Every role in managed care today is data driven, with success measured incrementally toward higher quality healthcare delivered at a lower cost. Practitioner data is at the center of it all. According to CAQH, a typical practice holds 12 managed care contracts, with each health plan requiring roughly 140 data elements. For a practice with five practitioners, this equates to managing 8,400 data points.
While it’s clear that we have more practitioner data than ever, we can’t always attest to maximizing its use for our organization. Ideally, we want quality data collection efforts to be fruitful in producing concrete plans and actions that improve:
- patient safety
- hospital fiscal performance
- resource utilization
- patient satisfaction
- practitioner satisfaction
Your senior leaders look to you to present your data as a package that makes sense and helps aid in decision making in these areas.
Spreadsheets are a good start, whether the information relates to patient event reporting, practitioner performance, or other measures. However, if you can’t easily interpret and share your data, it’s time for a reboot.
Five goals for quality care data
As a producer and a user of practitioner quality data, you handle a goldmine of information that others across the managed care spectrum use. If the data you produce is complex, simplify the story it tells for the end user. If you’re a user of data that others provide, make clear that your goals are to address specific questions. Start with these five general questions as a guideline:
- Should we maintain course or change direction?
- Are we using the right volume, scope, speed/duration of collection?
- Is the data showing what direction we’re headed (i.e., getting better/getting worse)?
- Is the data stable enough that we can draw a conclusion?
- Is there time to analyze the data, or should we make an immediate decision?
Quantitative vs. qualitative quality care data
Quantitative data is relayed in quantities and numbers, and by nature is measurable. Qualitative information relies on the perceptions of patients, clinical or professional staff, or others; is descriptive, and is associated with events that can be observed but not counted.
There are clear-cut methods for some practitioners quality data along the two lines of quantitative versus qualitative. For example, you can count patient falls and instances of provider hand washing, But when competence, behavior, and knowledge are the focus, your data may be qualitative or a bit of both. Data presented for the Six General Competencies for physicians, established by the Accreditation Council for Graduate Medical Education and the American Board of Medical Specialties, is a great example of requiring both qualitative and quantitative:
- Patient Care
- Medical/Clinical Knowledge
- Interpersonal and Communication Skills
- Systems-based Practice
- Practice-based Learning and Improvement
When presenting quantitative data, consider using graphs, charts, tables, maps, and scatter plot diagrams.
For qualitative data, visual representations can help focus end users on the trends in the data: Can it tell a story or can you show change over a progressive timeline?
Use comparisons and benchmarks for evaluating quality data
It's likely you’re using and/or producing data of various types for managed care practitioners. Keep in mind that your goal is to facilitate sound decision making, and consider a wide range of options for presenting practitioner quality data to end users:
- Benchmarks are useful for presenting on a scale. For example, use graphs and charts to show where your data subject (practitioner, department, facility, organization) compares with others and to highlight a need to change direction.
- Historic comparisons are look-backs based on what’s been delivered in the past. They’re useful when you don’t have access to state or national benchmarks. You can apply historical comparisons to single or multiple factors (e.g., just practitioners, and/or practitioner/department/organization)
- Standards can be those of an accreditation body or your own organizational goals (or often both, depending on your bylaws). When you set quality expectations based on standards (e.g., zero peer review cases with unacceptable results) you’re establishing a comparison goal for your performance data.
- Control charts help identify triggers outside of an acceptable zone. This type of chart goes hand-in-hand with peer review findings where there are three levels: excellent, acceptable, needs follow-up.
Include conclusions in your data presentations
Stepping up from presenting a simple spreadsheet full of data means you’ve answered the five questions outlined above and given life to data using charts, graphs, etc. And remember that your data is the start of a series of critical decisions for the lifecycle of practitioners in managed care.