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Although it is recognized that many people have duplicate private health insurance coverage, either through separate purchase or as health benefits in multi-earner families, there has been little analysis of the factors determining duplicate coverage rates. A new data source, the Survey of Income and Education, offers a comparison with the only previous source of state level data, the estimates from the Health Insurance Association of America. The R2 between the two sets is only .3 and certain problems can be traced to the methodology underlying the HIAA figures. Using figures for gross and net coverage, the ratio of total policies to people with private coverage ranges from .94 in Utah to 1.53 in Illinois. Measures of industry distribution, per capita income and employment explain a large portion of the variance, but it appears that these factors operate in opposite directions for group and non-group policies. Similar sociodemographic variables also explain net coverage. These findings have substantial implications for research and the structuring of employee health benefits.

Objective: To describe the initial impact of an organizational policy change on measurement of physician non-English language proficiency.Study Setting: Multispecialty health care organization in the San Francisco Bay Area.Study Design/data Collection: In response to preliminary findings suggesting that the organization's nonvalidated and undefined three-category tool for physician self-report of non-English language proficiency levels was likely inadequate, the organization asked physicians to rate their non-English language proficiency levels using an adapted Interagency Language Roundtable (ILR) scale, a validated measure with five rating levels and descriptors. We then compared the self-reported language proficiency on the original scale and the ILR for those physicians who completed both and used regression analysis to investigate physician characteristics potentially associated with a change in score on the old versus ILR scales.Principal Findings: Six months after the ILR scale was implemented throughout the organization, 75 percent (258/342) of physicians had updated their language proficiency ratings. Among clinicians who had previously rated themselves in the "Medical/Conversational" category, there were substantial variations in scores using the ILR scale. Physicians who spoke two or more non-English languages were significantly more likely to lower their self-reported proficiency when updating from the old scale to the ILR scale.Conclusions: The organization was willing to adopt a relatively straightforward change in how data were collected and presented to patients based on the face validity of initial findings. This organizational policy change appeared to improve how self-reported physician language proficiency was characterized. [ABSTRACT FROM AUTHOR]

In the past two decades, relationships among health plans, medical groups, and providers have grown more complex and the number of clinical management strategies has increased. In this context, determining the independent effect of a particular organizational strategy on quality of care has become more difficult. The authors review some of the issues a researcher must address when studying the relationship between organizational characteristics and quality of care. They offer criteria for selecting a research question, list organizational characteristics that may influence quality, and suggest sampling and study design techniques to reduce confounding. Since this type of research often requires a health care organization as collaborator, the authors discuss strategies for developing research partnerships and collecting data from the partner organization. Finally, they offer suggestions for translating research into policy. [ABSTRACT FROM AUTHOR]

The estimated impact of health system reform proposals on health expenditures influences the health system reform debate. Health expenditure growth rate (HEGR) estimates are particularly critical because small differences in rates over time create large differences in health expenditures. There currently is no hard scientific basis for estimating the impact of managed care/managed competition legislation and market developments on the HEGR. For the 1994 health system reform debate, the two leading nonpartisan modelers developed qualitative scenarios about future health system developments in which managed care/managed competition would have little or no impact on the HEGR. We conclude that there are alternative plausible scenarios, based on current health system changes interacting with potential legislation, in which managed care/managed competition will substantially lower the HEGR. We recommend that simulation models should provide a range of estimates that better reflect both market trends and the profound uncertainty about the effects of health system reform. [ABSTRACT FROM AUTHOR]

In 1993 the California Office of Statewide Health Planning and Development (OSHPD) began public release of risk-adjusted monitoring of outcomes (RAMO) under the California Hospital Outcomes Project. We studied how 17 acute are public hospitals in California used these RAMO data for quality improvement purposes following their initial distribution, first by analyzing the outcome data for San Francisco General Hospital Medical Center as recommended by OSHPD and, second, by querying the departments at the other 16 public hospitals to determine how their own analyses compared. We found that the hospitals generally did minimal analyses of the OSHPD RAMO data and considered the data of little value to them. Only 3 hospitals initiated quality improvement activities based on their data review. The major reasons given by the hospitals for not using the RAMO data were that their outcomes were adequate, as verified by a comparison of their observed outcomes and those expected after risk-adjustment; that the hospitals had too few patients in the diagnostic categories; that they had too few resources; and that they were not concerned with the data's public release. Other possible explanations were that awareness of the California Hospital Outcomes Project was not widespread at the time of the study, that the RAMO data were not distributed in a way that encouraged their use, and that public hospitals were not inclined to use the outcome data because the project was imposed on them. Whatever the explanation, our study suggests that the California Hospital Outcomes Project has had little effect on quality improvement in public hospitals. [ABSTRACT FROM AUTHOR]

Background: Adolescent dating violence (ADV) and mental illness are highly prevalent, interrelated public health priorities. Increasingly, scholars are looking to adverse childhood experiences (ACEs) to explain risk for these health concerns., Objectives: Determine prevalence of ACEs, ADV perpetration and victimization, and anxiety and depression symptoms among adolescents in the Dominican Republic (DR). Evaluate the association of ACEs with these ADV and mental health outcomes., Participants and Setting: Cross-sectional survey data were collected from 142 adolescents at middle and high schools in the DR., Methods: We assessed ACEs using the Childhood Experiences Survey, ADV using the Conflict in Dating Relationships Inventory, and depression/anxiety symptoms using Patient-Reported Outcomes Measurement Information System scales. Linear and logistic regressions were performed to test dose-response relationships between a cumulative ACE score and ADV and mental health outcomes while adjusting for age, gender, and rural/urban residence., Results: 80.6% of students reported at least one ACE. The most prevalent ACEs reported were physical abuse (49%) and witnessing domestic violence (48%). After adjusting for covariates, cumulative ACE scores were significantly associated with depression symptoms, anxiety symptoms, physical and emotional ADV perpetration, and physical and emotional ADV victimization., Conclusions: Structural and community-based interventions to prevent ADV and promote mental health for adolescents in the DR should address ACEs., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Although lung cancer incidence rates according to smoking status, sex, and detailed race/ethnicity have not been available, it is estimated that more than half of Asian American, Native Hawaiian, and Pacific Islander (AANHPI) females with lung cancer have never smoked., Methods: We calculated age-adjusted incidence rates for lung cancer according to smoking status and detailed race/ethnicity among females, focusing on AANHPI ethnic groups, and assessed relative incidence across racial/ethnic groups. We used a large-scale dataset that integrates data from electronic health records from 2 large health-care systems-Sutter Health in Northern California and Kaiser Permanente Hawai'i-linked to state cancer registries for incident lung cancer diagnoses between 2000 and 2013. The study population included 1 222 694 females (n = 244 147 AANHPI), 3297 of which were diagnosed with lung cancer (n = 535 AANHPI)., Results: Incidence of lung cancer among never-smoking AANHPI as an aggregate group was 17.1 per 100 000 (95% confidence interval [CI] = 14.9 to 19.4) but varied widely across ethnic groups. Never-smoking Chinese American females had the highest rate (22.8 per 100 000, 95% CI = 17.3 to 29.1). Except for Japanese American females, incidence among every never-smoking AANHPI female ethnic group was higher than that of never-smoking non-Hispanic White females, from 66% greater among Native Hawaiian females (incidence rate ratio = 1.66, 95% CI = 1.03 to 2.56) to more than 100% greater among Chinese American females (incidence rate ratio = 2.26, 95% CI = 1.67 to 3.02)., Conclusions: Our study revealed high rates of lung cancer among most never-smoking AANHPI female ethnic groups. Our approach illustrates the use of innovative data integration to dispel the myth that AANHPI females are at overall reduced risk of lung cancer and demonstrates the need to disaggregate this highly diverse population., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: A relatively high proportion of Asian American, Native Hawaiian, and Pacific Islander (AANHPI) females with lung cancer have never smoked. We used an integrative data approach to assemble a large-scale cohort to study lung cancer risk among AANHPIs by smoking status with attention to representation of specific AANHPI ethnic groups., Methods: We leveraged electronic health records (EHRs) from two healthcare systems-Sutter Health in northern California and Kaiser Permanente Hawai'i-that have high representation of AANHPI populations. We linked EHR data on lung cancer risk factors (i.e., smoking, lung diseases, infections, reproductive factors, and body size) to data on incident lung cancer diagnoses from statewide population-based cancer registries of California and Hawai'i for the period between 2000 and 2013. Geocoded address data were linked to data on neighborhood contextual factors and regional air pollutants., Results: The dataset comprises over 2.2 million adult females and males of any race/ethnicity. Over 250,000 are AANHPI females (19.6% of the female study population). Smoking status is available for over 95% of individuals. The dataset includes 7,274 lung cancer cases, including 613 cases among AANHPI females. Prevalence of never-smoking status varied greatly among AANHPI females with incident lung cancer, from 85.7% among Asian Indian to 14.4% among Native Hawaiian females., Conclusion: We have developed a large, multilevel dataset particularly well-suited to conduct prospective studies of lung cancer risk among AANHPI females who never smoked., Impact: The integrative data approach is an effective way to conduct cancer research assessing multilevel factors on cancer outcomes among small populations., (©2021 American Association for Cancer Research.)

Introduction: Recent guideline changes for lung cancer screening with low-dose computed tomography recommend smoking-cessation interventions be done in parallel with screening. The purpose of this study is to determine the post-guideline rates of smoking-cessation interventions among patients eligible and ineligible for lung cancer screening., Methods: Using electronic health records collected from a large ambulatory care system in northern California between 2010 and 2017, authors identified new patients who were current smokers aged 55-80 years visiting a primary care provider, and grouped patients into lung cancer screening-eligible heavy smokers, screening-ineligible moderate smokers, and screening-ineligible light smokers. Screening-eligible smokers versus screening-ineligible smokers were compared in receipt of smoking-cessation interventions before (2010-2013) and after (2014-2017) the guideline change, overall and by intervention type (formal counseling, informal counseling, pharmacotherapy) using hierarchical generalized linear models. Analyses were conducted in 2018-2019., Results: After the guideline change, the likelihood of receiving any smoking-cessation intervention (OR=1.44, 95% CI=1.28, 1.61, p<0.05), informal counseling (OR=1.29, 95% CI=1.15, 1.46, p<0.05), and pharmacotherapy (OR=1.24, 95% CI=1.02, 1.50, p<0.05) during a new patient visit significantly increased, with the increase not varying by level of smoking. For formal counseling, the post-guideline increase was greater for screening-eligible heavy smokers (OR=3.15, 95% CI=1.18, 8.36, p<0.05) and moderate smokers (OR=3.58, 95% CI=1.29, 9.95, p<0.05) relative to light smokers., Conclusions: Smoking-cessation interventions increased after new lung cancer screening guidelines. Given the sizable adverse impacts of smoking on morbidity and mortality, small increases in the implementation of smoking-cessation interventions could have substantial public health benefits., (Copyright © 2020 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: There is tremendous potential to leverage the value gained from integrating electronic health records (EHR) and population-based cancer registry data for research. Registries provide diagnosis details, tumor characteristics, and treatment summaries, while EHRs contain rich clinical detail. A carefully conducted cancer registry linkage may also be used to improve the internal and external validity of inferences made from EHR-based studies., Methods: We linked the EHRs of a large, multispecialty, mixed-payer health care system with the statewide cancer registry and assessed the validity of our linked population. For internal validity, we identify patients that might be "missed" in a linkage, threatening the internal validity of an EHR study population. For generalizability, we compared linked cases with all other cancer patients in the 22-county EHR catchment region., Results: From an EHR population of 4.5 million, we identified 306,554 patients with cancer, 26% of the catchment region patients with cancer; 22.7% of linked patients were diagnosed with cancer after they migrated away from our health care system highlighting an advantage of system-wide linkage. We observed demographic differences between EHR patients and non-EHR patients in the surrounding region and demonstrated use of selection probabilities with model-based standardization to improve generalizability., Conclusions: Our experiences set the foundation to encourage and inform researchers interested in working with EHRs for cancer research as well as provide context for leveraging linkages to assess and improve validity and generalizability., Impact: Researchers conducting linkages may benefit from considering one or more of these approaches to establish and evaluate the validity of their EHR-based populations. See all articles in this CEBP Focus section, "Modernizing Population Science.", (©2020 American Association for Cancer Research.)

Objectives: To assess quality, cost, physician productivity, and patient experience for 2 primary care physician (PCP) practice styles: the focused, who typically address only the patient's acute problem, versus the max-packers, who typically address additional conditions also., Study Design: Retrospective observational study using administrative data, electronic health record (EHR) data, and patient surveys. Data represent 285 PCPs (779 PCP-years) in a large, multispecialty group practice during 2011, 2012, and 2013., Methods: PCPs were ranked each year by their number of additional conditions addressed during acute care visits. The top one-third (max-packers) addressed 25.4% more "other problems" than expected, while focused PCPs (bottom one-third) addressed 20.3% fewer than expected. Outcomes were resource use, clinical quality metrics, patient-reported experience, physician time using the EHR, and physician productivity. All measures were risk-adjusted to account for patient mix. T tests were used to compare measures., Results: Relative to a focused pattern of care, max-packing was associated with 3.4% lower overall resource use, consistently better scores for the available clinical quality metrics, and comparable patient experience (except for worse wait time ratings). Patients of focused PCPs used 7.3% more specialist services, in terms of costs, than patients of max-packers ($1218 vs $1136; P <.001). Max-packers spent 40 minutes more per clinical day using the EHR. PCPs with less appointment availability and who used a mix of appointment slots were more likely to be max-packers., Conclusions: Max-packing behavior yields desirable outcomes at lower overall cost but involves more conventionally uncompensated PCP time. Alternatives to compensation just for face-to-face visits and using more flexible scheduling may be needed to support max-packing.

Importance: Accurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, using only the single worst value of laboratory tests and vital signs and largely ignoring information present in free-text notes. Whether capturing more of the available data and applying machine learning and natural language processing (NLP) can improve and automate the prediction of outcomes among patients in the ICU remains unknown., Objectives: To evaluate the change in power for a mortality prediction model among patients in the ICU achieved by incorporating measures of clinical trajectory together with NLP of clinical text and to assess the generalizability of this approach., Design, Setting, and Participants: This retrospective cohort study included 101 196 patients with a first-time admission to the ICU and a length of stay of at least 4 hours. Twenty ICUs at 2 academic medical centers (University of California, San Francisco [UCSF], and Beth Israel Deaconess Medical Center [BIDMC], Boston, Massachusetts) and 1 community hospital (Mills-Peninsula Medical Center [MPMC], Burlingame, California) contributed data from January 1, 2001, through June 1, 2017. Data were analyzed from July 1, 2017, through August 1, 2018., Main Outcomes and Measures: In-hospital mortality and model discrimination as assessed by the area under the receiver operating characteristic curve (AUC) and model calibration as assessed by the modified Hosmer-Lemeshow statistic., Results: Among 101 196 patients included in the analysis, 51.3% (n = 51 899) were male, with a mean (SD) age of 61.3 (17.1) years; their in-hospital mortality rate was 10.4% (n = 10 505). A baseline model using only the highest and lowest observed values for each laboratory test result or vital sign achieved a cross-validated AUC of 0.831 (95% CI, 0.830-0.832). In contrast, that model augmented with measures of clinical trajectory achieved an AUC of 0.899 (95% CI, 0.896-0.902; P < .001 for AUC difference). Further augmenting this model with NLP-derived terms associated with mortality further increased the AUC to 0.922 (95% CI, 0.916-0.924; P < .001). These NLP-derived terms were associated with improved model performance even when applied across sites (AUC difference for UCSF: 0.077 to 0.021; AUC difference for MPMC: 0.071 to 0.051; AUC difference for BIDMC: 0.035 to 0.043; P < .001) when augmenting with NLP at each site., Conclusions and Relevance: Intensive care unit mortality prediction models incorporating measures of clinical trajectory and NLP-derived terms yielded excellent predictive performance and generalized well in this sample of hospitals. The role of these automated algorithms, particularly those using unstructured data from notes and other sources, in clinical research and quality improvement seems to merit additional investigation.

Preventive visit rates are low among older adults in the United States. We evaluated changes in preventive visit utilization with Medicare's introduction of Annual Wellness Visits (AWVs) in 2011. We further assessed how coverage expansion differentially affected older adults who were previously underutilizing the service. The study included Medicare beneficiaries aged 65 to 85 from a mixed-payer multispecialty outpatient healthcare organization in northern California between 2007 and 2016. Data from the electronic health records were used, and the unit of analysis was patient-year (N = 456,281). Multivariable logistic regression models were used to assess determinants of "any preventive visit" use. Prior to the AWV coverage (2007-2010), Medicare beneficiaries who were older, with serious chronic conditions, and with a fee-for-services (FFS) plan underutilized preventive visits such that odds ratio (OR) for age groups (vs. age 65-69) ranges from 0.826 (age 70-74) to 0.522 (age 80-85); for Charlson comorbidity index (CCI) (vs. 0 CCI) ranges from 0.77 (1 CCI) to 0.65 (≥2 CCI); and for FFS (vs. HMO) is 0.236. With the Medicare coverage (2011-2016), the age-based gap reduced substantially, but the difference persisted, e.g., OR for age 80-85 (vs. 65-69) is 0.628, and FFS (vs. HMO) beneficiaries still have far lower odds of using a preventive visit (OR = 0.278). The gap based on comorbidity was not reduced. Medicare's coverage expansion facilitated the use of preventive visit particularly for older adults with more advanced age or with FFS, thereby reducing disparities in preventive visit use., (Copyright © 2018. Published by Elsevier Inc.)

Background: In 2013, the US Preventive Services Task Force (USPSTF) issued recommendations for low-dose computed tomography for lung cancer screening (LDCT-LCS), but there continues to be a dearth of information on the adoption of LDCT-LCS in healthcare systems. Using a multilevel perspective, our study aims to assess referrals for LDCT-LCS and identify facilitators and barriers to adoption following recent policy changes., Methods: A retrospective analysis of electronic medical record data from patients aged 55-80 years with no history of lung cancer who visited a primary care provider in a large healthcare system in California during 2010-2016 (1,572,538 patient years). Trends in documentation of smoking history, number of eligible patients, and lung cancer screening orders were assessed. Using Hierarchical Generalized Linear Models, we also evaluated provider-level and patient-level factors associated with lung cancer screening orders among 970 primary care providers and 12,801 eligible patients according to USPSTF guidelines between January 1st, 2014 and December 31st, 2016., Results: Documentation of smoking history to determine eligibility (59.2% in 2010 to 77.8% in 2016) and LDCT-LCS orders (0% in 2010 to 7.3% in 2016) have increased since USPSTF guidelines. Patient factors associated with increased likelihood of lung cancer screening orders include: younger patient age (78-80 vs. 55-64 years old: OR, 0.4; 95% CI, 0.3-0.7), Asian race (vs. Non-Hispanic White: OR, 1.6; 95% CI, 1.1-2.4), reported current smoking (vs. former smoker: OR, 1.7; 95% CI, 1.4-2.0), no severe comorbidity (severe vs. no major comorbidity: OR = 0.2, 95% CI = 0.1-0.3; moderate vs. no major comorbidity: OR = 0.5; 95% CI = 0.4-0.7), and making a visit to own primary care provider (vs. other primary care providers: OR, 2.4; 95% CI, 1.7-3.4). Appropriate referral for lung cancer screening varies considerably across primary care providers. Provider factors include being a female physician (vs. male: OR, 1.6; 95% CI, 1.1-2.3) and receiving medical training in the US (foreign vs. US medical school graduates: OR = 0.4, 95% CI = 0.3-0.7)., Conclusions: Future interventions to improve lung cancer screening may be more effective if they focus on accurate documentation of smoking history and target former smokers who do not regularly see their own primary care providers.

With the growth of precision medicine research on health data and biospecimens, research institutions will need to build and maintain long-term, trusting relationships with patient-participants. While trust is important for all research relationships, the longitudinal nature of precision medicine research raises particular challenges for facilitating trust when the specifics of future studies are unknown. Based on focus groups with racially and ethnically diverse patients, we describe several factors that influence patient trust and potential institutional approaches to building trustworthiness. Drawing on these findings, we suggest several considerations for research institutions seeking to cultivate long-term, trusting relationships with patients: (1) Address the role of history and experience on trust, (2) engage concerns about potential group harm, (3) address cultural values and communication barriers, and (4) integrate patient values and expectations into oversight and governance structures.

Purpose: To examine the characteristics of voluntary online commitment contracts that may be associated with greater weight loss., Design: Retrospective analysis of weight loss commitment contracts derived from a company that provides web-based support for personal commitment contracts. Using regression, we analyzed whether percentage weight loss differed between participants who incentivized their contract using monetary deposits and those who did not., Setting: Online., Participants: Users (N = 3857) who voluntarily signed up online in 2013 for a weight loss contract., Intervention: Participants specified their own weight loss goal, time period, and self-reported weekly weight. Deposits were available in the following 3 categories: charity, anticharity (a nonprofit one does not like), or donations made to a friend., Measures: Percentage weight loss per week., Analysis: Multivariable linear regressions., Results: Controlling for several participant and contract characteristics, contracts with anticharity, charity, and friend deposits had greater reported weight loss than nonincentivized contracts. Weight change per week relative to those without deposits was -0.33%, -0.28%, and -0.25% for anti-charity, charity, and friend, respectively ( P < 0.001). Contracts without a weight verification method claimed more weight loss than those with verification., Conclusion: Voluntary use of commitment contracts may be an effective tool to assist weight loss. Those who choose to use monetary incentives report more weight loss. It is not clear whether this is due to the incentives or higher motivation.

Objective This report examines ways to improve National Ambulatory Medical Care Survey (NAMCS) data on practice and physician characteristics in multispecialty group practices. Methods From February to April 2013, the National Center for Health Statistics (NCHS) conducted a pilot study to observe the collection of the NAMCS physician interview information component in a large multispecialty group practice. Nine physicians were randomly sampled using standard NAMCS recruitment procedures; eight were eligible and agreed to participate. Using standard protocols, three field representatives conducted NAMCS physician induction interviews (PIIs) while trained ethnographers observed and audio recorded the interviews. Transcripts and field notes were analyzed to identify recurrent issues in the data collection process. Results The majority of the NAMCS items appeared to have been easily answered by the physician respondents. Among the items that appeared to be difficult to answer, three themes emerged: (a) physician respondents demonstrated an inconsistent understanding of "location" in responding to questions; (b) lack of familiarity with administrative matters made certain questions difficult for physicians to answer; and (c) certain primary care‑oriented questions were not relevant to specialty care providers. Conclusions Some PII survey questions were challenging for physicians in a multispecialty practice setting. Improving the design and administration of NAMCS data collection is part of NCHS' continuous quality improvement process., (All material appearing in this report is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated.)

The 23rd annual conference of the Health Care Systems Research Network (HCSRN, formerly the HMO Research Network) was held in San Diego, California, March 21-23, 2017, attracting 387 attendees. As a consortium of 20 research organizations embedded in or affiliated with large health care delivery organizations, the HCSRN has held annual research conferences since 1994. The overall aim of the conferences is to bring researchers, project staff, research funders and other stakeholders together to share latest scientific findings and foster new research ideas and collaborations. The 2017 conference was hosted by the Palo Alto Medical Foundation Research Institute. Each host site takes responsibility for the content and structure of the conference, and the 2017 team introduced several new features. In particular, past conferences used concurrent sessions to present research results in different topical areas, such as chronic disease, cancer, health informatics, mental health or precision medicine. This year, concurrent sessions shifted to panel discussions about how research results were achieved, including the use of methods, partnerships and analytic approaches. The 35 panels were organized into tracks such as engagement, data and informatics, partnerships and research implementation. Scientific results from HCSRN projects were presented via 120 posters in two poster sessions. Plenary sessions included a town hall-style panel with different funding agency representatives, an opening presentation on the range of opportunities and benefits to studying health systems, and a concluding presentation on how researchers can apply design thinking in their work.

Time spent by physicians is a key resource in health care delivery. This study used data captured by the access time stamp functionality of an electronic health record (EHR) to examine physician work effort. This is a potentially powerful, yet unobtrusive, way to study physicians' use of time. We used data on physicians' time allocation patterns captured by over thirty-one million EHR transactions in the period 2011-14 recorded by 471 primary care physicians, who collectively worked on 765,129 patients' EHRs. Our results suggest that the physicians logged an average of 3.08 hours on office visits and 3.17 hours on desktop medicine each day. Desktop medicine consists of activities such as communicating with patients through a secure patient portal, responding to patients' online requests for prescription refills or medical advice, ordering tests, sending staff messages, and reviewing test results. Over time, log records from physicians showed a decline in the time allocated to face-to-face visits, accompanied by an increase in time allocated to desktop medicine. Staffing and scheduling in the physician's office, as well as provider payment models for primary care practice, should account for these desktop medicine efforts., (Project HOPE—The People-to-People Health Foundation, Inc.)

Objectives: We examined a wide range of performance outcomes after Lean methodology-a leading strategy to enhance efficiency and patient value-was implemented and scaled across all primary care clinics in a nonprofit, ambulatory care delivery system., Study Design: Using a stepped wedge approach, we assessed changes associated with the phased introduction of Lean-based redesigns across 46 primary care departments in 17 different clinic locations. Longitudinal analysis of operational metrics included: workflow efficiency, physician productivity, operating expenses, clinical quality, and satisfaction among patients, physicians, and staff., Methods: We used interrupted time series analysis with generalized linear mixed models to estimate Lean impacts over time. Projected outcomes in the absence of changes (ie, counterfactuals) were compared with observed outcomes after Lean redesigns were implemented, and mean differences were assessed using 95% bias-corrected bootstrap confidence intervals (CIs)., Results: We observed systemwide improvements in workflow efficiencies (eg, 95% CI, 5.8-10.4) and physician productivity (95% CI, 3.9-27.2), with no adverse effects on clinical quality. Patient satisfaction increased with respect to access to care (95% CI, 15.2-20.7), handling of personal issues (95% CI, 2.1-6.9), and overall experience of care (95% CI, 11.0-17.0), but decreased with respect to interactions with care providers (95% CI, -13.4 to -5.7). Departmental operating costs decreased, and annual staff and physician satisfaction scores increased particularly among early adopters, with key improvements in employee engagement, connection to purpose, relationships with staff, and physician time spent working., Conclusions: Lean redesigns can benefit primary care patients, physicians, and staff without negatively impacting the quality of clinical care. Study results may lead other delivery system leaders to innovate using Lean techniques and may further enhance support for Lean learning among public and private payers.

Objective: To develop a technology-based method for evaluating the nutritional quality of chain-restaurant menus to increase the efficiency and lower the cost of large-scale data analysis of food items., Design: Using a Modified Nutrient Profiling Index (MNPI), we assessed chain-restaurant items from the MenuStat database with a process involving three steps: (i) testing 'extreme' scores; (ii) crowdsourcing to analyse fruit, nut and vegetable (FNV) amounts; and (iii) analysis of the ambiguous items by a registered dietitian., Results: In applying the approach to assess 22 422 foods, only 3566 could not be scored automatically based on MenuStat data and required further evaluation to determine healthiness. Items for which there was low agreement between trusted crowd workers, or where the FNV amount was estimated to be >40 %, were sent to a registered dietitian. Crowdsourcing was able to evaluate 3199, leaving only 367 to be reviewed by the registered dietitian. Overall, 7 % of items were categorized as healthy. The healthiest category was soups (26 % healthy), while desserts were the least healthy (2 % healthy)., Conclusions: An algorithm incorporating crowdsourcing and a dietitian can quickly and efficiently analyse restaurant menus, allowing public health researchers to analyse the healthiness of menu items.

Purpose: The 21-gene recurrence score (RS) identifies patients with breast cancer who derive little benefit from chemotherapy; it may reduce unwarranted variability in the use of chemotherapy. We tested whether the use of RS seems to guide chemotherapy receipt across different cancer care settings., Methods: We developed a retrospective cohort of patients with breast cancer by using electronic medical record data from Stanford University (hereafter University) and Palo Alto Medical Foundation (hereafter Community) linked with demographic and staging data from the California Cancer Registry and RS results from the testing laboratory (Genomic Health Inc., Redwood City, CA). Multivariable analysis was performed to identify predictors of RS and chemotherapy use., Results: In all, 10,125 patients with breast cancer were diagnosed in the University or Community systems from 2005 to 2011; 2,418 (23.9%) met RS guidelines criteria, of whom 15.6% received RS. RS was less often used for patients with involved lymph nodes, higher tumor grade, and age < 40 or ≥ 65 years. Among RS recipients, chemotherapy receipt was associated with a higher score (intermediate v low: odds ratio, 3.66; 95% CI, 1.94 to 6.91). A total of 293 patients (10.6%) received care in both health care systems (hereafter dual use); although receipt of RS was associated with dual use (v University: odds ratio, 1.73; 95% CI, 1.18 to 2.55), there was no difference in use of chemotherapy after RS by health care setting., Conclusion: Although there was greater use of RS for patients who sought care in more than one health care setting, use of chemotherapy followed RS guidance in University and Community health care systems. These results suggest that precision medicine may help optimize cancer treatment across health care settings., (Copyright © 2016 by American Society of Clinical Oncology.)

Objectives: Antibiotics increase weight in farm animals and may cause weight gain in humans. We used electronic health records from a large primary care organization to determine the effect of antibiotics on weight and BMI in healthy adolescents with acne., Methods: We performed a retrospective cohort study of adolescents with acne prescribed ≥4 weeks of oral antibiotics with weight measurements within 18 months pre-antibiotics and 12 months post-antibiotics. We compared within-individual changes in weight-for-age Z-scores (WAZs) and BMI-for-age Z-scores (BMIZs). We used: (i) paired t-tests to analyse changes between the last pre-antibiotics versus the first post-antibiotic measurements; (ii) piecewise-constant-mixed models to capture changes between mean measurements pre- versus post-antibiotics; (iii) piecewise-linear-mixed models to capture changes in trajectory slopes pre- versus post-antibiotics; and (iv) χ(2) tests to compare proportions of adolescents with ≥0.2 Z-scores WAZ or BMIZ increase or decrease., Results: Our cohort included 1012 adolescents with WAZs; 542 also had BMIZs. WAZs decreased post-antibiotics in all analyses [change between last WAZ pre-antibiotics versus first WAZ post-antibiotics = -0.041 Z-scores (P < 0.001); change between mean WAZ pre- versus post-antibiotics = -0.050 Z-scores (P < 0.001); change in WAZ trajectory slopes pre- versus post-antibiotics = -0.025 Z-scores/6 months (P = 0.002)]. More adolescents had a WAZ decrease post-antibiotics ≥0.2 Z-scores than an increase (26% versus 18%; P < 0.001). Trends were similar, though not statistically significant, for BMIZ changes., Conclusions: Contrary to original expectations, long-term antibiotic use in healthy adolescents with acne was not associated with weight gain. This finding, which was consistent across all analyses, does not support a weight-promoting effect of antibiotics in adolescents., (© The Author 2016. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Policy makers (both public and private) are seeking ways to improve the value delivered within our health care system, that is, using fewer resources to provide the same benefit to patients, or using equivalent resources to provide more benefit. One strategy is to alter the predominant fee-for-service (FFS) economic incentives in the current system. To inform such policy changes, this paper identifies areas in which little is known about the effects of specific incentives (FFS, salary, etc.) on the two components of value: resource use and quality. Specific suggestions are offered regarding research that would be informative for policy makers, focusing on fundamental "building block" studies rather than overall evaluations of complex interventions, such as accountable care organizations. This research would better identify critical aspects of the FFS model and salary-based payments that are particularly problematic, as well as situations in which FFS or salary may be less problematic. The research would also explore when alternatives, such as episode-based payment might be feasible, or simply be hypothetical solutions. The availability of electronic health record-based data in various delivery systems would allow many of these studies to be accomplished in 3-5 years with budgets manageable by public and private funding sources., (© Health Research and Educational Trust.)

Introduction: In a fragmented health care system, research can be challenging when one seeks to follow cancer patients as they seek care which can continue for months or years and may reflect many physician and patient decisions. Claims data track patients, but lack clinical detail. Linking routine electronic health record (EHR) data with clinical registry data allows one to gain a more complete picture of the patient journey through a cancer care episode. However, valid analytical approaches to examining care trajectories must be longitudinal and account for the dynamic nature of what is "seen" in the EHR., Methods: The Oncoshare database combines clinical detail from the California Cancer Registry and EHR data from two large health care organizations in the same catchment area-a multisite community practice and an academic medical center-for all women treated in either organization for breast cancer from 2000 to 2012. We classified EHR encounters data according to typical periods of the cancer care episode (screening, diagnosis, treatment) and posttreatment surveillance, as well as by facility used to better characterize patterns of care for patients seen at both organizations., Findings: We identified a "treated" cohort consisting of women receiving interventions for their initial cancer diagnosis, and classified their encounters over time across multiple dimensions (type of care, provider of care, and timing of care with respect to their cancer diagnosis). Forty-three percent of the patients were treated at the academic center only, 42 percent at the community center only, and 16 percent of the patients obtained care at both health care organizations. Compared to women seen at only one organization, the last group had similar-length initial care episodes, but more frequently had multiple episodes and longer observation periods., Discussion: Linking EHR data from neighboring systems can enhance our information on care trajectories, but careful consideration of the complexity of the treatment process and data generating mechanisms is necessary to make valid inferences., Conclusion/next Steps: If analyzed as a timeline, and with careful characterization of diagnostic tests, surgical interventions, and type and frequency of physician encounters, the pathways taken by women through their breast cancer episode may lead to better understanding of patient decisions.

Under the Affordable Care Act (ACA), Medicare coverage expanded in 2011 to fully cover annual preventive care visits. We assessed the impact of coverage expansion, using 2007-13 data from primary care patients of Medicare-eligible age at the Palo Alto Medical Foundation (204,388 patient-years), which serves people in four counties near San Francisco, California. We compared trends in preventive visits and recommended preventive services among Medicare fee-for-service and Medicare health maintenance organization (HMO) patients as well as non-Medicare patients ages 65-75 who were covered by private fee-for-service and private HMO plans. Among Medicare fee-for-service patients, the annual use of preventive visits rose from 1.4 percent before the implementation of the ACA to 27.5 percent afterward. This increase was significantly larger than was seen for patients in the other insurance groups. Nevertheless, rates of annual preventive care visit use among Medicare fee-for-service patients remained 10-20 percentage points lower than was the case for people with private coverage (43-44 percent) or those in a Medicare HMO (53 percent). ACA policy changes led to increased preventive service use by Medicare fee-for-service beneficiaries, which suggests that Medicare coverage expansion is an effective way to increase seniors' use of preventive services., (Project HOPE—The People-to-People Health Foundation, Inc.)

Twenty-seven years after the first public release by the U.S. government of data on the quality of hospital care, public reporting for consumers has expanded substantially. Despite the growth in public reporting activities, there is limited evidence of their use by consumers in ways that significantly affect health care delivery. Support for public reporting continues, in part, because of the face value of transparency. The limited impact of reporting efforts is plausibly due to flaws in the content, design, and implementation of existing public reports rather than inherent limitations of reporting. Substantial work is still needed for public reports to achieve their potential for engaging and informing consumers. We present a vision statement and 10 recommendations to achieve this potential., (© The Author(s) 2014.)

Background: A national strategic framework to address multiple chronic conditions has called for further research on disease trajectories of patients with comorbidities., Methods: An observational study using multilevel models to analyze electronic health record data from a multispecialty practice from 2003 to 2010 to examine disease trajectories of patients with at least 2 of 3 common chronic conditions: overweight/obese, hypertension, and depression. Using longitudinal data on up to 110,000 patients, the effects of comorbidities on the probability of having a diagnosis for overweight/obesity or hypertension and on the trajectories of body mass index (BMI) and blood pressure (BP) over time were examined., Results: From 2003 to 2010, the percentage of patients with high BMI receiving an overweight/obesity diagnosis grew from 5.0% to 18.7%, and the percentage of patients with high BP having a hypertension diagnosis rose from 39.9% to 51.7%. The effect of time for patients with high BMI and depression was less than the effect of time for high BMI only patients (P<0.01) in receiving overweight/obesity diagnoses. Co-occurring depression and high BMI was positively associated with BMI trajectory (coefficient=0.06, P<0.01), whereas high BP and high BMI (coefficient=-0.07, P<0.01) or high BP and high BMI and depression (coefficient=-0.05, P<0.01) were negatively associated with BMI trajectories., Conclusions: Although physicians' recording of diagnoses for patients with high BMI and high BP has improved, significant gaps remain. Some co-occurrence patterns of these 3 conditions not only affected the recognition of overweight/obesity and hypertension over time, but also BMI trajectories over time. Quality improvement efforts should target patients with co-occurring depression and overweight/obesity.

Background: Understanding of cancer outcomes is limited by data fragmentation. In the current study, the authors analyzed the information yielded by integrating breast cancer data from 3 sources: electronic medical records (EMRs) from 2 health care systems and the state registry., Methods: Diagnostic test and treatment data were extracted from the EMRs of all patients with breast cancer treated between 2000 and 2010 in 2 independent California institutions: a community-based practice (Palo Alto Medical Foundation; "Community") and an academic medical center (Stanford University; "University"). The authors incorporated records from the population-based California Cancer Registry and then linked EMR-California Cancer Registry data sets of Community and University patients., Results: The authors initially identified 8210 University patients and 5770 Community patients; linked data sets revealed a 16% patient overlap, yielding 12,109 unique patients. The percentage of all Community patients, but not University patients, treated at both institutions increased with worsening cancer prognostic factors. Before linking the data sets, Community patients appeared to receive less intervention than University patients (mastectomy: 37.6% vs 43.2%; chemotherapy: 35% vs 41.7%; magnetic resonance imaging: 10% vs 29.3%; and genetic testing: 2.5% vs 9.2%). Linked Community and University data sets revealed that patients treated at both institutions received substantially more interventions (mastectomy: 55.8%; chemotherapy: 47.2%; magnetic resonance imaging: 38.9%; and genetic testing: 10.9% [P < .001 for each 3-way institutional comparison])., Conclusions: Data linkage identified 16% of patients who were treated in 2 health care systems and who, despite comparable prognostic factors, received far more intensive treatment than others. By integrating complementary data from EMRs and population-based registries, a more comprehensive understanding of breast cancer care and factors that drive treatment use was obtained., (© 2013 American Cancer Society.)

Comparative effectiveness research is sometimes best done using routinely collected data from multiple real world settings. This raises important issues with respect to the authorizations to use the data, dealing with the multiple IRBs that may have oversight authority, and protecting it from breaches. Careful delineation of what is quality improvement vs. research-driven interventions can help with IRB reviews, especially for low-risk projects. It is impossible to totally de-identify data without markedly reducing its value for research, but creative strategies can markedly reduce the risk of advertent disclosures of patient identities.

Objectives: To determine if patient clinical conditions need to be considered when assessing primary care physician (PCP) workload in the context of standardizing panel sizes., Study Design: Work resource value units (wRVUs) were used to standardize PCP panel workload. Standardized panels were created using (1) age and sex- and (2) clinical condition-based risk indicators. Billing data were used for all patients, regardless of insurance, for PCPs in a group practice (n = 190). Weighting methods were assessed for subgroups based on PCP specialty (family medicine, internal medicine, and pediatrics) and patient age (adults vs children) and for different levels of aggregation (patient vs PCP)., Methods: Groupwide weights based on wRVUs of all primary care services delivered during the year were applied to individual patients and then aggregated to PCP panels. For age/sex weighting, only patient age and sex were taken into account. For condition-based weighting, 1275 disease categories, based on a combination of episode treatment groups (ETGs) and age and/or sex, were used., Results: As expected, at the patient level, condition-based weights were far more discriminative than age/sex. At the PCP level, this discrimination was less important; panel weights varied 1.9- (age/sex-based) to 2.6-fold (condition-based) across PCPs. Correlations between the 2 weighting methods were high (r = 0.93) for child panels and moderate (r = 0.71) for adult panels (all P <.001)., Conclusions: The heterogeneity of PCP panels should be considered when assessing PCP workload for panel management. Panel variability in workload is well captured by age/sex-based weights for children, but for adults condition-based adjustment may be necessary.

Background: The current Medicare policy of non-payment to hospitals for Hospital-Acquired Conditions (HAC) seeks to avoid payment for preventable complications identified within a single admission. The financial impact ($1 million-$50 million/yr) underestimates the true financial impact of HACs when readmissions are taken into account., Objective: Define and quantify acute inpatient readmissions arising directly from, or completing the definition of, the current HACs., Research Design: Observational study., Subjects: All non-federal inpatient admissions to California hospitals, July 2006 to June 2007 with a recorded Social Security number., Measures: Readmission to acute care within 1 day for acute complications of poor glycemic control; 7 days for iatrogenic air emboli, incompatible blood transfusions, catheter-associated urinary tract infections and vascular catheter-associated infections; 30 days for deep vein thromboses or pulmonary emboli following hip or knee replacement surgery; and 183 days for foreign objects retained after surgery, mediastinitis following coronary artery bypass grafts, injuries sustained during inpatient care, infections following specific joint or bariatric surgery procedures, and pressure ulcers stages III & IV., Results: An additional estimated $103 million in payments would be withheld if Medicare expands the policy to include non-payment for HAC related readmissions. The majority (90%) of this impact involves mediastinitis, post-orthopedic surgery infection, or fall related injury., Conclusions: Limiting the current HAC policy focus to complications identified during the index admission omits consideration of many complications only identified in a subsequent admission. Non-payment for HAC-related readmissions would enhance incentives for prevention by increasing the frequency with which hospitals are held accountable for HACs.

Much of health services research seeks to inform particular policy choices and is best characterized as policy-driven research. The reverse, research-driven policy, occurs when studies alter how people perceive reality, which eventually leads to new policy. An example of the latter is nearly four decades of work by John Wennberg and colleagues. Observing variations in practice across small geographic areas led to the notion that some care is preference sensitive, whereas other care is supply constrained. For the former, patient, rather than physician, preferences should be honored, after acquiring and effectively communicating the best available information on the benefits and risks of treatment options. Finding that areas with high use of services have no better quality or outcomes than do areas with lower use led to the notion of accountable care organizations (ACOs). Eventually, both patient engagement and ACOs were written into the Affordable Care Act of 2010.

Advocates for consumer-friendly public reporting on the performance of health care providers anticipate that, at some point, well-vetted and standardized measures will be widely available to help patients choose clinicians who provide the best care. However, achieving that goal would require assembling standardized data from many sources. Such an effort would raise concerns, including privacy considerations about having a single massive data repository; questions of how such an effort would be funded; and potential misuse of the data. This paper proposes creating a public-private data aggregator that would receive patient and provider data from payers that are deidentified in such a way as to remain useful for consumer-reporting and research purposes. The aggregator could be funded through fees charged to commercial users. Meanwhile, registered researchers putting their methods and findings in the public domain could access the data aggregator for free.

Researchers often focus on the data and methods to assess policy changes, but data and methods can also be policy tools. To improve, health care systems need mechanisms and incentives for continually gathering, assessing, and acting on data. This requires (1) more comprehensive data, (2) converting data into information, and (3) incentives to apply that information. Restructured economic incentives can encourage clinicians to increase value (higher quality and/or lower cost) for their patients. While necessary, incentives are not sufficient-information is also needed. Incentives can lead clinicians to demand better information. Much of the necessary data is already used in patient care and billing; some additional variables will come directly from patients. The notion builds on two concepts: collective intelligence and positive deviance. The former characterizes knowledge gained from observing the behavior of many independent actors adapting to changing situations. Positive deviants are those who achieve far better results than expected. By rewarding positive deviants, rather than trying to identify and "correct" those who are problematic, providers will voluntarily identify themselves and their methods for achieving superior outcomes., (© Health Research and Educational Trust.)