The implications of APOBEC3-mediated C-to-U RNA editing for human disease

Intra-organism biodiversity is thought to arise from epigenetic modification of constituent genes and post-translational modifications of translated proteins. Here, we show that post-transcriptional modifications, like RNA editing, may also contribute. RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosine to uracil. RNAsee (RNA site editing evaluation) is a computational tool developed to predict the cytosines edited by these enzymes. We find that 4.5% of non-synonymous DNA single nucleotide polymorphisms that result in cytosine to uracil changes in RNA are probable sites for APOBEC3A/G RNA editing; the variant proteins created by such polymorphisms may also result from transient RNA editing. These polymorphisms are associated with over 20% of Medical Subject Headings across ten categories of disease, including nutritional and metabolic, neoplastic, cardiovascular, and nervous system diseases. Because RNA editing is transient and not organism-wide, future work is necessary to confirm the extent and effects of such editing in humans.

Defining the Subtypes of Long COVID and Risk Factors for Prolonged Disease: Population-Based Case-Crossover Study

BACKGROUND

There have been over 772 million confirmed cases of COVID-19 worldwide. A significant portion of these infections will lead to long COVID (post-COVID-19 condition) and its attendant morbidities and costs. Numerous life-altering complications have already been associated with the development of long COVID, including chronic fatigue, brain fog, and dangerous heart rhythms.

OBJECTIVE

We aim to derive an actionable long COVID case definition consisting of significantly increased signs, symptoms, and diagnoses to support pandemic-related clinical, public health, research, and policy initiatives.

METHODS

This research employs a case-crossover population-based study using International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) data generated at Veterans Affairs medical centers nationwide between January 1, 2020, and August 18, 2022. In total, 367,148 individuals with ICD-10-CM data both before and after a positive COVID-19 test were selected for analysis. We compared ICD-10-CM codes assigned 1 to 7 months following each patient's positive test with those assigned up to 6 months prior. Further, 350,315 patients had novel codes assigned during this window of time. We defined signs, symptoms, and diagnoses as being associated with long COVID if they had a novel case frequency of ≥1:1000, and they significantly increased in our entire cohort after a positive test. We present odds ratios with CIs for long COVID signs, symptoms, and diagnoses, organized by ICD-10-CM functional groups and medical specialty. We used our definition to assess long COVID risk based on a patient's demographics, Elixhauser score, vaccination status, and COVID-19 disease severity.

RESULTS

We developed a long COVID definition consisting of 323 ICD-10-CM diagnosis codes grouped into 143 ICD-10-CM functional groups that were significantly increased in our 367,148 patient post-COVID-19 population. We defined 17 medical-specialty long COVID subtypes such as cardiology long COVID. Patients who were COVID-19-positive developed signs, symptoms, or diagnoses included in our long COVID definition at a proportion of at least 59.7% (268,320/449,450, based on a denominator of all patients who were COVID-19-positive). The long COVID cohort was 8 years older with more comorbidities (2-year Elixhauser score 7.97 in the patients with long COVID vs 4.21 in the patients with non-long COVID). Patients who had a more severe bout of COVID-19, as judged by their minimum oxygen saturation level, were also more likely to develop long COVID.

CONCLUSIONS

An actionable, data-driven definition of long COVID can help clinicians screen for and diagnose long COVID, allowing identified patients to be admitted into appropriate monitoring and treatment programs. This long COVID definition can also support public health, research, and policy initiatives. Patients with COVID-19 who are older or have low oxygen saturation levels during their bout of COVID-19, or those who have multiple comorbidities should be preferentially watched for the development of long COVID.

Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and Evaluation of Machine Learning Models

BACKGROUND

Infants born at extremely preterm gestational ages are typically admitted to the neonatal intensive care unit (NICU) after initial resuscitation. The subsequent hospital course can be highly variable, and despite counseling aided by available risk calculators, there are significant challenges with shared decision-making regarding life support and transition to end-of-life care. Improving predictive models can help providers and families navigate these unique challenges.

OBJECTIVE

Machine learning methods have previously demonstrated added predictive value for determining intensive care unit outcomes, and their use allows consideration of a greater number of factors that potentially influence newborn outcomes, such as maternal characteristics. Machine learning-based models were analyzed for their ability to predict the survival of extremely preterm neonates at initial admission.

METHODS

Maternal and newborn information was extracted from the health records of infants born between 23 and 29 weeks of gestation in the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database. Applicable machine learning models predicting survival during the initial NICU admission were developed and compared. The same type of model was also examined using only features that would be available prepartum for the purpose of survival prediction prior to an anticipated preterm birth. Features most correlated with the predicted outcome were determined when possible for each model.

RESULTS

Of included patients, 37 of 459 (8.1%) expired. The resulting random forest model showed higher predictive performance than the frequently used Score for Neonatal Acute Physiology With Perinatal Extension II (SNAPPE-II) NICU model when considering extremely preterm infants of very low birth weight. Several other machine learning models were found to have good performance but did not show a statistically significant difference from previously available models in this study. Feature importance varied by model, and those of greater importance included gestational age; birth weight; initial oxygenation level; elements of the APGAR (appearance, pulse, grimace, activity, and respiration) score; and amount of blood pressure support. Important prepartum features also included maternal age, steroid administration, and the presence of pregnancy complications.

CONCLUSIONS

Machine learning methods have the potential to provide robust prediction of survival in the context of extremely preterm births and allow for consideration of additional factors such as maternal clinical and socioeconomic information. Evaluation of larger, more diverse data sets may provide additional clarity on comparative performance.

Buprenorphine, Norbuprenorphine, and Naloxone Levels in Adulterated Urine Samples: Can They be Detected When Buprenorphine/Naloxone Film is Dipped into Urine or Water?

Reportedly, various urine manipulations can be performed by opioid use disorder (OUD) patients who are on buprenorphine/naloxone medications to disguise their non-compliance to the treatment. One type of manipulation is known as "spiking" adulteration, directly dipping a buprenorphine/naloxone film into urine. Identifying this type of urine manipulation has been the aim of many previous studies. These studies have revealed urine adulterations through inappropriately high levels of "buprenorphine" and "naloxone" and a very small amount of "norbuprenorphine." So, does the small amount of "norbuprenorphine" in the adulterated urine samples result from dipped buprenorphine/naloxone film, or is it a residual metabolite of buprenorphine in the patient's system? This pilot study utilized 12 urine samples from 12 participants, as well as water samples as a control. The samples were subdivided by the dipping area and time, as well as the temperature and concentration of urine samples, and each sublingual generic buprenorphine/naloxone film was dipped directly into the samples. Then, the levels of "buprenorphine," "norbuprenorphine," "naloxone," "buprenorphine-glucuronide" and "norbuprenorphine-glucuronide" were examined by Liquid Chromatography with tandem mass spectrometry (LC-MS/MS). The results of this study showed that high levels of "buprenorphine" and "naloxone" and a small amount of "norbuprenorphine" were detected in both urine and water samples when the buprenorphine/naloxone film was dipped directly into these samples. However, no "buprenorphine-glucuronide" or "norbuprenorphine-glucuronide" were detected in any of the samples. In addition, the area and timing of dipping altered "buprenorphine" and "naloxone" levels, but concentration and temperature did not. This study's findings could help providers interpret their patients' urine drug test results more accurately, which then allows them to monitor patient compliance and help them identify manipulation by examining patient urine test results.

A cohort of patients in New York State with an alcohol use disorder and subsequent treatment information - A merging of two administrative data sources

OBJECTIVE

Despite the high prevalence of alcohol use disorder (AUD) in the United States, limited research is focused on the associations among AUD, pain, and opioids/benzodiazepine use. In addition, little is known regarding individuals with a history of AUD and their potential risk for pain diagnoses, pain prescriptions, and subsequent misuse. Moreover, the potential risk of pain diagnoses, prescriptions, and subsequent misuse among individuals with a history of AUD is not well known. The objective was to develop a tailored dataset by linking data from 2 New York State (NYS) administrative databases to investigate a series of hypotheses related to AUD and painful medical disorders.

METHODS

Data from the NYS Office of Addiction Services and Supports (OASAS) Client Data System (CDS) and Medicaid claims data from the NYS Department of Health Medicaid Data Warehouse (MDW) were merged using a stepwise deterministic method. Multiple patient-level identifier combinations were applied to create linkage rules. We included patients aged 18 and older from the OASAS CDS who initially entered treatment with a primary substance use of alcohol and no use of opioids between January 1, 2003, and September 23, 2019. This cohort was then linked to corresponding Medicaid claims.

RESULTS

A total of 177,685 individuals with a primary AUD problem and no opioid use history were included in the dataset. Of these, 37,346 (21.0%) patients had an OUD diagnosis, and 3,365 (1.9%) patients experienced an opioid overdose. There were 121,865 (68.6%) patients found to have a pain condition.

CONCLUSION

The integrated database allows researchers to examine the associations among AUD, pain, and opioids/benzodiazepine use, and propose hypotheses to improve outcomes for at-risk patients. The findings of this study can contribute to the development of a prognostic prediction model and the analysis of longitudinal outcomes to improve the care of patients with AUD.

The Role of C-to-U RNA Editing in Human Biodiversity

Intra-organism biodiversity is thought to arise from epigenetic modification of our constituent genes and post-translational modifications after mRNA is translated into proteins. We have found that post-transcriptional modification, also known as RNA editing, is also responsible for a significant amount of our biodiversity, substantively expanding this story. The APOBEC (apolipoprotein B mRNA editing catalytic polypeptide-like) family RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosines to uracils (C>U) in specific stem-loop structures.1,2 We used RNAsee (RNA site editing evaluation), a tool developed to predict the locations of APOBEC3A/G RNA editing sites, to determine whether known single nucleotide polymorphisms (SNPs) in DNA could be replicated in RNA via RNA editing. About 4.5% of non-synonymous SNPs which result in C>U changes in RNA, and about 5.4% of such SNPs labelled as pathogenic, were identified as probable sites for APOBEC3A/G editing. This suggests that the variant proteins created by these DNA mutations may also be created by transient RNA editing, with the potential to affect human health. Those SNPs identified as potential APOBEC3A/G-mediated RNA editing sites were disproportionately associated with cardiovascular diseases, digestive system diseases, and musculoskeletal diseases. Future work should focus on common sites of RNA editing, any variant proteins created by these RNA editing sites, and the effects of these variants on protein diversity and human health. Classically, our biodiversity is thought to come from our constitutive genetics, epigenetic phenomenon, transcriptional differences, and post-translational modification of proteins. Here, we have shown evidence that RNA editing, often stimulated by environmental factors, could account for a significant degree of the protein biodiversity leading to human disease. In an era where worries about our changing environment are ever increasing, from the warming of our climate to the emergence of new diseases to the infiltration of microplastics and pollutants into our bodies, understanding how environmentally sensitive mechanisms like RNA editing affect our own cells is essential.

Differential Perceptions of What Constitutes a Medical Error Associated with Electronic Medical Records

Perceptions of errors associated with healthcare information technology (HIT) often depend on the context and position of the viewer. HIT vendors posit very different causes of errors than clinicians, implementation teams, or IT staff. Even within the same hospital, members of departments and services often implicate other departments. Organizations may attribute errors to external care partners that refer patients, such as nursing homes or outside clinics. Also, the various clinical roles within an organization (e.g., physicians, nurses, pharmacists) can conceptualize errors and their root causes differently. Overarching all these perceptual factors, the definitions, mechanisms, and incidence of HIT-related errors are remarkably conflictual. There is neither a universal standard for defining or counting these errors. This paper attempts to enumerate and clarify the issues related to differential perceptions of medical errors associated with HIT. It then suggests solutions.

Association Between Buprenorphine Dose and the Urine "Norbuprenorphine" to "Creatinine" Ratio: Revised

Background

Utilizing a 1-year chart review as the data, Furo et al. conducted a research study on an association between buprenorphine dose and the urine "norbuprenorphine" to "creatinine" ratio and found significant differences in the ratio among 8-, 12-, and 16-mg/day groups with an analysis of variance (ANOVA) test. This study expands the data for a 2-year chart review and is intended to delineate an association between buprenorphine dose and the urine "norbuprenorphine" to "creatinine" ratio with a higher statistical power.

Methods

This study performed a 2-year chart review of data for the patients living in a halfway house setting, where their drug administration was closely monitored. The patients were on buprenorphine prescribed at an outpatient clinic for opioid use disorder (OUD), and their buprenorphine prescription and dispensing information were confirmed by the New York Prescription Drug Monitoring Program (PDMP). Urine test results in the electronic health record (EHR) were reviewed, focusing on the "buprenorphine," "norbuprenorphine," and "creatinine" levels. The Kruskal-Wallis H and Mann-Whitney U tests were performed to examine an association between buprenorphine dose and the "norbuprenorphine" to "creatinine" ratio.

Results

This study included 371 urine samples from 61 consecutive patients and analyzed the data in a manner similar to that described in the study by Furo et al. This study had similar findings with the following exceptions: (1) a mean buprenorphine dose of 11.0 ± 3.8 mg/day with a range of 2 to 20 mg/day; (2) exclusion of 6 urine samples with "creatinine" level <20 mg/dL; (3) minimum "norbuprenorphine" to "creatinine" ratios in the 8-, 12-, and 16-mg/day groups of 0.44 × 10-4 (n = 68), 0.1 × 10-4 (n = 133), and 1.37 × 10-4 (n = 82), respectively; however, after removing the 2 lowest outliers, the minimum "norbuprenorphine" to "creatinine" ratio in the 12-mg/day group was 1.6 × 10-4, similar to the findings in the previous study; and (4) a significant association between buprenorphine dose and the urine "norbuprenorphine" to "creatinine" ratios from the Kruskal-Wallis test (P < .01). In addition, the median "norbuprenorphine" to "creatinine" ratio had a strong association with buprenorphine dose, and this association could be formulated as: [y = 2.266 ln(x) + 0.8211]. In other words, the median ratios in 8-, 12-, and 16-mg/day groups were 5.53 × 10-4, 6.45 × 10-4, and 7.10 × 10-4, respectively. Therefore, any of the following features should alert providers to further investigate patient treatment compliance: (1) inappropriate substance(s) in urine sample; (2) "creatinine" level <20 mg/dL; (3) "buprenorphine" to "norbuprenorphine" ratio >50:1; (4) buprenorphine dose >24 mg/day; or (5) "norbuprenorphine" to "creatinine" ratios <0.5 × 10-4 in patients who are on 8 mg/day or <1.5 × 10-4 in patients who are on 12 mg/day or more.

Conclusion

The results of the present study confirmed those of the previous study regarding an association between buprenorphine dose and the "norbuprenorphine" to "creatinine" ratio, using an expanded data set. Additionally, this study delineated a clearer relationship, focusing on the median "norbuprenorphine" to "creatinine" ratios in different buprenorphine dose groups. These results could help providers interpret urine test results more accurately and apply them to outpatient opioid treatment programs for optimal treatment outcomes.

COVID-19 vaccination and venous thromboembolism risk in older veterans

Introduction

It is important for SARS-CoV-2 vaccine providers, vaccine recipients, and those not yet vaccinated to be well informed about vaccine side effects. We sought to estimate the risk of post-vaccination venous thromboembolism (VTE) to meet this need.

Methods

We conducted a retrospective cohort study to quantify excess VTE risk associated with SARS-CoV-2 vaccination in US veterans age 45 and older using data from the Department of Veterans Affairs (VA) National Surveillance Tool. The vaccinated cohort received at least one dose of a SARS-CoV-2 vaccine at least 60 days prior to 3/06/22 (N = 855,686). The control group was those not vaccinated (N = 321,676). All patients were COVID-19 tested at least once before vaccination with a negative test. The main outcome was VTE documented by ICD10-CM codes.

Results

Vaccinated persons had a VTE rate of 1.3755 (CI: 1.3752-1.3758) per thousand, which was 0.1 percent over the baseline rate of 1.3741 (CI: 1.3738-1.3744) per thousand in the unvaccinated patients, or 1.4 excess cases per 1,000,000. All vaccine types showed a minimal increased rate of VTE (rate of VTE per 1000 was 1.3761 (CI: 1.3754-1.3768) for Janssen; 1.3757 (CI: 1.3754-1.3761) for Pfizer, and for Moderna, the rate was 1.3757 (CI: 1.3748-1.3877)). The tiny differences in rates comparing either Janssen or Pfizer vaccine to Moderna were statistically significant (p < 0.001). Adjusting for age, sex, BMI, 2-year Elixhauser score, and race, the vaccinated group had a minimally higher relative risk of VTE as compared to controls (1.0009927 CI: 1.007673-1.0012181; p < 0.001).

Conclusion

The results provide reassurance that there is only a trivial increased risk of VTE with the current US SARS-CoV-2 vaccines used in veterans older than age 45. This risk is significantly less than VTE risk among hospitalized COVID-19 patients. The risk-benefit ratio favors vaccination, given the VTE rate, mortality, and morbidity associated with COVID-19 infection.

Trends in Prescribing Opioids, Benzodiazepines, and Both Among Adults with Alcohol Use Disorder in New York State

BACKGROUND

Alcohol use disorder (AUD) is a highly prevalent public health problem that contributes to opioid- and benzodiazepine-related morbidity and mortality. Even though co-utilization of these substances is particularly harmful, data are sparse on opioid or benzodiazepine prescribing patterns among individuals with AUD.

OBJECTIVE

To estimate temporal trends and disparities in opioid, benzodiazepine, and opioid/benzodiazepine co-prescribing among individuals with AUD in New York State (NYS).

DESIGN/PARTICIPANTS

Serial cross-sectional study analyzing merged data from the NYS Office of Addiction Services and Supports (OASAS) and the NYS Department of Health Medicaid Data Warehouse. Subjects with a first admission to an OASAS treatment program from 2005-2018 and a primary AUD were included. A total of 148,328 subjects were identified.

MEASURES

Annual prescribing rates of opioids, benzodiazepines, or both between the pre- (2005-2012) and post- (2013-2018) Internet System for Tracking Over-Prescribing (I-STOP) periods. I-STOP is a prescription monitoring program implemented in NYS in August 2013. Analyses were stratified based on sociodemographic factors (age, sex, race/ethnicity, and location).

RESULTS

Opioid prescribing rates decreased between the pre- and post-I-STOP periods from 25.1% (95% CI, 24.9-25.3%) to 21.3% (95% CI, 21.2-21.4; P <.001), while benzodiazepine (pre: 9.96% [95% CI, 9.83-10.1%], post: 9.92% [95% CI, 9.83-10.0%]; P =.631) and opioid/benzodiazepine prescribing rates remained unchanged (pre: 3.01% vs. post: 3.05%; P =.403). After I-STOP implementation, there was a significant decreasing trend in opioid (change, -1.85% per year, P <.0001), benzodiazepine (-0.208% per year, P =.0184), and opioid/benzodiazepine prescribing (-0.267% per year, P <.0001). Opioid, benzodiazepine, and co-prescription rates were higher in females, White non-Hispanics, and rural regions.

CONCLUSIONS

Among those with AUD, opioid prescribing decreased following NYS I-STOP program implementation. While both benzodiazepine and opioid/benzodiazepine co-prescribing rates remained high, a decreasing trend was evident after program implementation. Continuing high rates of opioid and benzodiazepine prescribing necessitate the development of innovative approaches to improve the quality of care.

Identification of patient characteristics associated with survival benefit from metformin treatment in patients with stage I non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) continues to be a major cause of cancer deaths. Previous investigation has suggested that metformin use can contribute to improved outcomes in NSCLC patients. However, this association is not uniform in all analyzed cohorts, implying that patient characteristics might lead to disparate results. Identification of patient characteristics that affect the association of metformin use with clinical benefit might clarify the drug's effect on lung cancer outcomes and lead to more rational design of clinical trials of metformin's utility as an intervention. In this study, we examined the association of metformin use with long-term mortality benefit in patients with NSCLC and the possible modulation of this benefit by body mass index (BMI) and smoking status, controlling for other clinical covariates.

METHODS

This was a retrospective cohort study in which we analyzed data from the Veterans Affairs (VA) Tumor Registry in the United States. Data from all patients with stage I NSCLC from 2000 to 2016 were extracted from a national database, the Corporate Data Warehouse that captures data from all patients, primarily male, who underwent treatment through the VA health system in the United States. Metformin use was measured according to metformin prescriptions dispensed to patients in the VA health system. The association of metformin use with overall survival (OS) after diagnosis of stage I NSCLC was examined. Patients were further stratified according to BMI and smoking status (previous vs current) to examine the association of metformin use with OS across these strata.

RESULTS

Metformin use was associated with improved survival in patients with stage I NSCLC (average hazard ratio, 0.82; P < .001). An interaction between the effect of metformin use and BMI on OS was observed (χ2 = 3268.42; P < .001) with a greater benefit of metformin use observed in patients as BMI increased. Similarly, an interaction between smoking status and metformin use on OS was also observed (χ2 = 2997.05; P < .001) with a greater benefit of metformin use observed in previous smokers compared with current smokers.

CONCLUSIONS

In this large retrospective study, we showed that a survival benefit is enjoyed by users of metformin in a robust stage I NSCLC patient population treated in the VA health system. Metformin use was associated with an 18% improved OS. This association was stronger in patients with a higher BMI and in previous smokers. These observations deserve further mechanistic study and can help rational design of clinical trials with metformin in patients with lung cancer.

CDS-Compare: A Web Application for Machine Learning Assisted Curation of Clinical Order Sets

Order sets that adhere to disease-specific guidelines have been shown to increase clinician efficiency and patient safety but curating these order sets, particularly for consistency across multiple sites, is difficult and time consuming. We created software called CDS-Compare to alleviate the burden on expert reviewers in rapidly and effectively curating large databases of order sets. We applied our clustering-based software to a database of NLP-processed order sets extracted from VA's Electronic Health Record, then had subject-matter experts review the web application version of our software for clustering validity.

Buprenorphine Dosage and Urine Quantitative Buprenorphine, Norbuprenorphine, and Creatinine Levels in an Office-Based Opioid Treatment Program

BACKGROUND

Treatment progress is routinely monitored by urine testing in patients with opioid use disorder (OUD) undergoing buprenorphine medication-assisted treatment (MAT). However, interpretation of urine test results could be challenging. This retrospective study aims to examine the results of quantitative buprenorphine, norbuprenorphine, and creatinine levels in urine testing in relation to sublingual buprenorphine dosage to facilitate an accurate interpretation of urine testing results.

METHODS

We reviewed the medical charts of 41 consecutive patients, who were residing in halfway houses where their medication intake was closely monitored and who had enrolled in an office-based MAT program at an urban clinic between July 2018 and June 2019. The patients' urine testing results were reviewed, and demographic variables were recorded. We focused on the patients treated with 8-, 12-, or 16-mg/day of buprenorphine, examining their urine buprenorphine, norbuprenorphine, and creatinine levels. Analysis of variance tested the statistical association between the dosage and urine testing results on the norbuprenorphine-to-creatinine ratio.

RESULTS

A total of 240 urine samples from 41 patients were included for this study. The 41 patients received a mean buprenorphine dose of 10.5 ± 3.7 mg/day (range, 4-20 mg/day). Then, this study examined the distribution of the 240 urine samples and then focused on 184 urine samples that came from the 33 patients who were treated with 8-, 12-, and 16-mg/day of buprenorphine, the 3 most common dosages. All of the 184 urine samples had a creatinine level of >20 mg/dL and buprenorphine-to-norbuprenorphine ratio <50:1. The average norbuprenorphine-to-creatinine ratio in the 8 mg/day dosage group was 3.85 ± 2.24 × 10^-4 (n = 66; range, 0.44-11.12). The respective ratios in the 12- and 16-mg dosage groups were 5.64 ± 3.40 × 10^-4 (n = 83; range, 1.55-22.72) and 6.23 ± 4.92 × 10^-4 (n = 35; range, 1.37-27.12). The 3 dosage groups differed significantly in the mean ratios (P < .01), except when the 12- and 16-mg dosage groups were compared (P = .58). The results of this study thus suggest that prescribers should pay attention to the following features: (1) unexpected substance(s) in urine testing, (2) creatinine level under 20 mg/dL, (3) buprenorphine-to-creatinine ratio over 50:1, (4) buprenorphine dosage over 24 mg/day, and (5) norbuprenorphine-to-creatinine ratio consistently under 0.5 × 10^-4 in patients treated with 8 mg/day or 1.5 × 10^-4 in patients treated with 12 mg/day or more.

CONCLUSION

This study suggested parameters for interpreting quantitative urine test results in relation to buprenorphine intake dose in office-based opioid treatment programs.

Automated Modeling of Clinical Narrative with High Definition Natural Language Processing Using Solor and Analysis Normal Form

OBJECTIVE

One important concept in informatics is data which meets the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). Standards, such as terminologies (findability), assist with important tasks like interoperability, Natural Language Processing (NLP) (accessibility) and decision support (reusability). One terminology, Solor, integrates SNOMED CT, LOINC and RxNorm. We describe Solor, HL7 Analysis Normal Form (ANF), and their use with the high definition natural language processing (HD-NLP) program.

METHODS

We used HD-NLP to process 694 clinical narratives prior modeled by human experts into Solor and ANF. We compared HD-NLP output to the expert gold standard for 20% of the sample. Each clinical statement was judged "correct" if HD-NLP output matched ANF structure and Solor concepts, or "incorrect" if any ANF structure or Solor concepts were missing or incorrect. Judgements were summed to give totals for "correct" and "incorrect".

RESULTS

113 (80.7%) correct, 26 (18.6%) incorrect, and 1 error. Inter-rater reliability was 97.5% with Cohen's kappa of 0.948.

CONCLUSION

The HD-NLP software provides useable complex standards-based representations for important clinical statements designed to drive CDS.

Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study

BACKGROUND

Nonvalvular atrial fibrillation (NVAF) affects almost 6 million Americans and is a major contributor to stroke but is significantly undiagnosed and undertreated despite explicit guidelines for oral anticoagulation.

OBJECTIVE

The aim of this study is to investigate whether the use of semisupervised natural language processing (NLP) of electronic health record's (EHR) free-text information combined with structured EHR data improves NVAF discovery and treatment and perhaps offers a method to prevent thousands of deaths and save billions of dollars.

METHODS

We abstracted 96,681 participants from the University of Buffalo faculty practice's EHR. NLP was used to index the notes and compare the ability to identify NVAF, congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category (CHA₂DS₂-VASc), and Hypertension, Abnormal liver/renal function, Stroke history, Bleeding history or predisposition, Labile INR, Elderly, Drug/alcohol usage (HAS-BLED) scores using unstructured data (International Classification of Diseases codes) versus structured and unstructured data from clinical notes. In addition, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF frequency, rates of CHA₂DS₂‑VASc ≥2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year's costs after stroke.

RESULTS

The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA₂DS₂-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion.

CONCLUSIONS

Artificial intelligence-informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences.

Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes

Identification of patient subtypes from retrospective Electronic Health Record (EHR) data is fraught with inherent modeling issues, such as missing data and variable length time intervals, and the results obtained are highly dependent on data pre-processing strategies. As we move towards personalized medicine, assessing accurate patient subtypes will be a key factor in creating patient specific treatment plans. Partitioning longitudinal trajectories from irregularly spaced and variable length time intervals is a well-established, but open problem. In this work, we present and compare k-means approaches for subtyping opioid use trajectories from EHR data. We then interpret the resulting subtypes using decision trees, examining how each subtype is influenced by opioid medication features and patient diagnoses, procedures, and demographics. Finally, we discuss how the subtypes can be incorporated in static machine learning models as features in predicting opioid overdose and adverse events. The proposed methods are general, and can be extended to other EHR prescription dosage trajectories.

Defining Data Migration Across Multidisciplinary Ambulatory Clinics Using Participatory Design

OBJECTIVE

This study aimed to develop an institutional approach for defining data migration based on participatory design principles.

METHODS

We outline a collaborative approach to define data migration as part of an electronic health record (EHR) transition at an urban hospital with 20 ambulatory clinics, based on participatory design. We developed an institution-specific list of data for migration based on physician end-user feedback. In this paper, we review the project planning phases, multidisciplinary governance, and methods used.

RESULTS

Detailed data migration feedback was obtained from 90% of participants. Depending on the specialty, requests for historical laboratory values ranged from 2 to as many as 145 unique laboratory types. Lookback periods requested by physicians varied and were ultimately assigned to provide the most clinical data. This clinical information was then combined to synthesize an overall proposed data migration request on behalf of the institution.

CONCLUSION

Institutions undergoing an EHR transition should actively involve physician end-users and key stakeholders. Physician feedback is vital for developing a clinically relevant EHR environment but is often difficult to obtain. Challenges include physician time constraints and overall knowledge about health information technology. This study demonstrates how a participatory design can serve to improve the clinical end-user's understanding of the technical aspects of an EHR implementation, as well as enhance the outcomes of such projects.

A Clinical Informatics Program Directors' Proposal to the American Board of Preventive Medicine

In 2013, the American Board of Preventive Medicine (ABPM) and the American Board of Pathology (ABPath) offered the first board certification examination in Clinical Informatics to eligible physicians in the United States. In 2022, the Practice Pathway will expire and in 2023 only candidates eligible through the Fellowship Pathway will be eligible for the board certification. To date, Clinical Informatics as a specialty has not had a regular match process and used a controlled offer-acceptance process that does not meet candidates' or programs' needs. Fellows may not be offered a position with their top choice program initially, and they may accept offers from other programs to avoid risk by ensuring that they have a fellowship position. Programs have to consider losing an applicant in the first round in the ranking of applicants. The process is open to manipulation including early agreements between program directors and candidates. In this open letter to the ABPM, program directors make the case for a third-party match and are calling on the ABPM to leverage its status as the Clinical Informatics certifying body and its existing infrastructure to implement a Clinical Informatics match.

Association of C>U RNA Editing with Human Disease Variants

RNA-editing is an important post-transcriptional RNA sequence modification performed by two catalytic enzymes, "ADAR"(A>I) and "APOBEC"(C>U). Although APOBEC-mediated C>U editing has been associated with a number of human cancers, the extent of C>U editing in human disease remains unclear. Here, we performed an association study and found that at least 1293 human disease variants occur at sites predicted by sequence motif analysis (RNASee protocol) to undergo APOBEC3A/G C>U editing. These variants were associated with a wide array of human disease conditions ranging from cancer, metabolic disorders, retinopathies, cardiomyopathies, neurodegenerative disorders and immunodeficiencies. These results indicate that APOBEC mediated C>U RNA editing may have widespread and previously unreported contributions to human disease conditions.

Rosacea Patients Are at Higher Risk for Obstructive Sleep Apnea: Automated Retrospective Research

Using big data science we employ NLP and a novel interface the BMI Investigator to answer clinically meaninful questions. The use case presented is the association between Rosacea and Obstructive Sleep Apnea.

Can Solo Practitioners Survive in Value-Based Healthcare? Validating a Predicative Model for ED Utilization

The health industry will see increased implementations of value-based models. This study validates a predictive model for determining emergency room utilization. Data from 2991 records are used for the analysis. To validate the model we used Poisson and random forest models. The results indicate that patients with one of six chronic conditions, who missed scheduled appointments or had higher body mass indexes were more likely to utilize the emergency department.

Machine Learning Methods to Predict Lung Cancer Survival Using the Veterans Affairs Research Precision Oncology Data Commons

We completed a pilot study to guide the development of the VA Research Precision Oncology Data Commons infrastructure as a collaboration platform with the greater research community. Our results using a small subset of patients from the VA's Precision Oncology Program demonstrate the feasibility of our data sharing platform to build predictive models for lung cancer survival using machine learning, as well as highlight the potential of target genome sequencing data.

Body Mass Index Influences the Salutary Effects of Metformin on Survival After Lobectomy for Stage I NSCLC

INTRODUCTION

Metformin, a common medication used in the treatment of diabetes mellitus is known to have anticancer effects. We hypothesized that the salutary effect of metformin on the survival of patients with stage I NSCLC is influenced by body mass index (BMI).

METHODS

Patients undergoing lobectomy for stage I NSCLC without neoadjuvant therapy were included. Univariate and multivariate survival analyses to examine the association between metformin use and overall survival (OS), disease-specific survival (DSS), and recurrence-free survival were performed, stratified by BMI (>25 kg/m² and ≤25 kg/m²). Expression of immune checkpoints in patients on metformin and not was performed in a separate cohort of 205 patients with advanced disease.

RESULTS

Four hundred thirty-four stage I patients (including 74 metformin users) were deemed eligible for analysis. Univariate and multivariate analysis revealed an association between metformin use and OS (hazard ratio [HR] = 0.52; p = 0.04) as well as DSS (HR = 0.21; p = 0.04) but not recurrence-free survival (HR = 0.67; p = 0.33) in high-BMI patients only. In a separate cohort of 205 patients with tumors of all stages (including 35 metformin users), downregulation of immune checkpoint gene expression (programmed cell death 1, cytotoxic T-lymphocyte associated protein 4, B and T lymphocyte associated, CD27 molecule, lymphocyte activating 3, and inducible T cell costimulator) in metformin users was seen only in high-BMI patients, with upregulation of these genes seen in low-BMI patients with metformin use.

CONCLUSIONS

Metformin use may be associated with better OS and DSS only in high-BMI patients. This hypothesis is supported by gene expression data of immune checkpoint genes in metformin users using a separate cohort of advanced-stage tumors. Further studies examining the interaction of BMI with metformin in NSCLC are worthwhile.

Re-Identification Risk in HIPAA De-Identified Datasets: The MVA Attack

We present a re-identification attack that uses indirect (non-HIPAA) identifiers to target a vulnerable subset of records de-identified to the HIPAA Safe Harbor standard, those involving motor vehicle accidents (MVAs). Documentation of an MVA in a patient note creates a significant risk to patient privacy through the MVA re-identification attack, with a relative risk of 537 compared to the general population. Patients in a significant MVA resulting in either permanent injury, hospitalization or death (for any victim) should have the accident location information omitted due to the significant risk of re-identification of HIPAA de-identified data. Clinicians should also consider omitting location information for any MVA, as it significantly increases the risk of re-identification.

Characteristics of the National Applicant Pool for Clinical Informatics Fellowships (2016-2017)

We conducted a national study to assess the numbers and diversity of applicants for 2016 and 2017 clinical informatics fellowship positions. In each year, we collected data on the number of applications that programs received from candidates who were ultimately successful vs. unsuccessful. In 2017, we also conducted an anonymous applicant survey. Successful candidates applied to an average of 4.2 and 5.5 programs for 2016 and 2017, respectively. In the survey, unsuccessful candidates reported applying to fewer programs. Assuming unsuccessful candidates submitted between 2-5 applications each, the total applicant pool numbered 42-69 for 2016 (competing for 24 positions) and 52-85 for 2017 (competing for 30 positions). Among survey respondents (n=33), 24% were female, 1 was black and none were Hispanic. We conclude that greater efforts are needed to enhance interest in clinical informatics among medical students and residents, particularly among women and members of underrepresented minority groups.

Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support

Evoking strength is one of the important contributions of the field of Biomedical Informatics to the discipline of Artificial Intelligence. The University at Buffalo's Orthopedics Department wanted to create an expert system to assist patients with self-diagnosis of knee problems and to thereby facilitate referral to the right orthopedic subspecialist. They had two independent sports medicine physicians review 469 cases. A board-certified orthopedic sports medicine practitioner, L.B., reviewed any disagreements until a gold standard diagnosis was reached. For each case, the patients entered 126 potential answers to 26 questions into a Web interface. These were modeled by an expert sports medicine physician and the answers were reviewed by L.B. For each finding, the clinician specified the sensitivity (term frequency) and both specificity (Sp) and the heuristic evoking strength (ES). Heuristics are methods of reasoning with only partial evidence. An expert system was constructed that reflected the posttest odds of disease-ranked list for each case. We compare the accuracy of using Sp to that of using ES (original model, p < 0.0008; term importance * disease importance [DItimesTI] model, p < 0.0001: Wilcoxon ranked sum test). For patient referral assignment, Sp in the DItimesTI model was superior to the use of ES. By the fifth diagnosis, the advantage was lost and so there is no difference between the techniques when serving as a reminder system.

Barriers, Facilitators, and Solutions to Optimal Patient Portal and Personal Health Record Use: A Systematic Review of the Literature

Patient portal and personal health record adoption and usage rates have been suboptimal. A systematic review of the literature was performed to capture all published studies that specifically addressed barriers, facilitators, and solutions to optimal patient portal and personal health record enrollment and use. Consistent themes emerged from the review. Patient attitudes were critical as either barrier or facilitator. Institutional buy-in, information technology support, and aggressive tailored marketing were important facilitators. Interface redesign was a popular solution. Quantitative studies identified many barriers to optimal patient portal and personal health record enrollment and use, and qualitative and mixed methods research revealed thoughtful explanations for why they existed. Our study demonstrated the value of qualitative and mixed research methodologies in understanding the adoption of consumer health technologies. Results from the systematic review should be used to guide the design and implementation of future patient portals and personal health records, and ultimately, close the digital divide.

Biomedical Informatics Investigator

The BMI Investigator is a computer human interface built in .Net which allows simultaneous query of structured data such as demographics, administrative codes, medications (coded in RxNorm), laboratory test results (coded in LOINC) and formerly unstructured data in clinical notes (coded in SNOMED CT). The ontology terms identified using SNOMED are all coded as either positive, negative or uncertain assertions. They are then where applicable built into compositional expressions and stored in both a graph database and a triple store. The SNOMED CT codes are stored in a NOSQL database, Berkley DB, and the structured data is stored in SQL using the OMOP/OHDSI format. The BMI investigator also lets you develop models for cohort selection (data driven recruitment to clinical trials) and automated retrospective research using genomic criteria and we are adding image feature data currently to the system. We performed a usability experiment and the users identified some usability flaws which were used to improve the software. Overall, the BMI Investigator was felt to be usable by subject matter experts. Next steps for the software are to integrate genomic criteria and image features into the query engine.

Safe Opioid Prescription: A SMART on FHIR Approach to Clinical Decision Support

Background Prescription opioid pain medication overuse, misuse and abuse have been a significant contributing factor in the opioid epidemic. The rising death rates from opioid overdose have caused healthcare practitioners and researchers to work on optimizing pain therapy and limiting the prescriptions for pain medications. The state of New York has implemented a prescription drug monitoring program(PDMP), amended public health law to limit the prescription of opioids for acute pain and utilized the resources of the state and county health departments to help in curbing this epidemic. The recent publication of guidelines for prescription opioids from CDC [1] and ASIPP (American Society of Interventional pain practitioners) have independently reviewed literature and found good evidence of limiting opioid prescription for acute and chronic non cancer pain. [2] Method Over the last decade, advanced technology has increased the complexity of electronic health records systems leading to the development of Clinical Decision Support Systems (CDSS) to aid the work flow of healthcare providers. There are several systematic reviews on the effectiveness and utility of CDSSs. A common consensus is that commercially and locally developed CDSS are effective in improving patient measures while actual workload improvement and efficient cost-cutting measure are not significantly improved by CDSS. Patient provider involvement in developing CDSS is a determinant of its success and utilization rates. [7] Therefore, a plug and play form of CDSS which can be implemented from an external platform through secure channels would be more effective. Design The Health Level Seven's (HL7) open licensed interoperability standard Fast Health Interoperability Resources (FHIR) has a platform, Substitutable Medical Applications and Reusable Technologies (SMART) for CDSS app development by a third party. [3] We adopted these open source standard to plan to develop an app for accessible and efficient implementation of the recently published guidelines for management of pain with prescription opioid medications.

AIM

Prescription Opioid Dependence in Western New York: Using Data Analytics to Find an Answer to the Opioid Epidemic

Opioid dependence and overdose is on the rise. One indicator is the increasing trends of prescription buprenorphine use among patient on chronic pain medication. In addition to the New York State Department of Health's prescription drug monitoring programs and training programs for providers and first responders to detect and treat a narcotic overdose, further examination of the population may provide important information for multidisciplinary interventions to address this epidemic. This paper uses an observational database with a Natural Language Processing (NLP) based Not Only Structured Query Language architecture to examine Electronic Health Record (EHR) data at a regional level to study the trends of prescription opioid dependence. We aim to help prioritize interventions in vulnerable population subgroups. This study provides a report of the demographic patterns of opioid dependent patients in Western New York using High Throughput Phenotyping NLP of EHR data.

Health Sciences Library Closings; A Context Sensitive Pilot Study

A significant number of U.S. health sciences libraries have closed since the mid-1990's. A pilot study was conducted with academic physicians to understand the impact of closing the health sciences library in the teaching hospital with which they were affiliated. A brief survey was designed and distributed to fourteen faculty members with thirteen useable responses received. The study elicited a context-sensitive perspective on the closing of the library with the most noteworthy outcome being the additional time required by attending physicians and trainees to perform the work that previously was performed by library staff. The loss of the expert literature search, instructional services, journal request, and interlibrary loan services had the most significant impact on study participants. Further research is needed to understand the long term consequences of closing hospital-based health sciences library on the education of physicians.

ICD9-CM Claims Data are Insufficient for Influenza Surveillance

Background:

Influenza and Influenza like illness are representative of a class of epidemic infectious diseases that have important public health implications. Early detection via biosurveillance can speed lifesaving public heath responses. In the United States, biosurveillance is typically conducted using ICD9 coded visit diagnoses and uncoded chief complaint data.

Objective:

To determine the accuracy of ICD9 diagnoses using laboratory confirmed cases as the gold standard.

Design:

A six-year retrospective cohort study.

Setting:

A tertiary referral center.

Patients:

All 3,825 patients with an ICD9-CM diagnosis of Influenza and all 1455 patients with laboratory confirmed Influenza.

Results:

Of the 3,828 patients assigned ICD9-CM visit codes indicating a diagnosis of Influenza, 2,825 were not confirmed by laboratory testing and 1,003 patients under went laboratory testing. Only 664 (66.2%) tested positive for Influenza. Of the 1,455 patients who tested positive for Influenza 45.6% were identified by ICD9-CM code.

Conclusion:

ICD9-CM had a low 66.2% Positive Predictive Value (precision) for Influenza and a low 45.6% Sensitivity (recall) for Influenza in patients tested for Influenza. ICD9 coded visit diagnoses/claims data are insufficient alone to serve as the basis for Influenza Surveillance.

Primary Funding Source:

CDC grants PH00022 and HK00014.

An observational study of the quality of care for chronic kidney disease: a Buffalo and Albany, New York metropolitan area study

Background

The database of a major regional health insurer was employed to identify the number and frequency of covered patients with chronic kidney disease (CKD). We then examined the characteristics of their care as defined, in part, by the frequency of physician visits and specialty referral, the characteristics of laboratory testing and total costs as indices of the quality of care of the subject population.

Methods

This retrospective, cross-sectional study analyzed insurance claims, laboratory results and medication prescription data. Patients with two estimated glomerular filtration rate readings below 60 ml/min/1.73 m² (n = 20,388) were identified and classified by CKD stage.

Results

The prevalence of CKD stages 3a and above was 12 %. Vascular comorbidities were common with prevalence increasing steadily from stage 3a through stage 5. Only 55.6 % of stage 4 CKD patients had claims for nephrology visits within one year of their index date. Fifty-nine percent of patients had claims for renin-angiotensin system (RAS) blockers. Twenty-five percent of patients in stage 3a CKD filled a prescription for non-steroidal anti-inflammatory drugs. Fifty-two percent of patients who developed end-stage renal disease received their first dialysis treatment as inpatients.

Conclusions

The pattern of medical practice observed highlights apparent deficiencies in the care of CKD patients including inappropriate medication use, delayed nephrology referral, and a lack of preparation for dialysis. This study shows the potential value of using large patient databases available through insurers to assess and likely improve regional CKD care.

Automatically Expanding the Synonym Set of SNOMED CT using Wikipedia

Clinical terminologies and ontologies are often used in natural language processing/understanding tasks as a method for semantically tagging text. One ontology commonly used for this task is SNOMED CT. Natural language is rich and varied: many different combinations of words may be used to express the same idea. It is therefore essential that ontologies and terminologies have a rich set of synonyms. One source of synonyms is Wikipedia. We examine methods for aligning concepts in SNOMED CT with articles in Wikipedia so that newly-found synonyms may be added to SNOMED CT. Our experiments show promising results and provide guidance to researchers who wish to use Wikipedia for similar tasks.

Automated quality measurement in Department of the Veterans Affairs discharge instructions for patients with congestive heart failure

Quality measurement is an important issue for the United States Department of Veterans Affairs (VA). In this study, we piloted the use of an informatics tool, the Multithreaded Clinical Vocabulary Server (MCVS), which extracted automatically whether the VA Office of Quality and Performance measures of quality of care were met for the completion of discharge instructions for inpatients with congestive heart failure. We used a single document, the discharge instructions, from one section of the medical records for 152 patients and developed a reference standard using two independent reviewers to assess performance. When evaluated against the reference standard, MCVS achieved a sensitivity of 0.87, a specificity of 0.86, and a positive predictive value of 0.90. The automated process using the discharge instruction document worked effectively. The use of the MCVS tool for concept-based indexing resulted in mostly accurate data capture regarding quality measurement, but improvements are needed to further increase the accuracy of data extraction.

Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives

Rapid, automated determination of the mapping of free text phrases to pre-defined concepts could assist in the annotation of clinical notes and increase the speed of natural language processing systems. The aim of this study was to design and evaluate a token-order-specific naïve Bayes-based machine learning system (RapTAT) to predict associations between phrases and concepts. Performance was assessed using a reference standard generated from 2860 VA discharge summaries containing 567,520 phrases that had been mapped to 12,056 distinct Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) concepts by the MCVS natural language processing system. It was also assessed on the manually annotated, 2010 i2b2 challenge data. Performance was established with regard to precision, recall, and F-measure for each of the concepts within the VA documents using bootstrapping. Within that corpus, concepts identified by MCVS were broadly distributed throughout SNOMED CT, and the token-order-specific language model achieved better performance based on precision, recall, and F-measure (0.95±0.15, 0.96±0.16, and 0.95±0.16, respectively; mean±SD) than the bag-of-words based, naïve Bayes model (0.64±0.45, 0.61±0.46, and 0.60±0.45, respectively) that has previously been used for concept mapping. Precision, recall, and F-measure on the i2b2 test set were 92.9%, 85.9%, and 89.2% respectively, using the token-order-specific model. RapTAT required just 7.2ms to map all phrases within a single discharge summary, and mapping rate did not decrease as the number of processed documents increased. The high performance attained by the tool in terms of both accuracy and speed was encouraging, and the mapping rate should be sufficient to support near-real-time, interactive annotation of medical narratives. These results demonstrate the feasibility of rapidly and accurately mapping phrases to a wide range of medical concepts based on a token-order-specific naïve Bayes model and machine learning.

ICD90CM claims data are insufficient for influenza surveillance

Influenza and Influenza like illness are representative of a class of epidemic infectious diseases that have important public health implications. Early detection via Biosurveillance can speed life saving public heath responses. In the United States Biosurveillance is typically conducted using ICD9 coded visit diagnoses and uncoded chief complaint data. To determine the accuracy of ICD9 diagnoses using laboratory confirmed cases as the gold standard. We determined the sensitivity and specificity of ICD9 in detecting laboratory confirmed vs unconfirmed Influenza. ICD9-CM had a low 66.2% Positive Predictive Value (precision) for Influenza and a low 45.6% Sensitivity (recall) for Influenza. ICD9-CM proved insufficient alone for use in biosurveillance.

The usability-error ontology

Clinical Systems have become standard partners with clinicians in the care of patients. As these systems become integral parts of the clinical workflow, they have the potential to help improve patient outcomes, however they have also in some cases have led to adverse events and has resulted in patients coming to harm. Often the root cause analysis of these adverse events can be traced back to Usability Errors in the Health Information Technology (HIT) or its interaction with users. Interoperability of the documentation of HIT related Usability Errors in a consistent fashion can improve our ability to do systematic reviews and meta-analyses. In an effort to support improved and more interoperable data capture regarding Usability Errors, we have created the Usability Error Ontology (UEO) as a classification method for representing knowledge regarding Usability Errors. We expect the UEO will grow over time to support an increasing number of HIT system types. In this manuscript, we present this Ontology of Usability Error Types and specifically address Computerized Physician Order Entry (CPOE), Electronic Health Records (EHR) and Revenue Cycle HIT systems.

Human Factors Engineering in HI: So What? Who Cares? and What's in It for You?

OBJECTIVES

Human factors engineering is a discipline that deals with computer and human systems and processes and provides a methodology for designing and evaluating systems as they interact with human beings. This review article reviews important current and past efforts in human factors engineering in health informatics in the context of the current trends in health informatics.

METHODS

The methodology of human factors engineering and usability testing in particular were reviewed in this article.

RESULTS

This methodology arises from the field of human factors engineering, which uses principles from cognitive science and applies them to implementations such as a computer-human interface and user-centered design.

CONCLUSIONS

Patient safety and best practice of medicine requires a partnership between patients, clinicians and computer systems that serve to improve the quality and safety of patient care. People approach work and problems with their own knowledge base and set of past experiences and their ability to use systems properly and with low error rates are directly related to the usability as well as the utility of computer systems. Unusable systems have been responsible for medical error and patient harm and have even led to the death of patients and increased mortality rates. Electronic Health Record and Computerized Physician Order Entry systems like any medical device should come with a known safety profile that minimizes medical error and harm. This review article reviews important current and past efforts in human factors engineering in health informatics in the context of the current trends in health informatics.

AMIA Board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is 'the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics sub-disciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

Comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes

BACKGROUND

An effective national biosurveillance system expedites outbreak recognition and facilitates response coordination at the federal, state, and local levels. The BioSense system, used at the Centers for Disease Control and Prevention, incorporates chief complaints but not data from the whole encounter note into its surveillance algorithms.

OBJECTIVE

To evaluate whether biosurveillance by using data from the whole encounter note is superior to that using data from the chief complaint field alone.

DESIGN

6-year retrospective case-control cohort study.

SETTING

Mayo Clinic, Rochester, Minnesota.

PARTICIPANTS

17,243 persons tested for influenza A or B virus between 1 January 2000 and 31 December 2006.

MEASUREMENTS

The accuracy of a model based on signs and symptoms to predict influenza virus infection in patients with upper respiratory tract symptoms, and the ability of a natural language processing technique to identify definitional clinical features from free-text encounter notes.

RESULTS

Surveillance based on the whole encounter note was superior to the chief complaint field alone. For the case definition used by surveillance of the whole encounter note, the normalized partial area under the receiver-operating characteristic curve (specificity, 0.1 to 0.4) for surveillance using the whole encounter note was 92.9% versus 70.3% for surveillance with the chief complaint field (difference, 22.6%; P < 0.001). Comparison of the 2 models at the fixed specificity of 0.4 resulted in sensitivities of 89.0% and 74.4%, respectively (P < 0.001). The relative risk for missing a true case of influenza was 2.3 by using the chief complaint field model.

LIMITATIONS

Participants were seen at 1 tertiary referral center. The cost of comprehensive biosurveillance monitoring was not studied.

CONCLUSION

A biosurveillance model for influenza using the whole encounter note is more accurate than a model that uses only the chief complaint field. Because case-defining signs and symptoms of influenza are commonly available in health records, the investigators believe that the national strategy for biosurveillance should be changed to incorporate data from the whole health record.

PRIMARY FUNDING SOURCE

Centers for Disease Control and Prevention.

Bioprospecting the Bibleome: Adding Evidence to Support the Inflammatory Basis of Cancer

BACKGROUND CANCER SIGNIFICANCE AND QUESTION

BioProspecting is a novel approach that enabled our team to mine genetic marker related data from the New England Journal of Medicine (NEJM) utilizing Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) and the Human Gene Ontology (HUGO). Genes associated with disorders using the Multi-threaded Clinical Vocabulary Server (MCVS) Natural Language Processing (NLP) engine, whose output was represented as an ontology-network incorporating the semantic encodings of the literature. Metabolic functions were used to identify potentially novel relationships between (genes or proteins) and (diseases or drugs). In an effort to identify genes important to transformation of normal tissue into a malignancy, we went on to identify the genes linked to multiple cancers and then mapped those genes to metabolic and signaling pathways.

FINDINGS

Ten Genes were related to 30 or more cancers, 72 genes were related to 20 or more cancers and 191 genes were related to 10 or more cancers. The three pathways most often associated with the top 200 novel cancer markers were the Acute Phase Response Signaling, the Glucocorticoid Receptor Signaling and the Hepatic Fibrosis/Hepatic Stellate Cell Activation pathway.

MEANING AND IMPLICATIONS OF THE ADVANCE

This association highlights the role of inflammation in the induction and perhaps transformation of mortal cells into cancers.

MAJOR FINDINGS

BioProspecting can speed our identification and understanding of synergies between articles in the biomedical literature. In this case we found considerable synergy between the Oncology literature and the Sepsis literature. By mapping these associations to known metabolic, regulatory and signaling pathways we were able to identify further evidence for the inflammatory basis of cancer.

Comparison of SNOMED CT versus Medcin terminology concept coverage for mild Traumatic Brain Injury

BACKGROUND

Traumatic Brain Injury (TBI) is a "signature" injury of the current wars in Iraq and Afghanistan. Structured electronic data regarding TBI findings is important for research, population health and other secondary uses but requires appropriate underlying standard terminologies to ensure interoperability and reuse. Currently the U.S. Department of Veterans Affairs (VA) uses the terminology SNOMED CT and the Department of Defense (DOD) uses Medcin.

METHODS

We developed a comprehensive case definition of mild TBI composed of 68 clinical terms. Using automated and manual techniques, we evaluated how well the mild TBI case definition terms could be represented by SNOMED CT and Medcin, and compared the results. We performed additional analysis stratified by whether the concepts were rated by a TBI expert panel as having High, Medium, or Low importance to the definition of mild TBI.

RESULTS

SNOMED CT sensitivity (recall) was 90% overall for coverage of mild TBI concepts, and Medcin sensitivity was 49%, p < 0.001 (using McNemar's chi square). Positive predictive value (precision) for each was 100%. SNOMED CT outperformed Medcin for concept coverage independent of import rating by our TBI experts.

DISCUSSION

SNOMED CT was significantly better able to represent mild TBI concepts than Medcin. This finding may inform data gathering, management and sharing, and data exchange strategies between the VA and DOD for active duty soldiers and veterans with mild TBI. Since mild TBI is an important condition in the civilian population as well, the current study results may be useful also for the general medical setting.

Hospital and health information systems - Current perspectives. Contribution of the IMIA Health Information Systems Working Group

OBJECTIVE

To celebrate over 30 years of health information systems' (HIS) evolution by bringing together pioneers in the field, members of the next generation of leaders, and government officials from several developing nations in Africa to discuss the past, present, and future of HISs.

METHODS

Participants gathered in Le Franschhoek, South Africa for a 2 1/2 day working conference consisting of scientific presentations followed by several concurrent breakout sessions. A small writing group prepared draft statements representing their positions on various topics of discussion which were circulated and revised by the entire group.

RESULTS

Many new tools, techniques and technologies were described and discussed in great detail. Interestingly, all of the key themes identified in the first HIS meeting held over 30 years ago are still of vital importance today: Patient Centered design, Clinical User Support, Real-time Education, Human-computer Factors and Measuring Clinical User Performance, Meaningful use.

CONCLUSIONS

As we continue to work to develop next-generation HISs, we must remember the lessons of the past as we strive to develop the solutions for tomorrow.

Automated identification of postoperative complications within an electronic medical record using natural language processing

CONTEXT

Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach.

OBJECTIVE

To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record.

DESIGN, SETTING, AND PATIENTS

Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006.

MAIN OUTCOME MEASURES

Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information.

RESULTS

The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses.

CONCLUSION

Among patients undergoing inpatient surgical procedures at VA medical centers, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding.