How digital health data is improving patient outcomes across healthcare
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The list of what data can do goes on – but healthcare cannot realize the full value of data without addressing the challenges holding us back as an industry.
Data remains siloed across the care journey. In many cases, it is not standardized, and sometimes, not even accurate. Advancements in care coordination, patient encounters, and research are dependent on progress in how we collect, store, share, and make sense of massive amounts of data.
So, what does the future look like? What can healthcare leaders do to fix broken data systems and truly harness the power of data? This blog explores how the current digital data landscape is benefiting patients, and emerging healthcare data trends to watch.
Understanding the healthcare data landscape today
While there are flaws in healthcare data utilization that must be addressed to further advance patient outcomes, it’s worth noting the significant impact that digital data has across healthcare today.
Quality measurement and value-based care
The use of digital health records and the incentives available for health plans and providers via value-based care have contributed noteworthy progress to healthcare in recent decades.
Value-based care brings payers and providers together with a focus on common goals. It gives both parties a means to track their performance against specific categories, such as identifying gaps in patient care, effective patient outreach, and accurate care documentation.
In a successful value-based care contract, the payer is managing data from multiple sources to deliver actionable insights to their provider partner. This may look like flagging inaccurate documentation or identifying a group of patients who have missed a checkup or have not been adhering to their care plan. Such insights can be invaluable to the success of a contract – and more importantly, the wellness of many patients – but the trick is for payers to have the right data and the appropriate resources to derive insights from it.
Without reliable measurements, value-based care is simply a theory. It’s only when the right data is applied to the right population, from accurate, reliable sources, that the intended impact of these incentives can be realized across member populations.
Population health analytics
Population health analytics help predict patient risk at scale, improve resource allocation across healthcare, and inform quality improvement initiatives.
By analyzing a combination of claims data, patient sociodemographic data, and clinical data, analysts can get a clearer picture of patient populations vs. what just one data source can show. Research in this space can impact many aspects of the care journey, from how to improve access to care to supporting drug development for new, better treatments.
Population health analytics is only limited by the amount and accuracy of the data available, and the methods applied to aggregate, standardize, and analyze it. The more that analysts can improve in these areas, the greater impact population health insights can have.
How the new era of digital health data is improving patient outcomes
As value-based care and population health analytics demonstrate, there is infinite potential to what healthcare data can help us accomplish – the key is knowing how to tap into its potential, while balancing time and resources.
Here are 3 examples of the “new age” of digital health data:
Standardization and consistency
The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is improving healthcare research standards, methods, and reliability by standardizing the way data is transmitted, received, and interpreted.
Essentially, OMOP empowers researchers around the world to “speak” the same data language. With various datasets available in an OMOP-ready format, it’s much faster and easier to combine data sources, increasing the speed in which teams can derive meaningful analytics.
OMOP is also improving data accuracy, since there is less room for human error when combining datasets that are in the same format as opposed to when data has to be normalized, then aggregated.
All these benefits during analysis lead to the most important impact: when the research makes a positive influence in patient outcomes. Decreasing the time it takes to determine the efficacy of a new drug, identify barriers to care in certain populations, or compare the outcomes of two similar treatments means that more people can benefit from key findings such as this.
Interoperability
Interoperability is the answer to data silos. It’s a key area to focus on in order to improve patient outcomes with data.
Establishing stronger connections across healthcare will enable greater data sharing, transparency, and even accuracy – while replacing outdated, manual healthcare data collection workflows with reliable technology.
Interoperability can eliminate the need for providers to collect the same data more than once, or for patients to spend more time filling out forms than actually speaking with their provider. This means greater data accuracy, and across encounters, better interoperability can save time in a provider’s day while improving patient satisfaction and decreasing risks during treatment.
For health plans, interoperability enables seamless data flow between plans and providers, helping ensure patient data is accurate, up to date, and easily accessible when needed. For patients, this means a better care experience and may even increase the likelihood of them adhering to their care plan, because they’re more engaged in the process by both their health plan and their provider.
Additionally, for life sciences and pharmaceutical companies, interoperability can unlock a newfound level of data access and visibility to inform research across a wide array of therapeutic areas. Some large organizations are already tapping into this by establishing their own real-world data centers. The next step for our industry is to connect these massive data repositories together, to truly realize interoperability in practice and unlock its full potential.
Innovation
Innovation from fax machines and printed forms contributed greatly to the state of healthcare today. However, there is still more progress to achieve as we continue to understand what healthcare data can help us do.
One of the biggest opportunities to advance digital health data is finding new ways to securely connect different data sources. This is different than interoperability, which could be applied across various systems and industry needs. Connecting data sources is more focused; it’s often related to specific research needs, and no two studies are the same.
For example, for too long, researchers avoided combining claims data with any other forms of data due to patient privacy and data security concerns. With the innovation of secure data linking practices such as the Safe Harbor and Expert Determination de-identification methods, researchers can expand their data visibility beyond what was previously possible.
This has contributed to studies such as:
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- Understanding Treatment Patterns in Patients Newly Diagnosed with Schizophrenia with Commercial Versus Medicaid Insurance Coverage (ISPOR 2022)
- Z Codes Can Provide Important Data on Social Determinants of Health and Improve Care: How Can We Improve Utilization? (Inovalon Research and Publications)
- Comparative Effectiveness of Carbidopa/Levodopa Enteral Suspension and Deep Brain Stimulation on Pill Burden Reduction in Medicare Fee-for-Service Patients with Advanced Parkinson’s Disease (Springer)
As more research teams harness new data linking opportunities, there is no telling how impactful their findings can be.
Improve patient outcomes with healthcare data supplementation and analytics from Inovalon
Digital health data is the key to the future. But researchers can’t unlock the power of data without the right amount and the appropriate support to analyze massive amounts of information.
Inovalon can you capture insights, improve outcomes, and convert your data to evidence. Ask our Data Solutions team how we can support your real-world evidence needs.
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