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Dealing with data chaos: how to mine your data

It’s a common misperception that payers and providers struggle to deliver coordinated care due to a lack of patient data. Thanks to the explosive growth of the health tech industry, most providers are actually inundated with data and their challenges are now around organizing it and extracting meaningful insight. This is particularly true of participants in value-based reimbursement programs, who must demonstrate continual progress in improving patient outcomes. From our experience working with healthcare organizations, we’ve gleaned tips in a few different areas to help payers and providers make sense of the data chaos that is today’s reality and how to leverage that data to improve patient outcomes.

Focus initial efforts on a few key data points that can move the needle on outcomes

There is so much data—data from EMRs, data from claims, data from HIEs, and more—that care coordination teams can easily become overwhelmed in the early days of participation in a value-based program. The teams need guidance on where to focus data collection and analysis efforts. Fortunately, CMS has zeroed in on a few foundational metrics that all organizations should track and measure, consistently inserting them across its suite of programs that span the care continuum. Readmission rates and average length of stay remain the baseline metrics for both acute and post-acute settings. More recently, CMS has also been looking beyond these care settings and creating measures across multiple programs that focus on how patients fare when they are back in their communities.

Once organizations have a handle on these core data points, they can begin to tackle more nuanced metrics that are relevant to their clinical specialties and the value-based programs that they participate in. Many of our clients have found that taking a gradual approach to building a data analytics program allows them to match their efforts to their available resources. On our recent webinar with Mount Sinai, the care coordination team from that health system, which is also an ACO and works daily with multiple payers, walked us through their journey of navigating their data chaos and developing an analytics program to help them assess the quality of their post-acute providers. Although the data points they selected are specific to their organizational goals, the process they used to select these metrics is instructive for any organization that is trying to extract more meaning from its data.

Evaluate your current data sources with an eye toward identifying gaps and limitations

Deciding which data points to prioritize is an important step but only the first. Someone at the organization still needs to aggregate the data from these disparate sources and take stock of the inventory, so to speak. Does the organization have the information it needs? Here are a few areas where many come up short:

  • Information about what’s happening to patients outside of the health system and its referral network: The EHR only shows patient activity at facilities within the system. What about when patients seek treatment at another system’s hospital or an out-of-network SNF? What about when the patient goes home? This is a gap for most organizations.
  • Information on readmissions and length of stay: Claims data, which has historically been a core source of patient data, does provide this information. However, claims data is at least six months old, which limits its value. By the time an organization receives the information, it’s too late to act on it. Current data is lacking.
  • Benchmarking data: What is the context for a hospital readmission rate or the average length of stay in a post-acute facility? A statistic on the anticipated length of stay for patients with a particular condition, for example, has more value than broad statistics that incorporate all patients. Benchmarking data also allows for comparisons between providers.

Invest in resources that fill the gaps and deliver the data that matters most

After identifying the metrics that matter and determining what’s missing from their current data sources, an organization usually needs some additional resources to organize the data it has decided to focus on and to fill in gaps. There are a number of different ways to attack these tasks and start to shape a strong data analytics program. Most payers and health systems choose to bring on a full-time data scientist. An FTE is certainly not a mandatory requirement, however. Many smaller payers and systems can benefit from a collaborative data analysis effort taken on by the care management team, which already has a deep investment in tracking patients and understanding trends across the population. In some cases the leadership of a payer or health system provides guidance on the most important metrics and dictates the measurement methods, allowing the care management team to focus on execution.

In addition to people resources, organizations need technology that supports their efforts to organize existing data and fill in data gaps. CarePort’s suite of care coordination tools helps a wide range of healthcare organizations –payers, health systems, hospitals, ACOs, and post-acute providers—obtain real-time data. It isolates the metrics that matter and provides that critical data along with the clinical context needed to extract meaningful insight.

Learn more about CarePort’s suite of EHR-agnostic care coordination solutions that support organizations developing a data-driven approach to patient care.

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