Predictors of Depression Screening Rates of Nurses Receiving a Personal Digital Assistant-based Reminder to Screen

Schnall, Rebecca; Currie, Leanne M.; Jia, Haomiao; John, Rita Marie; Lee, Nam Ju; Vélez, Olivia; Bakken, Suzanne

doi:10.1007/s11524-010-9464-2

Cited by 5 publications

(7 citation statements)

References 54 publications

(46 reference statements)

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“…Reflecting previous research, privately insured patients were more likely to be screened than those with Medicare or Medicaid (Jamal et al, 2012). This is also similar to Schnall et al’s (2010) work on depression screening and management. Similar to research by Jamal et al (2012), privately insured patients were also more likely to receive patient teaching and counseling and referrals.…”

Section: Discussionsupporting

confidence: 57%

“…As part of a randomized, controlled trial that tested the effect of an mHealth DSS on nurse adherence to guideline-based recommendations for screening and management of depression, obesity, and tobacco use (Bakken et al, 2008; Lee et al, 2009; Schnall et al, 2010), the authors used an observational design to study predictors of screening rates and use of guideline-based recommendations by RNs randomized to mHealth DSS for screening and management of tobacco use.…”

Section: Methodsmentioning

confidence: 99%

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Response to a Mobile Health Decision-Support System for Screening and Management of Tobacco Use

Cato

Hyun

Bakken

2014

Oncology Nursing Forum

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Purpose/Objectives To describe the predictors of nurse actions in response to a mobile health decision support system (mHealth DSS) for guideline-based screening and management of tobacco use. Design Observational design focused on experimental arm of a randomized, controlled trial. Setting Acute and ambulatory care settings in the New York City metropolitan area. Sample 14,115 patient encounters in which 185 registered nurses enrolled in advanced practice nurse (APN) training were prompted by a mHealth DSS to screen for tobacco use and select guideline-based treatment recommendations. Methods Data were entered and stored during nurse documentation in the mHealth DSS and subsequently stored in the study database where they were retrieved for analysis using descriptive statistics and logistic regressions. Main Research Variables Predictor variables included patient gender, patient race/ethnicity, patient payer source, APN specialty, and predominant payer source in clinical site. Dependent variables included number of patient encounters in which the nurse screened for tobacco use, provided smoking cessation teaching and counseling, or referred patient for smoking cessation for patients who were to quit. Findings Screening was more likely to occur in encounters where patients were female, Black, not of Hispanic origin, received care from a nurse in the Adult Nurse Practitioner specialty or in a clinical site in which the predominant payer source was Medicare, Medicaid, or State Children’s Health Insurance Program. In encounters where patient payer source was other, nurses were less likely to provide tobacco cessation teaching and counseling. Conclusions mHealth DSS has the potential to affect nurse provision of guideline-based care. However, patient, nurse, and setting factors influence nurse actions in response to a mHealth DSS for tobacco cessation. Implications for Nursing The combination of a reminder to screen and integration of guideline-based recommendations into the mHealth DSS may reduce racial or ethnic disparities to screening, as well as clinician barriers related to time, training and familiarity with resources. Knowledge Translation A mobile health decision support system (mHealth DSS) has the potential to affect nurse provision of guideline-based care. Nurse actions in response to a mHealth DSS reminder to screen were influenced by patient gender and race as well nurse and setting factors. Payer source and NP specialty influenced provision of tobacco cessation teaching and counseling and tobacco cessation referral.

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Section: Discussionsupporting

confidence: 57%

Section: Methodsmentioning

confidence: 99%

Response to a Mobile Health Decision-Support System for Screening and Management of Tobacco Use

Cato

Hyun

Bakken

2014

Oncology Nursing Forum

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“…This is similar to findings in the general clinical DSS literature 24 as well as in at least one mHealth study. 25 Moreover, the greater number of diagnoses is consistent with the relatively high screening rates that we have previously reported for mHealth DSS for obesity and overweight, 20 adult depression, 21 and tobacco use. 22 …”

Section: Discussionsupporting

confidence: 79%

“…In previous papers, we have reported screening rates in response to mHealth DSS reminders for obesity and overweight, 20 adult depression, 21 and tobacco use 22 and demonstrated that screening rates varied by NP specialty, patient race/ethnicity, and payer source. 21,22 The purpose of this study was to compare diagnostic rates and care planning by registered nurses in NP training randomized to mHealth DSS versus control group for obesity and overweight, tobacco use, and depression.…”

mentioning

confidence: 98%

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The Effect of a Mobile Health Decision Support System on Diagnosis and Management of Obesity, Tobacco Use, and Depression in Adults and Children

Bakken¹,

Jia²,

Chen³

et al. 2014

The Journal for Nurse Practitioners

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Acute care nurses’ perceptions of electronic health record use: A mixed method study

et al. 2018

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AimThe overall aim of this study is to examine nurses’ perceptions of electronic health record use in an acute care hospital setting.DesignThis study uses a sequential mixed methods design in two phases.MethodsPhase one consists of a survey of Registered Nurses to understand nurses’ perceptions of electronic health record use. Phase two is comprised of focus groups of a subsample from phase one. Data collection occurred from November 2015 ‐ August 2016 and was done in Toronto, Canada.ResultsIn phase one, navigation was found to be a predictor of nurses’ perceptions of electronic health record use. In phase two, participants discussed the following five topics: (1) navigation; (2) functionality; (3) organizational standards; (4) documentation workload and (5) issues of system performance and response time. This study has implications for organizations implementing electronic health records, nursing leaders and electronic health record vendors.

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Predictors of Depression Screening Rates of Nurses Receiving a Personal Digital Assistant-based Reminder to Screen

Cited by 5 publications

References 54 publications

Response to a Mobile Health Decision-Support System for Screening and Management of Tobacco Use

Response to a Mobile Health Decision-Support System for Screening and Management of Tobacco Use

The Effect of a Mobile Health Decision Support System on Diagnosis and Management of Obesity, Tobacco Use, and Depression in Adults and Children

Acute care nurses’ perceptions of electronic health record use: A mixed method study

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