Intelligent, Autonomous Machines in Surgery.

Surgeons perform two primary tasks: operating and engaging patients and caregivers in shared decision-making. Human dexterity and decision-making are biologically limited. Intelligent, autonomous machines have the potential to augment or replace surgeons. Rather than regarding this possibility with denial, ire, or indifference, surgeons should understand and steer these technologies. Closer examination of surgical innovations and lessons learned from the automotive industry can inform this process. Innovations in minimally invasive surgery and surgical decision-making follow classic S-shaped curves with three phases: (1) introduction of a new technology, (2) achievement of a performance advantage relative to existing standards, and (3) arrival at a performance plateau, followed by replacement with an innovation featuring greater machine autonomy and less human influence. There is currently no level I evidence demonstrating improved patient outcomes using intelligent, autonomous machines for performing operations or surgical decision-making tasks. History suggests that if such evidence emerges and if the machines are cost effective, then they will augment or replace humans, initially for simple, common, rote tasks under close human supervision and later for complex tasks with minimal human supervision. This process poses ethical challenges in assigning liability for errors, matching decisions to patient values, and displacing human workers, but may allow surgeons to spend less time gathering and analyzing data and more time interacting with patients and tending to urgent, critical-and potentially more valuable-aspects of patient care. Surgeons should steer these technologies toward optimal patient care and net social benefit using the uniquely human traits of creativity, altruism, and moral deliberation.

Donald A. B. Lindberg: Inspiring Leader and Visionary in Biomedicine, Healthcare, and Informatics.

BACKGROUND: As Director of the US National Library of Medicine (NLM) for 30 years, Dr. Donald A. B. Lindberg was instrumental in bringing biomedical research and healthcare worldwide into the age of genomic and translational medicine through the informatics systems developed by the NLM. Lindberg opened free access and worldwide public dissemination of all the NLM's biomedical literature and databases, thus helping transform not only biomedical research like the Human Genome Project and its successors, but also the practices of medicine and healthcare internationally. Guiding, leading, and teaching-by-example at national, regional, and global levels of biomedical and healthcare informatics, Lindberg helped coalesce a dynamic discipline that provides a foundation for the human understanding which promotes the future health of our world. OBJECTIVES: To provide historical insight into the scientific, technological, and practical clinical accomplishments of Donald Lindberg, and to describe how this led to contributions in the worldwide interdisciplinary evolution of informatics, and its impact on the biosciences and practices of medicine, nursing, and other healthcare-related disciplines. METHODS: Review and comment on the publications, scientific contributions, and leadership of Donald Lindberg in the evolution of biomedical and health informatics which anticipate the vision, scholarship, research in the field, and represent the deeply ethical humanism he exhibited throughout his life. These were essential in producing the informatics systems, such as the Unified Medical Language System (UMLS), MEDLINE, PubMed, PubMed Central, and ClinicalTrials.gov, which, together with NLM training programs and conferences, made possible the interactions among researchers and practitioners leading to the past quarter-century of rapid and dramatic advances in biomedical scientific inquiry and clinical discoveries, openly shared across the globe. CONCLUSION: Dr. Lindberg was a uniquely talented physician and pioneering researcher in biomedical and health informatics. As the main leader in developing and funding innovative informatics research for more than 30 years as Director of the National Library of Medicine, he helped bring together the most creative interdisciplinary researchers to bridge the worlds of biomedical research, education, and clinical practice. Lindberg's emphasis on open-access to the biomedical literature through publicly shared computer-mediated methods of search and inquiry are seen as an example of ethical scientific openness.

Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision.

OBJECTIVE: The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040. METHOD: Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS. RESULT: In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery. CONCLUSION: CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

Clinical Information Systems - From Yesterday to Tomorrow.

OBJECTIVES: To review the history of clinical information systems over the past twenty-five years and project anticipated changes to those systems over the next twenty-five years. METHODS: Over 250 Medline references about clinical information systems, quality of patient care, and patient safety were reviewed. Books, Web resources, and the author's personal experience with developing the HELP system were also used. RESULTS: There have been dramatic improvements in the use and acceptance of clinical computing systems and Electronic Health Records (EHRs), especially in the United States. Although there are still challenges with the implementation of such systems, the rate of progress has been remarkable. Over the next twenty-five years, there will remain many important opportunities and challenges. These opportunities include understanding complex clinical computing issues that must be studied, understood and optimized. Dramatic improvements in quality of care and patient safety must be anticipated as a result of the use of clinical information systems. These improvements will result from a closer involvement of clinical informaticians in the optimization of patient care processes. CONCLUSIONS: Clinical information systems and computerized clinical decision support have made contributions to medicine in the past. Therefore, by using better medical knowledge, optimized clinical information systems, and computerized clinical decision, we will enable dramatic improvements in both the quality and safety of patient care in the next twenty-five years.

Integration of Information for Patient Care: 2015 Redux.

Medical workstations are getting more and more powerful - however for the last decades they have been slow in making their way into the everyday life in medical care. In some resource rich organisations or smaller countries, their advance has gone further than elsewhere - however, a tipping point has not been reached. Again and again, new technologies and developments are pushing the need for integration of workstations into medical processes - currently the quantified self wave - however more options lead to more complexity and this growing complexity makes it more difficult to integrate the power of medical workstations in given culturally primed scenarios.

The Renewed Promise of Medical Informatics.

The promise of the field of Medical Informatics has been great and its impact has been significant. In 1999, the Yearbook editors of the International Medical Informatics Association (IMIA) - also the authors of the present paper - sought to assess this impact by selecting a number of seminal papers in the field, and asking experts to comment on these articles. In particular, it was requested whether and how the expectations, represented by these papers, had been fulfilled since their publication several decades earlier. Each expert was also invited to comment on what might be expected in the future. In the present paper, these areas are briefly reviewed again. Where did these early papers have an impact and where were they not as successful as originally expected? It should be noted that the extraordinary developments in computer technology observed in the last two decades could not have been foreseen by these early researchers. In closing, some of the possibilities and limitations of research in medical informatics are outlined in the context of a framework that considers six levels of computer applications in medicine and health care. For each level, some predictions are made for the future, concluded with thoughts on fruitful areas for ongoing research in the field.

Electronic Health Records: Then, Now, and in the Future.

OBJECTIVES: Describe the state of Electronic Health Records (EHRs) in 1992 and their evolution by 2015 and where EHRs are expected to be in 25 years. Further to discuss the expectations for EHRs in 1992 and explore which of them were realized and what events accelerated or disrupted/derailed how EHRs evolved. METHODS: Literature search based on "Electronic Health Record", "Medical Record", and "Medical Chart" using Medline, Google, Wikipedia Medical, and Cochrane Libraries resulted in an initial review of 2,356 abstracts and other information in papers and books. Additional papers and books were identified through the review of references cited in the initial review. RESULTS: By 1992, hardware had become more affordable, powerful, and compact and the use of personal computers, local area networks, and the Internet provided faster and easier access to medical information. EHRs were initially developed and used at academic medical facilities but since most have been replaced by large vendor EHRs. While EHR use has increased and clinicians are being prepared to practice in an EHR-mediated world, technical issues have been overshadowed by procedural, professional, social, political, and especially ethical issues as well as the need for compliance with standards and information security. There have been enormous advancements that have taken place, but many of the early expectations for EHRs have not been realized and current EHRs still do not meet the needs of today's rapidly changing healthcare environment. CONCLUSION: The current use of EHRs initiated by new technology would have been hard to foresee. Current and new EHR technology will help to provide international standards for interoperable applications that use health, social, economic, behavioral, and environmental data to communicate, interpret, and act intelligently upon complex healthcare information to foster precision medicine and a learning health system.

Evidence-based medicine: what has happened in the past 50 years?

Although the phrase 'evidence-based medicine' (EBM) was used for the first time in the medical literature less than 25 years ago, the history of EBM goes back for centuries. What is remarkable is how popular and how globally accepted the EBM movement has become in such a short time. Many famous, past clinicians have played major roles in the disciplines that preceded EBM, particularly 'clinical epidemiology'. It soon became clear to the early EBM champions that 'evidence' was only part of the clinical decision-making process. Consequently, both clinical expertise and the patient's values and preferences were rapidly incorporated into the concept we now know as 'EBM'. The current need for high-quality, easily accessible 'evidence-based summaries' for busy clinicians is now apparent, as traditional EBM requires both considerable time and skill. Consequently, there is a progressive move away from the primary literature (such as randomised controlled trials) to systematic reviews and other 'evidence-based summaries'. The future of EBM will almost certainly involve widespread utilisation of 'clinical (computer)-based decision support systems'.

RADIOLOGÍA, los clínicos y los radiólogos / RADIOLOGÍA, clinicians and radiologists

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Decision support in dermatology and medicine: history and recent developments.

This article is focused on diagnostic decision support tools and will provide a brief history of clinical decision support (CDS), examine the components of CDS and its associated terminology, and discuss recent developments in the use and application of CDS systems, particularly in the field of dermatology. For this article, we use CDS to mean an interactive system allowing input of patient-specific information and providing customized medical knowledge-based results via automated reasoning, for example, a set of rules and/or an underlying logic, and associations.

A history of the INTERNIST-1 and Quick Medical Reference (QMR) computer-assisted diagnosis projects, with lessons learned.

The INTERNIST-1/Quick Medical Reference (QMR) diagnostic decision support project spans four decades, from 1971-onward. This paper describes the history of the project and details insights gained of relevance to the general clinical and informatics communities.

A four-phase model of the evolution of clinical decision support architectures.

BACKGROUND: A large body of evidence over many years suggests that clinical decision support systems can be helpful in improving both clinical outcomes and adherence to evidence-based guidelines. However, to this day, clinical decision support systems are not widely used outside of a small number of sites. One reason why decision support systems are not widely used is the relative difficulty of integrating such systems into clinical workflows and computer systems. PURPOSE: To review and synthesize the history of clinical decision support systems, and to propose a model of various architectures for integrating clinical decision support systems with clinical systems. METHODS: The authors conducted an extensive review of the clinical decision support literature since 1959, sequenced the systems and developed a model. RESULTS: The model developed consists of four phases: standalone decision support systems, decision support integrated into clinical systems, standards for sharing clinical decision support content and service models for decision support. These four phases have not heretofore been identified, but they track remarkably well with the chronological history of clinical decision support, and show evolving and increasingly sophisticated attempts to ease integrating decision support systems into clinical workflows and other clinical systems. CONCLUSIONS: Each of the four evolutionary approaches to decision support architecture has unique advantages and disadvantages. A key lesson was that there were common limitations that almost all the approaches faced, and no single approach has been able to entirely surmount: (1) fixed knowledge representation systems inherently circumscribe the type of knowledge that can be represented in them, (2) there are serious terminological issues, (3) patient data may be spread across several sources with no single source having a complete view of the patient, and (4) major difficulties exist in transferring successful interventions from one site to another.

Decision support, knowledge representation and management: A broad methodological spectrum. Findings from the Decision Support, Knowledge Representation and Management.

OBJECTIVES: To summarize current excellent research in the field of decision support, knowledge management and representation. METHODS: Synopsis of the articles selected for the IMIA Yearbook 2006. RESULTS: Decision Support, Knowledge Representation and Management covers a broad spectrum of methods applied to a variety of medical problems and domains. Some particularly interesting and current topics were picked for the IMIA Yearbook 2006: the importance of ontologies for systematic system engineering of decision support systems, syndromic surveillance based on natural language processing, the evaluation of large semantic networks, and a comprehensive ontology for a randomised controlled trial database to support evidence-based practise. CONCLUSIONS: The best paper selection shows that methods for decision support, knowledge representation and management can decisively contribute to the solution of many different medical problems, but also that there is still a lot of exiting research to be done.