In hopes of sparking renewed commitment to applying improvement science to telehealth, we offer this Telehealth QI and QA Miniseries. Today is the third in the series.
Recall that data can come in many forms and doesn’t have to be a report out of your electronic medical record (EHR). It can be hashmarks, start-end times, glass globs in a jar and more. I’m not kidding about glass globs. Once when I visited the Institute for Healthcare Improvement (IHI) there were two jars; one was labeled “Having a good day” the other was labeled “Having a bad day”. Each person who checked in at reception put a glob in the jar that reflected how their day was going.
Ask these three questions when considering data collection.
- What is our question (that we hope to answer with this data)?
- Will the data answer our specific question?
- What will we do with the results?
If the answer to the last question is “We just want to know” consider carefully whether you need that data and whether the investment in time, energy, and will is worth it.
Know the differences among outcome, process, and balancing measures!
- Outcome measures – often considered high-level measures; frequently linked to patient outcomes. These are the ones that health plans want us to measure! These are also the measures that clinicians often counter that they cannot be responsible for what their patients do or do not do, especially around lifestyle changes and medication/treatment plan adherence. Examples of outcome measures include:
- Percent patients with diabetes with A1C < 7%
- Readmission rates
- Percent patients with improved PHQ9 scores
- Process measures – often an indication of how well a process is working; often targets the specific steps in a process that lead to increases or decreases in an outcome measure. Examples of process measures include:
- Percent patient with diabetes with A1C test completed within the past six months
- Percent patients discharged from inpatient admission that were called within 48 hours of discharge
- Percent patients screened for depression using the PHQ9 screening tool
- Balancing measure – often used to make sure there are no unintended consequences of concentrating on one measure. For example, if we focus our improvement efforts getting patients with diabetes in for A1C tests, will dilated eye exam rates go down?
Collect just enough data. You need just enough data to guide your next improvement steps. Don’t get bogged down; Know When Enough Data is Enough. If you have a statistician or data analyst on board – great! But if you don’t, it’s not a big issue. Most data do not require statistical analyses to determine if things are trending in the right direction. “Ultimately, the goal of using data in primary care is to guide quality improvement efforts. The challenge is getting enough data to direct improvement activities, but not so much to burden the effort.” ~Gerald Langley
Consider the best way to display the data. If we truly believe that data and measurement are for learning, then we need to share the data for everyone to learn. Know whether to use a histogram, run chart or other way of displaying the data to tell the story. Run charts are an excellent way to track data over time to 1) understand if change is an improvement (especially if you use an annotated run chart) and 2) to know if fluctuations are due to random or specific causes. You can find a run chart tool here.
Share data and measures transparently. Clinicians and other staff may push back with several protestations, including “my patients are different”; “I can’t be held accountable for what my patients do or do not do”; “I don’t trust the data”; “the data reflect our poor workflows”, etc. However, in my role as Quality Coordinator many years ago, I learned from a wise family residency director that if we truly believe that data and measurement are for learning not judgement, the messaging is that we are all in this together and if an aspect of our care delivery needs to be improved, it’s up to all of us as a team to work on it. Patients are part of the team, too, and truly evolved organizations share their data with patients, posting it in the waiting room. Transparency is not always possible and is dependent on the culture of the organization. I have worked with organizations that did not handle rolling out data transparency well and had clinicians quit over it.
Measure. Improve. Measure.
Use the SWTRC Contact Us form to share your telehealth measures and/or experiences with telehealth quality improvement.