Pediatric Aspects of Inpatient Health Information Technology Systems

Lehmann, Christoph U.; Weinberg, Stuart T.; Alexander, Gregg M.; Beyer, Eric L.; Beccaro, Mark A. Del; Francis, Anne B.; Handler, Eric G.; Johnson, Timothy D.; Kirkendall, Eric S.; Lighter, Donald E.; Morgan, Stephen James; Raskas, Mordechai; Tham, Eric; Webber, Emily C.

doi:10.1542/peds.2014-4148

Cited by 62 publications

(42 citation statements)

References 83 publications

(64 reference statements)

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“…36 Several HRSAfunded initiatives 37,38 and the National Institutes of Health-funded Safe to Sleep campaign 7 include provider training modules that integrate effective behavior change methods, such as motivational interviewing. New mobile health message interventions, 39 clinical decision support tools, [40][41][42] and quality improvement initiatives, [43][44][45][46] including CDC-funded perinatal quality collaborative initiatives, 47 also show promise to improve safe sleep practices.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and Factors Associated With Safe Infant Sleep Practices

et al. 2019

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OBJECTIVES: To examine prevalence of safe infant sleep practices and variation by sociodemographic, behavioral, and health care characteristics, including provider advice. METHODS: Using 2016 Pregnancy Risk Assessment Monitoring System data from 29 states, we examined maternal report of 4 safe sleep practices indicating how their infant usually slept: (1) back sleep position, (2) separate approved sleep surface, (3) room-sharing without bedsharing, and (4) no soft objects or loose bedding as well as receipt of health care provider advice corresponding to each sleep practice. RESULTS: Most mothers reported usually placing their infants to sleep on their backs (78.0%), followed by room-sharing without bed-sharing (57.1%). Fewer reported avoiding soft bedding (42.4%) and using a separate approved sleep surface (31.8%). Reported receipt of provider advice ranged from 48.8% (room-sharing without bed-sharing) to 92.6% (back sleep position). Differences by sociodemographic, behavioral, and health care characteristics were larger for safe sleep practices (∼10-20 percentage points) than receipt of advice (∼5-10 percentage points). Receipt of provider advice was associated with increased use of safe sleep practices, ranging from 12% for room-sharing without bed-sharing (adjusted prevalence ratio: 1.12; 95% confidence interval: 1.09-1.16) to 28% for back sleep position (adjusted prevalence ratio: 1.28; 95% confidence interval: 1.21-1.35). State-level differences in safe sleep practices spanned 20 to 25 percentage points and did not change substantially after adjustment for available characteristics. CONCLUSIONS: Safe infant sleep practices, especially those other than back sleep position, are suboptimal, with demographic and state-level differences indicating improvement opportunities. Receipt of provider advice is an important modifiable factor to improve infant sleep practices. WHAT'S KNOWN ON THIS SUBJECT: Approximately 3500 infants die annually in the United States from sleep-related sudden unexpected causes. Previous studies have indicated suboptimal adherence to safe infant sleep recommendations and highlighted various sociodemographic disparities and connections with provider advice. WHAT THIS STUDY ADDS: We update previous estimates of safe infant sleep and include a new composite measure assessing the use of separate approved sleep surfaces. We also explore state variation and examine associations between provider advice and each of 4 corresponding sleep-related practices.

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Section: Discussionmentioning

confidence: 99%

Prevalence and Factors Associated With Safe Infant Sleep Practices

et al. 2019

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“…Study strengths included a focus on the sometimes neglected areas of nursing and pediatric health IT, [53] quantitative assessment of perceptions and acceptance, strong theoretical basis, and relatively large response rate. The study sample size was relatively large for health IT acceptance research and was greater than that of 63% of technology acceptance studies with nurses [20].…”

Section: Discussionmentioning

confidence: 99%

Nurses’ perceptions, acceptance, and use of a novel in-room pediatric ICU technology: testing an expanded technology acceptance model

Holden

Asan

Wozniak

et al. 2016

BMC Med Inform Decis Mak

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BackgroundThe value of health information technology (IT) ultimately depends on end users accepting and appropriately using it for patient care. This study examined pediatric intensive care unit nurses’ perceptions, acceptance, and use of a novel health IT, the Large Customizable Interactive Monitor.MethodsAn expanded technology acceptance model was tested by applying stepwise linear regression to data from a standardized survey of 167 nurses.ResultsNurses reported low-moderate ratings of the novel IT’s ease of use and low to very low ratings of usefulness, social influence, and training. Perceived ease of use, usefulness for patient/family involvement, and usefulness for care delivery were associated with system satisfaction (R2 = 70%). Perceived usefulness for care delivery and patient/family social influence were associated with intention to use the system (R2 = 65%). Satisfaction and intention were associated with actual system use (R2 = 51%).ConclusionsThe findings have implications for research, design, implementation, and policies for nursing informatics, particularly novel nursing IT. Several changes are recommended to improve the design and implementation of the studied IT.

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“…These functionalities may not be supported by all electronic health record systems. 15, 16 Third, categorizing hospitalized patients based on ICD-9 codes would be difficult since these codes are not usually available until after discharge. However, as the analysis showed, diagnosis categories added surprisingly little to the prediction model.…”

Section: Limitations and Next Stepsmentioning

confidence: 99%

Predicting Discharge Dates From the NICU Using Progress Note Data

Temple¹,

Lehmann

Fabbri³

2015

Pediatrics

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Background and Objectives Discharging patients from the Neonatal Intensive Care Unit (NICU) may be delayed for non-medical reasons including the need for medical equipment, parental education, and children’s services. We describe a method to predict and identify patients that will be medically ready for discharge in the next 2–10 days – providing lead-time to address non-medical reasons for delayed discharge. Methods A retrospective study examined 26 features (17 extracted, 9 engineered) from daily progress notes of 4,693 patients (103,206 patient-days) from the NICU of a large, academic children’s hospital. These data were used to develop a supervised machine learning problem to predict days to discharge (DTD). Random forest classifiers were trained using combinations of examined features and ICD-9-based subpopulations to determine the most important features and those features which most accurately predicted days to discharge. Results Three of the four sub-populations (Premature, Cardiac, GI surgery) and all patients combined performed similarly at 2, 4, 7, and 10 DTD with AUC ranging from 0.854–0.865 at 2 DTD and 0.723–0.729 at 10 DTD. Neurosurgery patients performed worse at every DTD measure scoring 0.749 at 2 DTD and 0.614 at 10 DTD. This model was also able to identify important features and provide “rule-of-thumb” criteria for patients close to discharge. Using DTD equal to 4 and 2 features (oral percentage of feedings and weight) we constructed a model with an AUC of 0.843. Conclusion Using clinical features from daily progress notes provides an accurate method to predict when NICU patients are nearing discharge.

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Pediatric Aspects of Inpatient Health Information Technology Systems

Cited by 62 publications

References 83 publications

Prevalence and Factors Associated With Safe Infant Sleep Practices

Prevalence and Factors Associated With Safe Infant Sleep Practices

Nurses’ perceptions, acceptance, and use of a novel in-room pediatric ICU technology: testing an expanded technology acceptance model

Predicting Discharge Dates From the NICU Using Progress Note Data

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