共查询到20条相似文献,搜索用时 31 毫秒
1.
Background
Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text classification.Methods
We evaluated our system on biomedical WSD datasets and determined the contribution of our WSD system to clinical document classification on the 2007 Computational Medicine Challenge corpus.Results
Our system compared favorably with other knowledge-based methods. Machine learning classifiers trained on disambiguated concepts significantly outperformed those trained using all concepts.Conclusions
We developed a WSD system that achieves high disambiguation accuracy on standard biomedical WSD datasets and showed that our WSD system improves clinical document classification.Data sharing
We integrated our WSD system with MetaMap and the clinical Text Analysis and Knowledge Extraction System, two popular biomedical natural language processing systems. All codes required to reproduce our results and all tools developed as part of this study are released as open source, available under http://code.google.com/p/ytex. 相似文献2.
Objectives
This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models.Methods
We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation.Results
Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements.Conclusions
This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models. 相似文献3.
Daniel Albright Arrick Lanfranchi Anwen Fredriksen William F Styler IV Colin Warner Jena D Hwang Jinho D Choi Dmitriy Dligach Rodney D Nielsen James Martin Wayne Ward Martha Palmer Guergana K Savova 《J Am Med Inform Assoc》2013,20(5):922-930
Objective
To create annotated clinical narratives with layers of syntactic and semantic labels to facilitate advances in clinical natural language processing (NLP). To develop NLP algorithms and open source components.Methods
Manual annotation of a clinical narrative corpus of 127 606 tokens following the Treebank schema for syntactic information, PropBank schema for predicate-argument structures, and the Unified Medical Language System (UMLS) schema for semantic information. NLP components were developed.Results
The final corpus consists of 13 091 sentences containing 1772 distinct predicate lemmas. Of the 766 newly created PropBank frames, 74 are verbs. There are 28 539 named entity (NE) annotations spread over 15 UMLS semantic groups, one UMLS semantic type, and the Person semantic category. The most frequent annotations belong to the UMLS semantic groups of Procedures (15.71%), Disorders (14.74%), Concepts and Ideas (15.10%), Anatomy (12.80%), Chemicals and Drugs (7.49%), and the UMLS semantic type of Sign or Symptom (12.46%). Inter-annotator agreement results: Treebank (0.926), PropBank (0.891–0.931), NE (0.697–0.750). The part-of-speech tagger, constituency parser, dependency parser, and semantic role labeler are built from the corpus and released open source. A significant limitation uncovered by this project is the need for the NLP community to develop a widely agreed-upon schema for the annotation of clinical concepts and their relations.Conclusions
This project takes a foundational step towards bringing the field of clinical NLP up to par with NLP in the general domain. The corpus creation and NLP components provide a resource for research and application development that would have been previously impossible. 相似文献4.
Objective
To provide a natural language processing method for the automatic recognition of events, temporal expressions, and temporal relations in clinical records.Materials and Methods
A combination of supervised, unsupervised, and rule-based methods were used. Supervised methods include conditional random fields and support vector machines. A flexible automated feature selection technique was used to select the best subset of features for each supervised task. Unsupervised methods include Brown clustering on several corpora, which result in our method being considered semisupervised.Results
On the 2012 Informatics for Integrating Biology and the Bedside (i2b2) shared task data, we achieved an overall event F1-measure of 0.8045, an overall temporal expression F1-measure of 0.6154, an overall temporal link detection F1-measure of 0.5594, and an end-to-end temporal link detection F1-measure of 0.5258. The most competitive system was our event recognition method, which ranked third out of the 14 participants in the event task.Discussion
Analysis reveals the event recognition method has difficulty determining which modifiers to include/exclude in the event span. The temporal expression recognition method requires significantly more normalization rules, although many of these rules apply only to a small number of cases. Finally, the temporal relation recognition method requires more advanced medical knowledge and could be improved by separating the single discourse relation classifier into multiple, more targeted component classifiers.Conclusions
Recognizing events and temporal expressions can be achieved accurately by combining supervised and unsupervised methods, even when only minimal medical knowledge is available. Temporal normalization and temporal relation recognition, however, are far more dependent on the modeling of medical knowledge. 相似文献5.
Objective
Current techniques for knowledge-based Word Sense Disambiguation (WSD) of ambiguous biomedical terms rely on relations in the Unified Medical Language System Metathesaurus but do not take into account the domain of the target documents. The authors'' goal is to improve these methods by using information about the topic of the document in which the ambiguous term appears.Design
The authors proposed and implemented several methods to extract lists of key terms associated with Medical Subject Heading terms. These key terms are used to represent the document topic in a knowledge-based WSD system. They are applied both alone and in combination with local context.Measurements
A standard measure of accuracy was calculated over the set of target words in the widely used National Library of Medicine WSD dataset.Results and discussion
The authors report a significant improvement when combining those key terms with local context, showing that domain information improves the results of a WSD system based on the Unified Medical Language System Metathesaurus alone. The best results were obtained using key terms obtained by relevance feedback and weighted by inverse document frequency. 相似文献6.
Objective
To develop an automated, high-throughput, and reproducible method for reclassifying and validating ontological concepts for natural language processing applications.Design
We developed a distributional similarity approach to classify the Unified Medical Language System (UMLS) concepts. Classification models were built for seven broad biomedically relevant semantic classes created by grouping subsets of the UMLS semantic types. We used contextual features based on syntactic properties obtained from two different large corpora and used α-skew divergence as the similarity measure.Measurements
The testing sets were automatically generated based on the changes by the National Library of Medicine to the semantic classification of concepts from the UMLS 2005AA to the 2006AA release. Error rates were calculated and a misclassification analysis was performed.Results
The estimated lowest error rates were 0.198 and 0.116 when considering the correct classification to be covered by our top prediction and top 2 predictions, respectively.Conclusion
The results demonstrated that the distributional similarity approach can recommend high level semantic classification suitable for use in natural language processing. 相似文献7.
David A Hanauer Mohammed Saeed Kai Zheng Qiaozhu Mei Kerby Shedden Alan R Aronson Naren Ramakrishnan 《J Am Med Inform Assoc》2014,21(5):925-937
Objective
We describe experiments designed to determine the feasibility of distinguishing known from novel associations based on a clinical dataset comprised of International Classification of Disease, V.9 (ICD-9) codes from 1.6 million patients by comparing them to associations of ICD-9 codes derived from 20.5 million Medline citations processed using MetaMap. Associations appearing only in the clinical dataset, but not in Medline citations, are potentially novel.Methods
Pairwise associations of ICD-9 codes were independently identified in both the clinical and Medline datasets, which were then compared to quantify their degree of overlap. We also performed a manual review of a subset of the associations to validate how well MetaMap performed in identifying diagnoses mentioned in Medline citations that formed the basis of the Medline associations.Results
The overlap of associations based on ICD-9 codes in the clinical and Medline datasets was low: only 6.6% of the 3.1 million associations found in the clinical dataset were also present in the Medline dataset. Further, a manual review of a subset of the associations that appeared in both datasets revealed that co-occurring diagnoses from Medline citations do not always represent clinically meaningful associations.Discussion
Identifying novel associations derived from large clinical datasets remains challenging. Medline as a sole data source for existing knowledge may not be adequate to filter out widely known associations.Conclusions
In this study, novel associations were not readily identified. Further improvements in accuracy and relevance for tools such as MetaMap are needed to realize their expected utility. 相似文献8.
Berry de Bruijn Colin Cherry Svetlana Kiritchenko Joel Martin Xiaodan Zhu 《J Am Med Inform Assoc》2011,18(5):557-562
Objective
As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality. A critical part of that process is rigid benchmark testing of natural language processing methods on realistic clinical narrative. In this paper, the authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge.Design
The three systems perform three key steps in clinical information extraction: (1) extraction of medical problems, tests, and treatments, from discharge summaries and progress notes; (2) classification of assertions made on the medical problems; (3) classification of relations between medical concepts. Machine learning systems performed these tasks using large-dimensional bags of features, as derived from both the text itself and from external sources: UMLS, cTAKES, and Medline.Measurements
Performance was measured per subtask, using micro-averaged F-scores, as calculated by comparing system annotations with ground-truth annotations on a test set.Results
The systems ranked high among all submitted systems in the competition, with the following F-scores: concept extraction 0.8523 (ranked first); assertion detection 0.9362 (ranked first); relationship detection 0.7313 (ranked second).Conclusion
For all tasks, we found that the introduction of a wide range of features was crucial to success. Importantly, our choice of machine learning algorithms allowed us to be versatile in our feature design, and to introduce a large number of features without overfitting and without encountering computing-resource bottlenecks. 相似文献9.
Norris H Heintzelman Robert J Taylor Lone Simonsen Roger Lustig Doug Anderko Jennifer A Haythornthwaite Lois C Childs George Steven Bova 《J Am Med Inform Assoc》2013,20(5):898-905
Objectives
To test the feasibility of using text mining to depict meaningfully the experience of pain in patients with metastatic prostate cancer, to identify novel pain phenotypes, and to propose methods for longitudinal visualization of pain status.Materials and methods
Text from 4409 clinical encounters for 33 men enrolled in a 15-year longitudinal clinical/molecular autopsy study of metastatic prostate cancer (Project to ELIminate lethal CANcer) was subjected to natural language processing (NLP) using Unified Medical Language System-based terms. A four-tiered pain scale was developed, and logistic regression analysis identified factors that correlated with experience of severe pain during each month.Results
NLP identified 6387 pain and 13 827 drug mentions in the text. Graphical displays revealed the pain ‘landscape’ described in the textual records and confirmed dramatically increasing levels of pain in the last years of life in all but two patients, all of whom died from metastatic cancer. Severe pain was associated with receipt of opioids (OR=6.6, p<0.0001) and palliative radiation (OR=3.4, p=0.0002). Surprisingly, no severe or controlled pain was detected in two of 33 subjects’ clinical records. Additionally, the NLP algorithm proved generalizable in an evaluation using a separate data source (889 Informatics for Integrating Biology and the Bedside (i2b2) discharge summaries).Discussion
Patterns in the pain experience, undetectable without the use of NLP to mine the longitudinal clinical record, were consistent with clinical expectations, suggesting that meaningful NLP-based pain status monitoring is feasible. Findings in this initial cohort suggest that ‘outlier’ pain phenotypes useful for probing the molecular basis of cancer pain may exist.Limitations
The results are limited by a small cohort size and use of proprietary NLP software.Conclusions
We have established the feasibility of tracking longitudinal patterns of pain by text mining of free text clinical records. These methods may be useful for monitoring pain management and identifying novel cancer phenotypes. 相似文献10.
Objective
To build an effective co-reference resolution system tailored to the biomedical domain.Methods
Experimental materials used in this study were provided by the 2011 i2b2 Natural Language Processing Challenge. The 2011 i2b2 challenge involves co-reference resolution in medical documents. Concept mentions have been annotated in clinical texts, and the mentions that co-refer in each document are linked by co-reference chains. Normally, there are two ways of constructing a system to automatically discoverco-referent links. One is to manually build rules forco-reference resolution; the other is to use machine learning systems to learn automatically from training datasets and then perform the resolution task on testing datasets.Results
The existing co-reference resolution systems are able to find some of the co-referent links; our rule based system performs well, finding the majority of the co-referent links. Our system achieved 89.6% overall performance on multiple medical datasets.Conclusions
Manually crafted rules based on observation of training data is a valid way to accomplish high performance in this co-reference resolution task for the critical biomedical domain. 相似文献11.
Objective
To study existing problem list terminologies (PLTs), and to identify a subset of concepts based on standard terminologies that occur frequently in problem list data.Design
Problem list terms and their usage frequencies were collected from large healthcare institutions.Measurement
The pattern of usage of the terms was analyzed. The local terms were mapped to the Unified Medical Language System (UMLS). Based on the mapped UMLS concepts, the degree of overlap between the PLTs was analyzed.Results
Six institutions submitted 76 237 terms and their usage frequencies in 14 million patients. The distribution of usage was highly skewed. On average, 21% of unique terms already covered 95% of usage. The most frequently used 14 395 terms, representing the union of terms that covered 95% of usage in each institution, were exhaustively mapped to the UMLS. 13 261 terms were successfully mapped to 6776 UMLS concepts. Less frequently used terms were generally less ‘mappable’ to the UMLS. The mean pairwise overlap of the PLTs was only 21% (median 19%). Concepts that were shared among institutions were used eight times more often than concepts unique to one institution. A SNOMED Problem List Subset of frequently used problem list concepts was identified.Conclusions
Most of the frequently used problem list terms could be found in standard terminologies. The overlap between existing PLTs was low. The use of the SNOMED Problem List Subset will save developmental effort, reduce variability of PLTs, and enhance interoperability of problem list data. 相似文献12.
Objectives
The UMLS constitutes the largest existing collection of medical terms. However, little has been published about the users and uses of the UMLS. This study sheds light on these issues.Design
We designed a questionnaire consisting of 26 questions and distributed it to the UMLS user mailing list. Participants were assured complete confidentiality of their replies. To further encourage list members to respond, we promised to provide them with early results prior to publication. Sector analysis of the responses, according to employment organizations is used to obtain insights into some responses.Results
We received 70 responses. The study confirms two intended uses of the UMLS: access to source terminologies (75%), and mapping among them (44%). However, most access is just to a few sources, led by SNOMED, MeSH, and ICD. Out of 119 reported purposes of use, terminology research (37), information retrieval (19), and terminology translation (14) lead. Four important observations are that the UMLS is widely used as a terminology (77%), even though it was not designed as one; many users (73%) want the NLM to mark concepts with multiple parents in an indented hierarchy and to derive a terminology from the UMLS (73%). Finally, auditing the UMLS is a top budget priority (35%) for users.Conclusions
The study reports many uses of the UMLS in a variety of subjects from terminology research to decision support and phenotyping. The study confirms that the UMLS is used to access its source terminologies and to map among them. Two primary concerns of the existing user base are auditing the UMLS and the design of a UMLS-based derived terminology. 相似文献13.
Sunghwan Sohn Kavishwar B Wagholikar Dingcheng Li Siddhartha R Jonnalagadda Cui Tao Ravikumar Komandur Elayavilli Hongfang Liu 《J Am Med Inform Assoc》2013,20(5):836-842
Background
Temporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge.Objective
To construct automated systems for EVENT/TIMEX3 extraction and temporal link (TLINK) identification from clinical text.Materials and methods
The i2b2 organizers provided 190 annotated discharge summaries as the training set and 120 discharge summaries as the test set. Our Event system used a conditional random field classifier with a variety of features including lexical information, natural language elements, and medical ontology. The TIMEX3 system employed a rule-based method using regular expression pattern match and systematic reasoning to determine normalized values. The TLINK system employed both rule-based reasoning and machine learning. All three systems were built in an Apache Unstructured Information Management Architecture framework.Results
Our TIMEX3 system performed the best (F-measure of 0.900, value accuracy 0.731) among the challenge teams. The Event system produced an F-measure of 0.870, and the TLINK system an F-measure of 0.537.Conclusions
Our TIMEX3 system demonstrated good capability of regular expression rules to extract and normalize time information. Event and TLINK machine learning systems required well-defined feature sets to perform well. We could also leverage expert knowledge as part of the machine learning features to further improve TLINK identification performance. 相似文献14.
Jeffrey P Ferraro Hal Daumé III Scott L DuVall Wendy W Chapman Henk Harkema Peter J Haug 《J Am Med Inform Assoc》2013,20(5):931-939
Objective
Natural language processing (NLP) tasks are commonly decomposed into subtasks, chained together to form processing pipelines. The residual error produced in these subtasks propagates, adversely affecting the end objectives. Limited availability of annotated clinical data remains a barrier to reaching state-of-the-art operating characteristics using statistically based NLP tools in the clinical domain. Here we explore the unique linguistic constructions of clinical texts and demonstrate the loss in operating characteristics when out-of-the-box part-of-speech (POS) tagging tools are applied to the clinical domain. We test a domain adaptation approach integrating a novel lexical-generation probability rule used in a transformation-based learner to boost POS performance on clinical narratives.Methods
Two target corpora from independent healthcare institutions were constructed from high frequency clinical narratives. Four leading POS taggers with their out-of-the-box models trained from general English and biomedical abstracts were evaluated against these clinical corpora. A high performing domain adaptation method, Easy Adapt, was compared to our newly proposed method ClinAdapt.Results
The evaluated POS taggers drop in accuracy by 8.5–15% when tested on clinical narratives. The highest performing tagger reports an accuracy of 88.6%. Domain adaptation with Easy Adapt reports accuracies of 88.3–91.0% on clinical texts. ClinAdapt reports 93.2–93.9%.Conclusions
ClinAdapt successfully boosts POS tagging performance through domain adaptation requiring a modest amount of annotated clinical data. Improving the performance of critical NLP subtasks is expected to reduce pipeline error propagation leading to better overall results on complex processing tasks. 相似文献15.
Glenn T Gobbel Jennifer Garvin Ruth Reeves Robert M Cronin Julia Heavirland Jenifer Williams Allison Weaver Shrimalini Jayaramaraja Dario Giuse Theodore Speroff Steven H Brown Hua Xu Michael E Matheny 《J Am Med Inform Assoc》2014,21(5):833-841
Objective
To determine whether assisted annotation using interactive training can reduce the time required to annotate a clinical document corpus without introducing bias.Materials and methods
A tool, RapTAT, was designed to assist annotation by iteratively pre-annotating probable phrases of interest within a document, presenting the annotations to a reviewer for correction, and then using the corrected annotations for further machine learning-based training before pre-annotating subsequent documents. Annotators reviewed 404 clinical notes either manually or using RapTAT assistance for concepts related to quality of care during heart failure treatment. Notes were divided into 20 batches of 19–21 documents for iterative annotation and training.Results
The number of correct RapTAT pre-annotations increased significantly and annotation time per batch decreased by ∼50% over the course of annotation. Annotation rate increased from batch to batch for assisted but not manual reviewers. Pre-annotation F-measure increased from 0.5 to 0.6 to >0.80 (relative to both assisted reviewer and reference annotations) over the first three batches and more slowly thereafter. Overall inter-annotator agreement was significantly higher between RapTAT-assisted reviewers (0.89) than between manual reviewers (0.85).Discussion
The tool reduced workload by decreasing the number of annotations needing to be added and helping reviewers to annotate at an increased rate. Agreement between the pre-annotations and reference standard, and agreement between the pre-annotations and assisted annotations, were similar throughout the annotation process, which suggests that pre-annotation did not introduce bias.Conclusions
Pre-annotations generated by a tool capable of interactive training can reduce the time required to create an annotated document corpus by up to 50%. 相似文献16.
Background
Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging.Objective
To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort.Methods
As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images.Results
Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%.Conclusions
The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms. 相似文献17.
Objective
We aim to identify duplicate pairs of Medline citations, particularly when the documents are not identical but contain similar information.Materials and methods
Duplicate pairs of citations are identified by comparing word n-grams in pairs of documents. N-grams are modified using two approaches which take account of the fact that the document may have been altered. These are: (1) deletion, an item in the n-gram is removed; and (2) substitution, an item in the n-gram is substituted with a similar term obtained from the Unified Medical Language System Metathesaurus. N-grams are also weighted using a score derived from a language model. Evaluation is carried out using a set of 520 Medline citation pairs, including a set of 260 manually verified duplicate pairs obtained from the Deja Vu database.Results
The approach accurately detects duplicate Medline document pairs with an F1 measure score of 0.99. Allowing for word deletions and substitution improves performance. The best results are obtained by combining scores for n-grams of length 1–5 words.Discussion
Results show that the detection of duplicate Medline citations can be improved by modifying n-grams and that high performance can also be obtained using only unigrams (F1=0.959), particularly when allowing for substitutions of alternative phrases. 相似文献18.
Guergana K Savova Wendy W Chapman Jiaping Zheng Rebecca S Crowley 《J Am Med Inform Assoc》2011,18(4):459-465
Objective
The long-term goal of this work is the automated discovery of anaphoric relations from the clinical narrative. The creation of a gold standard set from a cross-institutional corpus of clinical notes and high-level characteristics of that gold standard are described.Methods
A standard methodology for annotation guideline development, gold standard annotations, and inter-annotator agreement (IAA) was used.Results
The gold standard annotations resulted in 7214 markables, 5992 pairs, and 1304 chains. Each report averaged 40 anaphoric markables, 33 pairs, and seven chains. The overall IAA is high on the Mayo dataset (0.6607), and moderate on the University of Pittsburgh Medical Center (UPMC) dataset (0.4072). The IAA between each annotator and the gold standard is high (Mayo: 0.7669, 0.7697, and 0.9021; UPMC: 0.6753 and 0.7138). These results imply a quality corpus feasible for system development. They also suggest the complementary nature of the annotations performed by the experts and the importance of an annotator team with diverse knowledge backgrounds.Limitations
Only one of the annotators had the linguistic background necessary for annotation of the linguistic attributes. The overall generalizability of the guidelines will be further strengthened by annotations of data from additional sites. This will increase the overall corpus size and the representation of each relation type.Conclusion
The first step toward the development of an anaphoric relation resolver as part of a comprehensive natural language processing system geared specifically for the clinical narrative in the electronic medical record is described. The deidentified annotated corpus will be available to researchers. 相似文献19.
Robert C Wu Vivian Lo Dante Morra Brian M Wong Robert Sargeant Ken Locke Rodrigo Cavalcanti Sherman D Quan Peter Rossos Kim Tran Mark Cheung 《J Am Med Inform Assoc》2013,20(4):766-777
Background
Effective clinical communication is critical to providing high-quality patient care. Hospitals have used different types of interventions to improve communication between care teams, but there have been few studies of their effectiveness.Objectives
To describe the effects of different communication interventions and their problems.Design
Prospective observational case study using a mixed methods approach of quantitative and qualitative methods.Setting
General internal medicine (GIM) inpatient wards at five tertiary care academic teaching hospitals.Participants
Clinicians consisting of residents, attending physicians, nurses, and allied health (AH) staff working on the GIM wards.Methods
Ethnographic methods and interviews with clinical staff (doctors, nurses, medical students, and AH professionals) were conducted over a 16-month period from 2009 to 2010.Results
We identified four categories that described the intended and unintended consequences of communication interventions: impacts on senders, receivers, interprofessional collaboration, and the use of informal communication processes. The use of alphanumeric pagers, smartphones, and web-based communication systems had positive effects for senders and receivers, but unintended consequences were seen with all interventions in all four categories.Conclusions
Interventions that aimed to improve clinical communications solved some but not all problems, and unintended effects were seen with all systems. 相似文献20.