Updated Review of Blood Culture Contamination

SUMMARY Blood culture contamination represents an ongoing source of frustration for clinicians and microbiologists alike. Ambiguous culture results often lead to diagnostic uncertainty in clinical management and are associated with increased health care costs due to unnecessary treatment and testing. A variety of strategies have been investigated and employed to decrease contamination rates. In addition, numerous approaches to increase our ability to distinguish between clinically significant bacteremia and contamination have been explored. In recent years, there has been an increase in the application of computer-based tools to support infection control activities as well as provide clinical decision support related to the management of infectious diseases. Finally, new approaches for estimating bacteremia risk which have the potential to decrease unnecessary blood culture utilization have been developed and evaluated. In this review, we provide an overview of blood culture contamination and describe the potential utility of a variety of approaches to improve both detection and prevention. While it is clear that progress is being made, fundamental challenges remain.

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Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

Applied Clinical Informatics ◽

10.4338/aci-2011-02-ra-0012 ◽

2011 ◽

Vol 02 (03) ◽

pp. 284-303 ◽

Cited By ~ 4

Author(s):

A. Wright ◽

M. Burton ◽

G. Fraser ◽

M. Krall ◽

S. Maviglia ◽

...

Keyword(s):

Diabetes Mellitus ◽

Health Care ◽

Decision Making ◽

Decision Support ◽

Clinical Decision Support ◽

Diabetes Care ◽

Clinical Decision ◽

The United States ◽

Computer Based

SummaryBackground: Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known.Objective: We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care.Methods: We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites.Results: The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines.Conclusion: Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

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DESIGN OF DECOUPLED CLINICAL DECISION SUPPORT FOR SERVICE-ORIENTED ARCHITECTURES

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194009004143 ◽

2009 ◽

Vol 19 (02) ◽

pp. 159-183 ◽

Cited By ~ 2

Author(s):

JENS WEBER-JAHNKE

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Service Oriented Architecture ◽

Empirical Evaluation ◽

Cost Savings ◽

Clinical Decision ◽

Service Oriented Architectures ◽

Distributed Services ◽

Service Oriented ◽

Computer Based

Computer-based clinical decision support (CDS) contributes to cost savings, increased patient safety and quality of medical care. Most existing CDS systems are stand-alone products (first generation) or part of complete electronic medical record packages (second generation). Experience shows that creating and maintaining CDS systems is expensive and requires effort that should be economized by sharing them among multiple users. It makes good economic sense to share CDS service installations among a larger set of client systems. The paradigm of a service-oriented architecture (SOA) embraces this idea of sharing distributed services. Some attempts making CDS services available to distributed health information systems exist. However, these approaches have not gained much adoption. We argue that they do not provide a sufficient level of decoupling between client and CDS in order to be broadly reusable in SOAs. In this paper, we present a new CDS service component called EGADSS, which has been designed and implemented with the declared objective to minimize the coupling between client and CDS server. We present our key design decisions, which are guided by empirical research in SOA development. We evaluate our result theoretically by measuring the level of decoupling achieved compared to existing CDS approaches. Furthermore, we report on an empirical evaluation of the resulting design, integrating the EGADSS service with an example client system.

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District nurses’ experiences of using a clinical decision support system and an assessment tool at elderly care units in primary health care: a qualitative study

Primary Health Care Research & Development ◽

10.1017/s1463423621000530 ◽

2021 ◽

Vol 22 ◽

Author(s):

Annica Lagerin ◽

Lena Törnkvist ◽

Johan Fastbom ◽

Lena Lundh

Keyword(s):

Older Adults ◽

Health Care ◽

Primary Health Care ◽

Decision Support ◽

Clinical Decision Support System ◽

Elderly Care ◽

Medication Use ◽

Clinical Decision ◽

Primary Health ◽

District Nurses

Abstract Aim: The present study aimed to describe the experience of district nurses (DNs) in using a clinical decision support system (CDSS) and the safe medication assessment (SMA) tool during patient visits to elderly care units at primary health care centres. Background: In Swedish primary health care, general practitioners (GPs) prescribe and have the responsibility to regularly review older adults’ medications, while DN (nurses specialised in primary health care) play an important role in assessing older adults’ ability to manage their medications, detecting potential drug-related problems and communicating with patients and GPs about such problems. In a previous feasibility study, we found that DNs who use a combination of a CDSS and the SMA tool identified numerous potentially harmful or dangerous factors and took a number of nursing care actions to improve the safety and quality of patients’ medication use. In telephone interviews, patients indicated that they were positive towards the assessment and interventions. Methods: Individual interviews with seven DNs who worked at six different primary health care centres in Region Stockholm were carried out in 2018. In 2019, an additional group interview was conducted with two of the seven DNs so they could discuss and comment on preliminary findings. Qualitative content analysis was used to analyse the interview transcripts. Findings: Using the tools, the DNs could have a natural conversation about medication use with older adults. They could get a clear picture of the older adults’ medication use and thus obtain information that could facilitate collaboration with GPs about this important component of health care for older adults. However, for the tools to be used in clinical practice, some barriers would have to be overcome, such as the time-consuming nature of using the tools and the lack of established routines for interprofessional collaboration regarding medication discussions.

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Does a mobile clinical decision-support system (CDSS) help improve the quality of primary health care?

Cochrane Clinical Answers ◽

10.1002/cca.3745 ◽

2021 ◽

Author(s):

Agustín Ciapponi

Keyword(s):

Health Care ◽

Primary Health Care ◽

Decision Support ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Primary Health

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Organizational Factors Influencing the Use of Clinical Decision Support for Improving Cancer Screening Within Community Health Centers

E-Health and Telemedicine ◽

10.4018/978-1-4666-8756-1.ch007 ◽

2016 ◽

pp. 118-148 ◽

Cited By ~ 1

Author(s):

Timothy Jay Carney ◽

Michael Weaver ◽

Anna M. McDaniel ◽

Josette Jones ◽

David A. Haggstrom

Keyword(s):

Colorectal Cancer ◽

Health Care ◽

Decision Support ◽

Cancer Screening ◽

Community Health ◽

Clinical Decision Support ◽

Community Health Centers ◽

Clinical Decision ◽

Health Centers ◽

Practice Setting

Adoption of clinical decision support (CDS) systems leads to improved clinical performance through improved clinician decision making, adherence to evidence-based guidelines, medical error reduction, and more efficient information transfer and to reduction in health care disparities in under-resourced settings. However, little information on CDS use in the community health care (CHC) setting exists. This study examines if organizational, provider, or patient level factors can successfully predict the level of CDS use in the CHC setting with regard to breast, cervical, and colorectal cancer screening. This study relied upon 37 summary measures obtained from the 2005 Cancer Health Disparities Collaborative (HDCC) national survey of 44 randomly selected community health centers. A multi-level framework was designed that employed an all-subsets linear regression to discover relationships between organizational/practice setting, provider, and patient characteristics and the outcome variable, a composite measure of community health center CDS intensity-of-use. Several organizational and provider level factors from our conceptual model were identified to be positively associated with CDS level of use in community health centers. The level of CDS use (e.g., computerized reminders, provider prompts at point-of-care) in support of breast, cervical, and colorectal cancer screening rate improvement in vulnerable populations is determined by both organizational/practice setting and provider factors. Such insights can better facilitate the increased uptake of CDS in CHCs that allows for improved patient tracking, disease management, and early detection in cancer prevention and control within vulnerable populations.

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Advanced Issues of Health Informatics and Clinical Decision Support System in Global Health Care

Next-Generation Mobile and Pervasive Healthcare Solutions - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-2851-7.ch010 ◽

2018 ◽

pp. 153-170 ◽

Cited By ~ 2

Author(s):

Kijpokin Kasemsap

Keyword(s):

Health Care ◽

Decision Support ◽

Global Health ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Health Informatics ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Global Health Care

This chapter indicates the advanced issues of health informatics; the advanced issues of Clinical Decision Support System (CDSS); CDSS and Computerized Physician Order Entry (CPOE); the false positive alerts in CDSS; and CDSS and biomedical engineering. Health informatics and CDSS are the advanced health care technologies with the support of many technological fields. Health informatics and CDSS apply various computerized devices to provide enhanced health-related outcomes in terms of problem solving, analytical thinking, and decision making. Health informatics and CDSS help clinicians and health care providers to make complex information useful in supporting clinical decisions, thus delivering the best standard of care for each patient. The chapter argues that utilizing health informatics and CDSS has the potential to increase health outcomes and reach strategic goals in global health care.

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Features of computer-based clinical decision support

Clinical Decision Support ◽

10.1016/b978-012369377-8/50004-0 ◽

2007 ◽

pp. 79-107

Author(s):

Robert A. Greenes

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Clinical Decision ◽

Computer Based

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P036: A clinical decision support intervention to increase usage of probenecid in the ED

CJEM ◽

10.1017/cem.2016.212 ◽

2016 ◽

Vol 18 (S1) ◽

pp. S90-S90

Author(s):

S. Dowling ◽

E. Lang ◽

D. Wang ◽

T. Rich

Keyword(s):

Health Care ◽

Decision Support ◽

Soft Tissue ◽

Clinical Decision Support ◽

Clinical Decision ◽

Soft Tissue Infections ◽

Post Intervention ◽

Support Intervention ◽

Intervention Phase ◽

Decision Support Intervention

Introduction: In certain circumstances, skin and soft tissue infections are managed with intravenous (IV) antibiotics. In our center, patients initiated on outpatient IV antibiotics are followed up by a home parental therapy program the following day. A significant number of these patients require a repeat visit to the ED because of clinic hours. Probenecid is a drug that can prolong the half-life of certain antibiotics (such as cefazolin) and can therefore avoid a repeat ED visit, reducing health care costs and improve ED capacity. Our goal was to increase probenecid usage in the ED in order to optimize management of skin and soft tissue infections (SSTI) in the ED. The primary outcome was to compare the usage of probenecid in the pre and post-intervention phase. Secondary outcomes were to compare revisit rates between patients receiving cefazolin alone vs cefazolin + probenecid. Methods: Using administrative data merged with Computerized Physician Order Entry (CPOE), we extracted data 90 days pre- and 90 post-intervention (February 11, 2015 to August 11, 2015). The setting for the study is an urban center (4 adult ED’s with an annual census of over 320,000 visits per year). Our CPOE system is fully integrated into the ED patient care. The multi-faceted intervention involved modifying all relevant SSTI order sets in the CPOE system to link any cefazolin order with an order for probenecid. Physicians and nurses were provided with a 1 page summary of probenecid (indications, contra-indications, pharmacology), as well as decision support with the CPOE. Any patients who were receiving outpatient cefazolin therapy were included in the study. Results: Our analysis included 2512 patients (1148 and 1364 patients in the pre/post phases) who received cefazolin in the ED and were discharged during the 180 day period. Baseline variables (gender, age, % admitted) and ED visits were similar in both phases. In the pre-intervention phase 30.2% of patients received probenecid and in the post-intervention phase 43.0%, for a net increase of 12.8% (p=<0.0001). Patients who received probenecid had a 2.2% (11.4% vs 13.6%, p=0.014) lower re-visit rate in the following 72H. Conclusion: We have implemented a CPOE based clinical decision support intervention that demonstrated significant increase in probenecid usage by emergency physician and resulted in a decrease in ED revisits. This intervention would result in health care cost-savings.

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