24 research outputs found
Providing Feedback Following Leadership Walkrounds is Associated with Better Patient Safety Culture, Higher Employee Engagement and Lower Burnout
Background There is a poorly understood relationship between Leadership WalkRounds (WR) and domains such as safety culture, employee engagement, burnout and work-life balance. Methods This cross-sectional survey study evaluated associations between receiving feedback about actions taken as a result of WR and healthcare worker assessments of patient safety culture, employee engagement, burnout and work-life balance, across 829 work settings. Results 16â797 of 23â853 administered surveys were returned (70.4%). 5497 (32.7% of total) reported that they had participated in WR, and 4074 (24.3%) reported that they participated in WR with feedback. Work settings reporting more WR with feedback had substantially higher safety culture domain scores (first vs fourth quartile Cohenâs d range: 0.34â0.84; % increase range: 15â27) and significantly higher engagement scores for four of its six domains (first vs fourth quartile Cohenâs d range: 0.02â0.76; % increase range: 0.48â0.70). Conclusion This WR study of patient safety and organisational outcomes tested relationships with a comprehensive set of safety culture and engagement metrics in the largest sample of hospitals and respondents to date. Beyond measuring simply whether WRs occur, we examine WR with feedback, as WR being done well. We suggest that when WRs are conducted, acted on, and the results are fed back to those involved, the work setting is a better place to deliver and receive care as assessed across a broad range of metrics, including teamwork, safety, leadership, growth opportunities, participation in decision-making and the emotional exhaustion component of burnout. Whether WR with feedback is a manifestation of better norms, or a cause of these norms, is unknown, but the link is demonstrably potent
The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey
ImportanceEmotional exhaustion (EE) rates in healthcare workers (HCWs) have reached alarming levels and been linked to worse quality of care. Prior research has shown linguistic characteristics of writing samples can predict mental health disorders. Understanding whether linguistic characteristics are associated with EE could help identify and predict EE.ObjectivesTo examine whether linguistic characteristics of HCW writing associate with prior, current, and future EE.Design, setting, and participantsA large hospital system in the Mid-West had 11,336 HCWs complete annual quality improvement surveys in 2019, and 10,564 HCWs in 2020. Surveys included a measure of EE, an open-ended comment box, and an anonymous identifier enabling HCW responses to be linked across years. Linguistic Inquiry and Word Count (LIWC) software assessed the frequency of one exploratory and eight a priori hypothesized linguistic categories in written comments. Analysis of covariance (ANCOVA) assessed associations between these categories and past, present, and future HCW EE adjusting for the word count of comments. Comments with <20 words were excluded.Main outcomes and measuresThe frequency of the linguistic categories (word count, first person singular, first person plural, present focus, past focus, positive emotion, negative emotion, social, power) in HCW comments were examined across EE quartiles.ResultsFor the 2019 and 2020 surveys, respondents wrote 3,529 and 3,246 comments, respectively, of which 2,101 and 1,418 comments (103,474 and 85,335 words) contained â„20 words. Comments using more negative emotion (p < 0.001), power (i.e., references relevant to status, dominance, and social hierarchies, e.g., own, order, and allow) words (p < 0.0001), and words overall (p < 0.001) were associated with higher current and future EE. Using positive emotion words (p < 0.001) was associated with lower EE in 2019 (but not 2020). Contrary to hypotheses, using more first person singular (p < 0.001) predicted lower current and future EE. Past and present focus, first person plural, and social words did not predict EE. Current EE did not predict future language use.ConclusionFive linguistic categories predicted current and subsequent HCW EE. Notably, EE did not predict future language. These linguistic markers suggest a language of EE, offering insights into EEâs etiology, consequences, measurement, and intervention. Future use of these findings could include the ability to identify and support individuals and units at high risk of EE based on their linguistic characteristics
A new look at an old well-being construct: evaluating the psychometric properties of 9, 5, and 1-item versions of emotional exhaustion metrics
ObjectiveTo compare the relative strengths (psychometric and convergent validity) of four emotional exhaustion (EE) measures: 9- and 5-item scales and two 1-item metrics.Patients and methodsThis was a national cross-sectional survey study of 1409 US physicians in 2013. Psychometric properties were compared using Cronbachâs alpha, Confirmatory Factor Analysis (CFA), Exploratory Factor Analysis (EFA), and Spearmanâs Correlations. Convergent validity with subjective happiness (SHS), depression (CES-D10), work-life integration (WLI), and intention to leave current position (ITL) was assessed using Spearmanâs Correlations and Fisherâs R-to-Z.ResultsThe 5-item EE scale correlated highly with the 9-item scale (Spearmanâs rho = 0.828), demonstrated excellent internal reliability (alpha = 0.87), and relative to the 9-item, exhibited superior CFA model fit (RMSEA = 0.082, CFI = 0.986, TLI = 0.972). The 5-item EE scale correlated as highly as the 9-item scale with SHS, CES-D10, and WLI, and significantly stronger than the 9-item scale to ITL. Both 1-item EE metrics had significantly weaker correlation with SHS, CES-D10, WLI, and ITL (Fisherâs R-to-Z; p < 0.05) than the 5- and 9-item EE scales.ConclusionThe 5-item EE scale was repeatedly found equivalent or superior to the 9-item version across analyses, particularly with respect to the CFA results. As there is no cost to using the briefer 5-item EE scale, the burden on respondents is smaller, and widespread access to administering and interpreting an excellent wellbeing metric is enhanced at a critical time in global wellbeing research. The single item EE metrics exhibited lower convergent validity than the 5- and 9-item scales, but are acceptable for detecting a signal of EE when using a validated EE scale is not feasible. Replication of psychometrics and open-access benchmarking results for use of the 5-tem EE scale further enhance access and utility of this metric
Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTICâHF: baseline characteristics and comparison with contemporary clinical trials
Aims:
The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTICâHF) trial. Here we describe the baseline characteristics of participants in GALACTICâHF and how these compare with other contemporary trials.
Methods and Results:
Adults with established HFrEF, New York Heart Association functional class (NYHA)ââ„âII, EF â€35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokineticâguided dosing: 25, 37.5 or 50âmg bid). 8256 patients [male (79%), nonâwhite (22%), mean age 65âyears] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NTâproBNP 1971âpg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTICâHF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressureâ<â100âmmHg (n = 1127), estimated glomerular filtration rate <â30âmL/min/1.73 m2 (n = 528), and treated with sacubitrilâvalsartan at baseline (n = 1594).
Conclusions:
GALACTICâHF enrolled a wellâtreated, highârisk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation
Development and evaluation of a Bluetooth EKG monitoring sensor
With the adoption of 2G and 3G cellular network technologies, mobile phones now have the bandwidth capability to stream data back to monitoring stations in real-time. Our paper describes the design and evaluation of a Bluetooth electrocardiogram sensor that transmits medical data to a cell phone. This data is displayed and stored on the phone. Future development of the system will relay this data over a cellular GPRS network. The current system provides a low cost and lightweight alternative to existing EKG event monitors. The final GPRS connected system will provide continuous monitoring of a patientâs heart anywhere cellular coverage is available
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Assessing the readability of ClinicalTrials.gov.
OBJECTIVE: ClinicalTrials.gov serves critical functions of disseminating trial information to the public and helping the trials recruit participants. This study assessed the readability of trial descriptions at ClinicalTrials.gov using multiple quantitative measures. MATERIALS AND METHODS: The analysis included all 165,988 trials registered at ClinicalTrials.gov as of April 30, 2014. To obtain benchmarks, the authors also analyzed 2 other medical corpora: (1) all 955 Health Topics articles from MedlinePlus and (2) a random sample of 100,000 clinician notes retrieved from an electronic health records system intended for conveying internal communication among medical professionals. The authors characterized each of the corpora using 4 surface metrics, and then applied 5 different scoring algorithms to assess their readability. The authors hypothesized that clinician notes would be most difficult to read, followed by trial descriptions and MedlinePlus Health Topics articles. RESULTS: Trial descriptions have the longest average sentence length (26.1 words) across all corpora; 65% of their words used are not covered by a basic medical English dictionary. In comparison, average sentence length of MedlinePlus Health Topics articles is 61% shorter, vocabulary size is 95% smaller, and dictionary coverage is 46% higher. All 5 scoring algorithms consistently rated CliniclTrials.gov trial descriptions the most difficult corpus to read, even harder than clinician notes. On average, it requires 18 years of education to properly understand these trial descriptions according to the results generated by the readability assessment algorithms. DISCUSSION AND CONCLUSION: Trial descriptions at CliniclTrials.gov are extremely difficult to read. Significant work is warranted to improve their readability in order to achieve CliniclTrials.govs goal of facilitating information dissemination and subject recruitment
Computer-Supported Expert-Guided Experiential Learning Tool for Advanced Healthcare Skills
Healthcare professionals, just like any other community, can exhibit implicit biases. These biases adversely impact patientsâ health outcomes. Promoting awareness of both social determinants of health (SDH) and the impact of implicit/explicit biases assists healthcare professionals to understand their patients well and improve care experiences. In addition, it helps to augment the long-lasting empathy and compassion in healthcare professionals towards patients for care treatments while maintaining better healthcare professional-patient relationships. Thus, this research provides Computer-Supported Expert-Guided Experiential Learning (CSEGEL) tools or mobile applications that facilitate healthcare professionals with a first-person learning experience to augment advanced healthcare skills (e.g., professional communication, cultural humility, awareness of both SDH and impact of biases on health outcomes). The CSEGEL tools in the form of mobile applications incorporate virtual reality-based serious role-playing scenarios along with a novel Life Course module to deliver first-person experiential learning capability to augment the advanced healthcare skills of healthcare professionals and public awareness. Finally, a preliminary data analysis is provided to demonstrate the positive influence of CSEGEL tools and measure the required number of sample sizes for concrete evidence to show effective results
Computer-Supported Expert-Guided Experiential Learning Tool for Advanced Healthcare Skills
Healthcare professionals, just like any other community, can exhibit implicit biases. These biases adversely impact patientsâ health outcomes. Promoting awareness of both social determinants of health (SDH) and the impact of implicit/explicit biases assists healthcare professionals to understand their patients well and improve care experiences. In addition, it helps to augment the long-lasting empathy and compassion in healthcare professionals towards patients for care treatments while maintaining better healthcare professional-patient relationships. Thus, this research provides Computer-Supported Expert-Guided Experiential Learning (CSEGEL) tools or mobile applications that facilitate healthcare professionals with a first-person learning experience to augment advanced healthcare skills (e.g., professional communication, cultural humility, awareness of both SDH and impact of biases on health outcomes). The CSEGEL tools in the form of mobile applications incorporate virtual reality-based serious role-playing scenarios along with a novel Life Course module to deliver first-person experiential learning capability to augment the advanced healthcare skills of healthcare professionals and public awareness. Finally, a preliminary data analysis is provided to demonstrate the positive influence of CSEGEL tools and measure the required number of sample sizes for concrete evidence to show effective results