After the Collaborative Is Over: What Sustains Quality Improvement Initiatives in Primary Care Practices?

2009 ◽  
Vol 35 (10) ◽  
pp. 502-AP3 ◽  
Author(s):  
Paul Bray ◽  
Doyle M. Cummings ◽  
Marti Wolf ◽  
Mark W. Massing ◽  
Janet Reaves
2014 ◽  
Vol 32 (31_suppl) ◽  
pp. 76-76
Author(s):  
Jeff Myers ◽  
Suzanne Strasberg ◽  
Kathi Carroll ◽  
Zabin Dhanji ◽  
Ingrid Harle ◽  
...  

76 Background: In Ontario, the Ministry of Health and Long Term Care’s (MOHLTC) uses Quality Improvement Plans (QIPs) to drive system improvement aimed at providing high value, high quality care for all. To support the introduction of QIPs into the primary care sector, Cancer Care Ontario has developed an Advance Care Planning (ACP) toolkit for practices that include ACP as part of their annual QIP. ACP is an ongoing and dynamic process that involves a capable individual reflecting on their current values and beliefs for their health care, communicating their personal wishes for future health care and identifying an individual who will make decisions on their behalf in the event that they are unable to provide informed consent. The process is iterative and wishes may change over time with changes in health status. Methods: The ACP QIP was developed based on the Plan, Do, Study, Act cycle of continuous quality improvement. The ACP QIP provides primary care practices with detailed instructions on how to implement, monitor and report on an ACP Quality Improvement initiative. Importantly, the ACP QIP provides guidance and practical tools for developing objectives, establishing targets, and identifying measures and baselines for performance. CCO is actively promoting the ACP QIP in an effort to encourage uptake and broad adoption across Ontario. Results: There is now evidence that with ACP there is a greater likelihood EOL wishes will be both known and followed resulting in improved EOL care. ACP is also associated with decreased distress among the family members. Conclusions: Creating an ACP QIP supports primary care’s focus on advancing quality patient care. Importantly, implementing the ACP QIP into primary care practices has the potential to improve EOL care and secondarily reduce health care costs ultimately working towards achieving the triple aim of “better care, better health, and lower costs”.


2019 ◽  
Author(s):  
Michael Parchman ◽  
Melissa L. Anderson ◽  
Katie F Coleman ◽  
LeAnn Michaels ◽  
Linnaea Schuttner ◽  
...  

Abstract Background: Healthy Hearts Northwest (H2N) is a study of external support strategies to build quality improvement (QI) capacity in primary care with a focus on cardiovascular risk factors: appropriate aspirin use, blood pressure control, and tobacco screening/cessation. Methods: To guide practice facilitator support, experts in practice transformation identified seven domains of QI capacity and mapped items from a previously validated medical home assessment tool to them. A practice facilitator (PF) met with clinicians and staff in each practice to discuss each item on the Quality Improvement Capacity Assessment (QICA) resulting in a practice-level response to each item. We examined the association between the QICA total and sub-scale scores, practice characteristics, a measure of prior experience with managing practice change, and performance on clinical quality measures (CQMs) for the three cardiovascular risk factors. Results: The QICA score was associated with prior experience managing change and moderately associated with two of the three CQMs: aspirin use (r=0.16, p=0.049) and blood pressure control (r=0.18, p=0.013). Rural practices and those with 2-5 clinicians had lower QICA scores. PFs notes provide examples of high scoring practices devoting time and attention to quality improvement whereas low scoring practices did not. Conclusions: The QICA is useful for assessing QI capacity within a practice and may serve as a guide for both facilitators and primary care practices in efforts to build this capacity and improve measures of clinical quality.


2015 ◽  
Vol 63 (5) ◽  
pp. 963-969 ◽  
Author(s):  
Bruce Leff ◽  
Christine M. Weston ◽  
Sarah Garrigues ◽  
Kanan Patel ◽  
Christine Ritchie ◽  
...  

2018 ◽  
Vol 16 (Suppl 1) ◽  
pp. S35-S43 ◽  
Author(s):  
Bijal A. Balasubramanian ◽  
Miguel Marino ◽  
Deborah J. Cohen ◽  
Rikki L. Ward ◽  
Alex Preston ◽  
...  

2019 ◽  
Vol 16 (2) ◽  
pp. 154-164 ◽  
Author(s):  
David G Bundy ◽  
Hardeep Singh ◽  
Ruth EK Stein ◽  
Tammy M Brady ◽  
Christoph U Lehmann ◽  
...  

Background: Diagnostic errors contribute to the large burden of healthcare-associated harm experienced by children. Primary care settings involve high diagnostic uncertainty and limited time and information, creating ideal conditions for diagnostic errors. We report on the design and conduct of Project RedDE, a stepped-wedge, cluster-randomized controlled trial of a virtual quality improvement collaborative aimed at reducing diagnostic errors in pediatric primary care. Methods: Project RedDE cluster-randomized pediatric primary care practices into one of three groups. Each group participated in a quality improvement collaborative targeting the same three diagnostic errors (missed diagnoses of elevated blood pressure and adolescent depression and delayed diagnoses of abnormal laboratory studies), but in a different sequence. During the quality improvement collaborative, practices worked both independently and collaboratively, leveraging general quality improvement strategies (e.g. process mapping) in addition to error-specific content (e.g. pocket guides for blood pressure norms) delivered during the intervention phase for each error. The quality improvement collaborative intervention included interactive learning sessions and webinars, quality improvement coaching at the team level, and repeated evaluation of failures via root cause analyses. Pragmatic data were collected monthly, submitted to a centralized data aggregator, and returned to the practices in the form of run charts comparing each practice’s progress over time to that of the group. The primary analysis used patients as the unit of analysis and compared diagnostic error proportions between the intervention and baseline periods, while secondary analyses evaluated the sustainability of observed reductions in diagnostic errors after the intervention period ended. Results: A total of 43 practices were recruited and randomized into Project RedDE. Eleven practices withdrew before submitting any data, and one practice merged with another participating practice, leaving 31 practices that began work on Project RedDE. All but one of the diverse, national pediatric primary care practices that participated ultimately submitted complete data. Quality improvement collaborative participation was robust, with an average of 63% of practices present on quality improvement collaborative webinars and 85% of practices present for quality improvement collaborative learning sessions. Complete data included 30 months of outcome data for the first diagnostic error worked on, 24 months of outcome data for the second, and 16 months of data for the third. Lessons learned and limitations: Contamination across study groups was a recurring concern; concerted efforts were made to mitigate this risk. Electronic health records played a large role in teams’ success. Conclusion: Project RedDE, a virtual quality improvement collaborative aimed at reducing diagnostic errors in pediatric primary care, successfully recruited and retained a diverse, national group of pediatric primary care practices. The stepped-wedge, cluster-randomized controlled trial design allowed for enhanced scientific efficiency.


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