Leveraging Lab Analytic Data to Include Actionable Details to Improve Quality
Posted May 06 2014 12:00am
This is a guest blog note by Philip Abrams who is the EVP of AccuCore Group with a general overview of how lab analytic software can improve lab quality and efficiency. Other lab analytics products place greater emphasis on customer relations and lab operational metrics.
Leveraging lab analytics to improve lab quality and efficiency should be the goal of every lab professional. However, LIS data is not comprehensive and must be supplemented. Data mining is not enough and is often limited to structured data. How about notes in text fields? Such data may not include triggers with exclusions so you are forced to manually parse the data, apply exclusions, and consider weighting factors. This is typically accomplished with the use of paper forms, spreadsheets, or various LIS data fields.
A solution for effective QA is to leverage analytic data in support the following goal: optimize the ability to provide accurate and timely diagnoses in support of clinical decision-making and patient safety. Such a solution should include monitoring the entire lab testing cycle and identifying pre-analytic, analytic and post-analytic variables that contribute to errors. It must leverage quantitative, detailed data that is actionable. Lastly, such a program must also support digital media such as digitized slides, gross videos and extramural reports.
Dashboards, provided by lab analytic software, can be used to provide summary information with supporting drill-downs for details. Thresholds should trigger traffic-light indicators with separate task alerts. Preestablished intervals for pro forma reports should also be available in support of weekly/monthly meetings QA meetings, or inspections. In the past there have been complaints about reporting on QA issues because many pathologists felt their efforts were wasted. This occurs when the information acquired from the LIS and supplemental forms are insufficient to be actionable. Reporting of issues drops off as quality is improved with continuous CQM.
Although mandated for pathologists, any peer review process should include PAs, lab techs, transcriptionists, and secretaries. By leveraging individualized dashboard data, individuals can view their personal performance compared to the departmental average, CAP thresholds, or their own key performance indicators (KPIs). Such individualized QA feedback process can eliminate the need for one-on-one meetings and stressful performance reviews. Managers should also be able to see the performance of their subordinates. LISs were designed around specimens and patient data and not QA. For automated QA to be effective, it should reduce the current effort and support proactive management.
Technology to consider in lab analytics should include a QA interface to an LIS, user-definable thresholds for triggers and exclusions, flow logic that assigns QA tasks to the proper individual, an in-box for task sorting, prioritization and management, menus that adjust to the type of QA being performed, definable KPIs, and the weighting of the various QA elements. Applying best practices to QA and peer review can reduce lab costs, improve patient safety, avoid problems of non-compliance with regulatory bodies, and reduce non-reimbursement and medical malpractice claims. With the improved QA achieved by lab analytic systems, lab professionals can obtain better information while maintaining regulatory compliance.