Medication for Opioid Use Disorder During Pregnancy - Maternal and Infant Network to Understand Outcomes Associated with Use of Medication for Opioid Use Disorder During Pregnancy (MAT-LINK), 2014-2021

Problem

Medication for opioid use disorder (MOUD) is recommended for persons with opioid use disorder (OUD) during pregnancy. However, knowledge gaps exist about best practices for management of OUD during pregnancy and these data are needed to guide clinical care.

Period Covered

2014-2021.

Description of the System

Established in 2019, the Maternal and Infant Network to Understand Outcomes Associated with Medication for Opioid Use Disorder During Pregnancy (MAT-LINK) is a surveillance network of seven clinical sites in the United States. Boston Medical Center, Kaiser Permanente Northwest, The Ohio State University, and the University of Utah were the initial clinical sites in 2019. In 2021, three clinical sites were added to the network (the University of New Mexico, the University of Rochester, and the University of South Florida). Persons receiving care at the seven clinical sites are diverse in terms of geography, urbanicity, race and ethnicity, insurance coverage, and type of MOUD received. The goal of MAT-LINK is to capture demographic and clinical information about persons with OUD during pregnancy to better understand the effect of MOUD on outcomes and, ultimately, provide information for clinical care and public health interventions for this population. MAT-LINK maintains strict confidentiality through robust information technology architecture. MAT-LINK surveillance methods, population characteristics, and evaluation findings are described in this inaugural surveillance report. This report is the first to describe the system, presenting detailed information on funding, structure, data elements, and methods as well as findings from a surveillance evaluation. The findings presented in this report are limited to selected demographic characteristics of pregnant persons overall and by MOUD treatment status. Clinical and outcome data are not included because data collection and cleaning have not been completed; initial analyses of clinical and outcome data will begin in 2023.

Results

The MAT-LINK surveillance network gathered data on 5,541 reported pregnancies with a known pregnancy outcome during 2014-2021 among persons with OUD from seven clinical sites. The mean maternal age was 29.7 (SD = ±5.1) years. By race and ethnicity, 86.3% of pregnant persons were identified as White, 25.4% as Hispanic or Latino, and 5.8% as Black or African American. Among pregnant persons, 81.6% had public insurance, and 84.4% lived in urban areas. Compared with persons not receiving MOUD during pregnancy, those receiving MOUD during pregnancy were more likely to be older and White and to have public insurance. The evaluation of the surveillance system found that the initial four clinical sites were not representative of demographics of the South or Southwest regions of the United States and had low representation from certain racial and ethnic groups compared with the overall U.S. population; however, the addition of three clinical sites in 2021 made the surveillance network more representative. Automated extraction and processing improved the speed of data collection and analysis. The ability to add new clinical sites and variables demonstrated the flexibility of MAT-LINK.

Interpretation

MAT-LINK is the first surveillance system to collect comprehensive, longitudinal data on pregnant person-infant dyads with perinatal outcomes associated with MOUD during pregnancy from multiple clinical sites. Analyses of clinical site data demonstrated different sociodemographic characteristics between the MOUD and non-MOUD treatment groups.

Public Health Actions

MAT-LINK is a timely and flexible surveillance system with data on approximately 5,500 pregnancies. Ongoing data collection and analyses of these data will provide information to support clinical and public health guidance to improve health outcomes among pregnant persons with OUD and their children.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Demonstration Project: Transitioning a Research Network to New Single IRB Platforms

Since the 2016 National Institutes of Health (NIH) mandate to use a single IRB (sIRB) in multicenter research, institutions have struggled to operationalize the process. In this demonstration project, the University of Utah Trial Innovation Center assisted the Collaborative Pediatric Critical Care Research Network to transition from using individually negotiated reliance agreements and paper-based documentation to a new sIRB master agreement and an informatics platform to capture reliance documentation. Lessons learned that can guide other academic institutions and IRBs as they operationalize sIRBs included the need for sites to understand what type of engagement or reliance is required and their need to understand the difference between reliance and activation. Requirements around local review remain poorly understood. Further research is needed to determine approaches that can achieve the NIH vision of reviews becoming more efficient and improving study start-up times, relieving administrative burden while advancing human research protections.

Consent Builder: an innovative tool for creating research informed consent documents

Objective

To describe process innovations related to research informed consent documents, and development and formative evaluation of Consent Builder, a platform for generating consent documents for multicenter studies.

Materials and Methods

Analysis of Institutional Review Board workflows and documents, followed by process redesign, document redesign, and software development. Locally developed software leverages REDCap and LaTeX. A small-scale usability study was conducted.

Results

Process innovations were combining document types, and conceptualizing 2-part informed consent documents: part 1 standardizing the study description and part 2 with local site verbiage. Consent Builder was implemented in the Trial Innovation Network. User survey scores were acceptable; but areas for improvement were noted. LaTeX coding was the biggest challenge for users.

Discussion

The process changes were generally well accepted. The software implementation uncovered un-accounted for assumptions, and variability in IRB review workflow across centers. Technical modifications may be needed before widespread implementation.

Conclusion

We demonstrated proof-of-concept of an approach to generate research consent documents that are consistent across sites in study description, but which allow for customization of local site verbiage. The Consent Builder tool is an example of an operational innovation, helping meet a need that arose in part due to regulations around use of Single IRB for multicenter trials.

Provider Preferences for Patient-Generated Health Data Displays in Pediatric Asthma: A Participatory Design Approach

Objective  There is a lack of evidence on how to best integrate patient-generated health data (PGHD) into electronic health record (EHR) systems in a way that supports provider needs, preferences, and workflows. The purpose of this study was to investigate provider preferences for the graphical display of pediatric asthma PGHD to support decisions and information needs in the outpatient setting.

Methods  In December 2019, we conducted a formative evaluation of information display prototypes using an iterative, participatory design process. Using multiple types of PGHD, we created two case-based vignettes for pediatric asthma and designed accompanying displays to support treatment decisions. Semi-structured interviews and questionnaires with six participants were used to evaluate the display usability and determine provider preferences.

Results  We identified provider preferences for display features, such as the use of color to indicate different levels of abnormality, the use of patterns to trend PGHD over time, and the display of environmental data. Preferences for display content included the amount of information and the relationship between data elements.

Conclusion  Overall, provider preferences for PGHD include a desire for greater detail, additional sources, and visual integration with relevant EHR data. In the design of PGHD displays, it appears that the visual synthesis of multiple PGHD elements facilitates the interpretation of the PGHD. Clinicians likely need more information to make treatment decisions when PGHD displays are introduced into practice. Future work should include the development of interactive interface displays with full integration of PGHD into EHR systems.

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

A Scoping Review and Content Analysis of Common Depressive Symptoms of Young People

School nurses are the most accessible health care providers for many young people including adolescents and young adults. Early identification of depression results in improved outcomes, but little information is available comprehensively describing depressive symptoms specific to this population. The aim of this study was to develop a taxonomy of depressive symptoms that were manifested and described by young people based on a scoping review and content analysis. Twenty-five journal articles that included narrative descriptions of depressive symptoms in young people were included. A total of 60 depressive symptoms were identified and categorized into five dimensions: behavioral (n = 8), cognitive (n = 14), emotional (n = 15), interpersonal (n = 13), and somatic (n = 10). This comprehensive depression symptom taxonomy can help school nurses to identify young people who may experience depression and will support future research to better screen for depression.

Patient-Generated Health Data in Pediatric Asthma: Exploratory Study of Providers' Information Needs

BACKGROUND

Adolescents are using mobile health apps as a form of self-management to collect data on symptoms, medication adherence, and activity. Adding functionality to an electronic health record (EHR) to accommodate disease-specific patient-generated health data (PGHD) may support clinical care. However, little is known on how to incorporate PGHD in a way that informs care for patients. Pediatric asthma, a prevalent health issue in the United States with 6 million children diagnosed, serves as an exemplar condition to examine information needs related to PGHD.

OBJECTIVE

In this study we aimed to identify and prioritize asthma care tasks and decisions based on pediatric asthma guidelines and identify types of PGHD that might support the activities associated with the decisions. The purpose of this work is to provide guidance to mobile health app developers and EHR integration.

METHODS

We searched the literature for exemplar asthma mobile apps and examined the types of PGHD collected. We identified the information needs associated with each decision in accordance with consensus-based guidelines, assessed the suitability of PGHD to meet those needs, and validated our findings with expert asthma providers.

RESULTS

We mapped guideline-derived information needs to potential PGHD types and found PGHD that may be useful in meeting information needs. Information needs included types of symptoms, symptom triggers, medication adherence, and inhaler technique. Examples of suitable types of PGHD were Asthma Control Test calculations, exposures, and inhaler use. Providers suggested uncontrolled asthma as a place to focus PGHD efforts, indicating that they preferred to review PGHD at the time of the visit.

CONCLUSIONS

We identified a manageable list of information requirements derived from clinical guidelines that can be used to guide the design and integration of PGHD into EHRs to support pediatric asthma management and advance mobile health app development. Mobile health app developers should examine PGHD information needs to inform EHR integration efforts.

Anemia Management in Rural Haitian Children: A Mixed Methods Study

INTRODUCTION

We examined factors influencing anemia outcomes in rural children following implementation of a prevention program.

METHOD

Mixed methods study of children, parents, and clinicians utilized statistical modeling and content/ethnographic analysis. Retrospective chart abstraction evaluated treatments administered and measured hemoglobin in children aged 6 to 59 months (n = 161). Prospective interviews/questionnaires examined parent (n = 51) and clinician (n = 19) perceptions.

RESULTS

Anemia prevalence decreased by 21.2%. Predictors of increased hemoglobin were clinic visit number and age at first visit. Once anemia improved, children were likely to remain improved (P = .65). Despite favorable program perceptions, stakeholders emphasized ecological barriers, including social disadvantage and local practices.

DISCUSSION

Socioeconomic factors prevented guideline concordant behaviors. Persistent attention to intrapersonal, interpersonal, and community social determinants is a sine qua non for successfully managing the epidemic. The first step to provide culturally congruent care is to explicitly acknowledge that guideline-concordant behaviors are often complex.

Patient-generated health data and electronic health record integration: a scoping review

Objectives

Patient-generated health data (PGHD) are clinically relevant data captured by patients outside of the traditional care setting. Clinical use of PGHD has emerged as an essential issue. This study explored the evidence to determine the extent of and describe the characteristics of PGHD integration into electronic health records (EHRs).

Methods

In August 2019, we conducted a systematic scoping review. We included studies with complete, partial, or in-progress PGHD and EHR integration within a clinical setting. The retrieved articles were screened for eligibility by 2 researchers, and data from eligible articles were abstracted, coded, and analyzed.

Results

A total of 19 studies met inclusion criteria after screening 9463 abstracts. Most of the study designs were pilots and all were published between 2013 and 2019. Types of PGHD were biometric and patient activity (57.9%), questionnaires and surveys (36.8%), and health history (5.3%). Diabetes was the most common patient condition (42.1%) for PGHD collection. Active integration (57.9%) was slightly more common than passive integration (31.6%). We categorized emergent themes into the 3 steps of PGHD flow. Themes emerged concerning resource requirements, data delivery to the EHR, and preferences for review.

Discussion

PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.

Loneliness as a mediator of the impact of social isolation on cognitive functioning of Chinese older adults

BACKGROUND AND OBJECTIVE

older adults have increased risk of social isolation, loneliness and cognitive functioning impairment, but the relationships among these factors are not conclusive. We investigated the potential mediation mechanism of loneliness on the association between social isolation and cognitive functioning among Chinese older adults within their cultural context.

DESIGN

secondary analysis of the baseline wave (2011-12) of the harmonised China Health and Retirement Longitudinal Study.

SETTING AND SUBJECTS

community-dwelling older adults in China (N = 7,410 participants aged 60-101 years).

METHODS

we applied a multiple indicator multiple cause approach to determine whether the construct of social isolation is well defined by four indicators (social activity engagement, weekly adult children contact, caregiving for grandchildren and living alone) and used structural equation modelling to examine the direct and indirect effects among variables of interest.

RESULTS

the results demonstrated that social activity engagement, weekly adult children contact and caregiving for grandchildren were significantly related to social isolation (β = -0.26 to -0.28) (Living alone was fixed to 1 for model identification.) The indirect effect of social isolation on cognitive functioning through loneliness was significant (β = -0.15), indicating loneliness was an important mediator. However, the direct effect of social isolation on cognitive functioning also remained significant (β = -0.83), suggesting a partial mediation effect.

CONCLUSIONS

our study highlights the mediation role of loneliness in the relationship between social isolation and cognitive functioning among Chinese older adults. The findings support the beneficial effects of maintaining social relations and coping with feelings of loneliness on older adults' cognitive functioning.

Patient-generated health data and electronic health record integration: protocol for a scoping review

INTRODUCTION

The objective of this study is to determine the extent and describe the nature of patient-generated health data (PGHD) integration into electronic health records (EHRs) using systematic scoping methods to review the available literature. PGHD have the potential to enhance decision making by providing the valuable information that may not be ordinarily captured during a routine care visit. These data which are captured from mobile devices, such as smartphones, activity trackers and other sensors, should be integrated into clinical workflows to allow for optimal use by clinicians.

METHODS AND ANALYSIS

This study aims to conduct a rigorous scoping review to explore evidence related to the integration of PGHD into EHRs. Using the framework developed by Arksey and O'Malley, we will create a systematic search strategy, chart data from the relevant articles, and use a qualitative, thematic approach to analyse the data. This review will enable the identification of types of integration and describe challenges and barriers to integrating PGHD.

ETHICS AND DISSEMINATION

Database searches will be initiated in June 2019. The review is expected to be completed by October 2019. As the content of the full-text articles emerges, the authors will summarise the characteristics related to the integration of PGHD. The findings of this scoping review will identify research gaps and present implications for future research.

Navigating the Search for Patient Generated Health Data

With massive amounts of mobile health data generated by patients, there is a growing amount of research conducted to understand their impact on patient care. The MeSH heading for patient generated health data was established in early 2018, complicating searches for PGHD research prior to 2018. In conducting a search of scientific databases, keywords are presented along with their degree of representation in the literature to help inform future searches.

Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis

Background

Both chronic obstructive pulmonary disease (COPD) and asthma incur heavy health care burdens. To support tailored preventive care for these 2 diseases, predictive modeling is widely used to give warnings and to identify patients for care management. However, 3 gaps exist in current modeling methods owing to rarely factoring in temporal aspects showing trends and early health change: (1) existing models seldom use temporal features and often give late warnings, making care reactive. A health risk is often found at a relatively late stage of declining health, when the risk of a poor outcome is high and resolving the issue is difficult and costly. A typical model predicts patient outcomes in the next 12 months. This often does not warn early enough. If a patient will actually be hospitalized for COPD next week, intervening now could be too late to avoid the hospitalization. If temporal features were used, this patient could potentially be identified a few weeks earlier to institute preventive therapy; (2) existing models often miss many temporal features with high predictive power and have low accuracy. This makes care management enroll many patients not needing it and overlook over half of the patients needing it the most; (3) existing models often give no information on why a patient is at high risk nor about possible interventions to mitigate risk, causing busy care managers to spend more time reviewing charts and to miss suited interventions. Typical automatic explanation methods cannot handle longitudinal attributes and fully address these issues.

Objective

To fill these gaps so that more COPD and asthma patients will receive more appropriate and timely care, we will develop comprehensible data-driven methods to provide accurate early warnings of poor outcomes and to suggest tailored interventions, making care more proactive, efficient, and effective.

Methods

By conducting a secondary data analysis and surveys, the study will: (1) use temporal features to provide accurate early warnings of poor outcomes and assess the potential impact on prediction accuracy, risk warning timeliness, and outcomes; (2) automatically identify actionable temporal risk factors for each patient at high risk for future hospital use and assess the impact on prediction accuracy and outcomes; and (3) assess the impact of actionable information on clinicians’ acceptance of early warnings and on perceived care plan quality.

Results

We are obtaining clinical and administrative datasets from 3 leading health care systems’ enterprise data warehouses. We plan to start data analysis in 2020 and finish our study in 2025.

Conclusions

Techniques to be developed in this study can boost risk warning timeliness, model accuracy, and generalizability; improve patient finding for preventive care; help form tailored care plans; advance machine learning for many clinical applications; and be generalized for many other chronic diseases.

International Registered Report Identifier (IRRID)

PRR1-10.2196/13783

Impact of computerized provider order entry (CPOE) on length of stay and mortality

Objective: To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE).

Materials and Methods: A 5-year retrospective pre-post study evaluated 66 186 patients and 104 153 admissions (49 683 pre-CPOE, 54 470 post-CPOE) at an academic medical center. Generalized linear mixed statistical tests controlled for 17 potential confounders with 2 models per outcome.

Results: After controlling for covariates, CPOE remained a significant statistical predictor of decreased LOS and mortality. LOS decreased by 0.90 days, P < .0001. Mortality decrease varied by model: 1 death per 1000 admissions (pre = 0.006, post = 0.0005, P < .001) or 3 deaths (pre = 0.008, post = 0.005, P < .01). Mortality and LOS decreased in medical and surgical units but increased in intensive care units.

Discussion: This study examined CPOE at multiple levels. Given the inability to randomize CPOE assignment, these results may only be applicable to the local setting. Temporal trends found in this study suggest that hospital-wide implementations may have impacted nursing staff and new residents. Differences in the results were noted at the patient care unit and room levels. These differences may partly explain the mixed results from previous studies.

Conclusion: Controlling for confounders, CPOE implementation remained a statistically significant predictor of LOS and mortality at this site. Mortality appears to be a sensitive outcome indicator with regard to hospital-wide implementations and should be further studied.

Mechanical Ventilation and Decision Support in Pediatric Intensive Care

Respiratory support is required in most children in the pediatric intensive care unit. Decision-support tools (paper or electronic) have been shown to improve the quality of medical care, reduce errors, and improve outcomes. Computers can assist clinicians by standardizing descriptors and procedures, consistently performing calculations, incorporating complex rules with patient data, and capturing relevant data. This article discusses computer decision-support tools to assist clinicians in making flexible but consistent, evidence-based decisions for equivalent patient states.

A Pediatric Intensive Care Unit Bedside Computer Clinical Decision Support Protocol for Hyperglycemia Is Feasible, Safe and Offers Advantages

BACKGROUND

Computer clinical decision support (CDS) systems are uncommon in the pediatric intensive care unit (PICU), despite evidence suggesting they improve outcomes in adult ICUs. We reasoned that a bedside CDS protocol for intravenous insulin titration, eProtocol-insulin, would be feasible and safe in critically ill children.

METHODS

We retrospectively reviewed data from non-diabetic children admitted to the PICU with blood glucose (BG) ≥140 mg/dL who were managed with intravenous insulin by either unaided clinician titration or eProtocol-insulin. Primary outcomes were BG measurements in target range (80-110 mg/dL) and severe hypoglycemia (BG ≤40 mg/dL); secondary outcomes were 60-day mortality and PICU length of stay. We assessed bedside nurse satisfaction with the eProtocol-insulin protocol by using a 5-point Likert scale and measured clinician compliance with eProtocol-insulin recommendations.

RESULTS

Over 5 years, 69 children were titrated with eProtocol-insulin versus 104 by unaided clinicians. eProtocol-insulin achieved target range more frequently than clinician titration (41% vs. 32%, P < 0.001). Severe hypoglycemia was uncommon in both groups (4.3% of patients in eProtocol-insulin, 8.7% in clinician titration, P = 0.37). There were no differences in mean time to BG target or median BG between the groups. Mortality was 23% in both groups. Clinician compliance with eProtocol-insulin recommendations was 89%. Nurses believed that eProtocol-insulin was easy to understand and safer than clinician titration.

CONCLUSIONS

eProtocol-insulin is safe for titration of intravenous insulin in critically ill children. Clinical research protocols and quality improvement initiatives aimed at optimizing BG control should utilize detailed computer protocols that enable replicable clinician decisions.

Computerized Decision Support Systems for Mechanical Ventilation in Children

Mechanical ventilation is an effective treatment in the ICU but can have significant adverse effects. Approaches from adult research have been adopted in pediatric critical care despite known differences in respiratory physiology and ICU processes. There continues to be considerable variation in how ventilators are managed. Computerized decision support systems implement explicit protocols, and are designed to make mechanical ventilation management safer, more consistent, and more lung protective. Variable results and low or unknown compliance with protocols and CDSS tools have been reported. To date, there has been limited research regarding CDSS for mechanical ventilation in children.

Virtualization of open-source secure web services to support data exchange in a pediatric critical care research network

OBJECTIVES

To examine the feasibility of deploying a virtual web service for sharing data within a research network, and to evaluate the impact on data consistency and quality.

MATERIAL AND METHODS

Virtual machines (VMs) encapsulated an open-source, semantically and syntactically interoperable secure web service infrastructure along with a shadow database. The VMs were deployed to 8 Collaborative Pediatric Critical Care Research Network Clinical Centers.

RESULTS

Virtual web services could be deployed in hours. The interoperability of the web services reduced format misalignment from 56% to 1% and demonstrated that 99% of the data consistently transferred using the data dictionary and 1% needed human curation.

CONCLUSIONS

Use of virtualized open-source secure web service technology could enable direct electronic abstraction of data from hospital databases for research purposes.

Qualitative evaluation of a text messaging intervention to support patients with active tuberculosis: implementation considerations

BACKGROUND

Tuberculosis (TB) remains a major global public health problem and mobile health (mHealth) interventions have been identified as a modality to improve TB outcomes. TextTB, an interactive text-based intervention to promote adherence with TB medication, was pilot-tested in Argentina with results supporting the implementation of trials at a larger scale.

OBJECTIVE

The objective of this research was to understand issues encountered during pilot-testing in order to inform future implementation in a larger-scale trial.

METHODS

A descriptive, observational qualitative design guided by a sociotechnical framework was used. The setting was a clinic within a public pulmonary-specialized hospital in Argentina. Data were collected through workflow observation over 115 days, text messages (n=2286), review of the study log, and stakeholder input. Emerging issues were categorized as organizational, human, technical, or sociotechnical considerations.

RESULTS

Issues related to the intervention included workflow issues (eg, human, training, security), technical challenges (eg, data errors, platform shortcomings), and message delivery issues (eg, unintentional sending of multiple messages, auto-confirmation problems). System/contextual issues included variable mobile network coverage, electrical and Internet outages, and medication shortages.

CONCLUSIONS

Intervention challenges were largely manageable during pilot-testing, but need to be addressed systematically before proceeding with a larger-scale trial. Potential solutions are outlined. Findings may help others considering implementing an mHealth intervention to anticipate and mitigate certain challenges. Although some of the issues may be context dependent, other issues such as electrical/Internet outages and limited resources are not unique issues to our setting. Release of new software versions did not result in solutions for certain issues, as specific features used were removed. Therefore, other software options will need to be considered before expanding into a larger-scale endeavor. Improved automation of some features will be necessary, however, a goal will be to retain the intervention capability to be interactive, user friendly, and patient focused. Continued collaboration with stakeholders will be required to conduct further research and to understand how such an mHealth intervention can be effectively integrated into larger health systems.

Fatal and near-fatal asthma in children: the critical care perspective

OBJECTIVE

To characterize the clinical course, therapies, and outcomes of children with fatal and near-fatal asthma admitted to pediatric intensive care units (PICUs).

STUDY DESIGN

This was a retrospective chart abstraction across the 8 tertiary care PICUs of the Collaborative Pediatric Critical Care Research Network (CPCCRN). Inclusion criteria were children (aged 1-18 years) admitted between 2005 and 2009 (inclusive) for asthma who received ventilation (near-fatal) or died (fatal). Data collected included medications, ventilator strategies, concomitant therapies, demographic information, and risk variables.

RESULTS

Of the 261 eligible children, 33 (13%) had no previous history of asthma, 218 (84%) survived with no known complications, and 32 (12%) had complications. Eleven (4%) died, 10 of whom had experienced cardiac arrest before admission. Patients intubated outside the PICU had a shorter duration of ventilation (median, 25 hours vs 84 hours; P < .001). African-Americans were disproportionately represented among the intubated children and had a shorter duration of intubation. Barotrauma occurred in 15 children (6%) before admission. Pharmacologic therapy was highly variable, with similar outcomes.

CONCLUSION

Of the children ventilated in the CPCCRN PICUs, 96% survived to hospital discharge. Most of the children who died experienced cardiac arrest before admission. Intubation outside the PICU was correlated with shorter duration of ventilation. Complications of barotrauma and neuromyopathy were uncommon. Practice patterns varied widely among the CPCCRN sites.

Executing medical logic modules expressed in ArdenML using Drools

The Arden Syntax is an HL7 standard language for representing medical knowledge as logic statements. Despite nearly 2 decades of availability, Arden Syntax has not been widely used. This has been attributed to the lack of a generally available compiler to implement the logic, to Arden's complex syntax, to the challenges of mapping local data to data references in the Medical Logic Modules (MLMs), or, more globally, to the general absence of decision support in healthcare computing. An XML representation (ArdenML) may partially address the technical challenges. MLMs created in ArdenML can be converted into executable files using standard transforms written in the Extensible Stylesheet Language Transformation (XSLT) language. As an example, we have demonstrated an approach to executing MLMs written in ArdenML using the Drools business rule management system. Extensions to ArdenML make it possible to generate a user interface through which an MLM developer can test for logical errors.

Computer protocols: how to implement

Variation in clinical practice impedes control, is associated with unwanted and widespread error, and may preclude replicability. Methodologic replicability enhances our ability to detect signals of interest by both increasing the signal through consistent application of the intervention, and by reducing the obscuring effects of noise. Decision-support tools are intended to standardize some aspect of clinical care and thereby help lead to uniform implementation of clinical interventions. This is realized by explicit replicable computer protocols that can produce appropriate patient-specific decisions and introduce control of process into clinical care. Development of such protocols has required around-the-clock implementation for patient management because of the influence of patient history and previous patient states on the output of the computer protocol. Three successful computer protocols for management of blood glucose provide compelling examples. This clinician driven "bottom-up" approach complements the common information technology service driven "top-down" approach to clinical problems.

Use of a Web-based game to teach pediatric content to medical students

OBJECTIVE

The aim of this study was to assess, using a Web-based format, third-year medical students' pediatric knowledge and perceptions of game playing with faculty facilitation compared with self-study computerized flash cards.

METHODS

This study used a repeated-measures experimental design with random assignment to a game group or self-study group. Pediatric knowledge was tested using multiple choice exams at baseline, week 6 of the clerkship following a 4-week intervention, and 6 weeks later. Perceptions about game playing and self-study were evaluated using a questionnaire at week 6.

RESULTS

The groups did not differ on content mastery, perceptions about content, or time involved in game playing or self-study. Perceptions about game playing versus self-study as a pedagogical method appeared to favor game playing in understanding content (P<.001), perceived help with learning (P<.05), and enjoyment of learning (P<.008). An important difference was increased game group willingness to continue participating in the intervention.

CONCLUSIONS

Games can be an enjoyable and motivating method for learning pediatric content, enhanced by group interactions, competition, and fun. Computerized, Web-based tools can facilitate access to educational resources and are feasible to apply as an adjunct to teaching clinical medicine.

Mapping clinicians' perceptions about computerized protocol use to an IT implementation framework

Previous studies have described the determinants of successful information technology (IT) implementation. In 2003, Kukafka et al. integrated several theoretical perspectives and proposed a framework for IT implementation. This framework is applicable to IT implementation in general but lacks the identification of factors affecting adoption, which are specific to the technology under consideration. We developed and validated a model that specifically identifies factors associated with clinicians' adoption of computerized protocols. The purpose of this paper is to identify the relations between the specific factors associated with intention to use computerized protocols and the high level variables that constitute the framework proposed by Kukafka et al. Incorporation of a specific model into a general schema for IT implementation allows implementers to assess the specific individual, organizational and environmental changes required to bring about successful implementation of computerized protocols. An understanding of clinicians' perceptions specific to the technology in use will allow its seamless integration into an organization's healthcare IT plan. Strategic planning requires enhancing the framework with additional detail related to the specific technology under consideration.

Phase I implementation of an academic medical record for integrating information management competencies into a nursing curriculum

This paper is the report of the first phase of a case study from the University of Utah to help students and faculty integrate electronic information management into the nursing curriculum. Cerner AES, a live-production clinical information system with an academic overlay, has been implemented into the first semester of an undergraduate nursing program. A consortium of schools that use Cerner AES collaborate in the design and implementation of forms used by students. The consortium also allows members to share strategies for using the system. By using the system students are developing needed informatics competencies for beginning level nurses. The paper discusses the implementation strategies used and initial results of this project. Plans for expanding the project throughout the nursing curriculum are also presented.