Mobile health (mHealth) is often reputed to be cost-effective or cost-saving. Despite optimism, the strength of the evidence supporting this assertion has been limited. In this systematic review the ...body of evidence related to economic evaluations of mHealth interventions is assessed and summarized.
Seven electronic bibliographic databases, grey literature, and relevant references were searched. Eligibility criteria included original articles, comparison of costs and consequences of interventions (one categorized as a primary mHealth intervention or mHealth intervention as a component of other interventions), health and economic outcomes and published in English. Full economic evaluations were appraised using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist and The PRISMA guidelines were followed.
Searches identified 5902 results, of which 318 were examined at full text, and 39 were included in this review. The 39 studies spanned 19 countries, most of which were conducted in upper and upper-middle income countries (34, 87.2%). Primary mHealth interventions (35, 89.7%), behavior change communication type interventions (e.g., improve attendance rates, medication adherence) (27, 69.2%), and short messaging system (SMS) as the mHealth function (e.g., used to send reminders, information, provide support, conduct surveys or collect data) (22, 56.4%) were most frequent; the most frequent disease or condition focuses were outpatient clinic attendance, cardiovascular disease, and diabetes. The average percent of CHEERS checklist items reported was 79.6% (range 47.62-100, STD 14.18) and the top quartile reported 91.3-100%. In 29 studies (74.3%), researchers reported that the mHealth intervention was cost-effective, economically beneficial, or cost saving at base case.
Findings highlight a growing body of economic evidence for mHealth interventions. Although all studies included a comparison of intervention effectiveness of a health-related outcome and reported economic data, many did not report all recommended economic outcome items and were lacking in comprehensive analysis. The identified economic evaluations varied by disease or condition focus, economic outcome measurements, perspectives, and were distributed unevenly geographically, limiting formal meta-analysis. Further research is needed in low and low-middle income countries and to understand the impact of different mHealth types. Following established economic reporting guidelines will improve this body of research.
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Electronic health records (EHRs) are linked with documentation burden resulting in clinician burnout. While clear classifications and validated measures of burnout exist, documentation burden ...remains ill-defined and inconsistently measured. We aim to conduct a scoping review focused on identifying approaches to documentation burden measurement and their characteristics.
Based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Extension for Scoping Reviews (ScR) guidelines, we conducted a scoping review assessing MEDLINE, Embase, Web of Science, and CINAHL from inception to April 2020 for studies investigating documentation burden among physicians and nurses in ambulatory or inpatient settings. Two reviewers evaluated each potentially relevant study for inclusion/exclusion criteria.
Of the 3482 articles retrieved, 35 studies met inclusion criteria. We identified 15 measurement characteristics, including 7 effort constructs: EHR usage and workload, clinical documentation/review, EHR work after hours and remotely, administrative tasks, cognitively cumbersome work, fragmentation of workflow, and patient interaction. We uncovered 4 time constructs: average time, proportion of time, timeliness of completion, activity rate, and 11 units of analysis. Only 45.0% of studies assessed the impact of EHRs on clinicians and/or patients and 40.0% mentioned clinician burnout.
Standard and validated measures of documentation burden are lacking. While time and effort were the core concepts measured, there appears to be no consensus on the best approach nor degree of rigor to study documentation burden.
Further research is needed to reliably operationalize the concept of documentation burden, explore best practices for measurement, and standardize its use.
•In all, 14 adult-ECMO experts participated in four focus groups and two interviews.•Seven themes were identified in this qualitative multidisciplinary global study.•Extubation during ECMO based on ...patient selection is recommended.•Extubation using an ECPG or team decision-making is recommended.
Extracorporeal membrane oxygenation in adults (adult-ECMO), a modification of cardiopulmonary bypass is increasingly used. Liberation from mechanical ventilation, or extubation, during adult-ECMO remains a challenge.
This study aimed to understand expert perceptions of the reasonableness of extubation during adult-ECMO and the usefulness of an extubation clinical practice guideline (ECPG).
Homogeneous purposive sampling, focus groups, and interviews with a discussion guide, and direct content, thematic analysis were used.
Fourteen volunteers participated with different educational levels (79% Doctor of Medicine, 14% Registered Nurse, 7% Nurse Practitioner), from high-volume ECMO centers of various annual ECMO runs (50% 30–49 ECMO/year, 36% 50–99 ECMO/year, 14% >100 ECMO/year) worldwide (64% North America, 21% South America, 7% Europe, 7% Asia). Seven themes were identified: paucity of evidence, mindsets towards using an ECPG, barriers, criteria and benefits of extubation, culture towards extubation and vision of the future. Participants recommended aiming for extubation based on patient selection, and a standardized extubation approach with an ECPG or team decision-making.
Application of adult-ECMO is expanding, during which extubation remains difficult. Experts recommend two methods of a standardized extubation approach.
About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve ...identification of patients at risk for negative outcomes.
The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).
This study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit HHC agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation).
Best performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/communication, and service use being the most frequent categories.
This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources.
Abstract
Objective
Understand the perceived role of electronic health records (EHR) and workflow fragmentation on clinician documentation burden in the emergency department (ED).
Methods
From ...February to June 2022, we conducted semistructured interviews among a national sample of US prescribing providers and registered nurses who actively practice in the adult ED setting and use Epic Systems’ EHR. We recruited participants through professional listservs, social media, and email invitations sent to healthcare professionals. We analyzed interview transcripts using inductive thematic analysis and interviewed participants until we achieved thematic saturation. We finalized themes through a consensus-building process.
Results
We conducted interviews with 12 prescribing providers and 12 registered nurses. Six themes were identified related to EHR factors perceived to contribute to documentation burden including lack of advanced EHR capabilities, absence of EHR optimization for clinicians, poor user interface design, hindered communication, increased manual work, and added workflow blockages, and five themes associated with cognitive load. Two themes emerged in the relationship between workflow fragmentation and EHR documentation burden: underlying sources and adverse consequences.
Discussion
Obtaining further stakeholder input and consensus is essential to determine whether these perceived burdensome EHR factors could be extended to broader contexts and addressed through optimizing existing EHR systems alone or through a broad overhaul of the EHR’s architecture and primary purpose.
Conclusion
While most clinicians perceived that the EHR added value to patient care and care quality, our findings underscore the importance of designing EHRs that are in harmony with ED clinical workflows to alleviate the clinician documentation burden.
Applications using artificial intelligence (AI) have shown potential to assist nurses with patient care activities. Nurses are in a position to influence the judicious use of AI that best supports ...practice needs in the future by participating in the design, development, testing, and evaluation of solutions. Thus, it is important for nurses at all levels to have a general understanding of the key concepts, potential benefits of AI, and unintended consequences as they consider its adoption in clinical practice. This article includes introductory level discussion about data management and components of AI that include real-world cases in use by nurses and offers implications for nursing practice.
We aimed to build prediction models for shift-level emergency department (ED) patient volume that could be used to facilitate prediction-driven staffing. We sought to evaluate the predictive power of ...rich real-time information and understand 1) which real-time information had predictive power and 2) what prediction techniques were appropriate for forecasting ED demand.
We conducted a retrospective study in an ED site in a large academic hospital in New York City. We examined various prediction techniques, including linear regression, regression trees, extreme gradient boosting, and time series models. By comparing models with and without real-time predictors, we assessed the potential gain in prediction accuracy from real-time information.
Real-time predictors improved prediction accuracy on models without contemporary information from 5% to 11%. Among extensive real-time predictors examined, recent patient arrival counts, weather, Google trends, and concurrent patient comorbidity information had significant predictive power. Out of all the forecasting techniques explored, SARIMAX (Seasonal Autoregressive Integrated Moving Average with eXogenous factors) achieved the smallest out-of-sample the root mean square error (RMSE) of 14.656 and mean absolute prediction error (MAPE) of 8.703%. Linear regression was the second best, with out-of-sample RMSE and MAPE equal to 15.366 and 9.109%, respectively.
Real-time information was effective in improving the prediction accuracy of ED demand. Practice and policy implications for designing staffing paradigms with real-time demand forecasts to reduce ED congestion were discussed.
Nurse practitioners (NPs) play a critical role in delivering primary care, particularly to chronically ill elderly. Yet, many NPs practice in poor work environments which may affect patient outcomes.
...We investigated the relationship between NP work environments in primary care practices and hospitalizations and emergency department (ED) use among chronically ill elderly.
We used a cross-sectional design to collect survey data from NPs about their practices. The survey data were merged with Medicare claims data.
In total, 979 primary care practices employing NPs and delivering care to chronically ill Medicare beneficiaries (n=452,931) from 6 US states were included.
NPs completed the Nurse Practitioner-Primary Care Organizational Climate Questionnaire-a valid and reliable measure for work environment. Data on hospitalizations and ED use was obtained from Medicare claims. We used Cox regression models to estimate risk ratios.
After controlling for covariates, we found statistically significant associations between practice-level NP work environment and 3 outcomes: Ambulatory Care Sensitive (ACS) ED visits, all-cause ED visits, and all-cause hospitalizations. With a 1-unit increase in the work environment score, the risk of an ACS-ED visit decreased by 4.4% risk ratio (RR)=0.956; 99% confidence interval (CI): 0.918-0.995; P=0.004, an ED visit by 3.5% (RR=0.965; 99% CI: 0.933-0.997; P=0.005), and a hospitalization by 4.0% (RR=0.960;99% CI: 0.928-0.993; P=0.002). There was no relationship between NP work environment and ACS hospitalizations.
Favorable NP work environments are associated with lower hospital and ED utilization. Practice managers should focus on NP work environments in quality improvement strategies.
Introduction
This study examines the relationships among recent adverse childhood experiences (ACEs), somatic symptoms, and anxiety/depression symptoms during adolescence and whether ...anxiety/depression symptoms mediate the relationship between ACEs and somatic symptoms.
Methods
Longitudinal prospective data from the Longitudinal Studies of Child Abuse and Neglect study of 1354 children and their primary caregivers in the United States was used in this study. A longitudinal cross‐lagged path analysis among recent ACEs, anxiety/depression symptoms, and somatic symptoms at three points during adolescence (ages 12, 14, and 16 years) was conducted.
Results
The sample was 51% female and 53% African American. The results indicated significant concurrent associations between recent ACEs and increased anxiety/depression symptoms at ages 12, 14, and 16 (β = .27, p < .001; β = .15, p < .001; β = .07, p < .05) and between anxiety/depression symptoms and increased somatic symptoms at ages 12, 14, and 16 years (β = .44, p < .001; β = .39, p < .001; β = .49, p < .001). Moreover, anxiety/depression symptoms significantly mediated the relationship between recent ACEs and concurrent somatic symptoms at ages 12, 14, and 16 years (β = .12, p < .001; β = .06, p < .001; β = .04, p < .05). However, there was no significant relationship between recent ACEs and somatic symptoms.
Conclusion
The findings suggest that anxiety/depression symptoms mediate the concurrent relationships between recent ACEs and somatic symptoms at ages 12, 14, and 16. Clinicians should consider assessing anxiety/depression symptoms and possible concurrent exposure to ACEs when caring for adolescents who present with somatic symptoms.
Early identification and interventions are imperative for mitigating the harmful effects of adverse childhood experiences (ACEs). Nonetheless, a substantial barrier persists in identifying ...adolescents experiencing ACEs. One understudied avenue for early identification of ACEs is through the examination of somatic symptoms endorsed by adolescents. Understanding the relationship between recent ACEs exposure and somatic symptoms may serve as a useful indicator for identifying adolescents affected by ACEs. This study examines the relationships between recent exposure to ACEs (within the past one to two years) and somatic symptoms across adolescence (ages 12–16 years). Longitudinal prospective data of 1354 child and caregiver dyads from the Longitudinal Studies of Child Abuse and Neglect were used in this study. Data from three time points, when adolescents were 12, 14, and 16, were used to conduct longitudinal path analyses. Somatic symptoms- defined as physical symptoms without known medical causes- were measured using the caregiver-report subscale of the Child Behavior Checklist. Recent ACEs in the past one to two years were measured using an index score summing exposure to nine ACE variables. The results indicated a significant association between recent ACEs and increased somatic symptoms at age 12. However, there were no significant associations between recent ACEs and somatic symptoms at ages 14 and 16. The findings indicate a notably stronger relationship between recent ACEs exposure and the presence of increased somatic symptoms at the age of 12, in contrast to what is observed at ages 14 and 16. This finding suggests that somatic symptoms during early adolescence may suggest underlying issues, potentially stemming from stressors such as ACEs.
Highlights
ACEs measured at ages 10–12 were associated with increased somatic symptoms at age 12.
Recent exposure to ACEs were significantly associated with increased somatic symptoms in early (age 12) adolescence, but not later adolescence (14–16).
Findings suggest the need for healthcare professionals to screen for somatic symptoms as potential indicators of recent ACEs, particularly in early adolescence.
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