The future of healthcare data: Q&A with Inovalon SVP of Enterprise Data, Patrick Brundage
With impressive innovations in technology, analytics, and industry connectivity, healthcare has clearly entered its data age. Data informs activity across the industry every day – in drug development, at the point of care, to close health equity gaps, and more. The future of healthcare data is bright, but there are varying opinions regarding what the next era of data utilization will look like.
We recently sat down with Patrick Brundage, SVP and General Manager of Inovalon Insights, to get his take on where healthcare data analytics is going.
What is healthcare real-world data?
“It’s what is happening to real patients in real life.” Real-world data is not like data collected during clinical trials. While valuable, clinical trial data comes from a controlled environment with a specific regiment.
Healthcare real-world data captures the nuances that occur in everyday life, providing a more accurate picture of what happens to patients. It can signal when a patient has not been compliant with their medication or if they haven’t been doing their regular check-ups. Real-world data can also provide a window to see any adverse outcomes a patient may encounter.
By adding in SDOH data, we can account for social risk factors that may affect access to healthcare and therefore further impact patient behavior in the real world. There are social risk indicators that can be linked with clinical data to provide a better understanding of the patient experience and their barriers to care.
How is data changing the healthcare industry?
At every point of the patient care journey, data is there. It’s helping facilitate patient registration, informing options for treatment, and flagging when a patient experiences gaps in care. It can help identify when a patient is at risk for a certain condition, when someone is eligible for a new treatment, and much more.
Data enhances “the background” of healthcare. At a macro level, real-world data allows for faster and more accurate drug development. Health plans can access massive datasets to better understand their patient populations and deliver better quality care.
Who benefits from using real-world data?
Large organizations and individual patients can benefit from real-world data. There is a trickle down effect that adds value at every level of the healthcare industry.
For health plans, providers, pharmacies, and life sciences companies…
The future of healthcare data for large organizations lies in the ability to take data from this big, complicated thing and distill it into insights that can be used in research, decision-making, and everyday behaviors when delivering care. The healthcare industry has laid the foundation for this through data collection, but there is still much more to gain from utilizing data analytics.
Health plans can gain a clearer picture of their patient populations without digging through documentation requirements or large silos of data.
Providers can see everything they need to know about a patient during intake such as their eligibility status and correct demographic information. Then in the treatment encounter, they can see the patient’s entire health history including their allergies, conditions, current medications, and adherence patterns. Pharmacies can perform faster prior authorizations and give better patient consultations with the right data.
For life sciences companies, the possibilities and applications of data are practically endless depending on the research question and the available data.
What does real-world data mean for patients? One patient may finally receive a prescription with more favorable side effects and outcomes. Another might receive more personalized care because their physician was able to see their complete health history, not just what was in their EHR. And the patient in the room next door might be back at the doctor for the first time in years because they received a call from their health plan that noticed there were some gaps in their care.
U.S. adults are the least likely to have a regular physician or place of care, or a longstanding relationship with a primary care provider compared to other high-income countries.1 So, having the data to identify when there is a gap and taking corrective action can greatly impact preventative care and/or early detection.
Patients of all conditions and socioeconomic backgrounds can benefit from large-scale healthcare data analytics. Perhaps the best part is that we are those patients; our families, friends, and communities can enjoy the positive impact that big data makes on their healthcare journey.
What kind of problems can healthcare real-world data solve?
In recent years, healthcare data has mostly provided benefit in time and cost savings, delivering better efficiencies to the groups mentioned above. We’ve seen data systems replace forms and faxes in most areas of healthcare, and during COVID-19, it accelerated identifying outbreaks and vaccine development.
That speed to impact can get faster. We can improve data sharing and transparency to continue breaking down healthcare silos while maintaining patient privacy and data security. We can further advance research, resource savings, and interoperability.
The data is there, the desire for better healthcare data analytics and utilization is there, and the value to be gained is clear.
How real-world data solves healthcare problems
To be more specific, Inovalon delivers data-driven software to solve two key healthcare problems.
Providers working with limited patient data during treatment
For most patients, a physician encounter often begins with confirming their current medications and any changes between their last visit. The patient has to inform the physician – and often, patients don’t know the name of every medication they’re on or they may forget to mention an important detail.
DataStream gives precious time back to healthcare providers by delivering patient health history to authorized providers at the point of care. It fills gaps in the provider’s EHR, eliminating the need to ask repetitive questions because all the information they need is right in front of them. The result is a more accurate view of the patient condition, which enables a better-informed and more personalized discussion with the patient during the encounter.
Clinical trial design and research
Clinical trial design is often extensive and complicated, with 9 out of 10 trials requiring the original timeline to be doubled in order to meet enrollment goals.2
Inovalon’s real-world dataset makes it faster and easier for researchers to create an incredible study design and identify eligible patients. With information on condition, age, race, location, and more, we empower life sciences to better focus on the appropriate subpopulations to drive their recruitment efforts and ensure the trial is clinically diverse.
What are the biggest data challenges in healthcare today?
Privacy and misperceptions.
As eager as many big players are for the future of healthcare data – one in which there’s true interoperability, where we share more data and can gather deeper insights – privacy must be at the core. There are laws and regulations that help uphold data privacy, but these concerns must remain top of mind.
Each decision made by RWD vendors around data access, data sharing, and data collection must undergo extensive review and continuous monitoring of existing data connections.
Misperception comes back to the patient. Some patients may wonder, “what about my data? Who is using it? Who is protecting it?” That’s where HIPAA comes in. HIPAA requires deidentification of patient data to the extent that the data cannot be reidentified.
What healthcare data trends do you think will emerge in 2023?
More sharing of data, data tokenization, and more patient engagement with their data.
Our data connections are just beginning. Many providers can still remember the days they did everything over fax or when a large portion of their day was spent pulling patient records from a file cabinet. We’ve made huge strides in how the industry works, but there is still a vast amount of opportunity for greater connectivity and fewer data silos.
Data tokenization is a method of identifying patient patterns without identifying the patient themselves. Tokenization creates a string of characters that is unique to the data without identifying who the data represents. This allows analysts to match that unique string of characters, or, “token”, with the same token in a different dataset.
Brundage states, “When you think about the way data is used in healthcare, very few people should ever see identifiable patient data – it’s really just the doctors and nurses treating the patient. However, many more people can benefit from data in research, trials, etcetera. They don’t need to know who the patient is, but they are interested in their conditions, their behavior patterns, their outcomes – and tokenized data can tell them that.”
Lastly, as healthcare professionals grow in their understanding of data capabilities, patients will follow. The industry will likely see patients become more engaged with their care journey, which could look like accessing their data through a secure health portal or tracking a certain condition in an app.
Leading the way for the future of healthcare data
As we continue to realize the power of data analytics throughout the industry, one thing is clear: data must be part of everything we do across treatment, research, reporting, and beyond. Even more important, data must be harnessed in a way that is applicable, relevant, and secure.
To learn more about how to harness real-world data and analytics, contact us or visit our Data Cloud page for more information.
1 “Primary Care in High-Income Countries: How the United States Compares,” Molly FitzGerald, Munira Z. Gunja, Roosa Tikkanen, The Commonwealth Fund, https://www.commonwealthfund.org/publications/issue-briefs/2022/mar/primary-care-high-income-countries-how-united-states-compares
2 “25+ useful clinical trial recruitment statistics for better results,” Eian Kantor, Antidote, July 30, 2022, https://www.antidote.me/blog/25-useful-clinical-trial-recruitment-statistics-for-better-results
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