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Identifying complexity in infectious diseases inpatient settings: An observation study.

ABSTRACT: Understanding complexity in healthcare has the potential to reduce decision and treatment uncertainty. Therefore, identifying both patient and task complexity may offer better task allocation and design recommendation for next-generation health information technology system design.To identify specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians.We observed and audio recorded clinical rounds of three infectious disease teams. Thirty cases were observed for a period of four consecutive days. Transcripts were coded based on clinical complexity-contributing factors from the clinical complexity model. Ratings of complexity on day 1 for each case were collected. We then used statistical methods to identify complexity-contributing factors in relationship to perceived complexity of clinicians.A factor analysis (principal component extraction with varimax rotation) of specific items revealed three factors (eigenvalues>2.0) explaining 47% of total variance, namely task interaction and goals (10 items, 26%, Cronbach's Alpha=0.87), urgency and acuity (6 items, 11%, Cronbach's Alpha=0.67), and psychosocial behavior (4 items, 10%, Cronbach's alpha=0.55). A linear regression analysis showed no statistically significant association between complexity perceived by the physicians and objective complexity, which was measured from coded transcripts by three clinicians (Multiple R-squared=0.13, p=0.61). There were no physician effects on the rating of perceived complexity.Task complexity contributes significantly to overall complexity in the infectious diseases domain. The different complexity-contributing factors found in this study can guide health information technology system designers and researchers for intuitive design. Thus, decision support tools can help reduce the specific complexity-contributing factors. Future studies aimed at understanding clinical domain-specific complexity-contributing factors can ultimately improve task allocation and design for intuitive clinical reasoning.

SUBMITTER: Roosan D

PROVIDER: S-EPMC5562543 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:BACKGROUND:The words 'infection' and 'inflammation' lack specific definitions. Here, such words are not defined. Instead, the ability to visualize host-microbial interactions was explored. METHODS:Leukocyte differential counts and four bacterial species (Staphylococcus aureus, Streptococcus dysgalactiae, Staphylococcus chromogenes, and Escherichia coli) were determined or isolated in a cross-sectional and randomized study conducted with 611 bovine milk samples. Two paradigms were evaluated: (i) the classic one, which measures non-structured (count or percent) data; and (ii) a method that, using complex data structures, detects and differentiates three-dimensional (3D) interactions among lymphocytes (L), macrophages (M), and neutrophils (N). RESULTS:Classic analyses failed to differentiate bacterial-positive (B+) from -negative (B-) observations: B- and B+ data overlapped, even when statistical significance was achieved. In contrast, the alternative approach showed distinct patterns, such as perpendicular data inflections, which discriminated microbial-negative/mononuclear cell-predominating (MCP) from microbial-positive/phagocyte-predominating (PP) subsets. Two PP subcategories were distinguished, as well as PP/culture-negative (false-negative) and MCP/culture-positive (false-positive) observations. In 3D space, MCP and PP subsets were perpendicular to one another, displaying ? 91% specificity or sensitivity. Findings supported five inferences: (i) disease is not always ruled out by negative bacterial tests; (ii) low total cell counts can coexist with high phagocyte percents; (iii) neither positive bacterial isolation nor high cell counts always coincide with PP profiles; (iv) statistical significance is not synonymous with discrimination; and (v) hidden relationships cannot be detected when simple (non-structured) data formats are used and statistical analyses are performed before data subsets are identified, but can be uncovered when complexity is investigated. CONCLUSIONS:Pattern recognition-based assessments can detect host-microbial interactions usually unobserved. Such cutoff-free, confidence interval-free, gold standard-free approaches provide interpretable information on complex entities, such as 'infection' and 'inflammation', even without definitions. To investigate disease dynamics, combinations of observational and experimental longitudinal studies, on human and non-human infections, are recommended.

Project description:BACKGROUND:General knowledge on climate change effects and adaptation strategies has increased significantly in recent years. However, there is still a substantial information gap regarding the influence of climate change on infectious diseases and how these diseases should be identified. From a One Health perspective, zoonotic infections are of particular concern. The climate in Northern regions is changing faster than the global average. This study sought to identify climate-sensitive infectious diseases (CSIs) of relevance for humans and/or animals living in Northern regions. Inclusion criteria for CSIs were constructed using expert assessments. Based on these principles, 37 potential CSIs relevant for Northern regions were identified. A systematic literature search was performed in three databases using an explicit stepwise approach to determine whether the literature supports selection of these 37 potential CSIs. RESULTS:In total, 1275 nominated abstracts were read and categorised using predefined criteria. Results showed that arthropod vector-borne diseases in particular are recognised as having potential to expand their distribution towards Northern latitudes and that tick-borne encephalitis and borreliosis, midge-borne bluetongue and the parasitic infection fasciolosis can be classified as climate-sensitive. Many of the other potential CSIs considered are affected by extreme weather events, but could not be clearly classified as climate-sensitive. An additional literature search comparing awareness of climate influences on potential CSIs between 1997-2006 and 2007-2016 showed an increase in the number of papers mentioning effects of climate change. CONCLUSIONS:The four CSIs identified in this study could be targeted in a systematic surveillance programme in Northern regions. It is evident that climate change can affect the epidemiology and geographical range of many infectious diseases, but there were difficulties in identifying additional CSIs, most likely because other factors may be of equal or greater importance. However, climate-ecological dynamics are constantly under change, and therefore diseases may fall in or out of the climate-sensitive definition over time. There is increasing awareness in the literature of the effects of climate change on infectious diseases over time.

Project description:IntroductionPeople who use alcohol and/or drugs (PWUAD) are at high risk of medical complications, frequent hospitalisation and drug-related death following discharge from inpatient settings and emergency departments (EDs). Harm reduction strategies implemented in these settings may mitigate negative health outcomes for PWUAD. However, the scope of harm reduction strategies used globally within inpatient settings and EDs is unknown. The objective of this review is to identify and synthesise reported harm reduction strategies that have been implemented across inpatient settings and EDs for PWUAD.Methods and analysisThis review will include studies from any country and health service reporting on harm reduction strategies implemented in inpatient settings or EDs. The population of interest includes people of any race, gender and age identifying as PWUAD, or individuals who provided care to PWUAD. Studies which describe implementation strategies and barriers and enablers to implementation will be included. Studies published in English, or those available for English translation will be included. The following databases will be searched: MEDLINE All (Ovid), Embase (Elsevier Embase.com), CINAHL with Full Text (EBSCOhost), PsycINFO (EBSCOhost) and SCOPUS (Elsevier Scopus.com). A grey literature search will be conducted. There will be no date restrictions on the search. Titles, abstracts and full texts will be screened in duplicate. Data will be extracted using a standardised form. The results will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews.Ethics and disseminationScoping reviews do not require ethical approval. Patient partners with lived experience and relevant knowledge users will be engaged as research team members throughout all phases of the research process. A report detailing context, methodology and findings from this review will be disseminated to knowledge users and relevant community stakeholders. This review will be submitted for publication to a relevant peer-reviewed journal.

Project description:Background Administrative hospital diagnostic coding data are increasingly used in "big data" research and to assess complication rates after surgery or acute medical conditions. Acute stroke is a common complication of several procedures/conditions, such as carotid interventions, but data are lacking on the sensitivity of administrative coding in identifying acute stroke during inpatient stay. Methods and Results Using all acute strokes ascertained in a population-based cohort (2002-2017) as the reference, we determined the sensitivity of hospital administrative diagnostic codes ( International Classification of Diseases, Tenth Revision; ICD-10) for identifying acute strokes that occurred during hospital admission for other reasons, stratified by coding strategies, study periods, and stroke severity (National Institutes of Health Stroke Score</≥5). Of 3011 acute strokes, 198 (6.6%) occurred during hospital admissions for procedures/other diseases, including 122 (61.6%) major strokes. Using stroke-specific codes ( ICD-10=I60-I61 and I63-I64) in the primary diagnostic position, 66 of the 198 cases were correctly identified (sensitivity for any stroke, 33.3%; 95% CI, 27.1-40.2; minor stroke, 30.3%; 95% CI, 21.0-41.5; major stroke, 35.2%; 95% CI, 27.2-44.2), with no improvement of sensitivity over time ( Ptrend=0.54). Sensitivity was lower during admissions for surgery/procedures than for other acute medical admissions (n/% 17/23.3% versus 49/39.2%; P=0.02). Sensitivity improved to 60.6% (53.6-67.2) for all and 61.6% (50.0-72.1) for surgery/procedures if other diagnostic positions were used, and to 65.2% (58.2-71.5) and 68.5% (56.9-78.1) respectively if combined with use of all possible nonspecific stroke-related codes (ie, adding ICD-10=I62 and I65-I68). Conclusions Low sensitivity of administrative coding in identifying acute strokes that occurred during admission does not support its use alone for audit of complication rates of procedures or hospitalization for other reasons.

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Identifying complexity in infectious diseases inpatient settings: An observation study.

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