From the Consultant’s Corner 2/6/13

There’s No Time Like the Present: Shifting to a Core Vendor Before ICD-10 Implementation
By Brad Boyd

Is your organization thinking about transitioning your patient access, EHR and/or revenue applications to a single core vendor? If you are, you are not alone. This has been a strong industry trend over the past three years, and many IT vendors have made this a development priority.

With healthcare reform ushering in initiatives like Meaningful Use, accountable care organizations, and value-based reimbursement models, technology systems that smoothly integrate clinical and business data are essential. In most cases, a core vendor that provides tight integration across these applications is better positioned to meet a healthcare organization’s business intelligence requirements.

I recommend that organizations evaluate the benefits of a core vendor from feature, functionality, total cost of ownership, and reporting perspectives. Truly integrated systems provide a variety of benefits which often include decreased operating expenses, reduced lost charges, improved reporting, and a rich functionality which can enhance the overall patient experience. However, with the benefits of a core vendor often come trade-offs from a best-of-breed perspective, particularly gaps in workflow capabilities.

Given the delay in ICD-10 implementation, now is an opportune time to engage in a focused assessment on long-term vendor strategy. While a system conversion is not an insignificant undertaking, there are efficiencies and cost advantages to make this switch in tandem with other changes required by ICD-10.

While switching to one core vendor may not be appropriate for your organization — or resource availability may not permit tackling both ICD-10 and a system conversion concurrently — examining the benefits, cost impact, and operational readiness is a valuable exercise. The delay in ICD-10 provides you with an opportunity for this analysis.

Brad Boyd is vice president of sales and marketing for Culbert Healthcare Solutions.

Guest Post: Committing to an ACO Model: Factors to Consider Before You Take the Leap

Just as value-based care can take many different forms, every path to prepare for this transition itself is unique. Some practices are moving into full-fledged ACOs quickly. Others are testing the waters with hybrid models and pilot programs.

Before taking a bold step toward accountable care, you should consider the following success factors:

Leadership and internal culture

To manage this transformation, your organization must be able to attain buy-in at all levels. This begins with a leadership team that is aligned with the vision and willing to invest in it. Leaders must spearhead change management efforts and effective internal communications. Driving quality and innovation will also require input from all staff, both clinical and non-clinical. This will be easier to accomplish if your culture already promotes collaboration and openness to new ideas.

Technology and infrastructure

If your practice uses basic electronic medical records (EMRs), there are additional investments ahead. Actionable patient data will be the driving force of improved quality of care. Rather than offering volumes of patient data to providers, information must be targeted and meaningful.

There is much focus on the need for HIE technology in an ACO or population-based care management initiative. However, first-generation HIE technology, which simply aggregates data, will not suffice. To support collaboration and decision-making for your panel of patients, providers will need advanced capabilities that enable the sharing of meaningful and complete data across the care continuum. This actionable data combined with analytics can also be used to create dynamic care plans that offer real-time insight for the care team and your patients.

Technology can empower your patients to make better healthcare decisions. Social media, mobile applications, and online tools are all effective outlets to engage patients. These resources can also help reduce network leakage and optimize utilization.

Level of clinical integration

Clinically integrated networks (CINs) are a strong foundation for ACOs. CINs can easily promote shared protocols, efficiency goals, education, and training. If your practice isn’t ready to move directly into a full ACO, this structure can be a good starting point.

Population health management expertise

ACOs must be able to stratify patients by risk. Based on this data, your practice can develop strategies to manage those with the highest risk / costly health conditions. Once patient data is matched with the latest clinical standards, new ways to improve outcomes may be found. This analysis of population health will also provide a good baseline for your goals around quality and measuring progress.

If your organization lacks expertise in this area, you should consider working with a health plan that has proven expertise. Care coordination payments from these health plans can help you plan for these infrastructure investments.

Market growth potential

Practices in a highly competitive market will likely be motivated to take on risk in hopes of greater rewards. Market growth will also be an important strategy to offset reduced utilization. Organizations that can readily attract and retain patients will have a distinct advantage.

Whether you’re creating a full ACO or a pilot program, these factors should be considered prior to launch. By preparing to face these challenges now, your practice can position itself for a sustainable future.

Bruce Henderson is head of Integrated Solutions, Aetna Accountable Care Solutions.

Bowtie Confidential 1/11/13

Data Governance in Practice

In today’s complex healthcare environment, data governance is an emerging discipline with an evolving definition. The discipline embodies a convergence of data quality, management and policy, business process, and risk management. Through data governance, organizations are exercising control over the processes and methods used by their data stewards and custodians. It is important that data governance, management, and architecture be seen as more than an IT responsibility, but also as the responsibility of end users.

Data governance is a quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information. It is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions, using which methods, with what information, and under what circumstances.

In other words, data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. It allows people to trust the data, and holds people accountable for any adverse event related to data quality. Governance is about putting people in charge of fixing and preventing data issues so that the enterprise can become more efficient.

Data governance also describes an evolutionary process for a company, altering the company’s way of thinking and setting up the processes to handle data so that it may be utilized by the entire organization. It’s about leveraging technology when necessary to help aid a process. When companies gain control of their data, they empower their people, set up processes, and get help from technology to do it. Goals may be defined at all levels of the enterprise and may aid in acceptance of processes by those who will use them. Some goals include:

• Increasing consistency and confidence in decision making
• Decreasing the risk of regulatory fines
• Improving data security
• Maximizing the income generation potential of data
• Designating accountability for information quality
• Improving planning by supervisory staff
• Minimizing or eliminating re-work
• Optimizing staff effectiveness
• Establishing process performance baselines to enable improvement efforts

These goals are realized by the implementation of data governance programs, or initiatives using change management techniques.

While data governance initiatives can be driven by a desire to improve data quality, they are more often driven by external regulations. Examples of these regulations include Sarbanes-Oxley, Basel I, Basel II, HIPAA, and a number of other data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes to govern data subject to regulations. Successful programs identify drivers meaningful to both supervisory and executive leadership.

The most common theme among the external regulations is the need to manage risk. These risks can be financial misstatement, inadvertent release of sensitive data, or poor data quality used to make key decisions. The proliferation of regulations and standards creates challenges for data governance professionals, particularly when multiple regulations overlap the data being managed. Organizations often launch data governance initiatives to address these challenges.

Understanding what data governance is not can help identify what it is. In particular, data governance is not:

• Change management
• Data cleansing or extract, transform and load data (ETL)
• Data warehousing
• Database design

Data governance applies to each of these disciplines or processes; however, it is not included in any of them.

Historically, data has been collected and managed at the individual department level for its own needs. Each department has developed procedures, formats, and terminology that fit its unique situation and preferences. Without the need for integration or exchange data, inconsistencies are harmless.

Today, however mission goals and legal mandates both require large organizations to report on their activities at the enterprise/organization level. This means that such organizations need to:

• Migrate data from legacy systems into new systems and formats
• Integrate and synchronize data from varied systems that use different formats, field names, and data characteristics
• Reconcile inconsistent or redundant terminology into a single data dictionary providing agreed upon definitions and properties for each element
• Report data in standard formats with standard interpretations

Data governance is a component of data management. It provides and enforces enterprise-wide data standards, common vocabulary, and reports and promotes the development and use of standardized data. It enables the organization to more easily integrate, synchronize and consolidate data from different departments, exchange data with other organizations in a common format, and communicate effectively through shared term and report formats. (Please see figure below for graphical representation)

The implementation of a data governance initiative may vary in scope as well as origin. Sometimes an executive mandate will arise to initiate an enterprise-wide effort. Other times, the mandate will be to create a pilot project or projects, limited in scope and objectives, aimed at either resolving existing issues or demonstrating value. The data governance initiative may originate lower down in the organization’s hierarchy, and will be deployed in a limited scope to demonstrate value to potential sponsors higher up in the organization. The initial scope of an implementation can vary greatly as well, from the review of a one-off IT system, to a cross-organization initiative.

With data governance, an organization can strategically focus on improving data quality, protecting sensitive data, promoting the efficient sharing of information, providing trusted business-critical data, and managing information throughout its lifecycle.

Data governance enables organizations to convert enterprise data into a strategic asset that can be used to create competitive advantage and drive economic value. It can improve financial performance, increase operational effectiveness and efficiency. and allow full compliance with regulatory requirements.

Rob Drewniak is vice president, strategic and advisory services, for Hayes Management Consulting.