OBJECTIVE: Clarify disciplinary foundations and internal structure of biomedical informatics. METHODS: We analyze BMI's emergence at disciplinary intersections and map its internal structure across 4 domains: theory and practice of knowledge discovery, knowledge representation and reasoning, knowledge architecture, and knowledge-driven transformation. We compare BMI with mathematics, computer science, biostatistics, and biomedical engineering, and illustrate emergent characteristics through a precision medicine example. RESULTS: BMI's distinctive contribution-elucidating the structure of biomedical knowledge and developing methods to discover, preserve, and make knowledge actionable-requires strength across all 4 domains. BMI developed these domains pragmatically: building systems, extracting principles, and formalizing theories. The discipline must now complement empirical approaches with rigorous theoretical work: assessing adequacy of existing theories, identifying gaps, and orchestrating collaborative development. CONCLUSIONS: BMI creates emergent capabilities across disciplines. As biomedicine becomes increasingly complex, BMI must strengthen its theoretical foundations while demonstrating transformative potential of knowledge spanning biological scales and time.

OBJECTIVES: We report on findings from a meeting convened by the American College of Medical Informatics (ACMI) to characterize aspects of the patient experience that could be improved using informatics. MATERIALS AND METHODS: The American College of Medical Informatics fellows were invited to share their experiences as patients and suggest informatics approaches that may improve the patient experience. RESULTS: We identified 4 themes: (1) getting the right care, (2) data sharing and data interoperability, (3) guiding low-cost evaluations, and (4) predictive analytics. DISCUSSION: Despite widespread adoption of health IT, patient experiences remain far from optimal. CONCLUSION: The American College of Medical Informatics fellows identified informatics approaches, applications, and research areas that have the potential to improve patient experiences with health care systems.

OBJECTIVE: This study uses probabilistic independence to disentangle patient-specific sources of disease and their signatures in Electronic Health Record (EHR) data. MATERIALS AND METHODS: We model a disease source as an unobserved root node in the causal graph of observed EHR variables (laboratory test results, medication exposures, billing codes, and demographics), and a signature as the set of downstream effects that a given source has on those observed variables. We used probabilistic independence to infer 2000 sources and their signatures from 9195 variables in 630,000 cross-sectional training instances sampled at random times from 269,099 longitudinal patient records. We evaluated the learned sources by using them to infer and explain the causes of benign vs. malignant pulmonary nodules in 13,252 records, comparing the inferred causes to an external reference list and other medical literature. We compared models trained by three different algorithms and used corresponding models trained directly from the observed variables as baselines. RESULTS: The model recovered 92% of malignant and 30% of benign causes in the reference standard. Of the top 20 inferred causes of malignancy, 14 were not listed in the reference standard, but had supporting evidence in the literature, as did 11 of the top 20 inferred causes of benign nodules. The model decomposed listed malignant causes by an average factor of 5.5 and benign causes by 4.1, with most stratifying by disease course or treatment regimen. Predictive accuracy of causal predictive models trained on source expressions (Random Forest AUC 0.788) was similar to (p = 0.058) their associational baselines (0.738). DISCUSSION: Most of the unrecovered causes were due to the rarity of the condition or lack of sufficient detail in the input data. Surprisingly, the causal model found many patients with apparently undiagnosed cancer as the source of the malignant nodules. Causal model AUC also suggests that some sources remained undiscovered in this cohort. CONCLUSION: These promising results demonstrate the potential of using probabilistic independence to disentangle complex clinical signatures from noisy, asynchronous, and incomplete EHR data that represent the confluence of multiple simultaneous conditions, and to identify patient-specific causes that support precise treatment decisions.

OBJECTIVE: 2017 blood pressure (BP) categories focus on cardiac risk. We hypothesize that studying the balance between mechanisms that increase or decrease BP across the medical phenome will lead to new insights. We devised a classifier that uses BP measures to assign individuals to mutually exclusive categories centered in the upper (Htn), lower (Hotn) and middle (Naf) zones of the BP spectrum; and examined the epidemiologic and phenotypic patterns of these BP-categories. METHODS: We classified a cohort of 832,560 deidentified electronic health records by BP-category; compared the frequency of BP-categories and four subtypes of Htn and Hotn by sex and age-decade; visualized the distributions of systolic, diastolic, mean arterial and pulse pressures stratified by BP-category; and ran Phenome-wide Association Studies (PheWAS) for Htn and Hotn. We paired knowledgebases for hypertension and hypotension and computed aggregate knowledgebase status (KB-status) indicating known associations. We assessed alignment of PheWAS results with KB-status for phecodes in the knowledgebase, and paired PheWAS correlations with KB-status to surface phenotypic patterns. RESULTS: BP-categories represent distinct distributions within the multimodal distributions of systolic and diastolic pressure. They are centered in the upper, lower, and middle zones of mean arterial pressure and provide a different signal than pulse pressure. For phecodes in the knowledgebase, 85% of positive correlations align with KB-status. Phenotypic patterns for Htn and Hotn overlap for several phecodes and are separate for others. Our analysis suggests five candidates for hypothesis testing research, two where the prevalence of the association with Htn or Hotn may be under appreciated, three where mechanisms that increase and decrease blood pressure may be affecting one another's expression. CONCLUSION: PairedPheWAS methods may open a phenome-wide path to disentangling hypertension and chronic hypotension. Our classifier provides a starting point for assigning individuals to BP-categories representing the upper, lower, and middle zones of the BP spectrum. 4.7 % of individuals matching 2017 BP categories for normal, elevated BP or isolated hypertension, have diastolic pressure < 60. Research is needed to fine-tune the classifier, provide external validation, evaluate the clinical significance of diastolic pressure < 60, and test the candidate hypotheses.

MOTIVATION: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. RESULTS: Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. AVAILABILITY AND IMPLEMENTATION: phecodeX is available at https://github.com/PheWAS/phecodeX.

Suzanne (Sue) Bakken, RN, PhD and Editor-in-Chief of the Journal of the American Medical Informatics Association (JAMIA), has been selected to receive the 2023 Morris F. Collen Award from the American College of Medical Informatics to honor her lifetime contributions to the field of biomedical informatics. Bakken has advanced the field by integrating leading-edge research with training successive generations of informaticians and by working with underserved communities to bring the results of research to people’s lives. We have created an on-line collection of selected papers from among Bakken’s publications in JAMIA that is available at academic.oup.com/jamia/pages/morris-collen-award-2023. The first paper in the collection appeared in the inaugural issue of JAMIA and reported her analysis of terms used by nurses to describe patient problems as a test of the feasibility of using SNOMED III as a nursing data standard.1 This article exhibits the hallmarks of her work: rigorous grounding in existing frameworks, in this case, a comparative review of healthcare classification schemes and published evaluation studies related to their use to represent clinical data; explicit research questions; appropriate use of both qualitative and quantitative analysis; and discussion including an actionable path forward for the field. The collection provides a glimpse of her progression from first author researcher1–6 to senior author with a mentee as first author,7–15 as well as the evolution of her research foci.

OBJECTIVE: Knowledgebases are needed to clarify correlations observed in real-world electronic health record (EHR) data. We posit design principles, present a unifying framework, and report a test of concept. MATERIALS AND METHODS: We structured a knowledge framework along 3 axes: condition of interest, knowledge source, and taxonomy. In our test of concept, we used hypertension as our condition of interest, literature and VanderbiltDDx knowledgebase as sources, and phecodes as our taxonomy. In a cohort of 832 566 deidentified EHRs, we modeled blood pressure and heart rate by sex and age, classified individuals by hypertensive status, and ran a Phenome-wide Association Study (PheWAS) for hypertension. We compared the correlations from PheWAS to the associations in our knowledgebase. RESULTS: We produced PhecodeKbHtn: a knowledgebase comprising 167 hypertension-associated diseases, 15 of which were also negatively associated with blood pressure (pos+neg). Our hypertension PheWAS included 1914 phecodes, 129 of which were in the PhecodeKbHtn. Among the PheWAS association results, phecodes that were in PhecodeKbHtn had larger effect sizes compared with those phecodes not in the knowledgebase. DISCUSSION: Each source contributed unique and additive associations. Models of blood pressure and heart rate by age and sex were consistent with prior cohort studies. All but 4 PheWAS positive and negative correlations for phecodes in PhecodeKbHtn may be explained by knowledgebase associations, hypertensive cardiac complications, or causes of hypertension independently associated with hypotension. CONCLUSION: It is feasible to assemble a knowledgebase that is compatible with EHR data to aid interpretation of clinical correlation research.

In this editorial, the first 4 Editors-in-Chief of the Journal of the American Medical Informatics Association (JAMIA) reflect on its history and future.The origins and characterization of each Editor's era represent the "lived experience" of each Editor rather than a comparison of common metrics over time.We also qualitatively assess JAMIA's progress in meeting its original vision and goals, and posit considerations for its future.Table 1 summarizes key JAMIA-related events.

OBJECTIVE: The capability to share data, and harness its potential to generate knowledge rapidly and inform decisions, can have transformative effects that improve health. The infrastructure to achieve this goal at scale--marrying technology, process, and policy--is commonly referred to as the Learning Health System (LHS). Achieving an LHS raises numerous scientific challenges. MATERIALS AND METHODS: The National Science Foundation convened an invitational workshop to identify the fundamental scientific and engineering research challenges to achieving a national-scale LHS. The workshop was planned by a 12-member committee and ultimately engaged 45 prominent researchers spanning multiple disciplines over 2 days in Washington, DC on 11-12 April 2013. RESULTS: The workshop participants collectively identified 106 research questions organized around four system-level requirements that a high-functioning LHS must satisfy. The workshop participants also identified a new cross-disciplinary integrative science of cyber-social ecosystems that will be required to address these challenges. CONCLUSIONS: The intellectual merit and potential broad impacts of the innovations that will be driven by investments in an LHS are of great potential significance. The specific research questions that emerged from the workshop, alongside the potential for diverse communities to assemble to address them through a 'new science of learning systems', create an important agenda for informatics and related disciplines.

Consistent collection and use of social and behavioral determinants of health can improve clinical care, prevention and general health, patient satisfaction, research, and public health. A recent Institute of Medicine committee defined a panel of 11 domains and 12 measures to be included in electronic health records. Incorporating the panel into practice creates a number of informatics research opportunities as well as challenges. The informatics issues revolve around standardization, efficient collection and review, decision support, and support for research. The informatics community can aid the effort by simultaneously optimizing the collection of the selected measures while also partnering with social science researchers to develop and validate new sources of information about social and behavioral determinants of health.

Clinical documentation is central to patient care. The success of electronic health record system adoption may depend on how well such systems support clinical documentation. A major goal of integrating clinical documentation into electronic heath record systems is to generate reusable data. As a result, there has been an emphasis on deploying computer-based documentation systems that prioritize direct structured documentation. Research has demonstrated that healthcare providers value different factors when writing clinical notes, such as narrative expressivity, amenability to the existing workflow, and usability. The authors explore the tension between expressivity and structured clinical documentation, review methods for obtaining reusable data from clinical notes, and recommend that healthcare providers be able to choose how to document patient care based on workflow and note content needs. When reusable data are needed from notes, providers can use structured documentation or rely on post-hoc text processing to produce structured data, as appropriate.

The American College of Medical Informatics (ACMI) is an honorary society established to recognize those who have made sustained contributions to the field. Its highest award, for lifetime achievement and contributions to the discipline of medical informatics, is the Morris F. Collen Award. Dr. Collen's own efforts as a pioneer in the field stand out as the embodiment of creativity, intellectual rigor, perseverance, and personal integrity. The Collen Award is given each year, when appropriate, to pioneers in the field of Medical Informatics who best exemplify the teaching and practice of Morris Collen. In 2007, the College was proud to present the Collen Award to William Wallace Stead (Figure 1). Throughout his career, Dr. Bill Stead, has been able to visualize better ways of achieving the purpose of work, and then to drop to ground level to figure out how to execute on the next step in that direction. William Wallace Stead 2005. Bill Stead was born in Durham NC in 1948 as third child to Eugene Anson Stead, Jr. and Evelyn Emogene Selby. Gene Stead was Chair of Medicine at Duke. He trained a generation of independent thinkers and constantly broke the mold; changing the Duke curriculum to replace memory with research, using the Cardiovascular Databank to improve practice, and launching the physician assistant profession to increase access to care. Evelyn was at Gene's side every step; raising Nancy, Lucy, and Bill, co-authoring an early low fat cook book, and editing the journals Gene took on. Every person or event she touched became special. To many observers through the years, it seemed that Gene and Bill Stead were practically the same person. As Harry Jacobson, MD, Vice-Chancellor, Vanderbilt University Medical Center, observed, “They had both a loving relationship, but a mutual respect—and a mutual curiosity, intellectual curiosity about what we need to do in health care…that I think created a bond between the two of them that I thought was very unique.” When Bill was four, the family purchased land on Kerr Lake in North Carolina. With Evelyn as Architect and Gene as Builder-in-Chief, they began their lake house. They started with a bath room and enclosed it with a screened area. This made a place to live while they built the house around it (Figure 2). The project took 20 years to complete, and it showed Bill how to break projects up into phases and that big things could happen if you kept laying one block after another. In this regard, Clem McDonald, MD, Regenstrief Professor Emeritus of Medical Informatics at Indiana University, said of Bill, “He's a master planner…you know step-wise, progressive, he thinks a long time ahead how to get there. In many respects he's like his Dad that way. The politics and the people and the funding and the motivation and building the infrastructure and keeping your eye on the prize…I think he probably did that even more focused than his dad.” Building the house on Kerr Lake—Bill, his father Eugene, and sister Nancy, 1960. Bill is an Eagle Scout. Harry Rodenhizer was his Scout Master and Harry believed in letting the boys make mistakes and learn from them. The troop camped out every month, often hiking in and out. Bill's first experience in leadership and organization came as a Patrol Leader and Junior Scout Master. When Bill was 10, he learned to ski behind a runabout with a 12 HP outboard. Almost 50 years later, water skiing is still his favorite sport. Bill began his education at Durham Academy, continued at Durham High School, and finished at Duke University, where he received his BA in Chemistry and an MD and then completed a residency in Internal Medicine and fellowship in Nephrology. Bill's interest in applying computing to medicine began in 1968 when he joined Dr. Frank Starmer as an undergraduate work study student in the Duke Myocardial Infarction Research Unit. The unit's Sigma V computer was located next to the cardiac care unit and supported real time data acquisition and interaction. When Bill started medical school, the Duke curriculum compressed requirements into 2 years, freeing a full year for basic research. Medical computing was not an established research track at the time, so Dr. Harvey Estes, Chairman of the Department of Community Health Sciences, agreed that Bill could work with Dr. Ed Hammond in his computer lab and receive credit for independent study. Bill's father suggested he start this “3rd year research” during the summer between college and medical school. With that head start, he was able to finish medical school in 3 years by taking courses year round. Ed Hammond, Ph.D., Professor Emeritus, Community and Family Medicine and Professor, Biomedical Engineering, Duke University recalls that time: “When Bill first joined me, and in typical Stead fashion, he actually did the third year rotation before he ever started his first year rotation at Duke. The first project that Bill was involved with was the development of a video interactive questionnaire for patients with headache. Not knowing Bill Stead very well, I came back after a week's vacation and walked into the room and Bill had the walls covered with large white wrapping paper on which he had outlined all the frames for the interactive questionnaires. This project was very successful but somewhat ahead of its time.” In the summer of 1970, Bill joined Ed Hammond as one of a team of five medical and undergraduate students (Figure 3). Together they developed assembly language programs for the PDP-12 and merged them into GEMISCH, an application development and database management environment. Working with Dr. Al Heyman in Neurology, Bill used this environment to develop an interactive video questionnaire to capture the history of present illness directly from patients presenting with headache. The program's differential diagnosis was comparable to that of attending neurologists. Checking a printout from the Obstetrical Medical Record—Bill 1973. By 1972, Ed's team had combined history and physical “exam takers” into a prenatal electronic health record. When Bill started his OBGYN clerkship, the attendings were unhappy because it took too long to find what they wanted in the long printouts. He went back to the computer center and stayed up all night to re-write the print format to match the outline the students had been given for admission notes. The resulting system was used at Duke until 2002. Bill was in the last internal medicine and resident group at Duke who were “on call” five out of seven nights of the week. This clinical immersion was intense and left no time for computing. However, in 1975, Bill became a fellow in nephrology under the tutelage of Dr. Ike (Roscoe R.) Robinson. Dr. Robinson switched the usual order of clinical and research training for Bill. Starting with research, he allowed Bill to build tools that he could then test and refine during his clinical year. It was during the research year that Bill developed a working prototype of a general purpose electronic medical record. A dictionary of metadata managed clinical content and entity-attribute-value data structures handled sparseness. In time, Ed Hammond and Bill would evolve this prototype into TMR, a full function practice management system, which supported at its peak over 40 sites in 20 different settings ranging from a 2 person practice to a 60 bed cancer research hospital. Morris Collen, MD, Emeritus Director of Research, Kaiser Permanente, recalls that “Soon the TMR system was used in other medical centers in the United States. Since some medical information such as Word Process Narrative still requires a paper base record, Bill published the succinct disclaimer that TMR was a chart-less record and not a paperless record.” Early in his clinical fellowship year, Bill met Janet Mackey. Janet was the daughter of John and Mary Mackey. She grew up in Beverly, MA and graduated from Salem Hospital School of Nursing. She had come to Duke the previous year to accept a position on the Duke Inpatient Renal Unit. Bill and Janet were never apart after their first date, and they married in 1977. Bill skied to the wedding dinner and he and Janet foiled the guests who wanted to decorate their car by leaving the wedding by boat. Bill then joined the Duke faculty in Nephrology. Since Ike Robinson was both Chief of Nephrology and CEO of Duke Hospital, he arranged to split Bill's time between directing the dialysis unit at the Durham VA Hospital and serving as physician liaison for the Duke Hospital Information System. The dialysis unit provided a test bed for TMR. The hospital system, DHIS, developed at Duke and marketed by IBM as PCS/ADS, provided an opportunity to test ideas on an enterprise scale. Bill's and Janet's daughter, Elizabeth Mackey Stead, was born in 1984. Elizabeth graduated from Lipscomb University last year with a degree in elementary education and she now teaches in Nashville (Figure 4). Bill, his wife Janet Mackey, and daughter Elizabeth, 2006. In 1985, Bill stepped in as Director of Medical Center Information Systems at Duke. Two seasoned medical center administrators, Mr. Bob Winfree and Dr. Jane Elchlepp met with Bill two hours every week for his first four years to guide him as he learned to manage at this level of the organization. Bill obtained model development and implementation grant funding from the National Library of Medicine's Integrated Academic Information Management System, or IAIMS, initiative (Figure 5. Through this work he developed what he views as his primary technical innovation—an enterprise information architecture to manage the information from all applications in repositories independent from, yet accessible to, the applications, allowing new functionality to be purchased and plugged in or developed on site with less hassle and without upsetting other programs and processes. This architecture underpins Vanderbilt's clinical IT and a derivative is the foundation of the “Volunteer eHealth Intitiative,” regional data exchange demonstration project in southwestern Tennessee. Integrated Advanced Information Management System (IAIMS) Fair—Bill back row center, 1990. In 1991, Ike Robinson recruited Bill to Vanderbilt. Ike had been at Vanderbilt as Medical Center as Vice Chancellor and the Medical Center CEO for 10 years, and he was ready to give Bill a fresh canvass to build Biomedical Informatics as Bill thought best. They agreed to focus on linking information into workflow to help people make better decisions. Carol Aronson, Bill's long time assistant, agreed to come to help with the effort. Bill decided to bring all information management and utilization functions, including the library, into a center managed at the level of the medical center; to implement the information architecture from his Duke plan, and to establish biomedical informatics as a scholarly discipline within the new center. Together, these decisions turned the operation and decision support infrastructure of the medical center into an applied laboratory for biomedical informatics. Clem McDonald observes that “It's absolutely essential to the field that there be informatics programs that have enough critical mass and are embedded in the health care system at their place well enough to be able to do the inventive and imaginative things that the health care system needs.” What Bill still calls “the Vanderbilt experiment” is a journey. The first leg (1991–1994) involved basic infrastructure: construction of the Eskind Biomedical Library to make space for the academic program, connecting buildings with fiber optics for networking, refreshing transaction processing systems, and adapting them to the enterprise information architecture. In this setting, Dr. Harry Jacobson observes, “It is amazing how much you can get done if you have a few principles—like: it doesn't matter who gets the credit, don't sacrifice success for control, and treat other people like you want to be treated. I mean very basic principles…and Bill lives them.” The second leg (1994–2003) applied techniques from the science of biomedical informatics to come up with new ways to help people do their work. This leg began when Dr. Randy Miller and his team from Pittsburgh (Nunzia Giuse, MD, MLS; Dario Giuse, DrIng; Antoine Geissbuhler, MD, and Steven H. Brown, MD) joined the Vanderbilt team. Other established leaders joined in quick succession as Bill sought to assemble a critical mass of talent. Biomedical Informatics gained departmental status in the School of Medicine in 2001. Applied informatics breakthroughs included an enterprise-wide electronic medical record together with workflow, communication, and decision support tools, and new models for librarianship. As Randolph A. Miller, MD, the Donald AB and Mary M Lindberg University Professor of Biomedical Informatics, Medicine, & Nursing at Vanderbilt, notes, “You're not going to win every argument, but if you know that the person listening to the points of view understands the points of view—weighs them carefully, and will make a fair decision that he can explain to everybody—that's about as good as it gets. And that's how Bill does things.” The third leg of informatics development, beginning in 2002, rethinks the nature of biomedical and healthcare work given the “art of the possible” through informatics. This leg began when Dr. Harry Jacobson (who had succeeded Ike Robinson in 1997) and Bill partnered to launch the Vanderbilt Center for Better Health as infrastructure to accelerate change in healthcare. The Center's methods are helping Vanderbilt and partners manage the transition to systems approaches to evidence based personalized care. Over the course of this journey, the Informatics Center grew from two to over 500. The Department of Biomedical Informatics, with Dr. Dan Masys as current Chair, is the 6th largest in the School of Medicine with 55 faculty spanning clinical, bio-, and educational informatics. As each new unit was assimilated or formed, Bill took it over, ran it for a period, and then recruited a leader to take it to the next stage. Dr. Miller recalls, “At one point in time he was director of the biomedical library, at another point in time he was the acting chair of the department, at other times earlier in his career he was a programmer and a clinician…so it's because he's been in all of these roles that he has the right experience and the right know-how to make things happen.” Similarly, Donald Lindberg, MD, Director of the National Library of Medicine, observes that “If you say, well, someone saw the future and he sees the importance—that's great but that's preaching to the choir and it doesn't get you anyplace. I think a lot of us have done that, I think Bill has done more…I think Bill has figured out the importance of everyone in the institution sharing that vision in his own way…and being supportive of those ideas.” Bill has been at the center of the emergence of biomedical informatics as an organized field. He was a member of the first executive committee of the American College of Medical Informatics and later served as President of the College. He was program chair of the 11th Symposium on Computer Applications in Medical Care. As President of the American Association for Medical Systems and Informatics, he worked with Dr. Ted Shortliffe, President of the College, and Dr. Clem McDonald, President of the Symposium, to bring the resources of those organizations together in what became the American Medical Informatics Association (AMIA). He led the committee that developed the business plan for the Journal of the American Medical Informatics Association (JAMIA) and served as JAMIA's Founding Editor-in-Chief. In this regard, Dr. Lindberg, who was AMIA's first president, observes, “I think another thing that we wouldn't want to forget about Bill having done really well is to be the first editor of JAMIA. And I recall very well when he expressed an interest in that…and fortunately both of us were holding glasses in our hands, and we agreed very, very quickly that he'd be the ideal editor and he took it on and was the ideal editor.” Bill has followed in his father's footsteps to membership in the Institute of Medicine and service on the Board of Regents of the National Library of Medicine. He served as Presidential appointee to the Systemic Interoperability Commission. As Chair of the Board of Regents of the National Library of Medicine, he guided development of the for as with of the In to Clem McDonald him help the Library plan the to long plan was a master for Lindberg, to have that of master so And Lindberg, in his as think he and both the of was the going to be during that years we were for and how would into that of the And I think they each other and both did a Bill is intense when focused on work. He with the family and He the at the on Center He is at his best working or over dinner on a In this Ed Hammond notes, is an person and what Bill does is he has his own that he to test and make on the the until it This is the same and that Bill into all of the projects that he Bill is a pioneer in the of for the right and for information enough to support he to organization approaches to develop the intellectual to both Dr. Collen, the observes Stead the highest level for in all of medical and the practice of medical Dr. Lindberg a on the Collen Award a transition for Bill in which he from his own group and his own more of a he more with at the to to these new ideas and to the I to receive this from in the College. Janet and Elizabeth, stand up and in This is a to the many people who have and given It is a to the I have had the to I each of When we established the Collen Award in we wanted to the of the in a that showed people their how a career over I now want to to the people in the I have good and The good is that we we our history will back on the next years as the of biomedical informatics. a of and the of and the data from the and will we come up with a new to make clinical decisions. Information is in the critical of The new is that you information into the current to healthcare and get the As in the such an will as many as it To get the from information you have to change the of to and information to on memory and to a allowing people to and What does this mean to people their in biomedical need to what be or what it of it be need to get your it to a real In the you will figure out how information can the you do these you will a person and your career will be To it this is but not need to both think and

OBJECTIVE: Authors evaluated whether displaying context sensitive links to infrequently accessed educational materials and patient information via the user interface of an inpatient computerized care provider order entry (CPOE) system would affect access rates to the materials. DESIGN: The CPOE of Vanderbilt University Hospital (VUH) included "baseline" clinical decision support advice for safety and quality. Authors augmented this with seven new primarily educational decision support features. A prospective, randomized, controlled trial compared clinicians' utilization rates for the new materials via two interfaces. Control subjects could access study-related decision support from a menu in the standard CPOE interface. Intervention subjects received active notification when study-related decision support was available through context sensitive, visibly highlighted, selectable hyperlinks. MEASUREMENTS: Rates of opportunities to access and utilization of study-related decision support materials from April 1999 through March 2000 on seven VUH Internal Medicine wards. RESULTS: During 4,466 intervention subject-days, there were 240,504 (53.9/subject-day) opportunities for study-related decision support, while during 3,397 control subject-days, there were 178,235 (52.5/subject-day) opportunities for such decision support, respectively (p = 0.11). Individual intervention subjects accessed the decision support features at least once on 3.8% of subject-days logged on (278 responses); controls accessed it at least once on 0.6% of subject-days (18 responses), with a response rate ratio adjusted for decision support frequency of 9.17 (95% confidence interval 4.6-18, p < 0.0005). On average, intervention subjects accessed study-related decision support materials once every 16 days individually and once every 1.26 days in aggregate. CONCLUSION: Highlighting availability of context-sensitive educational materials and patient information through visible hyperlinks significantly increased utilization rates for study-related decision support when compared to "standard" VUH CPOE methods, although absolute response rates were low.

People often use these terms interchangeably, and clarity about their differences and overlap is important when discussing these topics.d Next, we attempt to explain some of the challenges the United States faces in the NHII initiative and discuss approaches that might work on a national basis.We suggest, for example, that the NHII will sit beside today's electronic medical record systems, leverage their content, and make it easier to share information among these systems and stakeholders.d We conclude by outlining the potential building blocks that the NHII initiative can put in place in the short term, together with those that can be accomplished over a longer period.Where possible, we suggest assignment of responsibility. Components of the Health Care Information and Communication InfrastructureHealth care data are complex and used by various stakeholders for different purposes.The National Committee for Vital and Health Statistics defines three primary dimensions through which health care information can be viewed: d The patient view or Personal Health Dimension, d The Health Care Provider Dimension, d The community or Population Health Dimension.10

The American College of Medical Informatics (ACMI) is an honorary society established to recognize those who have made sustained contributions to the field. Its highest award, for lifetime achievement and contributions to the discipline of medical informatics, is the Morris F. Collen Award. Dr. Collen's own efforts as a pioneer in the field stand out as the embodiment of creativity, intellectual rigor, perseverance, and personal integrity. Once a year, when appropriate, the College gives its highest recognition to those whose attainments have, throughout their careers, substantially advanced the science and art of medical informatics. In 2003, the College was proud to present the Collen Award to Dr. William Edward Hammond II. Throughout his career, Ed has seen possibilities, and then figured out how to do whatever is needed to overcome challenges where others thought goals were unachievable. He is a pioneer in electronic health records and standards. He is a classic triple threat—breaking new scientific ground, developing young people, and providing services that make a difference in health care. He is at once an intense competitor and an effective collaborator. He is richly deserving of the recognition embodied in the Collen Award. Morrie Collen, MD, Emeritus Director of Research, Kaiser Permanente: “I congratulate Ed Hammond on receiving this award in recognition of his outstanding leadership and his significant technical contributions to medical informatics. I first learned of Ed's unusual technical abilities when he was working with Bill Stead at Duke University. Ed Hammond, an engineer, and Bill Stead, a physician, were an ideal pair for developing medical information systems. As early as 1969, using punch cards for some applications and a mini-computer, they were developing what they called a generalized medical information system for community health. Professor and Doctor William Ed Hammond is truly one of the great pioneers in medical informatics.” William Edward Hammond II, PhD. Ed Hammond was born in Hendersonville, North Carolina, in 1935, to Alfred and Kate Hammond. His father worked in the cotton mill. Ed was Alfred and Kate's fifth child. His father died when Ed was 4 and his mother remarried to Hicks Maybin, a farmer in the Green River Mountains of western North Carolina. His 13 children, her five, and one they had together made for a large immediate family. The farm was what now would be called a truck farm. The family had no electricity or running water. Ed started work at age 12 in a grocery store and, by his senior year in high school, he was working a full eight-hour shift at the cotton mill in addition to his course work. In 1953, Ed won an NROTC scholarship to Duke. He studied electrical engineering—in this vacuum tube era—spending his summers on navy cruises. He was elected to the Order of St. Patrick, received an armed forces communication award, and received an RCA best paper award. After graduating from Duke in 1957, Ed joined the Navy and went to Pensacola, Florida, for flight school. That October, he married Kay Stuart Forrester, a native of Durham, North Carolina. Ed continued active duty in the Navy until 1960, and during that period, became the Navy's first air intercept controller. In the fall of 1960, Ed returned to Duke as an instructor in electrical engineering (EE). While on the faculty, he completed a master's degree. During this time, he and Kay had two sons, William Edward Hammond III born in 1961 and Michael Lane Hammond born in 1963. Ed joined the first class of the EE PhD program when it was established in 1964. He had two research mentors. The first was Tom Wilson, who was working with NASA on power converters for spacecraft. His dissertation advisor was Harry Owens and the topic was development of a correlation data processing for the IMP series of satellites. From 1964 through 1967, Ed experienced rapid changes in technology—as vacuum tubes were replaced by transistors, then large component integration, and then large-scale integrated circuits. He developed his interest in computers, programming first in machine language on the IBM 620. After completing his PhD, Ed spent two years in a special postdoctoral program that let him take selected preclinical courses in the School of Medicine before joining the initial faculty of the new program in biomedical engineering in 1968. That summer, he used a Link 8 computer to develop the first real-time visualization of the cardiac activation sequence mapped on the body surface—a problem that had proven to be unsolvable on mainframe computers. He joined the newly formed Department of Community Health Sciences, based 65% in the School of Medicine while retaining 35% in the School of Engineering. Eugene Stead, MD, Emeritus Chairman of Medicine, Duke University: “Ed was always accommodating. He always wanted to do everything. So he agreed to do a great many things. In due time people appreciated the fact that if they got Ed interested, and he was easy to get interested, and it could be done in that unit, it would get done.” Harvey Estes, MD, Emeritus Chairman of Community and Family Medicine, Duke University: “Ed made the system work. He rolled up his sleeves and got to work and he learned how to work with [patients] relatively uninitiated with the use of computers. And he began to work with opinionated doctors, who were sure it wouldn't work anyway, and he made it work and he developed an entire medical information system which we used for many, many years after that.” Ed Hammond's interest in electronic health records began in 1969. He built a hardware interface between an optical scanner and a PDP-12 and wrote assembly language programs to print an initial medical history captured from the patient on mark sense forms. In the summer of 1970, he assembled a team of five medical and undergraduate students. Bill Stead, MD, Associate Vice Chancellor for Health Affairs, Vanderbilt University: “We each built a part of a mini-computer system to take a history directly from a patient. We worked with production code and real patients. We made changes and immediately saw the effect on usability. We were given advice and the freedom to try, to fail, and to try again.” Dr. Hammond inserting a disk into pdp-11. By 1972, history and physical examination “takers” had been combined into a prenatal electronic health record that was operational at Duke until 2002. By 1973, practice management such as appointment scheduling and billing permitted an early outpatient clinical system with a computer-based record as its core. From this foundation, the team went on to build GEMISCH—a command line language running on top of RSX and VMS Operating Systems. Multiple site-specific applications were replaced with generic application programs. A dictionary of metadata provided for site-specific variation and entity–attribute–value data structures handled sparseness. The result—TMR—was in use at its peak in over 40 sites in 20 different settings, ranging from a two-person practice to a 60-bed cancer research hospital. Clem McDonald, MD, Regenstrief Professor of Medical Informatics, Indiana University School of Medicine: “Ed Hammond has been a good friend since I met him in 1973 in his own development shop at Duke. At that time he was working on the development of an electronic medical record system, and had it installed in the Community Health Department at Duke University. Ed is one of the real pioneers in medical informatics and a most knowledgeable developer. He built every important part of an electronic medical record and had it all working well before 1980. And he still has things built that are not now running widely in commercial systems.” This variety led to Dr. Hammond's interest in standards. Beginning in 1983, Ed worked with Clem McDonald and others to create messaging standards for exchange of data among systems. The standard for the transmission of laboratory data was the first one adopted by the American Society for Testing and Materials (ASTM). Ed was a member of the group that formed Health Level 7 (HL7) in 1987, and he served as chair of that organization in 1990–1991 and again in 1996–1997. He played a major role in the integration of SGML/XML into HL7 and was instrumental in the creation of a number of Technical Committees and Special Interest Groups, including the Vocabulary, the Electronic Health Record, and most recently, the Patient Safety SIG. Stanley Huff, MD, Professor of Medical Informatics Intermountain Health Care and University of Utah: “Ed believes in people saying what they need. If people will say what they need—what their needs and wants are—then it's possible to find mutual benefit and relationship and to found relationships on a solid foundation that will last. One of the sayings that I've picked up from Ed is that ‘you can accomplish a lot if you don't worry about who gets the credit.’” With the formation of the International Standards Organization (ISO) Technical Committee TC 215 Health Informatics, Ed was elected as the convener of the Working Group on Messaging and Communications. This working group created the first standards coming out of the ISO effort. Ed clearly has established a trusting relationship among the international community. He also spearheaded the ISO/HL7 pilot process in which HL7 standards could move directly for approval as ISO standards. Joachim Dudeck, MD, Chairman of Medical Informatics, University Hospital in Giessen: “Standardization in health care is not imaginable without the initiatives and the work of Ed Hammond. By his many and always excellent, knowledgeable, and impressive presentations he motivated people in the U.S. and in the international arena to join these efforts. Without any doubts he is one of the fathers of the constitution of standardization in health care.” Dr. Hammond (third from left)during a naval exercise. Ed has advocated for what we now call the National Health Information Infrastructure (NHII) since the early 1990s. As Chair of the Computer-based Medical Record Institute, he introduced a proposal for the acceleration of the adoption of the computer-based patient record, identifying barriers and making specific recommendations on how to overcome these barriers by specific actions. More recently, he was chosen to be the chair of the Data Standards Working Group for the Connecting for Health Initiative. This group made recommendations for the identification and acceleration of necessary health data standards. He is a member of the Institute of Medicine (IOM) Patient Data Standards Committee. He is the bridge between that committee and HL7 regarding electronic health record functionality standards. Carol Diamond, MD, MPH, Managing Director, Markle Foundation: “Ed's passion, energy, and drive for the area of standards and informatics in health care are unparalleled. We greatly benefited from his leadership and guidance and came to call him our guru in “Connecting for Health” because he seemed to always be able to figure things out, and that always made us feel a little bit better.” Ed has worked to build each of the medical informatics organizations that existed during his career. He served two terms as chair of the Special Interest Group on Biomedical Computing (SIGBIO) of the Association for Computing Machinery (ACM). He served on the American Association of Medical Systems and Informatics (AAMSI) board as well as program chair for two meetings. He was part of the forming group of the American Medical Informatics Association (AMIA) and has served on the board continuously—as President, Treasurer, and Chair of the Membership Committee for six years. Along the way he served ACMI in many roles, including President. He is a founding fellow of ACMI and a founding member of the American Institute for Medical and Biological Engineering (AIMBE). HL7's Volunteer of the Year Award is named after Ed. In 2003, Ed received the Paul Elwood Award for lifetime achievements from the Foundation for Accountability. Mark McDougall, Executive Director, Health Level 7: “Ed is informed, well published, and highly respected. He is a teacher, an encourager, a mentor freely giving his time and talents to progress the work of our organization and those who participate in it. His intelligence, humor, and never-ending enthusiasm have inspired others to invest their time and talents in HL7. Years ago Ed shared with me some valuable insight. He told me that he believed that HL7's most valuable asset was not the standard that we produce but instead it is the collection of brilliant and dedicated people that HL7 was able to track and retain.” Dr. Hammond had an unusually active 33-year career in the U.S. Naval Reserve, retiring in 1989 as a Captain. During that period, he served as the commanding officer of several units, including Emergency Response Team 6, which had no active-duty equivalent. He created an underwater navigation system for unmanned deep submersibles. This system was used for the recovery of several aircraft that crashed at sea and for mapping the debris field for the Challenger spacecraft. Captain Hammond participated in several of these exercises directly. Ed Hammond is truly a pioneer in the tradition of Morrie Collen; he does what others think cannot be done, he works with others, and is trusted as a partner. As a result, he is sought out internationally as the expert on what is achievable related to health records and standards. It is with pride, admiration, and affection that the College recognizes his achievements with the Collen Award. Donald Lindberg, MD, Director, National Library of Medicine: “I don't imagine there was a time in the history of medical informatics when Ed Hammond wasn't present and smiling and encouraging the rest of us to do what was important to the field. I don't know when I met him, but I guess at least by 1971 when he was making TMR, a patient-based patient record system at Duke, using a minicomputer. That system was probably getting more done with a minicomputer than most of us were getting with what we then thought of as mainframe computers. But he stuck with it. Probably the contribution that everyone knows about from Ed is his insistence that we develop standards and that we perfect them, even if that takes decades and it turns out, it has. For that, we owe him a really great debt and I'm happy to see that debt partly paid by this well-deserved award today.”

Stephen Altschul is a Senior Investigator at the National Center for Biotechnology Information, which is part of the National Library of Medicine at the National Institutes of Health. He received his AB summa cum laude in mathematics from Harvard College and a PhD in mathematics from the Massachusetts Institute of Technology. Dr. Altschul held an IRTA postdoctoral fellowship at the Mathematics Research Branch of the National Institute of Diabetes and Digestive and Kidney Diseases before moving to the NCBI, where he has been for the past 12 years. His research has focused on developing measures, algorithms, and statistics for the comparison and analysis of DNA and protein sequences. He played a central role in developing the blast and psi-blast sequence database search programs, and his articles describing these programs have become, respectively, the most cited scientific papers published since 1990 and 1995. Dr. Altschul has served on grants committees for the National Human Genome Research Institute of the NIH and for the Medical Research Council of Canada. He has been a member of the editorial boards of Protein Sequences & Data Analysis, Gene-combis, and Genome Biology and is invited to be a keynote speaker at the Tenth Annual Conference on Intelligent Systems for Molecular Biology. Dennis Benson is Chief of the Information Resources Branch at the National Center for Biotechnology, National Library of Medicine. Dr. Benson received his undergraduate and graduate degrees in the neuroscience program at the University of Florida. Prior to his current position, Dr. Benson was a postdoctoral fellow in the Department of Biomedical Engineering, Johns Hopkins School of Medicine, where his research focused on the neurophysiology of the auditory cortex. He came to the Lister Hill Center for Biomedical Communications at the NLM in 1980 and worked on knowledge-based retrieval systems in the area of hepatitis and toxicology. He developed a test bed for evaluating statistical-based text retrieval algorithms, which evolved into an operational text retrieval system known as IRX. Early applications of IRX included the McKusick Mendelian Inheritance in Man database and a seminal integrated genetics data resource known as GenInfo. With the creation of the NCBI in 1988, he has had responsibility for designing and managing the computing and network infrastructure for research and public access to the information resources NCBI produces—in particular, the Gen Bank, Entrez, and PubMed databases. Mark Boguski is Senior Vice President for Research and Development at Rosetta Inpharmatics, Inc. He received his BA in natural sciences from Johns Hopkins University and his MD and PhD in molecular biology from the University of Washington. He was a resident in anatomic pathology at Barnes, Children's and Jewish Hospitals and a medical staff fellow at the Mathematical Research Branch of the National Institute of Diabetes and Digestive and Kidney Diseases at the National Institutes of Health. Dr. Boguski spent 11 years at the National Center for Biotechnology Information at the National Library of Medicine, rising from Senior Staff Fellow to Senior Investigator. Early in his career, he studied the organization and biological importance of repeated sequences in nucleic acids and proteins. He developed analytic methods to trace the molecular evolution of proteins from related species. At NCBI, he was instrumental in the design and implementation of a database system for representing expressed sequence tags. More recently, he led the development of several prototype database systems for storing and analyzing microarray-derived gene expression data. 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The September 2000 issue of JAMIA presented three papers on the intersection of bioinformatics and biomedical informatics.1–3 That set of papers was written by individuals who are grounded in biomedical informatics and working in areas of bioinformatics. Their papers explain aspects of bioinformatics research in the language of biomedical informatics. This issue of JAMIA contains the first of two sets of papers4 coming out of the Human Brain Project . With one exception, these papers are written by people who are grounded in neuroinformatics (the intersection of neuroscience and bioinformatics). They are working on problems that are analogous to the ones being tackled by researchers in biomedical informatics. Nonetheless, they come at these problems from a different perspective. Although they use many of the same words to describe what they do, the careful reader with a background in biomedical informatics will find that the words mean something different to them. For example, Gardner et al.5 use metadata as “the neurobiological descriptors characterizing neurophysiology datasets.” In other words, their metadata describe the experimental context in which the data were acquired. This difference in use is explicitly stated in the text, but it may be missed by someone who has a different expectation about the meaning. The review and revision process was lively. As Editor-in-Chief, I suggested that the work be re-framed to communicate clearly to the journal's audience. The authors disagreed, pointing out that these were the primary archival publications about this work and that they had to communicate clearly to people working directly in their area. On reflection, I came around to their view. We are fortunate to have these papers in the mainstream of biomedical informatics. Otherwise, most of our readers would not get an in-depth view of this important area of research. But, read the papers carefully. If you see a statement that you think is naive, you have probably come across a place where the author uses words differently than you do. After you finish the papers, think about how you might apply the techniques to problems in your area of work. I think you will find the extra effort worthwhile. If you take the time to bridge the communication gap, you always learn more from people tackling problems from a new perspective.

... David Bates is Chief, Division of General Medicine at Brigham and Women's Hospital, and Medical Director, Clinical and Quality Analysis at Partners HealthCare System, Inc. He is also an Associate Professor in Medicine at Harvard Medical School and has a joint appointment at Harvard School of Public Health in the Department of Health Policy and Management. Dr. Bates received his BS degree in Chemistry from Stanford University, his MD from Johns Hopkins School of Medicine, and his MSc from Harvard School of Public Health (Department of Health Policy and Management). He completed residency training in internal medicine at the Oregon Health Sciences University and a postdoctoral research fellowship in medicine at the Harvard Medical School. Dr. Bates' primary informatics interest has been the use of computerized decision support to improve safety and thereby reduce the costs of care. A particular focus has been on improving the systems by which drugs are given, to reduce the frequency of medication errors and adverse drug events. One study he led demonstrated that implementation of computerized physician order entry reduced the rate of serious medication errors by 55 percent. Other research interests include affecting physicians' decision making, particularly using computerized interventions; quality of care and cost-effectiveness in medical practice; and outcomes assessment.

The best medical informatics research is grounded in an understanding of both the medical content area and the informatics techniques. For example, it is not attention to the medical literature alone that makes medical informatics different from information science. The difference comes from the integration of medical knowledge and the strategies or structures needed to formalize or represent it. The paper by Piniewski-Bond et al.1 provides an example of the need for collaboration of informatics experts with application domain experts. It compares two methods for identifying the set of ideas contained in a test set of papers from the published literature. The researchers use the set of ideas contained in three published review papers as a gold standard, and they use the papers that are referenced in those three reviews as the test set. The reader will recognize two potential sources of error in this analysis. First, Piniewski-Bond et al. may not have correctly identified all the ideas contained in the three review papers. Second, the authors of those review papers may have included ideas based on their prior experience that did not come directly from any of the papers that they reference. Such limits are not unusual, and they do not constitute a reason to reject a manuscript for publication in a medical informatics journal as long as they are correctly stated in the manuscript. The review of the manuscript pointed out a limitation that may not be as obvious. The subject of the three published review papers is the specific association between human papillomavirus and cervical cancer. The expert in that clinical area among the reviewers of this paper stated clearly that the three published reviews were not the best available on this clinical topic. Accordingly, neither the ideas contained in them nor the ideas identified by examining their reference constitute a valid review of this clinical problem. In other words, the paper provides an apt demonstration of an information science technique; however, when viewed from the perspective of medicine, the clinical content is sub-par. We accepted the paper for publication because the author states clearly that she is not trying to provide a review of the clinical topic, the reviewers indicate that the paper adds to the base of knowledge in informatics; and we judge the risk of a clinical reader mistaking this paper for a clinical review as remote. Nonetheless, this limitation might have been easily avoided if a clinical subject expert had participated at the start of the project to identify a clinically valid test set. If the test set had been clinically valid, the quantitative data could be placed in clinical context. A reader could judge whether differences in results were significant clinically. One reviewer explained the problem with a fishing example. “The crappie is a round, slow fish not known for much of anything, pretty easy to catch. The musky, on the other hand, is a sleek and wily fish that hides in weeds, very difficult to catch. Would you have confidence in your new fishing rod if it had been tested on crappie and you wanted to catch musky?”

The papers by Stein et al.1 and Aronsky and Haug2 address the quality of the data found in clinical record systems. Stein et al. approach the problem as one of internal consistency. Their paper explores concordance within record systems, exploring the extent to which evidence found in one part of a clinical database is consistent with evidence found in another part. Specifically, they examine agreement between entries in a free-text narrative field with data found in coded fields. Aronsky and Haug examine concordance across two different clinical record systems—the HELP computerized clinical record system and a reference standard consisting of the sum of all information available in the paper chart and the computerized clinical record. Aronsky and Haug complement their appraisal of concordance with an outcome evaluation, determining the level of agreement in clinical severity indexes resulting from the information contained in the different record systems. Stein et al. use the measure of internal concordance to alert users of a computerized record system to the fact that they may get misleading answers unless they query each field that might contain a piece of information and resolve any discrepancies. Aronsky and Haug argue the need for equivalence in recommendations based on the clinical record, regardless of which form of a clinical record is used; they are saying, in effect, that records with higher concordance should lead to similar recommendations. The use of the term “concordance” is appropriate for framing the question posed in each of these papers. Concordance originated in the 14th century church, referring to a companion text to an original document, the companion text consisting of an enumeration of all terms appearing in the original document. These authors extended the target object from “terms” to “concepts” and restricted the enumeration of concepts to only those relevant to specific clinical phenomena. The authors' uses of the term “concordance” implies that the question of interest is the extent to which all clinically significant concepts found in one section (Stein et al.) or form (Aronsky and Haug) are evident in another section or form. The evidence presented in the two papers clearly indicates that the two sections or forms are not in complete accord, and the discussions evaluate the consequences of the discord. Clinical records, be they paper or electronic, are no more and no less than representations of the true state of the patient and the events occurring during the process of care for the patient. Concordance is a characteristic of representations: It is possible to estimate the degree of similarity within or between representations, such as patient records. While one could dispute the merits of different computational forms employed to characterize concordance, the basic intent of such a statistic is valid—to gauge the level of agreement between two documents. At the same time, it is critical to recognize that concordance offers no indication as to whether the representations themselves are true and accurate depictions of the real state of the patient. Clinical data are a scarce and expensive resource. These studies advance our understanding of the degree to which we can re-use data recorded in today's clinical information systems for practice management, decision support, and clinical or health services research. This type of work should be extended in two directions—prospective studies of data accuracy in extant clinical records and methodological studies of strategies to produce more robust language structures for representing clinical phenomena. Hogan and Wagner3 provide a model for examining data accuracy by assessing correctness and completeness. The approach they use enhances the concordance studies not only by examining data in the clinical record but by prospectively constructing a gold standard so that the patient and care provider can be used as information sources. In essence, this approach goes further than concordance to ensure that the record is a correct representation of the state of the patient. At best, however, data concordance and accuracy studies are only as good as the underlying representations. These studies have an inherent limitation, because they rely on the vocabulary primitive of term or phrase. Almost all studies in this realm employ some type of parsing strategy to select specific words or phrases on which to evaluate agreement. These words and phrases themselves may be overly limiting the ability of clinical records to represent the true state of the patient, because they force the reduction of expressions of complex clinical phenomena into atomic words and phrases. Studies based on the vocabulary primitive of “terms” rather than on more sophisticated representations of patient phenomena remain restricted by the fundamental nature of records. It is possible to envision language structures in the clinical record that are more robust than simple words and phrases. Emerging work in concept maps and compositional vocabularies promises to provide tools for characterizing patient phenomena in a manner that can be dynamically manipulated and provide a more meaningful image of the true state of the patient and the actual care process. Based on thinking originating more than 70 years ago in the work of Ogden and Richards, 4 it is logical to expect that any representation system should be appraised for its ability to provide truthful depictions of the real-life state of the patient and for equivalence across representational forms. The current state of research in formal language focuses on the development of computable language structures for creating textual representations of clinical phenomena. That work, in essence, strives to ensure that the words and phrases used to depict clinical observations in the patient record remain as true-to-life as possible, by capturing not only syntactic meaning but also semantic interpretability. Records based on these representations should be assessed for validity and manipulability. A valid representation would provide an honest, true-to-life depiction of the patient. A manipulable representation would support knowledge-based interpretation of clinical observations and automatic application of decision support tools. Therefore, there is need, and room, in the field of medical informatics for multiple research trajectories that converge on the problem of ensuring the validity of the clinical record as a representation of the true state of the patient. Papers such as those presented by Stein et al. and Aronsky and Haug are necessary but not sufficient endeavors in the quest for the Holy Grail of medical informatics—the computer based patient record.

The vision of integrating information-from a variety of sources, into the way people work, to improve decisions and process-is one of the cornerstones of biomedical informatics. Thoughts on how this vision might be realized have evolved as improvements in information and communication technologies, together with discoveries in biomedical informatics, and have changed the art of the possible. This review identified three distinct generations of "integration" projects. First-generation projects create a database and use it for multiple purposes. Second-generation projects integrate by bringing information from various sources together through enterprise information architecture. Third-generation projects inter-relate disparate but accessible information sources to provide the appearance of integration. The review suggests that the ideas developed in the earlier generations have not been supplanted by ideas from subsequent generations. Instead, the ideas represent a continuum of progress along the three dimensions of workflow, structure, and extraction.

This is the edited transcript of a discussion, among the authors and audience, that followed the presentation that led to the paper “Integration and Beyond: Linking Information from Disparate Sources and into Workflow,” which appears on p. 135. Mark Musen: Bill, when you presented the three generations of integration, the implication was that the third generation is at hand. It was all present tense. I think all of us agree that architectures that allow us to encapsulate knowledge and data in ways that permit reuse are quite exciting. But are we really in the present tense? Have we really achieved these kinds of architectures and, in particular, when you go to the vendor demonstrations, what do you see of this? Bill Stead: That is a very interesting question, because I think the third generation is more in hand than the second. I think “generation” may be the wrong word, because it suggests that the third supplants the second. Instead, techniques from each of the generations coexist in equilibrium. For example, the UMLS provides us with mapping between codes for the kind of things you order for a patient (diagnosis, tests, medications) and the literature. At Vanderbilt we use this mapping to let you ask, “What are the references relevant to the things that have been ordered?” So, to that degree, third generation exists. It is the second generation that is really hard, because it requires regularization. There's a difference between the Vanderbilt vegetable and the Columbia MED, in that the Columbia MED relates the source vocabularies of the various feed systems (third generation), whereas the Vanderbilt vegetable tries to build an enterprise-wide source vocabulary that is then reflected back into the source systems, prealigning their vocabularies. Back to your question, 3M is an example of a vendor that has pursued an architectural strategy. Member of the audience: I think one of the toughest things we all have to deal with is updating our dictionaries. In the simplest cases, the name of an organism is changed and we just have to do the maintenance. It is tougher, when, as with Citrobacter, they do genetic studies and say, “Oh, it's really six different organisms, not one.” We have the human genome project coming very quickly. Even that is just the tip of the iceberg. We're not only going to see all the genes; we're then going to see clinical tests based on gene expression. Essentially, you'll be able to look at something on the order of 180,000 gene products and whether they're up or down regulated. How are we going to integrate such an incredible amount of data at a time when we're going to also be changing how we think about these processes? Classification and simple mapping are not going to work, because the lumpers and splitters are going to be arguing furiously on a daily basis. Randy Miller: The problems you mentioned are clearly on the horizon and very important. But at a simple level, people are people and all of what you're talking about doesn't change how people will present to their primary care providers. At least that part of what exists will not get torn apart. I think what you're talking about is very rich, very vast information overlays on top of what we already have. We don't have to throw out what we have, we need to be ready to extend the linkages. How that will be done is an unanswered question that will result in multiple research grants. Bill Hersh: I think you allude to one of the key points, which is structuring the metadata with the right levels of granularity. Clearly, when we find an organism that can't fit in the existing framework, then that's problem. But if we find that an organism just represents a subcategory of others, and if there's a good hierarchic structure, it can be fit in. The same goes, for example, for diabetes. People classify diabetes with this complication and that complication, but often we just want to know whether the patient has diabetes. Again, a good hierarchic metadata structure can overcome some of those problems. I think we also need to recognize some of the practical limitations that face us. There are limits to the accuracy of the information that's in medical records; there are limits to the consistency in which people apply vocabulary terms. Computers can be completely precise in terms of mapping from this to that, but people will continue to have different conceptions of what a “grade II systolic murmur” is. Bill Stead: I agree with both answers, but I want to continue to clarify what we are talking about. We get in trouble because people use words to reduce concepts to something that we can manage in our heads. So we lump, and person A lumps differently from person B. So we are each a “legacy system,” and our information resources have grown from this starting point. I think we need to work at two ends of the spectrum. Whenever possible, capture data according to granular definitions. If we have an organism and we discover that it splits into six organisms, that's actually a very easy problem to solve, as you said. What you've got to do is say, “A is now B, C, and D and it mapped here.” That is straightforward. That's the end of the spectrum where we can stay granular. For example, never store a doctor and the doctor's service as one piece of information. At the other end of the spectrum, where the granular definitions are not obvious, do not try to classify the data. Instead, tag a “clump” of information with metadata. This tagging, together with increasingly sophisticated extraction techniques, will be used to approximate meaning. Over time, we will get to a complete set of coded data by working from the two ends. Mark Musen: I'm not sure that everything will ever be completely coded. Given the fact that the world is continuously changing, I don't think we can assume that Aristotle was correct that eventually there will be a classification that we will all accept. For example, I do not know whether gastric ulcer is an infectious disease or a gastrointestinal disease, and maybe it is both. As we continue to learn more about medicine and as our organizations change out from under us, I think we're going to be in the situation where the way we categorize the world is going to change. This is very hard stuff. Instead of working on the ultimate classification that will have all of the problems of the International Classification of Diseases, we need to build structures that not only allow us to enumerate the kinds of data that our programs operate on, but attempt as best as we can to enumerate the assumptions that we're making about our data and about the world. Then, as things change, we can, as human beings, try to update our ontologies. I think we have to be able to deal with changing worlds and with the fact that people and computers each need different views on the data, and that means different assumptions as well. Bill Hersh: To reiterate Mark's point, some people have heard this quote, that “perfect is the enemy of good.” We, especially us academic types, strive for perfection, but in reality the world is not perfect, and I don't know that everything will be perfectly coded. But we can reach compromises, such that we can code bits of information that enable us to do useful things. Bill Stead: I think human beings are each different, but we have an underlying genetic code that we are in the process of discovering. Next, we are going to have to work out the problem of going from genotype to phenotype. When I say that I think in the end things will be coded, I think we're going to discover something that is to information what DNA is to people. It will be a very granular base set of building blocks, which will be rolled up into concepts much as genes produce proteins. So I do not want to go to one ontology or one classification. Still, I like having ontologies, particularly ones that clearly represent the difference between themselves and the others. Member of the audience: I'd like to ask a question about capturing ontologies from multiple people. Imagine for a moment that knowledge freezes long enough for us to try to catch it. Do you have a vision of a tool that will allow multiple knowledge-domain people to act at once? To work out discrepancies in their visions? Mark Musen: Put differently, the question was how do we deal with the fact that there is no overarching ontology? How do we build the tools that will allow us to try to achieve consensus in ontologies? I think the answer to that question is that we do not know. I'm being a little bit facetious, but philosophers have been trying to deal with that problem for 2,000 to 3,000 years. I think you see two different approaches in the computer science community. You see the approach that Doug Lenat has taken. He is trying to create an ontology that he believes will provide all the knowledge that one needs to read the Encyclopaedia Britannica. Such an overarching ontology would need to capture most of human existence. The real problem, though, is how you ever validate the distinctions made in that ontology and have confidence that things have been captured in a way that is consistent and understandable? How do you record all the assumptions that you make while constructing the ontology? When you have concepts like “semi-tangible object” and “semi-intangible object,” it's very hard to know for sure whether what one records about those distinctions really makes sense. At the other end of the spectrum, you see people who really want a thousand flowers to bloom and who are not trying to achieve that kind of perfect alignment among views of the world. For example, the Knowledge Systems Laboratory at Stanford is trying to make constrained ontologies that deal with very narrow domains, so that the kinds of problems that you allude to do not happen, because the number of concepts in the ontology is relatively small. The answer lies somewhere between Doug Lenat's view of the world, that all we have to do is work hard enough and everything will fall into place, and the view that we can't possibly do this, so we have to have just a small number of constrained ontologies. We need to elucidate a set of principles that will provide the basis for tools that will help us try to, if not merge small ontologies, at least create the kinds of alignments that will allow us to bring them together in ways that make them useful. Randy Miller: One of the things that I learned from my mentor, Jack Myers, is that as an informatician, as opposed to a philosopher or a computer scientist, you do not need to represent everything. If you have a problem at hand, you represent it at a level that is tractable and doable. If you do what Doug Lenat's doing, you can spend your entire career representing stuff that is not ever going to be used in a real system, because there is no way to apply it. While that may sound harsh, the reality is that we do not know how to represent time, severity of finding, and severity of illness well at all, but we can still build systems that do diagnosis or a good job of making recommendations for therapy. So you do not have to capture the world in all its infinite detail. The trick is to understand what the critical information is and represent things at that level. Otherwise, you get mired in detail. Mark Musen: Let me underscore your last point. Doug Lenat actually felt pretty confident that his ontology covered all the areas that one would want to deal with, until last year, when HotBot contracted to use CYC as the basis for indexing Web pages. This contract showed, first of all, that ontologies have incredible commercial potential, but it also pointed out to Doug Lenat that there was a whole realm of human experience that was not well represented in the ontology. Specifically, there was a need to categorize different kinds of pornography, which Lenat had not thought about previously. Member of the audience: Health Level Seven's development of a set of reference information models is one of the major efforts for creating a structure for ontologies in the United States. Can you talk about how your organizations are participating in the development of that reference information model (RIM) and how you are using your academic experiences to contribute to that effort among providers, academics, and vendors? Bill Stead: Vanderbilt is an institutional member and a strong advocate of HL7. The central core of our communication subsystem uses HL7, and we build middle ware as needed to bridge between the core and legacy products. We have not put direct energy into the process for defining the reference information model. We use the HL7 model as a starting point, but we extend it as needed. In this way we incorporate it into immediate solutions to real problems, while providing useful information about future directions. Bill Hersh: None of us has been involved directly in that effort. However, our research into the nature of ontologies and the vocabulary projects such as the Cannon Grouping should useful to the effort. Mark Musen: I will just add that I think the vendor community is in the best position to work on ontology content, because they have the most direct connection with the needs of end users. I think that academicians need to follow this work very carefully. We are, we hope, in the best position to be developing the kinds of tools that will help us examine ontologies, relate them to each other, and allow them to evolve as our understanding of the world changes. Randy Miller: I have a slightly contrary view, partly out of ignorance about HL7 RIM. The key question is what problems it is trying to solve. That should drive what the content is. If you can state the problems it is going to be used to solve, then you can say whether it should clinically rich. In that case it will require lots of input from academic clinicians. If it is to solve the problem of interchange of data among vendors, then it needs vendor input. But until you explicitly state what it's going to be used for, just building it for the sake of building it is not useful. I know that the HL7 RIM is not being built that way. I am just saying that I think that's the way to address your question, to seek the specific purpose before giving an answer.

Health care is an information-intensive business. We have seen repeated calls for use of information systems to improve the health system. The decade began with the Institute of Medicine study1 championing the computer-based patient record. The Health Insurance Portability and Accountability Act of 19962 included requirements to support information exchange and monitoring of outcome data with goals of administrative efficiency and process improvement. Participants in the 1998 Symposium of the American College of Medical Informatics (ACMI) developed three audacious goals for health informatics in the next millennium: a virtual health care databank, a national health care knowledge base, and a personal clinical health record3. These calls to action are correct in identifying a need and the potential for benefit. However, they are simplistic in assuming that the focus provided by a legislative mandate or a Manhattan-style project will be adequate to achieve the desired impact on the health system. Experience suggests the opposite. Although progress is likely, it will fall far short of expectation. Simply put, the chief information officers (CIOs) and their vendors and consultants—the people charged with managing the information services of the health provider and payer communities—are not able to put in place systems that will meet the need in a timely fashion. The reasons for this inability to deliver are many. For example, a CIO may be asked to implement a system to automate a task that people in their enterprise are having trouble handling. When the nature of the enterprise or task changes, they are forced to start over. Even when they succeed in an implementation, the return on investment may be marginal unless the task involves an optimal mixture of people, process, and technology. An entire industry of health information technology vendors and consultants has grown up to provide information systems and support. Many developed as an extension of a system success in one enterprise or niche. Despite these origins, most try to market a capability for providing an integrated solution. Unfortunately, the integration may be only at the level of the vendor name, with each product having its own user interface and data structure, often being developed by different companies that were since acquired. The marketing strategy locks in market share and leverages the installed base through add-on sales. Unfortunately, the installed base also serves as an anchor, slowing transition to the current information technology that might provide solutions to data or work process integration challenges. Start-up vendors offer new technology but face barriers in terms of integration with products of established vendors. They also find a client base that does not understand either the new technology or the integration of people, process, and technology that is required to increase effectiveness. Health informatics, the science that deals with health information, its structure, acquisition, and use, holds several keys to better outcomes for both CIOs and the health information technology industry. The first key is information structures and communication methods that allow information to be linked into work process as needed, but managed as an asset outside the information systems that automate those processes. The second key is data mining techniques and filters that can locate information but limit reports to the immediate context. The third key is presentation metaphors that enhance biomedical users' cognition and exploration and adapt to individual learning styles. Finally, the key to the future is education and training programs that can produce people who know how to develop effective information-enabled work processes. Despite holding these keys, informatics groups based in academic medical centers have had minimal impact on the health system and the health information technology industry. The pioneering groups of the 1960s and 1970s developed in parallel with the information technology industry. They had to do everything from assembling processors to writing operating systems and languages, in addition to working on health-specific challenges. Groups that have not evolved away from this heritage have many of the problems of established vendors and have their time divided across too many responsibilities. Other groups have the problems of start-ups, an inability to get their ideas into operation in a real setting. The time has come for CIOs, the health information technology industry, and health informatics to come together to enable people to use information to improve health. Better cross-talk among the parties could establish a bridge, but such a bridge is not likely to be sufficient to harness the collective strength of potential partners to accomplish audacious goals. The roots are too strong and the differences in cultures and priorities of the moment are too great. We may need a new business model to achieve effective alliance. One where we identify the core competencies of each potential partner. One where we develop scenarios showing that each partner can win by focusing on putting their piece of the puzzle in place. One where the magnitude of the win is increased dramatically by the pieces provided by the other partners. In short, we need to change the game so that leverage among partners replaces competition. During the two years that I am president of ACMI, I will use my office to direct the attention of the College to this challenge. We will start with the 1999 ACMI symposium, where we will sharpen our own understanding of how we might better focus our energies. For example, what information problems are unique to health? Where can we reuse tools from the non-health information technology industry or ideas from the computer and information science disciplines to solve health problems? Where can health informatics make the most difference to the health system? Can we find benchmarks from other industries that demonstrate the value of solutions based on informatics? After this self-examination, we will need to reach out. How might we bootstrap the level of understanding of informatics by CIOs and the health information technology industry? How can we, in turn, get a better understanding of business models and health system management? How might we give industry access to academic laboratories and provide new revenue streams to the academic units? How might we give trainees access to real-world problems? Exploration of these questions can pay back in the near term, stimulating us to think outside the box and guiding our individual research agendas and applied informatics strategies. Over the long haul, the exploration can provide a basis for partnership among the factions that need to come together to translate informatics into better health. Success in this translation is a first step to establish credibility for audacious goals.

In his viewpoint paper,1 Dr. Hendee asserts that research in biomedical engineering, imaging, and informatics is “relatively” underfunded. Progress in key well-funded research areas—such as genetics, structural biology, and neuroscience—critically depends on progress in biomedical engineering, imaging, and informatics. Hendee concludes that the NIH should create a new institute or center to nurture these three domains. Such an institute would support fundamental research, coordinate activities throughout the federal government, educate and train investigators, and diffuse tools and techniques into research efforts of other institutes. I agree that the future success of the biomedical research enterprise depends on optimized interaction among the biomedical sciences, engineering, biomedical informatics, and the foundation provided by mathematics, computer science, and information science. Dr. Hendee is correct in drawing attention to the importance of work both at the intersection of these disciplines and in each discipline individually. The case for a new institute or center that combines support for biomedical engineering, imaging, and informatics is not as well established. Although there is much synergism among these domains, the people who work in them have had much less in common (training, research interests) than readers of Dr. Hendee's viewpoint might expect. For example, while biomedical engineers often take the position that biomedical informatics is a part of their discipline, and it is, the discipline of biomedical informatics also involves significant input and understanding from disciplines outside biomedicine and engineering, such as information sciences, cognitive sciences, and decision sciences. Because the cultures and histories of the various disciplines contrast with, as well as complement, one another, the goal of forming a new overarching institute or center will not be widely or uniformly supported by individuals who agree with Dr. Hendee's basic objectives. More progress might be made by identifying a key set of messages that promote support for important, directed work in the participating domains and emphasize coordination among them. For example, researchers in genetics think of bioinformatics as a core service that supports their work. They may not realize that advances in informatics knowledge are often required before techniques or tools can meet their objectives in areas such as population genetics. As previously demonstrated in developing informatics support for clinical practice, advances are most likely to come when someone working on a genetics research problem is working side by side with someone working on related informatics research problems. These workers are at their best in the disciplines that they bring to the intersection, when they are also part of the larger genetics or informatics intellectual community. Instead of creating a new monolithic organization, I would recommend a strategy similar to that followed by the High Performance Computing and Communication (HPCC) initiative over the last decade. Basic research goals should be cast in the form of grand challenges that can be met through targeted achievements in research and technology. This approach can effectively garner economic support, foster cross-institutional collaboration, and specify what must be accomplished. A coordinating office can bring people from the diverse institutions and agencies together to figure out how to get the work done. Through such a process, we might discover that we need some combination of new institutes or an augmentation of existing structures. It is my view that a change in organizational structure should not be the starting point, per se, for accomplishing these goals. Rather, organizational structure should naturally follow from lessons learned about how to work together to meet objectives.

Informatics and information technology do not appear to be valued by the health industry to the degree that they are in other industries. The agenda for health informatics should be presented so that value to the health system is linked directly to required investment. The agenda should acknowledge the foundation provided by the current health system and the role of financial issues, system impediments, policy, and knowledge in effecting change. The desired outcomes should be compelling, such as improved public health, improved quality as perceived by consumers, and lower costs. Strategies to achieve these outcomes should derive from the differentia of health, opportunities to leverage other efforts, and lessons from successes inside and outside the health industry. Examples might include using logistics to improve quality, mass customization to adapt to individual values, and system thinking to change the game to one that can be won. The justification for the informatics infrastructure of a virtual health care data bank, a national health care knowledge base, and a personal clinical health record flows naturally from these strategies.

The continuing development of the field of medical informatics has raised new questions and placed before us new dilemmas. Spurred by the proliferation of information systems to support the broad missions of our institutions, and the evolution of these systems from luxuries to necessities, organizational issues have assumed increasing prominence. Among a dazzling array of organizational issues now before us is the tension between the long-standing academic role of informatics groups within medical centers and the ever-expanding service role. In the academic role, we seek the knowledge to create improved technology and to train the next generation of informatics researchers. In the service role, we seek to put existing technology, developed internally or purchased from vendors, to best use across the full scope of medical center activities. The dilemma before us is not whether both roles are important—the answer to that is clear—but rather how to organize ourselves within our institutions to address both of them. How much organizational distance should exist between the people who carry out these different roles, and who should direct their efforts? Most academic medical centers are actively searching for answers to these organizational questions, and many AMIA members are engaged in this pursuit. The answers obtained will be of profound consequence for our field. The salience of this issue directed its selection as the focus of the ACMI Debate at the closing session of the 1997 AMIA Fall Symposium. The purpose of the debate was not to generate a universal answer, for no such answer exists, but rather to illuminate the many factors that must be considered as our institutions search for an appropriate organizational model. To frame the debate, we intentionally polarized the issue around a specific proposition: Resolved: Academic medical centers should have a single unit responsible for information systems supporting the clinical and academic missions and also should be charged to carry out high-quality education and research in medical informatics. The polarity is such that the affirmative team would argue in favor of one group under one leader who would carry out all roles. The negative team would argue for a significant level of separation. We adapted the standard high school and college debate format to fit the available time and to use competition as a device to promote deeper understanding of key issues. There were no judges and no declared winners. Each team had two members: Warner Slack and William Stead for the affirmative, Mark Frisse and Mark Musen for the negative. The format included eight-minute constructive statements, two-minute cross-examinations, and three-minute closing (rebuttal) statements in this order: First affirmative constructive statement, by Slack Cross-examination of Slack, by Musen First negative constructive statement, by Frisse Cross-examination of Frisse, by Slack Second affirmative constructive statement, by Stead Cross-examination of Stead, by Frisse Second negative constructive statement, by Musen Cross-examination of Musen, by Stead Closing (rebuttal) statements by Frisse, Slack, Musen, and Stead, in that order In preparing this summary, we sought to convey the substance and spirit of the debate in a manner suited to printed text. This narrative follows the order of the debate as it occurred on October 29, 1997, at the AMIA Fall Symposium in Nashville, Tennessee. The constructive statements included here were edited from the notes the debaters used to prepare their statements. The cross-examinations and closing statements were edited from the debate transcripts and retain much of the colloquial language used in the event. We include bibliographic references only to direct quotations and citations used by the debaters themselves. The format of this and any debate, and most notably the polarization of a multifaceted issue, often requires participants to take extreme positions. The debaters' views, in reality, overlap more than this report would suggest. Some of the debaters believed that they could, if asked, argue with equal effectiveness in support of their opponents' position. 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This issue of JAMIA contains a paper by Dr. Nordyke and Dr. Kulikowski that describes the interplay between a single specialty practice and a structured medical record/computer database over a period of 35 years.1 The story paints a clear picture of the many ways in which access to longitudinal information about a population of like patients can be used to improve practice and to satisfy intellectual curiosity over the course of the day. The story presents a contrast to much of the work on computer-based patient records. The sole focus is on the organization and utilization of information. The computer is used only for those parts of the process where it does something that cannot be done reasonably any other way (retrieval and analysis of a population of records) or something that can be done without added work (generation of patient reports). Structured data-capture forms and printed reports are used as the provider interface, avoiding use of the computer where it can be expensive and troublesome. Along the way, the practice experimented with a variety of decision support strategies, but found that they got in the way of practice and were less useful than straightforward access to the data about their patients. At first glance, the reader may question whether this story is relevant today. Technology is rapidly removing the barriers to direct provider-computer interaction, and we are learning how to incorporate decision support into the flow of practice. Work on these innovations is important, and it will increase the art of the possible over time. Few of the people involved in this work, however, have been as effective in using data to inform and change their practices as have the authors of this case study. The story has an important message for Medical Informatics today. We can make a big difference if we help people organize and access data to understand and improve practice patterns. We can establish credibility by using whatever processes and technology will work now to achieve that goal. In the process we can buy time to solve the challenges of technology and knowledge/data representation/exchange that must to be solved if we are to achieve what this one specialty practice did—across sites, specialties, and a patient's life time.

Informatics and information technology hold the promise of a consumer-centered health enterprise--one that provides quality care at a cost society is willing to pay; one where need-based, adaptive, competency-based learning results in cost-effectiveness of health education; one where team-based health and learning on demand, coupled with monitoring of process outcomes and network access to expertise, guarantee quality. The barriers to this promise are the professional guilds, the cross-subsidies that support the health enterprise of 1998, and the lack of respect for privacy. Collectively, the informatics community needs to develop a compelling vision that will galvanize the health community to action. If the health community does not step up to this challenge, consumers will take advantage of disintermediation. Empowered by the network, they will go outside the system into hands that meet their needs.

Patel and Kaufman make a clearly reasoned case for characterizing medical informatics as a local science of design.1 This analysis suggests that medical informatics may not be based on a set of discoverable universal truths. It goes on to point out that investigators can still discover general principles by limiting the scope of application. Lessons learned about what works, and what does not, can also be generalized, as can frameworks that organize what is known. People practicing medical informatics should strive to derive lessons that will generalize in this fashion. I agree that the practice of medical informatics has been challenged by the difficulty in transferring systems from one site to another. Similarly, in the science of medical informatics, a subdiscipline sometimes seems to try to dominate …

OBJECTIVE: Refine the understanding of the desirable skills for health sciences librarians as a basis for developing a training program model that reflects the fundamental changes in health care delivery and information technology. DESIGN: A four-step needs assessment process: focus groups developed lists of desirable skills; the research team organized candidate skills into a taxonomy; a survey of a random sample of librarians and library users assessed perception of importance of individual skills; and the research team framed, as a unifying hypothesis, a training model. SURVEY METHODS: The survey was distributed to random samples of 150 librarians, stratified by type of library, and 150 library users, stratified by type of use. A non-randomized sample was obtained by mounting the survey on a World Wide Web server. The survey instrument included 96 distinct skills organized into 13 categories. Respondents rated the importance of each skill on a Likert scale and provided a separate ranking by identifying the ten most important skills for the profession. RESULTS: Among the participants, 51% of librarians and 36% of library users responded to the survey. All categories of skills were rated above the midpoint of priority on the Likert scale. All groups rated personality characteristics and skills as most important, with an understanding of the health sciences, education, and research being rated comparably to technical skills. CONCLUSIONS: Health sciences librarians need a new educational model that provides them with broad-based tools to discover new roles and new resources for acquiring individual skills as the need arises. A unifying training model would involve trainees in developing their learning plan in a way that promotes proactive inquiry and self-directed learning, and it would rotate the trainees through projects to provide skills and an understanding of end-user work processes.

Occasional events observed by the editor during JAMIA's manuscript review process indicate that potential authors should become more familiar with their responsibilities to ensure the integrity of peer-reviewed publications. This responsibility is critical in communicating new findings and developments to the scientific community. Authorship is a responsibility, not an honor or acknowledgment. Huth has outlined five principles for determining authorship.1 The key idea is that each author should be in a position to take public responsibility for the content of the publication. To this end, Huth requires each author to participate in each of three steps: design or performance of the work; drafting or revision of the manuscript; and approval of the final version prior to publication. Contribution short of involvement in those three steps must be acknowledged but does not constitute authorship. Duplicative, or redundant, publication is both wasteful and misleading.2 The instructions for authors included in the January issue of the journal state that “authors should not submit material that substantively duplicates content previously published or in press.” Direct re-publication is a clear-cut violation of policy. The appropriateness of dividing reports from one project into multiple manuscripts is more subjective. A good rule is that a project should be reported in a single manuscript whenever “a single paper would be more cohesive and more informative than two, without being excessively long.”3 Regardless of how authors decide to best present their work, they must disclose any potentially redundant or related fragmented publications to the editors at the time the work is submitted for consideration. The journal's instructions for authors require that “any possibly duplicative published material” be included with a manuscript to expedite determination of the degree to which overlap exists. The Annals of the Rheumatic Diseases has taken a hard-line position against “the misconduct of redundant publication.”4 Its editors have outlined a process for investigating suspected duplicate or redundant publication. Upon confirmation of abuse of the policy, they notify the home institution of each author, and they refuse to review a paper by any of the authors for 5 years. The purpose of a manuscript that reports an original investigation is to communicate to other interested parties. Readers must understand the context of the work before they can apply it to their situations. References to prior work and parallel efforts, together with a discussion of the way in which the reported work builds on or extends other work, is a critical piece of that context. I would suggest avoiding claims of precedence. A variation on claiming precedence is the practice of citing references predominantly from the authors' own laboratory when similar work has been done elsewhere. Let historians judge whose work is important. In large part they will make that judgement based upon the rate at which work is built upon or re-used by others. Work that is presented in the correct context is more likely to be re-used than work that is presented with a bias. The same principle applies to the investigators' statements about the importance of a finding. Equal attention should be given to identifying each factor that may limit application or generalization of a finding. A finding with limited application can be re-used effectively if those limits are understood. Without that understanding, attempts at application of the finding will fail, and the finding will fall from sight. The health professions should be able to look to the field of medical informatics to set the standard with regard to practices that increase the integrity and efficiency of information communication. As a minimum, we should adopt Huth's principles of author-ship and the policy developed by the Annals of Internal Medicine regarding redundant publication. I would recommend that we extend the concept of author accountability to the areas of reference to prior work and discussion of limitations. I would also suggest that we require full disclosure of the history of prior submissions of a manuscript and the reports from any prior reviews. Such practices would emphasize the author's responsibility to make the message non-ambiguous through clarification and revision. Issues surrounding the integrity of our scientific communication are paramount. At the same time, we do not want to hold back ideas or have ideas blocked by differences of opinion. I would like to see these questions and trade-offs discussed by the Association's Publications and Ethics Committees. In the meantime, each of us should make an extra effort to set an example by acting responsibly.

Willima W. Stead, MD; Matching the Level of Evaluation to a project's Stage of Development, Journal of the American Medical Informatics Association, Volume 3, I

William W. Stead, MD; Focus on the Frontiers of Informatics: Call for Papers on Telehealth and the Informatics of Medical Imaging, Journal of the American Medic

Vanderbilt University Medical Center is implementing an Integrated Advanced Information Management System (IAIMS) using a fast-track approach. The elapsed time between start-up and completion of implementation will be 7.5 years. The Start-Up and Planning phases of the project are complete. The Implementation phase asks one question: How does an organization create an environment that redirects and coordinates a variety of individual activities so that they come together to provide an IAIMS? Four answers to this question are being tested. First, design resources to be "scalable"--i.e., capable of supporting enterprise-wide use. Second, provide information technology planning activities as ongoing core functions that direct local efforts. Third, design core infrastructure resources to be both reusable and expandable at the local level. Fourth, use milestones to measure progress toward selected endpoints to permit early refinement of plans and strategies.

Nancy M. Lorenzi, PhD, Reed M. Gardner, PhD, T. Allan Pryor, PhD, William W. Stead, MD; Medical Informatics: The Key to an Organization's Place in the New Healt

William W. Stead, MD; JAMIA—Status after the First Year, Journal of the American Medical Informatics Association, Volume 2, Issue 3, 1 May 1995, Pages 200–201,

Direct computer-based physician order entry has been the subject of debate for over 20 years. Many sites have implemented systems successfully. Others have failed outright or flirted with disaster, incurring substantial delays, cost overruns, and threatened work actions. The rationale for physician order entry includes process improvement, support of cost-conscious decision making, clinical decision support, and optimization of physicians' time. Barriers to physician order entry result from the changes required in practice patterns, roles within the care team, teaching patterns, and institutional policies. Key ingredients for successful implementation include: the system must be fast and easy to use, the user interface must behave consistently in all situations, the institution must have broad and committed involvement and direction by clinicians prior to implementation, the top leadership of the organization must be committed to the project, and a group of problem solvers and users must meet regularly to work out procedural issues. This article reviews the peer-reviewed scientific literature to present the current state of the art of computer-based physician order entry.

Journal Article JAMIA—why? Get access William W. Stead, MD William W. Stead, MD Correspondence and reprints: William W. Stead, MD, The Village at Vanderbilt, Suite 2000, 1500 21st Avenue S, Nashville, TN 37212. Search for other works by this author on: Oxford Academic PubMed Google Scholar Journal of the American Medical Informatics Association, Volume 1, Issue 1, January 1994, Pages 75–76, https://doi.org/10.1136/jamia.1994.95236138 Published: 01 January 1994 Article history Received: 29 June 1993 Revision received: 06 July 1993 Accepted: 06 July 1993 Published: 01 January 1994

William W. Stead, MD; Lessons from the Origins of Informatics, Journal of the American Medical Informatics Association, Volume 1, Issue 2, 1 March 1994, Pages 1

Careful study of medical informatics research and library-resource projects is necessary to increase the productivity of the research and development enterprise. Medical informatics research projects can present unique problems with respect to evaluation. It is not always possible to adapt directly the evaluation methods that are commonly employed in the natural and social sciences. Problems in evaluating medical informatics projects may be overcome by formulating system development work in terms of a testable hypothesis; subdividing complex projects into modules, each of which can be developed, tested and evaluated rigorously; and utilizing qualitative studies in situations where more definitive quantitative studies are impractical.