Towards Better Perioperative Pain Management in Mexico: A Study in a Network of Hospitals Using Quality Improvement Methods from PAIN OUT

Abstract

Objective: This was a pre-post study in a network of hospitals in Mexico-City, Mexico. Participants developed and implemented Quality Improvement (QI) interventions addressing perioperative pain management.

Methods: PAIN OUT, an international QI and research network, provided tools for web-based auditing and feedback of pain management and patient-reported outcomes (PROs) in the clinical routine. Ward- and patient-level factors were evaluated with multi-level models. Change in proportion of patients reporting worst pain ≥6/10 between project phases was the primary outcome.

Results: Participants created locally adapted resources for teaching and pain management, available to providers in the form of a website and a special issue of a national anesthesia journal. They offered teaching to anesthesiologists, surgeons, including residents, and nurses. Information was offered to patients and families. A total of 2658 patients were audited in 9 hospitals, between July 2016 and December 2018. Participants reported that the project made them aware of the importance of: training in pain management; auditing one's own patients to learn about PROs and that QI requires collaboration between multi-disciplinary teams. Participants reported being unaware that their patients experienced severe pain and lacked information about pain treatment options. Worst pain decreased significantly between the two project phases, as did PROs related to pain interfering with movement, taking a deep breath/coughing or sleep. The opportunity of patients receiving information about their pain treatment options increased from 44% to 77%.

Conclusions: Patients benefited from improved care and pain-related PROs. Clinicians appreciated gaining increased expertise in perioperative pain management and methods of QI.

Keywords: acute pain; auditing; patient-reported outcomes; perioperative pain management; quality improvement; surgery.

Conflict of interest statement

Drs Ana Lilia Garduño-López reports personal fees from ASPEN outside the submitted work. Dr Victor Manuel Acosta Nava reports grants from Pfizer during the conduct of the study. Dr Lisette Castro Garcés reports grants from Pfizer during the conduct of the study. Professor Winfried Meissner reports grants, personal fees from Grünenthal, grants from Pfizer, personal fees from TAD, personal fees from BioQPharm, personal fees from Bionorica, personal fees from Kyowa, personal fees from Northern Swan, grants from Mundipharma, personal fees from Tilray, outside the submitted work. The authors have no other conflicts of interest to declare.

© 2021 Garduño-López et al.

Figures

Figure 1

The flow chart depicts patient recruitment during the two project phases.

Figure 2

Distribution of the relative frequencies of the patient-reported outcomes are shown in (A) and for processes in (B). Each dot represents summarized data from one ward. Box plots filled in with gray, represent data for the first project phase and white plots represent data for the second phase.

Figure 3

Changes due to the QI work at the network level. The marginal effects for project phase on the patient-reported outcomes are shown in the upper panel, shaded in light gray, and the process variables are portrayed in the lower panel, shaded in dark gray. Squares depict the relative risk regarding project phases obtained by regression modelling and the black horizontal lines indicate the corresponding 95 % confidence intervals. *p

Figure 4

Results of the single ward analysis and whether improvement took place and its effect size. Cells with a green background indicate improvement, whereas, red signifies worsening of the PRO or decreased implementation of the process in phase 2. The effect size for each item is written in each cell. + signifies potential ceiling effects, indicating that the process was implemented in >90% of cases in phase 1.

Figure 5

Associations between the PROs and processes. Cells in green depict significant regression coefficients indicating a favorable association between process variable and PRO (e.g. receiving information about treatment options is associated with a lower risk of reporting worst ≥ 6/10 NRS). Correspondingly, red cells depict significant regression coefficients indicating an unfavorable association between process variable and PRO (eg, receiving systemic opioids is associated with a higher risk of reporting nausea ≥ 4/10 NRS). Asterisks indicate significant associations after applying the Bonferroni-Holm correction for multiple comparisons and adjusted for p values of less than 0.05.