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Studies have shown gender disparities in cholesterol care in patients with cardiovascular disease (CVD), with women less likely than men to have low-density lipoprotein cholesterol levels <100 mg/dl. Whether this is related to a lower evidence-based statin or high-intensity statin use is not known. We used a national cohort of 972,532 patients with CVD (coronary heart disease, peripheral artery disease, and ischemic stroke) receiving care in 130 Veterans Health Administration facilities from October 1, 2010, to September 30, 2011, to identify the proportion of male and female patients with CVD receiving any statin and high-intensity statin. Women with CVD (n = 13,371) were less likely than men to receive statins (57.6% vs 64.8%, p <0.0001) or high-intensity statins (21.1% vs 23.6%, p <0.0001). Mean low-density lipoprotein cholesterol levels (99 vs 85 mg/dl) were higher in women compared with men (p <0.0001). In adjusted models, female gender was independently associated with a lower likelihood of receiving statins (odds ratio 0.68, 95% confidence interval 0.66 to 0.71) or high-intensity statins (odds ratio 0.76, 95% confidence interval 0.73 to 0.80). The median facility-level rate of statin and high-intensity statin use among female patients (57.3% [interquartile range = 8.93%] for statin, 20% [interquartile range = 7.7%] for high-intensity statin use) showed significant variation. In conclusion, women with CVD are less likely to receive evidence-based statin and high-intensity statins compared with men, although, their use remains low in both genders. There is a significant facility-level variation in evidence-based statin or high-intensity statin use in female patients with CVD. With the “statin dose-based approach” proposed by the recent cholesterol guidelines, these results highlight areas for quality improvement.

The purpose of this article is to illustrate the application of an evidence-based, structured performance measurement methodology to identify, prioritize, and (when appropriate) generate new measures of health care quality, using primary care as a case example. Primary health care is central to the health care system and health of the American public; thus, ensuring high quality is essential. Due to its complexity, ensuring high-quality primary care requires measurement frameworks that can assess the quality of the infrastructure, workforce configurations, and processes available. This paper describes the use of the Productivity Measurement and Enhancement System (ProMES) to compile a targeted set of such measures, prioritized according to their contribution and value to primary care. We adapted ProMES to select and rank existing primary care measures according to value to the primary care clinic. Nine subject matter experts (SMEs) consisting of clinicians, hospital leaders and national policymakers participated in facilitated expert elicitation sessions to identify objectives of performance, corresponding measures, and priority rankings. The SMEs identified three fundamental objectives: access, patient-health care team partnerships, and technical quality. The SMEs also selected sixteen performance indicators from the 44 pre-vetted, currently existing measures from three different data sources for primary care. One indicator, Team 2-Day Post Discharge Contact Ratio, was selected as an indicator of both team partnerships and technical quality. Indicators were prioritized according to value using the contingency functions developed by the SMEs. Our article provides an actionable guide to applying ProMES, which can be adapted to the needs of various industries, including measure selection and modification from existing data sources, and proposing new measures. Future work should address both logistical considerations (e.g., data capture, common data/programming language) and lingering measurement challenges, such as operationalizating measures to be meaningful and interpretable across health care settings.

The objective was to compare quality of diabetes and cardiovascular disease (CVD) care between advanced practice providers (APPs) and physicians in a primary care setting. We identified diabetes (n=1,022,588) and CVD (n=1,187,035) patients receiving primary care between October 2013 and September 2014 in 130 Veterans Affairs facilities. We compared glycemic control (hemoglobin A1c <7%) in diabetic patients, blood pressure (BP) <140/90 mmHg in diabetic or CVD patients, cholesterol control (low-density lipoprotein cholesterol<100 mg/dL, receiving a statin) in diabetic or CVD patients, and those receiving a β-blocker (with history of myocardial infarction in the last 2 years) among patients receiving care from physicians and APPs. We also compared the proportion meeting composite measure (glycemic, BP, and cholesterol control in diabetic patients; BP, cholesterol control, and receipt of β-blocker among eligible CVD patients). Diabetic patients receiving care from APPs were statistically more likely to have glycemic (50% vs 51.4%, odds ratio [OR] 1.06 [1.05-1.08]) and BP control (77.5% vs 78.4%, OR 1.04 [1.03-1.06]), whereas patients receiving care from physicians were more likely to have cholesterol control (receipt of statin 68% vs 66.5%, OR 0.94 [0.93-0.95]) in adjusted models, although these differences are not clinically significant. Similar results were seen in CVD patients. Few patients met the composite measure (27.1% and 27.6% of diabetic and 54.0% and 54.8% of CVD patients receiving care from physicians and APPs, respectively). Diabetes and CVD care quality was comparable between physicians and APPs with clinically insignificant differences. Regardless of provider type, there is a need to improve performance on eligible measures in diabetes or CVD patients.

In this paper, we identify 8 methods used to measure errors and adverse events in health care and discuss their strengths and weaknesses. We focus on the reliability and validity of each, as well as the ability to detect latent errors (or system errors) versus active errors and adverse events. We propose a general framework to help health care providers, researchers, and administrators choose the most appropriate methods to meet their patient safety measurement goals.

Given the increase in financial-incentive programs nationwide, many physicians and physician groups are concerned about potential unintended consequences of providing financial incentives to improve quality of care. However, few studies examine whether actual unintended consequences result from providing financial incentives to physicians. We sought to document the extent to which the unintended consequences discussed in the literature were observable in a randomized clinical trial (RCT) of financial incentives. We conducted a qualitative observational study nested within a larger RCT of financial incentives to improve hypertension care. We conducted 30-minute telephone interviews with primary care personnel at facilities participating in the RCT housed at12 geographically dispersed Veterans Affairs Medical Centers nationwide. Participants answered questions about unintended effects, clinic team dynamics, organizational impact on care delivery, study participation. We employed a blend of inductive and deductive qualitative techniques for analysis. Sixty-five participants were recruited from RCT enrollees and personnel not enrolled in the larger RCT, plus one primary care leader per site. Emergent themes included possible patient harm, emphasis on documentation over improving care, reduced professional morale, and positive spillover. All discussions of unintended consequences involving patient harm were only concerns, not actual events. Several unintended consequences concerned ancillary initiatives for quality improvement (e.g., practice guidelines and performance measurement systems) rather than financial incentives. Many unintended consequences of financial incentives noted were either only concerns or attributable to ancillary quality-improvement initiatives. Actual unintended consequences included improved documentation of care without necessarily improving actual care, and positive unintended consequences. Clinicaltrials.gov Identifier: NCT00302718.

Background The science of effective teams is well documented; far less is known, however, about how specific team configurations may impact primary care team effectiveness. Further, teams experiencing frequent personnel changes (perhaps as a consequence of poor implementation) may have difficulty delivering effective, continuous, well-coordinated care. This study aims to examine the extent to which primary care clinics in the Veterans Health Administration have implemented team configurations consistent with recommendations based on the Patient-Centered Medical Home model and the extent to which adherence to said recommendations, team stability, and role stability impact healthcare quality. Specifically, we expect to find better clinical outcomes in teams that adhere to recommended team configurations, teams whose membership and configuration are more stable over time, and teams whose clinical manager role is more stable over time. Methods/design We will employ a combination of social network analysis and multilevel modeling to conduct a database review of variables extracted from the Veterans Health Administration’s Team Assignments Report (TAR) (one of the largest, most diverse existing national samples of primary care teams ( n teams  > 7000)), as well as other employee and clinical data sources. To ensure the examination of appropriate clinical outcomes, we will enlist a team of subject matter experts to select a concise set of clear, prioritized primary care performance metrics. We will accomplish this using the Productivity Measurement and Enhancement System, an evidence-based methodology for developing and implementing performance measurement. Discussion We are unaware of other studies of healthcare teams that consider team size, composition, and configuration longitudinally or with sample sizes of this magnitude. Results from this study can inform primary care team implementation policy and practice in both private- and public-sector clinics, such that teams are configured optimally, with adequate staffing, and the right mix of roles within the team. Trial registration Not applicable—this study does not involve interventions on human participants.

Cardiovascular disease (CVD) is a systemic process involving multiple vascular beds and includes coronary heart disease (CHD), ischemic cerebrovascular disease (ICVD), and peripheral arterial disease (PAD). All these manifestations are associated with an increased risk of subsequent myocardial infarction, stroke, and death. Guideline-directed medical therapy is recommended for all patients with CVD. In a cohort of US veterans, we identified 1,242,015 patients with CVD receiving care in 130 Veterans Affairs facilities from October 1, 2013 to September 30, 2014. CVD included diagnoses of CHD, PAD, or ICVD. We assessed the frequency of risk factor optimization and the use of guideline-directed medical therapy in patients with CHD, PAD alone, ICVD alone, and PAD + ICVD groups. A composite of 4 measures (blood pressure <140/90 mm Hg, A1c <7% in diabetics, statin use, and antiplatelet use in eligible patients), termed optimal medical therapy (OMT) was compared among groups. Multivariate logistic regression was performed with CHD as the referent category. CHD comprised 989,380 (79.7%), PAD alone 70,404 (5.7%), ICVD alone 163,730 (13.2%), and PAD + ICVD 18,501 (1.5%) of the cohort. Overall, only 36% received OMT with adjusted odds ratios of 0.54 (95% CI 0.53 to 0.55), 0.77 (0.76 to 0.78), and 0.97 (0.94 to 1.00) for patients with PAD alone, ICVD alone, and PAD + ICVD, respectively, compared with patients with CHD. In conclusion, OMT was low in all groups. Patients with PAD alone and ICVD alone were less likely to receive OMT than those with CHD and PAD + ICVD.

Zegers and colleagues’ study codifies the perceived burden of quality monitoring and improvement stemming from the work by clinicians of registering (documenting) quality information in the medical record. We agree with Zegers and colleagues’ recommendation that a smaller, more effective and curated set of measures is needed to reduce burden, confusion, and expense. We further note that focusing on validity of clinical evidence behind individual measures is critical, but insufficient. We therefore extend Zegers and colleagues’ work through a pragmatic, tripartite heuristic. To assess the value of and curate a targeted set of performance measures, we propose concentrating on the relationships among three factors: (1) The purpose of the performance measure, (2) the subject being evaluated, and (3) the consumer using information for decision-making. Our proposed tripartite framework lays the groundwork for executing the evidence-based recommendations proposed by Zegers et al, and provides a path forward for more effective healthcare performance-measurement systems.

Background Team-based primary care (PC) enhances the quality of and access to health care. The Veterans Health Administration (VHA) implements team-based care through Patient Aligned Care Teams (PACTs), consisting of four core members: a primary care provider, registered nurse (RN) care manager, licensed vocational nurse, and scheduling clerk. RNs play a central role: they coordinate patient care, manage operational needs, and serve as a patient point of contact. Currently, it is not known how varying levels of RN staffing on primary care teams impact patient outcomes. Objective This study aims to empirically assess how the stability of RN staffing within team-based primary care affects patient access to care. Methods A retrospective database review using clinical and administrative data from the VHA over 24 months. Participants included 5,897 PC PACTs across 152 VHA healthcare facilities in the United States and its territories. The stability of personnel in the RN role was categorized as: RN continuous churn, RN staffing instability and RN vacancy. All 3 categories were compared to teams with RN stability (i.e., same person in the role for the entire 24-month period). Access measures included: average third-next-available appointment, established patient average wait time in days, urgent care utilization, emergency room utilization, and total inbound-to-outbound PC secure messages ratio. Results RN continuous churn within PACTs had a significant impact on third-next-available appointment (b = 3.70, p  < 0.01). However, RN staffing instability and vacancy had no significant relationship with any of the access measures. Several risk adjustment variables, including team full-time equivalency, team stability, relative team size, and average team size, were significantly associated with access to health care. Conclusions Teams are impacted by churn on the team. Adequate staffing and team stability significantly predict patient access primary care services. Healthcare organizations should focus on personnel retention and strategies to mitigate the impact(s) of continuous RN turnover. Future research should examine the relative impact of turnover and stability of other roles (e.g., clerks) and how team members adapt to personnel changes.

Although current performance measures define low-density-lipoprotein cholesterol (LDL-C) levels <100 mg/dL in patients with cardiovascular disease (CVD) as good quality, they provide a snapshot and do not address whether treatment intensification was performed to manage elevated LDL-C levels. We determined the proportion of patients with CVD (n = 22,888) with LDL-C <100 mg/dL and the proportion with uncontrolled LDL-C levels (≥100 mg/dL) who received treatment intensification within the 45-day follow-up in a Veterans Affairs Network. We evaluated facility, provider, and patient correlates of treatment intensification. Low-density-lipoprotein cholesterol levels were at goal in 16,350 (71.4%) patients. An additional 2,093 (one third of those eligible for treatment intensification) received treatment intensification. Controlling for clustering between facilities and patient's illness severity: history of diabetes (odds ratio [OR] 1.15, 95% CI 1.01-1.32), hypertension (OR 1.19, 95% CI 1.01-1.42), good medication adherence (OR 2.20, 95% CI 1.91-2.54), and a higher number of lipid panels (OR 1.20, 95% CI 1.14-1.27) were associated with treatment intensification. Patients older than 75 years (OR 0.65, 95% CI 0.56-0.75) and women (OR 0.66, 95% CI 0.43-1.00) were less likely to receive treatment intensification. Teaching status of the facility, physician or specialist primary care provider, and patient's race were not associated with treatment intensification. Only one third of the CVD patients with elevated LDL-C received treatment intensification. Diabetic and hypertensive patients were more likely to receive treatment intensification, whereas, older patients, female patients, and patients with poor medication adherence were less likely to receive treatment intensification. Our findings highlight areas for quality improvement initiatives.