Ben Alexander, Vice President, PHSO Product and Solutions and Chief Medical Officer, Population Health Analytics
The healthcare industry is abuzz with talk about how information technology is transforming the practice of healthcare. Indeed, health systems, payers, and other players are gathering copious amounts of data that hold the promise of improving not just patient outcomes, but driving population health management and lowering the cost of care.
While the push toward value-based care has put providers and health systems on the front lines of this data revolution, healthcare organizations are discovering that technology alone will not get the job done. Data is only useful if it’s comprehensive, meaningfully analyzed, and disseminated to the right people at the right time—that is, the data is actionable. Many providers are struggling to build their capacity to utilize “Big Data” to their patients’ and their organization’s advantage. Here are some of the most challenging tasks at hand.
1. Aggregating data across the ecosystem
Tracking and managing population health requires creating a core system of record at the patient and population level. This task first requires identifying the types of data needed to build that ecosystem and the sources of that data. The difficulty lies in collecting the data from multiple sources, which often reside both inside and outside of provider group or health system data warehouses. The various internal and external sources often use different formats and have varying frequencies by which they update their data. Obtaining external data also requires creating carefully designed agreements with outside parties, such as payers, pharmacy benefit managers, independent providers, and labs. In addition, organizations must have the infrastructure and flexibility to incorporate newer data sources as they become relevant, such as social determinants of health data, social service referral platforms, and consumer engagement tools. Provider organizations and health systems are often challenged with allocating sufficient resources to manage these types of data activities at scale, often lacking automation capabilities to produce actionable system-level analytics.
2. Piecing together meaningful information
Data aggregation and analysis underpin both patient and population health management. Unfortunately, aggregating data from multiples sources that use varying formats creates many opportunities for error. At its core, the information system must be able to properly identify patients and match their data from different sources. It must attach all of the data for John Smith to the right John Smith and, for example, be able to link a lab encounter with the corresponding insurance claim, even though the provider and insurer might use different data languages. The system must also be able to identify incomplete, inaccurate, or duplicate data. Inaccurate data can sour physician opinion of patient management efforts and erode trust, and faulty data could result in cost or quality improvement initiatives based on incorrect assumptions.
3. Keeping it relevant
The information system must enable analytics with reporting that users want and need. The information must be user-friendly, timely, and actionable when it matters most, during the clinical encounter. For example, a list of patients who need preventive screenings drawn from three-month-old data is outdated by the time it reaches a care team responsible for reaching out to those patients.
In addition, decisions must be made about who gets what information, when, and where. Physicians may need monthly data comparing their individual performance with others in their group and their network, and in relation to the organization’s operating goals for the year. An executive, by contrast, might need a higher-level view delivered through quarterly reports with a different set of security requirements. Data should streamline workflows, enhance efficiency, and help answer questions that drive day-to-day operations of patients, practices, and systems.
4. Tracking financials
Value-based contracts incentivize providers to meet quality and cost goals, which means data cannot focus solely on clinical matters. Doctors need to get a full financial picture that includes accurate information on reimbursement, utilization, and costs. They need information that provides insight on how their behavior affects the cost of care, their incentives, and their overall financial and operating performance.
Navigating in a time of change
The work of building an information system capable of supporting the transformation necessary to make good on the promise of value-based care doesn’t happen overnight. Many providers and health systems don’t have the necessary resources, experience, or bandwidth to accomplish the task in a timeframe that matches the push for change posed by emerging industry disruptors.
Healthcare organizations that want to avoid the steep learning curve and speed the process can enter into an operating partnership with an entity that has a track record working hand-in-hand with providers throughout this complex, multi-step endeavor. Systems need to create a health IT strategy that aligns with the operational and programmatic needs for delivering value-based care.