Evaluation of Process and Effects of Change

Principles for Using Data
to Support Clinical Improvement ¹

1. Seek usefulness in measurement, not perfection
   The utility of data is directly related to the timeliness of feedback and the appropriateness of its level of detail for the persons who use it. The goal is continuous improvement with concurrent, ongoing measurement of impact.

2. Use a balanced set of process, outcome, and cost measures
   Medical care systems are comprised of sub-elements that interact with each other. They produce a fluid set of results that include: clinical outcomes, functional status, risk level, patient satisfaction, and costs. Balanced measures may cover "upstream" processes and "downstream" outcomes to link:

   • causes with effects
   • anticipated positive outcomes and potential adverse outcomes
   • results of interest to different stakeholders such as patient, family, employer, community, payer, and clinician
   • cumulative results related to the overall aim

   • specific outcomes for a particular change cycle

3. Keep measurement simple (think big, but start small)
   Focus data collection on a limited, manageable, and meaningful set of measures.

4. Use quantitative and qualitative data
   Quantitative performance data measure clinical behaviors and create tension for change. Qualitative data are used to learn how the patient and health care team experience a new procedure or system.

5. Write down the operational definitions of measures
   The better the operational definition, the better the data elements. The better the data elements, the more reliable and valid the aggregate measures.

6. Measure small, representative samples
   Use a sampling strategy that avoids the costs and trouble of collecting data on all patients and on all encounters. The emphasis is on usefulness, not perfection.

7. Build measurement into daily work
   Every process emits data that can reveal how the process is performing. Capture key data during the provision of routine care.

8. Develop a measurement team
   Team up to lighten the data collection workload, add knowledge, and boost morale.

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