How Can Data Science Help The Healthcare Industry?

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How does data science work?

Data science can be used in any industry. It combines algorithm development practices with other forms of technology to produce valuable knowledge. Some data science processes are used simply to create knowledge with no desire to solve a complex problem. Other data science processes, however, are performed specifically to solve an analytically-complex problem. In the healthcare industry, data science has produced massive amounts of knowledge related to populations as a whole and individual people. We have seen phenomenal advances take place thanks to data science. Each of the following is an example of how data science is saving lives.

Real-time Alerts and Wearables

Real-time alerts are an example of data science. These alerts are available through clinical decision support software, which analyzes various sources of medical data to provide physicians with on-the-spot prescriptive recommendations. Doctors, however, are finding it more efficient to keep patients away from the hospital as much as possible. And thanks to advances in technology, affordable wearable devices are making it easier to monitor patient data without having to have them in the hospital. These wearables, such as a FitBit, can provide real-time alerts, such as a notification about a sudden increase in blood pressure, allowing users to effectively monitor their health. These wearables can even be synced with patient portals to provide the data to the appropriate doctor.

Improved Diagnostic Accuracy

Even though we have massive amounts of data science, incorrect diagnostic rates are still high. In the United States alone, about five percent of all diagnostic outcomes are incorrect. This means nearly 12 million people each year receive an invalid diagnosis. Certain companies, however, are investing in data science and other technologies to increase the accuracy of diagnostics as well as its cost-efficiency. Enlitic is one of these companies and has received $15 million in funding. Using a very extensive learning algorithm, Enlitic takes data and runs it through a comprehensive database of laboratory experiments and clinical reports. The company claims it has been able to process data in such a way that diagnostic procedures are 70 percent more accurate.

Decreased Spreading of Diseases

The National Hospital Discharge Survey performed in 2010 showed that the average length of stay when being admitted to the hospital was 4.8 days. Another survey conducted in 2011 found that there were 136.3 million visits to the emergency department. Thanks to data science, we can analyze this type of data and use it to build a data narrative that improves hospital claims. Thanks to various methods, including predictive analytics and exploratory data analysis, we are able to pinpoint the most cost-effective treatment method for each patient. This data is especially beneficial in being able to identify unnecessary treatments, such as performing two lab tests that can be combined into one. It is also valuable because data narratives can lead to correct diagnoses and treatments the first time around, which decreases the spreading of diseases.

Cancer Moonshot Program

Before President Obama left office, he developed a program that focuses on “accomplishing 10 years worth of progress towards curing cancer in half that time.” Within the program, a panel has made much progress thanks to data science. Researchers have been able to take massive amounts of data and identify trends in cancer treatment and success rates. For example, they have discovered connections between certain cancer proteins and various treatment methods.

This program has seen its fair share of obstacles, though. For example, incompatible data systems have led to complicated interfaces, some of which cannot sync with one another, meaning the data cannot be combined to compute data science processes. There are also confidentiality issues that complicate the sharing of data from one interface to the other. Furthermore, many institutions that have invested in cancer research are not willing to share their dataset.

Telemedicine

Although telemedicine is nothing new — it has been present within the healthcare field for more than 40 years — it is becoming more popular. Thanks to online video conferences and wireless devices, like wearables, telemedicine can be used for a variety of purposes, including primary consultations. Even telesurgery has seen an increase in effectiveness and popularity over the past decade thanks to data science. Telesurgery allows doctors to use robots to conduct surgeries when they are not present with their patients. Instead, they use real-time data science to maneuver the robots and perform surgeries remotely.

Telemedicine has also led to the ability to better personalize treatment plans. This has helped reduce hospital re-admission rates. In fact, telemedicine has been the leading contributor to reduced costs of healthcare thanks to an improved quality of service via different forms of telecommunication. On the administrative side of things, data science has led to reduced paperwork and refined insurance claim processes. Those submitting claims can easily access the data they need to ensure each claim is processed effectively the first time around.

Electronic Health Records

The benefits of data science within the healthcare field can greatly be seen in the use of electronic health records (EHR). These records help medical facilities achieve higher rates of chronic disease management as well as increased efficiency in the cost of their services. The implementation of EHRs has occurred at varying maturities across the country. Those who are farther along in their maturity have seen firsthand the true value of EHR systems. For some medical facilities, though, achieving meaningful use of the data has been a challenge due to financial restraints affecting investment in proper technology and hardware. Nonetheless, as we continue to optimize the use of EHRs, we will also continue to discover new trends between diagnoses and treatment methods.

Predicting Patient Behaviors

Data science within the healthcare field has led to the development of strategic planning. Physicians are provided with much more in-depth overviews of patients than they used to have, which helps them better determine patient motivation. As a result, data can be analyzed to see which factors most affect treatment discouragement. Data science allows us to not only see how patients are behaving, but also pinpoint why they are behaving in that manner. For example, if location issues are seen as being a treatment discouragement for a specific population, altering treatment administration sites can serve as a solution to this type of issue.

Using Actionable Insights to Optimize Clinical Performance

When data science is integrated with predictive analytics, healthcare providers are able to improve their operational processes; especially their administrative tasks. Tapping into predictive analytics software can be costly, though. Thanks to CogntiveScale, a startup company based out of Austin, healthcare facilities can use the patented Deep Cognition Engine to help them make better sense of their data. CognitiveScale brings affordable predictive analytics to the table and enables medical professionals to improve their business performance by optimizing clinic staff scheduling as well as by attaining better inventory management. This company has greatly transformed medical facilities’ capabilities to build actionable programs for various epidemics, such as flu outbreaks.

The Takeaway

Data science is the term used to describe data-driven processes that produce valuable knowledge. Technologies are both improving patient outcomes and making office visits more efficient and effective than they used to be. Physicians now have access to more data than they’ve ever had, giving them a more in-depth overview of each patient they attend to. This in-depth overview of patients has been created thanks to data science. As more healthcare facilities start to take advantage of data science, diagnosis and treatment processes will continue to improve.

 

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