Clinical Decision Support at the Radiologist Point of Care

2018 ◽  
pp. 109-118
Author(s):  
Tarik K. Alkasab ◽  
Bernardo C. Bizzo ◽  
H. Benjamin Harvey
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision

Real-Time Clinical Decision Support at the Point of Care

2019 ◽  
pp. 125-133
Author(s):  
Ana Margarida Pereira ◽  
Cristina Jácome ◽  
Rita Amaral ◽  
Tiago Jacinto ◽  
João A Fonseca
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision

Red Book Point-of-Care Clinical Decision Support Tool

2018 ◽  
Keyword(s):  
Decision Support ◽  
Infectious Diseases ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Point Of Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Pediatric Health Care ◽  
Flip Chart

This convenient flip chart provides pediatric health care professionals with point-of-care guidance on the assessment, prevention, and treatment of childhood infectious diseases. https://shop.aap.org/red-book-pediatric-infectious-diseases-clinical-decision-support-chart/

scholarly journals Current Status and Future Prospects for Electronic Point-of-Care Clinical Decision Support in Diabetes Care

2012 ◽  
Vol 13 (2) ◽  
pp. 172-176 ◽  
Author(s):  
Patrick J. O’Connor ◽  
Jay R. Desai ◽  
John C. Butler ◽  
Elyse O. Kharbanda ◽  
JoAnn M. Sperl-Hillen
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Diabetes Care ◽  
Point Of Care ◽  
Clinical Decision ◽  
Current Status ◽  
Future Prospects

From Adverse Drug Event Detection to Prevention

2014 ◽  
Vol 53 (06) ◽  
pp. 482-492 ◽  
Author(s):  
P. McNair ◽  
V. Kilintzis ◽  
K. Skovhus Andersen ◽  
J. Niès ◽  
J.-C. Sarfati ◽  
...  
Keyword(s):  
Patient Safety ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Adverse Drug Event ◽  
Support Services ◽  
Medication Safety ◽  
Point Of Care ◽  
Clinical Decision ◽  
Substantial Contribution ◽  
Drug Event

Summary Background: Errors related to medication seriously affect patient safety and the quality of healthcare. It has been widely argued that various types of such errors may be prevented by introducing Clinical Decision Support Systems (CDSSs) at the point of care. Objectives: Although significant research has been conducted in the field, still medication safety is a crucial issue, while few research outcomes are mature enough to be considered for use in actual clinical settings. In this paper, we present a clinical decision support framework targeting medication safety with major focus on adverse drug event (ADE) prevention. Methods: The novelty of the framework lies in its design that approaches the problem holistically, i.e., starting from knowledge discovery to provide reliable numbers about ADEs per hospital or medical unit to describe their consequences and probable causes, and next employing the acquired knowledge for decision support services development and deployment. Major design features of the frame-work’s services are: a) their adaptation to the context of care (i.e. patient characteristics, place of care, and significance of ADEs), and b) their straightforward integration in the healthcare information technologies (IT) infrastructure thanks to the adoption of a service-oriented architecture (SOA) and relevant standards. Results: Our results illustrate the successful interoperability of the framework with two commercially available IT products, i.e., a Computerized Physician Order Entry (CPOE) and an Electronic Health Record (EHR) system, respectively, along with a Web prototype that is independent of existing health-care IT products. The conducted clinical validation with domain experts and test cases illustrates that the impact of the framework is expected to be major, with respect to patient safety, and towards introducing the CDSS functionality in practical use. Conclusions: This study illustrates an important potential for the applicability of the presented framework in delivering contextualized decision support services at the point of care and for making a substantial contribution towards ADE prevention. None-theless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.

scholarly journals Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19

2020 ◽  
Author(s):  
Michael P. McRae ◽  
Glennon W. Simmons ◽  
Nicolaos J. Christodoulides ◽  
Zhibing Lu ◽  
Stella K. Kang ◽  
...  
Keyword(s):  
Decision Support ◽  
Disease Severity ◽  
Clinical Decision Support ◽  
Severity Score ◽  
Troponin I ◽  
Point Of Care ◽  
Clinical Decision ◽  
Adverse Outcomes ◽  
Number Of Patients ◽  
Severity Scores

AbstractSARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase–myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89– 0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Point-of-care clinical decision support for cancer symptom management: Results of a group randomized trial.

2014 ◽  
Vol 32 (31_suppl) ◽  
pp. 1-1 ◽  
Author(s):  
Mary E. Cooley ◽  
Traci Blonquist ◽  
Paul Catalano ◽  
David Lobach ◽  
Ilana Braun ◽  
...  
Keyword(s):  
Palliative Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Symptom Management ◽  
Point Of Care ◽  
Symptom Assessment ◽  
Well Being ◽  
Clinical Decision ◽  
Group Randomized Trial ◽  
Significant Difference

1 Background: Integration of palliative care into oncology is recommended for quality care. Clinicians may benefit from assistance in assessing and managing multiple symptoms. Palliative care clinicians have the expertise but may not be available or are not consulted early in the course of a patient’s disease. Clinical decision support (CDS) offers an innovative way to deliver symptom management and trigger palliative care referrals at the point-of-care. Methods: Twenty clinicians and their patients were randomized to usual care (UC) or CDS using the symptom assessment and management intervention (SAMI), which provided tailored suggestions for pain, fatigue, depression, anxiety and/or dyspnea. One-hundred seventy-nine patients completed a Web-based symptom assessment prior to each visit for 6 months. A tailored report provided a longitudinal symptom report and suggestions for management were provided to clinicians in the SAMI arm prior to the visit. Standardized questionnaires were administered to patients at baseline, 2, 4 and 6 months later to measure communication about symptoms and health-related quality of life (HR-QOL). The treatment outcome index (TOI) was the primary outcome for HR-QOL. Management of the target symptoms was assessed through chart review. Linear mixed models and logistic regression were used for analyses. Results: Patient characteristics were: mean age of 63 years, 58% female, 88% white, and 32% had < HS education. No differences were noted in communication between patients and their clinicians. Significant differences were noted in physical well-being (p = 0.007, 0.08 adjusted for baseline) and a clinically significant difference in the TOI (62 vs. 68) at 4 months in SAMI as compared to UC. The odds of managing depression (1.6, 90% CI, 1.0-2.5), anxiety (1.7, 90% CI, 1.0-3.0) and fatigue (1.6, 90% CI, 1.1-2.5) were higher in SAMI as compared to UC. The odds of palliative care consults for pain (3.2, 90% CI, 0.7-13.4) appear to be higher in SAMI as compared to UC. Conclusions: Enhanced HR-QOL was noted among patients in the SAMI arm at 4 months. SAMI increased management of depression, fatigue and anxiety and appeared to increase palliative care consults for pain. Clinical trial information: NCT00852462.

scholarly journals Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example

2018 ◽  
Vol 25 (10) ◽  
pp. 1375-1381 ◽  
Author(s):  
Samuel Aronson ◽  
Lawrence Babb ◽  
Darren Ames ◽  
Richard A Gibbs ◽  
Eric Venner ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Genomic Medicine ◽  
Healthcare Providers ◽  
Clinical Decision ◽  
Organizational Boundaries ◽  
Genetic Test Results ◽  
Data Flows ◽  
Emerge Network

Abstract The eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.

An Overview of Efforts to Bring Clinical Knowledge to the Point of Care

2005 ◽  
pp. 285-296
Author(s):  
Dean F. Sittig
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Point Of Care ◽  
Clinical Decision Support Systems ◽  
Clinical Information ◽  
Clinical Decision ◽  
Clinical Knowledge ◽  
The Right

By bringing people the right information in the right format at the right time and place, state of the art clinical information systems with imbedded clinical knowledge can help people make the right clinical decisions. This chapter provides an overview of the efforts to develop systems capable of delivering such information at the point of care. The first section focuses on “library-type” applications that enable a clinician to look-up information in an electronic document. The second section describes a myriad of “real-time clinical decision support systems.” These systems generally deliver clinical guidance at the point of care within the clinical information system (CIS). The third section describes several “hybrid” systems, which combine aspects of real-time clinical decision support systems with library-type information. Finally, section four provides a brief look at various attempts to bring clinical knowledge, in the form of computable guidelines, to the point of care.be sufficiently expressive to explicitly capture the design rational (process and outcome intentions) of the guideline’s author, while leaving flexibility at application time to the attending physician and their own preferred methods.” (Shahar, 2001)

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