Bright Spots in Healthcare: What We Can Learn from Applying the Learning Healthcare System model

Track: Research

Session Number: 2014
Date: Tue, Sep 26th, 2017
Time: 5:00 PM - 5:45 PM

Description:

How are learning healthcare systems using their own data to inform clinical and organizational improvements in healthcare delivery, design, and efficiency?

Unique opportunities were afforded us, in this QI research initiative, to dive deeply to address research questions on the challenges of designing and implementing practice improvement/quality improvement projects within real-world clinic settings. Some of the most hard-won learnings came from our aim to use health systems’ own data as the baseline standpoint, from which the program intervention elements were designed. That is, the education and quality improvement components were developed to address specific gaps identified within each system partner, in an approach aligned with the tenets of a Learning Healthcare System framework.

This quality improvement research initiative aimed to address clinical gaps in two therapeutic areas: (1) oncology, specifically in chronic lymphocytic leukemia (CLL) and breast cancer. Patients participating in the oncology QI program were treated within oncology care network clinical sites in New York and Illinois. Retrospective chart audits identified that only 53% of CLL patients within this system had received fluorescence in situ hybridization (FISH) testing, which is key to identifying the appropriate therapy for the treatment of CLL. Further, retrospective chart audits identified that only 32% patients with HER2+ breast cancer had an opportunity to receive neoadjuvant therapy. In both of these patient populations, the clinical gaps, and the root causes, were examined by the associated healthcare providers in these systems, who contributed to the design of the set of solutions to address these gaps, which included healthcare provider education, patient tools, and system process change.

Using data to improve quality of care: Lessons learned from oncology QI

The aim for sharing these project examples is to provide insights on (1) what worked well, when system-specific clinical data were used to identify gaps and design interventions to address those gaps; and (2) what we learned about challenges in using clinical data for practice improvement, as these translatable lessons learned will have value to other health systems and planners of practice improvement initiatives. The project summaries are, indeed, brief synopses of the larger set of stories on lessons learned that could be told. In short, each system had specific gaps that we aimed to address; in doing so, we identified and used the systems’ own clinical data to demonstrate that gaps existed, which, was a necessary first step in securing buy-in from the relevant stakeholders. The interventions that were planned and executed, based on these identified gaps, used baseline data as the underpinning benchmark for improvement, and thus, also as the measure of program impact.

The oncology-focus practice improvement story: Using practice data to benchmark performance and assess progress

Through baseline data analysis, including surveys and chart audits within each of the participating clinics, we were able to identify discordance between patient and provider perceptions and gaps in clinical practices. Each participating clinic had the opportunity to benchmark their own data against those from the aggregated cohort of clinics participating in the QI program. The overarching goal of the project was to provide the necessary tools to facilitate the development of appropriate and individualized action plans for each clinic, based on their specific gaps.
• Within the project focused on clinical gaps in CLL care, suboptimal documentation of FISH testing was identified as a key barrier in CLL care among a number of oncology practices. Through qualitative analysis, the root causes of the lack of FISH testing were identified as being related to barriers in not efficiently obtaining results from diagnostic laboratories and healthcare provider misperception that testing was only needed among the patient populations who necessitate treatment. Expert faculty were engaged to support this project as independent experts emphasized, in a certified education program implemented as part of this program, the importance of FISH testing as an essential prognostic tool, which can both allay patients concerns about disease course and identify subpopulations that need more vigilant monitoring. Subsequently, clinic providers made commitments to increase FISH testing and investigate alternative vendors for diagnostic testing.
o As a result of this effort, follow-up chart audit analysis demonstrated that the percentage of appropriate patients receiving FISH testing increased from 53% to 60%.
• Within the project focused on clinical gaps in breast cancer care, care coordination between the medical oncologist and the oncology surgeons was identified as a significant barrier in patient access to neoadjuvant therapy. Within the CME component of the project, expert faculty provided key insights to medical oncologists on how to effectively develop relationships with oncology surgeons and improve co-productive healthcare delivery. This program was aimed to address the practice challenge identified among oncology surgeons that a proportion of patients will not be referred back to the surgeons after neoadjuvant therapy. As a result, medical oncologists made new commitments to attend multidisciplinary tumor boards to improve the aim of increased collaboration with oncology surgeons.
o Through follow-up chart audit analysis, we found that the percentage of patients receiving neoadjuvant therapy for HER2+ breast cancer increased from 25% to 32%.
Based on our experience in these projects as well as from our numerous other quality and practice improvement initiatives, we have summarized some key lessons learned to form a set of recommendations that we believe have value for planners of future practice improvement projects that are designed to be aligned with the tenets of a learning healthcare system approach, particularly related to methodological issues that we encountered in using evidence from the systems’ own data to support practice improvement initiatives.
Session Type: Case Based Learning

Learning Objective 1: Articulate how the tenets of the Learning Healthcare System model can be applied within the design of education initiatives
Learning Objective 2: Identify best practices for applying a healthcare system’s own data to benchmark performance
Learning Objective 3: Demonstrate the value of quality improvement education to improve healthcare system performance
Session Type: Case Based Learning

Learning Objective 1: Articulate how the tenets of the Learning Healthcare System model can be applied within the design of education initiatives
Learning Objective 2: Identify best practices for applying a healthcare system’s own data to benchmark performance
Learning Objective 3: Demonstrate the value of quality improvement education to improve healthcare system performance