Conformal Predictive Safety Filter for RL Controllers in Dynamic Environments

The interest in using reinforcement learning (RL) controllers in safety-critical applications such as robot navigation around pedestrians motivates the development of additional safety mechanisms. Running RL-enabled systems among uncertain dynamic agents may result in high counts of collisions and failures to reach the goal. The system could be safer if the pre-trained RL policy was uncertainty-informed. For that reason, we propose conformal predictive safety filters that: 1) predict the other agents' trajectories, 2) use statistical techniques to provide uncertainty intervals around these predictions, and 3) learn an additional safety filter that closely follows the RL controller but avoids the uncertainty intervals. We use conformal prediction to learn uncertainty-informed predictive safety filters, which make no assumptions about the agents' distribution. The framework is modular and outperforms the existing controllers in simulation. We demonstrate our approach with multiple experiments in a collision avoidance gym environment and show that our approach minimizes the number of collisions without making overly-conservative predictions

Recycling controllers

The problem of designing control schemes for teams of robots to satisfy complex high-level tasks is a challenging problem which becomes more difficult when adding constraints on relative locations of robots. This paper presents a method for automatically creating hybrid controllers that ensure a team of heterogeneous robots satisfy some user specified high-level task while guaranteeing collision avoidance and predicting and reducing deadlock. The generated hybrid controller composes atomic controllers based on information the robots gather during runtime; thus these atomic controllers can be reused in different scenarios for multiple tasks. As a demonstration of this general approach we examine a task in which a group of robots sort different items to be recycled

Comparing the implementation of team approaches for improving diabetes care in community health centers

Background: Patient panel management and community-based care management may be viable strategies for community health centers to improve the quality of diabetes care for vulnerable patient populations. The objective of our study was to clarify implementation processes and experiences of integrating office-based medical assistant (MA) panel management and community health worker (CHW) community-based management into routine care for diabetic patients. Methods: Mixed methods study with interviews and surveys of clinicians and staff participating in a study comparing the effectiveness of MA and CHW health coaching for improving diabetes care. Participants included 24 key informants in five role categories and 249 clinicians and staff survey respondents from 14 participating practices. We conducted thematic analyses of key informant interview transcripts to clarify implementation processes and describe barriers to integrating the new roles into practice. We surveyed clinicians and staff to assess differences in practice culture among intervention and control groups. We triangulated findings to identify concordant and disparate results across data sources. Results: Implementation processes and experiences varied considerably among the practices implementing CHW and MA team-based approaches, resulting in differences in the organization of health coaching and self-management support activities. Importantly, CHW and MA responsibilities converged over time to focus on health coaching of diabetic patients. MA health coaches experienced difficulty in allocating dedicated time due to other MA responsibilities that often crowded out time for diabetic patient health coaching. Time constraints also limited the personal introduction of patients to health coaches by clinicians. Participants highlighted the importance of a supportive team climate and proactive leadership as important enablers for MAs and CHWs to implement their health coaching responsibilities and also promoted professional growth. Conclusion: Implementation of team-based strategies to improve diabetes care for vulnerable populations was diverse, however all practices converged in their foci on health coaching roles of CHWs and MAs. Our study suggests that a flexible approach to implementing health coaching is more important than fidelity to rigid models that do not allow for variable allocation of responsibilities across team members. Clinicians play an instrumental role in supporting health coaches to grow into their new patient care responsibilities

Factors contributing to disparities in mortality among patients with non-small-cell lung cancer

Historically, non-small-cell lung cancer (NSCLC) patients who are non-white, have low incomes, low educational attainment, and non-private insurance have worse survival. We assessed whether differences in survival were attributable to sociodemographic factors, clinical characteristics at diagnosis, or treatments received. We surveyed a multiregional cohort of patients diagnosed with NSCLC from 2003 to 2005 and followed through 2012. We used Cox proportional hazard analyses to estimate the risk of death associated with race/ethnicity, annual income, educational attainment, and insurance status, unadjusted and sequentially adjusting for sociodemographic factors, clinical characteristics, and receipt of surgery, chemotherapy, and radiotherapy. Of 3250 patients, 64% were white, 16% black, 7% Hispanic, and 7% Asian; 36% of patients had income

Factors contributing to disparities in mortality among patients with non–small‐cell lung cancer

Historically, non–small‐cell lung cancer (NSCLC) patients who are non‐white, have low incomes, low educational attainment, and non‐private insurance have worse survival. We assessed whether differences in survival were attributable to sociodemographic factors, clinical characteristics at diagnosis, or treatments received. We surveyed a multiregional cohort of patients diagnosed with NSCLC from 2003 to 2005 and followed through 2012. We used Cox proportional hazard analyses to estimate the risk of death associated with race/ethnicity, annual income, educational attainment, and insurance status, unadjusted and sequentially adjusting for sociodemographic factors, clinical characteristics, and receipt of surgery, chemotherapy, and radiotherapy. Of 3250 patients, 64% were white, 16% black, 7% Hispanic, and 7% Asian; 36% of patients had incomes <$20 000/y; 23$60 000/y, not attending college, and not having private insurance were all associated with an increased risk of mortality. Black‐white differences were not statistically significant after adjustment for sociodemographic factors, although patients with patients without a high school diploma and patients with incomes <$40 000/y continued to have an increased risk of mortality. Differences by educational attainment were not statistically significant after adjustment for clinical characteristics. Differences by income were not statistically significant after adjustment for clinical characteristics and treatments. Clinical characteristics and treatments received primarily contributed to mortality disparities by race/ethnicity and socioeconomic status in patients with NSCLC. Additional efforts are needed to assure timely diagnosis and use of effective treatment to lessen these disparities.Using data from the Cancer Care Outcomes Research and Surveillance (CanCORS) consortium, a large, multi‐regional observational study of newly diagnosed cancer patients, we documented higher unadjusted mortality for NSCLC among patients who were black, have lower income, less well‐educated, and with non‐private insurance. We used a series of Cox proportional hazards model to estimate the increased risk of death associated with sociodemographic factors, clinical characteristics, and treatments received to determine what accounted for the disparities. We found that patients’ clinical characteristics and treatments received primarily contributed to the mortality disparities that we observed in patients with NSCLC.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146607/1/cam41796.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146607/2/cam41796_am.pd

Predictors of health-related quality of life in patients with colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Most studies that have identified variables associated with the health-related quality of life (HRQL) of patients with colorectal cancer have been cross-sectional or included patients with other diagnoses. The objectives of this study were to identify predictors of HRQL in patients with colorectal cancer and interpret the clinical importance of the results.</p> <p>Methods</p> <p>We conducted a population-based longitudinal study of patients identified through three regions of the California Cancer Registry. Surveys were completed by 568 patients approximately 9 and 19 months post-diagnosis. Three HRQL outcomes from the Functional Assessment of Cancer Therapy – Colorectal (FACT-C) were evaluated: social/family well-being (SWB), emotional well-being (EWB) and the Trial Outcome Index (TOI), which is a colorectal cancer-specific summary measure of physical function and well-being. Sociodemographic, cancer/health, and healthcare variables were assessed in multivariable regression models. We computed the difference in predicted HRQL scores corresponding to a large difference in a predictor variable, defined as a 1 standard deviation difference for interval variables or the difference relative to the reference category for nominal variables. The effect of an explanatory variable on HRQL was considered clinically meaningful if the predicted score difference was at least as large as the minimally important difference.</p> <p>Results</p> <p>Common predictors of better TOI, SWB and EWB were better general health and factors related to better perceived quality of cancer care. Predictor variables in addition to general health and perceived quality of care were identified only for SWB. Being married/living as married was associated with better SWB, whereas being male or of Hispanic ethnicity was associated with worse SWB. Among the sociodemographic, cancer/health, and healthcare variables evaluated, only Hispanic ethnicity had a clinically meaningful effect on an HRQL outcome.</p> <p>Conclusion</p> <p>Our findings, particularly the information on the clinical importance of predictor variables, can help clinicians identify patients who may be at risk for poor future HRQL. Potentially modifiable factors were related to perceived quality of cancer care; thus, future research should evaluate whether improving these factors improves HRQL.</p

Decision-making in percutaneous coronary intervention: a survey

<p>Abstract</p> <p>Background</p> <p>Few researchers have examined the perceptions of physicians referring cases for angiography regarding the degree to which collaboration occurs during percutaneous coronary intervention (PCI) decision-making. We sought to determine perceptions of physicians concerning their involvement in PCI decisions in cases they had referred to the cardiac catheterization laboratory at a major academic medical center.</p> <p>Methods</p> <p>An anonymous survey was mailed to internal medicine faculty members at a major academic medical center. The survey elicited whether responders perceived that they were included in decision-making regarding PCI, and whether they considered such collaboration to be the best process of decision-making.</p> <p>Results</p> <p>Of the 378 surveys mailed, 35% (133) were returned. Among responding non-cardiologists, 89% indicated that in most cases, PCI decisions were made solely by the interventionalist at the time of the angiogram. Among cardiologists, 92% indicated that they discussed the findings with the interventionalist prior to any PCI decisions. When asked what they considered the best process by which PCI decisions are made, 66% of non-cardiologists answered that they would prefer collaboration between either themselves or a non-interventional cardiologist and the interventionalist. Among cardiologists, 95% agreed that a collaborative approach is best.</p> <p>Conclusion</p> <p>Both non-cardiologists and cardiologists felt that involving another decision-maker, either the referring physician or a non-interventional cardiologist, would be the best way to make PCI decisions. Among cardiologists, there was more concordance between what they believed was the best process for making decisions regarding PCI and what they perceived to be the actual process.</p

Prostate Cancer and Race

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72215/1/j.1525-1497.2003.30801.x.pd

Principal inpatient diagnostic cost group model for Medicare risk adjustment

The Balanced Budget Act (BBA) of 1997 required HCFA to implement health-status-based risk adjustment for Medicare capitation payments for managed care plans by January 1, 2000. In support of this mandate, HCFA has been collecting inpatient encounter data from health plans since 1997. These data include diagnoses and other information that can be used to identify chronic medical problems that contribute to higher costs, so that health plans can be paid more when they care for sicker patients. In this article, the authors describe the risk-adjustment model HCFA is implementing in the year 2000, known as the Principal Inpatient Diagnostic Cost Group (PIPDCG) model