Chronic Health Conditions Among US Veterans Discharged From Military Service for Misconduct.

Veterans who are discharged from military service due to misconduct are vulnerable to negative health-related outcomes, including homelessness, incarceration, and suicide. We used national data from the Veterans Health Administration for 218,608 veterans of conflicts in Iraq and Afghanistan that took place after the events of September 11, 2001, to compare clinical diagnoses between routinely-discharged (n = 203,174) and misconduct-discharged (n = 15,433) veterans. Misconduct-discharged veterans had significantly higher risk for all mental health conditions (adjusted odds ratio [AOR] range, 2.5-8.0) and several behaviorally linked chronic health conditions (AOR range, 1.2-5.9). Misconduct-discharged veterans have serious and complex health care needs; prevention efforts should focus on behavioral risk factors to prevent the development and exacerbation of chronic health conditions among this vulnerable population.

Development and Validation of a Natural Language Processing Tool to Identify Patients Treated for Pneumonia across VA Emergency Departments.

BACKGROUND: Identifying pneumonia using diagnosis codes alone may be insufficient for research on clinical decision making. Natural language processing (NLP) may enable the inclusion of cases missed by diagnosis codes. OBJECTIVES: This article (1) develops a NLP tool that identifies the clinical assertion of pneumonia from physician emergency department (ED) notes, and (2) compares classification methods using diagnosis codes versus NLP against a gold standard of manual chart review to identify patients initially treated for pneumonia. METHODS: Among a national population of ED visits occurring between 2006 and 2012 across the Veterans Affairs health system, we extracted 811 physician documents containing search terms for pneumonia for training, and 100 random documents for validation. Two reviewers annotated span- and document-level classifications of the clinical assertion of pneumonia. An NLP tool using a support vector machine was trained on the enriched documents. We extracted diagnosis codes assigned in the ED and upon hospital discharge and calculated performance characteristics for diagnosis codes, NLP, and NLP plus diagnosis codes against manual review in training and validation sets. RESULTS: Among the training documents, 51% contained clinical assertions of pneumonia; in the validation set, 9% were classified with pneumonia, of which 100% contained pneumonia search terms. After enriching with search terms, the NLP system alone demonstrated a recall/sensitivity of 0.72 (training) and 0.55 (validation), and a precision/positive predictive value (PPV) of 0.89 (training) and 0.71 (validation). ED-assigned diagnostic codes demonstrated lower recall/sensitivity (0.48 and 0.44) but higher precision/PPV (0.95 in training, 1.0 in validation); the NLP system identified more "possible-treated" cases than diagnostic coding. An approach combining NLP and ED-assigned diagnostic coding classification achieved the best performance (sensitivity 0.89 and PPV 0.80). CONCLUSION: System-wide application of NLP to clinical text can increase capture of initial diagnostic hypotheses, an important inclusion when studying diagnosis and clinical decision-making under uncertainty.

Validation of an electronic tool for flagging surgical site infections based on clinical practice patterns for triaging surveillance: Operational successes and barriers.

BACKGROUND: Surveillance is an effective strategy for reducing surgical site infections (SSIs); however, current identification methods are resource-intensive. Therefore, we sought to validate an electronic SSI triaging tool for detection of probable infections and identify operational barriers and challenges. METHODS: A retrospective cohort study was conducted among all Veterans Affairs Surgical Quality Improvement Program (VASQIP)-reviewed surgeries at 2 Veterans Affairs medical centers from October 1, 2011-September 30, 2014. During the postoperative period, clinical and administrative variables associated with SSI (relevant microbiology order, antibiotic order, radiology order, and administrative codes) were extracted from the electronic medical record and used to score the probability (high, intermediate, and low) that an SSI occurred. VASQIP manual chart review was used as the gold standard of comparison. RESULTS: VASQIP manual review identified 118 SSIs out of 3,700 surgeries (3.2%). There were 2,041, 1,428, and 231 surgeries that met criteria for low, intermediate, and high probability for SSI. The tool's area under the curve was 0.86 (95% confidence interval, 0.82-0.89). The sensitivity among low-probability surgeries was 92.4%, and the specificity among high-probability surgeries was 95.1%. CONCLUSIONS: The electronic SSI tool has the potential to be used for triaging VASQIP surveillance toward the high-probability surgeries and to avoid manual review of surgeries with low probability of SSI.

Constructing Ebola transmission chains from West Africa and estimating model parameters using internet sources.

During the recent Ebola crisis in West Africa, individual person-level details of disease onset, transmissions, and outcomes such as survival or death were reported in online news media. We set out to document disease transmission chains for Ebola, with the goal of generating a timely account that could be used for surveillance, mathematical modeling, and public health decision-making. By accessing public web pages only, such as locally produced newspapers and blogs, we created a transmission chain involving two Ebola clusters in West Africa that compared favorably with other published transmission chains, and derived parameters for a mathematical model of Ebola disease transmission that were not statistically different from those derived from published sources. We present a protocol for responsibly gleaning epidemiological facts, transmission model parameters, and useful details from affected communities using mostly indigenously produced sources. After comparing our transmission parameters to published parameters, we discuss additional benefits of our method, such as gaining practical information about the affected community, its infrastructure, politics, and culture. We also briefly compare our method to similar efforts that used mostly non-indigenous online sources to generate epidemiological information.

Nursing Informatics Certification Worldwide: History, Pathway, Roles, and Motivation.

INTRODUCTION: Official recognition and certification for informatics professionals are essential aspects of workforce development. OBJECTIVE: To describe the history, pathways, and nuances of certification in nursing informatics across the globe; compare and contrast those with board certification in clinical informatics for physicians. METHODS: (1) A review of the representative literature on informatics certification and related competencies for nurses and physicians, and relevant websites for nursing informatics associations and societies worldwide; (2) similarities and differences between certification processes for nurses and physicians, and (3) perspectives on roles for nursing informatics professionals in healthcare Results: The literature search for 'nursing informatics certification' yielded few results in PubMed; Google Scholar yielded a large number of citations that extended to magazines and other non-peer reviewed sources. Worldwide, there are several nursing informatics associations, societies, and workgroups dedicated to nursing informatics associated with medical/health informatics societies. A formal certification program for nursing informatics appears to be available only in the United States. This certification was established in 1992, in concert with the formation and definition of nursing informatics as a specialty practice of nursing by the American Nurses Association. Although informatics is inherently interprofessional, certification pathways for nurses and physicians have developed separately, following long-standing professional structures, training, and pathways aligned with clinical licensure and direct patient care. There is substantial similarity with regard to the skills and competencies required for nurses and physicians to obtain informatics certification in their respective fields. Nurses may apply for and complete a certification examination if they have experience in the field, regardless of formal training. Increasing numbers of informatics nurses are pursuing certification. CONCLUSIONS: The pathway to certification is clear and wellestablished for U.S. based informatics nurses. The motivation for obtaining and maintaining nursing informatics certification appears to be stronger for nurses who do not have an advanced informatics degree. The primary difference between nursing and physician certification pathways relates to the requirement of formal training and level of informatics practice. Nurse informatics certification requires no formal education or training and verifies knowledge and skill at a more basic level. Physician informatics certification validates informatics knowledge and skill at a more advanced level; currently this requires documentation of practice and experience in clinical informatics and in the future will require successful completion of an accredited two-year fellowship in clinical informatics. For the profession of nursing, a graduate degree in nursing or biomedical informatics validates specialty knowledge at a level more comparable to the physician certification. As the field of informatics and its professional organization structures mature, a common certification pathway may be appropriate. Nurses, physicians, and other healthcare professionals with informatics training and certification are needed to contribute their expertise in clinical operations, teaching, research, and executive leadership.

Scaling-up NLP Pipelines to Process Large Corpora of Clinical Notes.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". OBJECTIVES: This paper describes the scale-up efforts at the VA Salt Lake City Health Care System to address processing large corpora of clinical notes through a natural language processing (NLP) pipeline. The use case described is a current project focused on detecting the presence of an indwelling urinary catheter in hospitalized patients and subsequent catheter-associated urinary tract infections. METHODS: An NLP algorithm using v3NLP was developed to detect the presence of an indwelling urinary catheter in hospitalized patients. The algorithm was tested on a small corpus of notes on patients for whom the presence or absence of a catheter was already known (reference standard). In planning for a scale-up, we estimated that the original algorithm would have taken 2.4 days to run on a larger corpus of notes for this project (550,000 notes), and 27 days for a corpus of 6 million records representative of a national sample of notes. We approached scaling-up NLP pipelines through three techniques: pipeline replication via multi-threading, intra-annotator threading for tasks that can be further decomposed, and remote annotator services which enable annotator scale-out. RESULTS: The scale-up resulted in reducing the average time to process a record from 206 milliseconds to 17 milliseconds or a 12- fold increase in performance when applied to a corpus of 550,000 notes. CONCLUSIONS: Purposely simplistic in nature, these scale-up efforts are the straight forward evolution from small scale NLP processing to larger scale extraction without incurring associated complexities that are inherited by the use of the underlying UIMA framework. These efforts represent generalizable and widely applicable techniques that will aid other computationally complex NLP pipelines that are of need to be scaled out for processing and analyzing big data.

Improving Bridging from Informatics Practice to Theory.

BACKGROUND: In 1962, Methods of Information in Medicine ( MIM ) began to publish papers on the methodology and scientific fundamentals of organizing, representing, and analyzing data, information, and knowledge in biomedicine and health care. Considered a companion journal, Applied Clinical Informatics ( ACI ) was launched in 2009 with a mission to establish a platform that allows sharing of knowledge between clinical medicine and health IT specialists as well as to bridge gaps between visionary design and successful and pragmatic deployment of clinical information systems. Both journals are official journals of the International Medical Informatics Association. OBJECTIVES: As a follow-up to prior work, we set out to explore congruencies and interdependencies in publications of ACI and MIM. The objectives were to describe the major topics discussed in articles published in ACI in 2014 and to determine if there was evidence that theory in 2014 MIM publications was informed by practice described in ACI publications in any year. We also set out to describe lessons learned in the context of bridging informatics practice and theory and offer opinions on how ACI editorial policies could evolve to foster and improve such bridging. METHODS: We conducted a retrospective observational study and reviewed all articles published in ACI during the calendar year 2014 (Volume 5) for their main theme, conclusions, and key words. We then reviewed the citations of all MIM papers from 2014 to determine if there were references to ACI articles from any year. Lessons learned in the context of bridging informatics practice and theory and opinions on ACI editorial policies were developed by consensus among the two authors. RESULTS: A total of 70 articles were published in ACI in 2014. Clinical decision support, clinical documentation, usability, Meaningful Use, health information exchange, patient portals, and clinical research informatics emerged as major themes. Only one MIM article from 2014 cited an ACI article. There are several lessons learned including the possibility that there may not be direct links between MIM theory and ACI practice articles. ACI editorial policies will continue to evolve to reflect the breadth and depth of the practice of clinical informatics and articles received for publication. Efforts to encourage bridging of informatics practice and theory may be considered by the ACI editors. CONCLUSIONS: The lack of direct links from informatics theory-based papers published in MIM in 2014 to papers published in ACI continues as was described for papers published during 2012 to 2013 in the two companion journals. Thus, there is little evidence that theory in MIM has been informed by practice in ACI.

The pandemic potential of avian influenza A(H7N9) virus: a review.

In March 2013 the first cases of human avian influenza A(H7N9) were reported to the World Health Organization. Since that time, over 650 cases have been reported. Infections are associated with considerable morbidity and mortality, particularly within certain demographic groups. This rapid increase in cases over a brief time period is alarming and has raised concerns about the pandemic potential of the H7N9 virus. Three major factors influence the pandemic potential of an influenza virus: (1) its ability to cause human disease, (2) the immunity of the population to the virus, and (3) the transmission potential of the virus. This paper reviews what is currently known about each of these factors with respect to avian influenza A(H7N9). Currently, sustained human-to-human transmission of H7N9 has not been reported; however, population immunity to the virus is considered very low, and the virus has significant ability to cause human disease. Several statistical and geographical modelling studies have estimated and predicted the spread of the H7N9 virus in humans and avian species, and some have identified potential risk factors associated with disease transmission. Additionally, assessment tools have been developed to evaluate the pandemic potential of H7N9 and other influenza viruses. These tools could also hypothetically be used to monitor changes in the pandemic potential of a particular virus over time.