Impact of Mobile Device-Based Clinical Decision Support Tool on Guideline Adherence and Mental Workload

Piloting a Mobile Clinical Decision Support Application for Pediatric Clinical Guidelines

Mobile Clinical Decision Support Systems (CDSSs) represent an increasingly utilized technology to promote clinical guideline use. We sought to explore clinician guideline use and access preferences during implementation of a mobile guideline app at a free-standing children's hospital integrating 23 guidelines. Surveys included demographic variables and access preferences among anonymous onboarded clinicians in January 2022. Response rate was 21.8% (57/261) among onboarded users, mostly attending (59.6%) and resident/fellow physicians (21.1%) in inpatient (42.1%) and emergency department (31.6%) settings. Onboarded users accessed guidelines on over half of shifts (68.4%) and quickly (80.7%, <1 minute). Overall, most users reported favorable patterns for adoption of mobile CDSSs as useful adjuncts to existing formats. Users reported more ease of access and frequent guideline usage, particularly for younger clinicians. Guidelines related to antibiotic decision-making or newer disease processes were most useful. Further study is needed on electronic health record incorporation, adherence, and patient outcomes.

Investigating Weekend Effect in the Management of Upper and Lower Extremity Degloving Injuries

Background

Weekend presentation has been associated with adverse outcomes in emergent conditions, including stroke, myocardial infarction, and critical limb ischemia. We examine whether a weekend effect exists in the management of and outcomes after extremity degloving injuries.

Methods

The cohort included adults presenting with open extremity degloving injuries to a tertiary level one trauma center between June 2018 and May 2022. We collected demographics, comorbidities, injury information, interventions, and complications. Propensity score weighting was used to minimize confounding differences between those presenting on weekends (Sat-Sun) versus weekdays (Mon-Fri). Weighted regressions were used to examine differences in interventions by day of presentation. Multivariable weighted regressions accounting for differences in interventions received were used to examine whether weekend presentation was associated with amputation risk, complications, or functional deficits.

Results

Ninety-five patients with 100 open extremity degloving injuries were included. In total, 39% of injuries were weekend-presenting. There was a higher rate of noninsulin-dependent diabetes among patients presenting on weekends (P = 0.03). Weekend-presenting injuries had higher median Injury Severity Scores (P = 0.04). Propensity-weighted regression analysis revealed differences in interventions received on weekends, including lower rates of pedicled and free flaps and bone graft, and increased rates of negative-pressure wound therapy (P ≤ 0.02). Multivariable regression analysis revealed weekend presentation was a significant independent risk factor for amputation of the affected extremity [odds ratio 2.27, 95% CI (1.01-5.33), P = 0.05].

Conclusion

Weekend presentation may impact interventions received and amputation risk in patients presenting with open extremity degloving injuries.

Clinicians' Perceptions of Artificial Intelligence: Focus on Workload, Risk, Trust, Clinical Decision Making, and Clinical Integration

Artificial intelligence (AI) offers the potential to revolutionize healthcare, from improving diagnoses to patient safety. However, many healthcare practitioners are hesitant to adopt AI technologies fully. To understand why, this research explored clinicians' views on AI, especially their level of trust, their concerns about potential risks, and how they believe AI might affect their day-to-day workload. We surveyed 265 healthcare professionals from various specialties in the U.S. The survey aimed to understand their perceptions and any concerns they might have about AI in their clinical practice. We further examined how these perceptions might align with three hypothetical approaches to integrating AI into healthcare: no integration, sequential (step-by-step) integration, and parallel (side-by-side with current practices) integration. The results reveal that clinicians who view AI as a workload reducer are more inclined to trust it and are more likely to use it in clinical decision making. However, those perceiving higher risks with AI are less inclined to adopt it in decision making. While the role of clinical experience was found to be statistically insignificant in influencing trust in AI and AI-driven decision making, further research might explore other potential moderating variables, such as technical aptitude, previous exposure to AI, or the specific medical specialty of the clinician. By evaluating three hypothetical scenarios of AI integration in healthcare, our study elucidates the potential pitfalls of sequential AI integration and the comparative advantages of parallel integration. In conclusion, this study underscores the necessity of strategic AI integration into healthcare. AI should be perceived as a supportive tool rather than an intrusive entity, augmenting the clinicians' skills and facilitating their workflow rather than disrupting it. As we move towards an increasingly digitized future in healthcare, comprehending the among AI technology, clinician perception, trust, and decision making is fundamental.

Team-focused implementation strategies to improve implementation of mental health screening and referral in rural Children's Advocacy Centers: study protocol for a pilot cluster randomized hybrid type 2 trial

BACKGROUND

Children's Advocacy Centers (CACs) use multidisciplinary teams to investigate and respond to maltreatment allegations. CACs play a critical role in connecting children with mental health needs to evidence-based mental health treatment, especially in low-resourced rural areas. Standardized mental health screening and referral protocols can improve CACs' capacity to identify children with mental health needs and encourage treatment engagement. In the team-based context of CACs, teamwork quality is likely to influence implementation processes and outcomes. Implementation strategies that target teams and apply the science of team effectiveness may enhance implementation outcomes in team-based settings.

METHODS

We will use Implementation Mapping to develop team-focused implementation strategies to support the implementation of the Care Process Model for Pediatric Traumatic Stress (CPM-PTS), a standardized screening and referral protocol. Team-focused strategies will integrate activities from effective team development interventions. We will pilot team-focused implementation in a cluster-randomized hybrid type 2 effectiveness-implementation trial. Four rural CACs will implement the CPM-PTS after being randomized to either team-focused implementation (n = 2 CACs) or standard implementation (n = 2 CACs). We will assess the feasibility of team-focused implementation and explore between-group differences in hypothesized team-level mechanisms of change and implementation outcomes (implementation aim). We will use a within-group pre-post design to test the effectiveness of the CPM-PTS in increasing caregivers' understanding of their child's mental health needs and caregivers' intentions to initiate mental health services (effectiveness aim).

CONCLUSIONS

Targeting multidisciplinary teams is an innovative approach to improving implementation outcomes. This study will be one of the first to test team-focused implementation strategies that integrate effective team development interventions. Results will inform efforts to implement evidence-based practices in team-based service settings.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT05679154 . Registered on January 10, 2023.

Workflow interruption and nurses' mental workload in electronic health record tasks: An observational study

BACKGROUND

Workflow interruptions are common in modern work systems. Electronic health record (EHR) tasks are typical tasks involving human-machine interactions in nursing care, but few studies have examined interruptions and nurses' mental workload in the tasks. Therefore, this study aims to investigate how frequent interruptions and multilevel factors affect nurses' mental workload and performance in EHR tasks.

METHODS

A prospective observational study was conducted in a tertiary hospital providing specialist and sub-specialist care from June 1^st to October 31^st, 2021. An observer documented nurses' EHR task interruptions, reactions and performance (errors and near errors) during one-shift observation sessions. Questionnaires were administered at the end of the electronic health record task observation to measure nurses' mental workload for the electronic health record tasks, task difficulty, system usability, professional experience, professional competency, and self-efficacy. Path analysis was used to test a hypothetical model.

RESULTS

In 145 shift observations, 2871 interruptions occurred, and the mean task duration was 84.69 (SD 56.68) minutes per shift. The incidence of error or near error was 158, while 68.35% of errors were self-corrected. The total mean mental workload level was 44.57 (SD 14.08). A path analysis model with adequate fit indices is presented. There was a relationship among concurrent multitasking, task switching and task time. Task time, task difficulty and system usability had direct effects on mental workload. Task performance was influenced by mental workload and professional title. Negative affect mediated the path from task performance to mental workload.

CONCLUSIONS

Nursing interruptions occur frequently in EHR tasks, come from different sources and may lead to elevated mental workload and negative outcomes. By exploring the variables related to mental workload and performance, we offer a new perspective on quality improvement strategies. Reducing harmful interruptions to decrease task time can avoid negative outcomes. Training nurses to cope with interruptions and improve competency in EHR implementation and task operation has the potential to decrease nurses' mental workload and improve task performance. Moreover, improving system usability is beneficial to nurses to mitigate mental workload.

EHR-Integrated Clinical Pathways Promote Education, Confidence, and Save Time for Primary Care Providers

Computerized clinical decision support tools are increasingly necessary and widespread in primary care due to rapidly evolving evidence and shifting clinical guidelines. Clinical pathways are a subtype of clinical decision support tool which aim to link evidence to practice and which require evaluation of benefits and barriers to use to inform user-centered design. The objective was to describe the use and perceived benefits and barriers to evidence-based, disease-specific electronic health record pathways for clinical decision support. Primary care providers at a large integrated health system were surveyed about their use of clinical pathways using an online questionnaire distributed via email in November 2021. Descriptive statistics were estimated and differences in the characteristics and responses by pathway use were assessed using chi-square or Fisher exact tests. The survey response rate was 26% (153/593). There were differences in the response rates between providers by practice type (42% academic versus 54% community; P < 0.001). No difference was found in the demographics of those that used the pathways versus those that did not according to role, age, or length of time in practice. Providers in the academic practice were more likely than those in community practices to have used the pathways. Among providers who used the pathways, 98% agree they have evidence-based information, 98% agree they allow them to take better care of patients, 88% agree they guide clinical-decisions, and 85% agree they save time. The main barrier for those who had used pathways was that they forget about them. Among those who had not used pathways, 35% were unaware that pathways existed. This analysis demonstrates that primary care providers who adopt clinical pathways perceive benefits in several domains. The largest barriers to adoption were that users forgot about pathways or were unaware of them. Future work should focus on dissemination and education, improving tool accessibility, and content optimization to balance complexity with efficiency.

Improving pediatric COVID-19 vaccine uptake using an mHealth tool (MoVeUp): study protocol for a randomized, controlled trial

BACKGROUND

Coronavirus disease 2019 (COVID-19) vaccines demonstrate excellent effectiveness against infection, severe disease, and death. However, pediatric COVID-19 vaccination rates lag among individuals from rural and other medically underserved communities. The research objective of the current protocol is to determine the effectiveness of a vaccine communication mobile health (mHealth) application (app) on parental decisions to vaccinate their children against COVID-19.

METHODS

Custodial parents/caregivers with ≥ 1 child eligible for COVID-19 vaccination who have not yet received the vaccine will be randomized to download one of two mHealth apps. The intervention app will address logistical and motivational barriers to pediatric COVID-19 vaccination. Participants will receive eight weekly push notifications followed by two monthly push notifications (cues to action) regarding vaccinating their child. Through branching logic, users will access customized content based on their locality, degree of rurality-urbanicity, primary language (English/Spanish), race/ethnicity, and child's age to address COVID-19 vaccine knowledge and confidence gaps. The control app will provide push notifications and information on general pediatric health and infection prevention and mitigation strategies based on recommendations from the American Academy of Pediatrics (AAP) and the Centers for Disease Control and Prevention (CDC). The primary outcome is the proportion of children who complete COVID-19 vaccination series. Secondary outcomes include the proportion of children who receive ≥ 1 dose of COVID-19 vaccine and changes in parent/caregiver scores from baseline to immediately post-intervention on the modified WHO SAGE Vaccine Hesitancy Scale adapted for the COVID-19 vaccine.

DISCUSSION

The COVID-19 pandemic inflicts disproportionate harm on individuals from underserved communities, including those in rural settings. Maximizing vaccine uptake in these communities will decrease infection rates, severe illness, and death. Given that most US families from these communities use smart phones, mHealth interventions hold the promise of broad uptake. Bundling multiple mHealth vaccine uptake interventions into a single app may maximize the impact of deploying such a tool to increase COVID-19 vaccination. The new knowledge to be gained from this study will directly inform future efforts to increase COVID-19 vaccination rates across diverse settings and provide an evidentiary base for app-based vaccine communication tools that can be adapted to future vaccine-deployment efforts.

CLINICAL TRIALS REGISTRATION

ClinicalTrials.gov NCT05386355 . Registered on May 23, 2022.

Shared Decision-Making for Drug-Drug Interactions: Formative Evaluation of an Anticoagulant Drug Interaction

Background

Warnings about drug-drug interactions (DDIs) between warfarin and nonsteroidal anti-inflammatory drugs (NSAIDs) within electronic health records indicate potential harm but fail to account for contextual factors and preferences. We developed a tool called DDInteract to enhance and support shared decision-making (SDM) between patients and physicians when both warfarin and NSAIDs are used concurrently. DDInteract was designed to be integrated into electronic health records using interoperability standards.

Objective

The purpose of this study was to conduct a formative evaluation of a DDInteract that incorporates patient and product contextual factors to estimate the risk of bleeding.

Methods

A randomized formative evaluation was conducted to compare DDInteract to usual care (UC) using physician-patient dyads. Using case vignettes, physicians and patients on warfarin participated in simulated virtual clinical encounters where they discussed the use of taking ibuprofen and warfarin concurrently and determined an appropriate therapeutic plan based on the patient’s individualized risk. Dyads were randomized to either DDInteract or UC. Participants completed a postsession interview and survey of the SDM process. This included the 9-item Shared Decision-Making Questionnaire (SDM-Q-9), tool usability and workload National Aeronautics and Space Administration (NASA) Task Load Index, Unified Theory of Acceptance and Use of Technology (UTAUT), Perceived Behavioral Control (PBC) scale, System Usability Scale (SUS), and Decision Conflict Scale (DCS). They also were interviewed after the session to obtain perceptions on DDInteract and UC resources for DDIs.

Results

Twelve dyad encounters were performed using virtual software. Most (n=11, 91.7%) patients were over 50 years of age, and 9 (75%) had been taking warfarin for more than 2 years (75%). Regarding scores on the SDM-Q-9, participants rated DDInteract higher than UC for questions pertaining to helping patients clarify the decision (P=.03), involving patients in the decision (P=.01), displaying treatment options (P<.001), identifying advantages and disadvantages (P=.01), and facilitating patient understanding (P=.01) and discussion of preferences (P=.01). Five of the 8 UTAUT constructs showed differences between the 2 groups, favoring DDInteract (P<.05). Usability ratings from the SUS were significantly higher (P<.05) for physicians using DDInteract compared to those in the UC group but showed no differences from the patient’s perspective. No differences in patient responses were observed between groups using the DCS. During the session debrief, physicians indicated little concern for the additional time or workload entailed by DDInteract use. Both clinicians and patients indicated that the tool was beneficial in simulated encounters to understand and mitigate the risk of harm from this DDI.

Conclusions

Overall, DDInteract may improve encounters where there is a risk of bleeding due to a potential drug-drug interaction involving anticoagulants. Participants rated DDInteract as logical and useful for enhancing SDM. They reported that they would be willing to use the tool for an interaction involving warfarin and NSAIDs.

Improving pediatric COVID-19 vaccine uptake using an mHealth tool (MoVeUP): a randomized, controlled trial

Background: Coronavirus disease 2019 (COVID-19) vaccines demonstrate excellent effectiveness against infection, severe disease, and death. However, pediatric COVID-19 vaccination rates lag among individuals from rural and other medically underserved communities. The research objective of the current protocol is to determine the effectiveness of a vaccine communication mobile health (mHealth) application (app) on parental decisions to vaccinate their children against COVID-19. Methods: Custodial parents/caregivers with ≥1 child eligible for COVID-19 vaccination who have not yet received the vaccine will be randomized to download one of two mHealth apps. The intervention app will address logistical and motivational barriers to pediatric COVID-19 vaccination. Participants will receive eight weekly push notifications followed by two monthly push notifications (cues to action) regarding vaccinating their child. Through branching logic, users will access customized content based on their locality, degree of rurality-urbanicity, primary language (English/Spanish), race/ethnicity, and child's age to address COVID-19 vaccine knowledge and confidence gaps. The control app will provide push notifications and information on general pediatric health and infection prevention and mitigation strategies based on recommendations from the American Academy of Pediatrics (AAP) and the Centers for Disease Control and Prevention (CDC). The primary outcome is the proportion of children who complete COVID-19 vaccination series. Secondary outcomes include the proportion of children who receive ≥1 dose of COVID-19 vaccine and changes in parent/caregiver scores from baseline to immediately post-intervention on the modified WHO SAGE Vaccine Hesitancy Scale adapted for the COVID-19 vaccine. Discussion: The COVID-19 pandemic inflicts disproportionate harm on individuals from underserved communities, including those in rural settings. Maximizing vaccine uptake in these communities will decrease infection rates, severe illness, and death. Given that most US families from these communities use smart phones, mHealth interventions hold the promise of broad uptake. Bundling multiple mHealth vaccine-uptake interventions into a single app may maximize the impact of deploying such a tool to increase COVID-19 vaccination. The new knowledge to be gained from this study will directly inform future efforts to increase COVID-19 vaccination rates across diverse settings and provide an evidentiary base for app-based vaccine communication tools that can be adapted to future vaccine-deployment efforts. Clinical Trials Registration: Name of the registry: clinicaltrials.gov Trial registration number: NCT05386355 Date of registration: May 23, 2022 URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT05386355.

Decision-support tools via mobile devices to improve quality of care in primary healthcare settings

BACKGROUND

The ubiquity of mobile devices has made it possible for clinical decision-support systems (CDSS) to become available to healthcare providers on handheld devices at the point-of-care, including in low- and middle-income countries. The use of CDSS by providers can potentially improve adherence to treatment protocols and patient outcomes. However, the evidence on the effect of the use of CDSS on mobile devices needs to be synthesized. This review was carried out to support a World Health Organization (WHO) guideline that aimed to inform investments on the use of decision-support tools on digital devices to strengthen primary healthcare.

OBJECTIVES

To assess the effects of digital clinical decision-support systems (CDSS) accessible via mobile devices by primary healthcare providers in the context of primary care settings.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, Global Index Medicus, POPLINE, and two trial registries from 1 January 2000 to 9 October 2020. We conducted a grey literature search using mHealthevidence.org and issued a call for papers through popular digital health communities of practice. Finally, we conducted citation searches of included studies.

SELECTION CRITERIA

Study design: we included randomized trials, including full-text studies, conference abstracts, and unpublished data irrespective of publication status or language of publication.  Types of participants: we included studies of all cadres of healthcare providers, including lay health workers and other individuals (administrative, managerial, and supervisory staff) involved in the delivery of primary healthcare services using clinical decision-support tools; and studies of clients or patients receiving care from primary healthcare providers using digital decision-support tools. Types of interventions: we included studies comparing digital CDSS accessible via mobile devices with non-digital CDSS or no intervention, in the context of primary care. CDSS could include clinical protocols, checklists, and other job-aids which supported risk prioritization of patients. Mobile devices included mobile phones of any type (but not analogue landline telephones), as well as tablets, personal digital assistants, and smartphones. We excluded studies where digital CDSS were used on laptops or integrated with electronic medical records or other types of longitudinal tracking of clients.

DATA COLLECTION AND ANALYSIS

A machine learning classifier that gave each record a probability score of being a randomized trial screened all search results. Two review authors screened titles and abstracts of studies with more than 10% probability of being a randomized trial, and one review author screened those with less than 10% probability of being a randomized trial. We followed standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care group. We used the GRADE approach to assess the certainty of the evidence for the most important outcomes.

MAIN RESULTS

Eight randomized trials across varying healthcare contexts in the USA,. India, China, Guatemala, Ghana, and Kenya, met our inclusion criteria. A range of healthcare providers (facility and community-based, formally trained, and lay workers) used digital CDSS. Care was provided for the management of specific conditions such as cardiovascular disease, gastrointestinal risk assessment, and maternal and child health. The certainty of evidence ranged from very low to moderate, and we often downgraded evidence for risk of bias and imprecision. We are uncertain of the effect of this intervention on providers' adherence to recommended practice due to the very low certainty evidence (2 studies, 185 participants). The effect of the intervention on patients' and clients' health behaviours such as smoking and treatment adherence is mixed, with substantial variation across outcomes for similar types of behaviour (2 studies, 2262 participants). The intervention probably makes little or no difference to smoking rates among people at risk of cardiovascular disease but probably increases other types of desired behaviour among patients, such as adherence to treatment. The effect of the intervention on patients'/clients' health status and well-being  is also mixed (5 studies, 69,767 participants). It probably makes little or no difference to some types of health outcomes, but we are uncertain about other health outcomes, including maternal and neonatal deaths, due to very low-certainty evidence. The intervention may slightly improve patient or client acceptability and satisfaction (1 study, 187 participants). We found no studies that reported the time between the presentation of an illness and appropriate management, provider acceptability or satisfaction, resource use, or unintended consequences.

AUTHORS' CONCLUSIONS

We are uncertain about the effectiveness of mobile phone-based decision-support tools on several outcomes, including adherence to recommended practice. None of the studies had a quality of care framework and focused only on specific health areas.   We need well-designed research that takes a systems lens to assess these issues.

Implementing a COVID-19 Discharge Pathway to Improve Patient Safety

The posthospital discharge period is vulnerable for patients with coronavirus disease 2019 (COVID-19). The authors implemented a COVID-19 discharge pathway in the electronic medical record for UCHealth, a 12-hospital health care system, including an academic medical center (University of Colorado Hospital [UCH]), to improve patient safety by standardizing discharge processes for COVID-19 patients. There were 3 key elements: (1) building consensus on discharge readiness criteria, (2) summarizing discharge criteria for disposition locations, and (3) establishing primary care follow-up protocols. The discharge pathway was opened 821 times between April 20, 2020, and June 7, 2020. Of the 436 patients discharged from the hospital medicine service at UCH from April 20, 2020, and June 7, 2020, 18 (4%) were readmitted and 13 (3%) had a 30-day emergency department visit. The main trend observed was venous thromboembolism. This pathway allowed real-time integration of clinical guidelines and complex disposition requirements, decreasing cognitive burden and standardizing care for a complex population.

Estimating the impact of deploying an electronic clinical decision support tool as part of a national practice improvement project

OBJECTIVE

Estimate the impact on clinical practice of using a mobile device-based electronic clinical decision support (mECDS) tool within a national standardization project.

MATERIALS AND METHODS

An mECDS tool (app) was released as part of a change package to provide febrile infant management guidance to clinicians. App usage was analyzed using 2 measures: metric hits per case (metric-related screen view count divided by site-reported febrile infant cases in each designated market area [DMA] monthly) and cumulative prior metric hits per site (DMA metric hits summed from study month 1 until the month preceding the index, divided by sites in the DMA). For each metric, a mixed logistic regression model was fit to model site performance as a function of app usage.

RESULTS

An increase of 200 cumulative prior metric hits per site was associated with increased odds of adherence to 3 metrics: appropriate admission (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06-1.18), appropriate length of stay (OR, 1.20; 95% CI, 1.12-1.28), and inappropriate chest x-ray (OR, 0.82; 95% CI, 0.75-0.91). Ten additional metric hits per case were also associated: OR were 1.18 (95% CI, 1.02-1.36), 1.36 (95% CI, 1.14-1.62), and 0.74 (95% CI, 0.62-0.89).

DISCUSSION

mECDS tools are increasingly being implemented, but their impact on clinical practice is poorly described. To our knowledge, although ecologic in nature, this report is the first to link clinical practice to mECDS use on a national scale and outside of an electronic health record.

CONCLUSIONS

mECDS use was associated with changes in adherence to targeted metrics. Future studies should seek to link mECDS usage more directly to clinical practice and assess other site-level factors.

Future Mobile Device Usage, Requirements, and Expectations of Physicians in German University Hospitals: Web-Based Survey

Background

The use of mobile devices in hospital care constantly increases. However, smartphones and tablets have not yet widely become official working equipment in medical care. Meanwhile, the parallel use of private and official devices in hospitals is common. Medical staff use smartphones and tablets in a growing number of ways. This mixture of devices and how they can be used is a challenge to persons in charge of defining strategies and rules for the usage of mobile devices in hospital care.

Objective

Therefore, we aimed to examine the status quo of physicians’ mobile device usage and concrete requirements and their future expectations of how mobile devices can be used.

Methods

We performed a web-based survey among physicians in 8 German university hospitals from June to October 2019. The online survey was forwarded by hospital management personnel to physicians from all departments involved in patient care at the local sites.

Results

A total of 303 physicians from almost all medical fields and work experience levels completed the web-based survey. The majority regarded a tablet (211/303, 69.6%) and a smartphone (177/303, 58.4%) as the ideal devices for their operational area. In practice, physicians are still predominantly using desktop computers during their worktime (mean percentage of worktime spent on a desktop computer: 56.8%; smartphone: 12.8%; tablet: 3.6%). Today, physicians use mobile devices for basic tasks such as oral (171/303, 56.4%) and written (118/303, 38.9%) communication and to look up dosages, diagnoses, and guidelines (194/303, 64.0%). Respondents are also willing to use mobile devices for more advanced applications such as an early warning system (224/303, 73.9%) and mobile electronic health records (211/303, 69.6%). We found a significant association between the technical affinity and the preference of device in medical care (χs2=53.84, P<.001) showing that with increasing self-reported technical affinity, the preference for smartphones and tablets increases compared to desktop computers.

Conclusions

Physicians in German university hospitals have a high technical affinity and positive attitude toward the widespread implementation of mobile devices in clinical care. They are willing to use official mobile devices in clinical practice for basic and advanced mobile health uses. Thus, the reason for the low usage is not a lack of willingness of the potential users. Challenges that hinder the wider adoption of mobile devices might be regulatory, financial and organizational issues, and missing interoperability standards of clinical information systems, but also a shortage of areas of application in which workflows are adapted for (small) mobile devices.

Predictors of Quality Improvement in Pediatric Asthma Care

BACKGROUND

Little is known about what hospital and emergency department (ED) factors predict performance in pediatric quality improvement efforts.

OBJECTIVES

Identify site characteristics and implementation strategies associated with improvements in pediatric asthma care.

METHODS

In this secondary analysis, we used data from a national quality collaborative. Data on site factors were collected via survey of implementation leaders. Data on quality measures were collected via chart review of children with a primary diagnosis of asthma. ED measures included severity assessment at triage, corticosteroid administration within 60 minutes, avoidance of chest radiographs, and discharge from the hospital. Inpatient measures included early administration of bronchodilator via metered-dose inhaler, screening for tobacco exposure, and caregiver referral to smoking cessation resources. We used multilevel regression models to determine associations between site factors and changes in mean compliance across all measures.

RESULTS

Sixty-four EDs and 70 inpatient units participated. Baseline compliance was similar by site characteristics. We found significantly greater increases in compliance in EDs within nonteaching versus teaching hospitals (12% vs 5%), smaller versus larger hospitals (10% vs 4%), and rural and urban versus suburban settings (6%-7% vs 3%). In inpatient units, we also found significantly greater increases in compliance in nonteaching versus teaching hospitals (36% vs 17%) and community versus children's hospitals (23% vs 14%). Changes in compliance were not associated with organizational readiness or number of audit and feedback sessions or improvement cycles.

CONCLUSIONS

Specific hospital and ED characteristics are associated with improvements in pediatric asthma care. Identifying setting-specific barriers may facilitate more targeted implementation support.

Optimizing the Cognitive Space of Nursing Work Through Electronic Medical Records

The incorporation of electronic medical records into nursing practice highlights the need to facilitate communication among nurses. The extensive use of information suggests that electronic medical records should be considered in the cognitive workspace to manage information and facilitate communication. The purpose of this study was to construct an integrative model to explain the role of electronic medical records in the cognitive workspace. This work is grounded in the Theory of Swift and Even Flow and Distributive Cognition. The Distributive Cognitive model views the workplace as a cognitive system, such that cognitive processes do not occur in individual clinicians, but as a collaborative effort among nurses. The Theory of Swift and Even Flow was used to explain the flow of information among nurses. We used a qualitative approach to gather data from nurses at local inpatient facilities. Seven focus groups among three facilities were completed (n = 34). A semistructured questionnaire guided the focus group sessions. The results suggest that electronic medical records contribute to the cognitive workspace by serving as a conduit for information to be collected and distributed. These systems may positively influence nursing care when the quality, quantity, and timeliness of information are optimized.