One of the most prominent changes in healthcare in the last decade has been the digital disruption in the industry. In what has been described as the "Uberization" of healthcare, key players have attempted to leverage the rapid developments in technology to disrupt patient care delivery and gain a competitive advantage. Healthcare systems and providers have now adopted electronic health records, remote monitoring systems, telemedicine, and other technologies to transform patient care.
This transformation has seen health data extensively stored, shared, accessed, analyzed, and used across digital platforms, including wearable devices, smartphone apps, medical devices, and AI-driven models. Consequently, this shift has provided healthcare systems and other stakeholders with access to a digital universe of large volumes of useful information that is integral to driving topline results and improving healthcare outcomes.
'Big data,' which is being harvested from this plethora of sources, facilitates solving real-time problems in everyday patient care flow. Patients can now schedule appointments with a push of a button without queueing for hours in the doctor's office. Healthcare providers can monitor patient indices and treatment progress from anywhere in the world and make early and timely decisions without even seeing the patient.
Now, data scientists and healthcare providers are scaling up this digital revolution and expanding its benefits to improve population health strategies, reshape businesses, and inform critical decisions organizations make. Simply put, industry key players, including healthcare providers, payers, and policymakers, are using predictive data analytics via patient-centric data ecosystems to improve value on a larger scale.
"Organizations are now, more than ever, in greater demand for data analytics, not in forms they expect, but in their raw forms to help them make better decisions and improve outcomes," says Thi Montalvo, North America Health Analytics Practice Leader at WTW. Montalvo said organizations are now focused on value-based use of data analytics.
In other words, predictive data analytics provides insight to healthcare providers as to when, where, and how healthcare should be delivered, which helps employers and organizations make important administrative decisions.
Here are the notable benefits of predictive analytics in healthcare:
Scaling Up Population Health
By using predictive modeling, healthcare providers can determine, for instance, the trend in infectious disease spread and which areas need more robust infectious disease control strategies even if the disease is yet to spread to those areas.
Researchers leveraged big data to develop a model of how COVID-19 was spreading across several areas. The researchers harvested large datasets from search engines, social media, contact tracing apps, and other sources to locate individuals exposed to the virus, check their primary source of the infection, and determine the areas of likely spread and emerging outbreaks.
Another team of researchers used predictive analytics to identify hospitalized patients at risk of heart failure and septic shock. The model leveraged volumes of data to risk-stratify patients to ensure that more healthcare resources are channeled toward the care of those at risk of these conditions, even when they had not developed these health problems.
Similarly, healthcare providers are able to identify patients at risk of decline using a predictive clinical analytics model. The AI-driven model is scanned against large datasets from health records and other sources to determine patients that may require emergency care to help clinicians triage care and provide timely care to those that need it most.
Integrating claims data and engagement data, with solutions data, according to Montalvo, is another key area of focus for data analytics in the future. It helps healthcare providers and payers to see the trend in chronic disease management and answer the question of which part of patient care flow is working and which isn't, and where they need to channel more resources and input.
Precision medicine, or personalized medicine, has become the new frontier of healthcare and data analytics in recent years. Big advances in genomic analytics and data science have created new datasets that have changed how clinical care providers deliver treatments and run diagnostics.
In the world of personalized medicine, data is where the precision lies. Patients dealing with the same type of cancer are no longer treated the same way. With access to an abundance of genomic metadata and as genetic testing becomes cheaper, clinicians are able to identify genetic variations that necessitate individualized treatments.
For instance, two patients with the same type of breast cancer may not benefit from the same drug. In a set of breast cancer patients, the presence of a specific gene coding for a receptor makes a drug work effectively such that patients without that gene expression will not benefit from that medicine. This precision requires vast amounts of health data.
Personalized medicine leverages five domains of data sources, including medical care, behavioral medicine, social factors, physical and social environment, and genomics biology. Experts are gradually improving the quality, quantity, and diversity of these data, integrating them into novel tools and ecosystems to expand precision medicine to broad populations and disease conditions.
Reduction in Healthcare Costs
Many organizations, including healthcare systems, have recorded a sharp drop in healthcare costs by using predictive data models.
First, the emergence of precision medicine will eliminate unnecessary treatments, admissions, and diagnostics. The massive sets of genomic data harnessed for precision or individualized treatment will mean that patients will only get treatments that are found to be effective for them.
Through using predictive models, healthcare providers are able to reduce operational expenses by focusing more on patients at risk of treatment non-compliance and complications. Data analytics also help employers to spot people at risk of chronic conditions and provide access to proactive and timely care, placing them one step ahead of events. This proactive care reduces the need for avoidable surgeries and healthcare complications that will cost millions of dollars.
Furthermore, accurate prediction of patient length of stay and readmission rates using data analytics helps providers plan and staff more efficiently. Timely and precise care is offered to people with high readmission risk, lowering hospital expenses associated with patient readmissions.
Montalvo said for employers, this means revamping corporate wellness strategies to address those conditions and statistics that are readily impactable. Suppose readmission rates for depression and anxiety attacks are spiking. In that case, employers need to improve mental health offerings in the workplace and rethink employee assistance programs to lower the burden of mental health issues among their workforce.
Big Data and the Future of Healthcare
The digital revolution is sweeping across industries, and healthcare isn't taking the back seat. The coronavirus pandemic has driven a sharp upswing in the digitalization of healthcare, and many healthcare systems are teaming up with data scientists to develop digital tools and models to change the healthcare landscape. Industry stakeholders, including healthcare providers, payers, and insurers, are leveraging the massive influx of data in healthcare to reshape the industry by driving optimal patient care outcomes and reducing healthcare costs at the same time.
You can learn more about how recent advances in data analytics are transforming healthcare, benefits, and well-being for employers at this year's virtual Healthcare Revolution conference. Registration is free!
Thi Montalvo is a speaker at this year's event, and you can enjoy more of her expert insight by checking out the panel session that she is speaking on at this year's event.