Leveraging clinical data for OPPE and FPPE
Introduction
More than a decade has passed since the Joint Commission created its standards for evaluating practitioners’ performance in 2008: Ongoing Professional Practice Evaluation (OPPE) and Focused Professional Practice Evaluation (FPPE). Through these processes, hospitals can identify and address performance issues that could impact patient care and make decisions about practitioners’ re-privileging. In the years since these standards were introduced, all of the approximately 80 percent of US hospitals that are accredited by the Joint Commission have incorporated OPPE and FPPE into their evaluation practices.
While the Joint Commission has provided general recommendations and guidelines about OPPE and FPPE, hospitals must determine some specifics on their own, such as what data are collected and used, how frequently OPPE data are reviewed each year, and who makes decisions about practitioners’ performance and privileging. Because hospitals are responsible for determining their OPPE and FPPE processes, approaches may differ widely—and may thus have varying degrees of success.
Data are critical to OPPE and FPPE, but finding and reviewing the right data has its challenges. Obtaining different types of data from a variety of sources—and then reviewing these data on an ongoing basis—can be time-consuming. Clinical staff may even be duplicating efforts if they are not aware of existing partnerships or data management practices within their health system. Staff may face difficulties using patient data to attribute providers to patients, who may see several different providers in one visit. Data integrity is a concern as well, given that errors in sources of medical data, such as electronic health records (EHRs), are common.
Fortunately, there are ways for hospitals to efficiently use data for OPPE and FPPE. Leveraging partnerships, relying on tools and technologies, and prioritizing data integrity enables health systems to meet requirements while getting the most out of their clinical data. This paper offers insights into using data to meet OPPE and FPPE requirements and shares best practices for success.
OPPE and FPPE at a glance
OPPE is a regular, ongoing process for evaluating the performance of all practitioners with privileges at a given hospital.
FPPE focuses on evaluating the performance of practitioners requesting new privileges or practitioners whose performance medical staff have concerns about.
Examples of quantitative data used in OPPE and FPPE may include:
- length of stay trends
- post-procedure infection rates
- Periodic Chart Review
Examples of qualitative data used in OPPE and FPPE may include:
- types of patient complaints
- code of conduct breaches
- peer recommendations
Challenges
Disparate data sources
The data required for OPPE and FPPE cannot be found in one singular system. Considering that three-quarters of hospitals use at least 10 different EHRs, capturing different types of data across many departments and systems for OPPE and EPPE can be challenging and complex. Obtaining data for specialists, who typically need to be evaluated using a more specific set of metrics, adds still more complexity to the process.
Time-consuming processes
Because OPPE must be conducted regularly—at least once a year—the time spent manually obtaining and analyzing relevant data from varied sources can quickly add up, particularly if hospitals are not employing any technologies to simplify the process.
Duplicating efforts
Quality departments often have their own data management processes in place, which may include partnerships with third parties. Clinical staff who attempt to collect OPPE and FPPE data without consulting with their quality departments may end up duplicating efforts and creating inefficiencies.
Poor data integrity
As data used in OPPE and FPPE inform decisions about providers’ privileging and reimbursements, it is crucial that these data are accurate. However, researchers estimate that at least half of EHRs (a common source of OPPE and FPPE data) may contain an error. Hospitals without policies to ensure data integrity may see their OPPE and FPPE efforts suffer.
Attributing cases to providers
One patient may see multiple physicians and specialists in the same visit. As a result, attributing a patient’s care to the appropriate physician is a common challenge in OPPE and FPPE. Hospital staff must have a process in place to decide which patient cases should be used to evaluate which physicians.
Best practices for using data for OPPE and FPPE
Include data with measurable objectives.
While a wide variety of both qualitative and quantitative data can be used for OPPE and FPPE, including some measurable data that can be compared against existing benchmarks or objectives can aid in interpretation. “Ideally, some of the data should have measurable objectives that can also be compared with other providers doing similar work,” noted researchers in the Journal for Nurse Practitioners.
Consider the relationships you have.
Look to existing relationships, whether within or outside the health system, for additional data resources. For example, many hospitals’ quality departments have already found efficient ways to conduct clinical data abstraction and analysis. Relevant data may therefore be readily available for use in OPPE and FPPE, saving clinical staff from duplicating efforts.
Prioritize data integrity.
Data integrity (defined as high-quality, timely, and valid data) affects many factors, including patient care and future treatment development. Using inaccurate data in OPPE and FPPE could lead to errors in making decisions about providers’ performance and privileges. If hospitals do not have data integrity processes in place, third-party partners can help them ensure greater data integrity by using AI to identify inconsistencies in data and employing clinical data experts who regularly perform quality checks on data.
Engage providers in decision-making.
Including providers in decisions about OPPE and FPPE—particularly decisions surrounding what metrics to use in evaluation and which patients to attribute to each provider—can engage providers in the OPPE process and ensure that OPPE is effective for all involved staff. This is especially useful when working with specialists, as general OPPE metrics may not adequately capture the specific level of care specialists provide.
Use data sources and tools that make OPPE easier.
If a data source or reporting tool is difficult to use or access, it will likely prove burdensome for OPPE and FPPE. For example, a widely used EHR that can produce automated reports would be a good source for evaluating physician performance, as opposed to a scarcely used EHR that requires manual processes. For hospitals struggling with a particular tool, it may be time to consider a change.
Automate the data reporting process.
Research has shown that relying on automation for OPPE can save valuable staff time. Automation also has additional benefits: AI-enabled automation allows experts to sort through larger quantities of clinical data, which can improve efficiency, reach, and data integrity.
Conclusion
Changes and challenges in health care are inevitable, and it is easy for routine processes such as OPPE and FPPE to fall to the wayside when other priorities emerge. However, hospital leaders should not lose sight of their importance in assessing patient care.
There is no one simple solution that can manage the entire OPPE or FPPE process from start to finish. Hospitals need to find the right combination of tools and practices that work for them. There are, however, steps that clinical and quality departments can take to improve their OPPE and FPPE processes and save on staff time. By using measurable data, collaborating with outside partners, and engaging providers and specialists when making decisions, hospitals can ensure their OPPE and FPPE processes are both efficient and effective.
Moreover, taking action to improve data integrity and automate data reporting efforts can have immense value beyond meeting OPPE and FPPE requirements. With greater data integrity and the AI-powered ability to find meaning in large sets of data, hospitals will be more equipped than ever to make data-based decisions to improve patient care.