Blog

Top 3 Ways Data Analytics Can Reduce Costs In Healthcare and Medicine

According to CNBC, healthcare is the number two largest industry with forecasted revenue growth of 2.3%. Hence, health and medicine are promising areas in which data can be managed and leveraged to create positive change. With health expenditures more than doubling from 2000 to 2017 to $3.5 trillion, proper use and implementation of data has the potential to reduce costs and give birth to MedTech and HealthTech industries. * With this, data analytics may improve medical decision accuracy and the lives of practitioners and patients.

Below are three key ways data analytics is cutting costs to change healthcare for the better.

1.  Electronic Health Records

Digitizing medical records can result in substantial savings. With EHR, each patient has his or her own digital record. This record may include medical history, allergies, lab test results and more. Such records are shared via secure information systems, so that they are available for providers across public and private sectors. An EHR is comprised of one modifiable file so that doctors can implement changes over time with no paperwork or danger of data replication.

Although 94% of hospitals in the US have adopted EHRs, that number should grow worldwide (HITECH research). The benefits are very apparent. According to a McKinsey Report on Big Data in Healthcare, an integrated system has already saved an estimated $1.0 billion from reduced office visits and lab tests.  A shared system of digitized patient records would save hospitals and healthcare centers substantial sums of money.

2. Fraud Prevention

Healthcare fraud is a national problem and rife in federal, state and private insurance programs.  Billions are lost each year due to improper claims.

In fact, the National Healthcare Anti-Fraud Association (NHCAA) estimates that 3% of all health care spending in the US is lost to fraud.  **

However, data analytics can provide organizations the ability to track incorrect or fraudulent payments. Claim review processes that incorporate rules-based data analytics, predictive modeling and linking technologies can help detect deviations in data. With this, commercial and government payers can identify fraud before an eligible claim is paid.  

3. Increased Engagement

 Have a smartwatch or FitBit? You are definitely engaging with your person health data! 

With smart devices, individuals can submit intel about their daily activities and health statistics. This information can be used to make better health-related decisions, saving long-term costs. For instance, if your smartwatch provides data on your daily steps and steps are below average, you may resolve to walk or run more tomorrow. Or, perhaps you decide to eat less to account for low energy expenditure.  

When a user modifies behavior to improve his or her health, he or she can stay out of the doctor’s office. This cuts down on costs paid by patients, medical centers, insurance and more for treatment.

Smart devices collect patient information such as heart rates and sleep habits which can be used in tandem with other medical records for faster, more accurate diagnoses. Patterns of symptoms can be detected across patients to allow for better medicine, patient care and comfort and ultimately more effective treatment plans. Finally, data can be transferred to the cloud for long-term monitoring and access.

Overall, data analytics can play a pivotal role in the healthcare industry and has spurred the rise of “MedTech” and/or “HealthTech” focused companies. Leveraging data in those sectors has become extremely important.

*National Health Expenditure “NHE” data

** Bending The Cost Curve: Analytics-Driven Enterprise Fraud Control

— Piece edited by Emily Hunt

Thanks for the comment
No Comments
Please rate*

Other Suggested Reads

  • Data Analysis: Smart Phones & Other Trends In Will Creation

    Writing a last will and testament is not usually an activity associated with millennials.  However, young people are thinking differently about protecting their families, and, in turn are "disrupti...
  • The Beautiful Binomial Logistic Regression

    The Logistic Regression is an important classification model to understand in all its complexity. There are a few reasons to consider it: It is faster to train than some other classification algo...
  • The Worst Kind of Data: Missing Data

    Most publicly available datasets or datasets at the workplace are complete. However, from time to time we encounter datasets where some or many entries are missing. The problem of missing data exists ...