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

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.

2020 ◽  
Author(s):  
Maria Beatriz Walter Costa ◽  
Mark Wernsdorfer ◽  
Alexander Kehrer ◽  
Markus Voigt ◽  
Carina Cundius ◽  
...  

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.


2021 ◽  
pp. 579-587
Author(s):  
Vivek A. Upadhyay ◽  
Adam B. Landman ◽  
Michael J. Hassett

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.


2018 ◽  
Author(s):  
Tim Bezemer ◽  
Mark C H de Groot ◽  
Enja Blasse ◽  
Maarten J ten Berg ◽  
Teus H Kappen ◽  
...  

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.


10.2196/20407 ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. e20407
Author(s):  
Maria Beatriz Walter Costa ◽  
Mark Wernsdorfer ◽  
Alexander Kehrer ◽  
Markus Voigt ◽  
Carina Cundius ◽  
...  

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.


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