Shweta Vyas: I see four major challenges:
Volume – Health systems generate a ton of data, but they don’t always know what data is meaningful for analytics and achieving value-based care. IT teams oversee data management, but no one within the organization is typically charged with determining who will use the data and how it will be governed or operationalized within the organization.
Accuracy – The adage, ‘You don’t know what you don’t know’ applies. Hospitals and health systems often have no idea that their patient and provider data is plagued by gaps and inaccuracies. This causes challenges with care coordination, revenue cycle management and more. The good news is that vendors can easily run diagnostics to analyze, validate, correct, monitor and augment the data.
Aggregation – Data is siloed and sometimes even managed within rudimentary spreadsheets. Hospitals and health systems need to pull all of the data together so we have a holistic, system-wide view to drive analytics for business intelligence.
Linking – Aggregating the data is only useful when organizations can also perform entity resolution (i.e., determining whether all pieces of data are tied to one patient or one provider). Data linking is critical as providers become more consolidated through mergers and acquisitions.
Has the shift to value-based care increased the focus on data management? If so, how?
Yes, I believe there is an increased focus on data management now that there are financial incentives to enhance healthcare quality. I don’t think you can improve quality, risk and patient outcomes without a holistic view of your patients and providers. However, many challenges remain. Organizations need a disciplined approach to data governance, data management, and data linking. They also need to overcome their ‘analysis paralysis’ and not let the sheer volume of the data overwhelm them. The industry has made great strides thus far, but we’ll likely continue to grapple with some of these challenges for years to come.
What advice can you give in terms of determining whether an organization’s health IT infrastructure can support the type of analytics that value-based care demands?
Consider these three questions:
• What are your goals and objectives? For example, what outcomes do you want to improve? On which patient populations do you want to focus? What cost outliers do you want to address?
• Can certain aspects of your existing IT infrastructure remain? Organizations may opt to keep their existing EHR, for example.
• What new elements will provide the biggest return on investment? For example, in-house data centers are becoming a thing of the past. Using a flexible data warehouse (i.e., the cloud) enables organizations to break down data silos without adding significant dollars to the IT budget. Using the cloud, we can also provide actionable insights within existing workflows and even at the point of care. Organizations will also need to invest in robust cybersecurity solutions as we move toward more digital interactions in healthcare.
Can you offer any predictions regarding data analytics and value-based care?
Consider the following five predictions:
More mature analytics. Organizations will use greater predictive analytics not only for population health management but also for managing provider networks and containing costs. They’ll also continue to experiment with artificial intelligence and machine learning to build better data models, create smarter algorithms and automate repetitive (and resource-intensive) tasks.
Industry-wide source of truth. Because of continued challenges with data management as discussed above, providers, payers and pharmacies will rely on a centralized, and single source of truth for cleansed and accurate data on patients and providers.
Greater emphasis on social determinants of health (SDOH). Medical care determines only 20 percent of overall health while social, economic and environmental factors determine 50 percent of overall health. Organizations will increasingly collect and analyze SDOH data, and they’ll need the ability to feed actionable insights directly into the care plan.
More efforts to combat provider burnout. The industry will find ways to streamline data entry on the front end and surface important insights at the point of care so providers can easily digest the information and act on it. This may not only improve physician satisfaction, but it may also address the problem of alert fatigue.
Integration of patient-generated health data. This important data feed will help the industry move beyond the notion that healthcare happens inside the four walls of a hospital, physician practice, or other clinical setting. However, we still need to question the accuracy of the data. Data from FDA-approved medical devices, for example, may be more reliable than data from consumer apps and wearables, though all of this data can help paint a picture of an individual’s health status.
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