Use of Quality Improvement Strategies Among Small to Medium-Size US Primary Care Practices

Process evaluation of a data-driven quality improvement program within a cluster randomised controlled trial to improve coronary heart disease management in Australian primary care

BACKGROUND

This study evaluates primary care practices' engagement with various features of a quality improvement (QI) intervention for patients with coronary heart disease (CHD) in four Australian states.

METHODS

Twenty-seven practices participated in the QI intervention from November 2019 -November 2020. A combination of surveys, semi-structured interviews and other materials within the QUality improvement in primary care to prevent hospitalisations and improve Effectiveness and efficiency of care for people Living with heart disease (QUEL) study were used in the process evaluation. Data were summarised using descriptive statistical and thematic analyses for 26 practices.

RESULTS

Sixty-four practice team members and Primary Health Networks staff provided feedback, and nine of the 63 participants participated in the interviews. Seventy-eight percent (40/54) were either general practitioners or practice managers. Although 69% of the practices self-reported improvement in their management of heart disease, engagement with the intervention varied. Forty-two percent (11/26) of the practices attended five or more learning workshops, 69% (18/26) used Plan-Do-Study-Act cycles, and the median (Interquartile intervals) visits per practice to the online SharePoint site were 170 (146-252) visits. Qualitative data identified learning workshops and monthly feedback reports as the key features of the intervention.

CONCLUSION

Practice engagement in a multi-featured data-driven QI intervention was common, with learning workshops and monthly feedback reports identified as the most useful features. A better understanding of these features will help influence future implementation of similar interventions.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR) number ACTRN12619001790134.

Factors facilitating the implementation of a clinical decision support system in primary care practices: a fuzzy set qualitative comparative analysis

BACKGROUND

Understanding how to implement innovations in primary care practices is key to improve primary health care. Aiming to contribute to this understanding, we investigate the implementation of a clinical decision support system (CDSS) as part of the innovation fund project AdAM (01NVF16006). Originating from complexity theory, the practice change and development model (PCD) proposes several interdependent factors that enable organizational-level change and thus accounts for the complex settings of primary care practices. Leveraging the PCD, we seek to answer the following research questions: Which combinations of internal and external factors based on the PCD contribute to successful implementation in primary care practices? Given these results, how can implementation in the primary care setting be improved?

METHODS

We analyzed the joint contributions of internal and external factors on implementation success using qualitative comparative analysis (QCA). QCA is a set-theoretic approach that allows to identify configurations of multiple factors that lead to one outcome (here: successful implementation of a CDSS in primary care practices). Using survey data, we conducted our analysis based on a sample of 224 primary care practices.

RESULTS

We identified two configurations of internal and external factors that likewise enable successful implementation. The first configuration enables implementation based on a combination of Strong Inside Motivation, High Capability for Development, and Strong Outside Motivation; the second configuration based on a combination of Strong Inside Motivators, Many Options for Development and the absence of High Capability for Development.

CONCLUSION

In line with the PCD, our results demonstrate the importance of the combination of internal and external factors for implementation outcomes. Moreover, the two identified configurations show that different ways exist to achieve successful implementation in primary care practices.

TRIAL REGISTRATION

AdAM was registered on ClinicalTrials.gov ( NCT03430336 ) on February 6, 2018.

What contributes to Primary Health Care effectiveness? Integrative literature review, 2010-2020

Primary Health Care (PHC) intends to rearrange services to make it more effective. Nevertheless, effectiveness in PHC is quite a challenge. This study reviews several articles regarding the effectiveness improvements in PHC between 2010 and 2020. Ninety out of 8,369 articles found in PubMed and the Virtual Health Library databases search were selected for thematic analysis using the Atlas.ti® 9.0 software. There were four categories identified: strategies for monitoring and evaluating health services, organizational arrangements, models and technologies applied to PHC. Studies concerning the sensitive conditions indicators were predominant. Institutional assessment programs, PHC as a structuring policy, appropriate workforce, measures to increase access and digital technologies showed positive effects. However, payment for performance is still controversial. The expressive number of Brazilian publications reveals the broad diffusion of PHC in the country and the concern on its performance. These findings reassure well-known aspects, but it also points to the need for a logical model to better define what is intended as effectiveness within primary health care as well as clarify the polysemy that surrounds the concept. We also suggest substituting the term "resolvability", commonly used in Brazil, for "effectiveness".

Estimating the Cardiovascular Disease Risk Reduction of a Quality Improvement Initiative in Primary Care: Findings from EvidenceNOW

BACKGROUND

This study estimates reductions in 10-year atherosclerotic cardiovascular disease (ASCVD) risk associated with EvidenceNOW, a multi-state initiative that sought to improve cardiovascular preventive care in the form of (A)spirin prescribing for high-risk patients, (B)lood pressure control for people with hypertension, (C)holesterol management, and (S)moking screening and cessation counseling (ABCS) among small primary care practices by providing supportive interventions such as practice facilitation.

DESIGN

We conducted an analytic modeling study that combined (1) data from 1,278 EvidenceNOW practices collected 2015 to 2017; (2) patient-level information of individuals ages 40 to 79 years who participated in the 2015 to 2016 National Health and Nutrition Examination Survey (n = 1,295); and (3) 10-year ASCVD risk prediction equations.

MEASURES

The primary outcome measure was 10-year ASCVD risk.

RESULTS

EvidenceNOW practices cared for an estimated 4 million patients ages 40 to 79 who might benefit from ABCS interventions. The average 10-year ASCVD risk of these patients before intervention was 10.11%. Improvements in ABCS due to EvidenceNOW reduced their 10-year ASCVD risk to 10.03% (absolute risk reduction: -0.08, P ≤ .001). This risk reduction would prevent 3,169 ASCVD events over 10 years and avoid $150 million in 90-day direct medical costs.

CONCLUSION

Small preventive care improvements and associated reductions in absolute ASCVD risk levels can lead to meaningful life-saving benefits at the population level.

A Medicaid Statewide Hypertension Quality Improvement Project: Initial Results

Background Hypertension control is critical to reducing cardiovascular disease, challenging to achieve, and exacerbated by socioeconomic inequities. Few states have established statewide quality improvement (QI) infrastructures to improve blood pressure (BP) control across economically disadvantaged populations. In this study, we aimed to improve BP control by 15% for all Medicaid recipients and by 20% for non-Hispanic Black participants. Methodology This QI study used repeated cross-sections of electronic health record data and, for Medicaid enrollees, linked Medicaid claims data for 17,672 adults with hypertension seen at one of eight high-volume Medicaid primary care practices in Ohio from 2017 to 2019. Evidence-based strategies included (1) accurate BP measurement; (2) timely follow-up; (3) outreach; (4) a standardized treatment algorithm; and (5) effective communication. Payers focused on a 90-day supply (vs. 30-day) of BP medications, home BP monitor access, and outreach. Implementation efforts included an in-person kick-off followed by monthly QI coaching and monthly webinars. Weighted generalized estimating equations were used to estimate the baseline, one-year, and two-year implementation change in the proportion of visits with BP control (<140/90 mm Hg) stratified by race/ethnicity. Results For all practices, the percentage of participants with controlled BP increased from 52% in 2017 to 60% in 2019. Among non-Hispanic Whites, the odds of achieving BP control in year one and year two were 1.24 times (95% confidence interval: 1.14, 1.34) and 1.50 times (1.38, 1.63) higher relative to baseline, respectively. Among non-Hispanic Blacks, the odds for years one and two were 1.18 times (1.10, 1.27) and 1.34 times (1.24, 1.45) higher relative to baseline, respectively. Conclusions A hypertension QI project as part of establishing a statewide QI infrastructure improved BP control in practices with a high volume of disadvantaged patients. Future efforts should investigate ways to reduce inequities in BP control and further explore factors associated with greater BP improvements and sustainability.

An Assessment of Weight Loss Management in Health System Primary Care Practices

BACKGROUND

Primary care practices can help patients address obesity through weight loss; however, there are many barriers to doing so. This study examined weight management services provided and factors associated with higher reported provision of services.

METHODS

A survey was given to practice members in 18 primary care practices in a Colorado-based health system. The survey assessed weight management services to determine the amount and type of weight loss assistance provided and other factors that may be important. We used descriptive statistics to summarize responses and linear regression with generalized estimating equations to assess the association between the practice and practice member characteristics and the amount of weight management services provided.

RESULTS

The overall response rate was 64% (254/399). On average, clinicians reported performing 73% of the services, and when grouped into minimal, basic, and extensive, the clinicians on average performed 87%, 68%, and 69% of them, respectively. In a multivariable model adjusted for demographics, factors associated with performing more services included perception of overall better practice culture and perception of weight management implementation climate.

CONCLUSIONS

Practice-associated factors such as culture and implementation climate may be worth examining to understand how to implement weight management in primary care.

Effective Facilitator Strategies for Supporting Primary Care Practice Change: A Mixed Methods Study

PURPOSE

Practice facilitation is an evidence-informed implementation strategy to support quality improvement (QI) and aid practices in aligning with best evidence. Few studies, particularly of this size and scope, identify strategies that contribute to facilitator effectiveness.

METHODS

We conducted a sequential mixed methods study, analyzing data from EvidenceNOW, a large-scale QI initiative. Seven regional cooperatives employed 162 facilitators to work with 1,630 small or medium-sized primary care practices. Main analyses were based on facilitators who worked with at least 4 practices. Facilitators were defined as more effective if at least 75% of their practices improved on at least 1 outcome measure-aspirin use, blood pressure control, smoking cessation counseling (ABS), or practice change capacity, measured using Change Process Capability Questionnaire-from baseline to follow-up. Facilitators were defined as less effective if less than 50% of their practices improved on these outcomes. Using an immersion crystallization and comparative approach, we analyzed observational and interview data to identify strategies associated with more effective facilitators.

RESULTS

Practices working with more effective facilitators had a 3.6% greater change in the mean percentage of patients meeting the composite ABS measure compared with practices working with less effective facilitators (P <.001). More effective facilitators cultivated motivation by tailoring QI work and addressing resistance, guided practices to think critically, and provided accountability to support change, using these strategies in combination. They were able to describe their work in detail. In contrast, less effective facilitators seldom used these strategies and described their work in general terms. Facilitator background, experience, and work on documentation did not differentiate between more and less effective facilitators.

CONCLUSIONS

Facilitation strategies that differentiate more and less effective facilitators have implications for enhancing facilitator development and training, and can assist all facilitators to more effectively support practice changes.

Data-driven quality improvement program to prevent hospitalisation and improve care of people living with coronary heart disease: Protocol for a process evaluation

BACKGROUND

Practice-level quality improvement initiatives using rapidly advancing technology offers a multidimensional approach to reduce cardiovascular disease burden. For the "QUality improvement in primary care to prevent hospitalisations and improve Effectiveness and efficiency of care for people Living with heart disease" (QUEL) cluster randomised controlled trial, a 12-month quality improvement intervention was designed for primary care practices to use data and implement progressive changes using "Plan, Do, Study, Act" cycles within their practices with training in a series of interactive workshops. This protocol aims to describe the systematic methods to conduct a process evaluation of the data-driven intervention within the QUEL study.

METHODS

A mixed-method approach will be used to conduct the evaluation. Quantitative data collected throughout the intervention period, via surveys and intervention materials, will be used to (1) identify the key elements of the intervention and how, for whom and in what context it was effective; (2) determine if the intervention is delivered as intended; and (3) describe practice engagement, commitment and capacity associated with various intervention components. Qualitative data, collected via semi-structured interviews and open-ended questions, will be used to gather in-depth understanding of the (1) satisfaction, utility, barriers and enablers; (2) acceptability, uptake and feasibility, and (3) effect of the COVID-19 pandemic on the implementation of the intervention.

CONCLUSION

Findings from the evaluation will provide new knowledge on the implementation of a complex, multi-component intervention at practice-level using their own electronic patient data to enhance secondary prevention of cardiovascular disease.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR) number ACTRN12619001790134.

Identifying Contextual Factors and Strategies for Practice Facilitation in Primary Care Quality Improvement Using an Informatics-Driven Model: Framework Development and Mixed Methods Case Study

BACKGROUND

The past decade has seen increasing opportunities and efforts to integrate quality improvement into health care. Practice facilitation is a proven strategy to support redesign and improvement in primary care practices that focuses on building organizational capacity for continuous improvement. Practice leadership, staff, and practice facilitators all play important roles in supporting quality improvement in primary care. However, little is known about their perspectives on the context, enablers, barriers, and strategies that impact quality improvement initiatives.

OBJECTIVE

This study aimed to develop a framework to enable assessment of contextual factors, challenges, and strategies that impact practice facilitation, clinical measure performance, and the implementation of quality improvement interventions. We also illustrated the application of the framework using a real-world case study.

METHODS

We developed the TITO (task, individual, technology, and organization) framework by conducting participatory stakeholder workshops and incorporating their perspectives to identify enablers and barriers to quality improvement and practice facilitation. We conducted a case study using a mixed methods approach to demonstrate the use of the framework and describe practice facilitation and factors that impact quality improvement in a primary care practice that participated in the Healthy Hearts in the Heartland study.

RESULTS

The proposed framework was used to organize and analyze different stakeholders' perspectives and key factors based on framework domains. The case study showed that practice leaders, staff, and practice facilitators all influenced the success of the quality improvement program. However, these participants faced different challenges and used different strategies. The framework showed that barriers stemmed from patients' social determinants of health, a lack of staff and time, and unsystematic facilitation resources, while enablers included practice culture, staff buy-in, implementation of effective practice facilitation strategies, practice capacity for change, and shared complementary resources from similar, ongoing programs.

CONCLUSIONS

Our framework provided a useful and generalizable structure to guide and support assessment of future practice facilitation projects, quality improvement initiatives, and health care intervention implementation studies. The practice leader, staff, and practice facilitator all saw value in the quality improvement program and practice facilitation. Practice facilitators are key liaisons to help the quality improvement program; they help all stakeholders work toward a shared target and leverage tailored strategies. Taking advantage of resources from competing, yet complementary, programs as additional support may accelerate the effective achievement of quality improvement goals. Practice facilitation-supported quality improvement programs may be opportunities to assist primary care practices in achieving improved quality of care through focused and targeted efforts. The case study demonstrated how our framework can support a better understanding of contextual factors for practice facilitation, which could enable well-prepared and more successful quality improvement programs for primary care practices. Combining implementation science and informatics thinking, our TITO framework may facilitate interdisciplinary research in both fields.

A Cohort Analysis of Statin Treatment Patterns Among Small-Sized Primary Care Practices

BACKGROUND

Small-sized primary care practices, defined as practices with fewer than 10 clinicians, delivered the majority of outpatient visits in the USA. Statin therapy in high-risk individuals reduces atherosclerotic cardiovascular disease (ASCVD) events, but prescribing patterns in small primary care practices are not well known. This study describes statin treatment patterns in small-sized primary care practices and examines patient- and practice-level factors associated with lack of statin treatment.

METHODS

We conducted a retrospective cohort analysis of statin-eligible patients from practices that participated in Healthy Hearts in the Heartland (H3), a quality improvement initiative aimed at improving cardiovascular care measures in small primary care practices. All statin-eligible adults who received care in one of 53 H3 practices from 2013 to 2016. Statin-eligible adults include those aged at least 21 with (1) clinical ASCVD, (2) low-density lipoprotein cholesterol (LDL-C) ≥ 190 mg/dL, or (3) diabetes aged 40-75 and with LDL-C 70-189 mg/dL. Eligible patients with no record of moderate- to high-intensity statin prescription are defined by ACC/AHA guidelines.

RESULTS

Among the 13,330 statin-eligible adults, the mean age was 58 years and 52% were women. Overall, there was no record of moderate- to high-intensity statin prescription among 5,780 (43%) patients. Younger age, female sex, and lower LDL-C were independently associated with a lack of appropriate intensity statin therapy. Higher proportions of patients insured by Medicaid and having only family medicine trained physicians (versus having at least one internal medicine trained physician) at the practice were also associated with lower appropriate intensity statin use. Lack of appropriate intensity statin therapy was higher in independent practices than in Federally Qualified Health Centers (FQHCs) (50% vs. 40%, p value < 0.01).

CONCLUSIONS

There is an opportunity for improved ASCVD risk reduction in small primary care practices. Statin treatment patterns and factors influencing lack of treatment vary by practice setting, highlighting the importance of tailored approaches to each setting.

Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives

BACKGROUND

Electronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings.

OBJECTIVE

This study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices.

METHODS

A qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors.

RESULTS

Cooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a "hybrid strategy" of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs.

CONCLUSION

Cooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

Understanding Factors Influencing Quality Improvement Capacity Among Ambulatory Care Practices Across the MidSouth Region: An Exploratory Qualitative Study

BACKGROUND AND OBJECTIVE

Success in choosing and implementing quality metrics, necessary in a value-based care model, depends on quality improvement (QI) capacity-the shared knowledge, understanding, and commitment to continuous improvement. We set out to explore factors influencing QI capacity among ambulatory care practices in the MidSouth Practice Transformation Network.

METHODS

As part of network participation, 82 practices submitted a plan for implementing self-selected quality metrics. This plan asked practices to identify factors that would assist or impede successful implementation of interventions to meet metric targets. We used a qualitative thematic analysis approach to explore barriers and facilitators to developing QI capacity among ambulatory care practices.

RESULTS

Recurrent facilitators included external change agents, protected time for QI, a framework for improvement, and infrastructure including electronic health record (EHR) capabilities. Frequent barriers included lack of QI knowledge, lack of time, frequent staff turnover, inadequate EHR capabilities, lack of an internal change agent, and a belief that performance was outside of the practice's control.

CONCLUSION

These findings provide insight into factors influencing the adoption and implementation of QI metrics across a diverse group of ambulatory care practices and suggest that targeting the Inner Setting of practices may be an appropriate approach for developing practice-level QI capacity, which is necessary for success in a value-based care model.

Place Matters: Closing the Gap on Rural Primary Care Quality Improvement Capacity-the Healthy Hearts Northwest Study

CONTEXT

To compare rural independent and health system primary care practices with urban practices to external practice facilitation support in terms of recruitment, readiness, engagement, retention, and change in quality improvement (QI) capacity and quality metric performance.

METHODS

The setting consisted of 135 small or medium-sized primary care practices participating in the Healthy Hearts Northwest quality improvement initiative. The practices were stratified by geography, rural or urban, and by ownership (independent [physician-owned] or system-owned [health/hospital system]). The quality improvement capacity assessment (QICA) survey tool was used to measure QI at baseline and after 12 months of practice facilitation. Changes in 3 clinical quality measures (CQMs)-appropriate aspirin use, blood pressure (BP) control, and tobacco use screening and cessation-were measured at baseline in 2015 and follow-up in 2017.

RESULTS

Rural practices were more likely to enroll in the study, with 1 out of 3.5 rural recruited practices enrolled, compared with 1 out of 7 urban practices enrolled. Rural independent practices had the lowest QI capacity at baseline, making the largest gain in establishing a regular QI process involving cross-functional teams. Rural independent practices made the greatest improvement in meeting the BP control CQM, from 55.5% to 66.1% (P ≤ .001) and the smoking cessation metric, from 72.3% to 86.7% (P ≤ .001).

CONCLUSIONS

Investing practice facilitation and sustained QI strategies in rural independent practices, where the need is high and resources are low, will yield benefits that outweigh centrally prescribed models.

The role of organizational learning and resilience for change in building quality improvement capacity in primary care

BACKGROUND

The extent that organizational learning and resilience for the change process, that is, adaptive reserve (AR), is a component of building practice capacity for continuous quality improvement (QI) is unknown.

PURPOSE

The aim of the study was to examine the association of AR and development of QI capacity.

METHODOLOGY

One hundred forty-two primary care practices were evaluated at baseline and 12 months in a randomized trial to improve care quality. Practice AR was measured by staff survey along with a validated QI capacity assessment (QICA). We assessed the association of baseline QICA with baseline AR and both baseline and change in AR with change in QICA from 0 to 12 months. Effect modification by presence of QI infrastructure in parent organizations and trial arm was examined.

RESULTS

Mean QICA increased from 6.5 to 8.1 (p < .001), and mean AR increased from 71.8 to 73.9 points (p < .001). At baseline, there was a significant association between AR and QICA scores: The QICA averaged 0.34 points higher (95% CI [0.04, 0.64], p = .03) per 10-point difference in AR. There was a significant association between baseline AR and 12-month QICA-which averaged 0.30 points higher (95% CI [0.02, 0.57], p = .04) per 10 points in baseline AR. There was no association between changes in AR and the QICA from 0 to 12 months and no effect modification by trial arm or external QI infrastructure.

CONCLUSIONS

Baseline AR was positively associated with both baseline and follow-up QI capacity, but there was no association between change in AR and change in the QICA, suggesting AR may be a precondition to growth in QI capacity.

PRACTICE IMPLICATIONS

Findings suggest that developing AR may be a valuable step prior to undertaking QI-oriented growth, with implications for sequencing of development strategies, including added gain in QI capacity development from building AR prior to engaging in transformation efforts.

A Cross-Sectional Analysis of Primary Care Practice Characteristics and Healthcare Professionals' Behavioral Responses to Change

The recent decade brought major changes to primary care practices. Previous research on change has focused on change processes, and change implementations rather than studying employee’s feelings, perceptions, and attitudes toward change. The objective of this cross-sectional study was to examine the relationship between healthcare professionals’ behavioral responses to change and practice characteristics. Our study, which builds upon Conner’s theory, addresses an extensive coverage of individual behaviors, feelings, and attitudes toward change. We analyzed survey responses of healthcare professionals (n = 1279) from 154 primary care practices in Virginia. Healthcare professionals included physicians, advanced practice clinicians, clinical support staff, and administrative staff. The Change Diagnostic Index^© (CDI) was used to measure behavioral responses in 7 domains: anxiety, frustration, delayed development, rejection of environment, refusal to participate, withdrawal, and global reaction. We used descriptive statistics and multivariate regression analysis. Our findings indicate that professionals had a significantly lower aptitude for change if they work in larger practices (≥16 clinicians) compared to solo practices (P < .05) and at hospital-owned practices compared to independent practices (P < .05). Being part of an accountable care organization was associated with significantly lower anxiety (P < .05). Understanding healthcare professionals’ responses to change can help healthcare leaders design and implement successful change management strategies for future transformation.

Readiness and Implementation of Quality Improvement Strategies Among Small- and Medium-Sized Primary Care Practices: an Observational Study

BACKGROUND

Little is known about what determines strategy implementation around quality improvement (QI) in small- and medium-sized practices. Key questions are whether QI strategies are associated with practice readiness and practice characteristics.

OBJECTIVE

Grounded in organizational readiness theory, we examined how readiness and practice characteristics affect QI strategy implementation. The study was a component of a larger practice-level intervention, Heart of Virginia Healthcare, which sought to transform primary care while improving cardiovascular care.

DESIGN

This observational study analyzed practice correlates of QI strategy implementation in primary care at 3 and 12 months. Data were derived from surveys completed by clinicians and staff and from assessments by practice coaches.

PARTICIPANTS

A total of 175 small- and medium-sized primary care practices were included.

MAIN MEASURES

Outcome was QI strategy implementation in three domains: (1) aspirin, blood pressure, cholesterol, and smoking cessation (ABCS); (2) care coordination; and (3) organizational-level improvement. Coaches assessed implementation at 3 and 12 months. Readiness was measured by baseline member surveys, 1831 responses from 175 practices, a response rate of 73%. Practice survey assessed practice characteristics, a response rate of 93%. We used multivariate regression.

KEY RESULTS

QI strategy implementation increased from 3 to 12 months: the mean for ABCS from 1.20 to 1.59, care coordination from 2.15 to 2.75, organizational improvement from 1.37 to 1.78 (95% CI). There was no statistically significant association between readiness and QI strategy implementation across domains. Independent practice implementation was statistically significantly higher than hospital-owned practices at 3 months for ABCS (95% CI, P = 0.01) and care coordination (95% CI, P = 0.03), and at 12 months for care coordination (95% CI, P = 0.04).

CONCLUSION

QI strategy implementation varies by practice ownership. Independent practices focus on patient care-related activities. FQHCs may need additional time to adopt and implement QI activities. Practice readiness may require more structural and organizational changes before starting a QI effort.

Development of competency model for family physicians against the background of 'internet plus healthcare' in China: a mixed methods study

BACKGROUND

Identification of the service competences of family physicians is central to ensuring high-quality primary care and improving patient outcomes. However, little is known about how to assess the family physicians' service competences in primary care settings. It is necessary to develop and validate a general model of core competences of the family physician under the stage of construction of family doctor system and implementation of 'Internet Plus Healthcare' service model in China.

METHODS

The literature review, behavioural event interviews, expert consultation and questionnaire survey were performed. The scale's 35 questions were measured by response rate, highest score, lowest score, and average score for each. Delphi method was used to assess content validity, Cronbach's α to estimate reliability, and factor analysis to test structural validity. Respondents were randomly divided into two groups; data for one group were used for exploratory factor analysis (EFA) to explore possible model structure. Confirmatory factor analysis (CFA) was then performed.

RESULTS

Effective response rate was 93.56%. Cronbach's α coefficient of the scale was 0.977. Factor analysis showed KMO of 0.988. Bartlett's test showed χ2 of 22 917.515 (df = 630), p < .001. Overall authority grade of expert consultation was 0.80, and Kendall's coefficient of concordance W was 0.194. By EFA, the five-factor model was retained after thorough consideration, and four items with factor loading less than 0.4 were proposed to obtain a five-dimension, 32-item scale. CFA was performed on the new structure, showing high goodness-of-fit test (NFI = 0.98, TLI = 0.91, SRMSR = 0.05, RMSEA = 0.04). Overall Cronbach's α coefficients of the scale and each sub-item were greater than 0.9.

CONCLUSIONS

The scale has good reliability, validity, and credibility and can therefore serve as an effective tool for assessment of Chinese family physicians' service competences.

Improving the structure of diabetes care in primary care: A pilot study

AIM

The aim of this pilot study was to determine whether glycemic control can be improved in patients with type 2 diabetes by implementing a workshop model to improve the structure of diabetes care at primary health care centers (PHCCs).

METHODS

The intervention consisted of 4 workshops at 12 PHCCs with HbA1c >70 mmol/mol (high HbA1c). Each PHCC could choose how many workshops they wished to attend and was to be represented by the manager, a diabetes nurse, and a GP. Participants analyzed the structure of diabetes care at their PHCC and developed an action plan to improve it. The percentage of patients with high HbA1c at baseline, 12, and 24 months was collected. Qualitative content analysis was also conducted.

RESULTS

All PHCCs reduced the percentage of patients with high HbA1c 12 months after the intervention, but not all maintained the reduction at 24 months. Participants experienced structuring diabetes care as central to reducing the percentage of patients with high HbA1c. Pillars of structured diabetes care included establishing routines, working in teams, and having and implementing an action plan.

CONCLUSIONS

Working with the structure of diabetes care improved care structure and had a positive impact on HbA1c. To sustain the positive impact, PHCCs had to set long-term goals and regularly evaluate performance.

Key Collaborative Factors When Medicaid Accountable Care Organizations Work With Primary Care Clinics to Improve Colorectal Cancer Screening: Relationships, Data, and Quality Improvement Infrastructure

Purpose

Accountable Care Organizations (ACOs) are implementing interventions to achieve triple-aim objectives of improved quality and experience of care while maintaining costs. Partnering across organizational boundaries is perceived as critical to ACO success.

Methods

We conducted a comparative case study of 14 Medicaid ACOs in Oregon and their contracted primary care clinics using public performance data, key informant interviews, and consultation field notes. We focused on how ACOs work with clinics to improve colorectal cancer (CRC) screening — one incentivized performance metric.

Results

ACOs implemented a broad spectrum of multi-component interventions designed to increase CRC screening. The most common interventions focused on reducing structural barriers (n = 12 ACOs), delivering provider assessment and feedback (n = 11), and providing patient reminders (n = 7). ACOs developed their processes and infrastructure for working with clinics over time. Facilitators of successful collaboration included a history of and commitment to collaboration (partnership); the ability to provide accurate data to prioritize action and monitor improvement (performance data), and supporting clinics’ reflective learning through facilitation, learning collaboratives; and support of ACO as well as clinic-based staffing (quality improvement infrastructure). Two unintended consequences of ACO–clinic partnership emerged: potential exclusion of smaller clinics and metric focus and fatigue.

Conclusion

Our findings identified partnership, performance data, and quality improvement infrastructure as critical dimensions when Medicaid ACOs work with primary care to improve CRC screening. Findings may extend to other metric targets.

Does Ownership Make a Difference in Primary Care Practice?

PURPOSE

We assessed differences in structural characteristics, quality improvement processes, and cardiovascular preventive care by ownership type among 989 small to medium primary care practices.

METHODS

This cross-sectional analysis used electronic health record and survey data collected between September 2015 and April 2017 as part of an evaluation of the EvidenceNOW: Advancing Heart Health in Primary Care Initiative by the Agency for Health Care Research and Quality. We compared physician-owned practices, health system or medical group practices, and Federally Qualified Health Centers (FQHC) by using 15 survey-based practice characteristic measures, 9 survey-based quality improvement process measures, and 4 electronic health record-based cardiovascular disease prevention quality measures, namely, aspirin prescription, blood pressure control, cholesterol management, and smoking cessation support (ABCS).

RESULTS

Physician-owned practices were more likely to be solo (45.0% compared with 8.1%, P < .001 for health system practices and 12.8%, P = .009 for FQHCs) and less likely to have experienced a major change (eg, moved to a new location) in the last year (43.1% vs 65.4%, P = .01 and 72.1%, P = .001, respectively). FQHCs reported the highest use of quality improvement processes, followed by health system practices. ABCS performance was similar across ownership type, with the exception of smoking cessation support (51.0% for physician-owned practices vs 67.3%, P = .004 for health system practices and 69.3%, P = .004 for FQHCs).

CONCLUSIONS

Primary care practice ownership was associated with differences in quality improvement process measures, with FQHCs reporting the highest use of such quality-improvement strategies. ABCS were mostly unrelated to ownership, suggesting a complex path between quality improvement strategies and outcomes.

EHR quality indicator tracking: A process improvement pilot project to meet MACRA requirements

The Centers for Medicare and Medicaid Services created the Quality Payment Program to award compensation to providers for offering evidence-based, high-value, and efficient care. This article outlines an information technology process improvement pilot project undertaken at a large primary care practice in western Florida to support readiness for Medicare Access and Children's Health Insurance Program (CHIP) Reauthorization Act of 2015 using new EHR quality indicator tracking features.

The medical home functions of primary care practices that care for adults with psychological distress: a cross-sectional study

BACKGROUND

Primary care practices are changing the way that they provide care by increasing their medical home functionality. Medical home functionality can improve access to care and increase patient-centeredness, which is essential for persons with mental health issues. This study aims to explore the degree to which medical home functions have been implemented by primary care practices that care for adults with psychological distress.

METHODS

Analysis of the 2015 Medical Expenditure Panel Survey Household Component and Medical Organizations Survey. This unique data set links data from a nationally representative sample of US households to the practices in which they receive primary care. This study focused on adults aged 18 and above.

RESULTS

As compared to adults without psychological distress, adults with psychological distress had significantly higher rates of chronic illness and poverty. Adults with psychological distress were more likely to receive care from practices that include advanced practitioners and are non-profit or hospital-based. Multivariate models that were adjusted for patient-level and practice-level characteristics indicated that adults with psychological distress are as likely to receive primary care from practices with medical home functionality, including case management, electronic health records, flexible scheduling, and PCMH certification, as adults without psychological distress.

CONCLUSIONS

Practices that care for adults with mental health issues have not been left behind in the transition towards medical home models of primary care. Policy makers should continue to prioritize adults with mental health issues to receive primary care through this model of delivery due to this population's great potential to benefit from improved access and care coordination.

TRIAL REGISTRATION

This study does not report the results of a health care intervention on human subject's participants.