ICU nurses' acceptance of electronic health records

Objective

To assess intensive care unit (ICU) nurses'' acceptance of electronic health records (EHR) technology and examine the relationship between EHR design, implementation factors, and nurse acceptance.

Design

The authors analyzed data from two cross-sectional survey questionnaires distributed to nurses working in four ICUs at a northeastern US regional medical center, 3 months and 12 months after EHR implementation.

Measurements

Survey items were drawn from established instruments used to measure EHR acceptance and usability, and the usefulness of three EHR functionalities, specifically computerized provider order entry (CPOE), the electronic medication administration record (eMAR), and a nursing documentation flowsheet.

Results

On average, ICU nurses were more accepting of the EHR at 12 months as compared to 3 months. They also perceived the EHR as being more usable and both CPOE and eMAR as being more useful. Multivariate hierarchical modeling indicated that EHR usability and CPOE usefulness predicted EHR acceptance at both 3 and 12 months. At 3 months postimplementation, eMAR usefulness predicted EHR acceptance, but its effect disappeared at 12 months. Nursing flowsheet usefulness predicted EHR acceptance but only at 12 months.

Conclusion

As the push toward implementation of EHR technology continues, more hospitals will face issues related to acceptance of EHR technology by staff caring for critically ill patients. This research suggests that factors related to technology design have strong effects on acceptance, even 1 year following the EHR implementation.     

Toward personalizing treatment for depression: predicting diagnosis and severity

Objective

Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment.

Materials and methods

We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35 000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute.

Results

Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70–0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy.

Conclusions

It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future.     

Automated concept-level information extraction to reduce the need for custom software and rules development

Objective

Despite at least 40 years of promising empirical performance, very few clinical natural language processing (NLP) or information extraction systems currently contribute to medical science or care. The authors address this gap by reducing the need for custom software and rules development with a graphical user interface-driven, highly generalizable approach to concept-level retrieval.

Materials and methods

A ‘learn by example’ approach combines features derived from open-source NLP pipelines with open-source machine learning classifiers to automatically and iteratively evaluate top-performing configurations. The Fourth i2b2/VA Shared Task Challenge''s concept extraction task provided the data sets and metrics used to evaluate performance.

Results

Top F-measure scores for each of the tasks were medical problems (0.83), treatments (0.82), and tests (0.83). Recall lagged precision in all experiments. Precision was near or above 0.90 in all tasks.

Discussion

With no customization for the tasks and less than 5 min of end-user time to configure and launch each experiment, the average F-measure was 0.83, one point behind the mean F-measure of the 22 entrants in the competition. Strong precision scores indicate the potential of applying the approach for more specific clinical information extraction tasks. There was not one best configuration, supporting an iterative approach to model creation.

Conclusion

Acceptable levels of performance can be achieved using fully automated and generalizable approaches to concept-level information extraction. The described implementation and related documentation is available for download.     

The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data

Objective

Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models.

Design

This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer''s disease in 1411 individuals who each had 312 318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB).

Measurement

Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time.

Results

The training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUC of 0.72, which is significantly better than the AUC of 0.59 by NB (p<0.00001), but not significantly different from the AUC of 0.71 by FSNB. MANB was better calibrated than NB, and FSNB was even better in calibration. A limitation was that only one dataset and two comparison algorithms were included in this study.

Conclusion

MANB performed comparatively well in predicting a clinical outcome from a high-dimensional genome-wide dataset. These results provide support for including MANB in the methods used to predict outcomes from large, genome-wide datasets.     

Use of population health data to refine diagnostic decision-making for pertussis

Objective

To improve identification of pertussis cases by developing a decision model that incorporates recent, local, population-level disease incidence.

Design

Retrospective cohort analysis of 443 infants tested for pertussis (2003–7).

Measurements

Three models (based on clinical data only, local disease incidence only, and a combination of clinical data and local disease incidence) to predict pertussis positivity were created with demographic, historical, physical exam, and state-wide pertussis data. Models were compared using sensitivity, specificity, area under the receiver-operating characteristics (ROC) curve (AUC), and related metrics.

Results

The model using only clinical data included cyanosis, cough for 1 week, and absence of fever, and was 89% sensitive (95% CI 79 to 99), 27% specific (95% CI 22 to 32) with an area under the ROC curve of 0.80. The model using only local incidence data performed best when the proportion positive of pertussis cultures in the region exceeded 10% in the 8–14 days prior to the infant''s associated visit, achieving 13% sensitivity, 53% specificity, and AUC 0.65. The combined model, built with patient-derived variables and local incidence data, included cyanosis, cough for 1 week, and the variable indicating that the proportion positive of pertussis cultures in the region exceeded 10% 8–14 days prior to the infant''s associated visit. This model was 100% sensitive (p<0.04, 95% CI 92 to 100), 38% specific (p<0.001, 95% CI 33 to 43), with AUC 0.82.

Conclusions

Incorporating recent, local population-level disease incidence improved the ability of a decision model to correctly identify infants with pertussis. Our findings support fostering bidirectional exchange between public health and clinical practice, and validate a method for integrating large-scale public health datasets with rich clinical data to improve decision-making and public health.     

Patient characteristics associated with venous thromboembolic events: a cohort study using pooled electronic health record data

Objective

To demonstrate the potential of de-identified clinical data from multiple healthcare systems using different electronic health records (EHR) to be efficiently used for very large retrospective cohort studies.

Materials and methods

Data of 959 030 patients, pooled from multiple different healthcare systems with distinct EHR, were obtained. Data were standardized and normalized using common ontologies, searchable through a HIPAA-compliant, patient de-identified web application (Explore; Explorys Inc). Patients were 26 years or older seen in multiple healthcare systems from 1999 to 2011 with data from EHR.

Results

Comparing obese, tall subjects with normal body mass index, short subjects, the venous thromboembolic events (VTE) OR was 1.83 (95% CI 1.76 to 1.91) for women and 1.21 (1.10 to 1.32) for men. Weight had more effect then height on VTE. Compared with Caucasian, Hispanic/Latino subjects had a much lower risk of VTE (female OR 0.47, 0.41 to 0.55; male OR 0.24, 0.20 to 0.28) and African-Americans a substantially higher risk (female OR 1.83, 1.76 to 1.91; male OR 1.58, 1.50 to 1.66). This 13-year retrospective study of almost one million patients was performed over approximately 125 h in 11 weeks, part time by the five authors.

Discussion

As research informatics tools develop and more clinical data become available in EHR, it is important to study and understand unique opportunities for clinical research informatics to transform the scale and resources needed to perform certain types of clinical research.

Conclusions

With the right clinical research informatics tools and EHR data, some types of very large cohort studies can be completed with minimal resources.     

Using statistical text classification to identify health information technology incidents

Objective

To examine the feasibility of using statistical text classification to automatically identify health information technology (HIT) incidents in the USA Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database.

Design

We used a subset of 570 272 incidents including 1534 HIT incidents reported to MAUDE between 1 January 2008 and 1 July 2010. Text classifiers using regularized logistic regression were evaluated with both ‘balanced’ (50% HIT) and ‘stratified’ (0.297% HIT) datasets for training, validation, and testing. Dataset preparation, feature extraction, feature selection, cross-validation, classification, performance evaluation, and error analysis were performed iteratively to further improve the classifiers. Feature-selection techniques such as removing short words and stop words, stemming, lemmatization, and principal component analysis were examined.

Measurements

κ statistic, F1 score, precision and recall.

Results

Classification performance was similar on both the stratified (0.954 F1 score) and balanced (0.995 F1 score) datasets. Stemming was the most effective technique, reducing the feature set size to 79% while maintaining comparable performance. Training with balanced datasets improved recall (0.989) but reduced precision (0.165).

Conclusions

Statistical text classification appears to be a feasible method for identifying HIT reports within large databases of incidents. Automated identification should enable more HIT problems to be detected, analyzed, and addressed in a timely manner. Semi-supervised learning may be necessary when applying machine learning to big data analysis of patient safety incidents and requires further investigation.     

Research and applications: Identifying medical terms in patient-authored text: a crowdsourcing-based approach

Background and objective

As people increasingly engage in online health-seeking behavior and contribute to health-oriented websites, the volume of medical text authored by patients and other medical novices grows rapidly. However, we lack an effective method for automatically identifying medical terms in patient-authored text (PAT). We demonstrate that crowdsourcing PAT medical term identification tasks to non-experts is a viable method for creating large, accurately-labeled PAT datasets; moreover, such datasets can be used to train classifiers that outperform existing medical term identification tools.

Materials and methods

To evaluate the viability of using non-expert crowds to label PAT, we compare expert (registered nurses) and non-expert (Amazon Mechanical Turk workers; Turkers) responses to a PAT medical term identification task. Next, we build a crowd-labeled dataset comprising 10 000 sentences from MedHelp. We train two models on this dataset and evaluate their performance, as well as that of MetaMap, Open Biomedical Annotator (OBA), and NaCTeM''s TerMINE, against two gold standard datasets: one from MedHelp and the other from CureTogether.

Results

When aggregated according to a corroborative voting policy, Turker responses predict expert responses with an F1 score of 84%. A conditional random field (CRF) trained on 10 000 crowd-labeled MedHelp sentences achieves an F1 score of 78% against the CureTogether gold standard, widely outperforming OBA (47%), TerMINE (43%), and MetaMap (39%). A failure analysis of the CRF suggests that misclassified terms are likely to be either generic or rare.

Conclusions

Our results show that combining statistical models sensitive to sentence-level context with crowd-labeled data is a scalable and effective technique for automatically identifying medical terms in PAT.     

Evaluation of safety profile of black shilajit after 91 days repeated administration in rats

Objective

To evaluate the safety of shilajit by 91 days repeated administration in different dose levels in rats.

Methods

In this study the albino rats were divided into four groups. Group I received vehicle and group II, III and IV received 500, 2 500 and 5 000 mg/kg of shilajit, respectively. Finally animals were sacrificed and subjected to histopathology and iron was estimated by flame atomic absorption spectroscopy and graphite furnace.

Results

The result showed that there were no significant changes in iron level of treated groups when compared with control except liver (5 000 mg/kg) and histological slides of all organs revealed normal except negligible changes in liver and intestine with the highest dose of shilajit. The weight of all organs was normal when compared with control.

Conclusions

The result suggests that black shilajit, an Ayurvedic formulation, is safe for long term use as a dietary supplement for a number of disorders like iron deficiency anaemia.     

Development and evaluation of an ensemble resource linking medications to their indications

Objective

To create a computable MEDication Indication resource (MEDI) to support primary and secondary use of electronic medical records (EMRs).

Materials and methods

We processed four public medication resources, RxNorm, Side Effect Resource (SIDER) 2, MedlinePlus, and Wikipedia, to create MEDI. We applied natural language processing and ontology relationships to extract indications for prescribable, single-ingredient medication concepts and all ingredient concepts as defined by RxNorm. Indications were coded as Unified Medical Language System (UMLS) concepts and International Classification of Diseases, 9th edition (ICD9) codes. A total of 689 extracted indications were randomly selected for manual review for accuracy using dual-physician review. We identified a subset of medication–indication pairs that optimizes recall while maintaining high precision.

Results

MEDI contains 3112 medications and 63 343 medication–indication pairs. Wikipedia was the largest resource, with 2608 medications and 34 911 pairs. For each resource, estimated precision and recall, respectively, were 94% and 20% for RxNorm, 75% and 33% for MedlinePlus, 67% and 31% for SIDER 2, and 56% and 51% for Wikipedia. The MEDI high-precision subset (MEDI-HPS) includes indications found within either RxNorm or at least two of the three other resources. MEDI-HPS contains 13 304 unique indication pairs regarding 2136 medications. The mean±SD number of indications for each medication in MEDI-HPS is 6.22±6.09. The estimated precision of MEDI-HPS is 92%.

Conclusions

MEDI is a publicly available, computable resource that links medications with their indications as represented by concepts and billing codes. MEDI may benefit clinical EMR applications and reuse of EMR data for research.     

Does user-centred design affect the efficiency,usability and safety of CPOE order sets?

Background

Application of user-centred design principles to Computerized provider order entry (CPOE) systems may improve task efficiency, usability or safety, but there is limited evaluative research of its impact on CPOE systems.

Objective

We evaluated the task efficiency, usability, and safety of three order set formats: our hospital''s planned CPOE order sets (CPOE Test), computer order sets based on user-centred design principles (User Centred Design), and existing pre-printed paper order sets (Paper).

Participants

27staff physicians, residents and medical students.

Setting

Sunnybrook Health Sciences Centre, an academic hospital in Toronto, Canada.

Methods

Participants completed four simulated order set tasks with three order set formats (two CPOE Test tasks, one User Centred Design, and one Paper). Order of presentation of order set formats and tasks was randomized. Users received individual training for the CPOE Test format only.

Main Measures

Completion time (efficiency), requests for assistance (usability), and errors in the submitted orders (safety).

Results

27 study participants completed 108 order sets. Mean task times were: User Centred Design format 273 s, Paper format 293 s (p=0.73 compared to UCD format), and CPOE Test format 637 s (p<0.0001 compared to UCD format). Users requested assistance in 31% of the CPOE Test format tasks, whereas no assistance was needed for the other formats (p<0.01). There were no significant differences in number of errors between formats.

Conclusions

The User Centred Design format was more efficient and usable than the CPOE Test format even though training was provided for the latter. We conclude that application of user-centred design principles can enhance task efficiency and usability, increasing the likelihood of successful implementation.     

Research and applications: Impact of an electronic medication management system on hospital doctors’ and nurses’ work: a controlled pre–post,time and motion study

Objective

To quantify and compare the time doctors and nurses spent on direct patient care, medication-related tasks, and interactions before and after electronic medication management system (eMMS) introduction.

Methods

Controlled pre–post, time and motion study of 129 doctors and nurses for 633.2 h on four wards in a 400-bed hospital in Sydney, Australia. We measured changes in proportions of time on tasks and interactions by period, intervention/control group, and profession.

Results

eMMS was associated with no significant change in proportions of time spent on direct care or medication-related tasks relative to control wards. In the post-period control ward, doctors spent 19.7% (2 h/10 h shift) of their time on direct care and 7.4% (44.4 min/10 h shift) on medication tasks, compared to intervention ward doctors (25.7% (2.6 h/shift; p=0.08) and 8.5% (51 min/shift; p=0.40), respectively). Control ward nurses in the post-period spent 22.1% (1.9 h/8.5 h shift) of their time on direct care and 23.7% on medication tasks compared to intervention ward nurses (26.1% (2.2 h/shift; p=0.23) and 22.6% (1.9 h/shift; p=0.28), respectively). We found intervention ward doctors spent less time alone (p=0.0003) and more time with other doctors (p=0.003) and patients (p=0.009). Nurses on the intervention wards spent less time with doctors following eMMS introduction (p=0.0001).

Conclusions

eMMS introduction did not result in redistribution of time away from direct care or towards medication tasks. Work patterns observed on these intervention wards were associated with previously reported significant reductions in prescribing error rates relative to the control wards.     

Point-of-care clinical documentation: assessment of a bladder cancer informatics tool (eCancerCareBladder): a randomized controlled study of efficacy,efficiency and user friendliness compared with standard electronic medical records

Objective

To compare the use of structured reporting software and the standard electronic medical records (EMR) in the management of patients with bladder cancer. The use of a human factors laboratory to study management of disease using simulated clinical scenarios was also assessed.

Design

eCancerCare^Bladder and the EMR were used to retrieve data and produce clinical reports. Twelve participants (four attending staff, four fellows, and four residents) used either eCancerCare^Bladder or the EMR in two clinical scenarios simulating cystoscopy surveillance visits for bladder cancer follow-up.

Measurements

Time to retrieve and quality of review of the patient history; time to produce and completeness of a cystoscopy report. Finally, participants provided a global assessment of their computer literacy, familiarity with the two systems, and system preference.

Results

eCancerCare^Bladder was faster for data retrieval (scenario 1: 146 s vs 245 s, p=0.019; scenario 2: 306 vs 415 s, NS), but non-significantly slower to generate a clinical report. The quality of the report was better in the eCancerCare^Bladder system (scenario 1: p<0.001; scenario 2: p=0.11). User satisfaction was higher with the eCancerCare^Bladder system, and 11/12 participants preferred to use this system.

Limitations

The small sample size affected the power of our study to detect differences.

Conclusions

Use of a specific data management tool does not appear to significantly reduce user time, but the results suggest improvement in the level of care and documentation and preference by users. Also, the use of simulated scenarios in a laboratory setting appears to be a valid method for comparing the usability of clinical software.     

Phospholipon 90H (P90H)-based PEGylated microscopic lipospheres delivery system for gentamicin: an antibiotic evaluation

Objective

To formulate gentamicin liposphere by solvent-melting method using lipids and polyethylene glycol 4 000 (PEG-4 000) for oral administration.

Methods

Gentamicin lipospheres were prepared by melt-emulsification using 30% w/w Phospholipon® 90H in Beeswax as the lipid matrix containing PEG-4 000. These lipospheres were characterized by evaluating on encapsulation efficiency, loading capacity, change in pH and the release profile. Antimicrobial activities were evaluated against Escherichia coli, Pseudomonas aeruginosa, Salmonella paratyphii and Staphylococcus aureus using the agar diffusion method.

Results

Photomicrographs revealed spherical particles within a micrometer range with minimal growth after 1 month. The release of gentamicin in vitro varied widely with the PEG-4 000 contents. Moreover, significant (P>0.05) amount of gentamicin was released in vivo from the formulation. The encapsulation and loading capacity were all high, indicating the ability of the lipids to take up the drug. The antimicrobial activities were very high especially against Pseudomonas compare to other test organisms. This strongly suggested that the formulation retain its bioactive characteristics.

Conclusions

This study strongly suggest that the issue of gentamicin stability and poor absorption in oral formulation could be adequately addressed by tactical engineering of lipid drug delivery systems such as lipospheres.     

Is the quality of data in an electronic medical record sufficient for assessing the quality of primary care?

Objective

Quality indicators for the treatment of type 2 diabetes are often retrieved from a chronic disease registry (CDR). This study investigates the quality of recording in a general practitioner''s (GP) electronic medical record (EMR) compared to a simple, web-based CDR.

Methods

The GPs entered data directly in the CDR and in their own EMR during the study period (2011). We extracted data from 58 general practices (8235 patients) with type 2 diabetes and compared the occurrence and value of seven process indicators and 12 outcome indicators in both systems. The CDR, specifically designed for monitoring type 2 diabetes and reporting to health insurers, was used as the reference standard. For process indicators we examined the presence or absence of recordings on the patient level in both systems, for outcome indicators we examined the number of compliant or non-compliant values of recordings present in both systems. The diagnostic OR (DOR) was calculated for all indicators.

Results

We found less concordance for process indicators than for outcome indicators. HbA1c testing was the process indicator with the highest DOR. Blood pressure measurement, urine albumin test, BMI recorded and eye assessment showed low DOR. For outcome indicators, the highest DOR was creatinine clearance <30 mL/min or mL/min/1.73 m² and the lowest DOR was systolic blood pressure <140 mm Hg.

Conclusions

Clinical items are not always adequately recorded in an EMR for retrieving indicators, but there is good concordance for the values of these items. If the quality of recording improves, indicators can be reported from the EMR, which will reduce the workload of GPs and enable GPs to maintain a good patient overview.     

Finding falls in ambulatory care clinical documents using statistical text mining

Objective

To determine how well statistical text mining (STM) models can identify falls within clinical text associated with an ambulatory encounter.

Materials and Methods

2241 patients were selected with a fall-related ICD-9-CM E-code or matched injury diagnosis code while being treated as an outpatient at one of four sites within the Veterans Health Administration. All clinical documents within a 48-h window of the recorded E-code or injury diagnosis code for each patient were obtained (n=26 010; 611 distinct document titles) and annotated for falls. Logistic regression, support vector machine, and cost-sensitive support vector machine (SVM-cost) models were trained on a stratified sample of 70% of documents from one location (dataset A_train) and then applied to the remaining unseen documents (datasets A_test–D).

Results

All three STM models obtained area under the receiver operating characteristic curve (AUC) scores above 0.950 on the four test datasets (A_test–D). The SVM-cost model obtained the highest AUC scores, ranging from 0.953 to 0.978. The SVM-cost model also achieved F-measure values ranging from 0.745 to 0.853, sensitivity from 0.890 to 0.931, and specificity from 0.877 to 0.944.

Discussion

The STM models performed well across a large heterogeneous collection of document titles. In addition, the models also generalized across other sites, including a traditionally bilingual site that had distinctly different grammatical patterns.

Conclusions

The results of this study suggest STM-based models have the potential to improve surveillance of falls. Furthermore, the encouraging evidence shown here that STM is a robust technique for mining clinical documents bodes well for other surveillance-related topics.     

Outbreak of Viral Hepatitis E in a Regimental Training Centre

Background

An outbreak of viral hepatitis occurred in a regimental centre with 265 cases occurring during a 3 months period.

Methods

190 serum samples were tested for IgM antibodies against viral hepatitis E by Enzyme Immuno Assay (EIA) and for antibodies against Hepatitis A and Hepatitis B viruses. Epidemiological investigation comprised review of surveillance data, filling up epidemiological case sheet, sanitary survey, inspection of water supplies and bacteriological examination of water for coliforms.

Result

97.4% of the serum samples were positive for IgM antibodies against Hepatitis E virus. Two leaks were detected in water pipelines, which were passing through contaminated areas around improperly functioning septic tanks and soak pits. The attack rate among recruits being supplied water through leaking pipelines was 11.1% whereas it was 2.89% in those not directly exposed. This difference was statistically significant (p<0.001). Bacteriological examination of water showed a high coliform count.

Conclusion

The outbreak of viral hepatitis E occurred due to sewage contamination of water pipelines.Key Words: Hepatitis E, Outbreak