Big Data and Population-Based Decision Support

Krall, Michael A.; Gundlapalli, Adi V.; Samore, Matthew H.

doi:10.1016/b978-0-12-398476-0.00013-0

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…Also, population-based decision support embedded in EHR systems has the potential to support System 2 with cognitive support for seeking help and watchful waiting [ 84 ]. Cognitive support to clinicians refers to maximizing clinicians’ cognitive abilities to improve clinical reasoning and decision-making with the aid of information technology [ 85 ].…”

Section: Discussionmentioning

confidence: 99%

“…Cognitive support to clinicians refers to maximizing clinicians’ cognitive abilities to improve clinical reasoning and decision-making with the aid of information technology [ 85 ]. Population-based decision support is a systematic application for analyzing population databases to improve the health of groups of individuals [ 84 ]. Such decision support can work as a “cognitive extension” for clinicians by providing information about treatment response for similar patients and interventions by other clinical experts.…”

Section: Discussionmentioning

confidence: 99%

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Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design

Islam

Weir

Jones

et al. 2015

BMC Med Inform Decis Mak

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BackgroundClinical experts’ cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. The purpose of the study is to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment.MethodsWe used Cognitive Task Analysis (CTA) methods to interview 10 Infectious Disease (ID) experts at the University of Utah and Salt Lake City Veterans Administration Medical Center. Participants were asked to recall a complex, critical and vivid antibiotic-prescribing incident using the Critical Decision Method (CDM), a type of Cognitive Task Analysis (CTA). Using the four iterations of the Critical Decision Method, questions were posed to fully explore the incident, focusing in depth on the clinical components underlying the complexity. Probes were included to assess cognitive and decision strategies used by participants.ResultsThe following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety. All these factors contribute to decision complexity. These factors almost always occurred together, creating unexpected events and uncertainty in clinical reasoning. Five themes emerged in the analyses of how experts deal with the complexity. Expert clinicians frequently used 1) watchful waiting instead of over- prescribing antibiotics, engaged in 2) theory of mind to project and simulate other practitioners’ perspectives, reduced very complex cases into simple 3) heuristics, employed 4) anticipatory thinking to plan and re-plan events and consulted with peers to share knowledge, solicit opinions and 5) seek help on patient cases.ConclusionThe cognitive strategies to deal with decision complexity found in this study have important implications for design future decision support systems for the management of complex patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0221-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design

Islam

Weir

Jones

et al. 2015

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

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“…Natural language processing (NLP), which describes the automated understanding and generation of human language, is a field of research in computer science relevant to population health management 7 . While applications in cardiovascular disease are spreading [8][9][10][11] , publications in HCM are limited 12,13 .…”

Section: Introductionmentioning

confidence: 99%

Making Words Count with Computerised Identification of Hypertrophic Cardiomyopathy Patients

Slater

Bradlow

Desai

et al. 2021

Preprint

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Background: The traditional outpatient model in hypertrophic cardiomyopathy (HCM) is under pressure. Population health management based on an accurate patient record provides an efficient, cost-effective alternative. Methods: To improve the accuracy of the HCM patient list in a single hospital, we developed a rule-based information extraction natural language processing (NLP) framework. The framework employed ontological expansion of vocabulary and exclusion-first annotation, and received training by an 'expert in the loop'. The output stratified patients with atrial fibrillation (AF) and heart failure (HF), those without active cardiology care and likely screened individuals. Results: The algorithm was validated against multiple data sources, including manual validation, for HCM, AF and HF and family history of the disease. Overall precision and recall were 0.854 and 0.865 respectively. The pipeline found 25,356 documents featuring HCM-related terms belonging to 11,083 patients. Excluding scanned documents resulted in 17,178 letters from 3,120 patients. Subsequent categorisation identified 1,753 real cases, of whom 357 had AF and 205 had HF. There were 696 likely screened individuals. Adjusting for 304 false-negative patients, the total HCM cohort was 2,045 patients. 214 were not under a cardiologist. NLP uncovered 709 patients who were absent in the registry or hospital disease codes. Conclusion: This novel NLP framework generated a hospital-wide record of patients with HCM and defined various cohorts, including the small set of HCM patients lacking current cardiology input. Existing data sources inadequately described this population, spotlighting NLP's essential role for clinical teams planning to move to a population health management model of care. Keywords: text mining, natural language processing, hypertrophic cardiomyopathy, population health management

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“…In the age of information explosion, big data has the potential to improve clinical decision-making and revolutionize research [76]. The past decade has witnessed a dramatic growth of electronic biomedical literature, which is generated and stored in digital form and provides a substantial amount of reliable data that forms a rich source for Evidence-Based Medicine (EBM) research.…”

Section: Problems and Challengesmentioning

confidence: 99%

Healthcare data mining from multi-source data

Chen¹

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The "big data" challenge is changing the way we acquire, store, analyse, and draw conclusions from data. How we effectively and efficiently "mine" the data from possibly multiple sources and extract useful information is a critical question. Increasing research attention has been drawn to healthcare data mining, with an ultimate goal to improve the quality of care. The human body is complex and so too the data collected in treating it. Data noise that is often introduced via the collection process makes building Data Mining models a challenging task.This thesis focuses on the classification tasks of mining healthcare data, with the goal of improving the effectiveness of health risk prediction. In particular, we developed algorithms to address issues identified from real healthcare data, such as feature extraction, heterogeneity, label uncertainty, and large unlabeled data.The three main contributions of this research are as follows. First, we developed a new health index called Personal Health Index (PHI) that scores a person's health status based on the examination records of a given population. Second, we identified the key characteristics of the real datasets and issues that were associated with the data. Third, we developed classification algorithms to cope with those issues, particularly, the label uncertainty and large unlabeled data issues.This research takes one step forward towards scoring personal health based on mining increasingly large health records. Particularly, it pioneers exploring the mining of GHE data and tackles the associated challenges. It is our anticipation that in the near future, more robust data-mining-based health scoring systems will be available for healthcare professionals to understand people's health status and thus improve the quality of care.

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Big Data and Population-Based Decision Support

Cited by 7 publications

References 31 publications

Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design

Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design

Making Words Count with Computerised Identification of Hypertrophic Cardiomyopathy Patients

Healthcare data mining from multi-source data

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