BACKGROUND: This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured information in the official databases.
METHODS: A Natural Language Processing (NLP) based pipeline has been developed to extract the waiting time information from the text of referrals for follow-up examinations in the Lombardy Region. A manually annotated dataset of 10 000 referrals has been used to develop the pipeline and another manually annotated dataset of 10 000 referrals has been used to test its performance. Subsequently, the pipeline has been used to analyze all 12 million referrals prescribed in 2021 and performed by May 2022 in the Lombardy Region.
RESULTS: The NLP-based pipeline exhibited high precision (0.999) and recall (0.973) in identifying waiting time information from referrals\' texts, with high accuracy in normalization (0.948-0.998). The overall reporting of timing indications in referrals\' texts for follow-up examinations was low (2%), showing notable variations across medical disciplines and types of prescribing physicians. Among the referrals reporting waiting times, 16% experienced delays (average delay = 19 days, standard deviation = 34 days), with significant differences observed across medical disciplines and geographical areas.
CONCLUSIONS: The use of NLP proved to be a valuable tool for assessing waiting times in follow-up examinations, which are particularly critical for the NHS due to the significant impact of chronic diseases, where follow-up exams are pivotal. Health authorities can exploit this tool to monitor the quality of NHS services and optimize resource allocation.

OBJECTIVE: Breast cancer (BC) is the most common type of cancer among women in Brazil. Evidence shows that delayed treatment onset is associated with increased mortality. This study aimed to evaluate median days between diagnosis and treatment and factors associated with delayed start of treatment (> 60 days after diagnosis): stage, treatment received, subtype, epidemiological characteristics, and type of healthcare coverage.
METHODS: This analysis included 1709 stage I-III BC patients from AMAZONA III, a prospective, observational study, diagnosed from January 2016 to March 2018 in 22 centers in Brazil.
RESULTS: The median number of days from diagnosis to beginning of first oncologic treatment was 46 days (IQR 28-75) overall, 43 days (IQR 25-75) for stage I disease, 49 days (IQR 28-81) for stage II, and 44 days (IQR 30-68) for stage III, (p = 0.1180). According to first treatment received, diagnosis-to-treatment interval was 43 days (IQR 29-65) for neoadjuvant chemotherapy and 48 days (IQR 26-81) for surgery. Diagnosis-to-treatment interval was higher in women treated in the public system versus the private system (56 vs. 34 days, p < 0.0001). Patients in the public system had an increased odds of delayed treatment initiation (OR 4.74 95% CI 3.09-7.26, p < .0001). The longer interval from diagnosis to treatment in the public system was independent of clinical stage, type of treatment (systemic vs surgery first), subtype and region of the country.
CONCLUSIONS: By characterizing the delays in care delivery, our study will aid stakeholders to better design interventions and allocate resource to improve timely treatment for breast cancer in Brazil.
RESULTS: gov Identifier: NCT02663973, registered on January, 26th, 2016.

This study aimed to investigate the perceptions of patient-centered care (PCC) among inpatients in Guangdong Province (GD), China. Based on these perspectives, we sought to understand existing PCC practices in medical institutions and identify the impacts of inpatients\' sociodemographic status on their perceived PCC. A self-developed PCC questionnaire was used to investigate inpatients\' perceptions of PCC. A cross-sectional survey was conducted in nine tertiary-level hospitals across five cities in GD. Descriptive statistics was used to describe the levels of PCC in GD. The differences in PCC levels across different sociodemographic groups were assessed using analysis of variance and multivariate linear regression. Valid responses were provided by 1863 inpatients. The mean overall PCC score was 8.58 (standard deviation [SD] = 1.36); inpatients from the Pearl River Delta and eastern GD area reported significantly higher scores than those from western and northern GD area (P<.01). Inpatients from rural areas tended to report lower PCC scores than their urban counterparts. Among the PCC questionnaire sub-domains, inpatients scored highest and lowest in \"patient experience\" (mean = 8.96, SD = 1.34) and \"medical insurance\" (mean = 7.93, SD = 2.05), respectively. This study provided a comprehensive overview of inpatients\' perceptions of PCC in the public healthcare system in GD, China. Our findings highlighted that a majority of inpatients were satisfied with the PCC in public healthcare system; however, a significant discrepancy between inpatients with different sociodemographic status remained.

OBJECTIVE: Inappropriate antimicrobial use favours the spread of resistance, and multidrug-resistant microorganisms (MDR) are currently of major concern. Antimicrobial stewardship programmes (ASPs) are essential for improving antibiotic use in hospitals. However, their impact on entire healthcare systems has not been thoroughly assessed. Our objective was to provide the results of an institutionally supported ASP involving 31 public hospitals in Andalusia, Spain.
METHODS: We designed an ecologic time-series study from 1 January 2014 to 31 December 2017. Quarterly, data on indicators were collected prospectively, and feedback reports were provided. PIRASOA is an ongoing clinically based quality-improvement programme whose key intervention is the educational interview, regular peer-to-peer interventions between advisors and prescribers to reinforce the appropriate use of antibiotics. Seventy-two indicators were monitored to measure prescribing quality (inappropriate treatments), antimicrobial consumption (defined daily doses per 1000 occupied bed-days), incidence density of MDR per 1000 occupied bed-days and crude mortality rate associated with bloodstream infections. We used Joinpoint regression software to analyse the trends.
RESULTS: The quality of antimicrobial prescribing improved markedly, and the inappropriate treatment rate was significantly lower, with quarterly percentage change (QPC) = -3.0%, p < 0.001. Total antimicrobial consumption decreased (QPC = -0.9%, p < 0.001), specifically carbapenems, amoxicillin/clavulanic acid, quinolones and antifungal agents, whereas antipseudomonal cephalosporin use increased. While the incidence of MDR showed a sustained decreasing trend (QPC = -1.8%; p 0.002), the mortality of patients with bloodstream infections remained stable (QPC = -0.2%, p 0.605).
CONCLUSIONS: To date, the PIRASOA programme has succeeded in optimizing the use of antimicrobial agents and has had a positive ecologic result on bacterial resistance at level of an entire healthcare system.