Quality Indicator System Lifts Pharmaceutical Care Adherence in Orthopedic Pain Management from 41% to 91%

Postoperative pain remains one of the most common and most undertreated problems in modern surgery, and orthopedic patients are among those who suffer most. Joint replacements, fracture repairs, and spinal procedures generate intense tissue trauma, yet surveys across many health systems have repeatedly shown that a substantial share of these patients receive analgesia that is delayed, incomplete, or poorly matched to their individual risk profiles. A new study published in PLOS One by Qian Zhu, Panfeng Feng, Xia Chen, Jiaojiao Huang, and Yong Chen now offers a concrete, measurable way to close that gap. The researchers implemented a structured quality indicator system for pharmaceutical care in orthopedic perioperative pain management and found that overall adherence to care standards more than doubled, rising from 41 percent before the intervention to 91 percent afterward.

The study is notable not only for the size of the improvement but also for the methodological machinery behind it. Rather than relying on vague notions of good practice, the team built on an indicator system developed in earlier methodological work using the Delphi method, a structured consensus technique in which panels of experts iteratively rate and refine candidate quality measures until stable agreement is reached. The indicators were then weighted using the Analytic Hierarchy Process, a mathematical framework that converts expert judgments into consistent numerical priorities. This combination produced a set of twenty indicators spanning three categories: structural indicators, which measure whether the resources, protocols, and personnel needed for good pharmaceutical care are in place; process indicators, which measure whether recommended actions such as pain assessment and medication counseling are actually performed; and outcome indicators, which measure whether patients achieve adequate pain control in the end.

To turn those indicators into a single, interpretable score, the researchers employed TOPSIS, the Technique for Order Preference by Similarity to Ideal Solution. TOPSIS is a multi-criteria decision analysis method that positions each evaluated case in a multidimensional space defined by the indicator values, then calculates how close that case sits to an ideal solution, in which every indicator reaches its best possible value, and how far it sits from the worst possible solution. The resulting closeness coefficient, denoted C(i), ranges from zero to one and serves as a composite measure of pharmaceutical care quality. The team pre-specified a threshold of C(i) of at least 0.6 as the definition of overall adherence, giving the study an objective and reproducible yardstick rather than a subjective judgment call.

The evaluation took the form of a single-center before-and-after study with a historical control. First, the researchers retrospectively assessed a pre-intervention cohort of 100 patients who underwent elective orthopedic surgery between November 2024 and January 2025, establishing the baseline reference for pharmaceutical care quality. They then designed and introduced targeted interventions aimed at the weaknesses revealed by the indicator analysis. Finally, they prospectively evaluated a post-intervention cohort of 100 patients treated between April and June 2025. Each group comprised 200 patients in total across the study, and the two cohorts were compared on overall adherence and on the full-score attainment rates of the individual quality indicators, with a Bonferroni correction applied to guard against false positives arising from multiple statistical comparisons.

The headline result was striking. Overall adherence to pharmaceutical care standards, defined as a TOPSIS closeness coefficient of at least 0.6, increased from 41 percent in the pre-intervention cohort to 91 percent in the post-intervention cohort. That corresponds to a risk difference of 50 percentage points, with a 95 percent confidence interval of 37.83 to 60.05 and a P value below 0.001. The median difference in the composite score itself was 0.32, with a 95 percent confidence interval of 0.26 to 0.38. In practical terms, roughly nine out of ten surgical patients after the intervention received pharmaceutical care that met the expert-defined standard, compared with fewer than half before.

Because before-and-after studies with historical controls are vulnerable to bias, particularly when some documentation is missing from the earlier records, the team performed a sensitivity analysis to test how robust their conclusion was. The concern is straightforward: if documentation quality improved over time for reasons unrelated to actual care, some of the apparent improvement might be an artifact. The researchers therefore recalculated the comparison under the most optimistic assumption for the missing pre-intervention data, that is, the assumption most favorable to the baseline group. Even under that scenario, the post-intervention group remained significantly higher, at 91 percent versus 55 percent, with P below 0.001. The finding, in other words, survived a deliberately conservative stress test.

Dissecting the results by indicator type revealed a nuanced pattern. The four structural indicators showed descriptive improvements, with absolute increases ranging from 34 to 60 percentage points, suggesting that the intervention strengthened the underlying infrastructure of pharmaceutical care, such as the availability of standardized protocols and dedicated pharmacist involvement. Among the remaining sixteen indicators, multimodal analgesia, the practice of combining several analgesic mechanisms to achieve better pain relief with lower opioid doses, showed no statistically significant difference, sitting at 96 percent versus 99 percent. This ceiling effect is itself informative: the baseline performance was already near maximal, leaving little room for improvement on that particular measure.

The process and outcome indicators told a more varied story. Of the fifteen indicators in these categories other than multimodal analgesia, thirteen remained statistically significant after the stringent Bonferroni correction, which set the significance threshold at an adjusted alpha of 0.0031. Their risk differences ranged from 14 to 33 percentage points, indicating substantial and reliable gains in the day-to-day execution of pharmaceutical care and in intermediate clinical results. Two outcome indicators, however, did not clear the adjusted threshold: achievement of target resting pain control improved by 17 percentage points, and the combined indicator covering breakthrough pain incidence and its management improved by 10 percentage points, but neither reached the corrected level of significance. This suggests that while the machinery of care, the assessments, the documentation, and the medication management processes, improved dramatically, the ultimate clinical endpoints moved more modestly and require further work to confirm.

The authors are candid about the limitations of their design. A historical control cannot exclude secular trends, such as parallel improvements in staffing, awareness, or documentation practices that occurred between the two study windows. The reliance on a composite TOPSIS measure, while mathematically elegant, means that the headline adherence figure aggregates many distinct aspects of care, and the single-center setting raises questions about whether the same gains could be replicated in hospitals with different resources, case mixes, and pharmacy services. The two-month post-intervention window also leaves open the question of whether the improvements can be sustained over the long term, a common challenge for quality improvement programs of all kinds.

Even with those caveats, the study delivers something the perioperative pain field has lacked: empirical evidence that a rigorously constructed quality indicator system, combining Delphi consensus, hierarchical weighting, and TOPSIS scoring, can be implemented in routine clinical practice and can drive measurable, robust improvements in pharmaceutical care. The work provides a practical reference for hospitals seeking to formalize the pharmacist’s role in orthopedic pain management, and it demonstrates that quality in pharmaceutical care need not remain an abstraction. If the approach generalizes, the humble checklist of well-weighted indicators may prove to be one of the most effective tools yet for ensuring that patients waking up from orthopedic surgery receive the pain care that guidelines have long promised them.

Subject of Research: Quality indicators for pharmaceutical care in orthopedic perioperative pain management

Article Title: Application and evaluation of a quality indicator system for pharmaceutical care in orthopedic perioperative pain management

Article References: Zhu, Q., Feng, P., Chen, X., Huang, J., & Chen, Y. (2026). Application and evaluation of a quality indicator system for pharmaceutical care in orthopedic perioperative pain management. PLOS One, 21(10), e0360057. https://doi.org/10.1371/journal.pone.0360057

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0360057

Keywords: pharmaceutical care, perioperative pain management, orthopedic surgery, quality indicators, TOPSIS, Delphi method, Analytic Hierarchy Process, multimodal analgesia, patient safety, clinical pharmacy, quality improvement, pain control