To analyze patient safety incidents associated with computer use to develop the basis for a classification of problems reported by health professionals.
Incidents submitted to a voluntary incident reporting database across one Australian state were retrieved and a subset (25%) was analyzed to identify ‘natural categories’ for classification. Two coders independently classified the remaining incidents into one or more categories. Free text descriptions were analyzed to identify contributing factors. Where available medical specialty, time of day and consequences were examined.
Descriptive statistics; inter-rater reliability.
A search of 42 616 incidents from 2003 to 2005 yielded 123 computer related incidents. After removing duplicate and unrelated incidents, 99 incidents describing 117 problems remained. A classification with 32 types of computer use problems was developed. Problems were grouped into information input (31%), transfer (20%), output (20%) and general technical (24%). Overall, 55% of problems were machine related and 45% were attributed to human–computer interaction. Delays in initiating and completing clinical tasks were a major consequence of machine related problems (70%) whereas rework was a major consequence of human–computer interaction problems (78%). While 38% (n=26) of the incidents were reported to have a noticeable consequence but no harm, 34% (n=23) had no noticeable consequence.
Only 0.2% of all incidents reported were computer related. Further work is required to expand our classification using incident reports and other sources of information about healthcare IT problems. Evidence based user interface design must focus on the safe entry and retrieval of clinical information and support users in detecting and correcting errors and malfunctions.
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.
Problem list terms and their usage frequencies were collected from large healthcare institutions.
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.
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.
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.
To evaluate the impact of generic substitution decision support on electronic (e-) prescribing of generic medications.
The authors analyzed retrospective outpatient e-prescribing data from an academic medical center and affiliated network for July 1, 2005–September 30, 2008 using an interrupted time-series design to assess the rate of generic prescribing before and after implementing generic substitution decision support. To assess background secular trends, e-prescribing was compared with a concurrent random sample of hand-generated prescriptions.
Proportion of generic medications prescribed before and after the intervention, evaluated over time, and compared with a sample of prescriptions generated without e-prescribing.
The proportion of generic medication prescriptions increased from 32.1% to 54.2% after the intervention (22.1% increase, 95% CI 21.9% to 22.3%), with no diminution in magnitude of improvement post-intervention. In the concurrent control group, increases in proportion of generic prescriptions (29.3% to 31.4% to 37.4% in the pre-intervention, post-intervention, and end-of-study periods, respectively) were not commensurate with the intervention. There was a larger change in generic prescribing rates among authorized prescribers (24.6%) than nurses (18.5%; adjusted OR 1.38, 95% CI 1.17 to 1.63). Two years after the intervention, the proportion of generic prescribing remained significantly higher for e-prescriptions (58.1%; 95% CI 57.5% to 58.7%) than for hand-generated prescriptions ordered at the same time (37.4%; 95% CI 34.9% to 39.9%) (p<0.0001). Generic prescribing increased significantly in every specialty.
Implementation of generic substitution decision support was associated with dramatic and sustained improvements in the rate of outpatient generic e-prescribing across all specialties.
To propose a centralized method for generating global unique identifiers to link collections of research data and specimens.
The work is a collaboration between the Simons Foundation Autism Research Initiative and the National Database for Autism Research. The system is implemented as a web service: an investigator inputs identifying information about a participant into a client application and sends encrypted information to a server application, which returns a generated global unique identifier. The authors evaluated the system using a volume test of one million simulated individuals and a field test on 2000 families (over 8000 individual participants) in an autism study.
Inverse probability of hash codes; rate of false identity of two individuals; rate of false split of single individual; percentage of subjects for which identifying information could be collected; percentage of hash codes generated successfully.
Large-volume simulation generated no false splits or false identity. Field testing in the Simons Foundation Autism Research Initiative Simplex Collection produced identifiers for 96% of children in the study and 77% of parents. On average, four out of five hash codes per subject were generated perfectly (only one perfect hash is required for subsequent matching).
The system must achieve balance among the competing goals of distinguishing individuals, collecting accurate information for matching, and protecting confidentiality. Considerable effort is required to obtain approval from institutional review boards, obtain consent from participants, and to achieve compliance from sites during a multicenter study.
Generic unique identifiers have the potential to link collections of research data, augment the amount and types of data available for individuals, support detection of overlap between collections, and facilitate replication of research findings.
Negation is a linguistic phenomenon that marks the absence of an entity or event. Negated events are frequently reported in both biological literature and clinical notes. Text mining applications benefit from the detection of negation and its scope. However, due to the complexity of language, identifying the scope of negation in a sentence is not a trivial task.
Conditional random fields (CRF), a supervised machine-learning algorithm, were used to train models to detect negation cue phrases and their scope in both biological literature and clinical notes. The models were trained on the publicly available BioScope corpus.
The performance of the CRF models was evaluated on identifying the negation cue phrases and their scope by calculating recall, precision and F1-score. The models were compared with four competitive baseline systems.
The best CRF-based model performed statistically better than all baseline systems and NegEx, achieving an F1-score of 98% and 95% on detecting negation cue phrases and their scope in clinical notes, and an F1-score of 97% and 85% on detecting negation cue phrases and their scope in biological literature.
This approach is robust, as it can identify negation scope in both biological and clinical text. To benefit text mining applications, the system is publicly available as a Java API and as an online application at http://negscope.askhermes.org.