A shift is occurring in how healthcare organizations use data to inform every aspect of their businesses. Planned investments in data sharing, management, and governance along with advanced analytics signal that healthcare leaders are preparing for more advanced uses of their data.
Predictive analytics is one area leaders are watching closely as early adopters begin to realize the technology’s potential to revolutionize healthcare and, more specifically, care delivery. Predictive consumer data in healthcare has the power to guide everything from consumer engagement to enhancing the health and well-being of patients and communities.
As organizations prepare to springboard out of planning and into implementation, there are five key areas where healthcare leaders can expect predictive consumer data to have a major impact.
1. Patient-centered, whole-person care
Leading organizations are already aggregating electronic health records (EHR), biometric, claims, and other data to predict the likelihood of disease for both individuals and broader patient populations. These insights combined with social determinants of health (SDOH) data and healthcare consumer preferences can transform how providers engage with patients and tailor care to address their individual needs.
Outcomes:
- Improved patient experience, consumer loyalty, and clinical outcomes
- Consumer Assessment of Healthcare Providers and Systems (CAHPS) improvements
2. Enhanced quality and safety
Industrywide drops in quality and safety scores driven by the pandemic continue to be exacerbated by workforce exhaustion and shortages. Organizations can rebound from this and build a culture of safety by making safety data accessible across the system and using predictive analytics to assess clinical variation and identify at-risk patients.
Outcomes:
- Decreased unwarranted variation
- Lower mortality rates and readmissions
- Improved patient safety
3. Network adequacy and resource allocation
Data helps organizations manage operations and coordination between acute and ambulatory settings to better understand patient utilization and capacity. Network analytics can be used to determine the next sites of care before applying it to workflow and deployment, enabling leaders to more effectively manage resources and match staffing levels to patient volume.
Outcomes:
- Cost savings through increased efficiency
- More seamless patient experiences
- Stronger consumer loyalty
- Increased data volume to drive new care models
4. Engaging patients across the continuum
Leading intelligence platforms provide organizations with longitudinal records of care that pinpoint where patients go for primary care, urgent or emergency care, specialty clinics, and other services. These insights help leaders understand how patients move throughout their systems, revealing gaps in the patient journey and vulnerable network leakage points.
Outcomes:
- Care optimization
- Patient retention
- Improved clinical outcomes
5. Advancing health equity agendas
Providers can influence the health of patients and communities by using data to understand nonhealthcare, unmet needs of consumers. Predictive data can be used to determine patients' risk across SDOH domains and identify potential disparities in health outcomes.
Outcomes:
- Mission fulfillment
- Healthier communities
- Effective public-private relationships
The Importance of Real-Time Data
While predictive data can provide the insights to enable healthcare organizations to deliver superior care, improve patient outcomes, and recognize network leakage, real-time use of consumer data is what will truly transform care delivery.
Evaluating data assets on a three-to-five-year schedule is not enough; leading organizations are urgently incorporating data insights at both the strategic and daily execution levels.
And even if real-time data is collected, it must be effectively distributed throughout an organization. Working from identical, real-time data assets across an enterprise is essential for understanding and stratifying the health status of both individuals and populations. Implementing the right cloud-based enterprise data management tool can mitigate this issue and should be a best practice for healthcare organizations.