Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition

Roosan, Don; Samore, Matthew H.; Jones, Makoto; Livnat, Yarden; Clutter, Justin

doi:10.1109/ichi.2016.39

Cited by 23 publications

(15 citation statements)

References 26 publications

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“…The interprofessional research team (including 9 clinicians with diverse clinical background and 6 researchers) will then iteratively review and revise the mock-up based on the written and verbal feedback related to usability (think-aloud methods), efficiency, and ease of use for 3 months or until no further revisions are identified. Think-aloud methods will provide rich verbal data about specific changes and functionalities of the initial mock-up [71-73]. We will audio record and screen record (using Camtasia Studio) the sessions to analyze verbal feedback and measure the mouse movements.…”

Section: Methodsmentioning

confidence: 99%

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

Roosan¹,

Law²,

Karim³

et al. 2019

JMIR Res Protoc

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BackgroundAccording to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis.ObjectiveThe overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management.MethodsTo achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3).ResultsBetween 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021.ConclusionsThe results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics.International Registered Report Identifier (IRRID)DERR1-10.2196/16047

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Section: Methodsmentioning

confidence: 99%

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

Roosan¹,

Law²,

Karim³

et al. 2019

JMIR Res Protoc

Self Cite

View full text Add to dashboard Cite

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“…• death: in-hospital mortality, defined as a discharge disposition of "expired" [9], [20]; • hospital-acquired infections (HAI) developed during the stay [21]; • admissions to intensive care unit (ICU) on or after the second day, excluding direct admissions on the first day; • pressure ulcers (PU) developed during the stay (not present at admission). Patients who experienced a given outcome are considered positive cases for this outcome; those who did not are considered negative cases.…”

Section: B Outcomesmentioning

confidence: 99%

Scalable and Interpretable Predictive Models for Electronic Health Records

Fejza

Genevès

Layaïda

et al. 2018

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)

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Early identification of patients at risk of developing complications during their hospital stay is currently one of the most challenging issues in healthcare. Complications include hospital-acquired infections, admissions to intensive care units, and in-hospital mortality. Being able to accurately predict the patients' outcomes is a crucial prerequisite for tailoring the care that certain patients receive, if it is believed that they will do poorly without additional intervention. We consider the problem of complication risk prediction, such as inpatient mortality, from the electronic health records of the patients. We study the question of making predictions on the first day at the hospital, and of making updated mortality predictions day after day during the patient's stay. We develop distributed models that are scalable and interpretable. Key insights include analysing diagnoses known at admission and drugs served, which evolve during the hospital stay. We leverage a distributed architecture to learn interpretable models from training datasets of gigantic size. We test our analyses with more than one million of patients from hundreds of hospitals, and report on the lessons learned from these experiments.

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“…Labeling the occurrence of hospital-acquired infections is slightly more involved since one must basically distinguish secondary infections occurring during hospital stay from infections existing before admission. For this purpose, medical experts guided us to label complications in terms of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes [6] that are used in medical records, inspired from the work of Roosan et al [7]. We implemented complication labeling as a one-pass algorithm that labels each admission with the complication(s) that occurred a posteori (if any).…”

Section: Considered Complications and Ground Truthmentioning

confidence: 99%

Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission

et al. 2018

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We show how the analysis of very large amounts of drug prescription data make it possible to detect, on the day of hospital admission, patients at risk of developing complications during their hospital stay. We explore, for the first time, to which extent volume and variety of big prescription data help in constructing predictive models for the automatic detection of at-risk profiles.Our methodology is designed to validate our claims that: (1) drug prescription data on the day of admission contain rich information about the patient's situation and perspectives of evolution, and (2) the various perspectives of big medical data (such as veracity, volume, variety) help in extracting this information. We build binary classification models to identify at-risk patient profiles. We use a distributed architecture to ensure scalability of model construction with large volumes of medical records and clinical data.We report on practical experiments with real data of millions of patients and hundreds of hospitals. We demonstrate how the fine-grained analysis of such big data can improve the detection of at-risk patients, making it possible to construct more accurate predictive models that significantly benefit from volume and variety, while satisfying important criteria to be deployed in hospitals.

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Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition

Cited by 23 publications

References 26 publications

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

Scalable and Interpretable Predictive Models for Electronic Health Records

Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission

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