Derivation of a Unique, Algorithm-Based Approach to Cancer Patient Navigator Workload Management.

PURPOSE: Cancer patient navigators (CPNs) can decrease the time from diagnosis to treatment, but workloads vary widely, which may lead to burnout and less optimal navigation. Current practice for patient distribution among CPNs at our institution approximates random distribution. A literature search did not uncover previous reports of an automated algorithm to distribute patients to CPNs. We sought to develop an automated algorithm to fairly distribute new patients among CPNs specializing in the same cancer type(s) and assess its performance through simulation on a retrospective data set. METHODS: Using a 3-year data set, a proxy for CPN work was identified and multiple models were developed to predict the upcoming week's workload for each patient. An XGBoost-based predictor was retained on the basis of its superior performance. A distribution model was developed to fairly distribute new patients among CPNs within a specialty on the basis of predicted work needed. The predicted work included the week's predicted workload from a CPN's existing patients plus that of newly distributed patients to the CPN. Resulting workload unfairness was compared between predictor-informed and random distribution. RESULTS: Predictor-informed distribution significantly outperformed random distribution for equalizing weekly workloads across CPNs within a specialty. CONCLUSION: This derivation work demonstrates the feasibility of an automated model to distribute new patients more fairly than random assignment (with unfairness assessed using a workload proxy). Improved workload management may help reduce CPN burnout and improve navigation assistance for patients with cancer.

Novel Techniques to Assess Predictive Systems and Reduce Their Alarm Burden.

Machine prediction algorithms (e.g., binary classifiers) often are adopted on the basis of claimed performance using classic metrics such as precision and recall. However, classifier performance depends heavily upon the context (workflow) in which the classifier operates. Classic metrics do not reflect the realized performance of a predictor unless certain implicit assumptions are met, and these assumptions cannot be met in many common clinical scenarios. This often results in suboptimal implementations and in disappointment when expected outcomes are not achieved. One common failure mode for classic metrics arises when multiple predictions can be made for the same event, particularly when redundant true positive predictions produce little additional value. This describes many clinical alerting systems. We explain why classic metrics cannot correctly represent predictor performance in such contexts, and introduce an improved performance assessment technique using utility functions to score predictions based on their utility in a specific workflow context. The resulting utility metrics (u-metrics) explicitly account for the effects of temporal relationships and other sources of variability in prediction utility. Compared to traditional measures, u-metrics more accurately reflect the real-world costs and benefits of a predictor operating in a realized context. The improvement can be significant. We also describe a formal approach to snoozing, a mitigation strategy in which some predictions are suppressed to improve predictor performance by reducing false positives while retaining event capture. Snoozing is especially useful for predictors that generate interruptive alarms. U-metrics correctly measure and predict the performance benefits of snoozing, whereas traditional metrics do not.

Closing the Gap: A Comparison of Engagement Interventions to Achieve Equitable Breast Cancer Screening in Rural Illinois.

Mammography screening rates are typically lower in those with less economic advantage (EA). This study, conducted at an integrated health care system covering a mixed rurality population, assessed the ability of interventions (text messages linking to a Web microsite, digital health care workers, and a community health fair) to affect mammography screening rates and disparity in those rates among different EA populations. Payor type served as a proxy for greater (commercially insured) versus lower (Medicaid insured) EA. 4,342 subjects were included across the preintervention ("Pre") and postintervention ("Post") periods. Interventions were prospectively applied to all Medicaid subjects and randomly selected commercial subjects. Applying interventions only to lower EA subjects reversed the screening rate disparity (2.6% Pre vs. -3.7% Post, odds ratio [OR] 2.4 P < 0.01). When intervention arms ("Least," "More," "Most") were equally applied, screening rates in both EA groups significantly increased in the More arm (Medicaid OR = 2.04 P = 0.04, Commercial OR = 3.08 P < 0.01) and Most arm (Medicaid OR 2.57 P < 0.01, Commercial OR 2.33 P < 0.01), but not in the Least (text-only) arm (Medicaid OR 1.83 P = 0.11, Commercial OR 1.72 P = 0.09), although this text-only arm was inadequately powered to detect a difference. In summary, targeting interventions to those with lower EA reversed screening rate disparities, text messaging combined with other interventions improved screening rates in both groups, and future research is needed to determine whether interventions can simultaneously improve screening rates for all without worsening the disparity.

Delphi consensus on the feasibility of translating the ACEP clinical policies into computerized clinical decision support.

Clinical practice guidelines are developed to reduce variations in clinical practice, with the goal of improving health care quality and cost. However, evidence-based practice guidelines face barriers to dissemination, implementation, usability, integration into practice, and use. The American College of Emergency Physicians (ACEP) clinical policies have been shown to be safe and effective and are even cited by other specialties. In spite of the benefits of the ACEP clinical policies, implementation of these clinical practice guidelines into physician practice continues to be a challenge. Translation of the ACEP clinical policies into real-time computerized clinical decision support systems could help address these barriers and improve clinician decision making at the point of care. The investigators convened an emergency medicine informatics expert panel and used a Delphi consensus process to assess the feasibility of translating the current ACEP clinical policies into clinical decision support content. This resulting consensus document will serve to identify limitations to implementation of the existing ACEP Clinical Policies so that future clinical practice guideline development will consider implementation into clinical decision support at all stages of guideline development.

Indistinguishable from magic: health and wellness in a future of sufficiently advanced technology.

We describe a future in which health and wellness are transformed by (1) the availability of definitive and unambiguous tests to prove or disprove each diagnosis, (2) new methods based in systems biology to help unravel the web of messages transmitted across cellular and subcellular networks, and (3) universal access to data that has been freed from data silos to produce true data liquidity for a constellation of purposes ranging from personal health management to population health research. We believe the resulting "connected health" environment will have a profound impact on every aspect of modern life.

Cancer-associated neutropenic fever: clinical outcome and economic costs of emergency department care.

Purpose. Febrile neutropenia (FN) is a common, costly, and potentially fatal complication in oncology. While FN in the inpatient setting has been extensively studied, only one study has evaluated emergency department (ED) care for FN cancer patients. That study found that 96% of patients survived the complication. We evaluated clinical and economic outcomes for cancer patients with chemotherapy-associated FN treated in an ED. Methods. ED records for consecutive oncology patients with FN were reviewed for information on death, intensive care unit (ICU) use, blood cultures, and costs. Results. Forty-eight patients (n = 57 visits) were evaluated. Six patients died from FN (12%) and four received ICU care within 2 weeks and survived (8%). Blood cultures were positive for 37% of the ED visits. The median ED time was 3.3 hours. In 91% of visits, i.v. antibiotics were administered in the ED, ordered at a median of 1.7 hours from triage (interquartile range [IQR], 1.2-2.8 hours). All patients with death or ICU in 2 weeks and all but one patient with positive blood cultures received antibiotics. The median per patient ED costs were $1,455 (IQR, $1,300-$1,579)-42.4% for hospital/nursing, 23.5% for radiology, 20.8% for physician services, 10.9% for diagnostic tests, and 2.4% for antibiotics. Conclusions. Cancer patients with FN in this sample presenting to the ED frequently had no identified source of infection. One third of the patients had positive ED blood cultures and one fifth died or required ICU care within 2 weeks. Costs of ED care were similar to the cost of a single day of inpatient care. Disclosure of potential conflicts of interest is found at the end of this article.

Metrics in the science of surge.

Metrics are the driver to positive change toward better patient care. However, the research into the metrics of the science of surge is incomplete, research funding is inadequate, and we lack a criterion standard metric for identifying and quantifying surge capacity. Therefore, a consensus working group was formed through a "viral invitation" process. With a combination of online discussion through a group e-mail list and in-person discussion at a breakout session of the Academic Emergency Medicine 2006 Consensus Conference, "The Science of Surge," seven consensus statements were generated. These statements emphasize the importance of funded research in the area of surge capacity metrics; the utility of an emergency medicine research registry; the need to make the data available to clinicians, administrators, public health officials, and internal and external systems; the importance of real-time data, data standards, and electronic transmission; seamless integration of data capture into the care process; the value of having data available from a single point of access through which data mining, forecasting, and modeling can be performed; and the basic necessity of a criterion standard metric for quantifying surge capacity. Further consensus work is needed to select a criterion standard metric for quantifying surge capacity. These consensus statements cover the future research needs, the infrastructure needs, and the data that are needed for a state-of-the-art approach to surge and surge capacity.

Keynote address: medical informatics and emergency medicine.

A personal look at some of the developments in practical clinical informatics over the past two decades, with discussion of several successful projects, including the National Center for Emergency Medicine Informatics, the Azyxxi system, Federal Project ER One, the Institutes for Innovation in Medicine, the Medical MediaLab, Project Sentinel, and others. Lessons learned, and hints and suggestions for future developers and informaticists.

Computerized physician order entry and online decision support.

Computerized physician order entry (CPOE) and decision support systems (DSS) can reduce certain types of error but often slow clinicians and may increase other types of error. The net effect of these systems on an emergency department (ED) is unknown. The consensus participants combined published evidence with expert opinion to outline recommendations for success. These include seamless integration of CPOE and DSS into systems and workflow; ensuring access to Internet-based and other online support material in the clinical arena; designing systems specifically for the ED and measuring their impact to ensure an overall benefit; ensuring that CPOE systems provide error and interaction checking and facilitate weight- and physiology-based dosing; using interruptive alerts only for the highest-severity events; providing a simple, vendor-independent interface for institutional customization of CPOE alert thresholds; maximizing the use of automated systems and passive data capture; and ensuring the widespread availability of CPOE and DSS using secure wireless and portable technologies where appropriate. Decisions regarding CPOE and DSS in the ED should be guided by the ED chair or designee. Much of what is believed to be true regarding CPOE and DSS has not been adequately studied. Additional CPOE and DSS research is needed quickly, and this research should receive funding priority. DSS and CPOE hold great promise to improve patient care, but not all systems are equal. Evidence must guide these efforts, and the measured outcomes must consider the many factors of quality care.

Clinical information systems: instant ubiquitous clinical data for error reduction and improved clinical outcomes.

Immediate access to existing clinical information is inadequate in current medical practice; lack of existing information causes or contributes to many classes of medical error, including diagnostic and treatment error. A review of the literature finds ample evidence to support a description of the problems caused by data that are missing or unavailable but little evidence to support one proposed solution over another. A primary recommendation of the Consensus Committee is that hospitals and departments should adopt systems that provide fast, ubiquitous, and unified access to all types of existing data. Additional recommendations cover a variety of related functions and operational concepts, from backups and biosurveillance to speed, training, and usability.

Clinical predictors of bioterrorism-related inhalational anthrax.

Limitation of a bioterrorist anthrax attack will require rapid and accurate recognition of the earliest victims. To identify clinical characteristics of inhalational anthrax, we compared 47 historical cases (including 11 cases of bioterrorism-related anthrax) with 376 controls with community-acquired pneumonia or influenza-like illness. Nausea, vomiting, pallor or cyanosis, diaphoresis, altered mental status, and raised haematocrit were more frequently recorded in the inhalational anthrax cases than in either the community-acquired pneumonia or influenza-like illness controls. The most accurate predictor of anthrax was mediastinal widening or pleural effusion on a chest radiograph. This finding was 100% sensitive (95% CI 84.6-100.0) for inhalational anthrax, 71.8% specific (64.8-78.1) compared with community-acquired pneumonia, and 95.6% specific (90.0-98.5) compared with influenza-like illness. Our findings represent preliminary efforts toward identifying clinical predictors of inhalational anthrax.