Ann Tinker, MSN, RN

Healthcare outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this article adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples. The top seven categories of outcome measures are:

1. Mortality
2. Readmissions
3. Safety of care
4. Effectiveness of care
5. Patient experience
6. Timeliness of care
7. Efficient use of medical imaging

CMS used these seven outcome measures to calculate overall hospital quality and arrive at its 2018 hospital star ratings. This article also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement.

How can health systems avoid just talking about improvement and instead achieve real progress in clinical quality performance? First, improvement teams need access to a robust data infrastructure that can provide a complete picture of performance. This analytic insight reveals process gaps and opportunity areas where the care team can target improvement efforts.

After selecting an opportunity area, care teams are ready to follow the three-step process to achieve meaningful clinical improvement:

1. The “why”: Identify the outcome goal.
2. The “what”: Select a written, measurable, and time-sensitive process metric to evaluate the process.
3. The “how”: Identify the best interventions that will support the desired change in a process.

通过致力于改革医疗保健分析，组织最终可以节省数亿美元(取决于其规模)，并实现全面的结果改善。The transformation helps organizations achieve the analytics efficiency needed to navigate the complex healthcare landscape of technology, regulatory, and financial challenges and the challenges of value-based care.

To achieve analytics transformation and ROI within a short timeframe, organizations can follow five phases to become data driven:

1. Establish a data-driven culture.
2. Acquire and access data.
3. Establish data stewardship.
4. Establish data quality.
5. Spread data use.