Moving Past the Barriers in Predictive Analytics Integration in Healthcare

Written by Guest author on 16 Feb 2021

This blog was written exclusively for KenSci by Ronica Judee. Ronica is a healthcare and content enthusiast, and shares her perspective on healthcare and predictive analytics. 

 

Data processing and analytics has improved dramatically in recent years. There are increasingly more companies that no longer rely on guesswork to help them in decision-making. Industries, such as construction, transportation, and healthcare, are using hard statistical data to verify and lead their judgments.

 

The Increasing Need for Data Analytics

The demand for usable information in every type of industry is leading to an increase in the number of careers in data analytics, with data analyst positions being the most in-demand. In fact, there’s a 33% expected growth rate for the job through 2026.

While nearly every industry sees data analytics as an investment, it’s become an essential part of the healthcare industry. With research and development (R&D) at its core, the healthcare industry depends on accurate data to inform improvements and discoveries in several areas like clinical decisions, health services, and especially predictive medicine.

 

The Rise of Predictive Analytics

Today’s modern healthcare providers are continuously trying to achieve better outcomes; and they’re using predictive data analytics to give them foresight. This specific branch of statistics makes use of past data and machine learning algorithms to predict future outcomes. And a staggering 93% of health organizations say predictive analytics is important to the future of their business, according to a study by the Society of Actuaries. That’s not to say it’s without challenges, but recent developments in the data analytics industry have found solutions to these issues.

 

Preventing Data Bias for More Accurate Risk Scoring

Risk scoring allows healthcare companies to make more accurate assessments of their population and anticipate future risks, based on existing data. This is a metric that defines and predicts aspects of patient care (cost, risk of hospitalization, etc.) in comparison to the standard population.

However, arriving at a correct risk score may be derailed by data bias. It usually occurs early on in the data collection process, and usually comes in the following forms:

  • Sample bias: Data sampled does not accurately represent the population
  • Prejudice bias: Data informed by stereotypes
  • Measurement bias: Data tainted by improper measuring

To prevent this bias, you must first need to identify and specify the goal of data collection; this helps you determine the proper sample. In addition, a diverse team of researchers would help equalize prejudices, and allow for a fair reading and assessment among different sample groups. We at KenSci advocate for a more progressive healthcare industry, and making resources on information transparency publicly available is one of the steps we’re taking.

Image courtesy: Unsplash

Integrating Data From Different Sources

The quality and accessibility of data are also huge factors in the success of predictive analytics. Data from multiple reliable sources—both internal and external—could help healthcare providers create a more accurate picture of a patient’s health journey. The challenge is that there’s a saturation of healthcare data vendors in the country, and an average hospital uses as many as 16 different platforms. Such disparate data may hinder a comprehensive view of patient care, costs, and treatment.

Seamless integration of this diverse data can be facilitated by the reliable systems that process the data. Such was the case of UNC Health Care (UNCHC), a non-profit integrated healthcare system in North Carolina. It allows clinicians to access and analyze unstructured patient data using natural-language processing, giving them insights on treatment courses and predictors of risks.

 

Preserving Patient Privacy

In data collection and analysis, data privacy is one of the highest concerns. For the healthcare industry—where patient trust is imperative for a successful treatment—this needs to be addressed. The stakes remain very high for healthcare providers to handle data responsibly, since the Health Insurance Portability and Accountability Act (HIPAA) was enacted into law in 1996.

Clinicians can now train an algorithm across multiple devices or servers holding local data samples without exchanging information—through an emerging approach called federated learning. Moreover, using this methodology, clinicians can monitor patterns in data access and utilization. This gives them an early warning when something within the server changes, especially when those changes indicate a cybersecurity threat or a network penetration.

In the time that it’s been around, predictive analytics has led to many positive changes, and it will undoubtedly continue to improve the healthcare industry moving forward.

Access Our Latest Thinking

Research, blogs, and other whitepapers on AI Led healthcare transformation

Case Study

See how Advocate Aurora Health is working on fighting the opioid battle

May 13, 2020

Blog

How to accelerate the movement of data onto Azure in FHIR format

March 17, 2020

Webinar

SLUHN is fighting COVID-19 by staying ahead with a real-time command center

September 4, 2020

PodCast

HIT Like a girl~ Health from Corinne Stroum on Healthcare Informatics

August 12, 2020

Press Release

SCAN Health and KenSci are leveraging AI to help the senior members

August 11, 2020