With seemingly endless amounts of data and the constant pressure to achieve data-driven healthcare improvement, health systems increasingly turn to data to solve problems and accelerate change. However, this onslaught of data has left many healthcare organizations questioning whether they use their data effectively to achieve better outcomes.
Health systems must respond to growing data demand and take charge of their data-driven futures. Adopting five data-centric mindset shifts will help organizations successfully apply data to outcomes improvement and track its performance.
#1. Focus on Data Orchestration, Not Data Computing
As cloud-based data platforms become more widely available to healthcare organizations, data interpretation is no longer the primary challenge. Instead, healthcare leaders need to focus on data orchestration: quickly combining data from different sources, then distributing the relevant data sets to appropriate end users throughout the organization.
An effective data platform must orchestrate data, alleviating the burden of data growth by collecting all the system’s data, no matter the volume, in one place: the data platform. The next part of data orchestration is distributing the relevant data insights to team members, enabling them to use the data in their everyday decision making.
#2: Real-Time Data Is Critical in a Pandemic
COVID-19 surfaced a critical need for real-time data from all data sources, both structured (e.g., claims and EHR data) and unstructured (e.g., clinician notes and imaging). As structured and unstructured data sources make the data technology landscape more complex, most organizations must manage both source types to gather timely data. Instead of relying on humans to manually compile data into spreadsheets, which has historically delayed access to real-time data, automation can increase access to real-time data. Such timely data is vital for organizations responding to health crises, such as a pandemic, that require quick decisions in a short timeframe.
#3: Data Governance Is Less About Control and More About Democratization
Health systems have historically been protective of their data largely due to privacy concerns. This defensive mindset has become a roadblock to data sharing. The broader data landscape beyond healthcare has used data to fuel growth and higher levels of efficiency without compromising quality, teaching the healthcare industry that data governance is less about control and more about data democratization.
In short, instead of withholding data from team members, leaders should enable teams with data along with the training they need to use it to make decisions. Giving team members visibility into data, data sources, and algorithms that derive analytic insight also helps team members trust the data, increasing the likelihood they will continue to use it. When team members feel confident using data, organizations achieve data-driven healthcare improvement at every level of the system, not just the top.
#4: If You’re Not Using AI, You Should Be
Internet of Things (IoT) data, namely consumer data from wearables, has vastly expanded the patient information that helps providers capture a more complete picture of a patient’s health. However, health systems can’t fully leverage this IoT data unless they’re using augmented intelligence (AI). Organizations have too many available data sets for humans to manually compute. With the rigor of AI, organizations can quickly make sense of IoT data and separate the value from the noise. AI is the key to making patient data actionable because it allows systems to take advantage of the breadth of IoT and patient-reported data and combine those insights with traditional data sources (e.g., EHR and claims data) for a comprehensive picture of patient health.
#5: Don’t Fit Data into Existing Care Models—Change Care Models to Fit Data
Healthcare leaders typically plug data into existing care models. Although this sounds like a sensible solution, organizations should take the opposite approach and alter their care models to fit their data sources. To take full advantage of a health system’s data and empower team members to work at the top of their license, leaders should identify the most relevant data for the appropriate end users, then alter their care models to support that data delivery. Additionally, this data-first, instead of process-first, approach helps end users, such as clinicians, gain confidence in the data and helps reduce all-too-common clinician data burdens.
Less Talk, More Data-Driven Healthcare Improvement
Data is not going away. It’s only becoming more prevalent in healthcare. To make sense of the growing data and maximize this valuable resource, organizations need to think about data differently, starting with these five data-centric mindset shifts.
The editorial staff had no role in this post's creation.