Data Mining In Healthcare | 5 mins read

6 Benefits to Data Mining in Healthcare

6 benefits to data mining in healthcare
Lauren Christiansen

By Lauren Christiansen

Insight Into Data Mining in Healthcare

The healthcare industry faces numerous challenges today. Providers need to decrease costs, be more transparent, and improve the consumer experience. With the rise of big data, providers must incorporate analytics to optimize consumer data access. It's also critical that patient data is secure to ensure compliance with regulations.

Failure to accommodate patients with a set of best practices results in lawsuits, fraudulent claims, and severed patient-provider relationships. Experts hope that improvements in data collection and mining efforts will revolutionize the healthcare industry. Read ahead to discover how healthcare organizations now use data mining to improve care, decrease fraud, and enhance physician knowledge.

  • Patients' increasing involvement in health care decision-making
  • Quick adoption to virtual health practices
  • Push for greater data analytics use
  • Unprecedented collaborations between the public sector and private sector

6 Ways Data Mining is Improving Healthcare

Different industries enjoy the many benefits of big data and business intelligence technologies. While banks use data mining to mitigate fraud abuse, other companies learn more about customers to improve marketing campaigns.

Out of all of the industries that capitalize on mining data, the healthcare industry perhaps does so the most. Those in the medical field use mining methods and big data analytics to optimize patient relationships, prevent disease, and much more. Here are the top 6 ways that mining data improves healthcare in the real world.

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1. Data Mining Improves Physician Decision-Making

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Health care providers use lab tests to improve patient care. Analysts now use predictive models and mining techniques to help doctors pinpoint patient problems. Many times, the lab results do not produce this type of insight on their own.

For example, researchers used mining algorithms to review urine sample patient data. As a result of using data mining, they could estimate life expectancy rates for these patients. This approach in data analysis enables healthcare providers to determine when patients are more ill than they appear.

A doctor can use insights derived from data mining algorithms to make a better clinical decision and prevent further harm to the patient.

2. Data Mining Mitigates Potential Drug Interactions

Healthcare organizations use mining tools to help doctors decide when to prescribe medication. For a patient to take certain drugs, he/she may need to stop taking another medication due to potentially fatal interactions. Analysts can use healthcare data to mitigate these interactions before they occur.

Because some interactions are less common, not all physicians know about them. Big data analytics can help scientists find these fewer common interactions before they generate any theories. While data mining helps to understand cardiovascular drug interactions, it can extract insights about other medications too.

3. Data Mining Determines Purchasing Patterns

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Patients don't always take the medication their doctor prescribes to them properly. Analysts use mining tools to study patient purchasing habits at the pharmacy. They use further analysis to see whether there is a correlation between this behavior and adherence to doctor orders.

For example, one research team studied patient analysis data from a pharmacy. They reviewed healthcare data and health records in a warehouse to identify the type of patients who don't adhere to protocols.

There was an association between those who spent more and purchased an item other than a prescription at the time of pickup with poorer adherence. Because this correlation wasn't enough to generate association rules, they were unable to predict purchasing patterns. However, the application data they collected did help them increase interventions in patients with low adherence rates.

  • Drug shortages
  • Escalating drug costs
  • Separation of medical and pharmacy benefits
  • Multitude of regulations
  • Changes in technology

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4. Data Mining Improves Patient Outcomes and Safety Precautions

The healthcare industry continues to find new ways to decrease costs and improve performance. Many analysts use data mining to do so. While there is no one right way to improve quality and decrease inefficiencies, patient safety is tied to performance.

Through several case studies, researchers know that length of stay and the number of treatments correlates to patient safety. Healthcare providers can use these insights to improve safety measures and decrease patient re-visits.

For example, healthcare providers can use electronic health records and data mining to analyze whether two or more adverse circumstances occurred at the same time. This can help hospitals eliminate or minimize any safety issues in the future.

5. Data Mining Reduces Fraud

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Many healthcare providers use data mining techniques and machine learning to reduce health insurance fraud. Traditionally, the claims department reviewed each document to look for fraud but did not have enough time to look for warning signs.

Analysts have discovered that providers can use data mining tools to find specific documents that may be illegitimate. This saves time and prevents fraudulent medical claims that cost millions of dollars per year.

  • Healthcare fraud costs the country $68 billion per year
  • Fraud occurs in about 10% of property-casualty insurance losses
  • The FBI estimates non-medical insurance fraud to be $40 billion per year

6. Data Mining Matches Specialist to Patient

Patients with unusual conditions cannot always find the specialist they need for treatment. This is frustrating for both healthcare providers and the patient. A recent study shows that health care providers can use data mining methods to improve a doctor's capacity to diagnose these patients.

It can also find the specific providers who are more likely to diagnose the medical issue. This eliminates potential error, saves time, reduces costs for both provider and patient, and optimizes each customer relationship.

  • Rare diseases impact roughly 3.5-5.9% of the worldwide population
  • 70% of genetic rare diseases begin in childhood
  • 72% of rare diseases are genetic, while others are the results of infections
  • Researchers believe that analytics may help to prevent rare diseases in the near future

Key Takeaways of Data Mining in Healthcare

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In conclusion, here is what to know about how providers use data to optimize care -

  • Data mining optimizes physician decision-making and predicts patient problems. It can also prevent less common and potentially fatal drug interactions in patients.
  • Data mining can predict some customer behavior and purchasing patterns at pharmacies. These insights help doctors perform interventions to ensure patients take their medication as prescribed.
  • Data mining improves patient outcomes and safety precautions to decrease costs and improve performance. It also helps ensure proper safety measures are in place to mitigate catastrophes.
  • Data mining reduces health claim fraud abuse and saves time. It can also match a specialist to a patient if the patient has a rare illness that is hard to diagnose.

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