Date: January 20, 2011
Measurement is a vital, but often overlooked, aspect of quality improvement projects in health care.
Last year, shortly after one of our clinics introduced electronic registration kiosks, a patient complained that the change had actually made the check-in process slower than when it was handled only by human beings.
However, we had a tracking system in place that told us how long each patient waited at different steps before actually seeing a physician. When Kelly Cavallio, the administrator of ambulatory services on our East Baltimore campus, looked at the system, she found that it took about 8 minutes combined for the patient to check in at the kiosk and make a copayment at the front desk. In the past this process took about 22 minutes. This patient’s delay actually occurred after the registration process, while he waited to be examined.
This is a simple example of how access to reliable measurement can fundamentally change our conversations about improving care. By focusing on data, we avoid extraneous discussions and anecdotes that may steer us toward the wrong conclusions, plus our interventions have greater credibility and buy-in.
Two articles in this issue of Quality Update underscore the power of measurement. Infection control specialists at The Johns Hopkins Hospital discovered that, as our hand hygiene performance improved, transmission of two drug-resistant pathogens dropped in our adult intensive care units. In addition to lending credibility to our hand-hygiene campaign, this finding will hopefully encourage skeptical caregivers to be more vigilant.
Beyond telling us if an effort has been successful, measurement can help us figure out what needs fixing in the first place. When the hospital’s kidney transplant program—also profiled here—realized that it was performing fewer procedures, it didn’t go straight to trying out solutions. Seeking to prime the referral pipeline, a team mapped their processes for handling referrals, calculated how long different steps took in that process, and used those figures to tell them where to focus attention. For example, the program learned that they had a high rate of patient no-shows for medical evaluations. That information led them to install new phone-call reminders before appointments, as well as new processes for backfilling slots on short notice.
In health care, we are surrounded by professionals who have been trained to value the power of data to help make decisions. If we want our colleagues to support efforts to improve the systems supporting that clinical care, we need to demonstrate the same commitment to evidence.