Screening Health-Related Social Needs in Hospitals: A Systematic Review of Health Care Professional and Patient Perspectives.

Health outcomes are markedly influenced by health-related social needs (HRSN) such as food insecurity and housing instability. Under new Joint Commission requirements, hospitals have recently increased attention to HRSN to reduce health disparities. To evaluate prevailing attitudes and guide hospital efforts, the authors conducted a systematic review to describe patients' and health care providers' perceptions related to screening for and addressing patients' HRSN in US hospitals. Articles were identified through PubMed and by expert recommendations, and synthesized by relevance of findings and basic study characteristics. The review included 22 articles, which showed that most health care providers believed that unmet social needs impact health and that screening for HRSN should be a standard part of hospital care. Notable differences existed between perceived importance of HRSN and actual screening rates, however. Patients reported high receptiveness to screening in hospital encounters, but cautioned to avoid stigmatization and protect privacy when screening. Limited knowledge of resources available, lack of time, and lack of actual resources were the most frequently reported barriers to screening for HRSN. Hospital efforts to screen and address HRSN will likely be facilitated by stakeholders' positive perceptions, but common barriers to screening and referral will need to be addressed to effectively scale up efforts and impact health disparities.

Artificial Intelligence and Machine Learning Technologies in Cancer Care: Addressing Disparities, Bias, and Data Diversity.

SUMMARY: Artificial intelligence (AI) and machine learning (ML) technologies have not only tremendous potential to augment clinical decision-making and enhance quality care and precision medicine efforts, but also the potential to worsen existing health disparities without a thoughtful, transparent, and inclusive approach that includes addressing bias in their design and implementation along the cancer discovery and care continuum. We discuss applications of AI/ML tools in cancer and provide recommendations for addressing and mitigating potential bias with AI and ML technologies while promoting cancer health equity.

Accuracy of an Artificial Intelligence System for Cancer Clinical Trial Eligibility Screening: Retrospective Pilot Study.

BACKGROUND: Screening patients for eligibility for clinical trials is labor intensive. It requires abstraction of data elements from multiple components of the longitudinal health record and matching them to inclusion and exclusion criteria for each trial. Artificial intelligence (AI) systems have been developed to improve the efficiency and accuracy of this process. OBJECTIVE: This study aims to evaluate the ability of an AI clinical decision support system (CDSS) to identify eligible patients for a set of clinical trials. METHODS: This study included the deidentified data from a cohort of patients with breast cancer seen at the medical oncology clinic of an academic medical center between May and July 2017 and assessed patient eligibility for 4 breast cancer clinical trials. CDSS eligibility screening performance was validated against manual screening. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for eligibility determinations were calculated. Disagreements between manual screeners and the CDSS were examined to identify sources of discrepancies. Interrater reliability between manual reviewers was analyzed using Cohen (pairwise) and Fleiss (three-way) κ, and the significance of differences was determined by Wilcoxon signed-rank test. RESULTS: In total, 318 patients with breast cancer were included. Interrater reliability for manual screening ranged from 0.60-0.77, indicating substantial agreement. The overall accuracy of breast cancer trial eligibility determinations by the CDSS was 87.6%. CDSS sensitivity was 81.1% and specificity was 89%. CONCLUSIONS: The AI CDSS in this study demonstrated accuracy, sensitivity, and specificity of greater than 80% in determining the eligibility of patients for breast cancer clinical trials. CDSSs can accurately exclude ineligible patients for clinical trials and offer the potential to increase screening efficiency and accuracy. Additional research is needed to explore whether increased efficiency in screening and trial matching translates to improvements in trial enrollment, accruals, feasibility assessments, and cost.

Variations in breast cancer surgical treatment and timing: determinants and disparities.

PURPOSE: To describe clinical and non-clinical factors associated with receipt of breast conserving surgery (BCS) versus mastectomy and time to surgical intervention. METHODS: Cross-sectional retrospective study of January 1, 2012 through March 31, 2018 data from the IBM MarketScan Commercial Claims and Encounter and Medicare Supplemental Databases. Area Health Resource Files provided non-clinical characteristics and sociodemographic data. Eligibility: Female sex, claim(s) with ICD-9-CM or ICD-10-CM diagnosis of non-metastatic invasive breast cancer, > 6 months of continuous insurance pre- and post-diagnosis, evidence of BCS or mastectomy following initial ICD9/10 code diagnosis. Logistic and quantile multivariable regression models assessed the association between clinical and non-clinical factors and the outcome of BCS and time to surgery, respectively. RESULTS: A total of 53,060 women were included in the study. Compared to mastectomy, BCS was significantly associated with older age (ORs: 1.54 to 2.99, 95% CIs 1.45 to 3.38; ps < .0001) and higher community density of medical genetics (OR: 5.88, 95% CIs 1.38 to 25.00; p = 0.02) or obstetrics and gynecology (OR: 1.13, 95% CI 1.02 to 1.25; p = .02) physicians. Shorter time-to-BCS was associated with living in the South (-2.96, 95% CI -4.39 to -1.33; p < .0001). Longer time-to-BCS was associated with residence in more urban (4.18, 95% CI 0.08 to 8.29; p = 0. 05), educated (9.02, 95% CI 0.13 to 17.91; p = 0.05), or plastic-surgeon-dense (4.62, 95% CI 0.50 to 8.73; p = 0.03) communities. CONCLUSIONS: Clinical and non-clinical factors are associated with adoption of BCS and time to treatment, suggesting opportunities to ensure equitable and timely care.

Comparison of an oncology clinical decision-support system's recommendations with actual treatment decisions.

OBJECTIVE: IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice. METHODS: This study included patients treated between 1/2017 and 7/2018 for breast, colon, lung, and rectal cancers at Bumrungrad International Hospital (BIH), Thailand. Treatments selected by clinicians were paired with therapeutic options presented by the CDSS and coded to mask the origin of options presented. The panel rated the acceptability of each treatment in the pair by consensus, with acceptability defined as compliant with BIH's institutional practices. Descriptive statistics characterized the study population and treatment-decision evaluations by cancer type and stage. RESULTS: Nearly 60% (187) of 313 treatment pairs for breast, lung, colon, and rectal cancers were identical or equally acceptable, with 70% (219) of WfO therapeutic options identical to, or acceptable alternatives to, BIH therapy. In 30% of cases (94), 1 or both treatment options were rated as unacceptable. Of 32 cases where both WfO and BIH options were acceptable, WfO was preferred in 18 cases and BIH in 14 cases. Colorectal cancers exhibited the highest proportion of identical or equally acceptable treatments; stage IV cancers demonstrated the lowest. CONCLUSION: This study demonstrates that a system designed in the US to support, rather than replace, cancer-treating clinicians provides therapeutic options which are generally consistent with recommendations from oncologists outside the US.

Clinical Decision Support for High-Risk Stage II Colon Cancer: A Real-World Study of Treatment Concordance and Survival.

BACKGROUND: Prognostic and pathologic risk factors typically guide clinicians and patients in their choice of surveillance or adjuvant chemotherapy when managing high-risk stage II colon cancer. However, variations in treatment and outcomes in patients with stage II colon cancer remain. OBJECTIVE: This study aimed to assess the survival benefits of treatments concordant with suggested therapeutic options from Watson for Oncology, a clinical decision support system. DESIGN: This is a retrospective observational study of concordance between actual treatment and Watson for Oncology therapeutic options. SETTING: This study was conducted at a top-tier cancer center in China. PATIENTS: Postoperative treatment data were retrieved from the electronic health records of 306 patients with high-risk stage II colon adenocarcinoma. MAIN OUTCOME MEASURES: The primary outcomes measured were the treatment patterns plus 3- and 5-year overall and disease-free survival for concordant and nonconcordant cases. RESULTS: Overall concordance was 90%. Most nonconcordant care resulted from adjuvant chemotherapy use (rather than surveillance) in patients with high-level microsatellite instability and ≥70 years old. No difference in overall survival (p = 0.56) or disease-free survival (p = 0.19) was observed between concordance groups. Patients receiving adjuvant chemotherapy had significantly higher 5-year overall survival than those undergoing surveillance (94% vs 84%, p = 0.01). LIMITATIONS: This study was limited by the use of retrospective cases drawn from patients presenting for surgery, the lack of complete follow-up data for 58% of patients who could not be included in the analysis, and a survival analysis that assumes no unmeasured correlation between survival and censoring. CONCLUSIONS: Watson for Oncology produced therapeutic options highly concordant with human decisions at a top-tier cancer center in China. Treatment patterns suggest that Watson for Oncology may be able to guide clinicians to minimize overtreatment of patients with high-risk stage II colon cancer with chemotherapy. Survival analyses suggest the need for further investigation to specifically assess the association between surveillance, single-agent and multiagent chemotherapy, and survival outcomes in this population. See Video Abstract at http://links.lww.com/DCR/B291. APOYO A LA DECISIÓN CLÍNICA DEL CÁNCER DE COLON EN ESTADIO II DE ALTO RIESGO: UN ESTUDIO DEL MUNDO REAL SOBRE LA CONCORDANCIA DEL TRATAMIENTO Y LA SUPERVIVENCIA: Los factores de riesgo pronósticos y patológicos generalmente guían a los médicos y pacientes en su elección de vigilancia o quimioterapia adyuvante cuando se trata el cáncer de colon en estadio II de alto riesgo. Sin embargo, las variaciones en el tratamiento y los resultados en pacientes con cáncer de colon en estadio II permanecen.Evaluar los beneficios de supervivencia de los tratamientos concordantes con las opciones terapéuticas sugeridas por "Watson for Oncology" (Watson para la oncología), un sistema de apoyo a la decisión clínica.Estudio observacional retrospectivo de concordancia entre el tratamiento real y las opciones terapéuticas de Watson para oncología.Un centro oncológico de primer nivel en China.Datos de tratamiento postoperatorio de registros de salud electrónicos de 306 pacientes con adenocarcinoma de colon en estadio II de alto riesgo.Patrones de tratamiento más supervivencia global y libre de enfermedad a 3 y 5 años para casos concordantes y no concordantes.La concordancia general fue del 90%. La mayoría de la atención no concordante resultó del uso de quimioterapia adyuvante (en lugar de vigilancia) en pacientes de alto nivel con inestabilidad de microsatélites y pacientes ≥70 años. No se observaron diferencias en la supervivencia global (p = 0,56) o la supervivencia libre de enfermedad (p = 0,19) entre los grupos de concordancia. Los pacientes que recibieron quimioterapia adyuvante tuvieron una supervivencia global a los 5 años significativamente más alta que los que fueron sometidos a vigilancia (94% frente a 84%, p = 0,01).Uso de casos retrospectivos extraídos de pacientes que se presentan para cirugía, falta de datos de seguimiento completos para el 58% de los pacientes que no pudieron ser incluidos en el análisis, y análisis de supervivencia que asume que no exite una correlación no medida entre supervivencia y censura.Watson para Oncología produjo opciones terapéuticas altamente concordantes con las decisiones humanas en un centro oncológico de primer nivel en China. Los patrones de tratamiento sugieren que Watson para Oncología puede guiar a los médicos para minimizar el sobretratamiento de pacientes con cáncer de colon en estadio II de alto riesgo con quimioterapia. Los análisis de supervivencia sugieren la necesidad de realizar mas investigaciónes para evaluar específicamente la asociación entre la vigilancia, la quimioterapia con uno solo o múltiples agentes y los resultados de supervivencia en esta población. Consulte Video Resumen en http://links.lww.com/DCR/B291. (Traducción-Dr. Gonzalo Hagerman).

Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients.

OBJECTIVE: The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital. METHODS: A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I-III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility. RESULTS: The study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53-100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243-4132). Median time for the system to run a query and return results was 15.5 s (range 7.2-37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen's kappa 0.70-1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%. DISCUSSION AND CONCLUSION: The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment.

Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center.

PURPOSE: Less than 5% of patients with cancer enroll in clinical trials, and 1 in 5 trials are stopped for poor accrual. We evaluated an automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials. PATIENTS AND METHODS: Medical records from 997 patients with breast cancer were assessed for trial eligibility at Highlands Oncology Group between May and August 2016. System and manual attribute extraction and eligibility determinations were compared using the percentage of agreement for 239 patients and 4 trials. Sensitivity and specificity of system-generated eligibility determinations were measured, and the time required for manual review and system-assisted eligibility determinations were compared. RESULTS: Agreement between system and manual attribute extraction ranged from 64.3% to 94.0%. Agreement between system and manual eligibility determinations was 81%-96%. System eligibility determinations demonstrated specificities between 76% and 99%, with sensitivities between 91% and 95% for 3 trials and 46.7% for the 4th. Manual eligibility screening of 90 patients for 3 trials took 110 minutes; system-assisted eligibility determinations of the same patients for the same trials required 24 minutes. CONCLUSION: In this study, the clinical trial matching system displayed a promising performance in screening patients with breast cancer for trial eligibility. System-assisted trial eligibility determinations were substantially faster than manual review, and the system reliably excluded ineligible patients for all trials and identified eligible patients for most trials.

Setting a National Agenda for Surgical Disparities Research: Recommendations From the National Institutes of Health and American College of Surgeons Summit.

Health care disparities (differential access, care, and outcomes owing to factors such as race/ethnicity) are widely established. Compared with other groups, African American individuals have an increased mortality risk across multiple surgical procedures. Gender, sexual orientation, age, and geographic disparities are also well documented. Further research is needed to mitigate these inequities. To do so, the American College of Surgeons and the National Institutes of Health-National Institute of Minority Health and Disparities convened a research summit to develop a national surgical disparities research agenda and funding priorities. Sixty leading researchers and clinicians gathered in May 2015 for a 2-day summit. First, literature on surgical disparities was presented within 5 themes: (1) clinician, (2) patient, (3) systemic/access, (4) clinical quality, and (5) postoperative care and rehabilitation-related factors. These themes were identified via an exhaustive preconference literature review and guided the summit and its interactive consensus-building exercises. After individual thematic presentations, attendees contributed research priorities for each theme. Suggestions were collated, refined, and prioritized during the latter half of the summit. Breakout sessions yielded 3 to 5 top research priorities by theme. Overall priorities, regardless of theme, included improving patient-clinician communication, fostering engagement and community outreach by using technology, improving care at facilities with a higher proportion of minority patients, evaluating the longer-term effect of acute intervention and rehabilitation support, and improving patient centeredness by identifying expectations for recovery. The National Institutes of Health and American College of Surgeons Summit on Surgical Disparities Research succeeded in identifying a comprehensive research agenda. Future research and funding priorities should prioritize patients' care perspectives, workforce diversification and training, and systematic evaluation of health technologies to reduce surgical disparities.