The role of comorbidities, medications, and social determinants of health in understanding urban-rural outcome differences among patients with heart failure.

PURPOSE: There is now a 20% disparity in all-cause, excess deaths between urban and rural areas, much of which is driven by disparities in cardiovascular death. We sought to explain the sources of these disparities for Medicare beneficiaries with heart failure with reduced ejection fraction (HFrEF). METHODS: Using a sample of Medicare Parts A, B, and D, we created a cohort of 389,528 fee-for-service beneficiaries with at least 1 heart failure hospitalization from 2008 to 2017. The primary outcome was 30-day mortality after discharge; 1-year mortality, readmissions, and return emergency room (ER) admissions were secondary outcomes. We used hierarchical, logistic regression modeling to determine the contribution of comorbidities, guideline-directed medical therapy (GDMT), and social determinants of health (SDOH) to outcomes. RESULTS: Thirty-day mortality rates after hospital discharge were 6.3% in rural areas compared to 5.7% in urban regions (P < .001); after adjusting for patient health and GDMT receipt, the 30-day mortality odds ratio for rural residence was 1.201 (95% CI 1.164-1.239). Adding the SDOH measure reduced the odds ratio somewhat (1.140, 95% CI 1.103-1.178) but a gap remained. Readmission rates in rural areas were consistently lower for all model specifications, while ER admissions were consistently higher. CONCLUSIONS: Among patients with HFrEF, living in a rural area is associated with an increased risk of death and return ER visits within 30 days of discharge from HF hospitalization. Differences in SDOH appear to partially explain mortality differences but the remaining gap may be the consequence of rural-urban differences in HF treatment.

Cancer Epidemiology in the Northeastern United States (2013-2017).

We tested the hypotheses that adult cancer incidence and mortality in the Northeast region and in Northern New England (NNE) were different than the rest of the United States, and described other related cancer metrics and risk factor prevalence. Using national, publicly available cancer registry data, we compared cancer incidence and mortality in the Northeast region with the United States and NNE with the United States overall and by race/ethnicity, using age-standardized cancer incidence and rate ratios (RR). Compared with the United States, age-adjusted cancer incidence in adults of all races combined was higher in the Northeast (RR, 1.07; 95% confidence interval [CI] 1.07-1.08) and in NNE (RR 1.06; CI 1.05-1.07). However compared with the United States, mortality was lower in the Northeast (RR, 0.98; CI 0.98-0.98) but higher in NNE (RR, 1.05; CI 1.03-1.06). Mortality in NNE was higher than the United States for cancers of the brain (RR, 1.16; CI 1.07-1.26), uterus (RR, 1.32; CI 1.14-1.52), esophagus (RR, 1.36; CI 1.26-1.47), lung (RR, 1.12; CI 1.09-1.15), bladder (RR, 1.23; CI 1.14-1.33), and melanoma (RR, 1.13; CI 1.01-1.27). Significantly higher overall cancer incidence was seen in the Northeast than the United States in all race/ethnicity subgroups except Native American/Alaska Natives (RR, 0.68; CI 0.64-0.72). In conclusion, NNE has higher cancer incidence and mortality than the United States, a pattern that contrasts with the Northeast region, which has lower cancer mortality overall than the United States despite higher incidence. Significance: These findings highlight the need to identify the causes of higher cancer incidence in the Northeast and the excess cancer mortality in NNE.

Inclusion of Social and Structural Determinants of Health to Advance Understanding of their Influence on the Biology of Chronic Disease.

Social Determinants of Health (SDOH) consider social, political, and economic factors that contribute to health disparities in patients and populations. The most common health-related SDOH exposures are food and housing insecurity, financial instability, transportation needs, low levels of education, and psychosocial stress. These domains describe risks that can impact health outcomes more than health care. Epidemiologic and translational research demonstrates that SDOH factors represent exposures that predict harm and impact the health of individuals. International and national guidelines urge health professionals to address SDOH in clinical practice and public health. The further implementation of these recommendations into basic and translational research, however, is lagging. Herein, we consider a precision health framework to describe how SDOH contributes to the exposome and exacerbates physiologic pathways that lead to chronic disease. SDOH factors are associated with various forms of stressors that impact physiological processes through epigenetic, inflammatory, and redox regulation. Many SDOH exposures may add to or potentiate the pathologic effects of additional environmental exposures. This overview aims to inform basic life science and translational researchers about SDOH exposures that can confound associations between classic biomedical determinants of disease and health outcomes. To advance the study of toxicology through either qualitative or quantitative assessment of exposures to chemical and biological substances, a more complete environmental evaluation should include SDOH exposures. We discuss common approaches to measure SDOH factors at individual and population levels and review the associations between SDOH risk factors and physiologic mechanisms that influence chronic disease. We provide clinical and policy-based motivation to encourage researchers to consider the impact of SDOH exposures on study results and data interpretation. With valid measures of SDOH factors incorporated into study design and analyses, future toxicological research may contribute to an evidence base that can better inform prevention and treatment options, to improve equitable clinical care and population health. © 2022 Wiley Periodicals LLC.

Multidisciplinary treatment of opioid use disorder in primary care using the collaborative care model.

Background: Treatment of opioid use disorder (OUD) is highly effective, but access is limited and care is often fragmented. Treatment in primary care can improve access to treatment and address psychiatric and physical co-morbidities in a holistic, efficient, and non-stigmatizing way. The Collaborative Care Model (CCM) of behavioral health integration into primary care has been widely disseminated and shown to improve outcomes and lower costs when studied for depression, but its use in treating substance use disorders has not been well documented. Methods: We used a mixed-methods approach to examine the impact of implementing multidisciplinary treatment of OUD in our health system's five primary care clinics using the framework of the CCM, with care shared between the primary care clinician (PCP), behavioral health clinician, and medical assistant. The implementation included staff education, creation of electronic health record tools, and implementation support, and was evaluated using data from the electronic health record, the medical staff office, and a clinician survey. Results: Over the last 2 years of implementation, the number of waivered providers increased from 11 to 35, providers prescribing for 5 or more patients increased from 2 to 18, and patients initiated on buprenorphine increased from 4/month to 18/month. 180-day treatment retention was 53%, and 81% of patients had consistently negative urine drug testing. Psychiatric and medical comorbidities were common, 70 and 44%, respectively. Although PCPs who prescribed buprenorphine found working in this model enjoyable and effective, the majority of non-waivered PCPs remained reluctant to participate. Conclusions: In our experience, treatment of OUD in primary care utilizing the CCM effectively addresses OUD and commonly comorbid anxiety and depression, and leads to an expansion of treatment. Successful implementation of OUD treatment requires addressing negative attitudes and perceptions.

International, national and local trends in the spread of COVID-19: a geographic view of COVID-19 spread and the role to be played by coproduction.

BACKGROUND: COVID-19, a respiratory disease caused by the SARS-CoV-2 virus, emerged in 2019 and led to a worldwide pandemic in 2020. The COVID-19 pandemic has been a massive natural experiment in the formation of mitigation strategies to prevent cases and to provide effective healthcare for those afflicted. Regional differences in the impact of the pandemic on morbidity and mortality have been driven by political and regional differences in the coproduction of public health and social policy. We explored the United States (US) experience of COVID-19 for trends and correlations with other nations and also at the national, regional, state and local levels. OBJECTIVE: To identify geographic and temporal trends in the spread of COVID-19 in the United States. METHODS: Population data on COVID-19 cases and mortality were acquired on a daily basis from multiple publicly available databases, including the New York Times and Johns Hopkins University. At each geographic level (national, state and county), geographic entities' reported cases were evaluated for correlations using linear least-squares methods to identify patterns of correlation in the cases independent of scale. We evaluated for two specific characteristics: (i) the nature of the curvature of the line linking across percentile scores, ranging from concave to convex and (ii) the area under this curve, indicating how effectively a selected region (nation, state and county) is linked to its entire containing unit (world, country and state). We used this approach to identify three distinct COVID behavior phenotypes, each of which consisted of a number of states in the USA. RESULTS: We found that COVID activity in the USA follows a unique trend compared to other countries and that within the USA during the first year of the pandemic, three initial COVID phenotypes emerged: (i) the metropolitan outbreak (early outbreak phenotype); (ii) the regional outbreak (summer peak phenotype) and (iii) trans-regional outbreak (fall/winter peak phenotype), which, taken in sum, represent the overall USA national trend. Each phenotype has specific behavioral characteristics and is composed of a cluster of different states experiencing different conditions. CONCLUSION: Our findings suggest a new opportunity for public health strategy in the pandemic, namely to apply targeted public health approaches to address the specific needs of each phenotype. In the future, we should create databases that capture key health and hardship data elements at the smallest geographic level possible and use these to track trends, predict the future and apply targeted coproduction approaches to more effectively and efficiently safeguard population health, economic vitality and social well-being.

A 'COVID Compass' for navigating the pandemic.

INTRODUCTION: The greatest challenge confronting political, public health, business, education and social welfare leaders in the COVID pandemic era is to restore the economy, businesses and schools without further risking public health. The 'COVID Compass' project aims to provide helpful information to guide local decisions by tracking state and local policies over time and their impact on a balanced set of outcomes-health metrics, economic trends and social hardship indicators. METHODS: We selected a parsimonious set of 'local level' health, economic and hardship outcomes and linked them to 'local level' actions aimed to decrease COVID-19 health effects and to mitigate hardship for people, businesses and the economy. Data trends will be released frequently (e.g. weekly and monthly) to show changes in health economic and social hardship 'outcomes' (based on quantitative data), alongside policy, health care, public health and individual/social 'actions' (based on both qualitative and quantitative data). RESULTS: Work on initial analytic and visualization prototypes of the COVID Compass is currently in progress at national, state and local levels. CONCLUSION: Building a national, regional and local integrated database platform that captures upstream policies, actions and behaviors and links them to downstream health, economic and social hardship outcomes will offer a more comprehensive view of the data necessary for decision-makers and citizens to more effectively and intelligently monitor and mitigate harms caused by the pandemic.

International, national and local trends in the spread of COVID-19: a geographic view of COVID-19 spread and the role to be played by coproduction.

BACKGROUND: COVID-19, a respiratory disease caused by the SARS-CoV-2 virus, emerged in 2019 and led to a worldwide pandemic in 2020. The COVID-19 pandemic has been a massive natural experiment in the formation of mitigation strategies to prevent cases and to provide effective healthcare for those afflicted. Regional differences in the impact of the pandemic on morbidity and mortality have been driven by political and regional differences in the coproduction of public health and social policy. We explored the United States (US) experience of COVID-19 for trends and correlations with other nations and also at the national, regional, state and local levels. OBJECTIVE: To identify geographic and temporal trends in the spread of COVID-19 in the United States. METHODS: Population data on COVID-19 cases and mortality were acquired on a daily basis from multiple publicly available databases, including the New York Times and Johns Hopkins University. At each geographic level (national, state and county), geographic entities' reported cases were evaluated for correlations using linear least-squares methods to identify patterns of correlation in the cases independent of scale. We evaluated for two specific characteristics: (i) the nature of the curvature of the line linking across percentile scores, ranging from concave to convex and (ii) the area under this curve, indicating how effectively a selected region (nation, state and county) is linked to its entire containing unit (world, country and state). We used this approach to identify three distinct COVID behavior phenotypes, each of which consisted of a number of states in the USA. RESULTS: We found that COVID activity in the USA follows a unique trend compared to other countries and that within the USA during the first year of the pandemic, three initial COVID phenotypes emerged: (i) the metropolitan outbreak (early outbreak phenotype); (ii) the regional outbreak (summer peak phenotype) and (iii) trans-regional outbreak (fall/winter peak phenotype), which, taken in sum, represent the overall USA national trend. Each phenotype has specific behavioral characteristics and is composed of a cluster of different states experiencing different conditions. CONCLUSION: Our findings suggest a new opportunity for public health strategy in the pandemic, namely to apply targeted public health approaches to address the specific needs of each phenotype. In the future, we should create databases that capture key health and hardship data elements at the smallest geographic level possible and use these to track trends, predict the future and apply targeted coproduction approaches to more effectively and efficiently safeguard population health, economic vitality and social well-being.

Developing primary care teams prepared to improve quality: a mixed-methods evaluation and lessons learned from implementing a microsystems approach.

BACKGROUND: Health systems in the United States are increasingly required to become leaders in quality to compete successfully in a value-conscious purchasing market. Doing so involves developing effective clinical teams using approaches like the clinical microsystems framework. However, there has been limited assessment of this approach within United States primary care settings. METHODS: This paper describes the implementation, mixed-methods evaluation results, and lessons learned from instituting a Microsystems approach across 6 years with 58 primary care teams at a large Midwestern academic health care system. The evaluation consisted of a longitudinal survey augmented by interviews and focus groups. Structured facilitated longitudinal discussions with leadership captured ongoing lessons learned. Quantitative analysis employed ordinal logistic regression and compared aggregate responses at 6-months and 12-months to those at the baseline period. Qualitative analysis used an immersion/crystallization approach. RESULTS: Survey results (N = 204) indicated improved perceptions of: organizational support, team effectiveness and cohesion, meeting and quality improvement skills, and team communication. Thematic challenges from the qualitative data included: lack of time and coverage for participation, need for technical/technology support, perceived devaluation of improvement work, difficulty aggregating or spreading learnings, tensions between team and clinic level change, a part-time workforce, team instability and difficulties incorporating a data driven improvement approach. CONCLUSIONS: These findings suggest that a microsystems approach is valuable for building team relationships and quality improvement skills but is challenged in a large, diverse academic primary care context. They additionally suggest that primary care transformation will require purposeful changes implemented across the micro to macro-level including but not only focused on quality improvement training for microsystem teams.

Primary care colorectal cancer screening correlates with breast cancer screening: implications for colorectal cancer screening improvement interventions.

OBJECTIVE: National colorectal cancer (CRC) screening rates have plateaued. To optimize interventions targeting those unscreened, a better understanding is needed of how this preventive service fits in with multiple preventive and chronic care needs managed by primary care providers (PCPs). This study examines whether PCP practices of other preventive and chronic care needs correlate with CRC screening. METHODS: We performed a retrospective cohort study of 90 PCPs and 33,137 CRC screening-eligible patients. Five PCP quality metrics (breast cancer screening, cervical cancer screening, HgbA1c and LDL testing, and blood pressure control) were measured. A baseline correlation test was performed between these metrics and PCP CRC screening rates. Multivariable logistic regression with clustering at the clinic-level estimated odds ratios and 95% confidence intervals for these PCP quality metrics, patient and PCP characteristics, and their relationship to CRC screening. RESULTS: PCP CRC screening rates have a strong correlation with breast cancer screening rates (r = 0.7414, p < 0.001) and a weak correlation with the other quality metrics. In the final adjusted model, the only PCP quality metric that significantly predicted CRC screening was breast cancer screening (OR 1.25; 95% CI 1.11-1.42; p < 0.001). CONCLUSIONS: PCP CRC screening rates are highly concordant with breast cancer screening. CRC screening is weakly concordant with cervical cancer screening and chronic disease management metrics. Efforts targeting PCPs to increase CRC screening rates could be bundled with breast cancer screening improvement interventions to increase their impact and success.

Dissemination and Implementation of Evidence Based Best Practice Across the High Value Healthcare Collaborative (HVHC) Using Sepsis as a Prototype - Rapidly Learning from Others.

The dissemination of evidence-based best practice through the entire health care system remains an elusive goal, despite public pressure and regulatory guidance. Many patients do not receive the same quality of care at different hospitals across the same health care system. We describe the role of a data driven learning collaborative, the High Value Healthcare Collaborative (HVHC), in the dissemination of best practice using adherence to the 3-hour-bundle for sepsis care. Compliance with and adoption of sepsis bundle care elements comparing sites with mature vs non-mature care delivery processes were measured during the improvement effort for a cohort of 20,758 patients. Non-mature sites increased their bundle compliance from 71.0 to 86.7 percent (p < 0.005). This compliance increase was primarily based on increased compliance with the fluid element of the bundle that improved for non-mature locations from 76.4 to 94.0 percent (p < 0.005).

Building the learning health system: Describing an organizational infrastructure to support continuous learning.

INTRODUCTION: Academic health centers are reorganizing in response to dramatic changes in the health-care environment. To improve value, they and other health systems must become a learning health system, specifically one that has the capacity to understand performance across the continuum of care and use that information to achieve continuous improvements in efficiency and effectiveness. While learning health system concepts have been well described, the practical steps to create such a system are not well defined. Establishing the necessary infrastructure is particularly challenging at academic health centers due to their tripartite missions and complex organizational structures. METHODS: Using an evidence-based framework, this article describes a series of organizational-level interventions implemented at an academic health center to create the structures and processes to support the functions of a learning health system. RESULTS: Following implementation of changes from 2008 to 2013, system-level performance improved in multiple domains: patient satisfaction, population health screenings, improvement education, and patient engagement. CONCLUSIONS: This experience can be applied to health systems that wrestle with making system-level change when existing cultures, structures, and processes vary. Using an evidence -based framework is useful when developing the structures and processes that support the functions of a learning health system.

Across the divide: "Primary care departments working together to redesign care to achieve the Triple Aim".

BACKGROUND: Primary care is considered the foundation of an effective health care system. However, primary care departments at academic health centers have numerous challenges to overcome when trying to achieve the Triple Aim. METHODS: As part of an organizational initiative to redesign primary care at a large academic health center, departments of internal medicine, general pediatrics and adolescent medicine, and family medicine worked together to comprehensively redesign primary care. This article describes the process of aligning these three primary care departments: defining panel size, developing a common primary care job description, redesigning the primary care compensation plan, redesigning the care model, and developing standardized staffing. RESULTS: Prior to the initiative, the rate of patient satisfaction was 85%, anticoagulation measurement 65%, pneumococcal vaccination 85%, breast cancer screening 79%, and colorectal cancer screening 69%. These rates all improved to 87%, 75%, 88%, 80%, and 80% respectively. Themes around key challenges to departmental integration are identified: (1) implementing effective communication strategies; (2) addressing specialty differences in primary care delivery; (3) working within resource limitations; and (4) developing long-term sustainability. CONCLUSIONS: Primary care in this large academic health center was transformed through developing a united primary care leadership team that bridged individual departments to create and adopt a common vision and solutions to shared problems. Our collaboration has achieved improvements across patient satisfaction, clinical safety metrics, and publicly-reported preventive care outcomes. IMPLICATIONS: The description of this experience may be useful for other academic health centers or other non-integrated delivery systems undertaking primary care practice transformation.

Strange Bedfellows: A Local Insurer/Physician Practice Partnership to Fund Innovation.

Despite an unprecedented urgency to control healthcare costs while simultaneously improving quality, there are many barriers to investing in quality improvement. Traditional fee-for-service reimbursement models fail to reward providers whose improved processes lead to decreases in billable clinical activity. In addition, providers may lack the necessary skills for improvement, or the organizational infrastructure to conduct these activities. Insurance firms lack incentives to invest in healthcare delivery system improvements that lead to benefits for all patients, even those covered by competitors. In this article, we describe a novel program in its sixth year of existence that funds ambulatory care improvements through a collaborative partnership between a local academic healthcare delivery system and an insurance firm. The program is designed as a competitive grant program and the payer and healthcare organization jointly benefit from completed improvement projects. Factors contributing to the ongoing success of the program and lessons learned are discussed in order to inform the potential development of similar programs in other markets.

A simple framework for complex system improvement.

The need to rapidly improve health care value is unquestioned, but the means to accomplish this task is unknown. Improving performance at the level of the health care organization frequently involves multiple interventions, which must be coordinated and sequenced to fit the specific context. Those responsible for achieving large-scale improvements are challenged by the lack of a framework to describe and organize improvement strategies. Drawing from the fields of health services, industrial engineering, and organizational behavior, a simple framework was developed and has been used to guide and evaluate improvement initiatives at an academic health center. The authors anticipate that this framework will be helpful for health system leaders responsible for improving health care quality.

Approaches and challenges to optimising primary care teams' electronic health record usage.

BACKGROUND: Although the presence of an electronic health record (EHR) alone does not ensure high quality, efficient care, few studies have focused on the work of those charged with optimising use of existing EHR functionality. OBJECTIVE: To examine the approaches used and challenges perceived by analysts supporting the optimisation of primary care teams' EHR use at a large U.S. academic health care system. METHODS: A qualitative study was conducted. Optimisation analysts and their supervisor were interviewed and data were analysed for themes. RESULTS: Analysts needed to reconcile the tension created by organisational mandates focused on the standardisation of EHR processes with the primary care teams' demand for EHR customisation. They gained an understanding of health information technology (HIT) leadership's and primary care team's goals through attending meetings, reading meeting minutes and visiting with clinical teams. Within what was organisationally possible, EHR education could then be tailored to fit team needs. Major challenges were related to organisational attempts to standardise EHR use despite varied clinic contexts, personnel readiness and technical issues with the EHR platform. Forcing standardisation upon clinical needs that current EHR functionality could not satisfy was difficult. CONCLUSIONS: Dedicated optimisation analysts can add value to health systems through playing a mediating role between HIT leadership and care teams. Our findings imply that EHR optimisation should be performed with an in-depth understanding of the workflow, cognitive and interactional activities in primary care.

Engaging Patients at the Front Lines of Primary Care Redesign: Operational Lessons for an Effective Program.

UNLABELLED: Article-at-a-Glance Background: The lack of patient engagement in quality improvement is concerning. As part of an enterprisewide initiative to redesign primary care at UW Health, interdisciplinary primary care teams received training in patient engagement. METHODS: Organizational stakeholders held a structured discussion and used nominal group technique to identify the key components critical to fostering a culture of patient engagement and critical lessons learned. These findings were augmented and illustrated by review of transcripts of two focus groups held with clinic managers and 69 interviews with individual microsystem team members. RESULTS: From late 2009 to 2014, 47 (81%) of 58 teams have engaged patients in various stages of practice improvement projects. Organizational components identified as critical to fostering a culture of patient engagement were alignment of the organization's vision that guided the redesign with national priorities, readily available external experts, involvement of all care team members in patient engagement, integration within an existing continuous improvement team development program, and an intervention deliberately matched to organizational readiness. Critical lessons learned were the need to embed patient engagement into current improvement activities, designate a neutral point person(s) or group to navigate organizational complexities, commit resources to support patient engagement activities, and plan for sustained team-patient interactions. CONCLUSIONS: Current national health care policy and local market pressures are compelling partnering with patients in efforts to improve the value of the health care delivery system. The UW Health experience may be useful for organizations seeking to introduce or strengthen the patient role in designing delivery system improvements.

Predictors of colorectal cancer screening variation among primary-care providers and clinics.

OBJECTIVES: Colorectal cancer (CRC) screening is underutilized. To effect change, we must understand reasons for underuse at multiple levels of the health-care system. We evaluated patient, provider, and clinic factors that predict variation in CRC screening among primary-care clinics and primary-care providers (PCPs). METHODS: We analyzed electronic medical record (EMR) data for 34,319 adults eligible for CRC screening, 19 clinics, and 97 PCPs in a large, academic physician group. Detailed data on potential patient, provider, and clinic predictors of CRC screening were obtained from the EMR. PCP perceptions of CRC screening barriers were measured via survey. The outcome was completion of CRC screening at the patient level. Multivariate logistic regression with clustering on clinics obtained adjusted odds ratios and 95% confidence intervals for potential predictors of CRC screening at each level. RESULTS: Seventy-one percentage of patients completed CRC screening. Variation in screening rates was seen among clinics (51-80%) and among PCPs (51-82%). Significant predictors of completing CRC screening were identified at all levels: patient (older age, white race, being married, primarily English-speaking, having commercial insurance plans vs. Medicare or Medicaid, and higher health-care resource utilization), provider (larger panel size of patients eligible for CRC screening), and clinic (hospital-owned, shorter distance to nearest optical colonoscopy center). CONCLUSIONS: Variation in CRC screening exists among primary-care clinics and providers within a single clinic. Predictors of variation can be identified at patient, provider, and clinic levels. Quality improvement interventions addressing CRC screening need to be directed at multiple levels of the health-care system.

Optical colonoscopy and virtual colonoscopy numbers after initiation of a CT colonography program: long term data.

BACKGROUND AND AIMS: The effect of CT colonography (CTC) screening on colonoscopy is unknown. The objective of this study is to determine the effect of a CTC screening program on the number of screening, therapeutic and total colonoscopies performed. METHODS: We compared the quarterly mean numbers of colonoscopic examinations performed for 50-79 year olds undergoing colorectal cancer screening in 2003, before initiation of a CTC program, to 2011, seven years after the CTC program began at our academic tertiary care facility. RESULTS: The CTC program began in 2004 with a peak number of 387 CTC examinations performed in the 3rd quarter of 2005 and 275 examinations in the final quarter of 2011. Screening colonoscopies increased from 555 mean/quarter in 2003 to 1460 in 2011 (P < 0.001). The mean/quarter number of total colonoscopies performed increased from 1104 in 2003 to 2382 in 2011 (P < 0.001). The number of overall colon cancer screening examinations (Colonoscopy + CTC) increased from 555/quarter in 2003 to 1736 in 2011 (P < 0.001). CONCLUSIONS: Since the initiation of CTC screening at our institution, the overall number of total colorectal cancer screening examinations (CTC + colonoscopy) has greatly increased. The initiation of a CTC screening program did not lead to a reduction in the number of colonoscopic examinations performed. Conversely, a significant increase in the number of screening and total colonoscopies completed was observed.

Effect of patient selection method on provider group performance estimates.

BACKGROUND: Performance measurement at the provider group level is increasingly advocated, but different methods for selecting patients when calculating provider group performance have received little evaluation. OBJECTIVE: We compared 2 currently used methods according to characteristics of the patients selected and impact on performance estimates. RESEARCH DESIGN, SUBJECTS, AND MEASURES: We analyzed Medicare claims data for fee-for-service beneficiaries with diabetes ever seen at an academic multispeciality physician group in 2003 to 2004. We examined sample size, sociodemographics, clinical characteristics, and receipt of recommended diabetes monitoring in 2004 for the groups of patients selected using 2 methods implemented in large-scale performance initiatives: the Plurality Provider Algorithm and the Diabetes Care Home method. We examined differences among discordantly assigned patients to determine evidence for differential selection regarding these measures. RESULTS: Fewer patients were selected under the Diabetes Care Home method (n=3558) than the Plurality Provider Algorithm (n=4859). Compared with the Plurality Provider Algorithm, the Diabetes Care Home method preferentially selected patients who were female, not entitled because of disability, older, more likely to have hypertension, and less likely to have kidney disease and peripheral vascular disease, and had lower levels of predicted utilization. Diabetes performance was higher under Diabetes Care Home method, with 67% versus 58% receiving >1 A1c tests, 70% versus 65% receiving ≥1 low-density lipoprotein (LDL) test, and 38% versus 37% receiving an eye examination. CONCLUSIONS: The method used to select patients when calculating provider group performance may affect patient case mix and estimated performance levels, and warrants careful consideration when comparing performance estimates.