The birth and evolution of a discipline devoted to information in biomedicine and health care. As reflected in its longest running journal.

BACKGROUND: The journal Methods of Information in Medicine, founded in 1962, has now completed its 50th volume. Its publications during the last five decades reflect the formation of a discipline that deals with information in biomedicine and health care. OBJECTIVES: To report about 1) the journal's origin, 2) the individuals who have significantly contributed to it, 3) trends in the journal's aims and scope, 4) influential papers and 5) major topics published in Methods over the years. METHODS: Methods included analysing the correspondence and journal issues in the archives of the editorial office and of the publisher, citation analysis using the ISI and Scopus databases, and analysing the articles' Medical Subject Headings (MeSH) in MEDLINE. RESULTS: In the journal's first 50 years 208 editorial board members and/or editors contributed to the journal's development, with most individuals coming from Europe and North America. The median time of service was 11 years. At the time of analysis 2,456 articles had been indexed with MeSH. Topics included computerized systems of various types, informatics methodologies, and topics related to a specific medical domain. Some MeSH topic entries were heavily and regularly represented in each of the journal's five decades (e.g. information systems and medical records), while others were important in a particular decade, but not in other decades (e.g. punched-card systems and systems integration). Seven papers were cited more than 100 times and these also covered a broad range of themes such as knowledge representation, analysis of biomedical data and knowledge, clinical decision support and electronic patient records. CONCLUSIONS: Methods of Information in Medicine is the oldest international journal in biomedical informatics. The journal's development over the last 50 years correlates with the formation of this new discipline. It has and continues to stress the basic methodology and scientific fundamentals of organizing, representing and analysing data, information and knowledge in biomedicine and health care. It has and continues to stimulate multidisciplinary communication on research that is devoted to high-quality, efficient health care, to quality of life and to the progress of biomedicine and the health sciences.

Evaluating the effects of increasing surgical volume on emergency department patient access.

AIM: To determine how increases in surgical patient volume will affect emergency department (ED) access to inpatient cardiac services. To compare how strategies to increase cardiology inpatient throughput can either accommodate increases in surgical volume or improve ED patient access. METHODS: A stochastic discrete event simulation was created to model patient flow through a cardiology inpatient system within a US, urban, academic hospital. The simulation used survival analysis to examine the relationship between anticipated increases in surgical volume and ED patient boarding time (ie, time interval from cardiology admission request to inpatient bed placement). RESULTS: ED patients boarded for a telemetry and cardiovascular intensive care unit (CVICU) bed had a mean boarding time of 5.3 (median 3.1, interquartile range 1.5-6.9) h and 2.7 (median 1.7, interquartile range 0.8-3.0) h, respectively. Each 10% incremental increase in surgical volume resulted in a 37 and 33 min increase in mean boarding time to the telemetry unit and CVICU, respectively. Strategies to increase cardiology inpatient throughput by increasing capacity and decreasing length of stay for specific inpatients was compared. Increasing cardiology capacity by one telemetry and CVICU bed or decreasing length of stay by 1 h resulted in a 7-9 min decrease in average boarding time or an 11-19% increase in surgical patient volume accommodation. CONCLUSIONS: Simulating competition dynamics for hospital admissions provides prospective planning (ie, decision making) information and demonstrates how interventions to increase inpatient throughput will have a much greater effect on higher priority surgical admissions compared with ED admissions.

Methods in year 50: preserving the past and preparing for the future.

BACKGROUND: Founded in 1962 and, therefore, the oldest international journal in medical informatics, Methods of Information in Medicine will publish its 50th volume in 2011. At the start of the journal's sixth decade, a discussion on the journal's profile seems appropriate. OBJECTIVES: To report on the new opportunities for online access to Methods publications as well as on the recent strategic decisions regarding the journal's aims and editorial policies. METHODS: Describing and analyzing the journal's aims and scope. Reflecting on recent publications and on the journal's development during the last decade. RESULTS: From 2011 forward all articles of Methods from 1962 until the present can be accessed online. Methods of Information in Medicine stresses the basic methodology and scientific fundamentals of processing data, information and knowledge in medicine and health care. Although the journal's major focus is on publications in medical informatics, it has never been restricted to publications only in this discipline. For example, articles in medical biometry, in or close to biomedical engineering, and, later, articles in bioinformatics continue to be a part of this journal. CONCLUSIONS: There is a continuous and, as it seems, ever growing overlap in the research methodology and application areas of the mentioned disciplines. As there is a continuing and even growing need for such a publication forum, Methods of Information in Medicine will keep its broad scope. As an organizational consequence, the journal's number of associate editors has increased accordingly.

The Student Editorial Board of Methods of Information in Medicine--an opportunity to educate tomorrow's peer reviewers.

OBJECTIVE: Peer review is a critical process in the publication of scientific findings; trainees and young investigators, however, have few opportunities to learn systematically how to review manuscripts. Journal editors have an opportunity to engage trainees and young investigators in the peer review process early during their career. METHODS: Methods of Information in Medicine, an official journal of the International Medical Informatics Association, is initiating a Student Editorial Board. The journal invites applications from international graduate and post-doctoral training programs that have a focus on health informatics, biomedical informatics, or a related field. RESULTS: Each year up to six trainees will be invited to join the Student Editorial Board. The trainees will go through a mentored training experience that includes an active involvement in the various aspects of peer review during their one to two-year term of appointment. CONCLUSIONS: The journal expects that the Student Editorial Board will benefit trainees and young investigators in becoming skilled reviewers and engaged peers who can offer professional, constructive, and informative feedback and enhance the process of scientific communication.

A comprehensive set of coded chief complaints for the emergency department.

OBJECTIVE: To develop a generally applicable set of coded chief complaints for the computerized patient records of emergency departments (EDs). METHODS: At an urban teaching ED the chief complaints of more than 50,000 patients were analyzed retrospectively during a 29-month period (June 1995-October 1997). Applying continuous quality improvement methods, a multidisciplinary team examined the current process documenting the patient's chief complaint. During two prospective periods (November 1997-December 1998; January 1999-June 1999), more than 34,000 chief complaints were analyzed. To reduce free-text charting practices, a variety of interventions on individual and team level were applied. Quantitative analysis was performed with statistical process control charts, and a qualitative evaluation was performed with a questionnaire. RESULTS: The charting of chief complaint in free-text format decreased from 23% to 1%. The range among individual ED staff members narrowed from 45% to 9%. During the refinement of the set of coded chief complaints, six infrequently charted items were removed. Five new chief complaints identified by analysis of free-text entries during the second study period were added. The current set of chief complaints consists of 54 codable and the three original free-text items. The ED staff members perceived all the interventions beneficial. A poster displaying all available terms as a visual aid, however, had the largest impact on charting the patient's chief complaint in coded format. CONCLUSIONS: Applying continuous quality improvement methods, the authors created a clinically developed and applicable set of codable chief complaints that can be easily integrated into a computerized patient record of an ED.

Automatic identification of patients eligible for a pneumonia guideline: comparing the diagnostic accuracy of two decision support models.

BACKGROUND: In busy clinical settings, physicians often do not have enough time to identify patients for specific therapeutic guidelines. As a solution, decision support systems could automatically identify eligible patients and trigger computerized guidelines for specific diseases. Applying this idea to community-acquired pneumonia (CAP), we developed a Bayesian network (BN) and an artificial neural network (ANN) for identifying patients who have CAP and are eligible for a pneumonia guideline. OBJECTIVE: The aim of this study was to determine whether the diagnostic accuracy of these two decision support models differs in terms of identifying CAP patients. METHODS: We trained and tested the networks with a data set of 32,662 adult patients. For each network, we (1) calculated the specificity, the positive predictive value (PPV), and the negative predictive value (NPV) at a sensitivity of 95%, and (2) determined the area under the receiver operating characteristic curve (AUC) as a measure of overall accuracy. We tested for statistical difference between the AUCs using the correlated area z statistic. RESULTS: At a sensitivity of 95%, the respective values for specificity, PPV, and NPV were: 92.3%, 15.1%, and 99.9% for the BN, and 94.0%, 18.6%, and 99.9% for the ANN. The BN had an AUC of 0.9795 (95% CI: 0.9736, 0.9843), and the ANN had an AUC of 0.9855 (95% CI: 0.9805, 0.9894). The difference between the AUCs was statistically significant (p=0.0044). CONCLUSIONS: The networks achieved high overall accuracies on the testing data set. Because the difference in accuracies is statistically significant but not clinically significant, both networks are equally suited to drive a guideline.

How should we make the admission decision in community-acquired pneumonia?

The spectrum of pneumonia patients ranges from only slightly compromised patients to patients who require life-sustaining measures. Admission decision support algorithms usually are not required for patients at either end of the spectrum. For patients presenting with intermediate severity of illness, decision support algorithms have shown that they can support clinicians in the admission decision and complement the clinicians' experience and clinical judgment with an objective tool. Clinical information systems may help overcome the existing obstacles to successful implementation. Successful guideline implementation in a clinical setting includes strategies that target not only the disease, but also include other forces that significantly influence the admission decision. Shared decision making and better managing of patients' expectations about treatment and prognosis need to be incorporated in the overall admission decision. The availability of improved outpatient management, such as outpatient intravenous antibiotic treatment and home health care, and a change in physicians' perspectives and patients' expectations may help to increase the proportion of outpatient management without compromising the quality of care. Decision support tools for pneumonia are available and show promising results. Further studies are needed, however, that show the successful dissemination and clinical implementation during routine patient care. Studies are needed that assess the impact of guidelines and prediction rules on patient outcomes. As the example of the PSI shows, the development, implementation, and dissemination of admission decision support systems is not a revolutionary, but a stepwise, evolutionary process that requires many years of research.

Evaluation of a computerized diagnostic decision support system for patients with pneumonia: study design considerations.

Planning the clinical evaluation of a computerized decision support system requires a strategy that encompasses the different aspects of the clinical problem, the technical difficulties of software and hardware integration and implementation, the behavioral aspects of the targeted users, and the discipline of study design. Although clinical information systems are becoming more widely available, only a few decision support systems have been formally evaluated in clinical environments. Published accounts of difficulties associated with the clinical evaluation of decision support systems remain scarce. The authors report on a variety of behavioral, logistical, technical, clinical, cost, and work flow issues that they had to address when choosing a study design for a clinical trial for the evaluation of an integrated, real-time decision support system for the automatic identification of patients likely to have pneumonia in an emergency department. In the absence of a true gold standard, they show how they created a credible, clinically acceptable, and economical reference standard for the diagnosis of pneumonia, to determine the overall accuracy of the system. For the creation of a reference standard, they describe the importance of recognizing verification bias and avoiding it. Finally, advantages and disadvantages of different study designs are explored with respect to the targeted users and the clinical setting.

Combining decision support methodologies to diagnose pneumonia.

OBJECTIVE: To evaluate the performance of a computerized decision support system that combines two different decision support methodologies (a Bayesian network and a natural language understanding system) for the diagnosis of patients with pneumonia. DESIGN: Evaluation study using data from a prospective, clinical study. PATIENTS: All patients 18 years and older who presented to the emergency department of a tertiary care setting and whose chest x-ray report was available during the encounter. METHODS: The computerized decision support system calculated a probability of pneumonia using information provided by the two systems. Outcome measures were the area under the receiver operating characteristic curve, sensitivity, specificity, predictive values, likelihood ratios, and test effectiveness. RESULTS: During the 3-month study period there were 742 patients (45 with pneumonia). The area under the receiver operating characteristic curve was 0.881 (95% CI: 0.822, 0.925) for the Bayesian network alone and 0.916 (95% CI: 0.869, 0.949) for the Bayesian network combined with the natural language understanding system (p=0.01). CONCLUSION: Combining decision support methodologies that process information stored in different data formats can increase the performance of a computerized decision support system.

A computer based intervention on the appropriate use of arterial blood gas.

OBJECTIVE: To evaluate impact of a computer-based intervention on arterial blood gas (ABG) usage in an intensive care setting. DESIGN: Retrospectively examined, via mixed group analysis, the effects of the intervention on ABG usage in the intensive care unit (ICU). SUBJECTS: Included all clinicians who placed ABG orders in an ICU using the computerized physician order-entry system, as well as controls in non-order entry units. METHODS: Computer-based intervention presenting ordering clinician with patient s previous ABG values and limiting forward duration of tests ordered. Study spanned 12 weeks, 5 weeks pre-intervention and 7-weeks post-intervention. Of 8 ICUs, intervention implemented in 6, not implemented in 2. Data analyzed using the repeated measure ANOVA. RESULTS: Physicians entered <40% ABG orders. 376 ABGs per week processed pre-intervention, 387 per week post. Results nonsignificant with a p= 0.09. Orders placed declined from 1039 per week, Jan 2000 to 662 per week, April 2001. DISCUSSION: Study did not demonstrate significant change; limited power. Need longer study periods. Impact improved in the future by targeting physician users and tailoring intervention to specific work flow pattern of high utilization units.

Automatic detection of acute bacterial pneumonia from chest X-ray reports.

OBJECTIVE: To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports. DESIGN: Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured. OUTCOME MEASURES: Recall, precision, specificity, and agreement (using Finn's R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia. RESULTS: Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference. CONCLUSION: In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.

Automatic identification of patients eligible for a pneumonia guideline.

OBJECTIVE: To assess the ability of an integrated, real-time diagnostic system (Bayesian network) to identify patients with community-acquired pneumonia who are eligible for a computerized pneumonia guideline without requiring clinicians to enter additional data. DESIGN: Prospective validation study. PATIENTS: All patients 18 years and older who presented to the emergency department of a tertiary care hospital. METHODS: The diagnostic system computed a probability of pneumonia for every patient. The final diagnosis was established using ICD-9 discharge diagnoses. Outcome measures were sensitivity, specificity, predictive values, likelihood ratios, area under the receiver operating characteristic curve, and test effectiveness. RESULTS: During the 9-week study period there were 4,361 patients (112 pneumonia patients). The area under the receiver operating characteristic curve was 0.930 (CI: 0.907, 0.948). At a fixed sensitivity of 95%, the specificity was 68.5%, the positive predictive value 7.3%, the negative predictive value 99.8%, the positive likelihood ratio 3.0, the negative likelihood ratio 0.08, and the test effectiveness 2.05. CONCLUSION: The diagnostic system was able to detect patients who are eligible for a pneumonia guideline. The detection of eligible patients can be applied to automatically initiate and evaluate computerized guidelines.

Contribution of a speech recognition system to a computerized pneumonia guideline in the emergency department.

OBJECTIVE: Evaluate the effect of a radiology speech recognition system on a real-time computerized guideline in the emergency department. METHODS: We collected all chest x-ray reports (n = 727) generated for patients in the emergency department during a six-week period. We divided the concurrently generated reports into those generated with speech recognition and those generated by traditional dictation. We compared the two sets of reports for availability during the patient's emergency department encounter and for readability. RESULTS: Reports generated by speech recognition were available seven times more often during the patients' encounters than reports generated by traditional dictation. Using speech recognition reduced the turnover time of reports from 12 hours 33 minutes to 2 hours 13 minutes. Readability scores were identical for both kinds of reports. CONCLUSION: Using speech recognition to generate chest x-ray reports reduces turnover time so reports are available while patients are in the emergency department.

Implementation of admission decision support for community-acquired pneumonia.

STUDY OBJECTIVES: Considerable variation exists in hospital admission rates for patients with community-acquired pneumonia. Logic to determine need for admission has been proposed by several authors. We compared Intermountain Health Care pneumonia guideline recommendations for inpatient vs outpatient care with actual physician decision making and clinical outcomes before vs after implementation. A secondary objective was to determine whether the pneumonia severity index predicts need for admission in this population. DESIGN: Prospective study after implementation vs historic controls. SETTING: Four ambulatory, urgent-care facilities. PATIENTS: Four hundred sixty-three immunocompetent adults with radiographically confirmed community-acquired pneumonia. INTERVENTION: A pneumonia practice guideline including decision support logic was implemented for a 12-month period. MEASUREMENTS AND RESULTS: After implementation, physicians used the pneumonia guideline form in 90% of cases. The percentage of patients admitted within 30 days decreased from 13.6% to 6.4% (p = 0.01). Only five patients before (2.5%) and three patients after (1.1%, p = 0.3) guideline implementation required subsequent hospital admission within 30 days after initial outpatient treatment. Only two deaths occurred in the study cohort, both outpatients before implementation. The positive predictive value was 14.4%, and the negative predictive value for admission was 98.8% after guideline implementation. Guideline recommendation for admission was more likely to be followed in patients with more risk factors and hypoxemia. CONCLUSIONS: Decreased admission rate was observed after implementation of admission decision support in combination with specific recommendations for outpatient antibiotic therapy. Favorable outpatient outcomes suggest that implementation of decision support was safe.

Assessing the quality of clinical data in a computer-based record for calculating the pneumonia severity index.

OBJECTIVE: This study examined whether clinical data routinely available in a computerized patient record (CPR) can be used to drive a complex guideline that supports physicians in real time and at the point of care in assessing the risk of mortality for patients with community-acquired pneumonia. SETTING: Emergency department of a tertiary-care hospital. DESIGN: Retrospective analysis with medical chart review. PATIENTS: All 241 inpatients during a 17-month period (Jun 1995 to Nov 1996) who presented to the emergency department and had a primary discharge diagnosis of community-acquired pneumonia. METHODS/MAIN OUTCOME MEASURES: The 20 guideline variables were extracted from the CPR (HELP System) and the paper chart. The risk score and the risk class of the Pneumonia Severity Index were computed using data from the CPR alone and from a reference standard of all data available in the paper chart and the CPR at the time of the emergency department encounters. Availability and concordance were quantified to determine data quality. The type and cause of errors were analyzed depending on the source and format of the clinical variables. RESULTS: Of the 20 guideline variables, 12 variables were required to be present for every computer-charted emergency department patient, seven variables were required for selected patients only, and one variable was not typically available in the HELP System during a patient's encounter. The risk class was identical for 86.7 percent of the patients. The majority of patients with different risk classes were assigned too low a risk class. The risk scores were identical for 72.1 percent of the patients. The average availability was 0.99 for the data elements that were required to be present and 0.79 for the data elements that were not required to be present. The average concordance was 0.98 when all a patient's variables were taken into account. The cause of error was attributed to the nurse charting in 77 percent of the cases and to the computerized evaluation in 23 percent. The type of error originated from the free-text fields in 64 percent, from coded fields in 21 percent, from vital signs in 14 percent, and from laboratory results in 1 percent. CONCLUSION: From a clinical perspective, the current level of data quality in the HELP System supports the automation and the prospective evaluation of the Pneumonia Severity Index as a computerized decision support tool.

An integrated decision support system for diagnosing and managing patients with community-acquired pneumonia.

Decision support systems that integrate guidelines have become popular applications to reduce variation and deliver cost-effective care. However, adverse characteristics of decision support systems, such as additional and time-consuming data entry or manually identifying eligible patients, result in a "behavioral bottleneck" that prevents decision support systems to become part of the clinical routine. This paper describes the design and the implementation of an integrated decision support system that explores a novel approach for bypassing the behavioral bottleneck. The real-time decision support system does not require health care providers to enter additional data and consists of a diagnostic and a management component.

Primary neuroendocrine tumors of the cystic duct.

Primary neuroendocrine tumors of the cystic duct are extremely rare. Only 4 cases have been described to date. We report 2 patients in whom a primary neuroendocrine tumor of the cystic duct was incidentally detected during histological examination following cholecystectomy. With regard to the primary neuroendocrine tumor both patients were asymptomatic at the time of diagnosis. However, histologic examination did not confirm that the neuroendocrine tumors had been completely removed. Both patients underwent a second procedure. They are well after 47 and 49 months, respectively.