This study aimed to investigate the estimated change in primary and revision arthroplasty rate in the Netherlands and Denmark for hips, knees, and shoulders during the COVID-19 pandemic in 2020 (COVID-period). Additional points of focus included the comparison of patient characteristics and hospital type (2019 vs COVID-period), and the estimated loss of quality-adjusted life years (QALYs) and impact on waiting lists. All hip, knee, and shoulder arthroplasties (2014 to 2020) from the Dutch Arthroplasty Register, and hip and knee arthroplasties from the Danish Hip and Knee Arthroplasty Registries, were included. The expected number of arthroplasties per month in 2020 was estimated using Poisson regression, taking into account changes in age and sex distribution of the general Dutch/Danish population over time, calculating observed/expected (O/E) ratios. Country-specific proportions of patient characteristics and hospital type were calculated per indication category (osteoarthritis/other elective/acute). Waiting list outcomes including QALYs were estimated by modelling virtual waiting lists including 0%, 5% and 10% extra capacity.Aims
Methods
Data of high quality are critical for the meaningful interpretation of registry information. The National Joint Registry (NJR) was established in 2002 as the result of an unexpectedly high failure rate of a cemented total hip arthroplasty. The NJR began data collection in 2003. In this study we report on the outcomes following the establishment of a formal data quality (DQ) audit process within the NJR, within which each patient episode entry is validated against the hospital unit’s Patient Administration System and vice-versa. This process enables bidirectional validation of every NJR entry and retrospective correction of any errors in the dataset. In 2014/15 baseline average compliance was 92.6% and this increased year-on-year with repeated audit cycles to 96.0% in 2018/19, with 76.4% of units achieving > 95% compliance. Following the closure of the audit cycle, an overall compliance rate of 97.9% was achieved for the 2018/19 period. An automated system was initiated in 2018 to reduce administrative burden and to integrate the DQ process into standard workflows. Our processes and quality improvement results demonstrate that DQ may be implemented successfully at national level, while minimizing the burden on hospitals. Cite this article:
The Oxford Hip and Knee Scores (OHS, OKS) have been demonstrated
to vary according to age and gender, making it difficult to compare
results in cohorts with different demographics. The aim of this
paper was to calculate reference values for different patient groups
and highlight the concept of normative reference data to contextualise an
individual’s outcome. We accessed prospectively collected OHS and OKS data for patients
undergoing lower limb joint arthroplasty at a single orthopaedic
teaching hospital during a five-year period.
T-scores were calculated based on the OHS and OKS distributions. Objectives
Methods
Satisfaction with care is important to both patients
and to those who pay for it. The Net Promoter Score (NPS), widely
used in the service industries, has been introduced into the NHS
as the ‘friends and family test’; an overarching measure of patient
satisfaction. It assesses the likelihood of the patient recommending
the healthcare received to another, and is seen as a discriminator
of healthcare performance. We prospectively assessed 6186 individuals
undergoing primary lower limb joint replacement at a single university
hospital to determine the Net Promoter Score for joint replacements
and to evaluate which factors contributed to the response. Achieving pain relief (odds ratio (OR) 2.13, confidence interval
(CI) 1.83 to 2.49), the meeting of pre-operative expectation (OR
2.57, CI 2.24 to 2.97), and the hospital experience (OR 2.33, CI
2.03 to 2.68) are the domains that explain whether a patient would
recommend joint replacement services. These three factors, combined
with the type of surgery undertaken (OR 2.31, CI 1.68 to 3.17),
drove a predictive model that was able to explain 95% of the variation
in the patient’s recommendation response. Though intuitively similar,
this ‘recommendation’ metric was found to be materially different
to satisfaction responses. The difference between THR (NPS 71) and
TKR (NPS 49) suggests that no overarching score for a department
should be used without an adjustment for case mix. However, the
Net Promoter Score does measure a further important dimension to
our existing metrics: the patient experience of healthcare delivery. Cite this article: