Quantifying emergency department nursing workload at the task level using NASA-TLX: An exploratory descriptive study.

BACKGROUND: Emergency departments (ED) nurses experience high mental workloads because of unpredictable work environments; however, research evaluating ED nursing workload using a tool incorporating nurses' perception is lacking. Quantify ED nursing subjective workload and explore the impact of work experience on perceived workload. METHODS: Thirty-two ED nurses at a tertiary academic hospital in the Republic of Korea were surveyed to assess their subjective workload for ED procedures using the National Aeronautics and Space Administration Task Load Index (NASA-TLX). Nonparametric statistical analysis was performed to describe the data, and linear regression analysis was conducted to estimate the impact of work experience on perceived workload. RESULTS: Cardiopulmonary resuscitation (CPR) had the highest median workload, followed by interruption from a patient and their family members. Although inexperienced nurses perceived the 'special care' procedures (CPR and defibrillation) as more challenging compared with other categories, analysis revealed that nurses with more than 107 months of experience reported a significantly higher workload than those with less than 36 months of experience. CONCLUSION: Addressing interruptions and customizing training can alleviate ED nursing workload. Quantified perceived workload is useful for identifying acceptable thresholds to maintain optimal workload, which ultimately contributes to predicting nursing staffing needs and ED crowding.

Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.

BACKGROUND: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non-standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)-based tools to identify older adults at risk of fall-related injuries in a primary care population and compared this approach to standard fall screening questionnaires. METHODS: Using patient-level clinical data from an integrated healthcare system consisting of 16-member institutions, we conducted a case-control study to develop and evaluate prediction models for fall-related injuries in older adults. Questionnaire-derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury-prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient-specific fall injury risk factors. RESULTS: Questionnaire-based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR-based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6-month and one-year prediction models. CONCLUSIONS: The current method of questionnaire-based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice.

Intravenous Medication Administration Safety with Smart Infusion Pumps in the Neonatal Intensive Care Unit: An Observational Study.

INTRODUCTION: Infants in the neonatal intensive care unit (NICU) are among the most vulnerable patient populations and medication errors are a significant source of risk and harm to neonates. Smart infusion pumps have been implemented to support the safe medication administration process; however, the effect of using smart infusion pumps on medication safety in the NICU is still unclear. METHODS: We conducted an observational study with a prospective point-prevalence approach to investigate intravenous (IV) medication administration errors in the NICU at one academic medical center in the USA. Observations were conducted in 48 days in a 3-month data collection period in 2019. RESULTS: We observed a total of 441 patients with 905 IV medication administrations during the data collection period. The total number of errors was 130 (14.4 per 100 administrations). Of these, the most frequent errors were selecting the wrong drug library entry (5.3 per 100 administrations), unauthorized medication (0.7 per 100 administrations), and wrong dose (0.6 per 100 administrations). Sixty-eight errors (7.5 per 100 administrations) were unlikely to cause harm despite reaching the patient (category C errors), while the rest did not reach the patient. CONCLUSION: We identified the medication errors, which was unique to NICU populations, but no harm to the patients were identified. Most errors occurred due to a lack of compliance of using smart pump technology; therefore, potential exists to maximize safety related to medication administration practices in the NICU through hospital policy change and increasing adherence to appropriate use of smart pump technology.

Implementation of a Continuous Patient Monitoring System in the Hospital Setting: A Qualitative Study.

BACKGROUND: Technology can improve care delivery, patient outcomes, and staff satisfaction, but integration into the clinical workflow remains challenging. To contribute to this knowledge area, this study examined the implementation continuum of a contact-free, continuous monitoring system (CFCM) in an inpatient setting. CFCM monitors vital signs and uses the information to alert clinicians of important changes, enabling early detection of patient deterioration. METHODS: Data were collected throughout the entire implementation continuum at a community teaching hospital. Throughout the study, 3 group and 24 individual interviews and five process observations were conducted. Postimplementation alarm response data were collected. Analysis was conducted using triangulation of information sources and two-coder consensus. RESULTS: Preimplementation perceived barriers were alarm fatigue, questions about accuracy and trust, impact on patient experience, and challenges to the status quo. Stakeholders identified the value of CFCM as preventing deterioration and benefitting patients who are not good candidates for telemetry. Educational materials addressed each barrier and emphasized the shared CFCM values. Mean alarm response times were below the desired target of two minutes. Postimplementation interview analysis themes revealed lessened concerns of alarm fatigue and improved trust in CFCM than anticipated. Postimplementation challenges included insufficient training for secondary users and impact on patient experience. CONCLUSION: In addition to understanding the preimplementation anticipated barriers to implementation and establishing shared value before implementation, future recommendations include studying strategies for optimal tailoring of education to each user group, identifying and reinforcing positive process changes after implementation, and including patient experience as the overarching element in frameworks for digital tool implementation.

Cost Savings Associated With Implementing 4 Total Joint Replacement Electronic Clinical Quality Measures Nationally: 2020-2040.

BACKGROUND: Digital transformation using widely available electronic data is a key component to improving health outcomes and customer choice and decreasing cost and measurement burden. Despite these benefits, existing information on the potential cost savings from electronic clinical quality measures (eCQMs) is limited. METHODS: We assessed the costs of implementing 4 eCQMs related to total hip and/or total knee arthroplasty into electronic health record systems across healthcare systems in the United States. We used published literature and technical expert panel consultation to calculate low-, mid-, and high-range hip and knee arthroplasty surgery projections, and used empirical testing, literature, and technical expert panel consultation to develop an economic model to assess projected cost savings of eCQMs when implemented nationally. RESULTS: Low-, mid-, and high-range projected cost savings for year's 2020, 2030, and 2040 were calculated for 4 orthopedic eCQMs. Mid-range projected cost savings for 2020 ranged from $7.9 to $31.9 million per measure per year. A breakeven of between 0.5% and 5.1% of adverse events (measure dependent) must be averted for cost savings to outweigh implementation costs. CONCLUSIONS: All measures demonstrated potential cost savings. These findings suggest that eCQMs have the potential to lower healthcare costs and improve patient outcomes without adding to physician documentation burden. The Centers for Medicare and Medicaid Services' investment in eCQMs is an opportunity to reduce adverse outcomes and excess costs in orthopedics.

A resilience perspective on healthcare personnels' experiences of managing the COVID-19 pandemic: a qualitative study in Norwegian nursing homes and come care services.

BACKGROUND: The COVID-19 pandemic led to new and unfamiliar changes in healthcare services globally. Most COVID-19 patients were cared for in primary healthcare services, demanding major adjustments and adaptations in care delivery. Research addressing how rural primary healthcare services coped during the COVID-19 pandemic, and the possible learning potential originating from the pandemic is limited. The aim of this study was to assess how primary healthcare personnel (PHCP) working in rural areas experienced the work situation during the COVID-19 outbreak, and how adaptations to changes induced by the pandemic were handled in nursing homes and home care services. METHOD: This study was conducted as an explorative qualitative study. Four municipalities with affiliated nursing homes and homecare services were included in the study. We conducted focus group interviews with primary healthcare personnel working in rural nursing homes and homecare services in western Norway. The included PHCP were 16 nurses, 7 assistant nurses and 2 assistants. Interviews were audio recorded, transcribed and analyzed using thematic analysis. RESULTS: The analysis resulted in three main themes and 16 subthemes describing PHCP experience of the work situation during the COVID-19 pandemic, and how they adapted to the changes and challenges induced by the pandemic. The main themes were: "PHCP demonstrated high adaptive capacity while being put to the test", "Adapting to organizational measures, with varying degree of success" and "Safeguarding the patient's safety and quality of care, but at certain costs". CONCLUSION: This study demonstrated PHCPs major adaptive capacity in response to the challenges and changes induced by the covid-19 pandemic, while working under varying organizational conditions. Many adaptations where long-term solutions improving healthcare delivery, others where short-term solutions forced by inadequate management, governance, or a lack of leadership. Overall, the findings demonstrated the need for all parts of the system to engage in building resilient healthcare services. More research investigating this learning potential, particularly in primary healthcare services, is needed.

Predicting COVID-19 severity: Challenges in reproducibility and deployment of machine learning methods.

The increasing use of electronic health records (EHR) based computable phenotypes in clinical research is providing new opportunities for development of data-driven medical applications. Adopted widely in the United States and globally, EHRs facilitate systematic collection of patients' longitudinal information, which serves as one of the important foundations for artificial intelligence applications in medicine. Harmonization of input variables and outcome definitions is critically important for wider clinical applicability of artificial intelligence (AI) methodologies. In this review, we focused on Coronavirus Disease 2019 (COVID-19) severity machine learning prediction models and explored the pipeline for standardizing future disease severity model development using EHR information. We identified 2,967 studies published between 01/01/2020 and 02/15/2022 and selected 135 independent studies that had built machine learning prediction models to predict severity related outcomes of COVID-19 patients based on EHR data for the final review. These 135 studies spanning across 27 counties covered a broad range of severity related prediction outcomes. We observed substantial inconsistency in COVID-19 severity phenotype definitions among models in these studies. Moreover, there was a gap between the outcome of these models and clinician-recognized clinical concepts. Accordingly, we recommend that robust clinical input metrics, with outcome definitions which eliminate ambiguity in interpretation, to reduce algorithmic bias, mitigate model brittleness and improve generalizability of a universal model for COVID-19 severity. This framework can potentially be extended to broader clinical application.

Refining Clinical Phenotypes to Improve Clinical Decision Support and Reduce Alert Fatigue: A Feasibility Study.

BACKGROUND: Chronic kidney disease (CKD) is common and associated with adverse clinical outcomes. Most care for early CKD is provided in primary care, including hypertension (HTN) management. Computerized clinical decision support (CDS) can improve the quality of care for CKD but can also cause alert fatigue for primary care physicians (PCPs). Computable phenotypes (CPs) are algorithms to identify disease populations using, for example, specific laboratory data criteria. OBJECTIVES: Our objective was to determine the feasibility of implementation of CDS alerts by developing CPs and estimating potential alert burden. METHODS: We utilized clinical guidelines to develop a set of five CPs for patients with stage 3 to 4 CKD, uncontrolled HTN, and indications for initiation or titration of guideline-recommended antihypertensive agents. We then conducted an iterative data analytic process consisting of database queries, data validation, and subject matter expert discussion, to make iterative changes to the CPs. We estimated the potential alert burden to make final decisions about the scope of the CDS alerts. Specifically, the number of times that each alert could fire was limited to once per patient. RESULTS: In our primary care network, there were 239,339 encounters for 105,992 primary care patients between April 1, 2018 and April 1, 2019. Of these patients, 9,081 (8.6%) had stage 3 and 4 CKD. Almost half of the CKD patients, 4,191 patients, also had uncontrolled HTN. The majority of CKD patients were female, elderly, white, and English-speaking. We estimated that 5,369 alerts would fire if alerts were triggered multiple times per patient, with a mean number of alerts shown to each PCP ranging from 0.07-to 0.17 alerts per week. CONCLUSION: Development of CPs and estimation of alert burden allows researchers to iteratively fine-tune CDS prior to implementation. This method of assessment can help organizations balance the tradeoff between standardization of care and alert fatigue.

Management of Fall Risk Among Older Adults in Diverse Primary Care Settings.

OBJECTIVES: Falls are persistent among community-dwelling older adults despite existing prevention guidelines. We described how urban and rural primary care staff and older adults manage fall risk and factors important to integration of computerized clinical decision support (CCDS). METHODS: Interviews, contextual inquiries, and workflow observations were analyzed using content analysis and synthesized into a journey map. Sociotechnical and PRISM domains were applied to identify workflow factors important to sustainable CCDS integration. RESULTS: Participants valued fall prevention and described similar approaches. Available resources differed between rural and urban locations. Participants wanted evidence-based guidance integrated into workflows to bridge skills gaps. DISCUSSION: Sites described similar clinical approaches with differences in resource availability. This implies that a single intervention would need to be flexible to environments with differing resources. Electronic Health Record's inherent ability to provide tailored CCDS is limited. However, CCDS middleware could integrate into different settings and increase evidence use.

Evaluation of an approach to clinical decision support for preventing inpatient falls: a pragmatic trial.

Objectives: To assess whether a fall-prevention clinical decision support (CDS) approach using electronic analytics that stimulates risk-targeted interventions is associated with reduced rates of falls and injurious falls. Materials and Methods: The CDS intervention included a machine-learning prediction algorithm, individual risk-factor identification, and guideline-based prevention recommendations. After a 5-month plan-do-study-act quality improvement initiative, the CDS intervention was implemented at an academic tertiary hospital and compared with the usual care using a pretest (lasting 24 months and involving 23 498 patients) and posttest (lasting 13 months and involving 17 341 patients) design in six nursing units. Primary and secondary outcomes were the rates of falls and injurious falls per 1000 hospital days, respectively. Outcome measurements were tested using a priori Poisson regression and adjusted with patient-level covariates. Subgroup analyses were conducted according to age. Results: The age distribution, sex, hospital and unit lengths of stay, number of secondary diagnoses, fall history, condition at admission, and overall fall rate per 1000 hospital days did not differ significantly between the intervention and control periods before (1.88 vs 2.05, respectively, P = .1764) or after adjusting for demographics. The injurious-falls rate per 1000 hospital days decreased significantly before (0.68 vs 0.45, P = .0171) and after (rate difference = -0.64, P = .0212) adjusting for demographics. The differences in injury rates were greater among patients aged at least 65 years. Conclusions: This study suggests that a well-designed CDS intervention employing electronic analytics was associated with a decrease in fall-related injuries. The benefits from this intervention were greater in elderly patients aged at least 65 years. Trial Registration: This study was conducted as part of a more extensive study registered with the Clinical Research Information Service (CRIS) (KCT0005378).

Cost of Inpatient Falls and Cost-Benefit Analysis of Implementation of an Evidence-Based Fall Prevention Program.

Importance: There is insufficient research on the costs of patient falls in health care systems, a leading source of nonreimbursable adverse events. Objective: To report the costs of inpatient falls and the cost savings associated with implementation of an evidence-based fall prevention program. Design, Setting, and Participants: In this economic evaluation, a matched case-control study used the findings from an interrupted time series analysis that assessed changes in fall rates following implementation of an evidence-based fall prevention program to understand the cost of inpatient falls. An economic analysis was then performed to assess the cost benefits associated with program implementation across 2 US health care systems from June 1, 2013, to August 31, 2019, in New York, New York, and Boston, Massachusetts. All adults hospitalized in participating units were included in the analysis. Data analysis was performed from October 2021 to November 2022. Interventions: Evidence-based fall prevention program implemented in 33 medical and surgical units in 8 hospitals. Main Outcomes and Measures: Primary outcome was cost of inpatient falls. Secondary outcome was the costs and cost savings associated with the evidence-based fall prevention program. Results: A total of 10 176 patients who had a fall event (injurious or noninjurious) with 29 161 matched controls (no fall event) were included in the case-control study and the economic analysis (51.9% were 65-74 years of age, 67.1% were White, and 53.6% were male). Before the intervention, there were 2503 falls and 900 injuries; after the intervention, there were 2078 falls and 758 injuries. Based on a 19% reduction in falls and 20% reduction in injurious falls from the beginning to the end of the postintervention period, the economic analysis demonstrated that noninjurious and injurious falls were associated with cost increases of $35 365 and $36 776, respectively. The implementation of the evidence-based fall prevention program was associated with $14 600 in net avoided costs per 1000 patient-days. Conclusions and Relevance: This economic evaluation found that fall-related adverse events represented a clinical and financial burden to health care systems and that the current Medicare policy limits reimbursement. In this study, costs of falls only differed marginally by injury level. Policies that incentivize organizations to implement evidence-based strategies that reduce the incidence of all falls may be effective in reducing both harm and costs.

Usability Testing of an Interoperable Computerized Clinical Decision Support Tool for Fall Risk Management in Primary Care.

BACKGROUND: Falls are a widespread and persistent problem for community-dwelling older adults. Use of fall prevention guidelines in the primary care setting has been suboptimal. Interoperable computerized clinical decision support systems have the potential to increase engagement with fall risk management at scale. To support fall risk management across organizations, our team developed the ASPIRE tool for use in differing primary care clinics using interoperable standards. OBJECTIVES: Usability testing of ASPIRE was conducted to measure ease of access, overall usability, learnability, and acceptability prior to pilot . METHODS: Participants were recruited using purposive sampling from two sites with different electronic health records and different clinical organizations. Formative testing rooted in user-centered design was followed by summative testing using a simulation approach. During summative testing participants used ASPIRE across two clinical scenarios and were randomized to determine which scenario they saw first. Single Ease Question and System Usability Scale were used in addition to analysis of recorded sessions in NVivo. RESULTS: All 14 participants rated the usability of ASPIRE as above average based on usability benchmarks for the System Usability Scale metric. Time on task decreased significantly between the first and second scenarios indicating good learnability. However, acceptability data were more mixed with some recommendations being consistently accepted while others were adopted less frequently. CONCLUSION: This study described the usability testing of the ASPIRE system within two different organizations using different electronic health records. Overall, the system was rated well, and further pilot testing should be done to validate that these positive results translate into clinical practice. Due to its interoperable design, ASPIRE could be integrated into diverse organizations allowing a tailored implementation without the need to build a new system for each organization. This distinction makes ASPIRE well positioned to impact the challenge of falls at scale.

Testing of an Electronic Clinical Quality Measure for Diagnostic Delay of Venous Thromboembolism (DOVE) in Primary Care.

Venous Thromboembolism (VTE) is a serious, preventable public health problem that requires timely treatment. Because signs and symptoms are non-specific, patients often present to primary care providers with VTE symptoms prior to diagnosis. Today there are no federal measurement tools in place to track delayed diagnosis of VTE. We developed and tested an electronic clinical quality measure (eCQM) to quantify Diagnostic Delay of Venous Thromboembolism (DOVE); the rate of avoidable delayed VTE events occurring in patients with a VTE who had reported VTE symptoms in primary care within 30 days of diagnosis. DOVE uses routinely collected EHR data without contributing to documentation burden. DOVE was tested in two geographically distant healthcare systems. Overall DOVE rates were 72.60% (site 1) and 77.14% (site 2). This novel, data-driven eCQM could inform healthcare providers and facilities about opportunities to improve care, strengthen incentives for quality improvement, and ultimately improve patient safety.

Development and Usability Testing of an Exercise-Based Primary Care Fall Prevention Clinical Decision Support Tool.

For older patients, falls are the leading cause offatal and nonfatal injuries. Guidelines recommend that at-risk older adults are referred to appropriate fall-prevention exercise programs, but many do not receive support for fall-risk management in the primary care setting. Advances in health information technology may be able to address this gap. This article describes the development and usability testing of a clinical decision support (CDS) tool for fall prevention exercise. Using rapid qualitative analysis and human-centered design, our team developed and tested the usability of our CDS prototype with primary care team members. Across 31 Health-Information Technology Usability Evaluation Scale surveys, our CDS prototype received a median score of 5.0, mean (SD) of 4.5 (0.8), and a range of 4.1-4.9. This study highlights the features and usability offall prevention CDS for helping primary care providers deliver patient-centeredfall prevention care.

Documentation Burden in Nursing and Its Role in Clinician Burnout Syndrome.

OBJECTIVES: The purpose of this study is to understand the relationship between documentation burden and clinician burnout syndrome in nurses working in direct patient care. The Office of the National Coordinator considers documentation burden a high priority problem. However, the presence of documentation burden in nurses working in direct patient care is not well known. Furthermore, the presence of documentation burden has not been linked to the development of clinician burnout syndrome. METHODS: This paper reports that the results of a cross-sectional survey study comprised of three tools: (1) The burden of documentation for nurses and mid-wives survey, (2) the system usability scale, and (3) Maslach's burnout inventory for medical professionals. RESULTS: Documentation burden has a weak to moderate correlation to clinician burnout syndrome. Furthermore, poor usability of the electronic health record (EHR) is also associated with documentation burden and clinician burnout syndrome. CONCLUSION: This study suggests that there is a relationship between documentation burden and clinician burnout syndrome. The correlation of poor usability and domains of clinician burnout syndrome implies the need for more work on improving the usability of EHR for nursing documentation. Further study regarding the presence of documentation burden and its correlation to clinician burnout syndrome should focus on specific areas of nursing to understand the drivers of documentation burden variation within and across specialty domains.

Effect of the STRIDE fall injury prevention intervention on falls, fall injuries, and health-related quality of life.

BACKGROUND: Falls are common in older adults and can lead to severe injuries. The Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial cluster-randomized 86 primary care practices across 10 health systems to a multifactorial intervention to prevent fall injuries, delivered by registered nurses trained as falls care managers, or enhanced usual care. STRIDE enrolled 5451 community-dwelling older adults age ≥70 at increased fall injury risk. METHODS: We assessed fall-related outcomes via telephone interviews of participants (or proxies) every 4 months. At baseline, 12 and 24 months, we assessed health-related quality of life (HRQOL) using the EQ-5D-5L and EQ-VAS. We used Poisson models to assess intervention effects on falls, fall-related fractures, fall injuries leading to hospital admission, and fall injuries leading to medical attention. We used hierarchical longitudinal linear models to assess HRQOL. RESULTS: For recurrent event models, intervention versus control incidence rate ratios were 0.97 (95% confidence interval [CI], 0.93-1.00; p = 0.048) for falls, 0.93 (95% CI, 0.80-1.08; p = 0.337) for self-reported fractures, 0.89 (95% CI, 0.73-1.07; p = 0.205) for adjudicated fractures, 0.91 (95% CI, 0.77-1.07; p = 0.263) for falls leading to hospital admission, and 0.97 (95% CI, 0.89-1.06; p = 0.477) for falls leading to medical attention. Similar effect sizes (non-significant) were obtained for dichotomous outcomes (e.g., participants with ≥1 events). The difference in least square mean change over time in EQ-5D-5L (intervention minus control) was 0.009 (95% CI, -0.002 to 0.019; p = 0.106) at 12 months and 0.005 (95% CI, -0.006 to 0.015; p = 0.384) at 24 months. CONCLUSIONS: Across a standard set of outcomes typically reported in fall prevention studies, we observed modest improvements, one of which was statistically significant. Future work should focus on patient-, practice-, and organization-level operational strategies to increase the real-world effectiveness of interventions, and improving the ability to detect small but potentially meaningful clinical effects. CLINICALTRIALS: gov identifier: NCT02475850.

Multi-Site Testing of an Opioid Prescribing Electronic Clinical Quality Measure Following Elective Primary Total Hip and/or Total Knee Arthroplasties.

As the United States faces the third wave of the ongoing opioid epidemic, development of measures which report on prolonged opioid prescribing (POP) rates, specifically following orthopedic surgeries, are needed to better understand and improve prescribing practices at the clinician group level. Brigham and Women's Hospital (BWH) has been contracted by the Centers for Medicare and Medicaid Services (CMS) to create a novel electronic clinical quality measure (eCQM) to quantify the prolonged opioid prescribing rate of opioid episodes lasting > 42 days in patients aged 18+ years following elective primary total hip arthroplasties (THA) and/or total knee arthroplasties (TKA) for use in the Merit-Based Incentive Payment System (MIPS). When this measure was tested on two geographically distinct sites, it was found that the THA rate was 3.80% and 16.07% at sites 1 and 2, respectively, and that the TKA rate is 7.65% and 24.15% at sites 1 and 2, respectively. This manuscript reports on the testing of this eCQM between these two sites, highlighting differences in state and organizational level policies regarding opioid prescribing and documentation practices.

Comparison of a Voluntary Safety Reporting System to a Global Trigger Tool for Identifying Adverse Events in an Oncology Population.

OBJECTIVE: There is a lack of research on adverse event (AE) detection in oncology patients, despite the propensity for iatrogenic harm. Two common methods include voluntary safety reporting (VSR) and chart review tools, such as the Institute for Healthcare Improvement's Global Trigger Tool (GTT). Our objective was to compare frequency and type of AEs detected by a modified GTT compared with VSR for identifying AEs in oncology patients in a larger clinical trial. METHODS: Patients across 6 oncology units (from July 1, 2013, through May 29, 2015) were randomly selected. Retrospective chart reviews were conducted by a team of nurses and physicians to identify AEs using the GTT. The VSR system was queried by the department of quality and safety of the hospital. Adverse event frequencies, type, and harm code for both methods were compared. RESULTS: The modified GTT detected 0.90 AEs per patient (79 AEs in 88 patients; 95% [0.71-1.12] AEs per patient) that were predominantly medication AEs (53/79); more than half of the AEs caused harm to the patients (41/79, 52%), but only one quarter were preventable (21/79; 27%). The VSR detected 0.24 AEs per patient (21 AEs in 88 patients; 95% [0.15-0.37] AEs per patient), a large plurality of which were medication/intravenous related (8/21); more than half did not cause harm (70%). Only 2% of the AEs (2/100) were detected by both methods. CONCLUSIONS: Neither the modified GTT nor the VSR system alone is sufficient for detecting AEs in oncology patient populations. Further studies exploring methods such as automated AE detection from electronic health records and leveraging patient-reported AEs are needed.

Clinical Decision Support for Fall Prevention: Defining End-User Needs.

BACKGROUND AND SIGNIFICANCE: Falls in community-dwelling older adults are common, and there is a lack of clinical decision support (CDS) to provide health care providers with effective, individualized fall prevention recommendations. OBJECTIVES: The goal of this research is to identify end-user (primary care staff and patients) needs through a human-centered design process for a tool that will generate CDS to protect older adults from falls and injuries. METHODS: Primary care staff (primary care providers, care coordinator nurses, licensed practical nurses, and medical assistants) and community-dwelling patients aged 60 years or older associated with Brigham & Women's Hospital-affiliated primary care clinics and the University of Florida Health Archer Family Health Care primary care clinic were eligible to participate in this study. Through semi-structured and exploratory interviews with participants, our team identified end-user needs through content analysis. RESULTS: User needs for primary care staff (n = 24) and patients (n = 18) were categorized under the following themes: workload burden; systematic communication; in-person assessment of patient condition; personal support networks; motivational tools; patient understanding of fall risk; individualized resources; and evidence-based safe exercises and expert guidance. While some of these themes are specific to either primary care staff or patients, several address needs expressed by both groups of end-users. CONCLUSION: Our findings suggest that there are many care gaps in fall prevention management in primary care and that personalized, actionable, and evidence-based CDS has the potential to address some of these gaps.