BMC Health Services Research最新文献

Background: Availability of mental health services is often limited, with many patients suffering from depression or anxiety either unidentified or unable to access care. Community pharmacists may be well-positioned to serve as behavioral healthcare extenders by offering timely screening, referrals, education, and medication management. However, evidence supporting feasibility and effectiveness of behavioral health (BH) care interventions in community pharmacy settings remains anecdotal. The purpose of this article is to summarize the findings from a feasibility evaluation of a BH intervention in 7 U.S. community pharmacies.

Methods: The BH intervention, delivered over 6-11 months, consisted of a screening and referral program and a 6-session education and medication management program. Participating pharmacies benefited from a multi-faceted implementation strategy (e.g., coaching, toolkits). An effectiveness-implementation hybrid Type II design was used to assess effectiveness of the intervention, while evaluating its implementation. Implementation outcomes involved program adoption rates, levels of program acceptability, appropriateness, and feasibility, intent to sustain, and fidelity rates. Intervention outcomes included: patient referral rates, perceived benefits, and changes in patient knowledge, clinical symptoms, and medication adherence. The data were collected using multiple methods (e.g., surveys, interviews, administrative data) and analyzed accordingly.

Results: Results indicated a 100% adoption rate by the pharmacies with intent to continue past the project period; significant increases in program acceptability, appropriateness, and feasibility; and high levels of program fidelity. All 206 patients were appropriately referred as needed, with patients enrolled in the 6-session program reporting statistically significant changes in knowledge, clinical symptoms, and a nonadherence to medications.

Conclusions: These findings lend support to the role that community pharmacists can play in bridging the mental health care gap and improving population health.

Objective: To develop and validate a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model for forecasting daily patient admissions to the Post-Anesthesia Care Unit (PACU), and to evaluate its potential for optimizing nursing staff allocation.

Method: Daily admission data from 16,637 patients between November 2, 2020, and January 2, 2022, were analyzed. The SARIMA model was developed on a training set (Nov 2020 - Dec 2021) and its forecasting accuracy was rigorously assessed on a test set (Dec 2021 - Jan 2022) using five-fold rolling cross-validation. Model selection was based on Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC), with residual diagnostics conducted to ensure validity. The model's performance was compared against a Long Short-Term Memory (LSTM) neural network. An operational simulation for nurse staffing was conducted based on the forecasts.

Results: The SARIMA(1,0,2)(0,1,2)7 model was identified as optimal. It demonstrated strong forecasting performance with a mean RMSE of 14.53, MAE of 11.14, and R² of 0.75 on cross-validation. Performance was superior during stable periods (e.g. Fold 4: RMSE = 9.27, R² = 0.88) but declined during periods of potential COVID-19 disruption. The model significantly outperformed the LSTM benchmark (LSTM RMSE = 15.51, R² = 0.483). A staffing simulation showed the model's recommendations could potentially reduce overstaffing on 32.1% of days while maintaining safe coverage.

Conclusion: The SARIMA model provides accurate and reliable short-term forecasts for PACU patient admissions under normal operational conditions. It serves as a valuable decision-support tool for optimizing nursing staff scheduling and improving resource allocation efficiency, demonstrating superior performance and practicality compared to a more complex LSTM model in this clinical setting.

Background: This study evaluates the feasibility and effectiveness of a tertiary hospital-led trusteeship model in strengthening radiology capacity within primary healthcare institutions under a high-level integrated healthcare system.

Methods: From 2018 to 2022, Zhejiang Hospital implemented a radiology department trusteeship model at Xihu District Hospital, comprising three core components: (1) deployment of expert resources through on-site and remote reporting, (2) full-process quality control, and (3) targeted personnel training. Clinical satisfaction was assessed via structured electronic questionnaires administered before (2016-2017) and after (2018-2022) implementation among frontline clinicians from internal medicine, surgery, orthopedics, and general wards. The survey utilized a 5-point Likert scale across five domains. Quantitative trends in Digital Radiography (DR) and Computed Tomography (CT) workload, as well as remote consultation volumes, were analyzed. Additionally, blinded quality assessments were conducted on 100 randomly selected radiology reports (50 DR and 50 CT, equally divided pre- and post-intervention) using a standardized 100-point scoring system. Statistical analyses were performed using SPSS 22.0.

Results: A total of 28 clinicians participated (male: 9, female: 19; median age: 40 years). Post-intervention, significant improvements were observed in timeliness (median [interquartile range, IQR]: 4.5 [4, 5] → 5 [5, 5], P = 0.006), diagnostic quality (4 [4, 5] → 5 [5, 5], P = 0.007), and clinical guidance (4 [4, 5] → 5 [5, 5], P = 0.003). No significant changes were noted in willingness to participate in case discussions or referral intentions. Clinical practicality was identified as the highest priority for improvement (46.4%). Notable structural shifts in imaging utilization were observed: DR examinations declined from 14,224 (2017) to 7,209 (2022), while CT examinations increased from 0 to 8,415 over the same period. Remote consultation volume rose from 342 (2018) to 507 (2022). Radiology report quality scores improved significantly (61 [57,63] → 85 [83,87], P < 0.001). Over 96% of respondents expressed support for the continued implementation of the model.

Conclusions: The "West Lake Model" trusteeship effectively addressed primary care radiology challenges, enhancing service capacity and clinical utility. It provides a scalable framework for improving integration within tiered healthcare systems.

Clinical trial number: Not applicable.