What is the benefit of patient education using computer assisted instruction?

Computer-assisted teaching refers to the use of computer programs, multimedia, and computer-based activities by teachers in the classroom environment. As technology has become an integral part of our daily lives, the field of Education could not possibly stay unaffected. Thus, more and more traditional blackboards are being substituted by computer screens and the teaching techniques are radically changing.

Both computers and the Internet offer a wide range of activities that focus on different aspects of the object taught, including instructional videos, practice exercises and brainstorming tasks. These activities can be used either independently or in combination with traditional teaching techniques. This way students are offered a fully customized learning experience based on their individual needs.

This interactive way of teaching appears to be more appealing to students, as today’s children grow up in highly mechanized environments and find technology fascinating. Computer-based lessons give learners the chance to explore ideas in a fun and intriguing way. Therefore, they are less prone to get bored during classes and more likely to actively participate in the activities taking place in the classroom.

Children, however, are not the only type of students that can benefit from computer-assisted teaching. Video instruction and other interactive activities have also been introduced in university classes around the world. These tools give students the opportunity to take a closer, more detailed look into complicated processes, such as medical procedures or scientific experiments.

It is important to say that computers cannot fully substitute the presence of a teacher who will guide students and answer all of their questions. After all, a computer activity is only as good as its programming, furthermore, a student’s inability to adjust to the demands of a mechanized learning process would cause him to feel frustrated and left behind without his teacher’s help. That is why we developed technology where teacher can interact with kids remotely, hands free via favorite cartoon characters. Video

To conclude, computer-assisted teaching can offer great benefits to both teachers and students. Combined with traditional teaching methods, it can optimize the results of the learning process and make learning an interesting and fun experience

Clinical education and the computer: A proposed model for computer-aided learning in the medical curriculum.

by Tim J. Lambert, Kenneth C. Kirkby, John C. Dunn

Reprinted with permission from Australasian Psychiatry Vol 5, No 1, February 1997, 19-21

Abstract

Learning to recognise the signs and symptoms of disease is a core clinical skill which is developed both by seeing a range and number of patients in clinical settings, and from knowledge acquired through formalised instruction. The availability of clinical cases varies widely, and the provision of formal instruction concerning cases which are not seen may not substitute fully for real-life learning experiences. This is in part because clinical exposure and formal instruction predominantly utilise different learning pathways, those of implicit and explicit learning respectively. Computer-aided learning has a particular role to play in supplementing clinical experience in order to maximise students opportunity to acquire clinical recognition skills.. The evidence that computer-aided approaches can enhance learning of medical skills is reviewed. The use of CAL is illustrated by a model which combines multi-media teaching techniques with knowledge derived from learning theories in ord er to develop clinical recognition skills in medical undergraduates.

Aim

The aim of this paper is to examine the proposition that computer-aided learning (CAL), which utilises interactive multimedia-based teaching strategies, positively enhances the acquisition of selected clinical recognition skills by undergraduate medical students.IntroductionThe use of interactive multimedia-based computer-aided learning techniques has been proposed as a method to supplement, and even replace, certain aspects of traditional teaching. Proponents cite its ability to provide: (i) the delivery of known and institution-accredited standards of education, (ii) a means of surmounting the difficulties of scheduling attendances at lecture sessions and clinical teaching meetings, (iii) up to date knowledge, with the additional ability to assess the retention of both new and previously understood principles, and (iv) the drive to acquire new knowledge by virtue of the stimulating medium by which it is delivered. Other features of computer-aided learning include its ability to: cater for individualised instruction, allow interaction with the courseware material, reduce tutor resources, be easily modified to fit local practice, be readily updated and expanded, and be consistently reliable. Finally, CAL may offer cost benefits (Ref 1)

Medical students have traditionally acquired clinical skills through gaining access to hospitalised patients. By interviewing and examining them, students obtain global impressions which are considered in the light of their formally taught knowledge. However, critical examination of existing mechanisms of teaching suggests a number of problem areas. First, there is the difficulty of integration between didactic coursework and the application of this knowledge in the clinical setting. Second, students have variable access to clinical material, which influences the amount and quality of clinical skills which they are able to acquire. In psychiatry, for example, key clinical skills are developed in part by an osmotic process the more clinical contact students have with patients, the greater their ability to recognise relevant psychopathology.

Computer-aided learning in medical education: research findings

The introduction of CAL resources into health science curricula has achieved partial acceptance by educators and students. However, opinions differ as to whether there is sufficient research evidence to confirm a positive pedagogical effect (Ref 2). In the literature a number of acronyms are used to describe computer-based teaching methods including computer-based instruction (CBI), computer-aided instruction (CAI), and computer -based teaching (CBT), as well as CAL. Cohen and Decaney, in a meta-analysis of computer-based instructional outcomes (Ref 3) found that Computer-enriched applications of CBI produced larger effects than computer-assisted instruction and computer-managed instruction applications, and published reports showed larger effects than did dissertations and theses.

On the other hand, Reeves (Ref 4) has called for new research to guide future development and cautions educators to dispel the notion that CAL automatically guarantees learning.There are two broad arguments for the use of CAL in medical education.

First, that it is at least as effective as traditional means in helping students to learn specific knowledge and to develop problem solving skills. Educators attribute this to CAL's greater capacity (a) to make problem-based learning easier using realistic clinical simulations which are risk free, and (b) to provide more personalised instruction which is learner-centred, learner-paced and learner-controlled. This can result in a more enjoyable learning experience which stimulates users to try to understand more and to utilise learning resources more frequently.

Second, CAL may result in students learning more quickly.The literature contains further, albeit unsubstantiated, support for CAL. Clayden and Wilson (Ref 5). for example conclude that "...computer-assisted learning can liberate students and teachers from the burden of memorising facts". Piemme has suggested that computers have the capacity to "reduce the time necessary to achieve a given level of mastery of subject material.." and can "..alter physician and student behaviour to the benefit of patient care" (Ref 6).Sceptics of CAL point out that research designs to date have often been limited and do not necessarily indicate that the computer resource itself has been the effective learning variable. Gillingham and Guthrie state that research in this area has not demonstrated either that the computer-delivered components are the effective agent in any educational outcome study, or that they produce an additional gain compared to traditional teaching strategies (Ref 7). Further, it is not clear that the improvement in learning attributed to CAL is sufficiently large to be pedagogically important (Ref 6). Despite such criticisms, there appears to be agreement that until further empirical outcome study is performed, CAL has potential to be a major component of future medical education (Ref 8).

Practical Use

A number of studies report that students have a high acceptance of CAL, when used as an adjunct to traditional teaching. (Ref 9-15). Xakellis and Gjerde reported a favourable response by students to a variety of CAL instructional methods. Whereas there was no difference in overall acceptance by students, those programs that encouraged exploratory and revelatory processes resulted in better learning outcomes than those based on more simple 'drill and practice' methods (Ref 16).

For medical students, Malakoff et al found that replacing lectures with simulation-based CAL improved scores in third year board examinations for internal medicine. They concluded that 'Computer simulations may be useful in teaching and assessing higher cognitive skills' (Ref 17).

A number of factors have been identified which may confound outcome estimations. Pradham et al (Ref 18) demonstrated that where CAL is being used to modify student misconceptions, it is important to clearly identify the misconceptions prior to the introduction of the software component. That is, to match course design to identified problem areas.Osman and Muir reported that medical students not exposed to computer training in their pre-clinical years were unlikely to adopt computer use later (Ref 19). This is an argument for computer literacy training prior to the incorporation of specific CAL teaching projects in the medical curriculum. This finding may have relevance for those reports which focus on CAL strategies undertaken in clinical years which have not controlled for computer literacy.

As computer proficiency improves in the student population, outcome research will require reevaluation.With respect to the introduction of CAL resources in Australia, South et al found that only 35% of medical school departments sampled were using such methods in teaching. In addition, the range and number of computer-based teaching resources varied widely (Ref 20). However, a large majority of medical departments indicated they were either planning to incorporate some element of CAL-based teaching or were in the throes of implementing it. South et al concluded that its use in medical schools in Australia has the potential to expand rapidly, albeit in an uncoordinated fashion.

The learning process

Given that computer-aided learning has shown potential to enhance the learning process, what aspects of our understanding of learning and memory are relevant to the design and administration of such approaches? In clinical disciplines, learning consists of a number of different components. These include factual knowledge, procedural skills and pattern recognition skills. The need for factual knowledge is well-recognised and all training programmes devote a considerable proportion of their time to its inculcation. However, clinical practice does not consist solely of the application of this kind of knowledge, skill-based learning must also take place. This kind of knowledge, although essential to clinical practice, is much harder to quantify, teach, and assess. Clinical lore holds that it cannot be learnt from a book but requires extensive practice in a wide range of different situations to develop mastery.

Clinical skills and factual knowledge appear to depend upon different modes of learning, termed implicit learning and explicit learning, respectively.Implicit learning has only recently been recognised as a mode of learning that is qualitatively different from explicit learning. Attention to some findings in experimental studies will illustrate how these different learning processes may be relevant to learning clinical skills.

In a recent review, Seger (Ref 21) proposes four criteria for distinguishing these two kinds of learning.

First, when learning occurs implicitly, while subjects are able to demonstrate that they have acquired knowledge, they are typically unable to provide an adequate account of what they have learned. That is, implicit learners have little insight into what they have learned, or how they have learned it. In an early and paradigmatic study, Posner and Keele (Ref 22) presented subjects with a series of dot patterns some of which were spatial distortions of a single, prototype pattern that was never presented. The subjects were unable to report any rule relating these patterns, nor were they able to detect global similarities. Nevertheless, in a subsequent recognition test, they were more likely to (incorrectly) remember having seen the prototype pattern than any variant that was actually presented. Clearly, some kind of learning had taken place that did not depend upon explicit awareness of stimulus structure.

Second, implicit learning is typically more complex than a simple association or frequency count. This is because if the object of learning was a simple relationship, then it would be readily noticed and an explicit account offered by the subject. In order to prevent this, and to exclude the possibility of explicit strategies, researchers typically present complex problems to subjects that overwhelm their ability to detect key underlying patterns.

The third criterion is that implicit learning does not arise from conscious hypothesis testing but occurs as a consequence of the cognitive processes engaged in during the performance of a task. In other words, implicit learning is not a deliberate, nor necessarily controllable act. It occurs as a by-product of normal engagement with the world.

The final criterion for implicit learning strongly suggests that it is a mode of learning that depends upon different neural mechanisms than that which underlies explicit memory and decision-making. This concerns the finding that implicit learning is preserved in cases of amnesia. Amnesics, in spite of their inability to remember the details of learning episodes, nevertheless are often able to acquire perceptual-motor, pattern-analysing, or problem-solving skills at the same rate as normals. For example, Cohen and Squire (Ref 23) demonstrated that densely amnesic subjects were able to learn to read mirror-reversed writing as quickly as controls. This study also demonstrated an additional involvement of explicit learning in the controls' performance. The authors found that control subjects enjoyed an advantage over the amnesics in their ability to read words which had been presented earlier in the learning sequence. In this case, these subjects were able to recognise these words from their mirror-reversed form and immediately call out their names. The amnesics, unaware of these repetitions were forced to laboriously decode these words in the same way as if they had been presented for the first time.

The pattern-analysing skill studied by Cohen and Squire (Ref 23) fulfills the four criteria of implicit learning. First, while subjects were aware that they were able to read mirror-reversed words with some facility, they were unable to offer any insight into how this was achieved. The skill of reading is not easily verbalisable. Second, the skill itself is quite complex, requiring the detection of individual letters, their transformation, recognition, and integration into a single word. Third, the skill arises directly in response to attempts to perform the task. No other strategies or explicit problem solving is required. Lastly, as we have seen, the skill is preserved in amnesics.Reber (Ref 24) has argued that implicit learning represents an evolutionary early mode of learning that permeates mental activity and is more resistant to disruption. There is evidence that implicit learning shows fewer individual differences than explicit learning and that it appears relatively early developmentally and is preserved later into old age (see Seger 1994, for a review).

Based on animal research, Mishkin, Malamut and Bachevalier (Ref 25) have argued for the existence of two neurologically and functionally distinct learning systems called System 1 or "habit learning" and System 2 or "memory", respectively.

System 1 learning, like much implicit learning, detects and preserves patterns occurring over time. In other words, it aims to abstract common features from a sequence of specific instantiations. Our capacity to recognise our friends, despite the continual change in their dress and looks might rely on this kind of learning.

System 2 learning, on the other hand focuses upon the details of each instantiation, the elements of which may be consciously analysed.Both implicit and explicit learning are crucial to clinical practice. Explicit skills are clearly important as they allow specific problem solving and planning to be tailored to the specific features of the current patient. For example, the doctor must know explicitly, that a particular drug will have either beneficial or deleterious effects for the patient in the present circumstances.

However, implicit knowledge is also an essential part of the clinician's armamentarium. The experienced diagnostician is able to quickly recognise a particular condition in a patient, or at least narrow it down to a few likely candidates. In addition, if the pattern of signs and symptoms does not quite fit a nominated diagnosis, the experienced clinician may feel this "in their bones". This process of direct pattern recognition may be a result of implicit learning reflecting knowledge acquired from the examination of hundreds of similar cases. Unlike the programming of computer diagnostic systems, it is not explicit since it does not follow the logical rules of hypothesis testing and is not verbalisable. The importance of medical knowledge acquired implicitly was shown in an experiment by Christensen-Szalanski and Bushyhead (Ref 26) This involved a diagnostic reasoning task in which subjects are asked to estimate the likelihood that a patient has a particular condition on the basis of the overall prevalence of the disorder and the results of a test of imperfect accuracy. When the physicians are asked to reason explicitly about an unknown disorder, they tend to overestimate its likelihood. This is a common error of reasoning known as "base rate neglect" since the low initial prevalence of the condition tends to be discounted. However, when physicians had been exposed to a series of cases in which the same information is acquired implicitly, no errors in reasoning result. In addition to showing that correct diagnosis is based on implicit learning, this study also shows that such learning is highly specific. Being a good diagnostician in a given field is a function of and hence does not imply that the sa me person will be a good diagnostician in any other field.

In summary, there is justification for considering medical education as comprising two distict and complementary streams involving different learning processes. Formal knowledge is underpinned by explicit learning, clinical recognition skills by implicit learning.Rationale for using computer-aided learning to develop clinical skills acquisitionIt is proposed that students build complex recognition skills from repeated clinical contact, a form of implicit pattern recognition. In keeping with implicit learning theories, students may be able to correctly identify pathological or other clinical forms but be unable to explain the operations by which they arrived at the correct identification. Where explicit instructions about classical signs and symptoms are taught to students, this may not lead to consistent improvement in their ability to recognise these features in clinical situations. In accord with the cognitive learning models outlined in the section above, the more often a student is exposed to realistic case scenarios, the more developed the implicit rule (clinical recognition skill) base becomes.

A number of questions now arise as to the relationship of CAL to clinical skill acquisition by students. Using exposure to live patients as a starting point, what features of computer-based approaches will activate such implicit learning processes as occur in the live situation? Will any clinical recognition skill acquired by this process be transferable to the live clinical domain?

We have developed a number of models of clinical teaching using interactive multimedia which attempt to incorporate an implicit learning framework through use of a simulated clinical situation. Such models include clinical interviewing, recognition of specific psychopathological states (mental states), and neuropsychiatric conditions.

A clinical interviewing model

The model is based on a staged clinical interview simulation. The simulated clinical situation is comprised of a digital-video based scenario. The student directs the clinical situation via a variety of means - selecting from option lists, typing short text answers, or pointing and clicking to drive specific events. The simulated patient responds with appropriate short descriptions of history and mental state. The courseware contains multiple conditional branches and the flow of the course is determined by the conditions and options chosen by the student. The decisions made by the student have either positive or negative clinical outcomes and are recorded by the courseware, creating an 'audit trail'.

Specific feedback at critical points in the clinical decision-making process is provided in order to reinforce the cognitive basis of the clinical decision being made. When students follow clinical paths that lead to termination of interviews, or other negative doctor-patient outcomes, explicit clues targeted at remedying mistakes, accompanied by sufficient didactic or explicit knowledge concerning specific interviewing skills, are provided (as a complete, stand-alone subtutorial if necessary). The subsequent trail of responses and decisions left by the student, ('audit trail'), is available for analysis by the courseware feedback logic and accessible for review by tutors.

A number of clinical situations are presented which cover a range of doctor-patient interviews. For example, patients from different social and cultural backgrounds are included so as to develop the student's ability to recognise cultural factors in medical interviewing.

Each interview is constructed with multiple conditional branch points, that is, students move from stage to stage of the interview in a manner shaped by their responses to the patient. This flexible interview strcture permits the student to experience both positive and negative outcomes in their interaction with the 'patient'. For example, they may arrive at a correct or incorrect diagnosis or the patient may terminate the interview.

The incorporation of a clinically-based self assessment module allows students to gauge their immediate performance. Extensive reporting on individual student's performance is available to clinical tutors so that traditional teaching techniques can be directed towards conceptual or problematical areas for students on either a group or individual basis.In any one medical school, undergraduates are taught in a number of teaching hospitals, not all of which have access to staff with requisite teaching experience. The model potentially provides students with access to patient-related clinical teaching independent of the availability of qualified teaching staff.

In the above model, it is the repetition of experience with a virtual patient which allows the student to safely abstract implicit rules governing interviewing techniques. A similar process occurs with other clinical skills such as the identification of mental state phenomena, such as hallucinations and delusions. Students may acquire recognition skills by interacting with a simulated patient or series of patients who all demonstrate a clinical form against the varied background of their own personalities, histories and so on.

With repeated interactions with the computer-based system, students implicitly abstract the 'rules' for delusion, for example, even if the simulated patients are from different genders, social and cultural backgrounds, or diagnostic clusters. Marrying explicit instruction to multiple exemplars of the condition allows students to combine both domains of knowledge in a situationally relevant manner.To date there has been little research into the application of CAL-based clinical teaching and the transferability of knowledge to real-life clinical situations.

Yoder (Ref 27) showed that when standard videotape teaching was compared to interactive computer-based instruction of nursing students in aseptic surgical techniques, there was a greater transfer of knowledge to the clinical domain when computer-based techniques were employed. From this it can be expected that the combination of multiple simulated patient interviews allowing implicit learning, and the interactivity inherent in CAL design, will allow for transferability of knowledge in medical student teaching. However, further research is required to test this proposition.

The final question posed above was whether the application of such teaching models is feasible. As South pointed out (Ref 20), there is considerable interest in CAL-based education in Australian medical schools. Skills acquired through this type of model may be applicable to clinical practice in all medical disciplines. With the advent of improved technology and different cognitive learning applications, adjunctive clinical teaching with the use of CAL may not only be feasible in the coming years, but an essential tool for teaching the medical curriculum.

References

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Correspondence to:

Tim J. Lambert, Associate Professor, FRANZCP, Dept. Of Psychiatry and Behavioural Science, University of Western Australia, 35 Mills Street, Bentley, AUSTRALIA 61022.

What are the benefits of computer assisted instruction?

Pros of Computer-Assisted Learning.
Students and Instructors Can Receive Real-Time Feedback. CAL reveals solutions and assesses student performance immediately. ... .
The Learning Process Is More Interactive and Engaging. ... .
Learning Can Be More Personalized. ... .
Technology Can Fill the Gaps for Students with Learning Differences..

Is computer assisted instruction effective in education?

While the use of CAL can be useful in any classroom, it's especially beneficial in language learning classrooms. In fact, it's so effective that it gets its own acronym too! CALL, or Computer Assisted Language Learning, is quickly becoming one of the preferred teaching tools among foreign language instructors.

What are the advantages of using a computer to monitor a patient in hospital?

Electronic records offer several advantages over paper records, including improved accuracy, efficiency, and patient safety. Electronic health record systems allow clinicians to instantly access a patient's medical history, making it easier to provide accurate diagnoses and high-quality care.