Incidence of Lung Cancer Among Never-Smoking Asian American, Native Hawaiian, and Pacific Islander Females.

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.

Integrating Electronic Health Record, Cancer Registry, and Geospatial Data to Study Lung Cancer in Asian American, Native Hawaiian, and Pacific Islander Ethnic Groups.

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.

Smoking-Cessation Interventions After Lung Cancer Screening Guideline Change.

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.

Primary care physician practice styles and quality, cost, and productivity.

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.

Population-Based Registry Linkages to Improve Validity of Electronic Health Record-Based Cancer Research.

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."

Preventive visit among older adults with Medicare's introduction of Annual Wellness Visit: Closing gaps in underutilization.

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.

New recommendation and coverage of low-dose computed tomography for lung cancer screening: uptake has increased but is still low.

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.

Beyond Consent: Building Trusting Relationships With Diverse Populations in Precision Medicine Research.

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.

Association Between Monetary Deposits and Weight Loss in Online Commitment Contracts.

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.

Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.

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.

Collecting Practice-level Data in a Changing Physician Office-based Ambulatory Care Environment: A Pilot Study Examining the Physician induction interview Component of the National Ambulatory Medical Care Survey.

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 care­oriented 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.

Electronic Health Record Logs Indicate That Physicians Split Time Evenly Between Seeing Patients And Desktop Medicine.

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.

Scaling Lean in primary care: impacts on system performance.

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.