scholarly journals The Clinical Decision Support System AMPEL for Laboratory Diagnostics: Implementation and Technical Evaluation

10.2196/20407 ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. e20407
Author(s):  
Maria Beatriz Walter Costa ◽  
Mark Wernsdorfer ◽  
Alexander Kehrer ◽  
Markus Voigt ◽  
Carina Cundius ◽  
...  
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Clinical Decision ◽  
Laboratory Diagnostics ◽  
Technical Requirements ◽  
Wide Range ◽  
Functional Components

Background Laboratory results are of central importance for clinical decision making. The time span between availability and review of results by clinicians is crucial to patient care. Clinical decision support systems (CDSS) are computational tools that can identify critical values automatically and help decrease treatment delay. Objective With this work, we aimed to implement and evaluate a CDSS that supports health care professionals and improves patient safety. In addition to our experiences, we also describe its main components in a general manner to make it applicable to a wide range of medical institutions and to empower colleagues to implement a similar system in their facilities. Methods Technical requirements must be taken into account before implementing a CDSS that performs laboratory diagnostics (labCDSS). These can be planned within the functional components of a reactive software agent, a computational framework for such a CDSS. Results We present AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings), a labCDSS that notifies health care professionals if a life-threatening medical condition is detected. We developed and implemented AMPEL at a university hospital and regional hospitals in Germany (University of Leipzig Medical Center and the Muldental Clinics in Grimma and Wurzen). It currently runs 5 different algorithms in parallel: hypokalemia, hypercalcemia, hyponatremia, hyperlactatemia, and acute kidney injury. Conclusions AMPEL enables continuous surveillance of patients. The system is constantly being evaluated and extended and has the capacity for many more algorithms. We hope to encourage colleagues from other institutions to design and implement similar CDSS using the theory, specifications, and experiences described in this work.

scholarly journals The Clinical Decision Support System AMPEL for Laboratory Diagnostics: Implementation and Technical Evaluation (Preprint)

2020 ◽  
Author(s):  
Maria Beatriz Walter Costa ◽  
Mark Wernsdorfer ◽  
Alexander Kehrer ◽  
Markus Voigt ◽  
Carina Cundius ◽  
...  
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Clinical Decision ◽  
Laboratory Diagnostics ◽  
Technical Requirements ◽  
Wide Range ◽  
Functional Components

BACKGROUND Laboratory results are of central importance for clinical decision making. The time span between availability and review of results by clinicians is crucial to patient care. Clinical decision support systems (CDSS) are computational tools that can identify critical values automatically and help decrease treatment delay. OBJECTIVE With this work, we aimed to implement and evaluate a CDSS that supports health care professionals and improves patient safety. In addition to our experiences, we also describe its main components in a general manner to make it applicable to a wide range of medical institutions and to empower colleagues to implement a similar system in their facilities. METHODS Technical requirements must be taken into account before implementing a CDSS that performs laboratory diagnostics (labCDSS). These can be planned within the functional components of a reactive software agent, a computational framework for such a CDSS. RESULTS We present AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings), a labCDSS that notifies health care professionals if a life-threatening medical condition is detected. We developed and implemented AMPEL at a university hospital and regional hospitals in Germany (University of Leipzig Medical Center and the Muldental Clinics in Grimma and Wurzen). It currently runs 5 different algorithms in parallel: hypokalemia, hypercalcemia, hyponatremia, hyperlactatemia, and acute kidney injury. CONCLUSIONS AMPEL enables continuous surveillance of patients. The system is constantly being evaluated and extended and has the capacity for many more algorithms. We hope to encourage colleagues from other institutions to design and implement similar CDSS using the theory, specifications, and experiences described in this work.

Landscape Analysis of Oncology Mobile Health Applications

2021 ◽  
pp. 579-587
Author(s):  
Vivek A. Upadhyay ◽  
Adam B. Landman ◽  
Michael J. Hassett
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Mobile Health ◽  
Health Care Professionals ◽  
English Language ◽  
Mobile Apps ◽  
Meta Analysis ◽  
Clinical Decision ◽  
Intended Audience

PURPOSE More than 325,000 mobile health (mhealth) applications (apps) have been developed. We sought to describe the state of oncology-specific apps and to highlight areas of strength and opportunities for future development. METHODS We searched for oncology apps in the Apple iOS and Google Play app stores in January 2020. Apps were classified by English language support, date of last update, downloads, intended audience, intended purpose, and developer type. RESULTS We identified 794 oncology-specific, English language applications; only 257 (32%) met basic recency standards and were considered evaluable. Of evaluable apps, almost half (47%) were found in the Medical Store Category and the majority were free (88%). The most common intended audience was health care professionals (45%), with 28% being geared toward the general public and 27% being intended for patients. The intended function was education for 36%, clinical decision support for 19.5%, and patient support for 18%. Only 23% of education apps and 40% of clinical decision support apps reported any formal app content review process. Web developers created 61.5% of apps, scientific societies created 10%, and hospitals or health care organizations created just 6%. Of 54 studies that used mobile apps in oncology identified by a recent meta-analysis, only two could be matched to commercially available apps from our study, suggesting a substantial divide between investigation and product dissemination. CONCLUSION Relatively few oncology-related apps exist in the commercial marketplace, up-to-date apps are uncommon, and there is a notable absence of key oncology stakeholders in app development. Meaningful development opportunities exist.

scholarly journals Models Used in Clinical Decision Support Systems Supporting Healthcare Professionals Treating Chronic Wounds: Systematic Literature Review (Preprint)

2017 ◽  
Author(s):  
Clara Schaarup ◽  
Louise Bilenberg Pape-Haugaard ◽  
Ole Kristian Hejlesen
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Literature Review ◽  
Clinical Decision Support ◽  
Systematic Literature Review ◽  
Health Care Professionals ◽  
Chronic Wounds ◽  
Wound Care ◽  
Clinical Decision ◽  
Decision Support Models

BACKGROUND Chronic wounds such as diabetic foot ulcers, venous leg ulcers, and pressure ulcers are a massive burden to health care facilities. Many randomized controlled trials on different wound care elements have been conducted and published in the Cochrane Library, all of which have only a low evidential basis. Thus, health care professionals are forced to rely on their own experience when making decisions regarding wound care. To progress from experience-based practice to evidence-based wound care practice, clinical decision support systems (CDSS) that help health care providers with decision-making in a clinical workflow have been developed. These systems have proven useful in many areas of the health care sector, partly because they have increased the quality of care, and partially because they have generated a solid basis for evidence-based practice. However, no systematic reviews focus on CDSS within the field of wound care to chronic wounds. OBJECTIVE The aims of this systematic literature review are (1) to identify models used in CDSS that support health care professionals treating chronic wounds, and (2) to classify each clinical decision support model according to selected variables and to create an overview. METHODS A systematic review was conducted using 6 databases. This systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for systematic reviews. The search strategy consisted of three facets, respectively: Facet 1 (Algorithm), Facet 2 (Wound care) and Facet 3 (Clinical decision support system). Studies based on acute wounds or trauma were excluded. Similarly, studies that presented guidelines, protocols and instructions were excluded, since they do not require progression along an active chain of reasoning from the clinicians, just their focus. Finally, studies were excluded if they had not undergone a peer review process. The following aspects were extracted from each article: authors, year, country, the sample size of data and variables describing the type of clinical decision support models. The decision support models were classified in 2 ways: quantitative decision support models, and qualitative decision support models. RESULTS The final number of studies included in the systematic literature review was 10. These clinical decision support models included 4/10 (40%) quantitative decision support models and 6/10 (60%) qualitative decision support models. The earliest article was published in 2007, and the most recent was from 2015. CONCLUSIONS The clinical decision support models were targeted at a variety of different types of chronic wounds. The degree of accessibility of the inference engines varied. Quantitative models served as the engine and were invisible to the health care professionals, while qualitative models required interaction with the user.

scholarly journals A Human(e) Factor in Clinical Decision Support Systems (Preprint)

2018 ◽  
Author(s):  
Tim Bezemer ◽  
Mark C H de Groot ◽  
Enja Blasse ◽  
Maarten J ten Berg ◽  
Teus H Kappen ◽  
...  
Keyword(s):  
Health Care ◽  
Big Data ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Expert Knowledge ◽  
Clinical Decision ◽  
Added Value ◽  
Support Design ◽  
Design Data

UNSTRUCTURED The overwhelming amount, production speed, multidimensionality, and potential value of data currently available—often simplified and referred to as big data —exceed the limits of understanding of the human brain. At the same time, developments in data analytics and computational power provide the opportunity to obtain new insights and transfer data-provided added value to clinical practice in real time. What is the role of the health care professional in collaboration with the data scientist in the changing landscape of modern care? We discuss how health care professionals should provide expert knowledge in each of the stages of clinical decision support design: data level, algorithm level, and decision support level. Including various ethical considerations, we advocate for health care professionals to responsibly initiate and guide interprofessional teams, including patients, and embrace novel analytic technologies to translate big data into patient benefit driven by human(e) values.

scholarly journals A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists

2021 ◽  
Vol 12 (02) ◽  
pp. 199-207
Author(s):  
Liang Yan ◽  
Thomas Reese ◽  
Scott D. Nelson
Keyword(s):  
Patient Safety ◽  
Quality Of Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Controlled Trials ◽  
Clinical Pharmacists ◽  
Randomized Controlled ◽  
The Impact

Abstract Objective Increasingly, pharmacists provide team-based care that impacts patient care; however, the extent of recent clinical decision support (CDS), targeted to support the evolving roles of pharmacists, is unknown. Our objective was to evaluate the literature to understand the impact of clinical pharmacists using CDS. Methods We searched MEDLINE, EMBASE, and Cochrane Central for randomized controlled trials, nonrandomized trials, and quasi-experimental studies which evaluated CDS tools that were developed for inpatient pharmacists as a target user. The primary outcome of our analysis was the impact of CDS on patient safety, quality use of medication, and quality of care. Outcomes were scored as positive, negative, or neutral. The secondary outcome was the proportion of CDS developed for tasks other than medication order verification. Study quality was assessed using the Newcastle–Ottawa Scale. Results Of 4,365 potentially relevant articles, 15 were included. Five studies were randomized controlled trials. All included studies were rated as good quality. Of the studies evaluating inpatient pharmacists using a CDS tool, four showed significantly improved quality use of medications, four showed significantly improved patient safety, and three showed significantly improved quality of care. Six studies (40%) supported expanded roles of clinical pharmacists. Conclusion These results suggest that CDS can support clinical inpatient pharmacists in preventing medication errors and optimizing pharmacotherapy. Moreover, an increasing number of CDS tools have been developed for pharmacists' roles outside of order verification, whereby further supporting and establishing pharmacists as leaders in safe and effective pharmacotherapy.

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/

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

2016 ◽  
pp. 118-148 ◽  
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.

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

2018 ◽  
pp. 153-170 ◽  
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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