Key Driver 3: Optimize Health Information Systems to Extract Data and Support Use of Evidence in Practice
Accurate and actionable data are needed to put evidence into practice, and clinical information systems can facilitate or hamper a practice’s ability to generate and use data. Practices may have focused their data efforts on producing information related to payment incentives and not yet harnessed their data for quality improvement (QI). Effective use of clinical information systems requires purposeful planning, effort, and allocation of resources. To produce and report on data efficiently, practices will want to master more advanced functions in electronic health records (EHR) and use other information systems, such as registries and laboratory systems, to fill the gaps. Involving care teams in the process can help practices increase the reliability of their data and generate meaningful reports.
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Create dashboard reports for selected measures
Dashboards – ongoing targeted reports on selected processes and outcomes related to the integration of new evidence, preferably graphical and in real time – allow practices to easily see their progress, let them make adjustments, and keep them engaged. Analyzing data and creating reports takes specialized skill and can be a labor-intensive process. When building dashboards, designers should think about how to present the data visually to make them easy to understand. Once developed, production of dashboard reports should be automated to the extent possible.
Improve data accuracy and transparency and secure staff trust
When QI teams first produce performance and QI data, they frequently encounter problems such as large amounts of missing data, documentation errors, or mistakes in data extraction algorithms. Collaborating with care team members on data documentation not only improves the accuracy of data, it also increases staff’s confidence in the data. Since data are only as good as the documentation in the information systems, QI teams can address some data errors by working with staff on the best ways to capture data and what needs to be documented. After these initial checks, QI teams can use additional validation strategies, such as sample chart audits to test data extraction processes, before sharing results with the entire practice. Even with this preparation, practice members may question the data. QI teams can review results with clinicians and teams privately to ascertain their comfort with the data. QI teams should try not to be defensive when data accuracy is challenged, and remain open to potential improvements and acknowledge that it may not be possible to get 100 percent accuracy. Time spent building confidence in the data early on usually pays off in trust and action down the road.
Identify and train a Data Coordinator
The demands on primary care practices mean that sometimes non-urgent, non-clinical activities can fall by the wayside. Practices gain efficiency by having a point person for data, a Data Coordinator, who has learned the ins and outs of the practice's information systems and ensures data tasks are completed. Having a Data Coordinator doesn't mean that one person has to collect all of the data and generate all of the reports. As the name implies, the Data Coordinator manages data operations and trouble shoots problems as they arise. Small practices may have one person doing the Data Coordinator job on a very part time basis. Larger practices may have more than one person serve as Data Coordinators. Responsibilities can include making changes to documentation in EHRs, developing auditing tools, designing and programming reports, using registries, and being a key member of the practice QI Team. In addition, Data Coordinators can be cheerleaders, motivating and engaging staff in using data to drive the QI process. Staff throughout the practice, even data novices, may be tapped to take on data coordination duties. It is therefore critical that practices give Data Coordinators training and, for part-time Data Coordinators, protected time from their other responsibilities to devote to data management tasks.
Involve care teams in refining documentation workflows to minimize burden
Clinicians and other team members often report that they are overwhelmed by documenting the care they provide. Involvement of care teams is key to balancing the need to document and track the delivery of evidence-based care and the need to keep documentation requirements manageable. Care teams provide vital information about how they currently record data as well as ideas and suggestions about what a better process might look like. Furthermore, documentation procedures developed by partnering with care teams are more likely to be followed.
Link patients to their clinicians and teams within information systems to improve usefulness of performance reports
Once care teams have taken responsibility for a panel of patients (go to Key Driver 4: Create and support high functioning teams to deliver high-quality evidence-based care), it is important to assign those patients to their teams in electronic or other information systems. Although practice-level data are useful, practices that link patients with individual clinicians and teams allow QI teams to identify high-performers from whom others can learn, and target places where performance is lagging for additional attention and support. Electronic systems may require the addition of new fields and paper-based systems will need a coherent coding system. Linking patients with care teams in the EHR has the added benefit of supporting population health efforts of the team.
Use electronic health records to improve data collection
For most practices, data collection for QI will be done using the electronic health record. Although it is possible, and sometimes preferred, to gather data for QI using paper-based records, paper-based methods result in collecting less data on fewer measures, which can limit QI progress. EHRs are nearly universal in primary care practices, yet their potential to be catalysts for the use of evidence and for quality improvement has not been realized for many. QI teams can coax actionable data out of their systems by learning to use existing untapped EHR functionality, standardizing documentation, and adopting innovations that other practices have developed. Implementing new clinical evidence, such as screening recommendations or treatment guidelines, usually entails implementing new data collection processes. However, some EHRs are better at supporting QI and QI measurement than others. Maximizing the use of EHRs to attain actionable data may require working closely with the EHR vendor. While data are key to improving quality, it is critical that practices not give up on meaningful QI work simply because they cannot get the data they want out of their EHR.
Use registries and other data sources creatively to track the provision of evidence-based care
Using registries to track the care provided to patients with particular diseases, provide care management services, or contribute to national surveillance efforts can be an alternative to wrangling data out of an uncooperative EHR. Creative use of other data sources such as a patient web portal, a laboratory system, a billing system, or a practice’s paper-based system, may provide the best solution. Using data that are already available in another system is often more efficient than investing time in revising an EHR to collect the same data. The goal, however, remains the same: making it simple for the practice and teams to be able to track their progress in delivering evidence-based care.
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