The health IT revolution is here and 2016 will be the year that actionable data brings it full circle.
Opportunities to achieve meaningful use with electronic health records (EHRs) are available and many healthcare organizations have already realized elevated care coordination with healthcare IT. However, improved care coordination is only a small piece of HIT’s full potential to produce a higher level synthesis of information that delivers actionable data to clinicians. As the healthcare industry transitions to a value-based model in which organizations are compensated not for services performed but for keeping patients and populations well, achieving a higher level of operational efficiency is what patient care requires and what executives expect to receive from their EHR investment. This approach emphasizes outcomes and value rather than procedures and fees, incentivizing providers to improve efficiency by better managing their populations. Garnering actionable insights for frontline clinicians through an evolved EHR framework is the unified responsibility of EHR providers, IT professionals and care coordination managers – and a task that will monopolize HIT in 2016.
The data void in historical EHR concepts Traditionally, care has been based on the “inside the four walls” EHR, which means insights are derived from limited data, and next steps are determined by what the patient’s problem is today or what they choose to communicate to their caregiver. If outside information is available from clinical and claims data, it is sparse and often inaccessible to the caregiver. This presents an unavoidable need to make clinical information actionable by readily transforming operational and care data that’s housed in care management tools into usable insights for care delivery and improvement. Likewise, when care management tools are armed with indicators of care gaps, they can do a better job at highlighting those patients during the care process, and feeding care activities to analytics appropriately tagged with metadata or other enhanced information to enrich further analysis.
Filling the gaps to achieve actionable data To deliver actionable data in a clinical context, HIT platform advancements must integrate and analyze data from across the community—including medical, behavioral, and social information—to provide the big picture of patient and population health. Further, the operational information about moving a patient through the care process (e.g., outreach, education, arranging a ride, etc.) is vital to tuning care delivery as a holistic system rather than just optimizing the points of care alone. This innovative approach consolidates diverse and fragmented data in a single comprehensive care plan, with meaningful insights that empowers the full spectrum of care, from clinical providers (e.g., physicians, nurses, behavioral health professionals, staff) to non-clinical providers (e.g., care managers, case managers, social workers), to patients and their caregivers. Armed with granular patient and population insights that span the continuum, care teams are able to proactively address gaps in patient care, allocate scarce resources, and strategically identify at-risk patients in time for cost-effective interventions. This transition also requires altering the way underlying data concepts are represented by elevating EHR infrastructures and technical standards to accommodate a high-level synthesis of information.
As clearly identified in the PCAST Report on Health Information Technology (2011), and as echoed in the recent GAO report Electronic Health Record Programs — Participation Has Increased, but Action Needed to Achieve Goals Including Improved Quality of Care (2014), healthcare continues to have a data problem. The country has invested significantly to advance EHR adoption.
In simpler terms, healthcare data is messy and makes for building of accurate, actionable metadata a problem. It’s clear that the next generation of standards that are being developed by the numerous committees and acronyms and professional societies tackling measure development, harmonization and testing will now need to address the relevance of each measure.
More than a decade ago, a coalition of purchasers, payers and providers came together across Wisconsin to form the Wisconsin Collaborative for Healthcare Quality (www.wchq.org). Groundbreaking initiatives like Get with the Guidelines, Leapfrog and JCAHO revealed that “quality” and “healthcare” could be used in the same sentence (or displayed on a website). These efforts were largely inpatient-focused. Measurement in the outpatient setting, long considered the keystone of payment reform, was an unsolved riddle. WCHQ, at the urging of the IOM, IHI and others accepted the challenge of tackling performance in the ambulatory arena.
At the direction of some very engaged employers, and with input from most of the state’s payers, WCHQ was charged with one very simple goal — apples to apples quality measurement, regardless of health IT infrastructure. The focus had to include both processes of care and outcomes. Oh, and if health systems didn’t have any health IT in place, data still needed to be included for these groups in the measurement effort. What transpired over an 18-month period was remarkable. With unwavering support from administrative and clinical leadership, health systems rolled up their sleeves and dug into their very messy data. Each Monday, we would devise a fiendish list of new tasks to be completed in the next four business days.
Guest post by Michelle Blackmer, director of marketing, healthcare, Informatica.
Several weeks into the New Year, our fitness resolutions are still top of mind. Whether tracking calories or steps, we are asking ourselves questions like “how many pounds have I lost?”, “how many calories did I eat?” and “how many steps did I take?” To take the guesswork out of it and to hold ourselves accountable, many of us put a Fitbit, Nike Fuel or Jawbone on our wish lists. Our physical fitness has become data-driven; these devices create data that provide insight, enable us to visualize patterns and generate millions of bytes of data, which helps account for the anticipated annual 40 percent growth in big data. However, this is only the tip of the iceberg for data-driven healthcare.
Health information leaders must continue to assess their business resolutions and take stock of their healthcare data fitness. This is especially important since an alarming 40 percent of healthcare executives gave their organizations a grade of “D” or “F” on their preparedness to manage the data deluge. What’s more is that none felt their organization deserved an “A.”
Successful transformation to value-driven care requires an investment in enterprise information management. However, healthcare organizations are tightening their belts and bracing for the hit to their bottom lines in response to the health reform law that took effect on January 1, 2014. Instead of scaling back, healthcare organizations must invest in the fitness of their data. After all, if the wrong data is analyzed (i.e., inaccurate, incomplete, missing or even unnecessary), organizations are going to make the wrong decisions. What is the cost of making the wrong decision?
Assess your data fitness. Ask yourself the following questions: