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Patient Safety

Improving Safety with Information Technology

David W. Bates, M.D., and Atul A. Gawande, M.D., M.P.H.

N Engl J Med 2003; 348:2526-2534June 19, 2003

Article

Health care is growing increasingly complex, and most clinical research focuses on new approaches to diagnosis and treatment. In contrast, relatively little effort has been targeted at the perfection of operational systems, which are partly responsible for the well-documented problems with medical safety.1 If medicine is to achieve major gains in quality, it must be transformed, and information technology will play a key part,2 especially with respect to safety.

In other industries, information technology has made possible what has been called “mass customization” — the efficient and reliable production of goods and services according to the highly personalized needs of individual customers.2 Computer retailers, for example, now use their Web sites to allow people to purchase computers built to their exact specifications, which can be shipped within two days. Medical care is, of course, orders of magnitude more complex than selling personal computers, and clinicians have always strived to provide carefully individualized care. However, safe care now requires a degree of individualization that is becoming unimaginable without computerized decision support. For example, computer systems can instantaneously identify interactions among a patient's medications. Even today, more than 600 drugs require adjustment of doses for multiple levels of renal dysfunction, a task that is poorly performed by human prescribers without assistance but can be done accurately by computers.3 Multiple studies now demonstrate that computer-based decision support can improve physicians' performance and, in some instances, patient outcomes.3-6

In the past decade, the risk of harm caused by medical care has received increasing scrutiny.1 The growing sophistication of computers and software should allow information technology to play a vital part in reducing that risk — by streamlining care, catching and correcting errors, assisting with decisions, and providing feedback on performance. Given the large potential risks and benefits as well as the costs involved, in this article we analyze what is known about the role and effect of information technology with respect to safety and consider the implications for medical care, research, and policy.

Ways That Information Technology Can Reduce Errors

Information technology can reduce the rate of errors in three ways: by preventing errors and adverse events, by facilitating a more rapid response after an adverse event has occurred, and by tracking and providing feedback about adverse events. Data now show that information technology can reduce the frequency of errors of different types and probably the frequency of associated adverse events.7-18 The main classes of strategies for preventing errors and adverse events include tools that can improve communication, make knowledge more readily accessible, require key pieces of information (such as the dose of a drug), assist with calculations, perform checks in real time, assist with monitoring, and provide decision support.

Improving Communication

Failures of communication, particularly those that result from inadequate “handoffs” between clinicians, remain among the most common factors contributing to the occurrence of adverse events.19-21 In one study, cross-coverage of medical inpatients was associated with an increase by a factor of 5.2 in the risk of an adverse event.22 A new generation of technology — including computerized coverage systems for signing out, hand-held personal digital assistants (Figure 1Figure 1Notification about a Critical Laboratory Result.), and wireless access to electronic medical records — may improve the exchange of information, especially if links between various applications and a common clinical data base are in place, since many errors result from inadequate access to clinical data. In the study mentioned above, the implementation of a “coverage list” application, which standardized the information exchanged among clinicians, eliminated the excess risk resulting from cross-coverage.16

Also, many serious laboratory abnormalities — for example, hypokalemia and a decreasing hematocrit — require urgent action but occur relatively infrequently, often when a clinician is not at hand, and such results can be buried amid less critical data. Information systems can identify and rapidly communicate these problems to clinicians automatically (Figure 1), unlike traditional systems in which such results are communicated to a clerk for the unit.12-15 In one controlled trial, this approach reduced the time to the administration of appropriate treatment by 11 percent and reduced the duration of dangerous conditions in patients by 29 percent.23

Providing Access to Information

Another key to improving safety will be improving access to reference information. A wide range of textbooks, references on drugs, and tools for managing infectious disease, as well as access to the Medline data base, are already available for desktop and even hand-held computers (e.g., through http://www.epocrates.com and http://www.unboundmedicine.com). Ease and rapidity of use at the point of care were initially problematic but appear to be improving, and hand-held devices are now widely used, especially for drug-reference information.24

Requiring Information and Assisting with Calculations

One of the main benefits of using computers for clinical tasks that is often overlooked is that it makes it possible to implement “forcing functions” — features that restrict the way in which tasks may be performed. For example, prescriptions written on a computer can be forced to be legible and complete. Similarly, applications can require constraints on clinicians' choices regarding the dose or route of administration of a potentially dangerous medication. Thus, a dose that is 10 times as large as it should be will be ordered much less frequently if it is not one of the options on a menu (Figure 2Figure 2Percentage of Medication Orders with Doses Exceeding the Maximum.). Indeed, forcing functions have been found to be one of the primary ways in which computerized order entry by physicians reduces the rate of errors.26 The usefulness of forcing functions may also apply to other types of information technology. For example, bar-coded patient-identification bracelets designed to prevent accidents, such as the performance in one patient of a procedure intended for another patient, function in this way.27 Similarly, many actions imply that another should be taken; these dependent actions have been termed “corollary orders” by Overhage et al.28 For example, prescribing bed rest for a patient would trigger the suggestion that the physician consider initiating prophylaxis against deep venous thrombosis. This approach — which essentially targets errors of omission — has resulted in a change in behavior in 46 percent of cases in the intervention group, as compared with 22 percent of cases in the control group, with regard to a broad range of actions.28

The use of computers can also reduce the frequency of errors of calculation, a common human failing.29 Such tools can be used on demand — for example, by a nurse in the calculation of an infusion rate.

Monitoring

Monitoring is inherently boring and is not performed well by humans. Moreover, so many data are collected now that it can be hard to sift through them to detect problems. However, if the monitoring of information is computerized, applications can perform this task, looking for relations and trends and highlighting them, which can permit clinicians to intervene before an adverse outcome occurs. For example, “smart” monitors can look for and highlight signals that suggest the occurrence of decompensation in a patient — signals that a human observer would often fail to detect (Figure 3Figure 3“Smart” Monitoring in an Intensive Care Unit.).30

A related approach that appears to be beneficial on the basis of early data is technology-enabled remote monitoring of intensive care. In one study, remote monitoring in a 10-bed intensive care unit was associated with a reduction in mortality of 68 percent and 46 percent as compared with two different base-line periods, and the average length of stay in the intensive care unit and related costs each decreased by about a third.17 Such monitoring is especially attractive in the intensive care unit because there is a national shortage of intensivists.

Decision Support

Information systems can assist in the flow of care in many important ways by making available such key information on patients as laboratory values, by calculating weight-based doses of medications, or by red-flagging patients for whom an order for imaging with intravenous contrast material may be inappropriate. A longer-term benefit will occur as more sophisticated tools — such as computerized algorithms and neural networks — become integrated with the provision of health care. Neural-network decision aids allow many factors to be considered simultaneously in order to predict a specific outcome. These tools have been developed in order to reduce diagnostic and treatment errors in numerous clinical settings, including the assessment of abdominal pain, chest pain, and psychiatric emergencies and the interpretation of radiologic images and tissue specimens.31 Controlled trials have demonstrated improvement in clinical accuracy with the use of such technical tools, including their use in the diagnosis of myocardial infarction,32,33 the detection of breast cancer on screening mammograms,34 and the finding of cervical neoplasia on Papanicolaou smears.35 However, of these practices, only neural-network–assisted cervical screening has had substantial use, and little of that use has been in the United States.31,36 Nonetheless, more widespread use of electronic medical records could lead to an expanded role for these applications and make it easier to integrate them into routine care.

Rapid Response to and Tracking of Adverse Events

Computerized tools can also be used with electronic medical records to identify, intervene early in, and track the frequency of adverse events — a major gap in the current safety-related armamentarium — since, to improve processes, it is important to be able to measure outcomes.37 Classen et al. pioneered an approach for combing clinical data bases to detect signals that suggest the presence of an adverse drug event in hospitalized patients, such as the use of an antidote; this approach identified 81 times as many events as did spontaneous reporting, which is the standard technique used today.38 Others have built applications that allow the detection of nosocomial infections in inpatients39 and adverse drug events in outpatients.40

Such tools may be useful both for the improvement of care and for research. Together with Indiana University, we are conducting a controlled trial to evaluate computerized prescribing for outpatients. In the first year of this study, we built a computerized monitor for adverse drug events, which goes through the electronic medical record to detect signals (such as high serum drug levels) that suggest that an adverse drug event may have occurred (Table 1Table 1Results of Screening for Drug-Related Adverse Events with the Use of Electronic Medical Records for Outpatients.). This approach inexpensively identifies large numbers of adverse drug events that are not routinely detected. We are now using the rates of events to assess the effect of computerized prescribing, first with simple and then with more advanced decision support.

Electronic tools designed to identify a broad array of adverse events in a variety of settings seem promising.41 Often, these signals may permit earlier intervention; for example, Raschke et al. found that 44 percent of the alerts generated by a tool that they built had not been identified by the team of clinicians.5

Medication Safety and the Prevention of Errors

After anesthesia, medication safety has perhaps been the most closely studied domain in patient safety. Efforts to reduce the rate of medication errors have involved all the strategies discussed above. Nearly half of serious medication errors have been found to result from the fact that clinicians have insufficient information about the patient and the drug. Other common factors include a failure to provide sufficient specificity in an order, illegibility of handwritten orders, errors of calculation, and errors in transcription.7 In one controlled trial involving inpatients, the implementation of a computerized application for order entry by physicians — which improves communication, makes knowledge accessible, includes appropriate constraints on choices of drugs, routes, frequencies, and doses, helps with calculations, performs real-time checks, and assists with monitoring — resulted in a 55 percent reduction in serious medication-related errors.8 In a further study, which evaluated serial improvements to this application with the addition of higher levels of support for clinical decisions (e.g., more comprehensive checking for drug allergies and drug–drug interactions), there was an 83 percent reduction in the overall rate of medication errors.9 The use of decision support for clinical decisions can also result in major reductions in the rate of complications associated with antibiotics, and can decrease costs and the rate of nosocomial infections.10 Other technological tools with substantial potential but less solid evidence of effectiveness include the bar coding of medications and the use of automated drug-delivery devices for both oral and intravenous medications.11

Summary of Approaches to Prevention

To date, studies have generally been conducted only in individual facilities and rarely in the outpatient setting; moreover, only a few types of technology have been well tested. However, the large benefits found in the improvement of fundamental aspects of patient care8,12,13,16-18 indicate that information technology can be an important tool for improving safety in many clinical settings.

Tools that can improve communication, make knowledge more accessible, require key information, and assist with calculations and clinical decision making are available today and should provide substantial benefit. More research is needed on such questions as how best to perform checks, how best to assist in monitoring, and especially, how to provide decision support most effectively in complex situations. In today's systems, many important warnings are ignored,42 and there are too many unimportant warnings. Approaches have been developed to highlight more serious warnings — for instance, by displaying a skull and crossbones — when a clinician tries to order a drug that has previously caused an anaphylactic reaction in the patient (Figure 4Figure 4Warning Displayed for a Drug Allergy.). However, many efforts directed at complex targets such as the management of hypertension44 or congestive heart failure45 have failed. Overcoming these difficulties will require bringing cognitive engineers and techniques for assessing and accommodating human factors, such as usability testing, into the design of medical processes.

Barriers and Directions for Improvement

Despite the substantial opportunities for improvement in patient safety, the development, testing, and adoption of information technology remain limited. Numerous barriers exist, although some approaches to overcoming them are at hand.

Financial Barriers

The development of medical applications of information technology has largely been commercially funded, and reimbursement has rewarded excellent billing rather than outstanding clinical care. As a result, the focus has been more on products to improve the “back-office” functions related to clinical practice than on those that might improve clinical practice itself. Since they depend on new capital, research and development efforts for clinical tools have had relatively limited funding. When companies have produced useful technological tools, their spending on clinical testing has been negligible, particularly in comparison with what is spent on the testing of medical devices or drugs.46 Furthermore, even for proven applications, such as computerized order entry for physicians, vendors do not have ready-made products.47 For clinicians and institutions seeking to adopt technological tools, the investment costs can be high,48 and the quality of the decision support that comes along with these applications remains highly variable.49

Progress on this front is unlikely to occur without considerable investment — particularly public investment — in clinical information technology. Incentives could make an important difference. To increase capital investment, legislation has been introduced in the U.S. Senate to provide nearly $1 billion over a period of 10 years to hospitals and Medicare-supported nursing homes that implement technology that improves medication safety.50 Of concern, however, are measures that mandate the adoption of such technology without providing the funding for doing so. California, for example, has passed a law requiring, as a condition of licensure, that all nonrural hospitals implement technology such as, but not limited to, computerized order entry for physicians by January 1, 2005.51 Neither an increase in reimbursement nor capital grants were provided to help hospitals to meet this requirement. A piece of national legislation in this area — the Patient Safety Improvement Act of 2003 (H.R. 877) — was passed by the House of Representatives on March 12, 2003. This bill would provide $50 million in grants over a two-year period to institutions that implement information technology intended to improve patient safety. Forms of technology that are named include electronic communication of patient data, computerized order entry by physicians, bar coding, and data support technology. Although this is a positive development, these incentives are sufficiently limited that their effect would most likely be small.52

Lack of Standards

We lack a single standard in the United States today for representation of most types of key clinical data, including conditions, procedures, medications, and laboratory data.53 The result has been that most applications do not communicate well, even within organizations, and the costs of interfaces are high. Another highly charged issue is that standards for some important types of data are privately held. Privately held standards are standards that are in general use but are licensed by a company or organization. Examples of privately held standards are diagnosis codes that are licensed by the College of American Pathologists and procedure codes that are licensed by the American Medical Association.

However, there are both short-term and longer-term opportunities in this area. The National Committee on Vital and Health Statistics recently released a report54 endorsing national standards for electronic data for key domains. The adoption of the Consolidated Health Informatics standards by the federal government on March 21, 2003, represents a major step forward.55 This initial set includes standards for messaging, images, and clinical laboratory tests. Such standardization will encourage innovation and the adoption of applications with relatively little cost to the government. Although standards are not fully developed for every important type of information, the identification of this area as a major priority should make it possible to do the additional work required, especially if federal funding to support it is provided. An important, open question is whether any organization should be able to hold a national standard privately. We believe that one appropriate approach would be to require organizations to sell such classification systems for a fair price.

Cultural Barriers

There is also a tendency for clinicians and policymakers to see information technology as relatively unimportant for either research efforts or incorporation into medical practice. Academic centers are more apt to seek and reward faculty members who pursue research on a drug or a device that might lead to a reduction of 0.5 percent in the rate of death from myocardial infarction than those who develop a decision-support system that could result in a far greater reduction. Furthermore, clinicians have been reluctant to adopt information technology even when it has been shown to be effective.

This reluctance appears to have a number of causes. It is still a new concept in medicine that computerized tools can have powerful benefits in practice. When errors occur, physicians are no less likely than the public to see the clinicians involved, rather than the system, as the central problem.2 In addition, many physicians are still uncomfortable with computers. Some are concerned about depending on them, particularly for clinical decision making. With regard to certain technological tools, such as e-mail between physicians and patients and electronic medical records, clinicians are also concerned about legal issues, including privacy.

Not only the government but clinicians too, in their practices and relationships with colleagues and health care facilities, must recognize that most preventable adverse events result from failures of systems, not individual failures. Investment in and adoption of new forms of information technology must be understood as being as vital to good patient care as the adoption of new technological tools for diagnosis and treatment.

Current Situation

Overall, few of the types of information technology that may improve safety are widely implemented. For example, few hospitals have adopted computerized order entry for physicians. However, the Leapfrog Group — a coalition of some of the nation's largest employers, such as General Electric and General Motors — has identified it as one of three changes that they believe would most improve safety,56 and many hospitals are beginning on this path. Use of computer-assisted decision making in diagnosis and the planning of treatment remains rare. Furthermore, the quality of the clinical software applications that are currently being developed remains unclear. Especially given the absence of widely used standards, organizations have been reluctant to make large financial commitments, fearing that they will select a dead-end solution. Another pivotal issue is that information technology has been seen by many health care organizations as a commodity, like plumbing, rather than as a strategic resource that is vitally important to the delivery of care. Exceptions are institutions such as the health systems of the Department of Veterans Affairs and Kaiser, and reported data suggest these strategies have been successful.57-59

Conclusions

The fundamental difficulty in modern medical care is execution. Providing reliable, efficient, individualized care requires a degree of mastery of data and coordination that will be achievable only with the increased use of information technology. Information technology can substantially improve the safety of medical care by structuring actions, catching errors, and bringing evidence-based, patient-centered decision support to the point of care to allow necessary customization. New approaches that improve customization and gather and sift through reams of data to identify key changes in status and then notify key persons should prove to be especially important.

Supported in part by a grant (PO1 HS11534) from the Agency for Healthcare Research and Quality (to Dr. Bates).

Dr. Bates reports having served as a paid lecturer for Eclipsys and as a consultant for MedManagement and Alaris.

We are indebted to Amar Desai for comments on previous versions of this manuscript and to Anne Kittler for assistance with the preparation of the manuscript.

Source Information

From the Division of General Medicine and Primary Care, Department of Medicine (D.W.B.), and the Department of Surgery (A.A.G.), Brigham and Women's Hospital; the Center for Applied Medical Information Systems, Partners HealthCare System (D.W.B.); and Harvard Medical School (D.W.B., A.A.G.) — all in Boston.

Address reprint requests to Dr. Bates at the Division of General Medicine and Primary Care, Brigham and Women's Hospital, 75 Francis St., Boston, MA 02115, or at .

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Citing Articles

  1. 1

    Kai Li, Shinji Naganawa, Kai Wang, Ping Li, Ken Kato, Xiu Li, Jie Zhang, Kazunobu Yamauchi. (2012) Study of the Cost-Benefit Analysis of Electronic Medical Record Systems in General Hospital in China. Journal of Medical Systems
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    V. Gómez Tello, J. Álvarez Rodríguez, A. Núñez Reiz, J.A. González Sánchez, A. Hernández Abadía de Barbará, M. Martínez Fresneda, P. Morrondo Valdeolmillos, J.M. Nicolás Arfelis, I. Pujol Varela, M. Calvete Chicharro. (2012) Technical and functional standards and implementation of a clinical information system in intensive care units. Medicina Intensiva (English Edition)
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    Min Joung Kim, Joon Min Park, Sang Mo Je, Je Sung You, Yoo Seok Park, Hyun Soo Chung, Sung Phil Chung, Hahn Shick Lee. (2012) Effects of a short text message reminder system on emergency department length of stay. International Journal of Medical Informatics
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    Joanne L. Callen, Johanna I. Westbrook, Andrew Georgiou, Julie Li. (2011) Failure to Follow-Up Test Results for Ambulatory Patients: A Systematic Review. Journal of General Internal Medicine
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    M McMahon, Erin Nystrom, John Miles. 2011. Parenteral Nutrition in Adults. , 135-144.
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    Bruce I. Reiner, Elizabeth Krupinski. (2011) The Insidious Problem of Fatigue in Medical Imaging Practice. Journal of Digital Imaging
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    J. W. Wilson, W. F. Marshall, L. L. Estes. (2011) Detecting Delayed Microbiology Results After Hospital Discharge: Improving Patient Safety Through an Automated Medical Informatics Tool. Mayo Clinic Proceedings 86:12, 1181-1185
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    Scott J. Ellner, Paul W. Joyner. (2011) Information Technologies and Patient Safety. Surgical Clinics of North America
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    Tommaso Bellandi, Sara Albolino, Riccardo Tartaglia, Sebastiano Bagnara. 2011. Human Factors and Ergonomics in Patient Safety Management. , 671-690.
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    Ben-Tzion Karsh, Richard Holden, Calvin Or. 2011. Human Factors and Ergonomics of Health Information Technology Implementation. , 249-264.
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    Laurie A. Huryk. (2011) Interview with an informaticist. Nursing Management (Springhouse) 42:11, 44-48
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    V. Gómez Tello, J. Álvarez Rodríguez, A. Núñez Reiz, J.A. González Sánchez, A. Hernández Abadía de Barbará, M. Martínez Fresneda, P. Morrondo Valdeolmillos, J.M. Nicolás Arfelis, I. Pujol Varela, M. Calvete Chicharro. (2011) Estándares técnicos y funcionales, y proceso de implantación, de un sistema de información clínica en unidades de cuidados intensivos. Medicina Intensiva 35:8, 484-496
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    Maree Johnson, Diana Jefferies, Daniel Nicholls. (2011) Developing a minimum data set for electronic nursing handover. Journal of Clinical Nursingno-no
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    Thomas D. Sequist, Shane M. Morong, Amy Marston, Carol A. Keohane, E. Francis Cook, E. John Orav, Thomas H. Lee. (2011) Electronic Risk Alerts to Improve Primary Care Management of Chest Pain: A Randomized, Controlled Trial. Journal of General Internal Medicine
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    Jeffrey R. Spina, Peter A. Glassman, Barbara Simon, Andrew Lanto, Martin Lee, Francesca Cunningham, Chester B. Good. (2011) Potential Safety Gaps in Order Entry and Automated Drug Alerts. Medical Care 49:10, 904-910
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    J. P. Palma, E. G. Van Eaton, C. A. Longhurst. (2011) Topics In Neonatal Informatics: Information Technology to Support Handoffs in Neonatal Care. NeoReviews 12:10, e560-e563
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    Mark L. Diana, Abby Swanson Kazley, Nir Menachemi. (2011) An Assessment of Health Care Information and Management Systems Society and Leapfrog Data on Computerized Provider Order Entry. Health Services Research 46:5, 1575-1591
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    K.M. Lyng, B.S. Pedersen. (2011) Participatory design for computerization of clinical practice guidelines. Journal of Biomedical Informatics 44:5, 909-918
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    Kelly Wulff, Greta G. Cummings, Patricia Marck, Ozden Yurtseven. (2011) Medication administration technologies and patient safety: a mixed-method systematic review. Journal of Advanced Nursing 67:10, 2080-2095
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    Ying-Jui Chang, Min-Li Yeh, Yu-Chuan Li, Chien-Yeh Hsu, Yung-Tai Yen, Po-Yen Wang, Thomas Waitao Chu. (2011) Potential drug interactions in dermatologic outpatient prescriptions—experience from nationwide population-based study in Taiwan. Dermatologica Sinica 29:3, 81-85
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    H. S. Nam, M.-J. Cha, Y. D. Kim, E. H. Kim, E. Park, H. S. Lee, C. M. Nam, J. H. Heo. (2011) Use of a handheld, computerized device as a decision support tool for stroke classification. European Journal of Neurologyno-no
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    Philip Moore, Gerry Armitage, John Wright, Stan Dobrzanski, Nafeesa Ansari, Ian Hammond, Andy Scally. (2011) Medicines Reconciliation Using a Shared Electronic Health Care Record. Journal of Patient Safety 7:3, 147-153
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    Paul St. Jacques, Brian Rothman. (2011) Enhancing Point of Care Vigilance Using Computers. Anesthesiology Clinics 29:3, 505-519
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    Michael F. Chiang, Michael V. Boland, Allen Brewer, K. David Epley, Mark B. Horton, Michele C. Lim, Colin A. McCannel, Sayjal J. Patel, David E. Silverstone, Linda Wedemeyer, Flora Lum. (2011) Special Requirements for Electronic Health Record Systems in Ophthalmology. Ophthalmology 118:8, 1681-1687
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    Richard J. Holden, Roger L. Brown, Samuel J. Alper, Matthew C. Scanlon, Neal R. Patel, Ben-Tzion Karsh. (2011) That’s nice, but what does IT do? Evaluating the impact of bar coded medication administration by measuring changes in the process of care. International Journal of Industrial Ergonomics 41:4, 370-379
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    Erin K. Lawler, Alan Hedge, Sonja Pavlovic-Veselinovic. (2011) Cognitive ergonomics, socio-technical systems, and the impact of healthcare information technologies. International Journal of Industrial Ergonomics 41:4, 336-344
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    Lee Wilbur, Gretchen Huffman, Stephanie Lofton, John T. Finnell. (2011) The Use of a Computer Reminder System in an Emergency Department Universal HIV Screening Program. Annals of Emergency Medicine 58:1, S71-S73.e1
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    Pascale Carayon, Ellen J. Bass, Tommaso Bellandi, Ayse P. Gurses, M. Susan Hallbeck, Vanina Mollo. (2011) Sociotechnical systems analysis in health care: a research agenda. IIE Transactions on Healthcare Systems Engineering 1:3, 145-160
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    Jamison Chang, Claudio Ronco, Mitchell H. Rosner. (2011) Computerized decision support systems: improving patient safety in nephrology. Nature Reviews Nephrology 7:6, 348-355
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    Christopher H.O. Olola, Scott Narus, Jonathan Nebeker, Mollie Poynton, Joseph Hales, Belle Rowan, Heather LeSieur, Cynthia Zumbrennen, Annemarie A. Edwards, Robert Crawford, Spencer Amundsen, Yasmin Kabir, Joseph Atkin, Cynthia Newberry, Jason Young, Tariq Hanifi, Ben Risenmay, Tyler Sorensen, R. Scott Evans. (2011) The perception of medical professionals and medical students on the usefulness of an emergency medical card and a continuity of care report in enhancing continuity of care. International Journal of Medical Informatics 80:6, 412-420
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    N.W. Cheung, N. Cinnadaio, A. O’Neill, L. Koller, H.L. Pratt, C. Zingle, D.R. Chipps. (2011) Implementation of a dedicated hospital subcutaneous insulin prescription chart: Effect on glycaemic control. Diabetes Research and Clinical Practice 92:3, 337-341
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    N. Lees, R. Hall. (2011) Information technology in anaesthesia and critical care. Continuing Education in Anaesthesia, Critical Care & Pain 11:3, 104-107
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    Bill G. Felkey, Brent I. Fox. (2011) Pharmacy Automation and Technology - Information Technology and the Medication Use Process. Hospital Pharmacy 46:4, 289-290
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    Elena Villamañán, Alicia Herrero, Rodolfo Álvarez-Sala. (2011) Prescripción electrónica asistida como nueva tecnología para la seguridad del paciente hospitalizado. Medicina Clínica 136:9, 398-402
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    E. Delgado Silveira, A. Álvarez Díaz, C. Pérez Menéndez-Conde, J. Serna Pérez, M.A. Rodríguez Sagrado, T. Bermejo Vicedo. (2011) Análisis modal de fallos y efectos del proceso de prescripción, validación y dispensación de medicamentos. Farmacia Hospitalaria
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    Robert M. Wachter. (2011) The hospitalist field turns 15: New opportunities and challenges. Journal of Hospital Medicine 6:4, E1-E4
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    MARIE E. GABE, GWYNETH A. DAVIES, FIONA MURPHY, MICHELLE DAVIES, LINZI JOHNSTONE, SUE JORDAN. (2011) Adverse drug reactions: treatment burdens and nurse-led medication monitoring. Journal of Nursing Management 19:3, 377-392
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    Terrence L. Trentman, Jeff T. Mueller, Keith J. Ruskin, Brie N. Noble, Christine A. Doyle. (2011) Adoption of anesthesia information management systems by US anesthesiologists. Journal of Clinical Monitoring and Computing 25:2, 129-135
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    Daniele Mascia, Americo Cicchetti. (2011) Physician social capital and the reported adoption of evidence-based medicine: Exploring the role of structural holes. Social Science & Medicine 72:5, 798-805
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    Ilkka Winblad, Päivi Hämäläinen, Jarmo Reponen. (2011) What Is Found Positive in Healthcare Information and Communication Technology Implementation?—The Results of a Nationwide Survey in Finland. Telemedicine and e-Health 17:2, 118-123
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    Elena Villamañán, Alicia Herrero, Rodolfo Álvarez Sala. (2011) La prescripción electrónica asistida en pacientes hospitalizados en un servicio de Neumología. Archivos de Bronconeumología 47:3, 138-142
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    C. H. O. Olola, S. Narus, M. Poynton, J. Nebeker, J. Hales, B. Rowan, M. Smith, R. S. Evans. (2011) Patient-perceived usefulness of an emergency medical card and a continuity-of-care report in enhancing the quality of care. International Journal for Quality in Health Care 23:1, 60-67
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    Takeshi Morimoto, Mio Sakuma, Kunihiko Matsui, Nobuo Kuramoto, Jinichi Toshiro, Junji Murakami, Tsuguya Fukui, Mayuko Saito, Atsushi Hiraide, David W. Bates. (2011) Incidence of Adverse Drug Events and Medication Errors in Japan: the JADE Study. Journal of General Internal Medicine 26:2, 148-153
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    D. Conen. (2011) Maßnahmen zur Verbesserung der Patientensicherheit. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 54:2, 171-175
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    Bartolome Bejarano, Mariangela Bianco, Dolores Gonzalez-Moron, Jorge Sepulcre, Joaquin Goñi, Juan Arcocha, Oscar Soto, Ubaldo Carro, Giancarlo Comi, Letizia Leocani, Pablo Villoslada. (2011) Computational classifiers for predicting the short-term course of Multiple sclerosis. BMC Neurology 11:1, 67
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    Carl J Reynolds, Jeremy C Wyatt. (2011) Open Source, Open Standards, and Health Care Information Systems. Journal of Medical Internet Research 13:1,
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    Christine A Goeschel. (2011) Nursing leadership at the crossroads: evidence-based practice ‘Matching Michigan-minimizing catheter related blood stream infections’*. Nursing in Critical Care 16:1, 36-43
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    Sabi Redwood, Anna Rajakumar, James Hodson, Jamie J Coleman. (2011) Does the implementation of an electronic prescribing system create unintended medication errors? A study of the sociotechnical context through the analysis of reported medication incidents. BMC Medical Informatics and Decision Making 11:1, 29
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    Jenelle Matic, Patricia M Davidson, Yenna Salamonson. (2011) Review: bringing patient safety to the forefront through structured computerisation during clinical handover. Journal of Clinical Nursing 20:1-2, 184-189
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    Ari Mwachofi, Stephen L. Walston, Badran A. Al-Omar. (2011) Factors affecting nurses' perceptions of patient safety. International Journal of Health Care Quality Assurance 24:4, 274-283
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    M. Fossum, M. Ehnfors, A. Fruhling, A. Ehrenberg. (2011) An Evaluation of the Usability of a Computerized Decision Support System for Nursing Homes. Applied Clinical Informatics 2:4, 420-436
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    O. Farri, K. A. Monsen, B. L. Westra, G. B. Melton. (2011) Analysis of Free Text with Omaha System Targets in Community-Based Care to Inform Practice and Terminology Development. Applied Clinical Informatics 2:3, 304-316
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    Elena Villamañán, Alicia Herrero, Rodolfo Álvarez Sala. (2011) The Assisted Electronic Prescription in Patients Hospitalised in a Chest Diseases Ward. Archivos de Bronconeumología (English Edition) 47:3, 138-142
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    Jonathan Z. Bakdash, Frank A. Drews. (2011) Using knowledge in the world to improve patient safety: Designing affordances in health care equipment to specify a sequential “checklist”. Human Factors and Ergonomics in Manufacturing & Service Industriesn/a-n/a
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    Ann Scheck McAlearney, Julie Robbins, Annemarie Hirsch, Maria Jorina, J. Phil Harrop. (2010) Perceived efficiency impacts following electronic health record implementation: An exploratory study of an urban community health center network. International Journal of Medical Informatics 79:12, 807-816
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    John Fox, David Glasspool, Vivek Patkar, Mark Austin, Liz Black, Matthew South, Dave Robertson, Charles Vincent. (2010) Delivering clinical decision support services: There is nothing as practical as a good theory. Journal of Biomedical Informatics 43:5, 831-843
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    Judy E. Kim-Hwang, Alice Hm Chen, Douglas S. Bell, David Guzman, Hal F. Yee, Margot B. Kushel. (2010) Evaluating Electronic Referrals for Specialty Care at a Public Hospital. Journal of General Internal Medicine 25:10, 1123-1128
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    MARILYN J. RANTZ, GREG ALEXANDER, COLLEEN GALAMBOS, MARCIA K. FLESNER, AMY VOGELSMEIER, LANIS HICKS, JILL SCOTT-CAWIEZELL, MARY ZWYGART-STAUFFACHER, LESLIE GREENWALD. (2010) The Use of Bedside Electronic Medical Record to Improve Quality of Care in Nursing Facilities. CIN: Computers, Informatics, Nursing1
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    Krish Thiru, Stuart Rowe, Nicola Shaw, Andrew Durward, David P. Inwald, Padmanabhan Ramnarayan. (2010) Survey of clinical information system usage by paediatric intensive care units in the UK. Intensive Care Medicine 36:9, 1616-1617
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    Anne Latva Draper, Colleen Karen Pouliot. (2010) Surgical Time-Out: Driving Change Through Electronic Documentation. Journal of Obstetric, Gynecologic, & Neonatal Nursing 39, S55-S56
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    Ann Scheck McAlearney, Paula H. Song, Julie Robbins, Annemarie Hirsch, Maria Jorina, Nina Kowalczyk, Deena Chisolm. (2010) Moving from Good to Great in Ambulatory Electronic Health Record Implementation. Journal for Healthcare Quality 32:5, 41-50
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    Marilyn J. Rantz, Lanis Hicks, Gregory F. Petroski, Richard W. Madsen, Greg Alexander, Colleen Galambos, Vicki Conn, Jill Scott-Cawiezell, Mary Zwygart-Stauffacher, Leslie Greenwald. (2010) Cost, Staffing and Quality Impact of Bedside Electronic Medical Record (EMR) in Nursing Homes. Journal of the American Medical Directors Association 11:7, 485-493
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    Michael Zalis, Mitchell Harris. (2010) Advanced Search of the Electronic Medical Record: Augmenting Safety and Efficiency in Radiology. Journal of the American College of Radiology 7:8, 625-633
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    L. Keoki Williams, Edward L. Peterson, Karen Wells, Janis Campbell, Mingqun Wang, Vimal K. Chowdhry, Matthew Walsh, Robert Enberg, David E. Lanfear, Manel Pladevall. (2010) A cluster-randomized trial to provide clinicians inhaled corticosteroid adherence information for their patients with asthma. Journal of Allergy and Clinical Immunology 126:2, 225-231.e4
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    Sophie Gerkens, Ralph Crott, Marie-Christine Closon, Yves Horsmans, Claire Beguin. (2010) Comparing the quality of care across Belgian hospitals from medical basic datasets: the case of thromboembolism prophylaxis after major orthopaedic surgery. Journal of Evaluation in Clinical Practice 16:4, 685-692
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    Sibel Kahraman, Richard P. Dutton, Peter Hu, Yan Xiao, Bizhan Aarabi, Deborah M. Stein, Thomas M. Scalea. (2010) Automated Measurement of “Pressure Times Time Dose” of Intracranial Hypertension Best Predicts Outcome After Severe Traumatic Brain Injury. The Journal of Trauma: Injury, Infection, and Critical Care 69:1, 110-118
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    Carlos Palacio, Jeffrey P. Harrison, David Garets. (2010) Benchmarking Electronic Medical Records Initiatives in the US: a Conceptual Model. Journal of Medical Systems 34:3, 273-279
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    2010. Using Information Technology to Reduce Error. , 246-266.
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    Poon, Eric G., Keohane, Carol A., Yoon, Catherine S., Ditmore, Matthew, Bane, Anne, Levtzion-Korach, Osnat, Moniz, Thomas, Rothschild, Jeffrey M., Kachalia, Allen B., Hayes, Judy, Churchill, William W., Lipsitz, Stuart, Whittemore, Anthony D., Bates, David W., Gandhi, Tejal K., . (2010) Effect of Bar-Code Technology on the Safety of Medication Administration. New England Journal of Medicine 362:18, 1698-1707
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    Jason S. Shapiro, Kevin M. Baumlin, Neal Chawla, Nicholas Genes, James Godbold, Fen Ye, Lynne D. Richardson. (2010) Emergency Department Information System Implementation and Process Redesign Result in Rapid and Sustained Financial Enhancement at a Large Academic Center. Academic Emergency Medicine 17:5, 527-535
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    Charlotte Tang, Sheelagh Carpendale, Stacey Scott. (2010) InfoFlow Framework for Evaluating Information Flow and New Health Care Technologies. International Journal of Human-Computer Interaction 26:5, 477-505
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    J. Fayn, P. Rubel. (2010) Toward a Personal Health Society in Cardiology. IEEE Transactions on Information Technology in Biomedicine 14:2, 401-409
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    Diane M. Doran, R. Brian Haynes, André Kushniruk, Sharon Straus, Jeremy Grimshaw, Linda McGillis Hall, Adam Dubrowski, Tammie Di Pietro, Kristine Newman, Joan Almost, Ha Nguyen, Jennifer Carryer, Dawn Jedras. (2010) Supporting Evidence-Based Practice for Nurses through Information Technologies. Worldviews on Evidence-Based Nursing 7:1, 4-15
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    Eric G. Poon, Adam Wright, Steven R. Simon, Chelsea A. Jenter, Rainu Kaushal, Lynn A. Volk, Paul D. Cleary, Janice A. Singer, Alexis Z. Tumolo, David W. Bates. (2010) Relationship Between Use of Electronic Health Record Features and Health Care Quality. Medical Care 48:3, 203-209
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    U.S. Food and Drug Administration. (2010) Food and Drug Administration's Safe Use Initiative Collaborating to Reduce Preventable Harm from Medications. Journal of Pain and Palliative Care Pharmacotherapy 24:1, 76-93
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    Feliciano Yu, Nir Menachemi, Thomas K. Houston. (2010) Hospital Patient Safety Levels among Healthcare's “Most Wired” Institutions. Journal for Healthcare Quality 32:2, 16-23
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    Kathryn Went, Patricia Antoniewicz, Deborah A. Corner, Stella Dailly, Peter Gregor, Judith Joss, Fiona B. McIntyre, Shaun McLeod, Ian W. Ricketts, Alfred J. Shearer. (2010) Reducing prescribing errors: can a well-designed electronic system help?. Journal of Evaluation in Clinical Practice
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    Jeongeun Kim, Sukwha Kim, Yoenyi Jung, Eun-Kyung Kim. (2010) Status and Problems of Adverse Event Reporting Systems in Korean Hospitals. Healthcare Informatics Research 16:3, 166
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    Joo-Yeon Lee, Yunling E. Du, Osode Coki, John T. Flynn, Justin Starren, Michael F. Chiang. (2010) Parental perceptions toward digital imaging and telemedicine for retinopathy of prematurity management. Graefe's Archive for Clinical and Experimental Ophthalmology 248:1, 141-147
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    Barbara Majchrowski. (2010) Medical Software's Increasing Impact on Healthcare and Technology Management. Biomedical Instrumentation & Technology 44:1, 70-74
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    M. CERLINCA, C. TURCU, T. CERLINCA, R. PRODAN. (2010) HL7 Messaging Engine with Customizable Translation System. Advances in Electrical and Computer Engineering 10:2, 98-101
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    D. Grandt. (2009) Verbesserung der Arzneimitteltherapiesicherheit. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 52:12, 1161-1165
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    (2009) FDA's Safe Use Initiative: Collaborating to Reduce Preventable Harm from Medications—Guidance for Industry. Biotechnology Law Report 28:6, 741-753
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    Corey M. Angst. (2009) Protect My Privacy or Support the Common-Good? Ethical Questions About Electronic Health Information Exchanges. Journal of Business Ethics 90:S2, 169-178
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    M. Völkel, A. Bußmann-Rolfes, J.C. Frölich. (2009) Hat sich die Arzneitherapiesicherheit in den letzten Jahren in Deutschland verbessert?. Der Internist 50:11, 1281-1289
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    Stephen Rogers, Dan Wilson, Simon Wan, Mark Griffin, Gurcharan Rai, John Farrell. (2009) Medication-Related Admissions in Older People. Drugs & Aging 26:11, 951-961
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    Varda Shalev, Gabriel Chodick, Anthony D. Heymann. (2009) Format change of a laboratory test order form affects physician behavior. International Journal of Medical Informatics 78:10, 639-644
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    Caroline Mir, Amina Gadri, Georges L. Zelger, Renaud Pichon, André Pannatier. (2009) Impact of a computerized physician order entry system on compliance with prescription accuracy requirements. Pharmacy World & Science 31:5, 596-602
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    Wachter, Robert M., Pronovost, Peter J., . (2009) Balancing “No Blame” with Accountability in Patient Safety. New England Journal of Medicine 361:14, 1401-1406
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    LAURA B. SMITH, LAURA BANNER, DIEGO LOZANO, CHRISTINE M. OLNEY, BRUCE FRIEDMAN. (2009) Connected Care. CIN: Computers, Informatics, Nursing 27:5, 318-323
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    Bob L. Lobo, Georgeta Vaidean, Joyce Broyles, Anne B. Reaves, Ronald I. Shorr. (2009) Risk of venous thromboembolism in hospitalized patients with peripherally inserted central catheters. Journal of Hospital Medicine 4:7, 417-422
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    Jenny L Gibb, Jarrod M Haar. (2009) e-Business connections in the health sector: IT challenges and the effects of practice size. Journal of Management & Organization 15:4, 500-513
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    William R. Hendee, Anthony B. Wolbarst. 2009. Biomedical Imaging Technologies. .
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    Jeffrey M. Riggio, Mandelin K. Cooper, Benjamin E. Leiby, Jeanine M. Walenga, Geno J. Merli, Jonathan E. Gottlieb. (2009) Effectiveness of a clinical decision support system to identify heparin induced thrombocytopenia. Journal of Thrombosis and Thrombolysis 28:2, 124-131
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    N. KUCHER, M. PUCK, J. BLASER, G. BUCKLAR, E. ESCHMANN, T. F. LÜSCHER. (2009) Physician compliance with advanced electronic alerts for preventing venous thromboembolism among hospitalized medical patients. Journal of Thrombosis and Haemostasis 7:8, 1291-1296
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    Jeff R. Schein, Rodney W. Hicks, Winnie W. Nelson, Vanja Sikirica, D. John Doyle. (2009) Patient-Controlled Analgesia-Related Medication Errors in the Postoperative Period. Drug Safety 32:7, 549-559
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    Ben-Tzion Karsh, Richard Holden, Kamisha Escoto, Samuel Alper, Matthew Scanlon, Judi Arnold, Kathleen Skibinski, Roger Brown. (2009) Do Beliefs About Hospital Technologies Predict Nurses' Perceptions of Quality of Care? A Study of Task-Technology Fit in Two Pediatric Hospitals. International Journal of Human-Computer Interaction 25:5, 374-389
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    Giorgio Da Rin. (2009) Pre-analytical workstations: A tool for reducing laboratory errors. Clinica Chimica Acta 404:1, 68-74
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    Anne Martin-Matthews, Robyn Tamblyn, Janice Keefe, Margaret Gillis. (2009) Associer les politiques et les recherches au sujet du vieillissement au Canada : reconnaître un anniversaire, constater une possibilité. Canadian Journal on Aging / La Revue canadienne du vieillissement 28:02, 195
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    Anne Martin-Matthews, Robyn Tamblyn, Janice Keefe, Margaret Gillis. (2009) Bridging Policy and Research on Aging in Canada: Recognizing an Anniversary, Realizing an Opportunity. Canadian Journal on Aging / La Revue canadienne du vieillissement 28:02, 185
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    George Ioannidis, Alexandra Papaioannou, Lehana Thabane, Amiram Gafni, Anthony Hodsman, Brent Kvern, Aleksandra Walsh, Famida Jiwa, Jonathan D. Adachi. (2009) Family Physicians' Personal and Practice Characteristics that Are Associated with Improved Utilization of Bone Mineral Density Testing and Osteoporosis Medication Prescribing. Population Health Management 12:3, 131-138
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    Joseph W. Rossano, Larry S. Jefferson, E. O'Brian Smith, Mark A. Ward, Antonio R. Mott. (2009) Automated External Defibrillators and Simulated In-Hospital Cardiac Arrests. The Journal of Pediatrics 154:5, 672-676.e1
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    Rahul K. Shah, Lina Lander, Peter Forbes, Kathy Jenkins, Gerald B. Healy, David W. Roberson. (2009) Safety on an inpatient pediatric otolaryngology service: Many small errors, few adverse events. The Laryngoscope 119:5, 871-879
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    Michael F. Chiang, Michael V. Boland, James W. Margolis, Flora Lum, Michael D. Abramoff, P. Lloyd Hildebrand. (2009) Author reply. Ophthalmology 116:5, 1019
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    Tobias Grundgeiger, Penelope Sanderson. (2009) Interruptions in healthcare: Theoretical views. International Journal of Medical Informatics 78:5, 293-307
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    Richard T. Griffey, Kathleen Wittels, Nicki Gilboy, Andrew T. McAfee. (2009) Use of a Computerized Forcing Function Improves Performance in Ordering Restraints. Annals of Emergency Medicine 53:4, 469-476
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