5 Steps to Achieving a Mature Enterprise-Class Quality Program
Jeffrey Springer, Sr. VP of Healthcare Solutions for CitiusTech
The game is changing faster than ever as more payer contracts and regulatory programs adopt risk-based models. To be successful, payer and provider organizations know they must increase quality scores and revenue while reducing avoidable medical costs. Yet, the providers who are critical to that success are burned out. According to a recent survey, nearly one-third of providers say their biggest frustration is constant “busy work,” such as electronic health record (EHR) data entry and prior authorizations. Instead of adding to these burdens, organizations need to build an environment that simultaneously reduces overhead, reaches targets and improves relationships with all stakeholders, including providers, administrators, and patients.
The difference often boils down to eliminating short-sighted, tactical approaches that do not support a mature and effective environment for quality improvement. While achieving maturity may seem daunting, it’s attainable when viewed as an evolutionary process. With five critical steps in mind, organizations can assess where they are along the path from tactical to strategic and create a roadmap that continually improves scores while meeting revenue targets for value-based programs – all while enabling providers to focus on caring for patients.
Step 1: Dissolve Data Silos
There is no shortage of valuable data available from a wide range of sources. The challenge is managing it all effectively. For example, a large integrated delivery network (IDN) purchased three functionally equivalent data warehouses, plus seven individual analytics solutions. It’s easy to imagine the user confusion, IT overhead, and lack of integration that ensued. Unfortunately, most organizations take a tactical approach and add a new repository for each new initiative.
With a mature program, data silos disappear. The strategic approach employs a single data strategy with strong governance built around a common organizational language. To set a course, assess from an organizational perspective: How many data repositories do you manage? Are there separate data sources for clinical, financial and operational reporting, regulatory programs, and contractual agreements? Also consider that regulatory programs require data to be separated. For example, stakeholders who are not involved in an accountable care organization (ACO) contract cannot see the financial data for at-risk patients. Creating a robust data strategy requires keen attention to complex data security and privacy needs.
Step 2: Coordinate Across Programs
As healthcare organizations become more intentional about engaging with patients and providers, coordination is an area that is often overlooked. For example, many organizations take a tactical approach and create specific outreach and engagement models for each program. However, the outreach typically occurs reactively when a gap is discovered, resulting in multiple contacts, and ultimately increasing costs, while failing to prevent avoidable ED visits and admissions.
As an organization’s program matures, they coordinate multiple programs and initiatives and bundle all touchpoints into one conversation or scorecard. Plus, each person engaging with a patient or provider has insight across all programs, along with the steps required to achieve program goals. Progress starts with assessment: How many times has each provider been approached? Does each communication bundle multiple initiatives? Adopt the consumerism mindset common in other industries to coordinate the key request for each program as well as the next step, so follow-ups are consolidated.
Step 3: Curate Data Across the Enterprise
With vast amounts of data from many disparate sources, trust quickly becomes a concern. Stakeholders, including providers and executives, are often frustrated when data seems wrong or doesn’t reflect program definitions. Tactical decisions based on up-front costs or return on investment metrics lead organizations to limit data to the types needed to answer only specific questions. As the program matures, enterprise data from a wide range of traditional and newly available sources resides in a single well-governed source that assures consistently defined terms and concepts.
Organizations progress toward this strategic approach by assessing: Do business users have separate sources for medical management, care management, registry, contractual and regulatory views of the patient? With today’s late-binding architecture, organizations can take only the data needed for a question, metric or pattern, and then curate additional data as needed. This opens the door for going beyond the standard healthcare sources, such as claims, HL7, and CCDA, to also include historically cost-prohibitive data, such as social media, benefit information and unstructured data. These data sets can be leveraged to determine the most effective engagement models, risk patterns and communications.
Step 4: Add Perspective to Measurement
Once organizations consistently measure key quality metrics, they often find that considering only short-term quality scores falls short for creating ongoing improvement. While scores are important, identifying a provider who is 55 percent compliant reveals a problem, but does not drive change.
In a mature program, a strategy for organizational learning enables effective engagement with patients and providers that anticipates and solves problems while supporting ongoing improvement. The key is to build measurement and intervention within workflows by asking: When should the provider be involved versus leveraging administration, care management, coding, and medical resources?
This leads to alignment across processes. For example, providers often perform the correct actions for a value-based contract, such as foot and eye checks during a routine diabetes management visit. However, medical coders are trained for fee-for-service, so specific actions can often be coded incorrectly, resulting in apparent care gaps months later. When coding practices are aligned with value-based contracts, coding accurately documents adherence to care protocols.
Step 5: Engage Proactively
Today, most payers and providers look at regulatory results only after the reporting period is over. HEDIS is structured this way. Not only are regulatory programs reactive, but many population health systems and data warehouse solutions use data from yesterday, last week or even last month to determine gaps and engagement models. This misses the opportunity to touch a patient or process once and solve the problem. What’s more, providers are already short on time and struggle do this work within today’s workflows.
With a mature program, interaction is no longer reactive. Organizations can use technology and processes to proactively supply information to the appropriate stakeholders at the right time to ensure gaps are understood, closed and documented.
Begin by asking: Is there a coordinated plan in place between payers and providers that includes sharing gaps, data, and resources? Use this insight along with technology to structure workflows that enable front and back-office administrators and care managers to provide the documentation. In parallel, the provider workflows facilitate quick attestation of actions, so they can focus on patient care.
Maturing the State of Healthcare
Healthcare is at a critical juncture. As it evolves beyond the tactics that currently create gaps, inefficiencies, and inaccuracies, a strategic approach to enterprise-class quality management becomes more pressing. To be successful, the focus must be on operationalizing what the patient needs with care processes, quality metrics, coding, and communications among stakeholders. All while assuring providers have the right amount of time to ensure all the right things happen for each patient. As organizations enter more at-risk agreements, they must evaluate their organizational maturity to achieve the results they need to not only stay in business but to thrive under the new business models healthcare will require in the future.
Step
Tactical Approach
Structured Approach
Dissolve Data Silos
Add a new repository for each new initiative.
Employ a single data strategy with strong governance.
Coordinate Across Programs
Create specific outreach and engagement models for each program.
Coordinate multiple programs and initiatives that bundle all touchpoints into one conversation or scorecard.
Curate Data Across the Enterprise
Data is limited to the types needed to answer specific questions.
Enterprise data from a wide range of traditional and newly available sources resides in a single well-governed source that assures consistently defined terms and concepts.
Measure with Perspective
Consider only short-term quality scores.
An organizational learning approach enables effective engagement with patients and providers that anticipates and solves problems while supporting ongoing improvement.
Engage Proactively
Use historical data to determine gaps and engagement models.
Use technology and processes to proactively supply information to the appropriate stakeholders at the right time to ensure gaps are understood, closed and documented.
About Jeffrey Springer – SVP of Healthcare Solutions at CitiusTech
Jeff Springer has been associated with CitiusTech for the past 4 years, driving product management, business analysis and product strategy for all products and solutions. He also currently leads an industry workgroup with WEDI focused on ACO payments. With over 20+ years of healthcare industry experience, Jeff has worked with some of the industry’s most leading healthcare technology vendors.
Prior to CitiusTech, he led the product management and strategy for analytics at Siemens and care management and analytics at MEDecision and also ran a business unit at McKesson to develop new products working with payers and providers. He is also the founder of the first payer-provider contract management company in the U.S.
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Source: 5 Steps to Achieving a Mature Enterprise-Class Quality Program