Several national health care-based smoking cessation initiatives have been recommended to facilitate the delivery of evidence-based treatments, such as quitline (telephone-based tobacco cessation ...services) assistance. The most notable examples are the 5 As (Ask, Advise, Assess, Assist, Arrange) and Ask. Advise. Refer. (AAR) programs. Unfortunately, rates of primary care referrals to quitlines are low, and most referred smokers fail to call for assistance.
To evaluate a new approach--Ask-Advise-Connect (AAC)--designed to address barriers to linking smokers with treatment.
A pair-matched, 2-treatment-arm, group-randomized design in 10 family practice clinics in a single metropolitan area. Five clinics were randomized to the AAC (intervention) and 5 to the AAR (control) conditions. In both conditions, clinic staff were trained to assess and record the smoking status of all patients at all visits in the electronic health record, and smokers were given brief advice to quit. In the AAC clinics, the names and telephone numbers of smokers who agreed to be connected were sent electronically to the quitline daily, and patients were called proactively by the quitline within 48 hours. In the AAR clinics, smokers were offered a quitline referral card and encouraged to call on their own. All data were collected from February 8 through December 27, 2011.
Ten clinics in Houston, Texas.
Smoking status assessments were completed for 42,277 patients; 2052 unique smokers were identified at AAC clinics, and 1611 smokers were identified at AAR clinics.
Linking smokers with quitline-delivered treatment.
Impact was based on the RE-AIM (Reach, Efficacy, Adoption, Implementation, and Maintenance) conceptual framework and defined as the proportion of all identified smokers who enrolled in treatment.
In the AAC clinics, 7.8% of all identified smokers enrolled in treatment vs 0.6% in the AAR clinics (t4 = 9.19 P < .001; odds ratio, 11.60 95% CI, 5.53-24.32), a 13-fold increase in the proportion of smokers enrolling in treatment.
The system changes implemented in the AAC approach could be adopted broadly by other health care systems and have tremendous potential to reduce tobacco-related morbidity and mortality.
A pay-for-performance scheme based on meeting targets for the quality of clinical care was introduced to family practice in England in 2004.
We conducted an interrupted time-series analysis of the ...quality of care in 42 representative family practices, with data collected at two time points before implementation of the scheme (1998 and 2003) and at two time points after implementation (2005 and 2007). At each time point, data on the care of patients with asthma, diabetes, or coronary heart disease were extracted from medical records; data on patients' perceptions of access to care, continuity of care, and interpersonal aspects of care were collected from questionnaires. The analysis included aspects of care that were and those that were not associated with incentives.
Between 2003 and 2005, the rate of improvement in the quality of care increased for asthma and diabetes (P<0.001) but not for heart disease. By 2007, the rate of improvement had slowed for all three conditions (P<0.001), and the quality of those aspects of care that were not associated with an incentive had declined for patients with asthma or heart disease. As compared with the period before the pay-for-performance scheme was introduced, the improvement rate after 2005 was unchanged for asthma or diabetes and was reduced for heart disease (P=0.02). No significant changes were seen in patients' reports on access to care or on interpersonal aspects of care. The level of the continuity of care, which had been constant, showed a reduction immediately after the introduction of the pay-for-performance scheme (P<0.001) and then continued at that reduced level.
Against a background of increases in the quality of care before the pay-for-performance scheme was introduced, the scheme accelerated improvements in quality for two of three chronic conditions in the short term. However, once targets were reached, the improvement in the quality of care for patients with these conditions slowed, and the quality of care declined for two conditions that had not been linked to incentives. Continuity of care was reduced after the introduction of the scheme.
Despite patient preference and many known benefits, continuity of care is in decline in general practice. The most common method of measuring continuity is the Usual Provider of Care (UPC) index. ...This requires a number of appointments per patient and a relatively long timeframe for accuracy, reducing its applicability for day-to-day performance management.
To describe the St Leonard's Index of Continuity of Care (SLICC) for measuring GP continuity regularly, and demonstrate how it has been used in service in general practice.
Analysis of appointment audit data from 2016-2017 in a general practice with 8823-9409 patients and seven part-time partners, in Exeter, UK.
The percentage of face-to-face appointments for patients on each doctor's list, with the patient's personal doctor (the SLICC), was calculated monthly. The SLICC for different demographic groupings of patients (for example, sex and frequency of attendance) was compared. The UPC index over the 2 years was also calculated, allowing comparisons between indices.
In the 2-year study period, there were 35 622 GP face-to-face appointments; 1.96 per patient per year. Overall, 51.7% (95% confidence interval = 51.2 to 52.2) of GP appointments were with the patients' personal doctor. Patients aged ≥65 years had a higher level of continuity with 64.9% of appointments being with their personal doctor. The mean whole-practice UPC score was 0.61 (standard deviation 0.23), with 'usual provider' being the personal GP for 52.8% and a trainee or locum for 8.1% of patients.
This method could provide working GPs with a simple way to track continuity of care and inform practice management and decision making.
ObjectiveCurrent healthcare reform in China has an overall goal of strengthening primary care and establishing a family practice system based on contract services. The objective of this study was to ...determine whether contracting a general practitioner (GP) could improve quality of primary care.DesignA cross-sectional study using two-stage sampling conducted from June to September 2014. Propensity score matching (PSM) was employed to control for confounding between patients with and without contracted GP.SettingThree community health centres in Guangzhou, China.Participants698 patients aged 18–89 years.Main outcome measuresThe quality of primary care was measured using a validated Chinese version of primary care assessment tool (PCAT). Eight domains are included (first contact utilisation, accessibility, continuity, comprehensiveness, coordination, family-centredness, community orientation and cultural competence from patient’s perceptions).ResultsA total of 692 effective samples were included for data analysis. After PSM, 94 pairs of patients were matched between the patients with and without contracted GPs. The total PCAT score, continuity (3.12 vs 2.68, p<0.01), comprehensiveness (2.31 vs 2.04, p<0.01) and family-centredness (2.11 vs 1.79, p<0.01) were higher in patients who contracted GPs than those did not. However, the domains of first contact utilisation (2.74 vs 2.87, p=0.14) and coordination (1.76 vs 1.93, p<0.05) were lower among patients contracted with GPs than in those who did not.ConclusionOur findings demonstrated that patients who had a contracted GP tend to experience higher quality of primary care. Our study provided evidence for health policies aiming to promote the implementation of family practice contract services. Our results also highlight further emphases on the features of primary care, first contact services and coordination services in particular.
Background
Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models.
Objective
To identify predictors of early hospital ...readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk.
Design
Prospective observational cohort study.
Patients
Participants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts.
Measurements
We identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk.
Results
Approximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, ≥1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of ≥25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively.
Conclusions
Select patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission.
Since December 2019, the dramatic escalation in coronavirus (COVID-19) cases worldwide has had a significant impact on health care systems. Family physicians (FPs) have played a critical role in the ...coordination of care.
In April 2020, we performed an online prospective survey to assess the impact of the pandemic on FPs' practices.
Three hundred FPs were included. Mean age was 53.6 ± 13.5 years. Before the pandemic, 60.2% reported >75 outpatient visits/week, which reduced down to an average of <20/week for 79.8% of FPs; 24.2% of FPs discontinued home visits, while for 94.7% of FPs there was a >50% increase in the number of telephone consultations. Concern related to the risk of contagion was elevated (≥3/5 in 74.6%) and even higher to the risk of infecting relatives and patients (≥3/5 in 93.3%). The majority of FPs (87%) supported the role of telemedicine in the near future. Satisfaction regarding the network with hospitals/COVID-19-dedicated wards received a score ≤2/5 in 46.9% of cases.
The COVID-19 pandemic has had a significant impact on the working practices of FPs. A collaboration is needed with well-established networks between FPs and referral centers to provide new insights and opportunities to inform future working practices.
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