Predicting Self-Reported Social Risk in Medically Complex Adults Using Electronic Health Data.

BACKGROUND: Social barriers to health care, such as food insecurity, financial distress, and housing instability, may impede effective clinical management for individuals with chronic illness. Systematic strategies are needed to more efficiently identify at-risk individuals who may benefit from proactive outreach by health care systems for screening and referral to available social resources. OBJECTIVE: To create a predictive model to identify a higher likelihood of food insecurity, financial distress, and/or housing instability among adults with multiple chronic medical conditions. RESEARCH DESIGN AND SUBJECTS: We developed and validated a predictive model in adults with 2 or more chronic conditions who were receiving care within Kaiser Permanente Northern California (KPNC) between January 2017 and February 2020. The model was developed to predict the likelihood of a "yes" response to any of 3 validated self-reported survey questions related to current concerns about food insecurity, financial distress, and/or housing instability. External model validation was conducted in a separate cohort of adult non-Medicaid KPNC members aged 35-85 who completed a survey administered to a random sample of health plan members between April and June 2021 (n = 2820). MEASURES: We examined the performance of multiple model iterations by comparing areas under the receiver operating characteristic curves (AUCs). We also assessed algorithmic bias related to race/ethnicity and calculated model performance at defined risk thresholds for screening implementation. RESULTS: Patients in the primary modeling cohort (n = 11,999) had a mean age of 53.8 (±19.3) years, 64.7% were women, and 63.9% were of non-White race/ethnicity. The final, simplified model with 30 predictors (including utilization, diagnosis, behavior, insurance, neighborhood, and pharmacy-based variables) had an AUC of 0.68. The model remained robust within different race/ethnic strata. CONCLUSIONS: Our results demonstrated that a predictive model developed using information gleaned from the medical record and from public census tract data can be used to identify patients who may benefit from proactive social needs assessment. Depending on the prevalence of social needs in the target population, different risk output thresholds could be set to optimize positive predictive value for successful outreach. This predictive model-based strategy provides a pathway for prioritizing more intensive social risk outreach and screening efforts to the patients who may be in greatest need.

Food Insecurity and Hypoglycemia among Older Patients with Type 2 Diabetes Treated with Insulin or Sulfonylureas: The Diabetes & Aging Study.

BACKGROUND: Severe hypoglycemia is a serious adverse drug event associated with hypoglycemia-prone medications; older patients with diabetes are particularly at high risk. Economic food insecurity (food insecurity due to financial limitations) is a known risk factor for hypoglycemia; however, less is known about physical food insecurity (due to difficulty cooking or shopping for food), which may increase with age, and its association with hypoglycemia. OBJECTIVE: Study associations between food insecurity and severe hypoglycemia. DESIGN: Survey based cross-sectional study. PARTICIPANTS: Survey responses were collected in 2019 from 1,164 older (≥ 65 years) patients with type 2 diabetes treated with insulin or sulfonylureas. MAIN MEASURES: Risk ratios (RR) for economic and physical food insecurity associated with self-reported severe hypoglycemia (low blood glucose requiring assistance) adjusted for age, financial strain, HbA1c, Charlson comorbidity score and frailty. Self-reported reasons for hypoglycemia endorsed by respondents. KEY RESULTS: Food insecurity was reported by 12.3% of the respondents; of whom 38.4% reported economic food insecurity only, 21.1% physical food insecurity only and 40.5% both. Economic food insecurity and physical food insecurity were strongly associated with severe hypoglycemia (RR = 4.3; p = 0.02 and RR = 4.4; p = 0.002, respectively). Missed meals ("skipped meals, not eating enough or waiting too long to eat") was the dominant reason (77.5%) given for hypoglycemia. CONCLUSIONS: Hypoglycemia prevention efforts among older patients with diabetes using hypoglycemia-prone medications should address food insecurity. Standard food insecurity questions, which are used to identify economic food insecurity, will fail to identify patients who have physical food insecurity only.

Development and Validation of the Life Expectancy Estimator for Older Adults with Diabetes (LEAD): the Diabetes and Aging Study.

BACKGROUND: Estimated life expectancy for older patients with diabetes informs decisions about treatment goals, cancer screening, long-term and advanced care, and inclusion in clinical trials. Easily implementable, evidence-based, diabetes-specific approaches for identifying patients with limited life expectancy are needed. OBJECTIVE: Develop and validate an electronic health record (EHR)-based tool to identify older adults with diabetes who have limited life expectancy. DESIGN: Predictive modeling based on survival analysis using Cox-Gompertz models in a retrospective cohort. PARTICIPANTS: Adults with diabetes aged ≥ 65 years from Kaiser Permanente Northern California: a 2015 cohort (N = 121,396) with follow-up through 12/31/2019, randomly split into training (N = 97,085) and test (N = 24,311) sets. Validation was conducted in the test set and two temporally distinct cohorts: a 2010 cohort (n = 89,563; 10-year follow-up through 2019) and a 2019 cohort (n = 152,357; 2-year follow-up through 2020). MAIN MEASURES: Demographics, diagnoses, utilization and procedures, medications, behaviors and vital signs; mortality. KEY RESULTS: In the training set (mean age 75 years; 49% women; 48% racial and ethnic minorities), 23% died during 5 years follow-up. A mortality prediction model was developed using 94 candidate variables, distilled into a life expectancy model with 11 input variables, and transformed into a risk-scoring tool, the Life Expectancy Estimator for Older Adults with Diabetes (LEAD). LEAD discriminated well in the test set (C-statistic = 0.78), 2010 cohort (C-statistic = 0.74), and 2019 cohort (C-statistic = 0.81); comparisons of observed and predicted survival curves indicated good calibration. CONCLUSIONS: LEAD estimates life expectancy in older adults with diabetes based on only 11 patient characteristics widely available in most EHRs and claims data. LEAD is simple and has potential application for shared decision-making, clinical trial inclusion, and resource allocation.

Telehealth During COVID-19 for Adults with Multiple Chronic Conditions: Associations with Self-Reported Food Insecurity and with Physical Limitations.

Background: Adults with chronic medical conditions complicated by food insecurity or physical limitations may have higher barriers to accessing telehealth implemented during the COVID-19 pandemic. Objective: To examine the relationships of self-reported food insecurity and physical limitations with changes in health care utilization and medication adherence comparing the year before (March 2019-February 2020) and the first year of the COVID-19 pandemic (April 2020-March 2021) among patients with chronic conditions insured by Medicaid or Medicare Advantage. Methods: A prospective cohort study of 10,452 Kaiser Permanente Northern California members insured by Medicaid and 52,890 Kaiser Permanente Colorado members insured by Medicare Advantage was conducted. Difference-in-differences (DID) between the pre-COVID and COVID years in telehealth versus in-person health care utilization and adherence to chronic disease medicines by food insecurity and by physical limitation status were measured. Results: Food insecurity and physical limitations were each associated with small but significantly greater shifts from in-person to telehealth. Medicare Advantage members with physical limitations also had significantly greater decline in adherence to chronic medications from year to year compared with those without physical limitations (DID from pre-COVID year to COVID year ranged from 0.7% to 3.6% greater decline by medication class, p < 0.01). Conclusions: Food insecurity and physical limitations did not present significant barriers to the transition to telehealth during the COVID pandemic. The greater decrease in medication adherence among older patients with physical limitations suggests that care systems must further address the needs of this high-risk population.

Associations Between Different Self-reported Social Risks and Neighborhood-level Resources in Medicaid Patients.

BACKGROUND: Adverse social conditions are a key contributor to health disparities. Improved understanding of how social risk factors interact with each other and with neighborhood characteristics may inform efforts to reduce health disparities. DATA: A questionnaire of 29,281 patients was collected through the enrollment of Medicaid beneficiaries in a large Northern California integrated health care delivery system between May 2016 and February 2020. EXPOSURES: Living in the least resourced quartile of neighborhoods as measured by a census-tract level Neighborhood Deprivation Index score. MAIN OUTCOMES: Five self-reported social risk factors: financial need, food insecurity, housing barriers, transportation barriers, and functional limitations. RESULTS: Nearly half (42.0%) of patients reported at least 1 social risk factor; 22.4% reported 2 or more. Mean correlation coefficient between social risk factors was ρ=0.30. Multivariable logistic models controlling for age, race/ethnicity, sex, count of chronic conditions, and insurance source estimated that living in the least resourced neighborhoods was associated with greater odds of food insecurity (adjusted odds ratio=1.07, 95% confidence interval: 1.00-1.13) and transportation barriers (adjusted odds ratio=1.20, 95% confidence interval: 1.11-1.30), but not financial stress, housing barriers, or functional limitations. CONCLUSIONS AND RELEVANCE: We found that among 5 commonly associated social risk factors, Medicaid patients in a large Northern California health system typically reported only a single factor and that these factors did not correlate strongly with each other. We found only modestly greater social risk reported by patients in the least resourced neighborhoods. These results suggest that individual-level interventions should be targeted to specific needs whereas community-level interventions may be similarly important across diverse neighborhoods.

Accounting for Social Risk Does not Eliminate Race/Ethnic Disparities in COVID-19 Infection Among Insured Adults: a Cohort Study.

BACKGROUND: Communities of color have been disproportionately impacted by the COVID-19 epidemic in the USA. OBJECTIVES: To examine the relationship of self-reported social health needs with SARS-COV-2 infection by race/ethnicity among insured adults with access to high-quality health care. DESIGN AND PARTICIPANTS: A prospective cohort study of 26,741 adult Kaiser Permanente Northern California members insured by Medicaid and 58,802 Kaiser Permanente Colorado members insured by Medicare Advantage who completed social risk assessments prior to the onset of the COVID-19 pandemic. MAIN MEASURES: We examined the independent relationships of demographic, medical, and social factors on SARS-COV-2 testing and positivity between March 1, 2020, and November 30, 2020, by race/ethnicity. KEY RESULTS: Findings were similar in the two cohorts, with Latino (16-18%), Asian (11-14%), and Black (11-12%) members having the highest prevalence of SARS-COV-2 infection (ORs adjusted for age, gender, and use of interpreter ranging from 1.68 to 2.23 compared to White member [7-8%], p < 0.001). Further adjustment for medical comorbidity (e.g., obesity, diabetes, chronic lung disease); neighborhood measures; and self-reported social risk factors (e.g., trouble paying for basics, food insecurity, housing concerns, transportation barriers) did not appreciably change these results. CONCLUSIONS: Compared to non-Latino White members, members of other race/ethnic groups had higher positivity rates that were only minimally reduced after controlling for medical and neighborhood conditions and self-reported social risk factors. These findings suggest that traditional infection transmission factors such as essential work roles and household size that have disproportionate representation among communities of color may be important contributors to SARS-COV-2 infection among insured adults.

Disclosure of New Type 2 Diabetes Diagnoses to Younger Adults: a Qualitative Study.

BACKGROUND: Adults diagnosed with type 2 diabetes at a younger age are at increased risk for poor outcomes. Yet, little is known about the early experiences of these individuals, starting with communication of the diagnosis. Addressing this knowledge gap is important as this initial interaction may shape subsequent disease-related perceptions and self-management. OBJECTIVE: We examined diagnosis disclosure experiences and initial reactions among younger adults with newly diagnosed type 2 diabetes. PARTICIPANTS: Purposive sample of adult members of Kaiser Permanente Northern California, an integrated healthcare delivery system, diagnosed with type 2 diabetes before age 45 years. APPROACH: We conducted six focus groups between November 2017 and May 2018. Transcribed audio recordings were coded by two coders using thematic analysis. KEY RESULTS: Participants (n = 41) were 38.4 (± 5.8) years of age; 10 self-identified as Latinx, 12 as Black, 12 as White, and 7 as multiple or other races. We identified variation in diagnosis disclosure experiences, centered on four key domains: (1) participants' sense of preparedness for diagnosis (ranging from expectant to surprised); (2) disclosure setting (including in-person, via phone, via secure message, or via review of results online); (3) perceived provider tone (from nonchalant, to overly fear-centered, to supportive); and (4) participants' emotional reactions to receiving the diagnosis (including acceptance, denial, guilt, and/or fear, rooted in personal and family experience). CONCLUSIONS: For younger adults, the experience of receiving a diabetes diagnosis varies greatly. Given the long-term consequences of inadequately managed diabetes and the need for early disease control, effective initial disclosure represents an opportunity to optimize initial care. Our results suggest several opportunities to improve the type 2 diabetes disclosure experience: (1) providing pre-test counseling, (2) identifying patient-preferred settings for receiving the news, and (3) developing initial care strategies that acknowledge and address the emotional distress triggered by this life-altering, chronic disease diagnosis.

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies.

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR-based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR-based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment-monitoring interventions, due to a large decrease in data support and concerns over finite-sample bias from near-violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.

Prompting Patients with Poorly Controlled Diabetes to Identify Visit Priorities Before Primary Care Visits: a Pragmatic Cluster Randomized Trial.

BACKGROUND: Most patients with diabetes do not meet all evidence-based goals of care, and many patients report poor communication and lack of involvement in decision-making during primary care visits. OBJECTIVE: To test the hypothesis that a "Pre-Visit Prioritization" secure email message could improve visit communication and glycemic control among patients with type 2 diabetes. DESIGN: We conducted a pragmatic, provider-randomized, multi-site clinical trial from March 2015 to October 2016 across 30 primary care practices within Kaiser Permanente Northern California (KPNC), a large integrated care delivery system. PARTICIPANTS: Eligible patients had at least 1 year of KPNC membership, type 2 diabetes with most recently measured hemoglobin A1c (HbA1c) > = 8.0%, and were registered users of the KPNC online patient portal. INTERVENTIONS: Patients in the intervention arm, upon booking an appointment, received a secure email through the KPNC online portal with a link to the EHR allowing them to submit their top one or two priorities prior to the visit. Control patients received usual care. MAIN MEASURES: Glycemic control; change in HbA1c 6 and 12 months after the initial visit; patient-reported outcomes related to patient-provider communication and patient care experiences. KEY RESULTS: During the study period, 1276 patients had at least one eligible visit. In post-visit surveys (n = 457), more intervention arm patients reported preparing questions for their visit (72% vs 63%, p = 0.048) and being given treatment choices to consider (81% vs 73%, p = 0.041). Patients in both arms had similar reductions in HbA1c over the 12-month study period (0.56% ± 1.45%), with no significant differences between arms. CONCLUSIONS: A "light touch" email-based pre-visit intervention resulted in improved measures of visit interaction but did not significantly improve glycemic control relative to usual care. Improving diabetes clinical outcomes through more effective primary care visits may require more intensive approaches to patient visit preparation. TRIAL REGISTRY: NCT02375932.

Targeted learning with daily EHR data.

Electronic health records (EHR) data provide a cost- and time-effective opportunity to conduct cohort studies of the effects of multiple time-point interventions in the diverse patient population found in real-world clinical settings. Because the computational cost of analyzing EHR data at daily (or more granular) scale can be quite high, a pragmatic approach has been to partition the follow-up into coarser intervals of pre-specified length (eg, quarterly or monthly intervals). The feasibility and practical impact of analyzing EHR data at a granular scale has not been previously evaluated. We start filling these gaps by leveraging large-scale EHR data from a diabetes study to develop a scalable targeted learning approach that allows analyses with small intervals. We then study the practical effects of selecting different coarsening intervals on inferences by reanalyzing data from the same large-scale pool of patients. Specifically, we map daily EHR data into four analytic datasets using 90-, 30-, 15-, and 5-day intervals. We apply a semiparametric and doubly robust estimation approach, the longitudinal Targeted Minimum Loss-Based Estimation (TMLE), to estimate the causal effects of four dynamic treatment rules with each dataset, and compare the resulting inferences. To overcome the computational challenges presented by the size of these data, we propose a novel TMLE implementation, the "long-format TMLE," and rely on the latest advances in scalable data-adaptive machine-learning software, xgboost and h2o, for estimation of the TMLE nuisance parameters.

Visit Planning Using a Waiting Room Health IT Tool: The Aligning Patients and Providers Randomized Controlled Trial.

PURPOSE: Time during primary care visits is limited. We tested the hypothesis that a waiting room health information technology (IT) tool to help patients identify and voice their top visit priorities would lead to better visit interactions and improved quality of care. METHODS: We designed a waiting room tool, the Visit Planner, to guide adult patients through the process of identifying their top priorities for their visit and effectively expressing these priorities to their clinician. We tested this tool in a cluster-randomized controlled trial with usual care as the control. Eligible patients had at least 1 clinical care gap (eg, overdue for cancer screening, suboptimal chronic disease risk factor control, or medication nonadherence). RESULTS: The study (conducted March 31, 2016 through December 31, 2017) included 750 English- or Spanish-speaking patients. Compared with usual care patients, intervention patients more often reported "definitely" preparing questions for their doctor (59.5% vs 45.1%, P <.001) and "definitely" expressing their top concerns at the beginning of the visit (91.3% vs 83.3%, P = .005). Patients in both arms reported high levels of satisfaction with their care (86.8% vs 89.9%, P = .20). With 6 months of follow-up, prevalence of clinical care gaps was reduced by a similar amount in each study arm. CONCLUSIONS: A simple waiting room-based tool significantly improved visit communication. Patients using the Visit Planner were more prepared and more likely to begin the visit by communicating their top priorities. These changes did not, however, lead to further reduction in aggregate clinical care gaps beyond the improvements seen in the usual care arm.

Which Complex Patients Should Be Referred for Intensive Care Management? A Mixed-Methods Analysis.

BACKGROUND: A large and increasing proportion of health care costs are spent caring for a small segment of medically and socially complex patients. To date, it has been difficult to identify which patients are best served by intensive care management. OBJECTIVE: To characterize factors that best identify which complex patients are most suited for intensive care management. DESIGN: We conducted a mixed-methods study involving 35 care managers (CMs; 10 licensed social workers and 25 registered nurses) working in intensive care management programs within Kaiser Permanente Northern California (KPNC) outpatient medical centers. We asked CMs to review a randomly selected list of up to 50 patients referred to them in the prior year and to categorize each patient as either (1) "good candidates" for care management, (2) "not needing" intensive care management, or (3) "needing more" than traditional care management could provide. We then conducted semi-structured interviews to understand how CMs separated patients into these three groups. RESULTS: CMs assigned 1178 patients into the 3 referral categories. Less than two thirds (62%, n = 736) of referred patients were considered good candidates, with 18% (n = 216) categorized as not needing care management and 19% (n = 226) as needing more. Compared to the other two categories, good candidates were older (76.2 years vs. 73.2 for not needing and 69.8 for needing more, p < 0.001), prescribed more medications (p = 0.02) and had more prior year outpatient visits (p = 0.04), while the number of prior year hospital and emergency room admissions were greater than not needing but less than needing more (p < 0.001). A logistic regression model using available electronic record data predicted good candidate designation with a c statistic of 0.75. Several qualitative themes emerged that helped define appropriateness for referral, including availability of social support, patient motivation, non-medical transitions, recent trajectory of medical condition, and psychiatric or substance use issues. CONCLUSION: Many apparently complex patients are not good candidates for intensive care management. Current electronic medical records do not capture several of the most salient characteristics that determine appropriateness for care management. Our findings suggest that systematic collection of social support, patient motivation, and recent non-medically related life change information may help identify which complex patients are most likely to benefit from care management.

A systematic review of care management interventions targeting multimorbidity and high care utilization.

BACKGROUND: Evidence supporting the effectiveness of care management programs for complex patients has been inconclusive. However, past reviews have not focused on complexity primarily defined by multimorbidity and healthcare utilization. We conducted a systematic review of care management interventions targeting the following three patient groups: adults with two or more chronic medical conditions, adults with at least one chronic medical condition and concurrent depression, and adults identified based solely on high past or predicted healthcare utilization. METHODS: Eligible studies were identified from PubMed, published between 06/01/2005 and 05/31/2015, and reported findings from a randomized intervention that tested a comprehensive, care management intervention. Identified interventions were grouped based on the three "complex" categories of interest (described above). Two investigators extracted data using a structured abstraction form and assessed RCT quality. RESULTS: We screened 989 article titles for eligibility from which 847 were excluded. After reviewing the remaining 142 abstracts, 83 articles were excluded. We reviewed the full-text of 59 full-text articles and identified 15 unique RCTs for the final analysis. Of these 15 studies, two focused on patients with two or more chronic medical conditions, seven on patients with at least one chronic medical condition and depression, and six on patients with high past or predicted healthcare utilization. Measured outcomes included utilization, chronic disease measures, and patient-reported outcomes. The seven studies targeting patients with at least one chronic medical condition and depression demonstrated significant improvement in depression symptoms (ranging from 9.2 to 48.7% improvement). Of the six studies that focused on high utilizers, two showed small reductions in utilization. The quality of the research methodology in most of the studies (12/15) was rated fair or poor. CONCLUSIONS: Interventions were more likely to be successful when patients were selected based on having at least one chronic medical condition and concurrent depression, and when patient-reported outcomes were assessed. Future research should focus on the role of mental health in complex care management, finding better methods for identifying patients who would benefit most from care management, and determining which intervention components are needed for which patients.

Defining Team Effort Involved in Patient Care from the Primary Care Physician's Perspective.

BACKGROUND: A better understanding of the attributes of patients who require more effort to manage may improve risk adjustment approaches and lead to more efficient resource allocation, improved patient care and health outcomes, and reduced burnout in primary care clinicians. OBJECTIVE: To identify and characterize high-effort patients from the physician's perspective. DESIGN: Cohort study. PARTICIPANTS: Ninety-nine primary care physicians in an academic primary care network. MAIN MEASURES: From a list of 100 randomly selected patients in their panels, PCPs identified patients who required a high level of team-based effort and patients they considered complex. For high-effort patients, PCPs indicated which factors influenced their decision: medical/care coordination, behavioral health, and/or socioeconomic factors. We examined differences in patient characteristics based on PCP-defined effort and complexity. KEY RESULTS: Among 9594 eligible patients, PCPs classified 2277 (23.7 %) as high-effort and 2676 (27.9 %) as complex. Behavioral health issues were the major driver of effort in younger patients, while medical/care coordination issues predominated in older patients. Compared to low-effort patients, high-effort patients were significantly (P < 0.01 for all) more likely to have higher rates of medical (e.g. 23.2 % vs. 6.3 % for diabetes) and behavioral health problems (e.g. 9.8 % vs. 2.9 % for substance use disorder), more frequent primary care visits (10.9 vs. 6.0 visits), and higher acute care utilization rates (25.8 % vs. 7.7 % for emergency department [ED] visits and 15.0 % vs. 3.9 % for hospitalization). Almost one in five (18 %) patients who were considered high-effort were not deemed complex by the same PCPs. CONCLUSIONS: Patients defined as high-effort by their primary care physicians, not all of whom were medically complex, appear to have a high burden of psychosocial issues that may not be accounted for in current chronic disease-focused risk adjustment approaches.

Influence of Language and Culture in the Primary Care of Spanish-Speaking Latino Adults with Poorly Controlled Diabetes: A Qualitative Study.

Background: We examined the role of language and culture in the interactions between Spanish-speaking Latino patients with poorly controlled diabetes - a fast-growing population in the United States - and their primary care providers. Methods: We conducted four focus groups with 36 non-US born Spanish-speaking patients with elevated HbA1c. Participants were insured health plan members with either English-speaking (2 groups) or Spanish-speaking (2 groups) primary care providers. Moderated discussions focused on visit preparation, communication during visit, and role of other care team members. Key themes derived from these discussions were then linked to corresponding Latino cultural constructs. Results: Patients had a mean age of 57.9 (±11.2) years and last measured HbA1c was 8.6% (1.5%). Two communication-related themes (reluctance to switch providers and use of intermediaries) and two visit-related themes (provider-driven visit agendas and problem-based visits) emerged from our analyses. These themes reflected the cultural constructs of confianza (trust), familismo (family), respeto (deference), and simpatía (harmonious relationship). Trust in the patient-provider relationship led many participants to remain with English-speaking providers who treated them well. Patients with either language concordant and discordant providers reported reliance on family or other intermediaries to close communication gaps. Deference to physician expertise and authority led to visit expectations that it is the doctor's job to know what to ask and that visits were intended to address specific, often symptom-driven problems. Conclusions: Spanish-speaking Latino patients' cultural expectations play an important role in framing their primary care interactions. Recognizing culturally influenced visit expectations is an important step toward improving patient-provider communication.

The Diabetes Telephone Study: Design and challenges of a pragmatic cluster randomized trial to improve diabetic peripheral neuropathy treatment.

BACKGROUND: Challenges to effective pharmacologic management of symptomatic diabetic peripheral neuropathy include the limited effectiveness of available medicines, frequent side effects, and the need for ongoing symptom assessment and treatment titration for maximal effectiveness. We present here the rationale and implementation challenges of the Diabetes Telephone Study, a randomized trial designed to improve medication treatment, titration, and quality of life among patients with symptomatic diabetic peripheral neuropathy. METHODS: We implemented a pragmatic cluster randomized controlled trial to test the effectiveness of an automated interactive voice response tool designed to provide physicians with real-time patient-reported data about responses to newly prescribed diabetic peripheral neuropathy medicines. A total of 1834 primary care physicians treating patients in the diabetes registry at Kaiser Permanente Northern California were randomized into the intervention or control arm. In September 2014, we began identification and recruitment of patients assigned to physicians in the intervention group who receive three brief interactive calls every 2 months after a medication is prescribed to alleviate diabetic peripheral neuropathy symptoms. These calls provide patients with the opportunity to report on symptoms, side effects, self-titration of medication dose and overall satisfaction with treatment. We plan to compare changes in self-reported quality of life between the intervention group and patients in the control group who receive three non-interactive automated educational phone calls. RESULTS: Successful implementation of this clinical trial required robust stakeholder engagement to help tailor the intervention and to address pragmatic concerns such as provider time constraints. As of 27 October 2015, we had screened 2078 patients, 1447 of whom were eligible for participation. We consented and enrolled 1206 or 83% of those eligible. Among those enrolled, 53% are women and the mean age is 67 (standard deviation = 12) years. The racial ethnic make-up is 56% White, 8% Asian, 13% Black or African American, and 19% Hispanic or Latino. CONCLUSION: Innovative strategies are needed to guide improvements in healthcare delivery for patients with symptomatic diabetic peripheral neuropathy. This trial aims to assess whether real-time collection and clinical feedback of patient treatment experiences can reduce patient symptom burden. Implementation of a clinical trial closely involving clinical care required researchers to partner with clinicians. If successful, this intervention provides a critical information feedback loop that would optimize diabetic peripheral neuropathy medication titration through widely available interactive voice response technology.

Randomized trial of a health IT tool to support between-visit-based laboratory monitoring for chronic disease medication prescriptions.

BACKGROUND: Lack of timely medication intensification and inadequate medication safety monitoring are two prevalent and potentially modifiable barriers to effective and safe chronic care. Innovative applications of health information technology tools may help support chronic disease management. OBJECTIVE: To examine the clinical impact of a novel health IT tool designed to facilitate between-visit ordering and tracking of future laboratory testing. DESIGN AND PARTICIPANTS: Clinical trial randomized at the provider level (n = 44 primary care physicians); patient-level outcomes among 3,655 primary care patients prescribed 5,454 oral medicines for hyperlipidemia, diabetes, and/or hypertension management over a 12-month period. MAIN MEASURES: Time from prescription to corresponding follow-up laboratory testing; proportion of follow-up time that patients achieved corresponding risk factor control (A1c, LDL); adverse event laboratory monitoring 4 weeks after medicine prescription. KEY RESULTS: Patients whose physicians were allocated to the intervention (n = 1,143) had earlier LDL laboratory assessment compared to similar patients (n = 703) of control physicians [adjusted hazard ratio (aHR): 1.15 (1.01-1.32), p = 0.04]. Among patients with elevated LDL (486 intervention, 324 control), there was decreased time to LDL goal in the intervention group [aHR 1.26 (0.99-1.62)]. However, overall there were no significant differences between study arms in time spent at LDL or HbA1c goal. Follow-up safety monitoring (e.g., creatinine, potassium, or transaminases) was relatively infrequent (ranging from 7 % to 29 % at 4 weeks) and not statistically different between arms. Intervention physicians indicated that lack of reimbursement for non-visit-based care was a barrier to use of the tool. CONCLUSIONS: A health IT tool to support between-visit laboratory monitoring improved the LDL testing interval but not LDL or HbA1c control, and it did not alter safety monitoring. Adoption of innovative tools to support physicians in non-visit-based chronic disease management may be limited by current visit-based financial and productivity incentives.

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network.

BACKGROUND: Improving the ability to risk-stratify patients is critical for efficiently allocating resources within healthcare systems. OBJECTIVE: The purpose of this study was to evaluate a physician-defined complexity prediction model against outpatient Charlson score (OCS) and a commercial risk predictor (CRP). DESIGN: Using a cohort in which primary care physicians reviewed 4302 of their adult patients, we developed a predictive model for estimated physician-defined complexity (ePDC) and categorized our population using ePDC, OCS and CRP. PARTICIPANTS: 143,372 primary care patients in a practice-based research network participated in the study. MAIN MEASURES: For all patients categorized as complex in 2007 by one or more risk-stratification method, we calculated the percentage of total person time from 2008-2011 for which eligible cancer screening was incomplete, HbA1c was ≥ 9 %, and LDL was ≥ 130 mg/dl (in patients with cardiovascular disease). We also calculated the number of emergency department (ED) visits and hospital admissions per person year (ppy). KEY RESULTS: There was modest agreement among individuals classified as complex using ePDC compared with OCS (36.7 %) and CRP (39.6 %). Over 4 follow-up years, eligible ePDC-complex patients had higher proportions (p < 0.001) of time with: incomplete cervical (17.8 % vs. 13.3 % for OCS; 19.4 % vs. 11.2 % for CRP), breast (21.4 % vs. 14.9 % for OCS; 22.7 % vs. 15.0 % for CRP), and colon (25.9 % vs. 18.7 % for OCS; 27.0 % vs. 18.2 % for CRP) cancer screening; HbA1c ≥ 9 % (15.6 % vs. 8.1 % for OCS; 15.9 % vs. 6.9 % for CRP); and LDL ≥ 130 mg/dl (12.4 % vs. 7.9 % for OCS; 11.8 % vs 9.0 % for CRP). ePDC-complex patients had higher rates (p < 0.003) of: ED visits (0.21 vs. 0.11 ppy for OCS; 0.17 vs. 0.15 ppy for CRP), and admissions in patients 45-64 and ≥ 65 years old (0.11 vs. 0.10 ppy AND 0.24 vs. 0.21 ppy for OCS). CONCLUSION: Our measure for estimated physician-defined complexity compared favorably to commonly used risk-prediction approaches in identifying future suboptimal quality and utilization outcomes.

Building Equity Improvement into Quality Improvement: Reducing Socioeconomic Disparities in Colorectal Cancer Screening as Part of Population Health Management.

BACKGROUND: Improving colorectal cancer (CRC) screening rates for patients from socioeconomically disadvantaged backgrounds is a recognized public health priority. OBJECTIVE: Our aim was to determine if implementation of a system-wide screening intervention could reduce disparities in the setting of improved overall screening rates. DESIGN: This was an interrupted time series (ITS) analysis before and after a population management intervention. PARTICIPANTS: Patients eligible for CRC screening (age 52-75 years without prior total colectomy) in an 18-practice research network from 15 June 2009 to 15 June 2012 participated in the study. INTERVENTION: The Technology for Optimizing Population Care (TopCare) intervention electronically identified patients overdue for screening and facilitated contact by letter or telephone scheduler, with or without physician involvement. Patients identified by algorithm as high risk for non-completion entered into intensive patient navigation. MAIN MEASURES: Patients were dichotomized as ≤ high school diploma (≤ HS), an indicator of socioeconomic disadvantage, vs. >HS diploma (> HS). The monthly disparity between ≤ HS and > HS with regard to CRC screening completion was examined. KEY RESULTS: At baseline, 72% of 47,447 eligible patients had completed screening, compared with 75% of 51,442 eligible patients at the end of follow-up (p < 0.001). CRC screening completion was lower in ≤ HS vs. >HS patients in June 2009 (65.7% vs. 74.5%, p < 0.001) and remained lower in June 2012 (69.4% vs. 76.7%, p < 0.001). In the ITS analysis, which accounts for secular trends, TopCare was associated with a significant decrease in the CRC screening disparity (0.7%, p < 0.001). The effect of TopCare represents approximately 99 additional ≤ HS patients screened above prevailing trends, or 26 life-years gained had these patients remained unscreened. CONCLUSIONS: A multifaceted population management intervention sensitive to the needs of vulnerable patients modestly narrowed disparities in CRC screening, while also increasing overall screening rates. Embedding interventions for vulnerable patients within larger population management systems represents an effective approach to increasing overall quality of care while also decreasing disparities.

Association of African genetic ancestry with fasting glucose and HbA1c levels in non-diabetic individuals: the Boston Area Community Health (BACH) Prediabetes Study.

AIMS/HYPOTHESIS: To test among diabetes-free urban community-dwelling adults the hypothesis that the proportion of African genetic ancestry is positively associated with glycaemia, after accounting for other continental ancestry proportions, BMI and socioeconomic status (SES). METHODS: The Boston Area Community Health cohort is a multi-stage 1:1:1 stratified random sample of self-identified African-American, Hispanic and white adults from three Boston inner city areas. We measured 62 ancestry informative markers, fasting glucose (FG), HbA1c, BMI and SES (income, education, occupation and insurance status) and analysed 1,387 eligible individuals (379 African-American, 411 Hispanic, 597 white) without clinical or biochemical evidence of diabetes. We used three-heritage multinomial linear regression models to test the association of FG or HbA1c with genetic ancestry proportion adjusted for: (1) age and sex; (2) age, sex and BMI; and (3) age, sex, BMI and SES. RESULTS: Mean age- and sex-adjusted FG levels were 5.73 and 5.54 mmol/l among those with 100% African or European ancestry, respectively. Using per cent European ancestry as the referent, each 1% increase in African ancestry proportion was associated with an age- and sex-adjusted FG increase of 0.0019 mmol/l (p = 0.01). In the BMI- and SES-adjusted model the slope was 0.0019 (p = 0.02). Analysis of HbA1c gave similar results. CONCLUSIONS/INTERPRETATION: A greater proportion of African genetic ancestry is independently associated with higher FG levels in a non-diabetic community-based cohort, even accounting for other ancestry proportions, obesity and SES. The results suggest that differences between African-Americans and whites in type 2 diabetes risk may include genetically mediated differences in glucose homeostasis.