide nurses with the necessary evidence to suggest that they may benefit from monitoring their diet more closely and providing care plans that will help them to follow their doctor’s orders and remain healthy.

A recent article in The New York Times titled “Studies Seek Ways to Better Screen for Alzheimer’s” makes the point that researchers are using data from Medicare, a universal health-insurance provider, to get a better picture of what the prevalence of Alzheimer’s disease is and how these people are being cared for in an effort to find better treatments.

Using data to guide treatment decisions that will improve outcomes and lower costs not only takes the guesswork out of the healthcare process but also allows us to apply evidence-based care in a way that is targeted to specific patient populations.

Predictive analytics can be used to help guide clinical decision-making in a number of different areas of healthcare. Using patient data to guide treatment decisions that will improve outcomes and optimize healthcare costs, is an important step in the journey toward a more evidence-based approach to care.

4) The Benefits of Predictive Analytics

The Benefits of Predictive Analytics
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The use of predictive analytics in healthcare is one way to help reduce the cost of treatment, improve the accuracy of diagnoses, and increase the speed with which treatments can be delivered.

Some of the ways that predictive analytics are being used in healthcare include:

  • Assisting clinicians with diagnostic decisions and surgical procedures by predicting patient outcomes.
  • Optimizing hospital capacity
  • Identifying high-risk populations for intervention
  • Improving public health initiatives
  • Evaluating clinical outcomes
  • Reducing the amount spent on readmission
  • Cutting down wait times for those who need critical care services.

Predictive analytics have helped doctors make smarter decisions, not just faster ones. One such example is the case of a woman who came into the emergency room complaining of chest pain after eating at a “spicy” restaurant. Doctors immediately knew she was likely suffering from heartburn and prescribed an over-the-counter remedy, saving her time and unnecessary tests.

Another benefit of predictive analytics in healthcare is the ability to determine patients’ risk factors and therefore anticipate how they will react to a specific medical event. For example, if someone has asthma, they will respond differently when exposed to allergens than someone without asthma. The data collected through predictive analysis can provide all parties involved – including providers, payers, and patients, – information for better decision-making that leads to a better quality of life for everyone involved.

To summarize, the benefits of predictive analytics in healthcare can include:

1) Can help clinicians predict patient outcomes and make better decisions about diagnostic procedures and surgeries.

2) Optimization of hospital capacity

3) Identify high-risk populations for intervention

4) Cut down wait times for those who need critical care services

5) Enable physicians to predict how people will respond medically based on their personal history and symptoms

5) How predictive analytics is changing the healthcare landscape

How Predictive Analytics Is Changing The Healthcare Landscape
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If we looked at how predictive analytics is changing the healthcare landscape, we would find that many systems, companies, and hospitals are looking to leverage the power of predictive analytics on a broader scale or system-wide scale to move healthcare from reactive to proactive.

When we are able to empower every patient to be more active and informed participants in the healthcare system, we can make improvements for everyone—starting with the patients who need it most.

Predictive analytics tool has been proven useful in other industries as well, such as banking and insurance, where it is used for fraud detection, but has not yet had wide adoption among healthcare providers due to some hesitation over whether this type of software will violate patient privacy.

Nevertheless, as we put more of an emphasis on process improvement, value-based reimbursement, and patient-quality care enhancements, predictive analysis will become a bigger and bigger part of our healthcare system. As a matter of fact, we are beginning to see a future where healthcare providers will not only focus on treating disease but also be expected to prevent it by using predictive analysis.

6) How can healthcare providers make use of predictive analytics?

Doctors and other healthcare providers often have to make important, life-changing decisions about their patients in a very short amount of time. For example, what treatment is best for the patient? What’s the patient’s prognosis?

These questions can be even more difficult to answer when they involve rare illnesses or diseases. While doctors always aim to provide the best care possible, they also want their patients’ treatments to be as efficient as possible so they don’t cost too much and/or cause undue stress on the patient.

One area where predictive analytics is currently being used is population health management. By identifying high-risk populations and taking action to prevent them from becoming ill, predictive analytics can help improve the overall health of a population.

7) Case Study: Dr. Smith and Patient X

Dr. Smith is a general practitioner with a practice near the university. Dr. Smith has noticed that some of his patients are dropping out of treatment too quickly, only to come back after their mental health symptoms have worsened. So he decided to investigate which patients were at risk for early discontinuation.

He used predictive analytics to identify patients who were most likely to drop out within three months based on patient utilization patterns, patient engagement, and predicted patient behaviors such as when a person would start showing signs of fatigue or sleep deprivation.

From there, he could take appropriate action by contacting the patient earlier than usual and making sure they had the help they needed from a healthcare provider.

The outcome? Early patient care leads to better outcomes—more patients stay engaged in care while decreasing unnecessary readmissions to the hospital!

8) The Drawbacks of Predictive Analytics

One of the drawbacks of predictive analytics is that it can’t take into account every possible scenario. It’s also hard to determine which patients might be at risk for an adverse event, but predictive analytics can help identify those who are most likely to have one.

Some predictive models use a data-driven approach with inputs such as demographics, clinical history, lab values, medication use, vital signs, and more. The model then predicts future outcomes like hospital readmissions or mortality rates within a specified period (usually 30 days) after discharge or some other life event.

In the U.S., predictive analytics is being used to predict people’s eligibility for health insurance based on their income levels and family size.

9) The future of predictive analytics in healthcare

The future of predictive analytics in healthcare
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Data and analytics technology are both crucial parts of a health system. Therefore, as with many other large organizations, doctors and hospitals use health data analytics tools to make the best possible decisions about their patients. In the near future, population health management will be at the center of health systems’ attention as they expand patient support and move beyond reactive treatment to preventative care.

Predictive analytics will continue to play an important role in healthcare, as it has the potential to improve outcomes and efficiency. As more data becomes available, predictive analytics will become even more powerful, helping to identify trends and patterns that may be otherwise hidden.

We expect that healthcare organizations will increasingly adopt the following approaches:

1) Developing data governance and business intelligence strategies

2) Aggregating and normalizing various datasets

3) Conducting analysis using descriptive, diagnostic, predictive, and prescriptive approaches

4) Implementing alerts and real-time reporting on key indicators

5) Adopting predictive analytics to support clinical decision-making

Healthcare predictive analytics is still in its early stages. Further advancements in technology enablers are expected in the future. And as datasets grow, the challenge of managing and analyzing all that data becomes greater with the increasing diversity of data sources. In addition, as health systems engage patients through new methods of data-driven engagement to drive population health management, it is imperative that their infrastructure be flexible and agile enough to keep up with an increasingly tech-savvy population. That’s why data analytics, machine learning, and predictive modeling will become important pillars in chronic disease management strategies and improve patient outcomes in the coming years.

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About the Author

Liviu Prodan

Liviu is an experienced trainer and LifeHacker. He’s been living the ‘Corpo life’ for more than 15 years now and has been a business developer for more than 12 years. His experience brings a lot of relevancy to his space, which he shares on this blog. Now he pursue a career in the Continuous Improvement & Business Development field, as a Lean Six Sigma Master Black Belt, a path that is coherent with his beliefs and gives him a lot of satisfaction.

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