Informatics is more than information technology, it is the development and application of IT systems to problems in health care, research, and education. (Masys et al., 2000) The fundamentals of informatics include: communication, knowledge management, decision support and the management of clinical information (Scherger, personal communication), without which clinicians cannot be truly effective. As practitioners attempt to keep abreast of the burgeoning information base – there are nearly 10,000 randomized controlled trials annually – it has become clear, as David Eddy has said, that the complexity of health care “exceeds the inherent limitations of the unaided human mind.” (Millenson, 1997)
Here is a diagram that shows these four areas of Medical Informatics. I will revisit this from time to time to illustrate a point. Currently there are largely stand alone products that fall in one of these areas.
In large part because of the Internet, the information technology landscape in health care has changed in the last five years. The biggest strides that have been made are in the communication and knowledge management arenas and include: health care organizations’ applications of information technology to administrative and financial transactions; a virtual explosion of health-related information available to consumers via the web; and gains in making syntheses of evidence, practice guidelines, and health services research more accessible to health professionals, researchers and patients (2001). And, informatics has begun to be applied in the clinical realm through such applications as reminder systems, telemedicine, tele-radiology, online prescribing, and e-mail (2001). Whatever advances have been made in integrating informatics into practice settings, IT innovations have had a modest effect on patient care (2001). Most clinical information is still stored in paper form (Hagland M., 2001 ) (Staggers et al., 2001), few patients have e-mail access to their care givers, most patients do not benefit from even the simplest decision aids, such as patient reminder systems, and an unacceptable number of medical errors occur because there are no information systems in place to process and check the vast amount of clinical data that flows through the system (2001).
Informatics applications will have their greatest impact on the quality of patient care and patient safety when in the future applications are merged into one seamless system. Evidence that this trend has begun can be seen in the latest clinical information systems that include communications and decision support as well as knowledge management.
The authors of the Quality Chasm report believe that, if a substantial improvement in quality is to be achieved over the next decade, informatics must play a central role in the redesign of the health system(2001). The Quality Chasm report envisions priority conditions providing a framework for the development of a standardized information infrastructure, which is a necessary first step. This infrastructure would support the work of care teams and enable organizations to effectively measure outcomes and processes of care, providing them with benchmarks for continuous improvement. Innovations in information systems would enhance safety and the way care is organized, coordinated across settings and time, and delivered (telemedicine, e-health). Standardized information systems would provide the infrastructure for payment methods that reward quality (2001).
An e-mail message posted soon after Boston Globe journalist Betsy Lehman died due to a massive overdose of a powerful anti-cancer drug is a powerful case in point. The message was posted by a University of Michigan bioengineer: “How long, Oh Lord, must this continue? In 1974 we had an on-line patient record system that flagged unusual lab results or unusual…prescriptions, and that was at a vet[erans] hospital. That’s 21 years ago…Isn’t it time that basic computerization be part of the expected, and required, care at medical facilities? That humans make 0.1 percent errors on prescriptions may be forgivable; that hospitals don’t take obvious actions to protect themselves and patients, well within state-of-the-art, is not” (Millenson, 1997).
More recently, hospitals are installing or planning to install computerized physician order entry systems, many at the behest of Leapfrog , a group of over 90 purchasers focused on select interventions such as computerized physician order entry systems to enhance quality (Delbanco, 2002). Research has shown that these systems as well as telemedicine, computerized reminder systems and broader based decision support systems can enhance clinician performance and improve patient care (2001). For example, select studies show dramatically reduced medication errors when a physician computer order entry system was put into place (Bates et al., 1998) and similar systems efficiently detected contraindicated drug combinations (McMullin et al., 1999). A review of controlled clinical trials assessing clinical decision support systems found enhanced performance for drug dosing and preventive care but not convincingly for diagnosis (Hunt et al., 1998).
In education settings, it is unclear what the extent of informatics training is in both preservice and continuing education realms. Institutions are now offering degrees (master’s and PhDs), fellowships, certificates, and short courses, some through remote learning (American Medical Informatics Association (AMIA), 2002). This training is mostly being offered through medical and nursing schools but also through public health and dental schools as well as through health care organizations, with some town-grown collaborations. One study in both Europe and the U.S. of informatics in academic settings suggests that informatics has not been embraced by nursing to the same extent as medicine (Hovenga, 2000).
The following were identified in the literature or through web searches. This is not an exhaustive list. Please review the list to add your own suggestions or modifications. Educational Services Department, New York University Medical Center, NYC NY. The department implemented a multidisciplinary informatics curriculum that includes a menu of offerings that may be adapted to meet the varying skills, needs and schedules of clinicians, basic scientists, residents, and medical, nursing and allied health students. Offerings include workshops in basic computer skills, identification of information resources, the structure of information, developing search strategies in support of evidence-based practice, identifying qualitative journal literature, and critical appraisal of the literature (Faraino, 1998). University of Utah School of Nursing, Salt Lake City. The University has had a nursing informatics program since 1990 which they renamed the clinical informatics program in 1999 in recognition of the growing emphasis on interdisciplinary efforts. The school grants degrees at the master’s and PhD level that focus on informatics. University of Sheffield, UK. They created a virtual classroom in health informatics in 1997 for primary care professionals that use a facilitated e-mail discussion list and Web site with on-line resources and an archive of teaching materials. An adult-learning approach encourages participants to identify their learning needs, emphasizes informatics skills in practice, and focuses on skills likely to enhance evidence-based practice. Pre- and post-intervention questionnaires were used to assess perceived skills in informatics and evidence-based practice with participants reporting statistically significant increases in eight informatics skills but no significant changes in evidence-based practice skills (Fox and Bennett, 1998). Stanford University Medical School, Palo Alto CA. The university created a highly integrated Web-based technology application -The Stanford Health Information Network for Education (SHINE) – with the goal of meeting the different information needs of physicians and incorporating such information into the clinical decision-making process, while overcoming traditional barriers such as the constraints of the work environment and physician education (Godin et al., 1999). Center for Advanced Technology in Surgery, Stanford University, Palo Alto CA. Over the next ten years, the Center anticipates selecting, training, credentialing, remediating, and recredentialing physicians and surgeons using simulation, virtual reality, and Web-based electronic learning. They anticipate that future physicians will be able to rehearse an operation on a projectable palpable hologram derived from patient-specific data, and deliver the data set of that operation with robotic assistance the next day (Gorman et al., 2000). There is currently a simulator-based curriculum which Stanford developed in conjunction with the Veterans Affairs Palo Alto Health Care System which has been built upon by other institutions including Harvard Medical School (Robeznieks, 2002).
Many of the studies identified thus far in the literature seem to focus on comparing traditional education to technology based education such as Internet-based education, educational videos, virtual classrooms, and simulation programs. In nursing, there are select studies focused on how well students retain information delivered through technology aided mediums and their satisfaction with the media (Mallow and Gilje, 1999). With respect to medical education, there are a small number of carefully controlled studies that have shown that computer assisted instruction is both more effective and less costly than traditional forms of education (Masys et al., 2000). There is even less evidence of the effects with respect to continuing education. A study evaluating a virtual informatics classroom for primary care physicians showed that this approach was effective for delivering informatics skills, although it did not result in clinicians’ enhanced evidence based practice skills (Fox and Bennett, 1998).
Barriers In the literature, one important area of study examines barriers or obstacles inhibiting the integration of technology into instruction. Based on the literature and practitioner experience, Leggett & Persichitte (1998) identify five categories of barriers to technology integration: time, expertise, access, resources, and support. They use the TEARS acronym to make these categories more memorable. While this acronym provides a useful mnemonic, important detail is lost in this subsumption and needs elaboration for a full understanding of these barriers. The lack of time is at the top of their list as the obstacle most often mentioned. This includes time to plan, collaborate with peers, prepare lessons and materials, explore, practice, and evaluate, as well as develop, maintain, and expand skills. Other articles also identify time as an important barrier (Adams, 2002; Kagima & Hausafus, 2001). Expertise is another potential barrier to technology integration. Technology training for teachers must be available. Effective technology training must be hands-on, systematic, and ongoing. Additionally, a variety of models and approaches should be available to accommodate different needs, schedules, and learning styles. Adams (2002) found similar barriers related to expertise, such as limited computer training, and Kagima & Hausafus (2001) identified lack of technology competence as a barrier. D. L. Rogers (2000) takes an even stronger position on this barrier. “The weak link in the knowledge infrastructure in most institutions is the skills and training in Information Age tools and processes for learners, faculty, staff, and other participants…. It is imperative that institutions realize that it is not only technology that is important, but also the learning methodologies utilized to employ the technology (p. 21).” She emphasizes that training focused on both technology use and effective use in instruction is necessary. Access is the third category used by Leggett & Persichitte (1998). Teachers must have uninterrupted, on-demand access to the technologies they intend to use, both while inside and outside of the classroom. Adams (2002) also reported hardware and software availability as a potential barrier. Their fourth category is resources. This includes resources to purchase, maintain, and upgrade hardware and software; to provide training and support; and for auxiliary costs, such as coordinating technology access, and continuing costs, such as purchasing printer ink. They also note a relationship where time, expertise, and access are dependent on resources. Support is their fifth barrier category, both administrative and technical. Administrative leadership and support may be the most critical factor. In addition to providing the needed financial resources, the administration can set expectations, develop a vision and plan for technology integration, and provide incentives and encouragement. Technical support not only includes the personnel for maintaining the technology, but it also includes personnel who are knowledgeable about pedagogical issues, such as appropriate instructional methods or media. P. L. Rogers (2000) identifies similar barriers based on the literature and two studies she conducted: availability and quality of hardware and software, faculty role models, funding, institutional support, models for using technology in instruction, staff development, student learning, teacher attitudes, technical support, and time to learn to use technology. However, she organizes these barriers differently. Some barriers have an internal source, such as a teacher’s attitude or perception about a technology as well as his or her competency with that technology. External barriers include: availability and accessibility of hardware and software, technical and institutional support, and stakeholder development. Time and funding barriers cross internal and external sources. P. L. Rogers (2000) then develops a model for visualizing the relationships among these barriers. As illustrated in Figure 1, stakeholder attitudes and perceptions toward technology, its use in education, and institutional support determine what will be considered. Once these possibilities are established, three external barriers can slow or halt the implementation: availability and access, technical support, and stakeholder development. Also, time and funding affect these three barriers.
Relationship/behavioral changes - New relationships, with all the disruption (including financial) and promise that they offer, would need to be formed between educators and students, between health professionals and institutions, between health professionals and their patients (Kleinke, 2000) (2001) and would require all parties to radically change behaviors (Sinclair and Gardner, 1999). Variability - There is great variability in existing informatics skills among and within schools, students, and health professionals and likely in the receptivity to informatics infusing into the system (2001) (Jwayyed S, 2002). Discipline specific issues - There is disagreement about whether informatics need to be thought about (and taught) in discipline specific ways or approached more generically (Masys et al., 2000) (Mihalas et al., 2000). Resources - Significant resources would be required to equip schools (and health care organizations) with the kind of sophisticated information technology and informatics education envisioned above, although the Internet can provide many efficiencies (2001). There are concerns about the financial effects on schools as more education programs are delivered over the Internet (Lindeman, 2000). Continuing education restrictions - IT offers a way to link continuing education with practice more directly but, in at least continuing medical education, self-initiated learning is not allowed for the majority of physician’s category I CME credit for state licensure (Godin et al., 1999).
Privacy - Consumer and policy maker concerns about privacy of health information. The U.S. lacks national standards for the protection of health data and information (2001). Laws – Those at the state level that govern practice, e.g., scope of practice acts and how they are not set up to regulate telemedicine, e-mail and other exchanges of information across state borders (2001). For example, Kaiser Permanente’s role out of a computerized information system that traces clinician ordering and interventions has posed problems due to scope of practice acts and raised liability issues (Chow, 2002). Financial - Payment issues for e-visits and other transactions that do not result in a visit to a clinician’s office (Maddox et al., 2001) (Borowitz and Wyatt, 1998).
Adopter Categories Innovators are eager to try new ideas, to the point where their venturesomeness almost becomes an obsession. Innovators’ interest in new ideas leads them out of a local circle of peers and into social relationships more cosmopolite than normal. Usually, innovators have substantial financial resources, and the ability to understand and apply complex technical knowledge. While others may consider the innovator to be rash or daring, it is the hazardous risk-taking that is of salient value to this type of individual. The innovator is also willing to accept the occasional setback when new ideas prove unsuccessful (Rogers, 1971). Early adopters tend to be integrated into the local social system more than innovators. The early adopters are considered to be localites, versus the cosmopolite innovators. People in the early adopter category seem to have the greatest degree of opinion leadership in most social systems. They provide advice and information sought by other adopters about an innovation. Change agents will seek out early adopters to help speed the diffusion process. The early adopter is usually respected by his or her peers and has a reputation for successful and discrete use of new ideas (Rogers, 1971). Members of the early majority category will adopt new ideas just before the average member of a social system. They interact frequently with peers, but are not often found holding leadership positions. As the link between very early adopters and people late to adopt, early majority adopters play an important part in the diffusion process. Their innovation-decision time is relatively longer than innovators and early adopters, since they deliberate some time before completely adopting a new idea. Seldom leading, early majority adopters willingly follow in adopting innovations (Rogers, 1971). The late majority are a skeptical group, adopting new ideas just after the average member of a social system. Their adoption may be borne out of economic necessity and in response to increasing social pressure. They are cautious about innovations, and are reluctant to adopt until most others in their social system do so first. An innovation must definitely have the weight of system norms behind it to convince the late majority. While they may be persuaded about the utility of an innovation, there must be strong pressure from peers to adopt (Rogers, 1971). Laggards are traditionalists and the last to adopt an innovation. Possessing almost no opinion leadership, laggards are localite to the point of being isolates compared to the other adopter categories. They are fixated on the past, and all decisions must be made in terms of previous generations. Individual laggards mainly interact with other traditionalists. An innovation finally adopted by a laggard may already be rendered obsolete by more recent ideas already in use by innovators. Laggards are likely to be suspicious not only of innovations, but of innovators and change agents as well (Rogers, 1971). Rogers, E. M. & Shoemaker, F. F. (1971). Communication of Innovation. New York: The Free Press.
Many argue that informatics should not be thought of as a single course or set of didactic ideas, but rather as a method of problem solving that facilitates going beyond an individual’s own knowledge base and encourages consulting the “growing body of electronically accessible biomedical knowledge early and often” (Masys et al., 2000). The kinds of informatics competencies identified in the literature include: word processing, information search and retrieval, information management, data analysis, presentation and a whole range of communication skills including e-mail, file transfer, and using the web among other skill (Gilje and Mallow, 1999) (Leino-Kilpi and Saranto, 1997; Gardner and Sinclair, 1999) (Masys et al., 2000) (Saba, 2001).
In February 1998 the Association of American Medical Colleges (AAMC) issued Report I of the Medical School Objectives Project (MSOP). The purposes of the MSOP were to set forth program level learning objectives that medical school deans and faculties could use as a guide in reviewing their medical student education programs (initial phase), and to suggest strategies that they might employ in implementing agreed upon changes in those programs (implementation phase). Issuing MSOP Report I concluded the initial phase of the project. That report set forth 30 program level learning objectives that represented a consensus within the medical education community on the knowledge, skills, and attitudes that students should possess prior to graduation from medical school. This report - MSOP II - is the second of a series of reports that will be issued by the AAMC during the course of the project. Each of the subsequent reports will address a particularly challenging contemporary issue that medical school deans and faculties must confront in order to align the content of their medical student education programs “with evolving societal needs, practice patterns, and scientific developments.” The Medical Informatics Advisory Panel was charged to provide guidance on learning objectives related to medical informatics. To this end, the panel has developed recommendations to help ensure that medical school graduates have a foundation in medical informatics that will support them, as physicians in the 21st century, to efficiently utilize increasingly complex information for problem solving and decision making. The recommendations consist of a set of learning objectives expressing the competencies medical schools should help their students attain, as well as a set of implementation strategies outlining ways schools can develop educational programs that address these learning objectives.
As a new medical school whose mission is to create 21st century physicians comfortable with technology and aware of the ways that technology can improve patient care, it was decided to include informatics as an integral part of the training. A curriculum taken from the AAMC Medical Schools Objectives project on Informatics (1998) was adopted. Informatics is taught as a part of the Doctoring course, which run throughout the 4 years and includes as well as informatics, clinical skills taught in the biopsychosocial model, such as history taking and physical examination, taught in our clinical learning center, and clinical problem solving and the students preceptor experience. The students get 3-4 workshops per semester. The skills that they learn are coordinated with needs in their other classes. Ie, they are taught PowerPoint just prior to having to do a presentation in their Histology class in the fall of their first years. They are taught Endnote just prior to their need to write a paper for another course in the Spring of their first year.
One of the major factors that makes an integrated informatics curriculum possible is the IT infrastructure that we have supporting all of the educational activities across all of our campuses. This diagram illustrates the core technology that supports all courses and clerkships, college wide. Included are a variety of hardware like our technology enhanced classrooms, wireless campus wide network, web, file and exchange servers, and server systems including Avantgo, SQL. Maintaining and supporting all of this is an IT section with a large range of expertise. Early planning allowed us to creat policy and budgets to allow us to standardize on hardware and software for all students and faculty across departments with planned replacement and upgrades. This includes wireless laptops and PDAs for students for all four years, loaded with the latest medical and productivity software. Faculty get either laptops or desktops based on their need to be mobile. Rounding this off is our decision to provide a virtual library to support all sites which Barbara Shearer will discuss, a state of the art Clinical Learning Center, and to utilize the institutionally supported Blackboard Courseware.
So let’s see what it is like to be a student of medical informatics or a doctor living in the 21st century. Here is a typical basic sciences classroom. Students who normally have a microscope in front of them now have a wireless laptop computer.
* Reguires a FSU COM logon and password
Knowledge management Journals Consumer Health information Evidence-based medical information
Knowledge management Journals Consumer Health information Evidence-based medical information
Decision Support Reminder systems Diagnostic Expert Systems Drug Interaction
Communication Telemedicine Tele-radiology Patient e-mail Presentations
Information Management Electronic Medical Records Billing transactions Ordering Systems
What is Medical Informatics? - Informatics
What is Medical Informatics?
Nancy B. Clark, M.Ed.
Director of Medical Informatics Education
Florida State University
Health (Medical) Informatics
Medical informatics is the application of
computers, communications and information
technology and systems to all fields of
medicine - medical care, medical education
and medical research.
MF Collen, MEDINFO '80, Tokyo
Health (Medical) Informatics
Medical informatics is the rapidly
developing scientific field that deals with
resources, devices and formalized methods
for optimizing the storage, retrieval and
management of biomedical information for
problem solving and decision making.
Edward Shortliffe, M.D., Ph.D. What is medical
informatics? Stanford University, 1995.
Health (Medical) Informatics
Medical Informatics is the branch of science
concerned with the use of computers and
communication technology to acquire, store,
analyze, communicate, and display medical
information and knowledge to facilitate
understanding and improve the accuracy,
timeliness, and reliability of decision-making.
Warner, Sorenson and Bouhaddou, Knowledge Engineering
in Health Informatics, 1997
Health Informatics Defined
Clinical Information Management
Communication Decision Support
Informatics Use in Health Care
Consumer Health information
Diagnostic Expert Systems
Electronic Medical Records
Communication Decision Support
Quality Chasm Report
Standardized information infrastructure
Support care teams - Enhances patient-
Supports care coordination
Measure outcomes - Improve outcomes
Enhance safety - Reduces errors
Enables quality measurement/monitoring -
The Case of Betsy A. Lehman
“How long, Oh Lord, must this continue?
…That’s 21 years ago…Isn’t it time that
basic computerization be part of the
expected, and required, care at medical
Order entry systems
Reduce medication errors
Detect potential drug interactions
Clinical decision support systems
Improve drug dosing
Improve preventive care
Public health schools
Health care organizations
Education/Schools in Informatics
NYU Medical Center Educational Services
U of Utah School of Nursing
U of Sheffield, UK
Stanford Medical School
Stanford Center for Advanced Technology in
Informatics Education Research
Comparisons traditional to electronic
Not evidence based practice skills
Barriers to Implementation of Technology
Rogers, P. L. (2000). Barriers to adopting emerging technologies in education. Journal of
Educational Computing Research, 22(4), 455-472.
Educational Barriers to
Lack of resources
Relationship/behavioral changes required
Variability and lack of receptivity
Discipline specific issues
Continuing education restrictions
Regulatory/Policy Barriers to
Concerns over privacy
Lack of financial resources
Lack of/bad experiences
Lack of standards
Stakeholder Attitudes and Perceptions
Rogers, E. M. & Shoemaker, F. F. (1971). Communication of Innovation. New York: The Free Press.
External Barriers in Medicine
Universally agreed-on medical vocabulary
Principled and standard formats for laboratory
data, medical images, medical record…
Standardization of medical literature formats--
Health care standards -- treatment guidelines
Standards for health data exchange
Didactic vs problem solving
E-mail, file transfer, web
Medical School Objectives Project
Informatics competencies for medical
Infrastructure to Support
Wireless Laptops for
PDAs for each student
Evidence Based Ref.
The CDCS system
Issued Palm devices to
Switched from HP
iPAQ to iPod Touch
Essential Evidence Plus
Pediatric Care Online
Hands on Experience
Order entry, EMR, Billing
Clinical Information Systems
SOAPware Electronic Medical Record
Loaded on each student computer
Used all 3rd
year in Longitudinal weekly clinic
Follow 6 patients with chronic disease
Build entire medical record
Use Flow Sheets, Reminder System