Frequent temporal patterns of physiological and biological biomarkers and their evolution in sepsis.

Sepsis is one of the most challenging health conditions worldwide, with relatively high incidence and mortality rates. It is shown that preventing sepsis is the key to avoid potentially irreversible organ dysfunction. However, data-driven early identification of sepsis is challenging as sepsis shares signs and symptoms with other health conditions.

Display and perception of risk: Analysis of decision support system display and its impact on perceived clinical risk of sepsis-induced health deterioration.

Despite acknowledging the value of clinical decision support systems (CDSS) in identifying risk for sepsis-induced health deterioration in-hospitalized patients, the relationship between display features, decision maker characteristics, and recognition of risk by the clinical decision maker remains an understudied, yet promising, area. The objective of this study is to explore the relationship between CDSS display design and perceived clinical risk of in-hospital mortality associated with sepsis. The study utilized data collected through in-person experimental sessions with 91 physicians from the general medical and surgical floors who were recruited across 12 teaching hospitals within the United States.

Proximity of Cellular and Physiological Response Failures in Sepsis.

Sepsis is a devastating multi-stage health condition with a high mortality rate. Its complexity, prevalence, and dependency of its outcomes on early detection have attracted substantial attention from data science and machine learning communities. Previous studies rely on individual cellular and physiological responses representing organ system failures to predict health outcomes or the onset of different sepsis stages.

The Available Criteria for Different Sepsis Scoring Systems in the Emergency Department-A Retrospective Assessment.

Objective: The goal of the study was to assess the criteria availability of eight sepsis scoring methods within 6 hours of triage in the emergency department (ED).

Design: Retrospective data analysis study.

Setting: ED of MedStar Washington Hospital Center (MWHC), a 912-bed urban, tertiary hospital.

Risky Business: Predicting patient mortality at a glance.

Clinicians are constantly forecasting patient trajectories to make critical point of care decisions intended to influence clinical outcomes. Little is known, however, about how providers interpret mortality risk against validated scoring systems. This research aims to understand how providers forecast mortality specifically for that of patients with sepsis.

FUTURES: Forecasting the Unexpected Transfer to Upgraded REsources in Sepsis.

Sepsis is one of the most deadly and costly diseases. The Emergency Department (ED) is the initial point of care for most patients who become hospitalized due to sepsis. Quantifying the accuracy of ED clinician forecasting regarding patients' clinical trajectories and outcomes can provide insight into clinical decision making and inform sepsis management.

Objective measures of workload in healthcare: a narrative review.

Purpose: Workload is a critical concept in the evaluation of performance and quality in healthcare systems, but its definition relies on the perspective (e.g. individual clinician-level vs unit-level workload) and type of available metrics (e.

The value of missing information in severity of illness score development.

Objective: We aim to investigate the hypothesis that using information about which variables are missing along with appropriate imputation improves the performance of severity of illness scoring systems used to predict critical patient outcomes.

Study Design And Setting: We quantify the impact of missing and imputed variables on the performance of prediction models used in the development of a sepsis-related severity of illness scoring system. Electronic health records (EHR) data were compiled from Christiana Care Health System (CCHS) on 119,968 adult patients hospitalized between July 2013 and December 2015.

Informatics and interaction: Applying human factors principles to optimize the design of clinical decision support for sepsis.

In caring for patients with sepsis, the current structure of electronic health record systems allows clinical providers access to raw patient data without imputation of its significance. There are a wide range of sepsis alerts in clinical care that act as clinical decision support tools to assist in early recognition of sepsis; however, there are serious shortcomings in existing health information technology for alerting providers in a meaningful way. Little work has been done to evaluate and assess existing alerts using implementation and process outcomes associated with health information technology displays, specifically evaluating clinician preference and performance.

Microsimulation Model Using Christiana Care Early Warning System (CEWS) to Evaluate Physiological Deterioration.

While physiological warning signs prior to deterioration events during hospitalization have been widely studied, evaluating clinical interventions, such as rapid response team (RRT) activations, based on scoring systems remains an understudied area. Simulation of physiological deterioration patterns represented by scoring systems can facilitate testing different RRT policies without disturbing care processes. Christiana Care Early Warning System (CEWS) is a scoring system developed at the study hospital to detect the physiological warning signs and inform RRT activations.

Not all organ dysfunctions are created equal - Prevalence and mortality in sepsis.

Purpose: While organ dysfunctions within sepsis have been widely studied, interaction between measures of organ dysfunction remains an understudied area. The objective of this study is to quantify the impact of organ dysfunction on in-hospital mortality in infected population.

Materials And Methods: Descriptive and multivariate analyses of retrospective data including patients (age ≥ 18 years) hospitalized at the study hospital from July 2013 to April 2016 who met the criteria for an infection visit (62,057 unique visits).

Data-driven approach to Early Warning Score-based alert management.

Background: Increasing adoption of electronic health records (EHRs) with integrated alerting systems is a key initiative for improving patient safety. Considering the variety of dynamically changing clinical information, it remains a challenge to design EHR-driven alerting systems that notify the right providers for the right patient at the right time while managing alert burden. The objective of this study is to proactively develop and evaluate a systematic alert-generating approach as part of the implementation of an Early Warning Score (EWS) at the study hospitals.

Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis.

Background: Hospitals are increasingly turning to clinical decision support systems for sepsis, a life-threatening illness, to provide patient-specific assessments and recommendations to aid in evidence-based clinical decision-making. Lack of guidelines on how to present alerts has impeded optimization of alerts, specifically, effective ways to differentiate alerts while highlighting important pieces of information to create a universal standard for health care providers.

Objective: To gain insight into clinical decision support systems-based alerts, specifically targeting nursing interventions for sepsis, with a focus on behaviors associated with and perceptions of alerts, as well as visual preferences.

The design of decisions: Matching clinical decision support recommendations to Nielsen's design heuristics.

Objective: While general design heuristics exist for graphic user interfaces, it remains a challenge to facilitate the implementation of these heuristics for the design of clinical decision support. Our goals were to map a set of recommendations for clinical decision support design found in current literature to Jakob Nielsen's traditional usability heuristics and to suggest usability areas that need more investigation.

Materials And Methods: Using a modified nominal group process, the research team discussed, classified, and mapped recommendations, organized as interface, information, and interaction, to design heuristics.

A Framework to Tackle Risk Identification and Presentation Challenges in Sepsis.

Introduction: Sepsis trajectories, including onset and recovery, can be difficult to assess, but electronic health records (EHRs) can accurately capture sepsis as a dynamic episode.

Methods: Retrospective dataset of 276,722 clinical observations (4,726 unique patients) during a two-month period in 2015 were extracted from the EHRs. A Cox proportional hazard model was built to test hazard ratios of risk factors to the first sepsis episode onset within 72 hours for patients with presumed infection.

We all make choices: A decision analysis framework for disposition decision in the ED.

Background: Emergency Department (ED) providers' disposition decision impacts patient care and safety. The objective of this brief report is to gain a better understanding of ED providers' disposition decision and risk tolerance of associated outcomes.

Methods: We synthesized qualitative and quantitative methods including decision mapping, survey research, statistical analysis, and word clouds.

Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support.

Objective: Provider acceptance and associated patient outcomes are widely discussed in the evaluation of clinical decision support systems (CDSSs), but critical design criteria for tools have generally been overlooked. The objective of this work is to inform electronic health record alert optimization and clinical practice workflow by identifying, compiling, and reporting design recommendations for CDSS to support the efficient, effective, and timely delivery of high-quality care.

Material And Methods: A narrative review was conducted from 2000 to 2016 in PubMed and The Journal of Human Factors and Ergonomics Society to identify papers that discussed/recommended design features of CDSSs that are associated with the success of these systems.

Optimizing usability and signal capture: a proactive risk assessment for the implementation of a wireless vital sign monitoring system.

Wearable vital sign monitors are a promising step towards optimal patient surveillance, providing continuous data to allow for early detection and treatment of patient deterioration. However, as wearable monitors become more widely adopted in healthcare, there is a corresponding need to carefully design the implementation of these tools to promote their integration into clinical workflows and defend against potential misuse and patient harm. Prior to the roll-out of these monitors, our multidisciplinary team of clinicians, clinical engineers, information technologists and research investigators conducted a modified Healthcare Failure Mode and Effect Analysis (HFMEA), a proactive evaluation of potential problems which could be encountered in the use of a wireless vital signs monitoring system.

Assessment of Nursing Response to a Real-Time Alerting Tool for Sepsis: A Provider Survey.

Background: An information technology solution to provide a real-time alert to the nursing staff is necessary to assist in identifying patients who may have sepsis and avoid the devastating effects of its late recognition. The objective of this study is to evaluate the perception and adoption of sepsis clinical decision support.

Methods: A cross-sectional survey over a three-week period in 2015 was conducted in a major tertiary care facility.

From Data to Improved Decisions: Operations Research in Healthcare Delivery.

Background: The Operations Research Interest Group (ORIG) within the Society of Medical Decision Making (SMDM) is a multidisciplinary interest group of professionals that specializes in taking an analytical approach to medical decision making and healthcare delivery. ORIG is interested in leveraging mathematical methods associated with the field of Operations Research (OR) to obtain data-driven solutions to complex healthcare problems and encourage collaborations across disciplines. This paper introduces OR for the non-expert and draws attention to opportunities where OR can be utilized to facilitate solutions to healthcare problems.

Improving Infusion Pump Safety Through Usability Testing.

With the recognition that the introduction of new technology causes changes in workflow and may introduce new errors to the system, usability testing was performed to provide data on nursing practice and interaction with infusion pump technology. Usability testing provides the opportunity to detect and analyze potentially dangerous problems with the design of infusion pumps that could cause or allow avoidable errors. This work will reduce preventable harm through the optimization of health care delivery.

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.

Background: Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations.

A stochastic model of acute-care decisions based on patient and provider heterogeneity.

The primary cause of preventable death in many hospitals is the failure to recognize and/or rescue patients from acute physiologic deterioration (APD). APD affects all hospitalized patients, potentially causing cardiac arrest and death. Identifying APD is difficult, and response timing is critical - delays in response represent a significant and modifiable patient safety issue.