Textbook process as a composite quality indicator for in-hospital hip fracture care

Poor prognosis and risk factors of nonoperative treatment hip fracture patients with end-stage renal disease

This study aims to summarize the prognosis and risk factors of nonoperative treatment patients of hip fracture with end-stage renal disease (ESRD). A total of 113 individuals of hip fracture with nonoperative treatment were retrospectively enrolled, 38 patients with ESRD were defined as the observation group, and the other 75 patients without ESRD were served as the control group. The difference in 30-day and 1-year mortality rate between the 2 groups was compared. The effects of risk factors on survival were estimated by the Cox proportional-hazards model. The survival difference was estimated by the method of Kaplan-Meier. In the subsequent subgroup analysis of the observation group, as before, the Cox proportional-hazards model and the Kaplan-Meier method were used. The 1-year mortality rate of the observation group was considerably higher than that of the control group, which was 86.84% and 32.0%, respectively (P < .005). For nonoperative treatment hip fracture subjects, ESRD, age ≥ 82, BMI (BMI) < 20, high Charlson Comorbidity Index (CCI) and low Barthel Index (BI) were associated with a low survival curve (P < .05). In the subsequent multivariable subgroup analyses, for nonoperative treatment hip fracture subjects with ESRD, high CCI and low BI were also the independent risk factors of mortality. For nonoperative treatment hip fracture subjects, ESRD, old age, low BMI, high CCI and low BI were the independent risk factors of mortality. For hip fracture subjects with ESRD, nonoperative treatment was associated with excess high 1-year mortality rate, especially for patients with high CCI and low BI.

Development and validation of automated electronic health record data reuse for a multidisciplinary quality dashboard

Objective

To describe the development and validation of automated electronic health record data reuse for a multidisciplinary quality dashboard.

Materials and methods

Comparative study analyzing a manually extracted and an automatically extracted dataset with 262 patients treated for HNC cancer in a tertiary oncology center in the Netherlands in 2020. The primary outcome measures were the percentage of agreement on data elements required for calculating quality indicators and the difference between indicators results calculated using manually collected and indicators that used automatically extracted data.

Results

The results of this study demonstrate high agreement between manual and automatically collected variables, reaching up to 99.0% agreement. However, some variables demonstrate lower levels of agreement, with one variable showing only a 20.0% agreement rate. The indicator results obtained through manual collection and automatic extraction show high agreement in most cases, with discrepancy rates ranging from 0.3% to 3.5%. One indicator is identified as a negative outlier, with a discrepancy rate of nearly 25%.

Conclusions

This study shows that it is possible to use routinely collected structured data to reliably measure the quality of care in real-time, which could render manual data collection for quality measurement obsolete. To achieve reliable data reuse, it is important that relevant data is recorded as structured data during the care process. Furthermore, the results also imply that data validation is conditional to development of a reliable dashboard.