Knowledge Translation in Nursing Through Decision Support at the Point of Care

2010 ◽  
pp. 314-330
Author(s):  
Diane Doran ◽  
Tammie Di Pietro
Keyword(s):  
Decision Support ◽  
Knowledge Translation ◽  
Patient Care ◽  
Real Time ◽  
Patient Outcomes ◽  
Health Professionals ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Future Research ◽  
Seeking Behavior

With advances in electronic health record systems and mobile computing technologies it is possible to re-conceptualize how health professionals access information and design appropriate decision-support systems to support quality patient care. This chapter uses the context of nursing-sensitive patient outcomes data collection to explore how technology can be used to increase nurses’ and other health professionals’ access to patient outcomes information in real time to continually improve patient care. The chapter draws upon literature related to: (1) case-based reasoning, (2) feedback, (3) and evidence-based nursing practice to provide the theoretical foundation for an electronic knowledge translation intervention that was developed and tested for usability. Directions for future research include the need to understand how nurses experience uncertainty in their practice, how this influences information seeking behavior, and how information resources can be designed to support real-time clinical decision making.

scholarly journals Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

2020 ◽  
Vol 33 (11) ◽  
pp. 2169-2185 ◽  
Author(s):  
Andrew J. Schaumberg ◽  
Wendy C. Juarez-Nicanor ◽  
Sarah J. Choudhury ◽  
Laura G. Pastrián ◽  
Bobbi S. Pritt ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Patient Care ◽  
Real Time ◽  
Improve Patient Care ◽  
Low Grade ◽  
Search Performance ◽  
Cell Nuclei ◽  
Disease States ◽  
Share Data

Abstract Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805–0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org. We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

scholarly journals Effects of computerised clinical decision support systems (CDSS) on nursing and allied health professional performance and patient outcomes: a systematic review of experimental and observational studies

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e053886
Author(s):  
Teumzghi F Mebrahtu ◽  
Sarah Skyrme ◽  
Rebecca Randell ◽  
Anne-Maree Keenan ◽  
Karen Bloor ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Patient Outcomes ◽  
Health Professionals ◽  
Comparative Research ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Link Type ◽  
Care Outcomes ◽  
Allied Health Professional

ObjectiveComputerised clinical decision support systems (CDSS) are an increasingly important part of nurse and allied health professional (AHP) roles in delivering healthcare. The impact of these technologies on these health professionals’ performance and patient outcomes has not been systematically reviewed. We aimed to conduct a systematic review to investigate this.Materials and methodsThe following bibliographic databases and grey literature sources were searched by an experienced Information Professional for published and unpublished research from inception to February 2021 without language restrictions: MEDLINE (Ovid), Embase Classic+Embase (Ovid), PsycINFO (Ovid), HMIC (Ovid), AMED (Allied and Complementary Medicine) (Ovid), CINAHL (EBSCO), Cochrane Central Register of Controlled Trials (Wiley), Cochrane Database of Systematic Reviews (Wiley), Social Sciences Citation Index Expanded (Clarivate), ProQuest Dissertations & Theses Abstracts & Index, ProQuest ASSIA (Applied Social Science Index and Abstract), Clinical Trials.gov, WHO International Clinical Trials Registry (ICTRP), Health Services Research Projects in Progress (HSRProj), OpenClinical(www.OpenClinical.org), OpenGrey (www.opengrey.eu), Health.IT.gov, Agency for Healthcare Research and Quality (www.ahrq.gov). Any comparative research studies comparing CDSS with usual care were eligible for inclusion.ResultsA total of 36 106 non-duplicate records were identified. Of 35 included studies: 28 were randomised trials, three controlled-before-and-after studies, three interrupted-time-series and one non-randomised trial. There were ~1318 health professionals and ~67 595 patient participants in the studies. Most studies focused on nurse decision-makers (71%) or paramedics (5.7%). CDSS as a standalone Personal Computer/LAPTOP-technology was a feature of 88.7% of the studies; only 8.6% of the studies involved ‘smart’ mobile/handheld-technology.DiscussionCDSS impacted 38% of the outcome measures used positively. Care processes were better in 47% of the measures adopted; examples included, nurses’ adherence to hand disinfection guidance, insulin dosing, on-time blood sampling and documenting care. Patient care outcomes in 40.7% of indicators were better; examples included, lower numbers of falls and pressure ulcers, better glycaemic control, screening of malnutrition and obesity and triaging appropriateness.ConclusionCDSS may have a positive impact on selected aspects of nurses’ and AHPs’ performance and care outcomes. However, comparative research is generally low quality, with a wide range of heterogeneous outcomes. After more than 13 years of synthesised research into CDSS in healthcare professions other than medicine, the need for better quality evaluative research remains as pressing.

scholarly journals Mapping multicenter randomized controlled trials in anesthesiology: a scoping review

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sylvain Boet ◽  
Joseph K. Burns ◽  
Olivia Cheng-Boivin ◽  
Hira Khan ◽  
Kendra Derry ◽  
...  
Keyword(s):  
Knowledge Translation ◽  
Randomized Controlled Trials ◽  
Patient Outcomes ◽  
Scoping Review ◽  
Anesthetic Technique ◽  
Future Research ◽  
Control Group ◽  
Controlled Trials ◽  
Cochrane Central Register ◽  
Randomized Controlled

Abstract Background Evidence suggests that there are substantial inconsistencies in the practice of anesthesia. There has not yet been a comprehensive summary of the anesthesia literature that can guide future knowledge translation interventions to move evidence into practice. As the first step toward identifying the most promising interventions for systematic implementation in anesthesia practice, this scoping review of multicentre RCTs aimed to explore and map the existing literature investigating perioperative anesthesia-related interventions and clinical patient outcomes. Methods Multicenter randomized controlled trials were eligible for inclusion if they involved a tested anesthesia-related intervention administered to adult surgical patients (≥ 16 years old), with a control group receiving either another anesthesia intervention or no intervention at all. The electronic databases Embase (via OVID), MEDLINE, and MEDLINE in Process (via OVID), and Cochrane Central Register of Control Trials (CENTRAL) were searched from inception to February 26, 2021. Studies were screened and data were extracted by pairs of independent reviewers in duplicate with disagreements resolved through consensus or a third reviewer. Data were summarized narratively. Results We included 638 multicentre randomized controlled trials (n patients = 615,907) that met the eligibility criteria. The most commonly identified anesthesia-related intervention theme across all studies was pharmacotherapy (n studies = 361 [56.6%]; n patients = 244,610 [39.7%]), followed by anesthetic technique (n studies = 80 [12.5%], n patients = 48,455 [7.9%]). Interventions were most often implemented intraoperatively (n studies = 233 [36.5%]; n patients = 175,974 [28.6%]). Studies typically involved multiple types of surgeries (n studies = 187 [29.2%]; n patients = 206 667 [33.5%]), followed by general surgery only (n studies = 115 [18.1%]; n patients = 201,028 [32.6%]) and orthopedic surgery only (n studies = 94 [14.7%]; n patients = 34,575 [5.6%]). Functional status was the most commonly investigated outcome (n studies = 272), followed by patient experience (n studies = 168), and mortality (n studies = 153). Conclusions This scoping review provides a map of multicenter RCTs in anesthesia which can be used to optimize future research endeavors in the field. Specifically, we have identified key knowledge gaps in anesthesia that require further systematic assessment, as well as areas where additional research would likely not add value. These findings provide the foundation for streamlining knowledge translation in anesthesia in order to reduce practice variation and enhance patient outcomes.

Did healthcare professional perspectives on the quality and safety environment in New Zealand public hospitals change from 2012 to 2017?

2020 ◽  
Vol 34 (7) ◽  
pp. 775-788
Author(s):  
Robin Gauld ◽  
Simon Horsburgh
Keyword(s):  
New Zealand ◽  
Patient Care ◽  
Health Professionals ◽  
Online Survey ◽  
Improve Patient Care ◽  
Public Hospitals ◽  
Quality And Safety ◽  
District Health ◽  
Content Type ◽  
Improve Patient

PurposeThe work environment is known to influence professional attitudes toward quality and safety. This study sought to measure these attitudes amongst health professionals working in New Zealand District Health Boards (DHBs), initially in 2012 and again in 2017.Design/methodology/approachThree questions were included in a national New Zealand health professional workforce survey conducted in 2012 and again in 2017. All registered health professionals employed with DHBs were invited to participate in an online survey. Areas of interest included teamwork amongst professionals; involvement of patients and families in efforts to improve patient care and ease of speaking up when a problem with patient care is perceived.FindingsIn 2012, 57% of respondents (58% in 2017) agreed health professionals worked as a team; 71% respondents (73% in 2017) agreed health professionals involved patients and families in efforts to improve patient care and 69% (65% in 2017) agreed it was easy to speak up in their clinical area, with none of these changes being statistically significant. There were some response differences by respondent characteristics.Practical implicationsWith no change over time, there is a demand for improvement. Also for leadership in policy, management and amongst health professionals if goals of improving quality and safety are to be delivered upon.Originality/valueThis study provides a simple three-question method of probing perceptions of quality and safety and an important set of insights into progress in New Zealand DHBs.

scholarly journals Geospatial Dashboards for Monitoring Smart City Performance

2019 ◽  
Vol 11 (20) ◽  
pp. 5648 ◽  
Author(s):  
Changfeng Jing ◽  
Mingyi Du ◽  
Songnian Li ◽  
Siyuan Liu
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Spatial Data ◽  
Smart City ◽  
Smart Cities ◽  
Research Progress ◽  
Future Research ◽  
Real Time Monitoring ◽  
Information Models ◽  
Technical Issues

Geospatial dashboards have attracted increasing attention from both user communities and academic researchers since the late 1990s. Dashboards can gather, visualize, analyze and advise on urban performance to support sustainable development of smart cities. We conducted a critical review of the research and development of geospatial dashboards, including the integration of maps, spatial data analytics, and geographic visualization for decision support and real-time monitoring of smart city performance. The research about this kind of system has mainly focused on indicators, information models including statistical models and geospatial models, and other related issues. This paper presents an overview of dashboard history and key technologies and applications in smart cities, and summarizes major research progress and representative developments by analyzing their key technical issues. Based on the review, we discuss the visualization model and validity of models for decision support and real-time monitoring that need to be further researched, and recommend some future research directions.

scholarly journals Machine Learning in Medicine: It Has Arrived, Let’s Embrace it

2021 ◽  
Author(s):  
scott Pappada
Keyword(s):  
Machine Learning ◽  
Patient Care ◽  
Data Science ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Orthotopic Heart Transplantation ◽  
Patient Counseling ◽  
Modeling Approach ◽  
One Year

Machine learning and artificial intelligence (AI) in medicine has arrived in medicine and the healthcare community is experiencing significant growth in its adoption across numerous patient care settings. There are countless applications for machine learning and AI in medicine ranging from patient outcome prediction, to clinical decision support, to predicting future patient therapeutic setpoints. This commentary discusses a recent application leveraging machine learning to predict one year patient survival following orthotopic heart transplantation. This modeling approach has significant implications in terms of improving clinical decision making, patient counseling, and ultimately organ allocation and has been shown to significantly outperform preexisting algorithms. This commentary also discusses how adoption and advancement of this modeling approach in the future can provide increased personalization of patient care. The continued expansion of information systems and growth of electronic patient data sources in healthcare will continue to pave the way for increased use and adoption of data science in medicine. Personalized medicine has been a long-standing goal of the healthcare community and with machine learning and AI now being continually incorporated into clinical settings and practice, this technology is well on the pathway to make a considerable impact to greatly improve patient care in the near future.

scholarly journals The role of artificial intelligence in healthcare: a structured literature review

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Silvana Secinaro ◽  
Davide Calandra ◽  
Aurelio Secinaro ◽  
Vivek Muthurangu ◽  
Paolo Biancone
Keyword(s):  
Artificial Intelligence ◽  
Health Services ◽  
Literature Review ◽  
Health Professionals ◽  
Clinical Decision Making ◽  
The United States ◽  
Healthcare Sector ◽  
Management Decision ◽  
Future Research ◽  
Structured Literature Review

Abstract Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. Methods The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. Results The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. Conclusions The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

How are allied health notes used for inpatient care and clinical decision-making? A qualitative exploration of the views of doctors, nurses and allied health professionals

2016 ◽  
Vol 46 (1) ◽  
pp. 23-31 ◽  
Author(s):  
Tilley Pain ◽  
Gail Kingston ◽  
Janet Askern ◽  
Rebecca Smith ◽  
Sandra Phillips ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Health Professionals ◽  
Inpatient Care ◽  
Information Transfer ◽  
Allied Health ◽  
Clinical Decision Making ◽  
Clinical Information ◽  
Allied Health Professionals ◽  
Effective Transfer

Background: Inpatient care is dependent upon the effective transfer of clinical information across multiple professions. However, documented patient clinical information generated by different professions is not always successfully transferred between them. One obstacle to successful information transfer may be the reader’s perception of the information, which is framed in a particular professional context, rather than the information per se. Objective: The aim of this research was to investigate how different health professionals perceive allied health documentation and to investigate how clinicians of all experience levels across medicine, nursing and allied health perceive and use allied health notes to inform their decision-making and treatment of patients. Method: The study used a qualitative approach. A total of 53 speech pathologists, nurses, doctors, occupational therapists, dieticians and social workers (8 males; 43 females) from an Australian regional tertiary hospital participated in eleven single discipline focus groups, conducted over 4 months in 2012. Discussions were recorded and transcribed verbatim and coded into themes by content analysis. Findings: Six themes contributing to the efficacy of clinical information transference emerged from the data: day-to-day care, patient function, discharge and discharge planning, impact of busy workloads, format and structure of allied health documentation and a holistic approach to patient care. Discussion: Other professions read and used allied health notes albeit with differences in focus and need. Readers searched for specific pieces of information to answer their own questions and professional needs, in a process akin to purposive sampling. Staff used allied health notes to explore specific aspects of patient function but did not obtain a holistic picture. Conclusion: Improving both the relationship between the various health professions and interpretation of other professions’ documented clinical information may reduce the frequency of communication errors, thereby improving patient care.

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