Effectiveness of Direct Admission Compared to Admission Through the Emergency Department: A Stepped-Wedge Cluster-Randomized Trial.

OBJECTIVE: Direct admission (DA) to hospital can reduce emergency department (ED) utilization by bypassing the ED during the admission process. We implemented a DA program across 3 health systems and compared timeliness of care, family experience of care, and post-admission clinical deterioration among children admitted via DA versus the ED after their clinic was randomized to begin the DA program. METHODS: Using a stepped-wedge design, 69 primary and urgent care clinics were randomized to 1 of 4 time points to begin a voluntary DA program, February 1, 2020 to April 30, 2023. Outcomes in children <18 years admitted with 7 common medical diagnoses were compared using adjusted logistic or linear regression. RESULTS: A total of 2599 children were admitted with eligible diagnoses during the study period , including 145 children admitted directly and 1852 admitted through EDs after program implementation at their clinic. Median age was 2.8 (interquartile range: 1.1-6.8) years, 994 (49.8%) were female, and 1324 (66.3%) were Medicaid-insured. Adjusted regression analyses showed that if each child was admitted via DA versus the ED, average time to initial clinical assessment was 3.1 minute (95% confidence interval: 1.7-4.5) slower, whereas time to initial therapeutic management was 49.6 minutes faster on average (95% confidence interval: 30.3.2-68.9). There were no significant differences in time to initial diagnostic testing or rates of post-admission clinical deterioration. CONCLUSIONS: Compared with ED admission, DA appears equally safe and acceptable to families, and may be associated with a significantly shorter time to initial therapeutic management with modestly longer time to initial clinical assessment.

Rural-Urban Disparities in Hospital Services and Outcomes for Children With Medical Complexity.

Importance: Limited availability of inpatient pediatric services in rural regions has raised concerns about access, safety, and quality of hospital-based care for children. This may be particularly important for children with medical complexity (CMC). Objectives: To describe differences in the availability of pediatric services at acute care hospitals where rural- and urban-residing CMC presented for hospitalization; identify rural-urban disparities in health care quality and in-hospital mortality; and determine whether the availability of pediatric services at index hospitals or the experience of interfacility transfer modified rural-urban differences in outcomes. Design, Setting, and Participants: This retrospective cohort study examined all-payer claims data from Colorado, Massachusetts, and New Hampshire from 2012 to 2017. Analysis was conducted from May 2023 to July 2024. Participants included CMC younger than 18 years residing in these states and hospitalized during the study period. Exposures: Rural or urban residence was determined using Rural-Urban Commuting Area codes. Hospitals were categorized as children's hospitals or general hospitals with comprehensive, limited, or no dedicated pediatric services using American Hospital Association survey data. Interfacility transfers between index and definitive care hospitals were identified using health care claims. Main Outcomes and Measures: In-hospital mortality, all-cause 30-day readmission, medical-surgical safety events, and surgical safety events were operationalized using Agency for Healthcare Research and Quality measure specifications. Results: Among 36 943 CMC who experienced 79 906 hospitalizations, 16 525 (44.7%) were female, 26 034 (70.5%) were Medicaid-insured, and 34 008 (92.1%) were urban-residing. Rural-residing CMC were 6.55 times more likely to present to hospitals without dedicated pediatric services (rate ratio [RR], 6.55 [95% CI, 5.86-7.33]) and 2.03 times more likely to present to hospitals without pediatric beds (RR, 2.03 [95% CI, 1.88-2.21]) than urban-residing CMC, with no significant differences in interfacility transfer rates. In unadjusted analysis, rural-residing CMC had a 44% increased risk of in-hospital mortality (RR, 1.44 [95% CI, 1.03-2.02]) with no significant differences in other outcomes. Adjusting for clinical characteristics, the difference in in-hospital mortality was no longer significant. Index hospital type was not a significant modifier of observed rural-urban outcomes, but interfacility transfer was a significant modifier of rural-urban differences in surgical safety events. Conclusions and Relevance: In this cohort study, rural-residing CMC were significantly more likely to present to hospitals without dedicated pediatric services. These findings suggest that efforts are justified to ensure that all hospital types are prepared to care for CMC.

Safety Net Primary Care Capabilities After the COVID-19 Pandemic.

Importance: Federally qualified health centers (FQHCs) provide care to 30 million patients in the US and have shown better outcomes and processes than other practice types. Little is known about how the COVID-19 pandemic contributed to FQHC capabilities compared with other practices. Objective: To compare postpandemic operational characteristics and capabilities of FQHCs with non-FQHC safety net practices and non-FQHC, non-safety net practices. Design, Setting, and Participants: This nationally representative survey conducted from June 2022 to February 2023 with an oversampling of safety net practices in the US included practice leaders working in stratified random selection of practices based on FQHC status, Area Deprivation Index category, and ownership type per a health care network dataset. Exposures: Practice type: FQHC vs non-FQHC safety net and non-FQHC practices. Main Outcomes and Measures: Primary care capabilities, including 2 measures of access and 11 composite measures. Results: A total of 1245 practices (221 FQHC and 1024 non-FQHC) responded of 3498 practices sampled. FQHCs were more likely to be independently owned and have received COVID-19 funding. FQHCs and non-FQHC safety net practices were more likely to be in rural areas. FQHCs significantly outperformed non-FQHCs on several capabilities even after controlling for practice size and ownership, including behavioral health provision (mean score, 0.53; 95% CI, 0.51-0.56), culturally informed services (mean score, 0.55; 95% CI, 0.53-0.58), screening for social needs (mean score, 0.43; 95% CI, 0.39-0.47), social needs referrals (mean score, 0.53; 95% CI, 0.48-0.57), social needs referral follow-up (mean score, 0.31; 95% CI, 0.27-0.36), and shared decision-making and motivational interviewing training (mean score, 0.53; 95% CI, 0.51-0.56). No differences were found in behavioral and substance use screening, care processes for patients with complex and high levels of need, use of patient-reported outcome measures, decision aid use, or after-hours access. Across all practices, most of the examined capabilities showed room for improvement. Conclusions and Relevance: The results of this survey study suggest that FQHCs outperformed non-FQHC practices on important care processes while serving a patient population with lower incomes who are medically underserved compared with patients in other practice types. Legislation to expand funding for the FQHC program should improve services for underserved populations and target current non-FQHC safety net practices to serve these populations. Increased support for these practices could improve primary care for rural populations.

Surgeon and Care Team Network Measures and Timely Breast Cancer Treatment.

Importance: Cancer treatment delay is a recognized marker of worse outcomes. Timely treatment may be associated with physician patient-sharing network characteristics, yet this remains understudied. Objective: To examine the associations of surgeon and care team patient-sharing network measures with breast cancer treatment delay. Design, Setting, and Participants: This cross-sectional study of Medicare claims in a US population-based setting was conducted from 2017 to 2020. Eligible participants included patients with breast cancer who received surgery and the subset who went on to receive adjuvant therapy. Patient-sharing networks were constructed for treating physicians. Data were analyzed from September 2023 to February 2024. Exposures: Surgeon linchpin score (a measure of local uniqueness or scarcity) and care density (a measure of physician team familiarity) were assessed. Surgeons were considered linchpins if their linchpin score was in the top 15%. The care density of a patient's physician team was calculated on preoperative teams for surgically-treated patients and postoperative teams for adjuvant therapy-receiving patients. Main Outcomes and Measures: The primary outcomes were surgical and adjuvant delay, which were defined as greater than 60 days between biopsy and surgery and greater than 60 days between surgery and adjuvant therapy, respectively. Results: The study cohort included 56 433 patients (18 004 aged 70-74 years [31.9%]) who were mostly from urban areas (44 931 patients [79.6%]). Among these patients, 8009 (14.2%) experienced surgical delay. Linchpin surgeon status (locally unique surgeon) was not statistically associated with surgical delay; however, patients with high preoperative care density (ie, high team familiarity) had lower odds of surgical delay compared with those with low preoperative care density (odds ratio [OR], 0.58; 95% CI, 0.53-0.63). Of the 29 458 patients who received adjuvant therapy after surgery, 5700 (19.3%) experienced adjuvant delay. Patients with a linchpin surgeon had greater odds of adjuvant delay compared with those with a nonlinchpin surgeon (OR, 1.30; 95% CI, 1.13-1.49). Compared with those with low postoperative care density, there were lower odds of adjuvant delay for patients with high postoperative care density (OR, 0.77; 95% CI, 0.69-0.87) and medium postoperative care density (OR, 0.85; 95% CI, 0.77-0.94). Conclusions and Relevance: In this cross-sectional study of Medicare claims, network measures capturing physician scarcity and team familiarity were associated with timely treatment. These results may help guide system-level interventions to reduce cancer treatment delays.

Regional variation in diagnostic intensity of dementia among older U.S. adults: An observational study.

INTRODUCTION: Geographic variation in diagnosed cases of Alzheimer's disease and related dementias (ADRD) could be due to underlying population risk or differences in intensity of new case identification. Areas with low ADRD diagnostic intensity could be targeted for additional surveillance efforts. METHODS: Medicare claims were used for a cohort of older adults across hospital referral regions (HRRs). ADRD-specific regional diagnosis intensity was measured as the ratio of expected new ADRD cases (estimated using population demographics, risk factors, and practice intensity) compared to observed ADRD-diagnosed cases. RESULTS: Crude new ADRD diagnosis rate ranged from 1.7 to 5.4 per 100 across HRRs. ADRD-specific diagnosis intensity ranged from 0.69 to 1.47 and varied most for Black, Hispanic, and the youngest (66-74) subgroups. Across all subgroups, ADRD diagnosis intensity was associated with 2-fold difference in receiving an ADRD diagnosis. DISCUSSION: Where one resides influences the likelihood of receiving an ADRD diagnosis, particularly among those 66-74 years of age and minoritized groups. HIGHLIGHTS: Rate of new Alzheimer's disease and related dementias (ADRD) case identification varies geographically across the United States. Variation in case identification is greatest in Black, Hispanic, and young-old groups. Intensity of diagnosis (ie, case identification) unrelated to population risk differs across place. Likelihood of receiving an ADRD diagnosis varies 2-fold based on place of residence.

Neurodevelopmental and Mental Health Conditions in Children With Medical Complexity.

BACKGROUND AND OBJECTIVES: Children with medical complexity (CMC) may be at a high risk of neurodevelopmental and mental health conditions given disease comorbidities and lived experiences. Little is known about the prevalence of these conditions at a population level. In this study, we estimated the prevalence of neurodevelopmental and mental health diagnoses in CMC relative to children without medical complexity and measured associations between these diagnoses in CMC and subsequent health care utilization and in-hospital mortality. METHODS: We applied the Child and Adolescent Mental Health Disorders Classification System to identify neurodevelopmental and mental health diagnoses using all-payer claims data from three states (2012-2017). Poisson regression was used to compare outcomes in CMC with neurodevelopmental and mental health diagnoses to CMC without these diagnoses, adjusting for sociodemographic and clinical characteristics. RESULTS: Among 85 581 CMC, 39 065 (45.6%) had ≥1 neurodevelopmental diagnoses, and 31 703 (37.0%) had ≥1 mental health diagnoses, reflecting adjusted relative risks of 3.46 (3.42-3.50) for neurodevelopmental diagnoses and 2.22 (2.19-2.24) for mental health diagnoses compared with children without medical complexity. CMC with both neurodevelopmental and mental health diagnoses had 3.00 (95% confidence interval [CI]: 2.98-3.01) times the number of ambulatory visits, 69% more emergency department visits (rate ratio = 1.69, 95% CI: 1.66-1.72), 58% greater risk of hospitalization (rate ratio = 1.58, 95% CI: 1.50-1.67), and 2.32 times (95% CI: 2.28-2.36) the number of hospital days than CMC without these diagnoses. CONCLUSIONS: Neurodevelopmental and mental health diagnoses are prevalent among CMC and associated with increased health care utilization across the continuum of care. These findings illustrate the importance of recognizing and treating neurodevelopmental and mental health conditions in this population.

Emergency Department Visits And Hospital Capacity In The US: Trends In The Medicare Population During The COVID-19 Pandemic.

Although emergency department (ED) and hospital overcrowding were reported during the later parts of the COVID-19 pandemic, the true extent and potential causes of this overcrowding remain unclear. Using data on the traditional fee-for-service Medicare population, we examined patterns in ED and hospital use during the period 2019-22. We evaluated trends in ED visits, rates of admission from the ED, and thirty-day mortality, as well as measures suggestive of hospital capacity, including hospital Medicare census, length-of-stay, and discharge destination. We found that ED visits remained below baseline throughout the study period, with the standardized number of visits at the end of the study period being approximately 25 percent lower than baseline. Longer length-of-stay persisted through 2022, whereas hospital census was considerably above baseline until stabilizing just above baseline in 2022. Rates of discharge to postacute facilities initially declined and then leveled off at 2 percent below baseline in 2022. These results suggest that widespread reports of overcrowding were not driven by a resurgence in ED visits. Nonetheless, length-of-stay remains higher, presumably related to increased acuity and reduced available bed capacity in the postacute care system.

Network spillover effects associated with the ChooseWell 365 workplace randomized controlled trial to promote healthy food choices.

Food choices are closely linked to culture, social relationships, and health. Because many adults spend up to half their time at work, the workplace provides a venue for changing population health-related behaviors and norms. It is unknown whether the effects of a workplace intervention to improve health behaviors might spread beyond participating employees due to social influence. ChooseWell 365 was a randomized controlled trial testing a 12-month healthy eating intervention grounded in principles of behavioral economics. This intervention leveraged an existing cafeteria traffic-light labeling system (green = healthy; red = unhealthy) in a large hospital workplace and demonstrated significant improvements in healthy food choices by employees in the intervention vs. control group. The current study used data from over 29 million dyadic purchasing events during the trial to test whether social ties to a trial participant co-worker (n = 299 intervention, n = 302 control) influenced the workplace food choices of non-participants (n = 7900). There was robust evidence that non-participants who were socially tied to more intervention group participants made healthier workplace food purchases overall, and purchased a greater proportion of healthy (i.e., green) food and beverages, and fewer unhealthy (i.e., red) beverages and modest evidence that the benefit of being tied to intervention participants was greater than being tied to control participants. Although individual-level effect sizes were small, a range of consistent findings indicated that this light-touch intervention yielded spillover effects of healthy eating behaviors on non-participants. Results suggest that workplace healthy eating interventions could have population benefits extending beyond participants.

Exploiting relationship directionality to enhance statistical modeling of peer-influence across social networks.

Risky-prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other "risky" drugs is a national public health concern. We use a novel data framework-a directed network connecting physicians who encounter the same patients in a sequence of visits-to investigate if risky-prescribing diffuses across physicians through a process of peer-influence. Using a shared-patient network of 10 661 Ohio-based physicians constructed from Medicare claims data over 2014-2015, we extract information on the order in which patients encountered physicians to derive a directed patient-sharing network. This enables the novel decomposition of peer-effects of a medical practice such as risky-prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer-effects for contagion in risky-prescribing behavior as well as spillover effects. The latter is measured in terms of adverse health events suspected to be related to risky-prescribing in patients of peer-physicians. Estimated peer-effects were strongest when the patient-sharing relationship was mutual as opposed to directional. Using simulations we confirmed that our modeling and estimation strategies allows simultaneous estimation of each type of peer-effect (mutual and directional) with accuracy and precision. We also show that failing to account for these distinct mechanisms (a form of model mis-specification) produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared-patient networks. These findings suggest network-based interventions for reducing risky-prescribing.

Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of geographic homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and could inform interventions to reduce risky-prescribing (e.g., should interventions target groups of physicians or select physicians at random). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques - groups of actors that are fully connected to each other - such as closed triangles in the case of three actors), this would further strengthen the case for targeting of select physicians for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing in both the state-wide and multiple HRR sub-networks, and that the level of homophily varied across HRRs. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology could be applied to arbitrary shared-patient networks and even more generally to other kinds of network data that underlies other kinds of social phenomena.

Communicating visit information to family caregivers: How does method matter? A national survey.

OBJECTIVES: The clinic visit is a critical point of contact for family caregivers. However, only 37% of family caregivers are able to accompany patients to visits. When they cannot attend, caregivers receive visit information to assist with their caregiving. However, little is known about how method of receiving information from clinic visits is associated with important caregiver outcomes. This study sought to determine whether mode of receiving clinic visit information (speaking with the patient, attending the visit, or using an after-visit summary [AVS]) was associated with changes in caregiver burden, caregiver preparedness, and the positive aspects of caregiving. METHODS: Cross-sectional web-based survey of a national sample of adult family caregivers. Multiple linear regression models determined associations between communication modes and caregivers' burden, preparedness, and positive aspects of caregiving, adjusting for sociodemographic covariates. RESULTS: Respondents (N = 340) were mostly male (58%), White (59%), ranged from 18 to 85 years old, and supported patients with conditions including diabetes, dementia, and cancer. Speaking with patients was associated with increases in positive aspects of caregiving (95% CI = 2.01, 5.42) and an AVS was associated with increases in positive aspects of caregiving (95% CI = 0.4, 3.56) and preparedness for caregiving (95% CI = 0.61, 3.15). Using any method of receiving information from visits was associated with the greatest increase in preparedness, compared to not receiving visit information. We did not observe an association between method of communication and caregiver burden. SIGNIFICANCE OF RESULTS: Method of communicating visit information is associated with improvements in caregiver preparedness and the positive aspects of caregiving, though caregiver burden may be unaffected by information exchange. Given the limitations of current communication methods, future work should explore directionality of the associations we found and identify visit communication strategies with caregivers that optimize caregiver and patient outcomes.

The Association Between Oncology Outreach and Timely Treatment for Rural Patients with Breast Cancer: A Claims-Based Approach.

BACKGROUND: Oncology outreach is a common strategy for increasing rural access to cancer care, where traveling oncologists commute across healthcare settings to extend specialized care. Examining the extent to which physician outreach is associated with timely treatment for rural patients is critical for informing outreach strategies. METHODS: We identified a 100% fee-for-service sample of incident breast cancer patients from 2015 to 2020 Medicare claims and apportioned them into surgery and adjuvant therapy cohorts based on treatment history. We defined an outreach visit as the provision of care by a traveling oncologist at a clinic outside of their primary hospital service area. We used hierarchical logistic regression to examine the associations between patient receipt of preoperative care at an outreach visit (preoperative outreach) and > 60-day surgical delay, and patient receipt of postoperative care at an outreach visit (postoperative outreach) and > 60-day adjuvant delay. RESULTS: We identified 30,337 rural-residing patients who received breast cancer surgery, of whom 4071 (13.4%) experienced surgical delay. Among surgical patients, 14,501 received adjuvant therapy, of whom 2943 (20.3%) experienced adjuvant delay. In adjusted analysis, we found that patient receipt of preoperative outreach was associated with reduced odds of surgical delay (odds ratio [OR] 0.75, 95% confidence interval [CI] 0.61-0.91); however, we found no association between patient receipt of postoperative outreach and adjuvant delay (OR 1.04, 95% CI 0.85-1.25). CONCLUSIONS: Our findings indicate that preoperative outreach is protective against surgical delay. The traveling oncologists who enable such outreach may play an integral role in catalyzing the coordination and timeliness of patient-centered care.

Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients.

When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient's own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effect pathways between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study is used to assess the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply our Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables.

Characterizing the Traveling Oncology Workforce and Its Influence on Patient Travel Burden: A Claims-Based Approach.

PURPOSE: Oncology outreach is a common strategy for extending cancer care to rural patients. However, a nationwide characterization of the traveling workforce that enables this outreach is lacking, and the extent to which outreach reduces travel burden for rural patients is unknown. METHODS: This cross-sectional study analyzed a rural (nonurban) subset of a 100% fee-for-service sample of 355,139 Medicare beneficiaries with incident breast, colorectal, and lung cancers. Surgical, medical, and radiation oncologists were linked to patients using Part B claims, and traveling oncologists were identified by observing hospital service area (HSA) transition patterns. We defined oncology outreach as the provision of cancer care by a traveling oncologist outside of their primary HSA. We used hierarchical gamma regression models to examine the separate associations between patient receipt of oncology outreach and one-way patient travel times to chemotherapy, radiotherapy, and surgery. RESULTS: On average, 9,935 of 39,960 oncologists conducted annual outreach, where 57.8% traveled with low frequency (0-1 outreach visits/mo), 21.1% with medium frequency (1-3 outreach visits/mo), and 21.1% with high frequency (>3 outreach visits/mo). Oncologists provided surgery, radiotherapy, and chemotherapy to 51,715, 27,120, and 5,874 rural beneficiaries, respectively, of whom 2.5%, 6.9%, and 3.6% received oncology outreach. Rural patients who received oncology outreach traveled 16% (95% CI, 11 to 21) and 11% (95% CI, 9 to 13) less minutes to chemotherapy and radiotherapy than those who did not receive oncology outreach, corresponding to expected one-way savings of 15.9 (95% CI, 15.5 to 16.4) and 11.9 (95% CI, 11.7 to 12.2) minutes, respectively. CONCLUSION: Our study introduces a novel claims-based approach for tracking the nationwide traveling oncology workforce and supports oncology outreach as an effective means for improving rural access to cancer care.

Rural-urban disparities in health care delivery for children with medical complexity and moderating effects of payer, disability, and community poverty.

PURPOSE: Children with medical complexity (CMC) may be at increased risk of rural-urban disparities in health care delivery given their multifaceted health care needs, but these disparities are poorly understood. This study evaluated rural-urban disparities in health care delivery to CMC and determined whether Medicaid coverage, co-occurring disability, and community poverty modified the effects of rurality on care delivery. METHODS: This retrospective cohort study of 2012-2017 all-payer claims data from Colorado, Massachusetts, and New Hampshire included CMC <18 years. Health care delivery measures (ambulatory clinic visits, emergency department visits, acute care hospitalizations, total hospital days, and receipt of post-acute care) were compared for rural- versus urban-residing CMC in multivariable regression models, following established methods to evaluate effect modification. FINDINGS: Of 112,475 CMC, 7307 (6.5%) were rural residing and 105,168 (93.5%) were urban residing. A total of 68.9% had Medicaid coverage, 33.9% had a disability, and 39.7% lived in communities with >20% child poverty. In adjusted analyses, rural-residing CMC received significantly fewer ambulatory visits (risk ratio [RR] = 0.95, 95% confidence interval [CI]: 0.94-0.96), more emergency visits (RR = 1.12, 95% CI: 1.08-1.16), and fewer hospitalization days (RR = 0.90, 95% CI = 0.85-0.96). The estimated modification effects of rural residence by Medicaid coverage, disability, and community poverty were each statistically significant. Differences in the odds of having a hospitalization and receiving post-acute care did not persist after incorporating sociodemographic and clinical characteristics and interaction effects. CONCLUSIONS: Rural- and urban-residing CMC differed in their receipt of health care, and Medicaid coverage, co-occurring disabilities, and community poverty modified several of these effects. These modifying effects should be considered in clinical and policy initiatives to ensure that such initiatives do not widen rural-urban disparities.

Clinician-Spoken Plain Language in Health Care Encounters: A Qualitative Analysis to Assess Measurable Elements.

PURPOSE: Good communication and use of plain language in health care encounters improve outcomes, including emotional health, symptom resolution, and functional status. Yet there is limited research on how to measure and report spoken plain language, which is the use of familiar, clear language. The authors aimed to describe key, measurable elements of spoken plain language that can be assessed and reported back to clinicians for self-reflection. METHOD: The authors conducted secondary analysis of transcripts from recorded encounters between breast cancer surgeons and patients with early-stage breast cancer. Two coders used a hybrid qualitative analysis with a framework based on U.S. Federal Plain Language Guidelines. To develop major themes, they examined (1) alignment with the Guidelines and (2) code frequencies within and across transcripts. They also noted minor themes. RESULTS: From 74 transcripts featuring 13 surgeons, the authors identified 2 major themes representing measurable elements of spoken plain language: (1) clinicians had a propensity to use both explained and unexplained medical terms, and (2) clinicians delivered information using either short turns (one unit of someone speaking) with 1 topic or long turns with multiple topics. There were 3 minor themes that were not indicative of whether or not clinicians used spoken plain language. First, clinicians regularly used absolute risk communication techniques. Second, question-asking techniques varied and included open-ended, close-ended, and comprehension checks. Third, some clinicians used imagery to describe complex topics. CONCLUSIONS: Clinicians' propensity to use medical terms with and without explanation and parse encounters into shorter or longer turns are measurable elements of spoken plain language. These findings will support further research on the development of a tool that can be used in medical education and other settings. This tool could provide direct and specific feedback to improve the plain language practices of clinicians in training and beyond.

Association between a network-based physician linchpin score and cancer patient mortality: a SEER-Medicare analysis.

BACKGROUND: Patients with cancer frequently require multidisciplinary teams for optimal cancer outcomes. Network analysis can capture relationships among cancer specialists, and we developed a novel physician linchpin score to characterize "linchpin" physicians whose peers have fewer ties to other physicians of the same oncologic specialty. Our study examined whether being treated by a linchpin physician was associated with worse survival. METHODS: In this cross-sectional study, we analyzed Surveillance, Epidemiology, and End Results-Medicare data for patients diagnosed with stage I to III non-small cell lung cancer or colorectal cancer (CRC) in 2016-2017. We assembled patient-sharing networks and calculated linchpin scores for medical oncologists, radiation oncologists, and surgeons. Physicians were considered linchpins if their linchpin score was within the top 15% for their specialty. We used Cox proportional hazards models to examine associations between being treated by a linchpin physician and survival, with a 2-year follow-up period. RESULTS: The study cohort included 10 081 patients with non-small cell lung cancer and 9036 patients with CRC. Patients with lung cancer treated by a linchpin radiation oncologist had a 17% (95% confidence interval = 1.04 to 1.32) greater hazard of mortality, and similar trends were observed for linchpin medical oncologists. Patients with CRC treated by a linchpin surgeon had a 22% (95% confidence interval = 1.03 to 1.43) greater hazard of mortality. CONCLUSIONS: In an analysis of Medicare beneficiaries with nonmetastatic lung cancer or CRC, those treated by linchpin physicians often experienced worse survival. Efforts to improve outcomes can use network analysis to identify areas with reduced access to multidisciplinary specialists.

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

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

Constructing within and between hospital physician social networks for modeling physician research participation.

BACKGROUND: Physician participation in clinical trials is essential for the progress of modern medicine. However, the demand for physician research partners is outpacing physicians' interest in participating in scientific studies. Understanding the factors that influence physician participation in research is crucial to addressing this gap. METHODS: In this study, we used a physician's social network, as constructed from patient billing data, to study if the research choices of a physician's immediate peers influence their likelihood to participate in scientific research. We analyzed data from 348 physicians across 40 hospitals. We used logistic regression models to examine the relationship between a physician's participation in clinical trials and the participation of their social network peers, adjusting for age, years of employment, and influences from other hospital facilities. RESULTS: We found that the likelihood of a physician participating in clinical trials increased dramatically with the proportion of their social network-defined colleagues at their primary hospital who were participating ([Formula: see text] for a 1% increase in the proportion of participating peers, [Formula: see text]). Additionally, physicians who work regularly at multiple facilities were more likely to participate ([Formula: see text], [Formula: see text]) and increasingly so as the extent to which they have social network ties to colleagues at hospitals other than their primary hospital increases ([Formula: see text], [Formula: see text]). These findings suggest an inter-hospital peer participation process. CONCLUSION: Our study provides evidence that the social structure of a physician's work-life is associated with their decision to participate in scientific research. The results suggest that interventions aimed at increasing physician participation in clinical trials could leverage the social networks of physicians to encourage participation. By identifying factors that influence physician participation in research, we can work towards closing the gap between the demand for physician research partners and the number of physicians willing to participate in scientific studies.

Clinically informed machine learning elucidates the shape of hospice racial disparities within hospitals.

Racial disparities in hospice care are well documented for patients with cancer, but the existence, direction, and extent of disparity findings are contradictory across the literature. Current methods to identify racial disparities aggregate data to produce single-value quality measures that exclude important patient quality elements and, consequently, lack information to identify actionable equity improvement insights. Our goal was to develop an explainable machine learning approach that elucidates healthcare disparities and provides more actionable quality improvement information. We infused clinical information with engineering systems modeling and data science to develop a time-by-utilization profile per patient group at each hospital using US Medicare hospice utilization data for a cohort of patients with advanced (poor-prognosis) cancer that died April-December 2016. We calculated the difference between group profiles for people of color and white people to identify racial disparity signatures. Using machine learning, we clustered racial disparity signatures across hospitals and compared these clusters to classic quality measures and hospital characteristics. With 45,125 patients across 362 hospitals, we identified 7 clusters; 4 clusters (n = 190 hospitals) showed more hospice utilization by people of color than white people, 2 clusters (n = 106) showed more hospice utilization by white people than people of color, and 1 cluster (n = 66) showed no difference. Within-hospital racial disparity behaviors cannot be predicted from quality measures, showing how the true shape of disparities can be distorted through the lens of quality measures. This approach elucidates the shape of hospice racial disparities algorithmically from the same data used to calculate quality measures.