DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation.

Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as healthcare should ideally be (i) accurate, and (ii) provide faithful explanations. However, current solutions are inadequate: state-of-the-art black-box models do not supply explanations, post-hoc explainers for black-box models lack faithfulness guarantees, and self-interpretable models greatly compromise accuracy. To address these issues, we propose DISCRET, a self-interpretable ITE framework that synthesizes faithful, rule-based explanations for each sample. A key insight behind DISCRET is that explanations can serve dually as database queries to identify similar subgroups of samples. We provide a novel RL algorithm to efficiently synthesize these explanations from a large search space. We evaluate DISCRET on diverse tasks involving tabular, image, and text data. DISCRET outperforms the best self-interpretable models and has accuracy comparable to the best black-box models while providing faithful explanations. DISCRET is available at https://github.com/wuyinjun-1993/DISCRET-ICML2024.

Ethical Implications of Artificial Intelligence in Gastroenterology: The Co-pilot or the Captain?

Though artificial intelligence (AI) is being widely implemented in gastroenterology (GI) and hepatology and has the potential to be paradigm shifting for clinical practice, its pitfalls must be considered along with its advantages. Currently, although the use of AI is limited in practice to supporting clinical judgment, medicine is rapidly heading toward a global environment where AI will be increasingly autonomous. Broader implementation of AI will require careful ethical considerations, specifically related to bias, privacy, and consent. Widespread use of AI raises concerns related to increasing rates of systematic errors, potentially due to bias introduced in training datasets. We propose that a central repository for collection and analysis for training and validation datasets is essential to overcoming potential biases. Since AI does not have built-in concepts of bias and equality, humans involved in AI development and implementation must ensure its ethical use and development. Moreover, ethical concerns regarding data ownership and health information privacy are likely to emerge, obviating traditional methods of obtaining patient consent that cover all possible uses of patient data. The question of liability in case of adverse events related to use of AI in GI must be addressed among the physician, the healthcare institution, and the AI developer. Though the future of AI in GI is very promising, herein we review the ethical considerations in need of additional guidance informed by community experience and collective expertise.

Spending Analysis of Machine Learning-Based Communication Nudges in Oncology.

BACKGROUND: Serious illness conversations (SICs) in the outpatient setting may improve mood and quality of life among patients with cancer and decrease aggressive end-of-life care. Interventions informed by behavioral economics may increase rates of SICs between oncology clinicians and patients, but the impact of these interventions on end-of-life spending is unknown. METHODS: This study is a secondary analysis of a stepped-wedge cluster randomized, controlled trial that involved nine medical oncology practices and their high-risk patients at a large academic institution between June 2019 and April 2020. The study included 1187 patients who were identified by a machine-learning algorithm as high risk of 180-day mortality and who died by December 2020. The patients were randomly assigned to standard of care (controls) or to a behavioral intervention designed to increase clinician-initiated SICs. We abstracted spending - defined as inflation-adjusted costs for acute care (inpatient plus emergency room), office/outpatient care, intravenous systemic therapy, other therapy (e.g., radiation), long-term care, and hospice - from the institution's accounting system, and we captured spending at inpatient, outpatient, and pharmacy settings. To evaluate intervention impacts on spending, we used a two-part model, first using logistic regression to model zero versus nonzero spending and second using generalized linear mixed models with gamma distribution and log-link function to model daily mean spending in the last 180days of life. Models were adjusted for clinic and wedge fixed effects, and they were clustered at the oncologist level. For all patients with at least one SIC within 6 months of death, we also calculated their mean daily spending before and after SIC. RESULTS: Median age at death was 68years (interquartile range, 15.5), 317 patients (27%) were Black or of ethnicities other than white, and 448 patients (38%) had an SIC. The intervention was associated with lower unadjusted mean daily spending in the last 6 months of life for the intervention group versus controls ($377.96 vs. $449.92; adjusted mean difference, -$75.33; 95% confidence interval, -$136.42 to -$14.23; P=0.02), translating to $13,747 total adjusted savings per decedent and $13 million in cumulative savings across all decedents in the intervention group. Compared with controls, patients in the intervention group incurred lower mean daily spending for systemic therapy (adjusted difference, -$44.59; P=0.001), office/outpatient care (-$9.62; P=0.001), and other therapy (-$8.65; P=0.04). The intervention was not associated with differences in end-of-life spending for acute care, long-term care, or hospice. Results were consistent for spending in the last 1 and 3 months of life and after adjusting for age, race, and ethnicity. For patients with SICs, mean daily spending decreased by $37.92 following the first SIC ($329.87 vs. $291.95). CONCLUSIONS: A machine learning-based, behaviorally informed intervention to prompt SICs led to end-of-life savings among patients with cancer, driven by decreased systemic therapy and outpatient spending. (Funded by the Penn Center for Precision Medicine and the National Institutes of Health; ClinicalTrials.gov number, NCT03984773.).

Clinician- and Patient-Directed Communication Strategies for Patients With Cancer at High Mortality Risk: A Cluster Randomized Trial.

Importance: Serious illness conversations (SICs) that elicit patients' values, goals, and care preferences reduce anxiety and depression and improve quality of life, but occur infrequently for patients with cancer. Behavioral economic implementation strategies (nudges) directed at clinicians and/or patients may increase SIC completion. Objective: To test the independent and combined effects of clinician and patient nudges on SIC completion. Design, Setting, and Participants: A 2 × 2 factorial, cluster randomized trial was conducted from September 7, 2021, to March 11, 2022, at oncology clinics across 4 hospitals and 6 community sites within a large academic health system in Pennsylvania and New Jersey among 163 medical and gynecologic oncology clinicians and 4450 patients with cancer at high risk of mortality (≥10% risk of 180-day mortality). Interventions: Clinician clusters and patients were independently randomized to receive usual care vs nudges, resulting in 4 arms: (1) active control, operating for 2 years prior to trial start, consisting of clinician text message reminders to complete SICs for patients at high mortality risk; (2) clinician nudge only, consisting of active control plus weekly peer comparisons of clinician-level SIC completion rates; (3) patient nudge only, consisting of active control plus a preclinic electronic communication designed to prime patients for SICs; and (4) combined clinician and patient nudges. Main Outcomes and Measures: The primary outcome was a documented SIC in the electronic health record within 6 months of a participant's first clinic visit after randomization. Analysis was performed on an intent-to-treat basis at the patient level. Results: The study accrued 4450 patients (median age, 67 years [IQR, 59-75 years]; 2352 women [52.9%]) seen by 163 clinicians, randomized to active control (n = 1004), clinician nudge (n = 1179), patient nudge (n = 997), or combined nudges (n = 1270). Overall patient-level rates of 6-month SIC completion were 11.2% for the active control arm (112 of 1004), 11.5% for the clinician nudge arm (136 of 1179), 11.5% for the patient nudge arm (115 of 997), and 14.1% for the combined nudge arm (179 of 1270). Compared with active control, the combined nudges were associated with an increase in SIC rates (ratio of hazard ratios [rHR], 1.55 [95% CI, 1.00-2.40]; P = .049), whereas the clinician nudge (HR, 0.95 [95% CI, 0.64-1.41; P = .79) and patient nudge (HR, 0.99 [95% CI, 0.73-1.33]; P = .93) were not. Conclusions and Relevance: In this cluster randomized trial, nudges combining clinician peer comparisons with patient priming questionnaires were associated with a marginal increase in documented SICs compared with an active control. Combining clinician- and patient-directed nudges may help to promote SICs in routine cancer care. Trial Registration: ClinicalTrials.gov Identifier: NCT04867850.

Hospice Administrators' and Providers' Perspectives on Providing Upstream Palliative Care: Facilitators, Barriers, and Policy Prescriptions.

Background: Among patients with serious illness, palliative care before hospice enrollment is associated with improved quality of life, reduced symptom burden, and earlier transitions to hospice. However, fewer than half of eligible patients receive specialty palliative care referrals. As most hospice clinicians and administrators have experience in specialty palliative care, several emerging programs propose engaging hospice clinicians to provide early palliative care. Objective: We sought to identify barriers and facilitators to upstream palliative care. Design: We conducted a key informant qualitative study among hospice administrators and clinicians. Setting/Subjects: We conducted semi-structured interviews with 23 hospice administrators and clinicians in eight states from March to August 2022. We identified participants using snowball and purposive sampling using states that participate in Medicare Advantage's value-based insurance design Model. Results: Respondents indicated that barriers to early palliative care included inadequate staffing and reimbursement. Hospice clinicians providing community-based palliative care can address access barriers and improve transitions to hospice. Respondents expressed desire for payer guidance in identifying eligible patients but were cautious about payers acting as direct palliative care providers. However, payers could facilitate uptake by broadening and specifying coverage of services to include goals of care conversations and symptom management. Routine referrals initiated by objective measures could potentially increase access. Conclusions: Utilizing hospice providers to provide upstream palliative care can increase access, improve outcomes, and ease the transition to hospice.

The COVID-19 Pandemic Led To A Large Decline In Physician Gross Revenue Across All Specialties In 2020.

US health care use declined during the initial phase of the COVID-19 pandemic in 2020. Although utilization is known to have recovered in 2021 and 2022, it is unknown how revenue in 2020-22 varied by physician specialty and practice setting. This study linked medical claims from a large national federation of commercial health plans to physician and practice data to estimate pandemic-associated impacts on physician revenue (defined as payments to eligible physicians) by specialty and practice characteristics. Surgical specialties, emergency medicine, and medical subspecialties each experienced a greater than 9 percent adjusted gross revenue decline in 2020 relative to prepandemic baselines. By 2022, pathology and psychiatry revenue experienced robust recovery, whereas surgical and oncology revenue remained at or below baseline. Revenue recovery in 2022 was greater for physicians practicing in hospital-owned practices and in practices participating in accountable care organizations. Pandemic-associated revenue recovery in 2021 and 2022 varied by specialty and practice type. Given that physician financial instability is associated with health care consolidation and leaving practice, policy makers should closely monitor revenue trends among physicians in specialties or practice settings with sustained gross revenue reductions during the pandemic.

Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial.

BACKGROUND: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. OBJECTIVE: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. METHODS: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. RESULTS: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. CONCLUSIONS: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-054675.

Cancer Treatment Before and After Physician-Pharmacy Integration.

Importance: Integration of pharmacies with physician practices, also known as medically integrated dispensing, is increasing in oncology. However, little is known about how this integration affects drug use, expenditures, medication adherence, or time to treatment initiation. Objective: To examine the association of physician-pharmacy integration with oral oncology drug expenditures, use, and patient-centered measures. Design, Setting, and Participants: This cohort study used claims data from a large commercial insurer in the US to analyze changes in outcome measures among patients treated by pharmacy-integrating vs nonintegrating community oncologists in 14 states between January 1, 2011, and December 31, 2019. Commercially insured patients were aged 18 to 64 years with 1 of the following advanced-stage diagnoses: breast cancer, colorectal cancer, kidney cancer, lung cancer, melanoma, or prostate cancer. Data analysis was conducted from May 2023 to March 2024. Exposure: Treatment by a pharmacy-integrating oncologist, ascertained by the presence of an on-site pharmacy or nonpharmacy dispensing site. Main Outcomes and Measures: Oral, intravenous (IV), total, and out-of-pocket drug expenditures for a 6-month episode of care; share of patients prescribed oral drugs; days' supply of oral drugs; medication adherence measured by proportion of days covered; and time to treatment initiation. The association between an oncologist's pharmacy integration and each outcome of interest was estimated using the difference-in-differences estimator. Results: Between 2012 and 2019, 3159 oncologists (745 females [27.1%], 2002 males [72.9%]) treated 23 968 patients (66.4% female; 53.4% aged 55-64 years). Of the 3159 oncologists, 578 (18.3%) worked in practices that integrated with pharmacies (with a low rate in 2011 of 0% and a high rate in 2019 of 31.5%). In the full sample (including all cancer sites), after physician-pharmacy integration, no significant changes were found in oral drug expenditures, IV drug expenditures, or total drug expenditures. There was, however, an increase in days' supply of oral drugs (5.96 days; 95% CI, 0.64-11.28 days; P = .001). There were no significant changes in out-of-pocket expenditures, medication adherence, or time to treatment initiation of oral drugs. In the breast cancer sample, there was an increase in oral drug expenditures ($244; 95% CI, $41-$446; P = .02) and a decrease in IV drug expenditures (-$4187; 95% CI, -$8293 to -$80; P = .05). Conclusions and Relevance: Results of this cohort study indicated that the integration of oncology practices with pharmacies was not associated with significant changes in expenditures or clear patient-centered benefits.

Real-world evaluation of deep learning algorithms to classify functional pathogenic germline variants.

Deep learning models for variant pathogenicity prediction can recapitulate expert-curated annotations, but their performance remains unexplored on actual disease phenotypes in a real-world setting. Here, we apply three state-of-the-art pathogenicity prediction models to classify hereditary breast cancer gene variants in the UK Biobank. Predicted pathogenic variants in BRCA1, BRCA2 and PALB2, but not ATM and CHEK2, were associated with increased breast cancer risk. We explored gene-specific score thresholds for variant pathogenicity, finding that they could improve model performance. However, when specifically tasked with classifying variants of uncertain significance, the deep learning models were generally of limited clinical utility.

Trends in low-value cancer care during the COVID-19 pandemic.

OBJECTIVE: To assess the association between the onset of the COVID-19 pandemic and change in low-value cancer services. STUDY DESIGN: In this retrospective cohort study, we used administrative claims from the HealthCore Integrated Research Environment, a repository of medical and pharmacy data from US health plans representing more than 80 million members, between January 1, 2016, and March 31, 2021. METHODS: We used linear probability models to investigate the relation between the onset of the COVID-19 pandemic and 4 guideline-based metrics of low-value cancer care: (1) conventional fractionation radiotherapy instead of hypofractionated radiotherapy for early-stage breast cancer; (2) non-guideline-based antiemetic use for minimal-, low-, or moderate- to high-risk chemotherapies; (3) off-pathway systemic therapy; and (4) aggressive end-of-life care. We identified patients with new diagnoses of breast, colorectal, and/or lung cancer. We excluded members who did not have at least 6 months of continuous insurance coverage and members with prevalent cancers. RESULTS: Among 117,116 members (median [IQR] age, 60 [53-69] years; 72.4% women), 59,729 (51.0%) had breast cancer, 25,751 (22.0%) had colorectal cancer, and 31,862 (27.2%) had lung cancer. The payer mix was 18.7% Medicare Advantage or Medicare supplemental and 81.2% commercial non-Medicare. Rates of low-value cancer services exhibited minimal changes during the pandemic, as adjusted percentage-point differences were 3.93 (95% CI, 1.50-6.36) for conventional radiotherapy, 0.82 (95% CI, -0.62 to 2.25) for off-pathway systemic therapy, -3.62 (95% CI, -4.97 to -2.27) for non-guideline-based antiemetics, and 2.71 (95% CI, -0.59 to 6.02) for aggressive end-of-life care. CONCLUSIONS: Low-value cancer care remained prevalent throughout the pandemic. Policy makers should consider changes to payment and incentive design to turn the tide against low-value cancer care.

Racial Differences in Germline Genetic Testing Completion Among Males With Pancreatic, Breast, or Metastatic Prostate Cancers.

BACKGROUND: Germline genetic testing is a vital component of guideline-recommended cancer care for males with pancreatic, breast, or metastatic prostate cancers. We sought to determine whether there were racial disparities in germline genetic testing completion in this population. PATIENTS AND METHODS: This retrospective cohort study included non-Hispanic White and Black males with incident pancreatic, breast, or metastatic prostate cancers between January 1, 2019, and September 30, 2021. Two nationwide cohorts were examined: (1) commercially insured individuals in an administrative claims database, and (2) Veterans receiving care in the Veterans Health Administration. One-year germline genetic testing rates were estimated by using Kaplan-Meier methods. Cox proportional hazards regression was used to test the association between race and genetic testing completion. Causal mediation analyses were performed to investigate whether socioeconomic variables contributed to associations between race and germline testing. RESULTS: Our cohort consisted of 7,894 males (5,142 commercially insured; 2,752 Veterans). One-year testing rates were 18.0% (95% CI, 16.8%-19.2%) in commercially insured individuals and 14.2% (95% CI, 11.5%-15.0%) in Veterans. Black race was associated with a lower hazard of testing among commercially insured individuals (adjusted hazard ratio [aHR], 0.73; 95% CI, 0.58-0.91; P=.005) but not among Veterans (aHR, 0.99; 95% CI, 0.75-1.32; P=.960). In commercially insured individuals, income (aHR, 0.90; 95% CI, 0.86-0.96) and net worth (aHR, 0.92; 95% CI, 0.86-0.98) mediated racial disparities, whereas education (aHR, 0.98; 95% CI, 0.94-1.01) did not. CONCLUSIONS: Overall rates of guideline-recommended genetic testing are low in males with pancreatic, breast, or metastatic prostate cancers. Racial disparities in genetic testing among males exist in a commercially insured population, mediated by net worth and household income; these disparities are not seen in the equal-access Veterans Health Administration. Alleviating financial and access barriers may mitigate racial disparities in genetic testing.

Applying Machine Learning Techniques to Implementation Science.

Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.

Real-world use, dose intensity, and adherence to enfortumab vedotin in locally advanced or metastatic urothelial cancer.

BACKGROUND: Enfortumab vedotin (EV) monotherapy is approved for the treatment of advanced urothelial cancer as later-line therapy (post-immunotherapy and -platinum-chemotherapy) and as earlier-line therapy (cisplatin-ineligible, at least 1 prior therapy). We examined real-world EV monotherapy use, dose intensity and adherence across 280 US cancer clinics. METHODS: This postmarketing study used data from a nationwide (United States) deidentified patient-level electronic health record-derived database. Included were patients with advanced urothelial cancer initiating EV on or after December 19, 2019 (date of accelerated approval). We summarized characteristics of EV users using descriptive statistics and computed metrics of EV use, EV dose intensity, and EV treatment adherence. RESULTS: We identified 416 advanced urothelial cancer patients initiating EV monotherapy. More than half of patients (55.3%) received EV as later-line therapy (3L+), and nearly half (44.7%) received EV as earlier line therapy (1 or 2L). Dosing frequency (mean [SD] 2.4 [0.5] treatments per 28 day cycle) and dose (1.1 [0.2] mg/kg) were lower than label indication guidelines (1.25 mg/kg, Day 1, 8, 15 of a 28 day cycle). Only 58.8% of patients received an average of >2 treatments per 28-day cycle. CONCLUSIONS: Among patients with advanced urothelial cancer treated with EV monotherapy in contemporary practice, EV dosing frequency, and dosage was lower in clinical practice than recommended in the product labeling. Further research is required to understand clinical factors and outcomes associated with the differences observed.

Uses and limitations of artificial intelligence for oncology.

Modern artificial intelligence (AI) tools built on high-dimensional patient data are reshaping oncology care, helping to improve goal-concordant care, decrease cancer mortality rates, and increase workflow efficiency and scope of care. However, data-related concerns and human biases that seep into algorithms during development and post-deployment phases affect performance in real-world settings, limiting the utility and safety of AI technology in oncology clinics. To this end, the authors review the current potential and limitations of predictive AI for cancer diagnosis and prognostication as well as of generative AI, specifically modern chatbots, which interfaces with patients and clinicians. They conclude the review with a discussion on ongoing challenges and regulatory opportunities in the field.

US Medicare Hospice and Palliative Medicine Physician Workforce and Service Delivery in 2008-2020.

CONTEXT: Despite clinical benefits of early palliative care, little is known about Medicare physician workforce specialized in Hospice and Palliative Medicine (HPM) and their service delivery settings. OBJECTIVES: To examine changes in Medicare HPM physician workforce and their service delivery settings in 2008-2020. METHODS: Using the Medicare Data on Provider Practice and Specialty from 2008 to 2020, we identified 2375 unique Medicare Fee-For-Service (FFS) physicians (15,565 physician-year observations) with self-reported specialty in "Palliative Care and Hospice". We examined changes in the annual number of HPM physicians, average number of Medicare services overall and by care setting, total number of Medicare FFS beneficiaries, and total Medicare allowed charges billed by the physician. RESULTS: The number of Medicare HPM physicians increased 2.32 times from 771 in 2008 to 1790 in 2020. The percent of HPM physicians practicing in metropolitan areas increased from 90% to 96% in 2008-2020. Faster growth was also observed in female physicians (52.4% to 60.1%). Between 2008 and 2020, we observed decreased average annual Medicare FFS beneficiaries (170 to 123), number of FFS services (467 to 335), and Medicare allowed charges billed by the physician ($47,230 to $37,323). The share of palliative care delivered in inpatient settings increased from 47% to 68% in 2008-2020; whereas the share of services delivered in outpatient settings decreased from 37% to 19%. CONCLUSION: Despite growth in Medicare HPM physician workforce, access is disproportionately concentrated in metropolitan and inpatient settings. This may limit receipt of early outpatient specialized palliative care, especially in nonmetropolitan areas.

Clinician Perspectives on Virtual Specialty Palliative Care for Patients With Advanced Illnesses.

Background: Patients with serious illnesses have unmet symptom and psychosocial needs. Specialty palliative care could address many of these needs; however, access varies by geography and health system. Virtual visits and automated referrals could increase access and lead to improved quality of life, health outcomes, and patient-centered care for patients with serious illness. Objectives: We sought to understand referring clinician perspectives on barriers and facilitators to utilizing virtual tools to increase upstream access to palliative care. Design: Participants in this multisite qualitative study included practicing clinicians who commonly place palliative care referrals across multiple specialties, including hematology/oncology, family medicine, cardiology, and geriatrics. All interviews were transcribed and subsequently coded and analyzed by trained research coordinators using Atlas.ti software. Settings/Subjects: This study included 23 clinicians (21 physicians, 2 nonphysicians) across 5 specialties, 4 practice settings, and 7 states in the United States. Results: Respondents felt that community-based specialty palliative services including symptom management, advance care planning, physical therapy, and mental health counseling would benefit their patients. However, they had mixed feelings about automated referrals, with some clinicians feeling hesitant about not being alerted to such referrals. Many respondents were supportive of virtual palliative care, particularly for those who may have difficulty accessing physician offices, but most respondents felt that such care should only be provided after an initial in-person consultation where clinicians can meet face-to-face with patients. Conclusion: Clinicians believe that automated referrals and virtual palliative care could increase access to the benefits of specialty palliative care. However, virtual palliative care models should give attention to iterative communication with primary clinicians and the perceived need for an initial in-person visit.

Patient perspectives on treatment and prevention of recurrent urinary tract infections: a focus group study.

INTRODUCTION AND HYPOTHESIS: We sought to understand factors that are important to patients for the management of recurrent urinary tract infections (UTI) during both an acute episode and for the prevention of future episodes. METHODS: This was a qualitative study with focus groups in women with recurrent UTIs. Participants filled out information about prior recurrent UTI treatment and the Belief about Medicines Questionnaire (BMQ). Each 90-minute focus group was moderated by a nonphysician psychologist. Line-by-line coding of each transcript by three independent physicians was used to develop emergent concepts and themes using Grounded Theory methodology. RESULTS: Twenty-six women participated in six focus groups. The average age of participants was 62 years and 77% were post-menopausal. All women had already tried multiple prevention strategies for their recurrent UTIs. The average BMQ-specific scores indicated a net positive attitude toward medicines specifically prescribed for recurrent UTI prevention. Several themes emerged from the focus groups. First, patients wanted providers to acknowledge the high burden imposed by frequent interactions with the health care system for the management of recurrent UTI. Second, patients wanted earlier access to providers knowledgeable in the management of this condition. Third, patients wanted to self-manage their condition through a structured treatment plan with support from their providers. Finally, patients wanted greater emphasis on education and prevention strategies to reduce their antibiotic intake. CONCLUSIONS: Patients with recurrent UTI want more efficient workflows, a framework that promotes self-management in partnership with their providers, and a greater emphasis on prevention.

Eliminating Algorithmic Racial Bias in Clinical Decision Support Algorithms: Use Cases from the Veterans Health Administration.

The Veterans Health Administration uses equity- and evidence-based principles to examine, correct, and eliminate use of potentially biased clinical equations and predictive models. We discuss the processes, successes, challenges, and next steps in four examples. We detail elimination of the race modifier for estimated kidney function and discuss steps to achieve more equitable pulmonary function testing measurement. We detail the use of equity lenses in two predictive clinical modeling tools: Stratification Tool for Opioid Risk Mitigation (STORM) and Care Assessment Need (CAN) predictive models. We conclude with consideration of ways to advance racial health equity in clinical decision support algorithms.