Clinical implications and validity of nursing assessments: a longitudinal measure of patient condition from analysis of the Electronic Medical Record

Nurses' Documentation Practice and Associated Factors in Eight Public Hospitals, Amhara Region, Ethiopia: A Cross-Sectional Study

Background

Nursing care documentation, which is the record of nursing care that is planned for and delivered to individual patients, can enhance patient outcomes while advancing the nursing profession. However, its practice and associated factors among Ethiopian nurses are not well investigated.

Objective

To assess the level of nursing care documentation practice and associated factors among nurses working at public hospitals in Ethiopia.

Methods

An institutional-based cross-sectional study was conducted from May 1 to 30, 2022. A total of 378 nurses and corresponding charts were randomly selected with a multistage sampling technique. Self-administered structured questionnaires and structured checklists were used to collect data about independent variables and nurses' documentation practice, respectively. Epi Data 4.6 was used for data entry and SPSS version 25 for analysis. Descriptive statistics and binary logistic regression analysis have been employed. The STROBE checklist was used to report the study.

Results

In this study, 372 nurses participated, and 30.4% (95% confidence interval [CI]: 26%-35%) of them had good nursing care documentation practice. Adequate knowledge about nursing care documentation(adjusted odds ratio [AOR] = 4.16, 95% CI: [2.36-7.33]), favorable attitude toward nursing care documentation (AOR = 3.43, 95% CI: [1.85-6.36]), adequacy of documenting sheets (AOR = 2.02, 95% CI: [1.14-3.59]), adequacy of time (AOR = 3.85, 95% CI: [2.11-7.05]), nurse-to-patient ratio (AOR = 2.78, 95% CI: [1.13-6.84]), and caring patients who had no stress, anxiety, pain, and distress (AOR = 3.56, 95% CI: [1.69-7.52]) were significantly associated with proper nursing care documentation practices.

Conclusion

Nursing documentation practice was poor in this study compared to the health sector transformation in quality standards due to the identified factors. Improving nurses' knowledge and attitude toward nursing care documentation and increasing access to documentation materials can contribute to improving documentation practice.

Systems-Based Physical Assessments: Earlier Detection of Clinical Deterioration and Reduced Mortality

BACKGROUND

Despite efforts to improve early detection of deterioration in a patient's condition, delays in activating the rapid response team remain common.

OBJECTIVES

To evaluate delays in activating the rapid response team and the occurrence of serious adverse events before and after implementation of a quality improvement initiative aimed at nurses' performing systems-based physical assessments.

METHODS

A retrospective observational cohort design was used to evaluate all patients who had a rapid response team activation during the study period.

RESULTS

A total of 1080 patients were included in the analysis: 536 patients before the quality improvement initiative and 544 patients after the quality improvement initiative. The delay in activating the rapid response team decreased from 11.7 hours in the before group to 9.6 hours in the after group (P < .001). In the after group, fewer patients were transferred to the intensive care unit (36% vs 41%, P = .02) and those who were transferred had 3.58 times greater odds of death than those who stayed at the same level of care. The after group had a 44% reduction in the odds of mortality compared with the before group.

CONCLUSIONS

When nurses focus on conducting a systems-based physical assessment early in their shift, delays in recognizing a patient's deteriorating condition are reduced, fewer patients are admitted to the intensive care unit, and mortality is significantly reduced.

Frontline Nurses' clinical judgment in recognizing, understanding, and responding to patient deterioration: A qualitative study

BACKGROUND

Early warning systems and rapid response teams have been widely implemented in hospitals worldwide to facilitate early recognition and response to patient deterioration. Unfortunately, evidence suggests that these interventions have made little impact on unexpected cardiac or respiratory arrest, hospital mortality, unplanned admission to intensive care units, or hospital length of stay. These programs depend on nurses recognizing at risk patients and initiating a timely response. Although physiologic abnormalities commonly precede serious adverse events, nurses often fail to recognize or respond effectively. Clinical judgment is a critical component in the effective response to deterioration, yet little is known about factors that influence nurses' clinical judgment in these situations. Noticing, interpreting, and responding are aspects of clinical judgment and are essential to preventing further patient deterioration and serious adverse events.

OBJECTIVE

To describe medical-surgical nurses' perceptions of factors that influenced their clinical judgment in situations of patient deterioration.

DESIGN

A qualitative descriptive design using individual, semi-structured interviews. Tanner's Clinical Judgment Model served as the framework for interview questions and data analysis.

PARTICIPANTS

A purposive sample of 20 medical-surgical registered nurses were recruited from 10 adult medical-surgical units at an academic medical center hospital in the United States.

METHODS

Telephone interviews occurred between March and July 2018. A directed approach to content analysis was used to code the transcribed data and identify themes.

RESULTS

Eight themes related to each aspect of clinical judgment emerged from the analysis: Knowing the patient, Experience matters, Lots of small points where the system can fail, Making sense of the data, Something doesn't go together, Caught in the middle, Culture of teamwork, and Increased nursing workload. An overarching theme was Nurses' keen sense of responsibility. Findings revealed that factors within the nurse, the patient, and the work environment influence each component of noticing, interpreting, and initiating an effective response to deteriorating patients.

CONCLUSIONS

Findings have implications for health care systems regarding interventions to support timely recognition and response to deterioration. Nurses' clinical judgment and factors that influence each aspect (noticing, interpreting, and responding) should be a key consideration in organizational efforts to improve the overall response to patient deterioration. Research is needed to enhance understanding of the contextual factors that impact nurses' clinical judgment to inform interventions to support timely recognition and response.

The roles of bedside nurses in Japan in antimicrobial stewardship

BACKGROUND

In the United States and the United Kingdom, the roles of nurses in antimicrobial stewardship (AS) have been described in guidelines. However, in Japan, no previous studies have clarified nurses' recognition of the role of AS. Moreover, how the AS roles were implemented among nurses in Japan has not been fully clarified. The objectives of this study were to determine the perceptions of infection control nurses (ICNs) in Japan regarding the AS role of nurses and the extent of nurses' practice.

METHODS

A questionnaire survey of ICNs was conducted.

RESULTS

Four hundred responses (response rate, 30.8%) were analyzed. Some of the items that have already gained consensus as the AS role of nurses were not recognized as the AS role of nurses by ICNs or had low implementation rates in Japan. Meanwhile, both recognition and implementation rates were high for the 5 types of care proposed.

DISCUSSION

The reason the ICNs agreed that these 5 types of care are AS roles for nurses is that they know that such care can prevent infection and thereby obviate the need for antimicrobial administration. However, whether nurses themselves understand that these are roles for nurses in AS is unclear. To promote AS in Japan, communicating the fact that nurses already contribute to AS, strengthening nurse education, and improving staffing are desirable.

A reusable learning object for assessment cardiovascular and respiratory responses

OBJECTIVES

Produce and determine the validity of a reusable learning object for assessment cardiovascular and respiratory responses from the taxonomy of the North American Association of Nursing Diagnosis Domain 4. Activity/Rest, Class 4. Cardiovascular/Pulmonary Responses.

METHODS

A descriptive methodological study was developed that included three phases (1) construction of the reusable learning object incorporating Gagné's nine instructional events, (2) content validation was carried out with 24 nurses who served as experts, and (3) and Usability was evaluated by 22 nursing students from a Public University in Colombia.

RESULTS

The reusable learning object was organized into three modules: introduction, assessment of cardiovascular responses, and assessment of pulmonary responses. The learning object obtained a content validation index of 0.86; the usability indicators had proportions of agreement greater than 85%.

CONCLUSIONS

The reusable learning object is valid and can be used for teaching the assessment of cardiovascular and respiratory responses in nursing students.

An evaluation of an electronic audit and feedback system for patient safety in a tertiary hospital setting: A study protocol

An electronic audit and feedback (e-A&F) system was developed to support healthcare providers' awareness of their own performance, improve delivery of care and ultimately the safety of patients while in hospital. The point-of-care e-A&F system provides healthcare providers, from a 600-bed tertiary hospital in Western Australia, with near real-time feedback via web-based dashboards. The aim of this evaluation is to determine the implications of e-A&F across multiple dimensions and domains of care in a tertiary hospital setting. The study also aims to address the paucity in the literature by validating hypothesised design and implementation mechanisms on its effectiveness. Key datasets to be examined include those related to patient outcomes, staff behaviour and costs. Quantitative methods, such as interrupted time series analysis and multiple logistic regression analysis, amongst other methods, will be employed to achieve these aims.

Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS)

OBJECTIVE

We aimed to develop a model for accurate prediction of general care inpatient deterioration.

MATERIALS AND METHODS

Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call.

RESULTS

Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score.

DISCUSSION

Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering.

CONCLUSIONS

MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

OBJECTIVE

There are signals of clinicians' expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals).

MATERIALS AND METHODS

We employed an iterative framework development approach that combined data-driven modeling and simulation testing to define and refine a process for phenotyping clinician behaviors. Our framework was developed and evaluated based on the Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) predictive model to detect and leverage signals of clinician expertise for prediction of patient trajectories.

RESULTS

Seven themes-identified during development and simulation testing of the CONCERN model-informed framework development. The HPM-ExpertSignals conceptual framework includes a 3-step modeling technique: (1) identify patterns of clinical behaviors from user interaction with CIS; (2) interpret patterns as proxies of an individual's decisions, knowledge, and expertise; and (3) use patterns in predictive models for associations with outcomes. The CONCERN model differentiated at risk patients earlier than other early warning scores, lending confidence to the HPM-ExpertSignals framework.

DISCUSSION

The HPM-ExpertSignals framework moves beyond transactional data analytics to model clinical knowledge, decision making, and CIS interactions, which can support predictive modeling with a focus on the rapid and frequent patient surveillance cycle.

CONCLUSIONS

We propose this framework as an approach to embed clinicians' knowledge-driven behaviors in predictions and inferences to facilitate capture of healthcare processes that are activated independently, and sometimes well before, physiological changes are apparent.

Predictors of adverse outcomes using a multidimensional nursing assessment in an Italian community hospital

BACKGROUND

There is growing evidence about the role of nurses in patient outcomes in several healthcare settings. However, there is still a lack of evidence about the transitional care setting. We aimed to assess the association between patient characteristics identified in a multidimensional nursing assessment and outcomes of mortality and acute hospitalization during community hospital stay.

METHODS

A retrospective observational study was performed on patients consecutively admitted to a community hospital (CH) in Loreto (Ancona, Italy) between January 1st, 2018 and May 31st, 2019. The nursing assessment included sociodemographic characteristics, functional status, risk of falls (Conley Score) and pressure damage (Norton scale), nursing diagnoses, presence of pressure sores, feeding tubes, urinary catheters or vascular access devices and comorbidities. Two logistic regression models were developed to assess the association between patient characteristics identified in a multidimensional nursing assessment and outcomes of mortality and acute hospitalization during CH stay.

RESULTS

We analyzed data from 298 patients. The mean age was 83 ± 9.9 years; 60.4% (n = 180) were female. The overall mean length of stay was 42.8 ± 36 days (32 ± 32 days for patients who died and 33.9 ± 35 days for patients who had an acute hospitalization, respectively). An acute hospitalization was reported for 13.4% (n = 40) of patients and 21.8% (n = 65) died. An increased risk of death was related to female sex (OR 2.25, 95% CI 1.10-4.62), higher Conley Score (OR 1.19; 95% CI 1.03-1.37) and having a vascular access device (OR 3.64, 95% CI 1.82-7.27). A higher Norton score was associated with a decreased risk of death (OR 0.71, 95% CI 0.62-0.81). The risk for acute hospitalization was correlated with younger age (OR 0.94, 95% CI 0.91-0.97), having a vascular access device (OR 2.33, 95% CI 1.02-5.36), impaired walking (OR 2.50, 95% CI 1.03-6.06) and it is inversely correlated with a higher Conley score (OR 0.84, 95% CI 0.77-0.98).

CONCLUSION

Using a multidimensional nursing assessment enables identification of risk of nearness of end of life and acute hospitalization to target care and treatment. The present study adds further knowledge on this topic and confirms the importance of nursing assessment to evaluate the risk of patients' adverse outcome development.

Stratifying Deterioration Risk by Acuity at Admission Offers Triage Insights for Coronavirus Disease 2019 Patients

OBJECTIVES

Triaging patients at admission to determine subsequent deterioration risk can be difficult. This is especially true of coronavirus disease 2019 patients, some of whom experience significant physiologic deterioration due to dysregulated immune response following admission. A well-established acuity measure, the Rothman Index, is evaluated for stratification of patients at admission into high or low risk of subsequent deterioration.

DESIGN

Multicenter retrospective study.

SETTING

One academic medical center in Connecticut, and three community hospitals in Connecticut and Maryland.

PATIENTS

Three thousand four hundred ninety-nine coronavirus disease 2019 and 14,658 noncoronavirus disease 2019 adult patients admitted to a medical service between January 1, 2020, and September 15, 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Performance of the Rothman Index at admission to predict in-hospital mortality or ICU utilization for both general medical and coronavirus disease 2019 populations was evaluated using the area under the curve. Precision and recall for mortality prediction were calculated, high- and low-risk thresholds were determined, and patients meeting threshold criteria were characterized. The Rothman Index at admission has good to excellent discriminatory performance for in-hospital mortality in the coronavirus disease 2019 (area under the curve, 0.81-0.84) and noncoronavirus disease 2019 (area under the curve, 0.90-0.92) populations. We show that for a given admission acuity, the risk of deterioration for coronavirus disease 2019 patients is significantly higher than for noncoronavirus disease 2019 patients. At admission, Rothman Index-based thresholds segregate the majority of patients into either high- or low-risk groups; high-risk groups have mortality rates of 34-45% (coronavirus disease 2019) and 17-25% (noncoronavirus disease 2019), whereas low-risk groups have mortality rates of 2-5% (coronavirus disease 2019) and 0.2-0.4% (noncoronavirus disease 2019). Similarly large differences in ICU utilization are also found.

CONCLUSIONS

Acuity level at admission may support rapid and effective risk triage. Notably, in-hospital mortality risk associated with a given acuity at admission is significantly higher for coronavirus disease 2019 patients than for noncoronavirus disease 2019 patients. This insight may help physicians more effectively triage coronavirus disease 2019 patients, guiding level of care decisions and resource allocation.

Opportunities for machine learning to improve surgical ward safety

BACKGROUND

Delayed recognition of decompensation and failure-to-rescue on surgical wards are major sources of preventable harm. This review assimilates and critically evaluates available evidence and identifies opportunities to improve surgical ward safety.

DATA SOURCES

Fifty-eight articles from Cochrane Library, EMBASE, and PubMed databases were included.

CONCLUSIONS

Only 15-20% of patients suffering ward arrest survive. In most cases, subtle signs of instability often occur prior to critical illness and arrest, and underlying pathology is reversible. Coarse risk assessments lead to under-triage of high-risk patients to wards, where surveillance for complications depends on time-consuming manual review of health records, infrequent patient assessments, prediction models that lack accuracy and autonomy, and biased, error-prone decision-making. Streaming electronic heath record data, wearable continuous monitors, and recent advances in deep learning and reinforcement learning can promote efficient and accurate risk assessments, earlier recognition of instability, and better decisions regarding diagnosis and treatment of reversible underlying pathology.

Admission Braden Skin Score Independently Predicts Mortality in Cardiac Intensive Care Patients

OBJECTIVE

To determine whether a low Braden skin score (BSS), reflecting increased risk for skin pressure injury, would predict lower survival in cardiac intensive care unit (CICU) patients after adjustment for illness severity and comorbidities.

PATIENTS AND METHODS

This retrospective cohort study included consecutive unique adult patients admitted to a single tertiary care referral hospital CICU from January 1, 2007, through December 31, 2015, who had a BSS documented on CICU admission. The primary outcome was all-cause hospital mortality, using elastic net penalized logistic regression to determine predictors of hospital mortality. The secondary outcome was all-cause post-discharge mortality, using Cox proportional hazards models to determine predictors of post-discharge mortality.

RESULTS

The study included 9552 patients with a mean age of 67.4±15.2 years (3589 [37.6%] were females) and a hospital mortality rate of 8.3%. Admission BSS was inversely associated with hospital mortality (unadjusted odds ratio, 0.70; 95% CI, 0.68-0.72; P<.001; area under the receiver operator curve, 0.80; 95% CI, 0.78-0.82), with increased short-term mortality as a function of decreasing admission BSS. After adjustment for illness severity and comorbidities using multivariable analysis, admission BSS remained inversely associated with hospital mortality (adjusted odds ratio, 0.88; 95% CI, 0.85-0.92; P<.001). Among hospital survivors, admission BSS was inversely associated with post-discharge mortality after adjustment for illness severity and comorbidities (adjusted hazard ratio, 0.89; 95% CI, 0.88-0. 90; P<.001).

CONCLUSION

The admission BSS, a simple inexpensive bedside nursing assessment potentially reflecting frailty and overall illness acuity, was independently associated with hospital and post-discharge mortality when added to established multiparametric illness severity scores among contemporary CICU patients.

Can proactive rapid response team rounding improve surveillance and reduce unplanned escalations in care? A controlled before and after study

BACKGROUND

Unplanned escalations manifest as a breakdown of hospital care attributable to clinician error through missed or delayed identification of physiological instability, ineffective treatment, or iatrogenic harm.

OBJECTIVES

To examine the impact of an Early Warning Score-based proactive rapid response team model on the frequency of unplanned intra-hospital escalations in care compared with a rapid response team model based on staff nurse identification of vital sign derangements.

DESIGN

Pre- and post Early Warning Score-guided proactive rapid response team model intervention.

SETTING

237-bed community hospital in the southeastern United States.

PARTICIPANTS

All hospitalized adults (n = 12,148) during a pre- and post-intervention period.

METHODS

Logistic regressions used to examine the relationship between unplanned ICU transfers and rapid response team models (rapid response team vs. Early Warning Score-guided proactive rapid response team).

RESULTS

Unplanned ICU transfers were 1.4 times more likely to occur during the rapid response team baseline period (OR = 1.392, 95% CI [1.017-1.905]) compared with the Early Warning Score-guided proactive rapid response team intervention period.

CONCLUSIONS

This study reports a difference in the frequency of unplanned escalations using different rapid response models, with fewer unplanned ICU transfers occurring during the use of Early Warning Score-guided proactive rapid response team model while accounting for differences in admission volumes, age, gender and comorbidities. Implementation of this model has implications for patient outcomes, hospital operations and costs.

A Novel Model for Enhanced Prediction and Understanding of Unplanned 30-Day Pediatric Readmission

OBJECTIVES

To develop a model to assist clinicians in reducing 30-day unplanned pediatric readmissions and to enhance understanding of risk factors leading to such readmissions.

METHODS

Data consisting of 38 143 inpatient clinical encounters at a tertiary pediatric hospital were retrieved, and 50% were used for training on a multivariate logistic regression model. The pediatric Rothman Index (pRI) was 1 of the novel candidate predictors considered. Multivariate model selection was conducted by minimization of Akaike Information Criteria. The area under the receiver operator characteristic curve (AUC) and values for sensitivity, specificity, positive predictive value, relative risk, and accuracy were computed on the remaining 50% of the data.

RESULTS

The multivariate logistic regression model of readmission consists of 7 disease diagnosis groups, 4 measures of hospital resource use, 3 measures of disease severity and/or medical complexities, and 2 variables derived from the pRI. Four of the predictors are novel, including history of previous 30-day readmissions within last 6 months (P < .001), planned admissions (P < .001), the discharge pRI score (P < .001), and indicator of whether the maximum pRI occurred during the last 24 hours of hospitalization (P = .005). An AUC of 0.79 (0.77-0.80) was obtained on the independent test data set.

CONCLUSIONS

Our model provides significant performance improvements in the prediction of unplanned 30-day pediatric readmissions with AUC higher than the LACE readmission model and other general unplanned 30-day pediatric readmission models. The model is expected to provide an opportunity to capture 39% of readmissions (at a selected operating point) and may therefore assist clinicians in reducing avoidable readmissions.

Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data

RATIONALE

Patients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.

OBJECTIVES

We aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.

METHODS

This observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database (n = 42,303 intensive care unit transfers).

RESULTS

Eleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning-derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value < 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort.

CONCLUSIONS

A machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.

Analysis of Rothman Index Data to Predict Postdischarge Adverse Events in a Medical Intensive Care Unit

OBJECTIVE

Currently, there are no objective metrics included in the intensive care unit (ICU) discharge decision making process. In this study, we evaluate Rothman Index(RI) data for a possible metric as part of a quality improvement project. Our objectives were to determine whether RI could predict adverse events occurring within 72 hours of ICU discharge decision, the optimal clinical cutoff value for this metric, and to determine whether there is a relation between the RI warning alert 24 hours prior to discharge and adverse events postdischarge.

DESIGN

Retrospective observational study.

SETTING

Single center tertiary hospital.

PATIENTS

Adult medical ICU patients discharged from the ICU between January 20, 2015 and March 14, 2015.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 194 patients were studied with mean age of 62.74 (18.37) years. Data collection included RI at the time of decision-making for ICU discharge and the presence of any warning signals in the previous 24 hours. A 72-hour follow-up chart review recorded any adverse events, including readmission to a higher level of care, discontinuation of discharge due to clinical status change, emergency department visit if discharged home, rapid response activation, or cardiopulmonary arrest postdischarge. Adverse events after ICU discharge were observed in 31 (16%) patients with 9 events being ICU readmission (4.6%). Based on an age-adjusted multivariate model, a higher RI was associated with lower odds of an adverse event (odds ratio [OR] = 0.969, P = .006, confidence interval [CI]: 0.9487-0.9911). An RI value ≥ 50 was associated with 72% lower odds of an adverse event (OR = 0.2887, 95% CI = 0.1278-0.6517 and P = .003) compared to RI < 50. This RI cutoff value was associated with the largest decrease in odds of events. As expected, patients with a very high-risk warning alert had a higher proportion of adverse events compared to patients who did not. (31.75% vs 12.65%, P = < .02).

CONCLUSIONS

Patients who have an RI < 50 or a very high-risk warning alert have a higher risk of adverse events postdischarge from the ICU. Rothman Index may be a useful metric for ICU discharge decision-making.

Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements

Background

Early warning scores aid in the detection of pediatric clinical deteriorations but include limited data inputs, rarely include data trends over time, and have limited validation.

Objective

Machine learning methods that make use of large numbers of predictor variables are now commonplace. This work examines how different types of predictor variables derived from the electronic health record affect the performance of predicting unplanned transfers to the intensive care unit (ICU) at three large children’s hospitals.

Methods

We trained separate models with data from three different institutions from 2011 through 2013 and evaluated models with 2014 data. Cases consisted of patients who transferred from the floor to the ICU and met one or more of 5 different priori defined criteria for suspected unplanned transfers. Controls were patients who were never transferred to the ICU. Predictor variables for the models were derived from vitals, labs, acuity scores, and nursing assessments. Classification models consisted of L1 and L2 regularized logistic regression and neural network models. We evaluated model performance over prediction horizons ranging from 1 to 16 hours.

Results

Across the three institutions, the c-statistic values for our best models were 0.892 (95% CI 0.875-0.904), 0.902 (95% CI 0.880-0.923), and 0.899 (95% CI 0.879-0.919) for the task of identifying unplanned ICU transfer 6 hours before its occurrence and achieved 0.871 (95% CI 0.855-0.888), 0.872 (95% CI 0.850-0.895), and 0.850 (95% CI 0.825-0.875) for a prediction horizon of 16 hours. For our first model at 80% sensitivity, this resulted in a specificity of 80.5% (95% CI 77.4-83.7) and a positive predictive value of 5.2% (95% CI 4.5-6.2).

Conclusions

Feature-rich models with many predictor variables allow for patient deterioration to be predicted accurately, even up to 16 hours in advance.

Recognising older frail patients near the end of life: What next?

Frailty is a state of vulnerability resulting from cumulative decline in many physiological systems during a lifetime. It is progressive and considered largely irreversible, but its progression may be controlled and can be slowed down and its precursor -pre-frailty- can be treated with multidisciplinary intervention. The aim of this narrative review is to provide an overview of the different ways of measuring frailty in community settings, hospital, emergency, general practice and residential aged care; suggest occupational groups who can assess frailty in various services; discuss the feasibility of comprehensive geriatric assessments; and summarise current evidence of its management guidelines. We also suggest practical recommendations to recognise frail patients near the end of life, so discussions on goals of care, advance care directives, and shared decision-making including early referrals to palliative and supportive care can take place before an emergency arises. We acknowledge the barriers to systematically assess frailty and the absence of consensus on best instruments for different settings. Nevertheless, given its potential consequences including prolonged suffering, disability and death, we recommend identification of frailty levels should be universally attempted in older people at any health service, to facilitate care coordination, and honest discussions on preferences for advance care with patients and their caregivers.

Nurse in patients' health status assessment: Data from a pilot study assessing agreement among nurse and gastroenterologist in computing IBD-clinical scores

INTRODUCTION

Crohn's Disease (CD) and Ulcerative Colitis (UC) are chronic, systemic Inflammatory Bowel Diseases (IBDs) that need a multidisciplinary approach involving not only different medical specialists but also qualified nurses.

AIM

We evaluated the concordance between IBD-nurse and physician in computing Clinical Activity Scores in IBD-patients treated with biologics.

METHODS

We enrolled all consecutive IBD-patients treated with biologics in two referral centers for IBD-care. For each patient, a gastroenterologist and a nurse blindly filled-out a form to assess the Harvey-Bradshaw Index (HBI) in CD or the partial MAYO score in UC. All data were recorded to assess the beyond chance agreement (concordance) using the k statistic.

RESULTS

87 patients were enrolled. The agreement in all patients by k value was substantial (66%), ranging from moderate to substantial (95% CI from 51% to 80%). The main reason of disagreement was about the scoring of remission versus mild activity, and that of mild versus moderate activity, both in CD and UC. For the HBI, the best agreement was for well-being on the previous day (k 62%) and the least one for abdominal mass (k 35%).

CONCLUSIONS

Our study shows an acceptable strength of agreement among nurse and gastroenterologist in evaluating the disease activity of IBD-patients through the calculation of clinical scores.

The nursing assessment of infants with bronchiolitis

Bronchiolitis is the leading cause of morbidity and hospitalization in infants under the age of one year. Supportive treatments and regular assessment remain the mainstay of care for infants admitted to hospital. Nurses play an important role in the assessment of infants with bronchiolitis; however, this is not well described in the literature and consequently little is known about what strategies nurses employ in assessing infants with bronchiolitis. The aim of this study was to explore bronchiolitis assessment in the context of nursing practice. A naturalistic inquiry study was undertaken using think aloud and retrospective probing data collection methods. The results revealed that the information gathered by nurses in their assessment of infants with bronchiolitis was varied and the process of acquiring and evaluating this information was multifaceted and holistic in nature. A close partnership between the nurse and mother was identified, and the mother's expert knowledge and ability to identify subtle changes in the infant's clinical condition over time was essential to the assessment process. The assessment partnership with families provides nurses with the most comprehensive and holistic view of the infant's clinical condition and vital assessment information could be lost if this partnership does not occur.

Patients Negatively Impacted by Discontinuity of Nursing Care During Acute Hospitalization

BACKGROUND

Continuity of nursing care in hospitals remains poor and not prioritized, and we do not know whether discontinuous nursing care is negatively impacting patient outcomes.

OBJECTIVES

This study aims to examine nursing care discontinuity and its effect on patient clinical condition over the course of acute hospitalization.

RESEARCH DESIGN

Retrospective longitudinal analysis of electronic health records (EHR). Average point-in-time discontinuity was estimated from time of admission to discharge and compared with theoretical predictions for optimal continuity and random nurse assignment. Mixed-effects models estimated within-patient change in clinical condition following a discontinuity.

SUBJECTS

A total of 3892 adult medical-surgical inpatients were admitted to a tertiary academic medical center in the Eastern United States during July 1, 2011 and December 31, 2011.

MEASURES

Exposure: discontinuity of nursing care was measured at each nurse assessment entry into a patient's EHR as assignment of the patient to a nurse with no prior assignment to that patient.

OUTCOME

patient's clinical condition score (Rothman Index) continuously tracked in the EHR.

RESULTS

Discontinuity declined from nearly 100% in the first 24 hours to 70% at 36 hours, and to 50% by the 10th postadmission day. Discontinuity was higher than predicted for optimal continuity, but not random. Each instance of discontinuity lead to a 0.12-0.23 point decline in the Rothman Index score, with more pronounced effects for older and high-mortality risk patients.

CONCLUSIONS

Discontinuity in acute care nurse assignments was high and negatively impacted patient clinical condition. Improved continuity of provider-patient assignment should be advocated to improve patient outcomes in acute care.

Rankings matter: nurse graduates from higher-ranked institutions have higher productivity

Background

Increasing demand for baccalaureate-prepared nurses has led to rapid growth in the number of baccalaureate-granting programs, and to concerns about educational quality and potential effects on productivity of the graduating nursing workforce. We examined the association of individual productivity of a baccalaureate-prepared nurse with the ranking of the degree-granting institution.

Methods

For a sample of 691 nurses from general medical-surgical units at a large magnet urban hospital between 6/1/2011–12/31/2011, we conducted multivariate regression analysis of nurse productivity on the ranking of the degree-granting institution, adjusted for age, hospital tenure, gender, and unit-specific effects. Nurse productivity was coded as “top”/“average”/“bottom” based on a computation of individual nurse value-added to patient outcomes. Ranking of the baccalaureate-granting institution was derived from the US News and World Report Best Colleges Rankings’ categorization of the nurse’s institution as the “first tier” or the “second tier”, with diploma or associate degree as the reference category.

Results

Relative to diploma or associate degree nurses, nurses who had attended first-tier universities had three-times the odds of being in the top productivity category (OR = 3.18, p < 0.001), while second-tier education had a non-significant association with productivity (OR = 1.73, p = 0.11). Being in the bottom productivity category was not associated with having a baccalaureate degree or the quality tier.

Conclusions

The productivity boost from a nursing baccalaureate degree depends on the quality of the educational institution. Recognizing differences in educational outcomes, initiatives to build a baccalaureate-educated nursing workforce should be accompanied by improved access to high-quality educational institutions.

"Deterioration to Door Time": An Exploratory Analysis of Delays in Escalation of Care for Hospitalized Patients

BACKGROUND

Timely escalation of care for patients experiencing clinical deterioration in the inpatient setting is challenging. Deterioration on a general floor has been associated with an increased risk of death, and the early period of deterioration may represent a time during which admission to the intensive care unit (ICU) improves survival. Previous studies examining the association between delay from onset of clinical deterioration to ICU transfer and mortality are few in number and were conducted more than 10 years ago.

OBJECTIVE

We aimed to evaluate the impact of delays in the escalation of care among clinically deteriorating patients in the current era of inpatient medicine.

DESIGN AND PARTICIPANTS

This was a retrospective cohort study that analyzed data from 793 patients transferred from non-intensive care unit (ICU) inpatient floors to the medical intensive care unit (MICU), from 2011 to 2013 at an urban, tertiary, academic medical center.

MAIN MEASURES

"Deterioration to door time (DTDT)" was defined as the time between onset of clinical deterioration (as evidenced by the presence of one or more vital sign indicators including respiratory rate, systolic blood pressure, and heart rate) and arrival in the MICU.

KEY RESULTS

In our sample, 64.6 % had delays in care escalation, defined as greater than 4 h based on previous studies. Mortality was significantly increased beginning at a DTDT of 12.1 h after adjusting for age, gender, and severity of illness.

CONCLUSIONS

Delays in the escalation of care for clinically deteriorating hospitalized patients remain frequent in the current era of inpatient medicine, and are associated with increased in-hospital mortality. Development of performance measures for the care of clinically deteriorating inpatients remains essential, and timeliness of care escalation deserves further consideration.

Development of a tool for defining and identifying the dying patient in hospital: Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL)

OBJECTIVE

To develop a screening tool to identify elderly patients at the end of life and quantify the risk of death in hospital or soon after discharge for to minimise prognostic uncertainty and avoid potentially harmful and futile treatments.

DESIGN

Narrative literature review of definitions, tools and measurements that could be combined into a screening tool based on routinely available or obtainable data at the point of care to identify elderly patients who are unavoidably dying at the time of admission or at risk of dying during hospitalisation.

MAIN MEASUREMENTS

Variables and thresholds proposed for the Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL screening tool) were adopted from existing scales and published research findings showing association with either in-hospital, 30-day or 3-month mortality.

RESULTS

Eighteen predictor instruments and their variants were examined. The final items for the new CriSTAL screening tool included: age ≥65; meeting ≥2 deterioration criteria; an index of frailty with ≥2 criteria; early warning score >4; presence of ≥1 selected comorbidities; nursing home placement; evidence of cognitive impairment; prior emergency hospitalisation or intensive care unit readmission in the past year; abnormal ECG; and proteinuria.

CONCLUSIONS

An unambiguous checklist may assist clinicians in reducing uncertainty patients who are likely to die within the next 3 months and help initiate transparent conversations with families and patients about end-of-life care. Retrospective chart review and prospective validation will be undertaken to optimise the number of prognostic items for easy administration and enhanced generalisability. Development of an evidence-based tool for defining and identifying the dying patient in hospital: CriSTAL.

Nurse value-added and patient outcomes in acute care

OBJECTIVE

The aims of the study were to (1) estimate the relative nurse effectiveness, or individual nurse value-added (NVA), to patients' clinical condition change during hospitalization; (2) examine nurse characteristics contributing to NVA; and (3) estimate the contribution of value-added nursing care to patient outcomes.

DATA SOURCES/STUDY SETTING

Electronic data on 1,203 staff nurses matched with 7,318 adult medical-surgical patients discharged between July 1, 2011 and December 31, 2011 from an urban Magnet-designated, 854-bed teaching hospital.

STUDY DESIGN

Retrospective observational longitudinal analysis using a covariate-adjustment value-added model with nurse fixed effects.

DATA COLLECTION/EXTRACTION METHODS

Data were extracted from the study hospital's electronic patient records and human resources databases.

PRINCIPAL FINDINGS

Nurse effects were jointly significant and explained 7.9 percent of variance in patient clinical condition change during hospitalization. NVA was positively associated with having a baccalaureate degree or higher (0.55, p = .04) and expertise level (0.66, p = .03). NVA contributed to patient outcomes of shorter length of stay and lower costs.

CONCLUSIONS

Nurses differ in their value-added to patient outcomes. The ability to measure individual nurse relative value-added opens the possibility for development of performance metrics, performance-based rankings, and merit-based salary schemes to improve patient outcomes and reduce costs.

Measuring the modified early warning score and the Rothman index: advantages of utilizing the electronic medical record in an early warning system

Early detection of an impending cardiac or pulmonary arrest is an important focus for hospitals trying to improve quality of care. Unfortunately, all current early warning systems suffer from high false-alarm rates. Most systems are based on the Modified Early Warning Score (MEWS); 4 of its 5 inputs are vital signs. The purpose of this study was to compare the accuracy of MEWS against the Rothman Index (RI), a patient acuity score based upon summation of excess risk functions that utilize additional data from the electronic medical record (EMR). MEWS and RI scores were computed retrospectively for 32,472 patient visits. Nursing assessments, a category of EMR inputs only used by the RI, showed sharp differences 24 hours before death. Receiver operating characteristic curves for 24-hour mortality demonstrated superior RI performance with c-statistics, 0.82 and 0.93, respectively. At the point where MEWS triggers an alarm, we identified the RI point corresponding to equal sensitivity and found the positive likelihood ratio (LR+) for MEWS was 7.8, and for the RI was 16.9 with false alarms reduced by 53%. At the RI point corresponding to equal LR+, the sensitivity for MEWS was 49% and 77% for RI, capturing 54% more of those patients who will die within 24 hours.

Shared decision-making at end-of-life is aided by graphical trending of illness severity

The Rothman Index (RI) gives a visual picture of patient's condition and progress for the physician and family to view together. This case demonstrates how the RI graph facilitates physician-family communication. An 85-year-old man with normal pressure hydrocephalus and ventriculoperitoneal shunt presented with a subdural haematoma. He required a temporoparietal craniotomy and evacuation of left subdural haematoma, followed by care in an intensive inpatient rehabilitation unit. His course was complicated by aspiration pneumonia, dehydration, renal failure and phenytoin toxicity. During hospitalisation, the patient's RI graph was reviewed daily with his family. The RI provided an unambiguous visualisation of the trend of patient acuity, which depicted the patient's persistent decline in health, and made clear to the family the situation of the patient. This clarity was instrumental in prompting frank discussions of prognosis and consideration of comfort measures, resulting in timely transfer to hospice.

Identifying patients at increased risk for unplanned readmission

BACKGROUND

Reducing readmissions is a national priority, but many hospitals lack practical tools to identify patients at increased risk of unplanned readmission.

OBJECTIVE

To estimate the association between a composite measure of patient condition at discharge, the Rothman Index (RI), and unplanned readmission within 30 days of discharge.

SUBJECTS

Adult medical and surgical patients in a major teaching hospital in 2011.

MEASURES

The RI is a composite measure updated regularly from the electronic medical record based on changes in vital signs, nursing assessments, Braden score, cardiac rhythms, and laboratory test results. We developed 4 categories of RI and tested its association with readmission within 30 days, using logistic regression, adjusted for patient age, sex, insurance status, service assignment (medical or surgical), and primary discharge diagnosis.

RESULTS

Sixteen percent of the sample patients (N=2730) had an unplanned readmission within 30 days of discharge. The risk of readmission for a patient in the highest risk category (RI<70) was >1 in 5 while the risk of readmission for patients in the lowest risk category was about 1 in 10. In multivariable analysis, patients with an RI<70 (the highest risk category) or 70-79 (medium risk category) had 2.65 (95% confidence interval, 1.72-4.07) and 2.40 (95% confidence interval, 1.57-3.67) times higher odds of unplanned readmission, respectively, compared with patients in the lowest risk category.

CONCLUSION

Clinicians can use the RI to help target hospital programs and supports to patients at highest risk of readmission.

Development and validation of a continuous measure of patient condition using the Electronic Medical Record

Patient condition is a key element in communication between clinicians. However, there is no generally accepted definition of patient condition that is independent of diagnosis and that spans acuity levels. We report the development and validation of a continuous measure of general patient condition that is independent of diagnosis, and that can be used for medical-surgical as well as critical care patients. A survey of Electronic Medical Record data identified common, frequently collected non-static candidate variables as the basis for a general, continuously updated patient condition score. We used a new methodology to estimate in-hospital risk associated with each of these variables. A risk function for each candidate input was computed by comparing the final pre-discharge measurements with 1-year post-discharge mortality. Step-wise logistic regression of the variables against 1-year mortality was used to determine the importance of each variable. The final set of selected variables consisted of 26 clinical measurements from four categories: nursing assessments, vital signs, laboratory results and cardiac rhythms. We then constructed a heuristic model quantifying patient condition (overall risk) by summing the single-variable risks. The model's validity was assessed against outcomes from 170,000 medical-surgical and critical care patients, using data from three US hospitals. Outcome validation across hospitals yields an area under the receiver operating characteristic curve(AUC) of ≥0.92 when separating hospice/deceased from all other discharge categories, an AUC of ≥0.93 when predicting 24-h mortality and an AUC of 0.62 when predicting 30-day readmissions. Correspondence with outcomes reflective of patient condition across the acuity spectrum indicates utility in both medical-surgical units and critical care units. The model output, which we call the Rothman Index, may provide clinicians with a longitudinal view of patient condition to help address known challenges in caregiver communication, continuity of care, and earlier detection of acuity trends.

Placing clinical variables on a common linear scale of empirically based risk as a step towards construction of a general patient acuity score from the electronic health record: a modelling study

OBJECTIVE

To explore the hypothesis that placing clinical variables of differing metrics on a common linear scale of all-cause postdischarge mortality provides risk functions that are directly correlated with in-hospital mortality risk.

DESIGN

Modelling study.

SETTING

An 805-bed community hospital in the southeastern USA.

PARTICIPANTS

42302 inpatients admitted for any reason, excluding obstetrics, paediatric and psychiatric patients.

OUTCOME MEASURES

All-cause in-hospital and postdischarge mortalities, and associated correlations.

RESULTS

Pearson correlation coefficients comparing in-hospital risks with postdischarge risks for creatinine, heart rate and a set of 12 nursing assessments are 0.920, 0.922 and 0.892, respectively. Correlation between postdischarge risk heart rate and the Modified Early Warning System (MEWS) component for heart rate is 0.855. The minimal excess risk values for creatinine and heart rate roughly correspond to the normal reference ranges. We also provide the risks for values outside that range, independent of expert opinion or a regression model. By summing risk functions, a first-approximation patient risk score is created, which correctly ranks 6 discharge categories by average mortality with p<0.001 for differences in category means, and Tukey's Honestly Significant Difference Test confirmed that the means were all different at the 95% confidence level.

CONCLUSIONS

Quantitative or categorical clinical variables can be transformed into risk functions that correlate well with in-hospital risk. This methodology provides an empirical way to assess inpatient risk from data available in the Electronic Health Record. With just the variables in this paper, we achieve a risk score that correlates with discharge disposition. This is the first step towards creation of a universal measure of patient condition that reflects a generally applicable set of health-related risks. More importantly, we believe that our approach opens the door to a way of exploring and resolving many issues in patient assessment.