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.

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.

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.

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.

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.

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.

Racial differences in low value care among older adult Medicare patients in US health systems: retrospective cohort study.

OBJECTIVE: To characterize racial differences in receipt of low value care (services that provide little to no benefit yet have potential for harm) among older Medicare beneficiaries overall and within health systems in the United States. DESIGN: Retrospective cohort study SETTING: 100% Medicare fee-for-service administrative data (2016-18). PARTICIPANTS: Black and White Medicare patients aged 65 or older as of 2016 and attributed to 595 health systems in the United States. MAIN OUTCOME MEASURES: Receipt of 40 low value services among Black and White patients, with and without adjustment for patient age, sex, and previous healthcare use. Additional models included health system fixed effects to assess racial differences within health systems and separately, racial composition of the health system's population to assess the relative contributions of individual patient race and health system racial composition to low value care receipt. RESULTS: The cohort included 9 833 304 patients (6.8% Black; 57.9% female). Of 40 low value services examined, Black patients had higher adjusted receipt of nine services and lower receipt of 20 services than White patients. Specifically, Black patients were more likely to receive low value acute diagnostic tests, including imaging for uncomplicated headache (6.9% v 3.2%) and head computed tomography scans for dizziness (3.1% v 1.9%). White patients had higher rates of low value screening tests and treatments, including preoperative laboratory tests (10.3% v 6.5%), prostate specific antigen tests (31.0% v 25.7%), and antibiotics for upper respiratory infections (36.6% v 32.7%; all P<0.001). Secondary analyses showed that these differences persisted within given health systems and were not explained by Black and White patients receiving care from different systems. CONCLUSIONS: Black patients were more likely to receive low value acute diagnostic tests and White patients were more likely to receive low value screening tests and treatments. Differences were generally small and were largely due to differential care within health systems. These patterns suggest potential individual, interpersonal, and structural factors that researchers, policy makers, and health system leaders might investigate and address to improve care quality and equity.

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.

Adoption and Diffusion of Transcarotid Artery Revascularization in Contemporary Practice.

BACKGROUND: In 2015, the FDA approved transcarotid artery revascularization (TCAR) as an alternative to carotid endarterectomy (CEA) and transfemoral carotid artery stenting (TF-CAS) for high-risk patients with carotid stenosis. This was granted in the absence of level 1 evidence to support TCAR. We aimed to document trends in TCAR utilization, its diffusion over time, and the clinical phenotypes of patients undergoing TCAR, CEA, and TF-CAS. METHODS: We used the Vascular Quality Initiative to study patients who underwent TCAR. We calculated the number of TCARs performed and the percent of TCAR utilization versus CEA/TF-CAS. Using data from before TCAR was widespread, we calculated propensity scores for patients to receive CEA. We applied this model to patients undergoing carotid revascularization from 2016 to 2022 and grouped patients by the procedure they ultimately underwent, examining overlap in score distribution to measure patient similarity. We measured the trend of in-hospital stroke/death after TCAR. RESULTS: We studied 31 447 patients who underwent TCAR from January 1, 2016 to March 31, 2022. The number of centers performing TCAR increased from 29 to 606. In 2021, TCAR represented 22.5% of carotid revascularizations at centers offering all 3 procedures. The percentage of patients that underwent TCAR who met approved high-risk criteria decreased from 88.5% to 80.9% (P<0.001). Those with a prior ipsilateral carotid procedure decreased from 20.6% in 2016 to 12.0% in 2021 (P<0.001). Patients undergoing TCAR after stroke increased from 19.7% to 30.7% (P<0.001). Propensity-score overlap was 55.4% for TCAR/CEA, and 58.6% for TCAR/TF-CAS, demonstrating that TCAR patients have a clinical phenotype mixed between those who undergo CEA and TF-CAS. The average in-hospital stroke/death risk after TCAR was 2.3% in 2016 and 1.7% in 2022 (P trend: 0.954). CONCLUSIONS: TCAR now represents nearly 1-in-4 procedures at centers offering it. TCAR was increasingly performed among standard-risk patients and as a first-line procedural option after stroke. The absence of level 1 evidence underscores the importance of high-quality registry-based analyses to document TCAR's real-world outcomes and durability.

Pediatric Hospitalizations at Rural and Urban Teaching and Nonteaching Hospitals in the US, 2009-2019.

Importance: National analyses suggest that approximately 1 in 5 US hospitals closed their pediatric units between 2008 and 2018. The extent to which pediatric hospitalizations at general hospitals in rural and urban communities decreased during this period is not well understood. Objective: To describe changes in the number and proportion of pediatric hospitalizations and costs at urban teaching, urban nonteaching, and rural hospitals vs freestanding children's hospitals from 2009 to 2019; to estimate the number and proportion of hospitals providing inpatient pediatric care; and to characterize changes in clinical complexity. Design, Setting, and Participants: This study is a retrospective cross-sectional analysis of the 2009, 2012, 2016, and 2019 Kids' Inpatient Database, a nationally representative data set of US pediatric hospitalizations among children younger than 18 years. Data were analyzed from February to June 2023. Exposures: Pediatric hospitalizations were grouped as birth or nonbirth hospitalizations. Hospitals were categorized as freestanding children's hospitals or as rural, urban nonteaching, or urban teaching general hospitals. Main Outcomes and Measures: The primary outcomes were annual number and proportion of birth and nonbirth hospitalizations and health care costs, changes in the proportion of hospitalizations with complex diagnoses, and estimated number and proportion of hospitals providing pediatric care and associated hospital volumes. Regression analyses were used to compare health care utilization in 2019 vs that in 2009. Results: The data included 23.2 million (95% CI, 22.7-23.6 million) weighted hospitalizations. From 2009 to 2019, estimated national annual pediatric hospitalizations decreased from 6 425 858 to 5 297 882, as birth hospitalizations decreased by 10.6% (95% CI, 6.1%-15.1%) and nonbirth hospitalizations decreased by 28.9% (95% CI, 21.3%-36.5%). Concurrently, hospitalizations with complex chronic disease diagnoses increased by 45.5% (95% CI, 34.6%-56.4%), and hospitalizations with mental health diagnoses increased by 78.0% (95% CI, 61.6%-94.4%). During this period, the most substantial decreases were in nonbirth hospitalizations at rural hospitals (4-fold decrease from 229 263 to 62 729) and urban nonteaching hospitals (6-fold decrease from 581 320 to 92 118). In 2019, birth hospitalizations occurred at 2666 hospitals. Nonbirth pediatric hospitalizations occurred at 3507 hospitals, including 1256 rural hospitals and 843 urban nonteaching hospitals where the median nonbirth hospitalization volumes were fewer than 25 per year. Conclusions and Relevance: Between 2009 and 2019, the largest decreases in pediatric hospitalizations occurred at rural and urban nonteaching hospitals. Clinical and policy initiatives to support hospitals with low pediatric volumes may be needed to maintain hospital access and pediatric readiness, particularly in rural communities.

Who's Accountable? Low-Value Care Received By Medicare Beneficiaries Outside Of Their Attributed Health Systems.

Policy makers and payers increasingly hold health systems accountable for spending and quality for their attributed beneficiaries. Low-value care-medical services that offer little or no benefit and have the potential for harm in specific clinical scenarios-received outside of these systems could threaten success on both fronts. Using national Medicare data for fee-for-service beneficiaries ages sixty-five and older and attributed to 595 US health systems, we describe where and from whom they received forty low-value services during 2017-18 and identify factors associated with out-of-system receipt. Forty-three percent of low-value services received by attributed beneficiaries originated from out-of-system clinicians: 38 percent from specialists, 4 percent from primary care physicians, and 1 percent from advanced practice clinicians. Recipients of low-value care were more likely to obtain that care out of system if age 75 or older (versus ages 65-74), male (versus female), non-Hispanic White (versus other races or ethnicities), rural dwelling (versus metropolitan dwelling), more medically complex, or experiencing lower continuity of care. However, out-of-system service receipt was not associated with recipients' health systems' accountable care organization status. Health systems might improve quality and reduce spending for their attributed beneficiaries by addressing out-of-system receipt of low-value care-for example, by improving continuity.

Using a Video Game Intervention to Increase Hospitalists' Advance Care Planning Conversations with Older Adults: a Stepped Wedge Randomized Clinical Trial.

BACKGROUND: Guidelines recommend Advance Care Planning (ACP) for seriously ill older adults to increase the patient-centeredness of end-of-life care. Few interventions target the inpatient setting. OBJECTIVE: To test the effect of a novel physician-directed intervention on ACP conversations in the inpatient setting. DESIGN: Stepped wedge cluster-randomized design with five 1-month steps (October 2020-February 2021), and 3-month extensions at each end. SETTING: A total of 35/125 hospitals staffed by a nationwide physician practice with an existing quality improvement initiative to increase ACP (enhanced usual care). PARTICIPANTS: Physicians employed for ≥ 6 months at these hospitals; patients aged ≥ 65 years they treated between July 2020-May 2021. INTERVENTION: Greater than or equal to 2 h of exposure to a theory-based video game designed to increase autonomous motivation for ACP; enhanced usual care. MAIN MEASURE: ACP billing (data abstractors blinded to intervention status). RESULTS: A total of 163/319 (52%) invited, eligible hospitalists consented to participate, 161 (98%) responded, and 132 (81%) completed all tasks. Physicians' mean age was 40 (SD 7); most were male (76%), Asian (52%), and reported playing the game for ≥ 2 h (81%). These physicians treated 44,235 eligible patients over the entire study period. Most patients (57%) were ≥ 75; 15% had COVID. ACP billing decreased between the pre- and post-intervention periods (26% v. 21%). After adjustment, the homogeneous effect of the game on ACP billing was non-significant (OR 0.96; 95% CI 0.88-1.06; p = 0.42). There was effect modification by step (p < 0.001), with the game associated with increased billing in steps 1-3 (OR 1.03 [step 1]; OR 1.15 [step 2]; OR 1.13 [step 3]) and decreased billing in steps 4-5 (OR 0.66 [step 4]; OR 0.95 [step 5]). CONCLUSIONS: When added to enhanced usual care, a novel video game intervention had no clear effect on ACP billing, but variation across steps of the trial raised concerns about confounding from secular trends (i.e., COVID). TRIAL REGISTRATION: Clinicaltrials.gov; NCT04557930, 9/21/2020.

The impact of adding cost information to a conversation aid to support shared decision making about low-risk prostate cancer treatment: Results of a stepped-wedge cluster randomised trial.

BACKGROUND: Decision aids help patients consider the benefits and drawbacks of care options but rarely include cost information. We assessed the impact of a conversation-based decision aid containing information about low-risk prostate cancer management options and their relative costs. METHODS: We conducted a stepped-wedge cluster randomised trial in outpatient urology practices within a US-based academic medical center. We randomised five clinicians to four intervention sequences and enroled patients newly diagnosed with low-risk prostate cancer. Primary patient-reported outcomes collected postvisit included the frequency of cost conversations and referrals to address costs. Other patient-reported outcomes included: decisional conflict postvisit and at 3 months, decision regret at 3 months, shared decision-making postvisit, financial toxicity postvisit and at 3 months. Clinicians reported their attitudes about shared decision-making pre- and poststudy, and the intervention's feasibility and acceptability. We used hierarchical regression analysis to assess patient outcomes. The clinician was included as a random effect; fixed effects included education, employment, telehealth versus in-person visit, visit date, and enrolment period. RESULTS: Between April 2020 and March 2022, we screened 513 patients, contacted 217 eligible patients, and enroled 117/217 (54%) (51 in usual care, 66 in the intervention group). In adjusted analyses, the intervention was not associated with cost conversations (ß = .82, p = .27), referrals to cost-related resources (ß = -0.36, p = .81), shared decision-making (ß = -0.79, p = .32), decisional conflict postvisit (ß = -0.34, p= .70), or at follow-up (ß = -2.19, p = .16), decision regret at follow-up (ß = -9.76, p = .11), or financial toxicity postvisit (ß = -1.32, p = .63) or at follow-up (ß = -2.41, p = .23). Most clinicians and patients had positive attitudes about the intervention and shared decision-making. In exploratory unadjusted analyses, patients in the intervention group experienced more transient indecision (p < .02) suggesting increased deliberation between visit and follow-up. DISCUSSION: Despite enthusiasm from clinicians, the intervention was not significantly associated with hypothesised outcomes, though we were unable to robustly test outcomes due to recruitment challenges. Recruitment at the start of the COVID-19 pandemic impacted eligibility, sample size/power, study procedures, and increased telehealth visits and financial worry, independent of the intervention. Future work should explore ways to support shared decision-making, cost conversations, and choice deliberation with a larger sample. Such work could involve additional members of the care team, and consider the detail, quality, and timing of addressing these issues. PATIENT OR PUBLIC CONTRIBUTION: Patients and clinicians were engaged as stakeholder advisors meeting monthly throughout the duration of the project to advise on the study design, measures selected, data interpretation, and dissemination of study findings.

Implementing shared decision making for early-stage breast cancer treatment using a coproduction learning collaborative: the SHAIR Collaborative protocol.

BACKGROUND: Shared decision making (SDM) in breast cancer care improves outcomes, but it is not routinely implemented. Results from the What Matters Most trial demonstrated that early-stage breast cancer surgery conversation aids, when used by surgeons after brief training, improved SDM and patient-reported outcomes. Trial surgeons and patients both encouraged using the conversation aids in routine care. We will develop and evaluate an online learning collaborative, called the SHared decision making Adoption Implementation Resource (SHAIR) Collaborative, to promote early-stage breast cancer surgery SDM by implementing the conversation aids into routine preoperative care. Learning collaboratives are known to be effective for quality improvement in clinical care, but no breast cancer learning collaborative currently exists. Our specific aims are to (1) provide the SHAIR Collaborative resources to clinical sites to use with eligible patients, (2) examine the relationship between the use of the SHAIR Collaborative resources and patient reach, and (3) promote the emergence of a sustained learning collaborative in this clinical field, building on a partnership with the American Society of Breast Surgeons (ASBrS). METHODS: We will conduct a two-phased implementation project: phase 1 pilot at five sites and phase 2 scale up at up to an additional 32 clinical sites across North America. The SHAIR Collaborative online platform will offer free access to conversation aids, training videos, electronic health record and patient portal integration guidance, a feedback dashboard, webinars, support center, and forum. We will use RE-AIM for data collection and evaluation. Our primary outcome is patient reach. Secondary data will include (1) patient-reported data from an optional, anonymous online survey, (2) number of active sites and interviews with site champions, (3) Normalization MeAsure Development questionnaire data from phase 1 sites, adaptations data utilizing the Framework for Reporting Adaptations and Modifications-Extended/-Implementation Strategies, and tracking implementation facilitating factors, and (4) progress on sustainability strategy and plans with ASBrS. DISCUSSION: The SHAIR Collaborative will reach early-stage breast cancer patients across North America, evaluate patient-reported outcomes, engage up to 37 active sites, and potentially inform engagement factors affecting implementation success and may be sustained by ASBrS.

Association of Functional Status, Cognition, Social Support, and Geriatric Syndrome With Admission From the Emergency Department.

Importance: The role of patient-level factors that are unrelated to the specific clinical condition leading to an emergency department (ED) visit, such as functional status, cognitive status, social supports, and geriatric syndromes, in admission decisions is not well understood, partly because these data are not available in administrative databases. Objective: To determine the extent to which patient-level factors are associated with rates of hospital admission from the ED. Design, Setting, and Participants: This cohort study analyzed survey data collected from participants (or their proxies, such as family members) enrolled in the Health and Retirement Study (HRS) from January 1, 2000, to December 31, 2018. These HRS data were linked to Medicare fee-for-service claims data from January 1, 1999, to December 31, 2018. Information on functional status, cognitive status, social supports, and geriatric syndromes was obtained from the HRS data, whereas ED visits, subsequent hospital admission or ED discharge, and other claims-derived comorbidities and sociodemographic characteristics were obtained from Medicare data. Data were analyzed from September 2021 to April 2023. Main Outcomes and Measures: The primary outcome measure was hospital admission after an ED visit. A baseline logistic regression model was estimated, with a binary indicator of admission as the dependent variable of interest. For each primary variable of interest derived from the HRS data, the model was reestimated, including the HRS variable of interest as an independent variable. For each of these models, the odds ratio (OR) and average marginal effect (AME) of changing the value of the variable of interest were calculated. Results: A total of 42 392 ED visits by 11 783 unique patients were included. At the time of the ED visit, patients had a mean (SD) age of 77.4 (9.6) years, and visits were predominantly for female (25 719 visits [60.7%]) and White (32 148 visits [75.8%]) individuals. The overall percentage of patients admitted was 42.5%. After controlling for ED diagnosis and demographic characteristics, functional status, cognition status, and social supports all were associated with the likelihood of admission. For instance, difficulty performing 5 activities of daily living was associated with an 8.5-percentage point (OR, 1.47; 95% CI, 1.29-1.66) AME increase in the likelihood of admission. Having dementia was associated with an AME increase in the likelihood of admission of 4.6 percentage points (OR, 1.23; 95% CI, 1.14-1.33). Living with a spouse was associated with an AME decrease in the likelihood of admission of 3.9 percentage points (OR, 0.84; 95% CI, 0.79-0.89), and having children living within 10 miles was associated with an AME decrease in the likelihood of admission of 5.0 percentage points (OR, 0.80; 95% CI, 0.71-0.89). Other common geriatric syndromes, including trouble falling asleep, waking early, trouble with vision, glaucoma or cataract, use of hearing aids or trouble with hearing, falls in past 2 years, incontinence, depression, and polypharmacy, were not meaningfully associated with the likelihood of admission. Conclusion and Relevance: Results of this cohort study suggest that the key patient-level characteristics, including social supports, cognitive status, and functional status, were associated with the decision to admit older patients to the hospital from the ED. These factors are critical to consider when devising strategies to reduce low-value admissions among older adult patients from the ED.