Methodological Challenges in Examining the Impact of Healthcare Predictive Analytics on Nursing-Sensitive Patient Outcomes

Jeffery, Alvin D.

doi:10.1097/cin.0000000000000154

Cited by 15 publications

(21 citation statements)

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“…Overall, all models performed substantially better than only a history of past suicide attempt (AUC > 0.80 vs. AUC = 0.57 for detecting outcomes 24 months post-index event). Ridge regression had the highest AUC at 24 months postindex case (AUC = 0.87) indicating good diagnostic accuracy of future risk for suicide attempt (Hajian-Tilaki, 2013). Positive predictive value was quite low across all models with the highest value of PPV = 0.14 for the elastic net, decision trees, and unregularized regression for predicating attempt at 6 months postindex event.…”

Section: Resultsmentioning

confidence: 96%

“…Several large health care settings have started to use these approaches linked to their electronic medical records data (EHR) (Kessler, Hwang, et al, ; Kessler, Stein, et al, ; Reger, McClure, Ruskin, Carter, & Reger, ; Walsh et al, , ), suggesting this is a potentially scalable approach throughout Indian Health Service (IHS) and tribally run hospitals and clinics. More research is needed to validate this model in other tribal settings, continuously improve this model with data from participating tribes, and/or generate new models with EHR data from other tribal settings, involving key stakeholders in every stage of development, implementation, and analysis (Jeffery, ).…”

Section: Discussionmentioning

confidence: 99%

“…In a national survey among adult NAs, approximately 1.2% reported having attempted suicide in the past year compared with 0.5% of adults in the US All Races population (SAMHSA, 2011). Among NA adolescents, lifetime rates of self-report attempted suicide ranged from 21.8% of girls to 11.8% of boys, with rates on reservations being higher than rates in urban areas (Borowsky, Resnick, Ireland, & Blum, 1999;Freedenthal & Stiffman, 2004). More generally, a past suicide attempt is one of the strongest risk factors for dying by suicide and often goes undetected.…”

mentioning

confidence: 99%

“…Many individuals do not seek medical attention for suicide attempt (Claassen & Larkin, 2005;Kemball, Gasgarth, Johnson, Patil, & Houry, 2008;King, O'mara, Hayward, & Cunningham, 2009), and those who do obtain little or no follow-up mental health treatment (Bridge, Marcus, & Olfson, 2012;Knesper, 2010). This problem is compounded in many reservation settings due to significant barriers to mental health care, including lack of available professional services, long waiting periods, poor access to transportation, long distances to travel, stigma, and poor cultural fit of existing clinical services (Barlow & Walkup, 1998;Freedenthal & Stiffman, 2007;Novins, 2009;O'Keefe, Cwik, Haroz, & Barlow, 2019;Probst et al, 2006;Pullmann, VanHooser, Hoffman, & Heflinger, 2010).…”

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confidence: 99%

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Reaching Those at Highest Risk for Suicide: Development of a Model Using Machine Learning Methods for use With Native American Communities

Haroz

Walsh

Goklish

et al. 2019

Suicide & Life Threat Behav

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Objective Suicide prevention is a major priority in Native American communities. We used machine learning with community‐based suicide surveillance data to better identify those most at risk. Method This study leverages data from the Celebrating Life program operated by the White Mountain Apache Tribe in Arizona and in partnership with Johns Hopkins University. We examined N = 2,390 individuals with a validated suicide‐related event between 2006 and 2017. Predictors included 73 variables (e.g., demographics, educational history, past mental health, and substance use). The outcome was suicide attempt 6, 12, and 24 months after an initial event. We tested four algorithmic approaches using cross‐validation. Results Area under the curves ranged from AUC = 0.81 (95% CI ± 0.08) for the decision tree classifiers to AUC = 0.87 (95% CI ± 0.04) for the ridge regression, results that were considerably higher than a past suicide attempt (AUC = 0.57; 95% CI ± 0.08). Selecting a cutoff value based on risk concentration plots yielded 0.88 sensitivity, 0.72 specificity, and a positive predictive value of 0.12 for detecting an attempt 24 months postindex event. Conclusion These models substantially improved our ability to determine who was most at risk in this community. Further work is needed including developing clinical guidance and external validation.

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Section: Resultsmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Reaching Those at Highest Risk for Suicide: Development of a Model Using Machine Learning Methods for use With Native American Communities

Haroz

Walsh

Goklish

et al. 2019

Suicide & Life Threat Behav

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“…Knowledge is just data that can be compelled to yield 'operational value'. However, while such claims attempt to shape the future environment, the evidence at present, perhaps unsurprisingly, is less conclusive (Jeffrey 2015). In many cases, digital analytics are used to enhance professional decision-making rather than displace it.…”

Section: Computer Technology and Professional Practicementioning

confidence: 99%

Digital analytics in professional work and learning

Edwards

Fenwick

2015

Studies in Continuing Education

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In a wide range of fields, professional practice is being transformed by the increasing influence of digital analytics: the massive volumes of big data, and software algorithms that are collecting, comparing and calculating that data to make predictions and even decisions. Researchers in a number of social sciences have been calling attention to the far-reaching and accelerating consequences of these forces, claiming that many professionals, researchers, policymakers and the public are just beginning to realise the enormous potentials and challenges these analytics are producing. Yet, outside of particular areas of research and practice, such as learning analytics, there has been little discussion of this to date in the broader education literature. This article aims to set out some key issues particularly relevant to the understandings of professional practice, knowledge and learning posed by the linkages of big data and software code. It begins by outlining definitions, forms and examples of these analytics, their potentialities and some of the hidden impact, and then presents issues for researchers and educators. It seeks to contribute to and extend debates taking place in certain quarters to a broader professional education and work audience. ARTICLE HISTORY

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Informatics Solutions for Application of Decision-Making Skills

Nibbelink

Young

Carrington

et al. 2018

Critical Care Nursing Clinics of North America

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Critical care nurses practice in a challenging environment that requires responses to patients with complex, often unstable health conditions. The electronic health record, access to clinical data, and Clinical Decision Support Systems informed by data from clinical databases are informatics tools designed to work together to facilitate decision-making in nursing practice. The complex decision-making environment of critical care requires informatics tools that support nursing practice through integration of current evidence with clinical data. Recommendations include continuing efforts toward the development of clinical decision support tools based on patient data that include predictive models to support increased patient safety.

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Methodological Challenges in Examining the Impact of Healthcare Predictive Analytics on Nursing-Sensitive Patient Outcomes

Cited by 15 publications

References 15 publications

Reaching Those at Highest Risk for Suicide: Development of a Model Using Machine Learning Methods for use With Native American Communities

Reaching Those at Highest Risk for Suicide: Development of a Model Using Machine Learning Methods for use With Native American Communities

Digital analytics in professional work and learning

Informatics Solutions for Application of Decision-Making Skills

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