A framework for an intelligent decision support system: A case in pathology test ordering

Zhuang, Zoe Yan; Wilkin, Carla; Ceglowski, Andrzej Stefan

doi:10.1016/j.dss.2012.10.006

Cited by 31 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some series of studies reported on multiple stages of a project, from AI development to pilot-testing; these projects included intended end users located in a primary care setting. [53][54][55][56][57][58][59][60] A small minority of studies (21 studies, 5.2%) had multiple purposes. Figure 2 presents all combinations.…”

Section: Study Purposementioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

View full text Add to dashboard Cite

PURPOSE Rapid increases in technology and data motivate the application of artificial intelligence (AI) to primary care, but no comprehensive review exists to guide these efforts. Our objective was to assess the nature and extent of the body of research on AI for primary care. METHODSWe performed a scoping review, searching 11 published or gray literature databases with terms pertaining to AI (eg, machine learning, bayes* network) and primary care (eg, general pract*, nurse). We performed title and abstract and then full-text screening using Covidence. Studies had to involve research, include both AI and primary care, and be published in English. We extracted data and summarized studies by 7 attributes: purpose(s); author appointment(s); primary care function(s); intended end user(s); health condition(s); geographic location of data source; and AI subfield(s). RESULTSOf 5,515 unique documents, 405 met eligibility criteria. The body of research focused on developing or modifying AI methods (66.7%) to support physician diagnostic or treatment recommendations (36.5% and 13.8%), for chronic conditions, using data from higher-income countries. Few studies (14.1%) had even a single author with a primary care appointment. The predominant AI subfields were supervised machine learning (40.0%) and expert systems (22.2%).CONCLUSIONS Research on AI for primary care is at an early stage of maturity. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed. Ann Fam Med 2020;18:250-258. https://doi.

show abstract

Section: Study Purposementioning

confidence: 99%

Artificial Intelligence and Primary Care Research: A Scoping Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The newer solution can be a cost-effective alternative to conventional healthcare surveys and studies. The physician’s overuse of screening and testing often leads to surplus data and excess costs [ 29 ]. The present practice in pathology is restricted by the emphasis on illness.…”

Section: Healthcare Administrationmentioning

confidence: 99%

The role of data science in healthcare advancements: applications, benefits, and future prospects

et al. 2021

View full text Add to dashboard Cite

Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical examinations, insurance, etc. The data collected from the Internet of Things (IoT) devices attract the attention of data scientists. Data science provides aid to process, manage, analyze, and assimilate the large quantities of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to acquire factual results. The process of data cleansing, data mining, data preparation, and data analysis used in healthcare applications is reviewed and discussed in the article. The article provides an insight into the status and prospects of big data analytics in healthcare, highlights the advantages, describes the frameworks and techniques used, briefs about the challenges faced currently, and discusses viable solutions. Data science and big data analytics can provide practical insights and aid in the decision-making of strategic decisions concerning the health system. It helps build a comprehensive view of patients, consumers, and clinicians. Data-driven decision-making opens up new possibilities to boost healthcare quality.

show abstract

“…There were series of studies that reported on multiple stages of a project, from AI development to pilot testing; these studies included intended end users located in a PC setting (49)(50)(51)(52)(53)(54)(55)(56). A minority of studies (n=21, 5.2%) had more than one study purpose.…”

Section: Searchesmentioning

confidence: 99%

Research on Artificial Intelligence and Primary Care: A Scoping Review

Kueper

Terry

Zwarenstein

et al. 2019

Preprint

View full text Add to dashboard Cite

Objective: The purpose of this study was to assess the nature and extent of the body of research on artificial intelligence (AI) and primary care. Methods:We performed a scoping review, searching 11 published and grey literature databases with subject headings and key words pertaining to the concepts of 1) AI and 2) primary care: MEDLINE, EMBASE, Cinahl, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, arXiv. Screening included title and abstract and then full text stages. Final inclusion criteria: 1) research study of any design, 2) developed or used AI, 3) used primary care data and/or study conducted in a primary care setting and/or explicit mention of study applicability to primary care; exclusion criteria: 1) narrative, editorial, or textbook chapter, 2) not applicable to primary care population or settings, 3) full text inaccessible in the English Language. We extracted and summarized seven key characteristics of included studies: overall study purpose(s), author appointments, primary care functions, author intended target end user(s), target health condition(s), location of data source(s) (if any), subfield(s) of AI.Results: Of 5,515 non-duplicate documents, 405 met our eligibility criteria. The body of literature is primarily focused on creating novel AI methods or modifying existing AI methods to support physician diagnostic or treatment recommendations, for chronic conditions, using data from higher income countries. Meaningfully more studies had at least one author with a technology, engineering, or math appointment than with a primary care appointment (57 (14%) compared to 217 (54%)). Predominant AI subfields were supervised machine learning and expert systems. Discussion:Overall, AI research associated with primary care is at an early stage of maturity with respect to widespread implementation in practice settings. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed.

show abstract

A framework for an intelligent decision support system: A case in pathology test ordering

Cited by 31 publications

References 41 publications

Artificial Intelligence and Primary Care Research: A Scoping Review

Artificial Intelligence and Primary Care Research: A Scoping Review

The role of data science in healthcare advancements: applications, benefits, and future prospects

Research on Artificial Intelligence and Primary Care: A Scoping Review

Contact Info

Product

Resources

About