A Guide to Successful Outcomes Using Population Health Analytics

will rise to the challenge and improve their performance, the rest of the providers who were already above that standard will not have changed.

Punishing the outliersFigure 13: A Less Effective Approach to Improvement: Punishing the Outliers

A better approach—and one that will produce improvement throughout the organization for all providers—will focus on reducing variation rather than just dictating a minimum standard. Organizational leaders share best practices and establish a “shared baseline” for typical cases then encourage physicians to be consistent about using those best practice guidelines when it clinically makes sense and deviating from that shared baseline when a patient requires it (yet records their reason for the deviation so that the shared baseline can be improved over time). Even the best performing physicians often improve using this approach.

Everyone improves shift curveFigure 14: A Better Approach to Improvement: Shift the Curve and Everyone Improves

Improvement Prioritization

To be successful and make a visible impact as soon as possible, an organization needs to prioritize its improvement projects. One way to do this is to consider resource consumption and potential to reduce variability. As seen in figure 15, the best place to start is on projects that will address care consuming large amounts of resources with high variability.

This method to prioritization follows the Pareto principle or 80/20 Rule, where a very small number of projects account for a majority of the opportunity for improvement.

variation vs consumption

Figure 15: A Variation and Resource Consumption Chart Showing the Best Place to Start for Improvement Efforts

HOW DOES THE ORGANIZATION TRANSFORM?: ADOPTION

Adoption means understanding an organization’s capacity for change, implementing excellent governance principles, standardizing improvement methodology, and providing great training. Adoption is all about how an organization transforms. This means getting an innovation or improvement deployed broadly through the health system, including every clinician providing care.

Improvement Capacity Assessment

First, the organization and its leaders need to understand the current capacity for improvement by evaluating the organization’s current capabilities, challenges, and gaps. This involves a self-assessment of an organization’s performance for best practices, analytics, and adoption. Organizational leaders answer 70+ questions in one of three ways: 1. Just Starting; 2. Mid-Journey; or 3. Mature.

For example, an assessment criterion for best practices evaluates the standard protocols for population management in the organization by asking, “What types of standardized content have you implemented to support Population Health Management?” Organizations respond one of three ways: Just Starting (We have not standardized content to support Population Health Management. Our clinicians use their best judgement based on their individual training.); Mid-Journey (We have begun to standardize some content [e.g. CPOE to implement standardized order sets provided by our EMR vendor]. We have not yet created standard content for both workflow and clinical domains across the continuum of care.); or Mature (We have implemented standardized content to manage ambulatory and inpatient care management [e.g., ambulatory treatment algorithms, order sets, bedside care protocols] and utilization criteria [e.g., diagnostic algorithms, triage criteria, indications for referral and intervention] regardless of what unit or facility a patient enters the same workflow and care delivery content is followed and measured.)

For adoption, one of the elements the assessment considers is data governance and data quality process by asking, “Who manages the quality of data?” Again, organizations can respond one of three ways: Just Starting (No team owns data quality); Mid-Journey (Quality is managed at the report level. Individual analysts scrub data before reports are distributed); or Mature (Data governance has been established by giving clinical and business owners the role of data stewards to identify source system errors and correct problems at the source).

These are just two questions from the comprehensive assessment organizations can undertake to see if they are set-up for sustainable success—and to show the areas where they might need to do some work. Figure 16 is a visualization showing a health system’s assessment results. This system has the greatest amount of work to do in its analytics. However, it got a bulls-eye in the best practices element, standardized protocols for population health.

Self-assessment VisualizationFigure 16: An Example of a Self-assessment Visualization

Governance Teams

Next, the organization needs to tackle one of the most challenging elements of the improvement process: establishing a good governance model that allows for both data governance and data stewardship, as well as advanced organizational governance and prioritization. Getting the governance structure right will accelerate the improvement with population health.

Getting it wrong will stall all efforts. Figure 17 shows the set up of teams that will result in success.

First, an executive leadership team must sponsor any kind of outcomes improvement effort. This group will oversee the permanent guidance teams, approve board-level outcomes goals, review progress, and remove road blocks. The leadership team will also determine and create guidance teams to tackle prioritization efforts based on the opportunities for greatest improvement, as discussed above.

Permanent Teams for care improvementFigure 17: The Permanent Teams Required for Successful, Sustainable Care Improvement

The guidance teams will prioritize innovations and can be formed around a clinical support service or a clinical program. The team should have expert and clinicians that will meet quarterly to determine aim statements, guide the small teams that are drafting the changes, and review progress.

Small, integrated, working teams of physicians, nurses, technical staff, and analysts will actually design the innovation (implementation team). These teams meet weekly and share those changes with the larger, broad team, which provides guidance. These small teams can find way to drive adoption for those who will need to change their behavior.

Improvement Types

There are three types of improvements that range from easier to difficult to achieve.

Opportunity identification improvements involve looking at variation and calculating how much might be saved by standardizing care. Process improvements address the root causes of improvement opportunities and take next steps to improving outcomes. Finally, outcomes improvements measure the project’s overall goals such as an improvement in health function, a reduction in mortality, or actual dollar savings.

Figure 18 follows a skiing metaphor, with green (easy runs) being the opportunity identification improvement, blue (intermediate runs) covering process improvements, and black (expert runs) denoting outcomes improvements.

Improvement types in terms of difficultyFigure 18: Three Improvement Types in Terms of Difficulty and Using a Ski Run Metaphor

Improvement Methodology

The organization needs to implement the right tools at the right time, meaning incorporating an improvement methodology that incorporates LEAN and PDSA, as well as Agile software development principles. Figure 19 shows how to systematically approach the real work of identifying the content and best practices, defining patient cohorts, choosing a goal with aim statements, selecting the metrics, and then redesigning the entire process to get closer to the goal.

systematically approach improvementFigure 19: How to Systematically Approach Improvement

Traditional “Waterfall” Approach vs. Agile Approach

The agile approach to software development is critical to successfully driving improvement. The traditional way, the waterfall method, requires a lot of documentation upfront about what the IT team will build. First, the project plan, estimates, and requirements are gathered—creating documentation. Then, the possible use cases and functional specifications are hammered out–resulting in more documentation. Next, design specifications are created—causing even more documentation. Then, some coding actually starts, and finally the customer sees the product. The customer then tests the software resulting in more—documentation. Developers take the test results…

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