Racial, ethnic, and socioeconomic disparities in trajectories of morbidity accumulation among older Americans.

Introduction: Multimorbidity, the presence of multiple chronic health conditions, generally starts in middle and older age but there is considerable heterogeneity in the trajectory of morbidity accumulation. This study aimed to clarify the number of distinct trajectories and the potential associations between race/ethnicity and socioeconomic status and these trajectories. Methods: Data from 13,699 respondents (age ≥51) in the Health and Retirement Study between 1998 and 2016 were analyzed with growth mixture models. Nine prevalent self-reported morbidities (arthritis, cancer, cognitive impairment, depressive symptoms, diabetes, heart disease, hypertension, lung disease, stroke) were summed for the morbidity count. Results: Three trajectories of morbidity accumulation were identified: low [starting with few morbidities and accumulating them slowly (i.e., low intercept and low slope); 80% of sample], increasing (i.e., low intercept and high slope; 9%), and high (i.e., high intercept and low slope; 11%). Compared to non-Hispanic (NH) White adults in covariate-adjusted models, NH Black adults had disadvantages while Hispanic adults had advantages. Our results suggest a protective effect of education for NH Black adults (i.e., racial health disparities observed at low education were ameliorated and then eliminated at increasing levels of education) and a reverse pattern for Hispanic adults (i.e., increasing levels of education was found to dampen the advantages Hispanic adults had at low education). Compared with NH White adults, higher levels of wealth were protective for both NH Black adults (i.e., reducing or reversing racial health disparities observed at low wealth) and Hispanic adults (i.e., increasing the initial health advantages observed at low wealth). Conclusion: These findings have implications for addressing health disparities through more precise targeting of public health interventions. This work highlights the imperative to address socioeconomic inequalities that interact with race/ethnicity in complex ways to erode health.

Depressive Multimorbidity and Trajectories of Functional Status among Older Americans: Differences by Racial/Ethnic Group.

OBJECTIVE: This study aims to evaluate the impact of depressive multimorbidity (ie, including depressive symptoms) on the long-term development of activities of daily living (ADL) and instrumental activities of daily living (IADL) limitations according to racial/ethnic group in a representative sample of US older adults. DESIGN: Prospective, observational, population-based 16-year follow-up study of nationally representative sample. SETTING AND PARTICIPANTS: Sample of older non-Hispanic Black, Hispanic, and nonHispanic White Americans from the Health and Retirement Study (2000‒2016, N = 16,364, community-dwelling adults ≥65 years of age). METHODS: Data from 9 biennial assessments were used to evaluate the accumulation of ADL-IADL limitations (range 0‒11) among participants with depressive (8-item Center for Epidemiologic Studies Depression score≥4) vs somatic (ie, physical conditions only) multimorbidity vs those without multimorbidity (no or 1 condition). Generalized estimating equations included race/ethnicity (non-Hispanic Black, Hispanic, non-Hispanic White), baseline age, sex, body mass index, education, partnered, and net worth. RESULTS: Depressive and somatic multimorbidity were associated with 5.18 and 2.95 times greater accumulation of functional limitations, respectively, relative to no disease [incidence rate ratio (IRR) = 5.18, 95% confidence interval, CI (4.38,6.13), IRR = 2.95, 95% CI (2.51,3.48)]. Hispanic and Black respondents experienced greater accumulation of ADL-IADL limitations than White respondents [IRR = 1.27, 95% CI (1.14, 1.41), IRR = 1.31, 95% CI (1.20, 1.43), respectively]. CONCLUSIONS AND IMPLICATIONS: Combinations of somatic diseases and high depressive symptoms are associated with greatest accumulation of functional limitations over time in adults ages 65 and older. There is a more rapid growth in functional limitations among individuals from racial/ethnic minority groups. Given the high prevalence of multimorbidity and depressive symptomatology among older adults and the availability of treatment options for depression, these results highlight the importance of screening/treatment for depression, particularly among older adults with socioeconomic vulnerabilities, to slow the progression of functional decline in later life.

Issues With Variability in Electronic Health Record Data About Race and Ethnicity: Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave.

BACKGROUND: The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. OBJECTIVE: This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. METHODS: At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as "Declined" were grouped with "Refused," and "Multiple Race" was grouped with "Two or more races" and "Multiracial." RESULTS: "No matching concept" was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. CONCLUSIONS: Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy.

Advancing Interoperability of Patient-level Social Determinants of Health Data to Support COVID-19 Research.

Including social determinants of health (SDoH) data in health outcomes research is essential for studying the sources of healthcare disparities and developing strategies to mitigate stressors. In this report, we describe a pragmatic design and approach to explore the encoding needs for transmitting SDoH screening tool responses from a large safety-net hospital into the National Covid Cohort Collaborative (N3C) OMOP dataset. We provide a stepwise account of designing data mapping and ingestion for patient-level SDoH and summarize the results of screening. Our approach demonstrates that sharing of these important data - typically stored as non-standard, EHR vendor specific codes - is feasible. As SDoH screening gains broader use nationally, the approach described in this paper could be used for other screening instruments and improve the interoperability of these important data.

The Transforming Outcomes for Patients Through Medical Home Evaluation and reDesign (TOPMED) Cluster Randomized Controlled Trial: Cost and Utilization Results.

BACKGROUND: Primary Care Medical Home (PCMH) redesign efforts are intended to enhance primary care's ability to improve population health and well-being. PCMH transformation that is focused on "high-value elements" (HVEs) for cost and utilization may improve effectiveness. OBJECTIVES: The objective of this study was to determine if a focus on achieving HVEs extracted from successful primary care transformation models would reduce cost and utilization as compared with a focus on achieving PCMH quality improvement goals. RESEARCH DESIGN: A stratified, cluster randomized controlled trial with 2 arms. All practices received equal financial incentives, health information technology support, and in-person practice facilitation. Analyses consisted of multivariable modeling, adjusting for the cluster, with difference-in-difference results. SUBJECTS: Eight primary care clinics that were engaged in PCMH reform. MEASURES: We examined: (1) total claims payments; (2) emergency department (ED) visits; and (3) hospitalizations among patients during baseline and intervention years. RESULTS: In total, 16,099 patients met the inclusion criteria. Intervention clinics had significantly lower baseline ED visits (P=0.02) and claims paid (P=0.01). Difference-in-difference showed a decrease in ED visits greater in control than intervention (ED per 1000 patients: +56; 95% confidence interval: +96, +15) with a trend towards decreased hospitalizations in intervention (-15; 95% confidence interval: -52, +21). Costs were not different. In modeling monthly outcome means, the generalized linear mixed model showed significant differences for hospitalizations during the intervention year (P=0.03). DISCUSSION: The trial had a trend of decreasing hospitalizations, increased ED visits, and no change in costs in the HVE versus quality improvement arms.

Physical Activity as a Mediator Between Race/Ethnicity and Changes in Multimorbidity.

OBJECTIVES: Studies report racial/ethnic disparities in multimorbidity (≥2 chronic conditions) and their rate of accumulation over time as well as differences in physical activity. Our study aimed to investigate whether racial/ethnic differences in the accumulation of multimorbidity were mediated by physical activity among middle-aged and older adults. METHOD: We assessed racial/ethnic differences in the accumulation of multimorbidity (of 9 conditions) over 12 years (2004-2016) in the Health and Retirement Study (N = 18,264, mean age = 64.4 years). Structural equation modeling was used to estimate latent growth curve models of changes in multimorbidity and investigate whether the relationship of race/ethnicity (non-Hispanic Black, Hispanic, non-Hispanic White participants) to changes in the number of chronic conditions was mediated by physical activity after controlling for age, sex, education, marital status, household wealth, insurance coverage, smoking, alcohol, and body weight. RESULTS: There was a significant increase in multimorbidity over time. Initial levels and changes in multimorbidity over time varied significantly across individuals. Indirect effects of the relationship between race/ethnicity and changes in multimorbidity as mediated by physical activity were significant, consistent with the mediational hypothesis. Black respondents engaged in significantly lower levels of physical activity than White respondents after controlling for covariates, but there were no differences between Hispanic and White respondents once education was included. Discussion: These results provide important new information for understanding how modifiable lifestyle factors may help explain disparities in multimorbidity in mid-to-late life, suggesting greater need to intervene to reduce sedentary behavior and increase physical activity.

Primary care practices' ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation.

BACKGROUND: Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores. METHODS: Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score. RESULTS: In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71-0.88) but sensitivity and PPV were low (0.16-0.40). Practice-created scores had 0.02-0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity. CONCLUSIONS: Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.

Understanding enterprise data warehouses to support clinical and translational research.

OBJECTIVE: Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, adoption of electronic data warehouses for research (EDW4R) containing data from electronic health record systems is nearly ubiquitous. Although benefits of EDW4R include more effective, efficient support of scientists, little is known about how CTSA hubs have implemented EDW4R services. The goal of this qualitative study was to understand the ways in which CTSA hubs have operationalized EDW4R to support clinical and translational researchers. MATERIALS AND METHODS: After conducting semistructured interviews with informatics leaders from 20 CTSA hubs, we performed a directed content analysis of interview notes informed by naturalistic inquiry. RESULTS: We identified 12 themes: organization and data; oversight and governance; data access request process; data access modalities; data access for users with different skill sets; engagement, communication, and literacy; service management coordinated with enterprise information technology; service management coordinated within a CTSA hub; service management coordinated between informatics and biostatistics; funding approaches; performance metrics; and future trends and current technology challenges. DISCUSSION: This study is a step in developing an improved understanding and creating a common vocabulary about EDW4R operations across institutions. Findings indicate an opportunity for establishing best practices for EDW4R operations in academic medicine. Such guidance could reduce the costs associated with developing an EDW4R by establishing a clear roadmap and maturity path for institutions to follow. CONCLUSIONS: CTSA hubs described varying approaches to EDW4R operations that may assist other institutions in better serving investigators with electronic patient data.

Using Healthcare Data in Embedded Pragmatic Clinical Trials among People Living with Dementia and Their Caregivers: State of the Art.

Embedded pragmatic clinical trials (ePCTs) are embedded in healthcare systems as well as their data environments. For people living with dementia (PLWD), settings of care can be different from the general population and involve additional people whose information is also important. The ePCT designs have the opportunity to leverage data that becomes available through the normal delivery of care. They may be particularly valuable in Alzheimer's disease and Alzheimer's disease-related dementia (AD/ADRD), given the complexity of case identification and the diversity of care settings. Grounded in the objectives of the Data and Technical Core of the newly established National Institute on Aging Imbedded Pragmatic Alzheimer's Disease and AD-Related Dementias Clinical Trials Collaboratory (IMPACT Collaboratory), this article summarizes the state of the art in using existing data sources (eg, Medicare claims, electronic health records) in AD/ADRD ePCTs and approaches to integrating them in real-world settings. J Am Geriatr Soc 68:S49-S54, 2020.

Unmet information needs of clinical teams delivering care to complex patients and design strategies to address those needs.

OBJECTIVES: To identify the unmet information needs of clinical teams delivering care to patients with complex medical, social, and economic needs; and to propose principles for redesigning electronic health records (EHR) to address these needs. MATERIALS AND METHODS: In this observational study, we interviewed and observed care teams in 9 community health centers in Oregon and Washington to understand their use of the EHR when caring for patients with complex medical and socioeconomic needs. Data were analyzed using a comparative approach to identify EHR users' information needs, which were then used to produce EHR design principles. RESULTS: Analyses of > 300 hours of observations and 51 interviews identified 4 major categories of information needs related to: consistency of social determinants of health (SDH) documentation; SDH information prioritization and changes to this prioritization; initiation and follow-up of community resource referrals; and timely communication of SDH information. Within these categories were 10 unmet information needs to be addressed by EHR designers. We propose the following EHR design principles to address these needs: enhance the flexibility of EHR documentation workflows; expand the ability to exchange information within teams and between systems; balance innovation and standardization of health information technology systems; organize and simplify information displays; and prioritize and reduce information. CONCLUSION: Developing EHR tools that are simple, accessible, easy to use, and able to be updated by a range of professionals is critical. The identified information needs and design principles should inform developers and implementers working in community health centers and other settings where complex patients receive care.

Implementing Risk Stratification in Primary Care: Challenges and Strategies.

INTRODUCTION: Primary care risk stratification (RS) has been shown to help practices better understand their patient populations' needs and may improve health outcomes and reduce expenditures by targeting and tailoring care to high-need patients. This study aims to understand key considerations practices faced and practice experiences as they began to implement RS models. METHODS: We conducted semistructured interviews about experiences in RS with 34 stakeholders from 15 primary care practices in Oregon and Colorado and qualitatively analyzed the data. RESULTS: Three decisions were important in shaping practices' experiences with RS: choosing established versus self-created algorithms or heuristics, clinical intuition, or a combination; selecting mechanisms for assigning risk scores; determining how to integrate RS approaches into care delivery. Practices using clinical intuition found stratification time-consuming and difficult to incorporate into existing workflows, but trusted risk scores more than those using algorithms. Trust in risk scores was influenced by data extraction capabilities; practices often lacked sufficient data to calculate their perceived optimal risk score. Displaying the scores to the care team was a major issue. Finally, obtaining buy-in from care team members was challenging, requiring repeated cycles of improvement and workflow integration. DISCUSSION: Practices used iterative approaches to RS implementation. As a result, procedural and algorithmic changes were introduced and were influenced by practices' health IT, staffing, and resource capacities. Practices were most successful when able to make iterative changes to their approaches, incorporated both automation and human process in RS, educated staff on the importance of RS, and had readily accessible risk scores.

Defining High Value Elements for Reducing Cost and Utilization in Patient-Centered Medical Homes for the TOPMED Trial.

INTRODUCTION: Like most patient-centered medical home (PCMH) models, Oregon's program, the Patient-Centered Primary Care Home (PCPCH), aims to improve care while reducing costs; however, previous work shows that PCMH models do not uniformly achieve desired outcomes. Our objective was to describe a process for refining PCMH models to identify high value elements (HVEs) that reduce cost and utilization. METHODS: We performed a targeted literature review of each PCPCH core attribute. Value-related concepts and their metrics were abstracted, and studies were assessed for relevance and strength of evidence. Focus groups were held with stakeholders and patients, and themes related to each attribute were identified; calculation of HVE attainment versus PCPCH criteria were completed on eight primary care clinics. Analyses consisted of descriptive statistics and criterion validity with stakeholder input. RESULTS: 2,126 abstracts were reviewed; 22 met inclusion criteria. From these articles and focus groups of stakeholders/experts (n = 49; 4 groups) and patients (n = 7; 1 group), 12 HVEs were identified that may reduce cost and utilization. At baseline, clinics achieved, on average, 31.3 percent HVE levels compared to an average of 87.9 percent of the 35 PCMH measures. DISCUSSION: A subset of measures from the PCPCH model were identified as "high value" in reducing cost and utilization. HVE performance was significantly lower than standard measures, and may better calibrate clinic ability to reduce costs. CONCLUSION: Through literature review and stakeholder engagement, we created a novel set of high value elements for advanced primary care likely to be more related to cost and utilization than other models.

Data-Driven Diffusion Of Innovations: Successes And Challenges In 3 Large-Scale Innovative Delivery Models.

Failed diffusion of innovations may be linked to an inability to use and apply data, information, and knowledge to change perceptions of current practice and motivate change. Using qualitative and quantitative data from three large-scale health care delivery innovations-accountable care organizations, advanced primary care practice, and EvidenceNOW-we assessed where data-driven innovation is occurring and where challenges lie. We found that implementation of some technological components of innovation (for example, electronic health records) has occurred among health care organizations, but core functions needed to use data to drive innovation are lacking. Deficits include the inability to extract and aggregate data from the records; gaps in sharing data; and challenges in adopting advanced data functions, particularly those related to timely reporting of performance data. The unexpectedly high costs and burden incurred during implementation of the innovations have limited organizations' ability to address these and other deficits. Solutions that could help speed progress in data-driven innovation include facilitating peer-to-peer technical assistance, providing tailored feedback reports to providers from data aggregators, and using practice facilitators skilled in using data technology for quality improvement to help practices transform. Policy efforts that promote these solutions may enable more rapid uptake of and successful participation in innovative delivery system reforms.

A Randomized Trial of High-Value Change Using Practice Facilitation.

PURPOSE: To understand how focused versus general practice facilitation can impact goal setting, action planning, and team performance in primary care transformation. BACKGROUND: Practice transformation in primary care is a crucial part of health reform, but can fatigue teams, leading to variable results. Practice facilitation may reduce primary care fatigue to help teams reach challenging transformation goals, but may require a more focused approach than previous studies suggest. METHODS: We performed a 12-month cluster randomized trial, during which 8 primary care clinics received practice facilitation. Four practices in the intervention arm received targeted facilitation to focus quality improvement (QI) goals on high-value elements (HVEs) intended to reduce cost and utilization, whereas 4 control practices received generalized QI facilitation. We investigated the impact of the targeted versus generalized approach on goal selection, action item selection and achievement, HVE attainment, and collaborative practice, using quantitative and qualitative methods. RESULTS: Intervention clinics selected an average of 7 goals and 29 action items, compared with 8 goals and 40 action items among controls. Eighty-three percent of intervention goals were related to HVEs, compared with 27% of goals among controls. Intervention clinics selected 101 HVE goals and met 68%, while controls selected 41 and met 61%. Analysis of pre-post practice surveys indicated greater improvement among intervention across 4 of 8 domains of collaborative practice. CONCLUSION: Targeted facilitation may be more effective than a generalized approach to support practices in reaching high-value change goals, as well as fostering improvement of team focus on goals, roles and responsibilities.

Clinical Decision Support Reduces Overuse of Red Blood Cell Transfusions: Interrupted Time Series Analysis.

BACKGROUND: Red blood cell transfusion is the most common procedure in hospitalized patients in the US. Growing evidence suggests that a sizeable percentage of these transfusions are inappropriate, putting patients at significant risk and increasing costs to the health care system. METHODS: We performed a retrospective quasi-experimental study from November 2008 until November 2014 in a 576-bed tertiary care hospital. The intervention consisted of an interruptive clinical decision support alert shown to a provider when a red blood cell transfusion was ordered in a patient whose most recent hematocrit was ≥21%. We used interrupted time series analysis to determine whether our primary outcome of interest, rate of red blood cell transfusion in patients with hematocrit ≥21% per 100 patient (pt) days, was reduced by the implementation of the clinical decision support tool. The rate of platelet transfusions was used as a nonequivalent dependent control variable. RESULTS: A total of 143,000 hospital admissions were included in our analysis. Red blood cell transfusions decreased from 9.4 to 7.8 per 100 pt days after the clinical decision support intervention was implemented. Interrupted time series analysis showed that significant decline of 0.05 (95% confidence interval [CI], 0.03-0.07; P < .001) units of red blood cells transfused per 100 pt days per month was already underway in the preintervention period. This trend accelerated to 0.1 (95% CI, 0.09-0.12; P < .001) units of red blood cells transfused per 100 pt days per month following the implementation of the clinical decision support tool. There was no statistical change in the rate of platelet transfusion resulting from the intervention. CONCLUSIONS: The implementation of an evidence-based clinical decision support tool was associated with a significant decline in the overuse of red blood cell transfusion. We believe this intervention could be easily replicated in other hospitals using commercial electronic health records and a similar reduction in overuse of red blood cell transfusions achieved.

Problem list completeness in electronic health records: A multi-site study and assessment of success factors.

OBJECTIVE: To assess problem list completeness using an objective measure across a range of sites, and to identify success factors for problem list completeness. METHODS: We conducted a retrospective analysis of electronic health record data and interviews at ten healthcare organizations within the United States, United Kingdom, and Argentina who use a variety of electronic health record systems: four self-developed and six commercial. At each site, we assessed the proportion of patients who have diabetes recorded on their problem list out of all patients with a hemoglobin A1c elevation>=7.0%, which is diagnostic of diabetes. We then conducted interviews with informatics leaders at the four highest performing sites to determine factors associated with success. Finally, we surveyed all the sites about common practices implemented at the top performing sites to determine whether there was an association between problem list management practices and problem list completeness. RESULTS: Problem list completeness across the ten sites ranged from 60.2% to 99.4%, with a mean of 78.2%. Financial incentives, problem-oriented charting, gap reporting, shared responsibility, links to billing codes, and organizational culture were identified as success factors at the four hospitals with problem list completeness at or near 90.0%. DISCUSSION: Incomplete problem lists represent a global data integrity problem that could compromise quality of care and put patients at risk. There was a wide range of problem list completeness across the healthcare facilities. Nevertheless, some facilities have achieved high levels of problem list completeness, and it is important to better understand the factors that contribute to success to improve patient safety. CONCLUSION: Problem list completeness varies substantially across healthcare facilities. In our review of EHR systems at ten healthcare facilities, we identified six success factors which may be useful for healthcare organizations seeking to improve the quality of their problem list documentation: financial incentives, problem oriented charting, gap reporting, shared responsibility, links to billing codes, and organizational culture.

Racial and ethnic differences in clinical outcome trajectories for care managed patients.

BACKGROUND: Care management has demonstrated improvements in quality of care for patients with complex care needs. The extent to which these interventions benefit race/ethnic minority populations is unclear. OBJECTIVES: To characterize race/ethnic differences in the longitudinal control of clinical outcomes for patients with complex care needs enrolled in Care Management Plus, a health information technology-enabled care coordination intervention. RESEARCH DESIGN: Multilevel models of repeated observations from clinical encounters before and after program enrollment for 6 Oregon and California primary care clinics. SUBJECTS: A total of 18,675 clinic patients were examined. We estimated multilevel models for 1481 and 5320 care-managed individuals with repeated hemoglobin A1c and blood pressure measurements, respectively. MEASURES: Primary outcomes were changes over time for 2 clinical markers of health status for complex care patients: (1) hemoglobin A1c for patients with diabetes; and (2) mid-blood pressure (BP) (average systolic and diastolic blood pressure). RESULTS: We found significant reductions in A1c for patients with previously uncontrolled A1c (preperiod slope, b=1.03 [0.83, 1.24]; postperiod slope, b=-0.63 [-0.91, -0.35]). For mid-BP we found increasing unconditional preperiod trajectories (b=3.52 [2.39, 4.64]) and decreasing postperiod trajectories (b=-5.21 [-5.70, -4.72]). We also found the trajectories of A1c and mid-BP were not statistically different for black, Latino, and white patients. CONCLUSIONS: These analyses demonstrate some promising results for intermediate clinical outcomes for underrepresented patients with complex chronic care needs. It remains to be seen whether these health care system delivery redesigns yield long-term benefits for patients, such as improvements in function and quality of life.

Defining the medical home: the Oregon experience.

OBJECTIVE: The patient-centered medical home (PCMH) is emerging as a key strategy to improve health outcomes, reduce total costs, and strengthen primary care, but a myriad of operational measures of the PCMH have emerged. In 2009, the state of Oregon convened a public, legislatively mandated committee charged with developing PCMH measures. We report on the process of, outcomes of, and lessons learned by this committee. METHODS: The Oregon PCMH advisory committee was appointed by the director of the Oregon Department of Human Services and held 7 public meetings between October 2009 and February 2010. The committee engaged a diverse group of Oregon stakeholders, including a variety of practicing primary care physicians. RESULTS: The committee developed a PCMH measurement framework, including 6 core attributes, 15 standards, and 27 individual measures. Key successes of the committee's work were to describe PCMH core attributes and functions in patient-centered language and to achieve consensus among a diverse group of stakeholders. CONCLUSIONS: Oregon's PCMH advisory committee engaged local stakeholders in a process that resulted in a shared PCMH measurement framework and addressed stakeholders' concerns. The state of Oregon now has implemented a PCMH program using the framework developed by the PCMH advisory committee. The Oregon experience demonstrates that a brief public process can be successful in producing meaningful consensus on PCMH roles and functions and advancing PCMH policy.

A business case for HIT adoption: effects of "meaningful use" EHR financial incentives on clinic revenue.

The goal of this study is to describe a framework that allows decision makers to efficiently evaluate factors that affect Electronic Health Record (EHR) adoption and test suitable interventions; specifically financial incentives. The United States healthcare delivery system is experiencing a transformation to improve population health. There is strong agreement that "meaningful use" of Health Information Technology (HIT) is a major enabler in this effort. However it's also understood that the high cost of implementing an EHR is an obstacle for adoption. To help understand these complexities we developed a simulation model designed to capture the dynamic nature of policy interventions that affect the adoption of EHR. We found that "Effective" use of HIT approaches break-even-point and larger clinic revenue many times faster that "average" or "poor" use of HIT. This study uses a systems perspective to the evaluate EHR adoption process through the "meaningful use" redesign as proposed in the American Reinvestment and Recovery Act 2009 in the United States healthcare industry by utilizing the System Dynamics methodology and Scenario Analysis.

User centered design in complex healthcare workflows: the case of care coordination and care management redesign.

We present the User Need Analysis process for the Integrated Care Coordination Information System (ICCIS) research project at Oregon Health & Science University (OHSU). The primary goal of the process was to select a mix of methodologies to perform user need analysis which would lead to generation of system requirements. The ICCIS Team developers would use these prioritized requirements to build the next generation of the ICCIS software that would help meet the overall goals of the care model. First, semi-structured User Discussion Guides and structured questioners were used to collect raw needs from Physicians, Nurses, IT and Administrative personal. Later Conjoint Analysis (CA) and Dimensional Analysis (DA) were used to generate emerging concepts and priorities from the discussions with the end-user community.