scholarly journals Integrating Patient-Generated Health Data Into Clinical Care Settings or Clinical Decision-Making: Lessons Learned From Project HealthDesign

2016 ◽  
Vol 3 (2) ◽  
pp. e26 ◽  
Author(s):  
Deborah J Cohen ◽  
Sara R Keller ◽  
Gillian R Hayes ◽  
David A Dorr ◽  
Joan S Ash ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Health Data ◽  
Lessons Learned

Clinical Decision Making in the ICU: Dysphagia Screening, Assessment, and Treatment

2019 ◽  
Vol 40 (03) ◽  
pp. 170-187 ◽  
Author(s):  
Martin B. Brodsky ◽  
Emily B. Mayfield ◽  
Roxann Diez Gross
Keyword(s):  
Intensive Care Unit ◽  
Decision Making ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Level Of Care ◽  
Assessment And Treatment ◽  
Proper Training ◽  
Screening Assessment

AbstractClinicians often perceive the intensive care unit as among the most intimidating environments in patient care. With the proper training, acquisition of skill, and approach to clinical care, feelings of intimidation may be overcome with the great rewards this level of care has to offer. This review—spanning the ages of birth to senescence and covering oral/nasal endotracheal intubation and tracheostomy—presents a clinically relevant, directly applicable review of screening, assessment, and treatment of dysphagia in the patients who are critically ill for clinical speech–language pathologists and identifies gaps in the clinical peer-reviewed literature for researchers.

Why Machine Learning Should Be Taught in Medical Schools (Preprint)

2021 ◽  
Author(s):  
Matthew Nagy ◽  
Nathan Radakovich ◽  
Aziz Nazha
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Rapid Development ◽  
Clinical Care ◽  
Clinical Decision ◽  
Medical Schools ◽  
Medical Workforce ◽  
Ethical Concerns ◽  
Clinical Tools

UNSTRUCTURED The rapid development of machine learning (ML) applications in healthcare promises to transform the landscape of healthcare. In order for ML advancements to be effectively utilized in clinical care, it is necessary for the medical workforce to be prepared to handle these changes. As physicians in training are exposed to a wide breadth of clinical tools during medical school, this offers an ideal opportunity to introduce ML concepts. A foundational understanding of ML will not only be practically useful for clinicians, but will also address ethical concerns for clinical decision making. While select medical schools have made effort to integrate ML didactics and practice into their curriculum, we argue that foundational ML principles should be taught to broadly to medical students across the country.

scholarly journals Predicting Clinical Outcomes Using Molecular Biomarkers

2016 ◽  
Vol 8 ◽  
pp. BIC.S33380 ◽  
Author(s):  
Harry B. Burke
Keyword(s):  
Decision Making ◽  
Clinical Outcomes ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Molecular Biomarkers ◽  
The Past ◽  
Diagnosis And Prognosis ◽  
Molecular Therapies ◽  
Physician Patient

Over the past 20 years, there has been an exponential increase in the number of biomarkers. At the last count, there were 768,259 papers indexed in PubMed.gov directly related to biomarkers. Although many of these papers claim to report clinically useful molecular biomarkers, embarrassingly few are currently in clinical use. It is suggested that a failure to properly understand, clinically assess, and utilize molecular biomarkers has prevented their widespread adoption in treatment, in comparative benefit analyses, and their integration into individualized patient outcome predictions for clinical decision-making and therapy. A straightforward, general approach to understanding how to predict clinical outcomes using risk, diagnostic, and prognostic molecular biomarkers is presented. In the future, molecular biomarkers will drive advances in risk, diagnosis, and prognosis, they will be the targets of powerful molecular therapies, and they will individualize and optimize therapy. Furthermore, clinical predictions based on molecular biomarkers will be displayed on the clinician's screen during the physician–patient interaction, they will be an integral part of physician–patient-shared decision-making, and they will improve clinical care and patient outcomes.

scholarly journals Clinical Informatics during the COVID-19 Pandemic: Lessons Learned and Implications for Emergency Department and Inpatient Operations

2020 ◽  
Author(s):  
Hanson Hsu ◽  
Peter W Greenwald ◽  
Matthew R Laghezza ◽  
Peter Steel ◽  
Richard Trepp ◽  
...  
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Medical Care ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Clinical Informatics ◽  
Security And Privacy ◽  
Care Pathways ◽  
Provider Education

Abstract In response to a pandemic, hospital leaders can use clinical informatics to aid clinical decision-making, virtualizing medical care, coordinating communication, and defining workflow and compliance. Clinical informatics procedures need to be implemented nimbly, with governance measures in place to properly oversee and guide novel patient care pathways, diagnostic and treatment workflows, and provider education and communication. The authors’ experience recommends: (1) creating flexible ordersets that adapt to evolving guidelines that meet needs across specialties (2) enhancing and supporting inherent telemedicine capability (3) electronically enabling novel workflows quickly and suspending non-critical administrative or billing functions in the EHR and (4) using communication platforms based on tiered urgency that do not compromise security and privacy.

Differences in comprehension of somatic genomic profiling between younger and older adults with advanced genitourinary cancer.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 228-228
Author(s):  
Cristiane Decat Bergerot ◽  
Paulo Gustavo Bergerot ◽  
Joann Hsu ◽  
Nazli Dizman ◽  
Stacy W. Gray ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Older Patients ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Genomic Profiling ◽  
Younger Patients ◽  
Low Health Literacy ◽  
Genitourinary Cancers

228 Background: Genomic profiling (GP) plays an important role in the care of patients diagnosed with advanced cancer, and has been used to guide clinical decision making. As age has been associated with low health literacy, we sought to determine comprehension of the goals and objectives of GP between younger (age < 65) and older (age ≥65) with genitourinary cancers. Methods: Eligible patients had agreed to receive somatic GP as a part of routine clinical care through a CLIA-certified commercially available platform. Participating physicians conducted a standardized dialogue with patients pertaining to the rationale for and clinical utility of somatic GP. Patients then received an in-person survey lasting approximately 10-15 min and assessing a broad range of perceptions related to GP. Results: Among 47 patients, 62% were characterized as older adults. Diagnoses encountered included kidney (43%), prostate (32%), and bladder (25%). Only older adults perceived any shortcomings in the description of GP. These shortcomings related to the clarity of the descriptions of genomic data, as well as the accuracy, detail and compassion with which this information was conveyed. Older adults demonstrated a very strong reliance on physician input in their decision to obtain somatic GP - 42% of older adults suggested that trust in their physician was among the top three reasons for which they opted to do genomic testing, in contrast to just 10% of younger patients (P = 0.04). Both older and younger patients demonstrated frequent misconceptions pertaining to the role of GP. For example, the majority of younger (78%) and older (52%) patients suggested the test was being performed for prognostic purposes. Both groups also frequently held the notion that somatic testing could identify hereditary cancer-related disorders (younger: 78% vs older: 66%). Conclusions: Detailed surveys of patients with genitourinary cancers reveal varied comprehension of somatic GP between younger and older patients. Interventions to enhance understanding of the principles of GP may be helpful in facilitating shared decision-making, particularly among older patients.

scholarly journals Hospital Libraries Have a Positive Impact on Clinical Decision Making and Patient Care

2007 ◽  
Vol 2 (4) ◽  
pp. 104 ◽  
Author(s):  
Martha I. Preddie
Keyword(s):  
Decision Making ◽  
Rural Communities ◽  
Patient Care ◽  
Sample Size ◽  
Clinical Decision Making ◽  
Positive Impact ◽  
Clinical Care ◽  
Clinical Decision ◽  
Sources Of Information ◽  
The Impact

A review of: Marshall, Joanne Gard. “The Impact of the Hospital Library on Clinical Decision Making: the Rochester Study.” Bulletin of the Medical Library Association 80.2 (1992): 169-78. Objective – To determine the impact of hospital library services on clinical decision making. Design – A descriptive survey. Setting – Fifteen hospitals in the Rochester area of New York, United States of America. Seven hospitals were in the city of Rochester, and eight were in surrounding rural communities. Subjects – Active physicians and residents affiliated with the Rochester hospitals. Methods – This study built upon the methodology used in an earlier study by D. N. King of the contribution of hospital libraries to clinical care in Chicago. Lists were compiled of all the active physicians and residents who were affiliated with the Rochester hospitals. In order to ensure that there was a reasonable number of participants from each hospital, and that librarians in hospitals with larger numbers of staff were not overburdened with requests, predetermined percentages were set for the sample: 10% of active physicians from hospitals with more than 25 medical staff members, 30% from hospitals with less staff, and 30% of residents and rural physicians. This resulted in a desirable sample size of 448. A systematic sample with a random start was then drawn from each hospital’s list, and physicians and residents were recruited until the sample size was achieved. Participants were asked to request information related to a clinical case from their hospital library, and to evaluate its impact on patient care, by responding to a two-page questionnaire. Main results – Based on usable questionnaires, there was an overall response rate of 46.4% (208 of 448). Eighty percent of the respondents stated that they probably (48%) or definitely (32.4%) handled a clinical situation differently due to the information received from the library. In terms of the specific aspects of care for which changes were made, 71.6% reported a change in advice given to the patient, 59.6% cited a change in treatment, 50.5% a change in diagnostic tests, 45.2% a change in drugs, and 38.5% a change in post-hospital care or treatment. Physicians credited the information provided by the library as contributing to their ability to avoid additional tests and procedures (49%), additional outpatient visits (26.4%), surgery (21.2%), patient mortality (19.2%), hospital admission (11.5%), and hospital-acquired infections (8.2%). In response to a question about the importance of several sources of information, the library received the highest rating amidst other sources including lab tests, diagnostic imaging, and discussions with colleagues. Conclusion – This study validates earlier research findings that physicians view the information provided by hospital libraries as having a significant impact on clinical decision making. Library supplied information influences changes to specific aspects of care as well as the avoidance of adverse events for patients. The significance of this influence is underscored by the finding that relative to other sources, information obtained from the hospital library was rated more highly.

scholarly journals Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review

Informatics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Mahanazuddin Syed ◽  
Shorabuddin Syed ◽  
Kevin Sexton ◽  
Hafsa Bareen Syeda ◽  
Maryam Garza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Medical Information ◽  
Descriptive Analysis ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Quantitative Descriptive Analysis ◽  
Quantitative Descriptive

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.

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