Journal of the American Medical Informatics Association (J AM MED INFORM ASSN)
The Journal of the American Medical Informatics Association is a bimonthly journal dedicated to the burgeoning field of medical informatics. Medical informatics is broadly defined as the application of computers and information technology to health care as well as to medical education and biomedical research. The Journal of the American Medical Informatics Association presents peer reviewed, state-of-the-art material to assist physicians, informaticians, scientists, nurses, and other health care professionals to develop and apply medical informatics to patient care, teaching, research, and health care administration.
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- WebsiteJournal of the American Medical Informatics Association website
Other titlesJournal of the American Medical Informatics Association, JAMIA
Material typePeriodical, Internet resource
Document typeJournal / Magazine / Newspaper, Internet Resource
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Publications in this journal
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ABSTRACT: Objective Medical visualization tools have traditionally been constrained to tethered imaging workstations or proprietary client viewers, typically part of hospital radiology systems. To improve accessibility to real-time, remote, interactive, stereoscopic visualization and to enable collaboration among multiple viewing locations, we developed an open source approach requiring only a standard web browser with no added client-side software. Materials and Methods Our collaborative, web-based, stereoscopic, visualization system, CoWebViz, has been used successfully for the past 2 years at the University of Chicago to teach immersive virtual anatomy classes. It is a server application that streams server-side visualization applications to client front-ends, comprised solely of a standard web browser with no added software. Results We describe optimization considerations, usability, and performance results, which make CoWebViz practical for broad clinical use. We clarify technical advances including: enhanced threaded architecture, optimized visualization distribution algorithms, a wide range of supported stereoscopic presentation technologies, and the salient theoretical and empirical network parameters that affect our web-based visualization approach. Discussion The implementations demonstrate usability and performance benefits of a simple web-based approach for complex clinical visualization scenarios. Using this approach overcomes technical challenges that require third-party web browser plug-ins, resulting in the most lightweight client. Conclusions Compared to special software and hardware deployments, unmodified web browsers enhance remote user accessibility to interactive medical visualization. Whereas local hardware and software deployments may provide better interactivity than remote applications, our implementation demonstrates that a simplified, stable, client approach using standard web browsers is sufficient for high quality three-dimensional, stereoscopic, collaborative and interactive visualization.Journal of the American Medical Informatics Association 10/2012;
Article: Secure messaging & diabetes management: Experiences and perspectives of patient portal usersJournal of the American Medical Informatics Association 01/2012;
Article: A system for classifying disease comorbidity status from medical discharge summaries using automated hotspot and negated concept detection.[show abstract] [hide abstract]
ABSTRACT: OBJECTIVE Free-text clinical reports serve as an important part of patient care management and clinical documentation of patient disease and treatment status. Free-text notes are commonplace in medical practice, but remain an under-used source of information for clinical and epidemiological research, as well as personalized medicine. The authors explore the challenges associated with automatically extracting information from clinical reports using their submission to the Integrating Informatics with Biology and the Bedside (i2b2) 2008 Natural Language Processing Obesity Challenge Task. DESIGN A text mining system for classifying patient comorbidity status, based on the information contained in clinical reports. The approach of the authors incorporates a variety of automated techniques, including hot-spot filtering, negated concept identification, zero-vector filtering, weighting by inverse class-frequency, and error-correcting of output codes with linear support vector machines. MEASUREMENTS Performance was evaluated in terms of the macroaveraged F1 measure. RESULTS The automated system performed well against manual expert rule-based systems, finishing fifth in the Challenge's intuitive task, and 13(th) in the textual task. CONCLUSIONS The system demonstrates that effective comorbidity status classification by an automated system is possible.Journal of the American Medical Informatics Association 05/2009; 16(4):590-5.
Article: Medication administration errors in nursing homes using an automated medication dispensing system.[show abstract] [hide abstract]
ABSTRACT: OBJECTIVE To identify the frequency of medication administration errors as well as their potential risk factors in nursing homes using a distribution robot. DESIGN The study was a prospective, observational study conducted within three nursing homes in the Netherlands caring for 180 individuals. MEASUREMENTS Medication errors were measured using the disguised observation technique. Types of medication errors were described. The correlation between several potential risk factors and the occurrence of medication errors was studied to identify potential causes for the errors. RESULTS In total 2,025 medication administrations to 127 clients were observed. In these administrations 428 errors were observed (21.2%). The most frequently occurring types of errors were use of wrong administration techniques (especially incorrect crushing of medication and not supervising the intake of medication) and wrong time errors (administering the medication at least 1 h early or late).The potential risk factors female gender (odds ratio (OR) 1.39; 95% confidence interval (CI) 1.05-1.83), ATC medication class antibiotics (OR 11.11; 95% CI 2.66-46.50), medication crushed (OR 7.83; 95% CI 5.40-11.36), number of dosages/day/client (OR 1.03; 95% CI 1.01-1.05), nursing home 2 (OR 3.97; 95% CI 2.86-5.50), medication not supplied by distribution robot (OR 2.92; 95% CI 2.04-4.18), time classes "7-10 am" (OR 2.28; 95% CI 1.50-3.47) and "10 am-2 pm" (OR 1.96; 1.18-3.27) and day of the week "Wednesday" (OR 1.46; 95% CI 1.03-2.07) are associated with a higher risk of administration errors. CONCLUSIONS Medication administration in nursing homes is prone to many errors. This study indicates that the handling of the medication after removing it from the robot packaging may contribute to this high error frequency, which may be reduced by training of nurse attendants, by automated clinical decision support and by measures to reduce workload.Journal of the American Medical Informatics Association 05/2009; 16(4):486-92.
Article: A rule-based approach for identifying obesity and its comorbidities in medical discharge summaries.[show abstract] [hide abstract]
ABSTRACT: OBJECTIVE Evaluate the effectiveness of a simple rule-based approach in classifying medical discharge summaries according to indicators for obesity and 15 associated co-morbidities as part of the 2008 i2b2 Obesity Challenge. METHODS The authors applied a rule-based approach that looked for occurrences of morbidity-related keywords and identified the types of assertions in which those keywords occurred. The documents were then classified using a simple scoring algorithm based on a mapping of the assertion types to possible judgment categories. MEASUREMENTS RESULTS for the challenge were evaluated based on macro F-measure. We report micro and macro F-measure results for all morbidities combined and for each morbidity separately. Results Our rule-based approach achieved micro and macro F-measures of 0.97 and 0.77, respectively, ranking fifth out of the entries submitted by 28 teams participating in the classification task based on textual judgments and substantially outperforming the average for the challenge. CONCLUSIONS As shown by its ranking in the challenge results, this approach performed relatively well under conditions in which limited training data existed for some judgment categories. Further, the approach held up well in relation to more complex approaches applied to this classification task. The approach could be enhanced by the addition of expert rules to model more complex medical reasoning.Journal of the American Medical Informatics Association 05/2009; 16(4):576-9.
Article: What evidence supports the use of computerized alerts and prompts to improve clinicians' prescribing behavior?[show abstract] [hide abstract]
ABSTRACT: Alerts and prompts represent promising types of decision support in electronic prescribing to tackle inadequacies in prescribing. A systematic review was conducted to evaluate the efficacy of computerized drug alerts and prompts searching EMBASE, CINHAL, MEDLINE, and PsychINFO up to May 2007. Studies assessing the impact of electronic alerts and prompts on clinicians' prescribing behavior were selected and categorized by decision support type. Most alerts and prompts (23 out of 27) demonstrated benefit in improving prescribing behavior and/or reducing error rates. The impact appeared to vary based on the type of decision support. Some of these alerts (n = 5) reported a positive impact on clinical and health service management outcomes. For many categories of reminders, the number of studies was very small and few data were available from the outpatient setting. None of the studies evaluated features that might make alerts and prompts more effective. Details of an updated search run in Jan 2009 are included in the supplement section of this review.Journal of the American Medical Informatics Association 05/2009; 16(4):531-8.
Article: Description of a rule-based system for the i2b2 challenge in natural language processing for clinical data.[show abstract] [hide abstract]
ABSTRACT: The Obesity Challenge, sponsored by Informatics for Integrating Biology and the Bedside (i2b2), a National Center for Biomedical Computing, asked participants to build software systems that could "read" a patient's clinical discharge summary and replicate the judgments of physicians in evaluating presence or absence of obesity and 15 comorbidities. The authors describe their methodology and discuss the results of applying Lockheed Martin's rule-based natural language processing (NLP) capability, ClinREAD. We tailored ClinREAD with medical domain expertise to create assigned default judgments based on the most probable results as defined in the ground truth. It then used rules to collect evidence similar to the evidence that the human judges likely relied upon, and applied a logic module to weigh the strength of all evidence collected to arrive at final judgments. The Challenge results suggest that rule-based systems guided by human medical expertise are capable of solving complex problems in machine processing of medical text.Journal of the American Medical Informatics Association 05/2009; 16(4):571-5.
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ABSTRACT: OBJECTIVE In this study the authors describe the system submitted by the team of University of Szeged to the second i2b2 Challenge in Natural Language Processing for Clinical Data. The challenge focused on the development of automatic systems that analyzed clinical discharge summary texts and addressed the following question: "Who's obese and what co-morbidities do they (definitely/most likely) have?". Target diseases included obesity and its 15 most frequent comorbidities exhibited by patients, while the target labels corresponded to expert judgments based on textual evidence and intuition (separately). DESIGN The authors applied statistical methods to preselect the most common and confident terms and evaluated outlier documents by hand to discover infrequent spelling variants. The authors expected a system with dictionaries gathered semi-automatically to have a good performance with moderate development costs (the authors examined just a small proportion of the records manually). MEASUREMENTS Following the standard evaluation method of the second Workshop on challenges in Natural Language Processing for Clinical Data, the authors used both macro- and microaveraged Fbeta=1 measure for evaluation. RESULTS The authors submission achieved a microaverage F(beta=1) score of 97.29% for classification based on textual evidence (macroaverage F(beta=1) = 76.22%) and 96.42% for intuitive judgments (macroaverage F(beta=1) = 67.27%). CONCLUSIONS The results demonstrate the feasibility of the authors approach and show that even very simple systems with a shallow linguistic analysis can achieve remarkable accuracy scores for classifying clinical records on a limited set of concepts.Journal of the American Medical Informatics Association 05/2009; 16(4):601-5.
Article: Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency.[show abstract] [hide abstract]
ABSTRACT: OBJECTIVE To determine whether a computerized clinical decision support system providing patient-specific recommendations in real-time improves the quality of prescribing for long-term care residents with renal insufficiency. DESIGN Randomized trial within the long-stay units of a large long-term care facility. Randomization was within blocks by unit type. Alerts related to medication prescribing for residents with renal insufficiency were displayed to prescribers in the intervention units and hidden but tracked in control units. Measurement The proportions of final drug orders that were appropriate were compared between intervention and control units within alert categories: (1) recommended medication doses; (2) recommended administration frequencies; (3) recommendations to avoid the drug; (4) warnings of missing information. RESULTS The rates of alerts were nearly equal in the intervention and control units: 2.5 per 1,000 resident days in the intervention units and 2.4 in the control units. The proportions of dose alerts for which the final drug orders were appropriate were similar between the intervention and control units (relative risk 0.95, 95% confidence interval 0.83, 1.1) for the remaining alert categories significantly higher proportions of final drug orders were appropriate in the intervention units: relative risk 2.4 for maximum frequency (1.4, 4.4); 2.6 for drugs that should be avoided (1.4, 5.0); and 1.8 for alerts to acquire missing information (1.1, 3.4). Overall, final drug orders were appropriate significantly more often in the intervention units-relative risk 1.2 (1.0, 1.4). CONCLUSIONS Clinical decision support for physicians prescribing medications for long-term care residents with renal insufficiency can improve the quality of prescribing decisions. Trial Registration: http://clinicaltrials.gov Identifier: NCT00599209.Journal of the American Medical Informatics Association 05/2009; 16(4):480-5.
Article: A text mining approach to the prediction of disease status from clinical discharge summaries.[show abstract] [hide abstract]
ABSTRACT: OBJECTIVE The authors present a system developed for the Challenge in Natural Language Processing for Clinical Data-the i2b2 obesity challenge, whose aim was to automatically identify the status of obesity and 15 related co-morbidities in patients using their clinical discharge summaries. The challenge consisted of two tasks, textual and intuitive. The textual task was to identify explicit references to the diseases, whereas the intuitive task focused on the prediction of the disease status when the evidence was not explicitly asserted. DESIGN The authors assembled a set of resources to lexically and semantically profile the diseases and their associated symptoms, treatments, etc. These features were explored in a hybrid text mining approach, which combined dictionary look-up, rule-based, and machine-learning methods. MEASUREMENTS The methods were applied on a set of 507 previously unseen discharge summaries, and the predictions were evaluated against a manually prepared gold standard. The overall ranking of the participating teams was primarily based on the macro-averaged F-measure. RESULTS The implemented method achieved the macro-averaged F-measure of 81% for the textual task (which was the highest achieved in the challenge) and 63% for the intuitive task (ranked 7(th) out of 28 teams-the highest was 66%). The micro-averaged F-measure showed an average accuracy of 97% for textual and 96% for intuitive annotations. CONCLUSIONS The performance achieved was in line with the agreement between human annotators, indicating the potential of text mining for accurate and efficient prediction of disease statuses from clinical discharge summaries.Journal of the American Medical Informatics Association 05/2009; 16(4):596-600.
Article: Perceptions of standards-based electronic prescribing systems as implemented in outpatient primary care: a physician survey.[show abstract] [hide abstract]
ABSTRACT: OBJECTIVE To compare the experiences of e-prescribing users and nonusers regarding prescription safety and workload and to assess the use of information from two e-prescribing standards (for medication history and formulary and benefit information), as they are implemented. DESIGN Cross-sectional survey of physicians who either had installed or were awaiting installation of one of two commercial e-prescribing systems. MEASUREMENTS Perceptions about medication history and formulary and benefit information among all respondents, and among e-prescribing users, experiences with system usability, job performance impact, and amount of e-prescribing. RESULTS Of 395 eligible physicians, 228 (58%) completed the survey. E-prescribers (n = 139) were more likely than non-e-prescribers (n = 89) to perceive that they could identify clinically important drug-drug interactions (83 versus 67%, p = 0.004) but not that they could identify prescriptions from other providers (65 versus 60%, p = 0.49). They also perceived no significant difference in calls about drug coverage problems (76 versus 71% reported getting 10 or fewer such calls per week; p = 0.43). Most e-prescribers reported high satisfaction with their systems, but 17% had stopped using the system and another 46% said they sometimes reverted to handwriting for prescriptions that they could write electronically. The volume of e-prescribing was correlated with perceptions that it enhanced job performance, whereas quitting was associated with perceptions of poor usability. CONCLUSIONS E-prescribing users reported patient safety benefits but they did not perceive the enhanced benefits expected from using standardized medication history or formulary and benefit information. Additional work is needed for these standards to have the desired effects.Journal of the American Medical Informatics Association 05/2009; 16(4):493-502.
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ABSTRACT: OBJECTIVE The authors developed a natural language processing (NLP) framework that could be used to extract clinical findings and diagnoses from dictated physician documentation. DESIGN De-identified documentation was made available by i2b2 Bio-informatics research group as a part of their NLP challenge focusing on obesity and its co-morbidities. The authors describe their approach, which used a combination of concept detection, context validation, and the application of a variety of rules to conclude patient diagnoses. RESULTS The framework was successful at correctly identifying diagnoses as judged by NLP challenge organizers when compared with a gold standard of physician annotations. The authors overall kappa values for agreement with the gold standard were 0.92 for explicit textual results and 0.91 for intuited results. The NLP framework compared favorably with those of the other entrants, placing third in textual results and fourth in intuited results in the i2b2 competition. CONCLUSIONS The framework and approach used to detect clinical conditions was reasonably successful at extracting 16 diagnoses related to obesity. The system and methodology merits further development, targeting clinically useful applications.Journal of the American Medical Informatics Association 05/2009; 16(4):585-9.
Article: A randomized trial comparing telemedicine case management with usual care in older, ethnically diverse, medically underserved patients with diabetes mellitus: 5 year results of the IDEATel study.[show abstract] [hide abstract]
ABSTRACT: CONTEXT Telemedicine is a promising but largely unproven technology for providing case management services to patients with chronic conditions and lower access to care. OBJECTIVES To examine the effectiveness of a telemedicine intervention to achieve clinical management goals in older, ethnically diverse, medically underserved patients with diabetes. DESIGN, Setting, and Patients A randomized controlled trial was conducted, comparing telemedicine case management to usual care, with blinded outcome evaluation, in 1,665 Medicare recipients with diabetes, aged >/= 55 years, residing in federally designated medically underserved areas of New York State. Interventions Home telemedicine unit with nurse case management versus usual care. Main Outcome Measures The primary endpoints assessed over 5 years of follow-up were hemoglobin A1c (HgbA1c), low density lipoprotein (LDL) cholesterol, and blood pressure levels. RESULTS Intention-to-treat mixed models showed that telemedicine achieved net overall reductions over five years of follow-up in the primary endpoints (HgbA1c, p = 0.001; LDL, p < 0.001; systolic and diastolic blood pressure, p = 0.024; p < 0.001). Estimated differences (95% CI) in year 5 were 0.29 (0.12, 0.46)% for HgbA1c, 3.84 (-0.08, 7.77) mg/dL for LDL cholesterol, and 4.32 (1.93, 6.72) mm Hg for systolic and 2.64 (1.53, 3.74) mm Hg for diastolic blood pressure. There were 176 deaths in the intervention group and 169 in the usual care group (hazard ratio 1.01 [0.82, 1.24]). CONCLUSIONS Telemedicine case management resulted in net improvements in HgbA1c, LDL-cholesterol and blood pressure levels over 5 years in medically underserved Medicare beneficiaries. Mortality was not different between the groups, although power was limited. Trial Registration http://clinicaltrials.gov Identifier: NCT00271739.Journal of the American Medical Informatics Association 05/2009; 16(4):446-56.
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ABSTRACT: In order to survey, facilitate, and evaluate studies of medical language processing on clinical narratives, i2b2 (Informatics for Integrating Biology to the Bedside) organized its second challenge and workshop. This challenge focused on automatically extracting information on obesity and fifteen of its most common comorbidities from patient discharge summaries. For each patient, obesity and any of the comorbidities could be Present, Absent, or Questionable (i.e., possible) in the patient, or Unmentioned in the discharge summary of the patient. i2b2 provided data for, and invited the development of, automated systems that can classify obesity and its comorbidities into these four classes based on individual discharge summaries. This article refers to obesity and comorbidities as diseases. It refers to the categories Present, Absent, Questionable, and Unmentioned as classes. The task of classifying obesity and its comorbidities is called the Obesity Challenge. The data released by i2b2 was annotated for textual judgments reflecting the explicitly reported information on diseases, and intuitive judgments reflecting medical professionals' reading of the information presented in discharge summaries. There were very few examples of some disease classes in the data. The Obesity Challenge paid particular attention to the performance of systems on these less well-represented classes. A total of 30 teams participated in the Obesity Challenge. Each team was allowed to submit two sets of up to three system runs for evaluation, resulting in a total of 136 submissions. The submissions represented a combination of rule-based and machine learning approaches. Evaluation of system runs shows that the best predictions of textual judgments come from systems that filter the potentially noisy portions of the narratives, project dictionaries of disease names onto the remaining text, apply negation extraction, and process the text through rules. Information on disease-related concepts, such as symptoms and medications, and general medical knowledge help systems infer intuitive judgments on the diseases.Journal of the American Medical Informatics Association 05/2009; 16(4):561-70.
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ABSTRACT: A systematic literature review was performed to identify variables promoting consumer health information technology (CHIT) acceptance among patients. The electronic bibliographic databases Web of Science, Business Source Elite, CINAHL, Communication and Mass Media Complete, MEDLINE, PsycArticles, and PsycInfo were searched. A cited reference search of articles meeting the inclusion criteria was also conducted to reduce misses. Fifty-two articles met the selection criteria. Among them, 94 different variables were tested for associations with acceptance. Most of those tested (71%) were patient factors, including sociodemographic characteristics, health- and treatment-related variables, and prior experience or exposure to computer/health technology. Only ten variables were related to human-technology interaction; 16 were organizational factors; and one was related to the environment. In total, 62 (66%) were found to predict acceptance in at least one study. Existing literature focused largely on patient-related factors. No studies examined the impact of social and task factors on acceptance, and few tested the effects of organizational or environmental factors on acceptance. Future research guided by technology acceptance theories should fill those gaps to improve our understanding of patient CHIT acceptance, which in turn could lead to better CHIT design and implementation.Journal of the American Medical Informatics Association 05/2009; 16(4):550-60.
Article: Physicians' use of key functions in electronic health records from 2005 to 2007: a statewide survey.[show abstract] [hide abstract]
ABSTRACT: OBJECTIVE Electronic health records (EHRs) have potential to improve quality and safety, but many physicians do not use these systems to full capacity. The objective of this study was to determine whether this usage gap is narrowing over time. DESIGN Follow-up mail survey of 1,144 physicians in Massachusetts who completed a 2005 survey. MEASUREMENTS Adoption of EHRs and availability and use of 10 EHR functions. RESULTS The response rate was 79.4%. In 2007, 35% of practices had EHRs, up from 23% in 2005. Among practices with EHRs, there was little change between 2005 and 2007 in the availability of nine of ten EHR features; the notable exception was electronic prescribing, reported as available in 44.7% of practices with EHRs in 2005 and 70.8% in 2007. Use of EHR functions changed inconsequentially, with more than one out of five physicians not using each available function regularly in both 2005 and 2007. Only electronic prescribing increased substantially: in 2005, 19.9% of physicians with this function available used it most or all the time, compared with 42.6% in 2007 (p < 0.001). CONCLUSIONS By 2007, more than one third of practices in Massachusetts reported having EHRs; the availability and use of electronic prescribing within these systems has increased. In contrast, physicians reported little change in the availability and use of other EHR functions. System refinements, certification efforts, and health policies, including standards development, should address the gaps in both EHR adoption and the use of key functions.Journal of the American Medical Informatics Association 05/2009; 16(4):465-70.
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ABSTRACT: OBJECTIVE Electronic health records (EHRs) have the potential to advance the quality of care, but studies have shown mixed results. The authors sought to examine the extent of EHR usage and how the quality of care delivered in ambulatory care practices varied according to duration of EHR availability. METHODS The study linked two data sources: a statewide survey of physicians' adoption and use of EHR and claims data reflecting quality of care as indicated by physicians' performance on widely used quality measures. Using four years of measurement, we combined 18 quality measures into 6 clinical condition categories. While the survey of physicians was cross-sectional, respondents indicated the year in which they adopted EHR. In an analysis accounting for duration of EHR use, we examined the relationship between EHR adoption and quality of care. RESULTS The percent of physicians reporting adoption of EHR and availability of EHR core functions more than doubled between 2000 and 2005. Among EHR users in 2005, the average duration of EHR use was 4.8 years. For all 6 clinical conditions, there was no difference in performance between EHR users and non-users. In addition, for these 6 clinical conditions, there was no consistent pattern between length of time using an EHR and physicians performance on quality measures in both bivariate and multivariate analyses. CONCLUSIONS In this cross-sectional study, we found no association between duration of using an EHR and performance with respect to quality of care, although power was limited. Intensifying the use of key EHR features, such as clinical decision support, may be needed to realize quality improvement from EHRs. Future studies should examine the relationship between the extent to which physicians use key EHR functions and their performance on quality measures over time.Journal of the American Medical Informatics Association 05/2009; 16(4):457-64.
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