E-SCOPE: A Strategic Approach to Identify and Accelerate Implementation of Evidence-based Best Practices

Evidence-Based Antibiotic Prophylaxis in Clean Hand Surgeries: An Educational Intervention to Decrease Use

PURPOSE

Evidence for the use of prophylactic antibiotics in clean hand surgery is limited, yet surgeons continue to administer antibiotics to prevent postoperative infections. We sought to assess the effect of a program directed at reducing the use of antibiotic prophylaxis in carpal tunnel release surgery and elicit reasons for continued use.

METHODS

A surgeon leader implemented a program between September 1, 2018 and September 30, 2019 to reduce antibiotic prophylaxis in clean hand surgeries in a hospital system of 10 medical centers. It consisted of (1) an evidence-based educational session for all participating orthopedic and hand surgeons during which the elimination of the use of antibiotics in clean hand surgeries was requested and (2) a year-long, monthly antibiotic use audit and feedback cycle using carpal tunnel release (CTR) as a proxy for clean hand surgery. The rate of antibiotic use in the year of the intervention was compared to the rate prior to the intervention. Multivariable regression was used to determine patient-related risk factors for receiving antibiotics. Participating surgeons completed a survey to elucidate factors that contributed to continued use.

RESULTS

Antibiotic prophylaxis decreased from 1223/2379 (51%) in 2017-2018 to 531/2550 (21%) in 2018-2019. During the last month of evaluation, the rate decreased to 28/208 (14%). Logistic regression revealed a higher rate of antibiotic use during the period after the intervention among patients who had diabetes mellitus or who were operated upon by an older surgeon. The follow-up surgeon survey revealed a strong positive correlation between surgeon willingness to administer antibiotics and patient hemoglobin A1c and body mass index.

CONCLUSIONS

The rate of antibiotic use in carpal tunnel release decreased from 51% the year prior to 14% the final month of implementing a surgeon-led program to reduce antibiotic prophylaxis. Multiple barriers to the implementation of evidence-based practice were identified.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prognostic IV.

The Alliance for Healthier Communities as a Learning Health System for primary care: A qualitative analysis in Ontario, Canada

RATIONALE, AIMS AND OBJECTIVES

A learning health system model can be used to efficiently evaluate and incorporate evidence-based care into practice. However, there is a paucity of evidence describing key organizational attributes needed to ensure a successful learning health system within primary care. We interviewed stakeholders for a primary care learning health system in Ontario, Canada (the Alliance for Healthier Communities) to identify strengths and areas for improvement.

METHOD

We conducted a qualitative descriptive study using individual semistructured interviews with Alliance stakeholders between December 2019 and March 2020. The Alliance delivers community-governed primary healthcare through 109 organizations including Community Health Centres (CHCs). All CHC staff within the Alliance were invited to participate. Interviews were audio-recorded and transcribed verbatim. We performed a thematic analysis using a team approach.

RESULTS

We interviewed 29 participants across six CHCs, including Executive Directors, managers, healthcare providers and data support staff. We observed three foundational elements necessary for a successful learning health system within primary care: shared organizational goals and culture, data quality and resources. Building on this foundation, people are needed to drive the learning health system, and this is conditional on their level of engagement. The main factors motivating staff member's engagement with the learning health system included their drive to help improve patient care, focusing on initiatives of personal interest and understanding the purpose of different initiatives. Areas for improvement were identified such as the ability to extract and use data to inform changes in real-time, better engagement and protected time for providers to do improvement work, and more staff dedicated to data extraction and analysis.

CONCLUSIONS

We identified key components needed to establish a learning health system in primary care. Similar primary care organizations in Canada and elsewhere can use these insights to guide their development as learning health systems.

Carbon Footprint of General, Regional, and Combined Anesthesia for Total Knee Replacements

BACKGROUND

Health care itself contributes to climate change. Anesthesia is a "carbon hotspot," yet few data exist to compare anesthetic choices. The authors examined the carbon dioxide equivalent emissions associated with general anesthesia, spinal anesthesia, and combined (general and spinal anesthesia) during a total knee replacement.

METHODS

A prospective life cycle assessment of 10 patients in each of three groups undergoing knee replacements was conducted in Melbourne, Australia. The authors collected input data for anesthetic items, gases, and drugs, and electricity for patient warming and anesthetic machine. Sevoflurane or propofol was used for general anesthesia. Life cycle assessment software was used to convert inputs to their carbon footprint (in kilogram carbon dioxide equivalent emissions), with modeled international comparisons.

RESULTS

Twenty-nine patients were studied. The carbon dioxide equivalent emissions for general anesthesia were an average 14.9 (95% CI, 9.7 to 22.5) kg carbon dioxide equivalent emissions; spinal anesthesia, 16.9 (95% CI, 13.2 to 20.5) kg carbon dioxide equivalent; and for combined anesthesia, 18.5 (95% CI, 12.5 to 27.3) kg carbon dioxide equivalent. Major sources of carbon dioxide equivalent emissions across all approaches were as follows: electricity for the patient air warmer (average at least 2.5 kg carbon dioxide equivalent [20% total]), single-use items, 3.6 (general anesthesia), 3.4 (spinal), and 4.3 (combined) kg carbon dioxide equivalent emissions, respectively (approximately 25% total). For the general anesthesia and combined groups, sevoflurane contributed an average 4.7 kg carbon dioxide equivalent (35% total) and 3.1 kg carbon dioxide equivalent (19%), respectively. For spinal and combined, washing and sterilizing reusable items contributed 4.5 kg carbon dioxide equivalent (29% total) and 4.1 kg carbon dioxide equivalent (24%) emissions, respectively. Oxygen use was important to the spinal anesthetic carbon footprint (2.8 kg carbon dioxide equivalent, 18%). Modeling showed that intercountry carbon dioxide equivalent emission variability was less than intragroup variability (minimum/maximum).

CONCLUSIONS

All anesthetic approaches had similar carbon footprints (desflurane and nitrous oxide were not used for general anesthesia). Rather than spinal being a default low carbon approach, several choices determine the final carbon footprint: using low-flow anesthesia/total intravenous anesthesia, reducing single-use plastics, reducing oxygen flows, and collaborating with engineers to augment energy efficiency/renewable electricity.

EDITOR’S PERSPECTIVE

Learning health systems in primary care: a systematic scoping review

BACKGROUND

Learning health systems have been gaining traction over the past decade. The purpose of this study was to understand the spread of learning health systems in primary care, including where they have been implemented, how they are operating, and potential challenges and solutions.

METHODS

We completed a scoping review by systematically searching OVID Medline®, Embase®, IEEE Xplore®, and reviewing specific journals from 2007 to 2020. We also completed a Google search to identify gray literature.

RESULTS

We reviewed 1924 articles through our database search and 51 articles from other sources, from which we identified 21 unique learning health systems based on 62 data sources. Only one of these learning health systems was implemented exclusively in a primary care setting, where all others were integrated health systems or networks that also included other care settings. Eighteen of the 21 were in the United States. Examples of how these learning health systems were being used included real-time clinical surveillance, quality improvement initiatives, pragmatic trials at the point of care, and decision support. Many challenges and potential solutions were identified regarding data, sustainability, promoting a learning culture, prioritization processes, involvement of community, and balancing quality improvement versus research.

CONCLUSIONS

We identified 21 learning health systems, which all appear at an early stage of development, and only one was primary care only. We summarized and provided examples of integrated health systems and data networks that can be considered early models in the growing global movement to advance learning health systems in primary care.