Muge Capan

Individualizing and optimizing the use of early warning scores in acute medical care for deteriorating hospitalized patients.

AIM While early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models. METHODS The setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011. The national early warning score (NEWS) was used to measure the patient health condition. Chi-square and Kruskal Wallis tests were used to identify statistically significant subpopulations as a function of the admission type (medical or surgical), frailty as measured by the Braden skin score, and history of prior clinical deterioration (RRT, cardiopulmonary arrest, or unscheduled ICU transfer). RESULTS Statistical tests identified 12 statistically significant subpopulations which differed clinically, as measured by length of stay and time to re-admission (P < .001). The Chi-square test of independence results showed a dependency structure between subsequent states in the embedded Markov chains (P < .001). The SMDP models identified two sets of subpopulation-specific RRT activation rules for each statistically unique subpopulation. Clinical deterioration experience in prior hospitalizations did not change the RRT activation rules. The thresholds differed as a function of admission type and frailty. CONCLUSIONS EWS were used to identify personalized thresholds for RRT activation for statistically significant Markovian patient subpopulations as a function of frailty and admission type. The full potential of EWS for personalizing acute care delivery is yet to be realized.

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.

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. Hospitals have instituted rapid response systems or teams (RRT) to provide timely critical care for APD, with thresholds that trigger the involvement of critical care expertise. The National Early Warning Score (NEWS) was developed to define these thresholds. However, current triggers are inconsistent and ignore patient-specific factors. Further, acute care is delivered by providers with different clinical experience, resulting in quality-of-care variation. This article documents a semi-Markov decision process model of APD that incorporates patient and provider heterogeneity. The model allows for stochastically changing health states, while determining patient subpopulation-specific RRT-activation thresholds. The objective function minimizes the total time associated with patient deterioration and stabilization; and the relative values of nursing and RRT times can be modified. A case study from January 2011 to December 2012 identified six subpopulations. RRT activation was optimal for patients in “slightly concerning” health states (NEWS > 0) for all subpopulations, except surgical patients with low risk of deterioration for whom RRT was activated in “concerning” states (NEWS > 4). Clustering methods identified provider clusters considering RRT-activation preferences and estimation of stabilization-related resource needs. Providers with conservative resource estimates preferred waiting over activating RRT. This study provides simple practical rules for personalized acute care delivery.

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. Methods. Decision making is the process of choosing between possible solutions to a problem with respect to certain metrics. OR concepts can help systematically improve decision making through efficient modeling techniques while accounting for relevant constraints. Depending on the problem, methods that are part of OR (e.g., linear programming, Markov Decision Processes) or methods that are derived from related fields (e.g., regression from statistics) can be incorporated into the solution approach. This paper highlights the characteristics of different OR methods that have been applied to healthcare decision making and provides examples of emerging research opportunities. Examples. We illustrate OR applications in healthcare using previous studies, including diagnosis and treatment of diseases, organ transplants, and patient flow decisions. Further, we provide a selection of emerging areas for utilizing OR. Conclusions. There is a timely need to inform practitioners and policy makers of the benefits of using OR techniques in solving healthcare problems. OR methods can support the development of sustainable long-term solutions across disease management, service delivery, and health policies by optimizing the performance of system elements and analyzing their interaction while considering relevant constraints.

Using electronic health records and nursing assessment to redesign clinical early recognition systems

As health-care organizations transition from paper to electronic documentation systems, capturing the nursing assessment electronically can play a fundamental role in transforming health-care delivery. Especially in preventive health, electronic capture of nursing assessment, combined with vital sign-based monitoring, can support early detection of physiological deterioration of patients. While vital sign-based Early Warning Systems have the potential to detect signals of physiological deterioration, their clinical interpretation and integration into the workflow in hospital-based care setting remain a challenge. This study presents a clinical early recognition algorithm using electronic health records (EHRs) coupled with an electronic Nurse Screening Assessment (NSA) that targets various health assessment categories and its integration into the nursing workflow. Data was collected retrospectively from a single institution (N=2,405 visits). χ2 tests showed significant differences between algorithms with and without NSA (P<0.01). This study provides a practical framework for facilitating the meaningful use of EHRs in hospitals.

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. Results Fourteen papers were included as meeting the criteria and were found to have a total of 42 unique recommendations; 11 were classified as interface features, 10 as information features, and 21 as interaction features. Discussion Features are defined and described, providing actionable guidance that can be applied to CDSS development and policy. To our knowledge, no reviews have been completed that discuss/recommend design features of CDSS at this scale, and thus we found that this was important for the body of literature. The recommendations identified in this narrative review will help to optimize design, organization, management, presentation, and utilization of information through presentation, content, and function. The designation of 3 categories (interface, information, and interaction) should be further evaluated to determine the critical importance of the categories. Future work will determine how to prioritize them with limited resources for designers and developers in order to maximize the clinical utility of CDSS. Conclusion This review will expand the field of knowledge and provide a novel organization structure to identify key recommendations for CDSS.

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. OBJECTIVE Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. METHODS We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. RESULTS The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. CONCLUSIONS Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

Sepsis alert presentation: Developing a framework to optimize alert design for clinical decision support

The Value Institute at Christiana Care Health System is developing and evaluating a framework to optimize alert design for clinical decision support (CDS) to target sepsis, the most impactful disease process to our system. Our multifaceted approach takes into account technological, organizational, and provider factors that influence how providers interact with alerts throughout the hospitalization process when appropriate clinical care can change the trajectory of a patient with a systemic infection. Effective presentation of the alert, including how and what is displayed, may help providers extract information quickly, offering better cognitive support during busy patient encounters. Our simulated usability evaluation, using real clinical scenarios, investigates how clinicians detect sepsis and respond to CDS based on the way the information is presented visually. Usability testing is designed to better understand the decision-making process analyzing varied designs that utilize various levels of visual presentations to promote situational awareness and measure diagnosis- and treatment- related variables.

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. METHODS An interactive survey to display a novel user interface for clinical decision support systems for sepsis was developed and then administered to members of the nursing staff. RESULTS A total of 43 nurses participated in 2 interactive survey sessions. Participants preferred alerts that were based on an established treatment protocol, were presented in a pop‐up format, and addressed the patients clinical condition rather than regulatory guidelines. CONCLUSIONS The results can be used in future research to optimize electronic medical record alerting and clinical practice workflow to support the efficient, effective, and timely delivery of high‐quality care to patients with sepsis. The research also may advance the knowledge base of what information health care providers want and need to improve the health and safety of their patients.

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. Predisposition, infection, response, and organ failure (PIRO) score-based framework was used in a logistic regression to identify factors associated with in-hospital mortality within the sepsis population. Results 47.54% of patients with an infection episode experienced at least one sepsis episode (N=1,044 out of 2,196) within 72 hours of admission. The mortality rate was higher for patients with sepsis episodes (7.24%) compared to patient with only organ dysfunction episodes (4.84%) or only with infection episodes (3.96%). Analysis identified factors associated with the first sepsis episode onset and those associated with in-hospital mortality. Discussion Our study addresses identification of infection, organ dysfunction, and sepsis as dynamic episodes utilizing EHR data and provides a systematic approach to detect risk factors related to sepsis onset and in-hospital mortality.