Michael A. Fauman, MD, PhD

Several readers have responded with comments and concerns regarding my column, "Do Physicians Use Practice Guidelines?" (Psychiatric Times, June 2006, page 13). The concerns center on the relevance of clinical practice guidelines (CPGs) to the practice of psychiatry; specifically, what makes a CPG more valid than other forms of research such as literature, meetings, and lectures, and how can physicians be sure that CPGs are free of bias from managed care and insurance companies? Since the issues these readers raised are important and concern many psychiatrists, they merit some discussion.


Many CPGs are created through a process of expert consensus in which clinicians who specialize in studying a specific psychiatric illness are asked to rate the appropriateness of various treatments for the disorder. For example, in the development of a consensus guideline for the pharmacologic treatment of psychosis, 50 expert clinicians were asked to rate the appropriateness of 11 antipsychotic medications for the treatment of an initial episode of psychosis with predominantly positive psychopathology.

 Medications were subsequently designated as first-, second-, and third-line choices based on statistical analysis of the combined expert ratings.It is reasonable to question how these experts were selected and why they should be considered more qualified to draw conclusions from the research data than practicing psychiatrists who are, by definition, also experts.

 Clinicians and researchers, like those who create CPGs, often disagree among themselves about the meaning of research results and the relative effectiveness of the various treatments for a psychiatric disorder. Sometimes they cannot provide convincing evidence that certain treatments are better than others.

However, these problems don't necessarily invalidate the use of CPGs. In an ideal world, research would provide definitive answers about the effective treatment of various disorders. Every clinician would know this and could appropriately apply it in patient care.	

Unfortunately, the world of psychiatric treatment is far from ideal. Research is a messy proposition that often produces tentative and inconclusive results that are open to various interpretations. If disagreement about research results were the criterion for rejection, science would make little progress in discovering successful treatments for medical disorders. In fact, science actually thrives on disagreement because it drives further research and exploration. The challenge for clinicians and researchers is to provide the best possible interpretation of the data while controlling for the cognitive biases that inevitably enter into any such judgment process.

I don't know exactly how clinical experts are chosen, but I presume they are selected by their colleagues based on who has published the most relevant papers on the diagnosis and treatment of specific psychiatric disorders. There is certainly potential for bias in this process, but I cannot think of a better way to identify experts in the field. Selecting a large number of experts ensures that the guidelines will include the broadest possible knowledge about treating the disorder. It also provides a peer review process that decreases the likelihood of bias in the interpretation of research results. This does not mean that bias is extinguished-it is simply reduced.

Physicians often argue that it is sufficient to attend professional meetings, read the literature, listen to lectures, and consult with colleagues in order to draw their own conclusions about how to treat their patients. They maintain that they do not need anyone to tell them how to practice medicine. Yet practitioners, like experts, need to guard against bias in their own interpretation of clinical and research data. Individual clinical experts may be no more skillful than practicing psychiatrists in the management of a complex case, but the collective wisdom of a large number of experts is more likely to be correct than is the judgment of a single clinician.

An expert consensus panel is also a useful method for filling in the gaps in formal research. No study can address all of the questions associated with the clinical treatment of a disorder. As the number of treatment subgroups in a research study increases, a greater number of subjects will be required to provide the statistical power necessary to detect differences among the subgroups. Eventually, the number of subjects and the cost of the study will become too large to manage. An expert consensus guideline provides some direction to practitioners about treatments that are not included in the formal research.

CPGs represent a compilation of the current best practices or standards in the treatment of various disorders. They are meant to be a supplement to physician education, not a "cookbook" for treatment. Every guideline that I have seen emphasizes the role of clinical judgment. Knowing when and how to apply the recommendations in the guidelines requires knowledge and experience. Most CPGs, even algorithms, contain broad outlines of care with multiple choices at each stage of treatment. These choices are under the clinician's control. Encouraging compliance with CPGs simply means encouraging psychiatrists to provide treatment that lies within the broad acceptable standards of care.


When psychiatrists argue that CPGs have "limited applicability" to their patients, they are usually referring to the inability of guidelines to effectively address the variations in patient responses to treatment. This is a valid and important issue. Although many patients respond to initial treatment with a single medication, a substantial number appear to require a more complex regimen of multiple medications. To be useful, CPGs must address these real-world clinical problems.

One of the readers who responded to my column correctly observed that this limitation is currently being addressed in studies such as the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) and the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD).

 The STAR*D, in particular, focuses on the clinical management of patients who have failed 1 or more treatment trials with a single medication. When the results from this and other new research are incorporated into CPGs, they should be able to address more of the clinical variability seen in everyday practice.	

It may also be worth studying the types of variability and problems that clinicians face in the treatment of complex cases. Yet, even these types of studies cannot control for all the sources of patient variation. There will always be a role for clinical judgment in adjusting treatment for patients who do not quite fit the guidelines or who do not respond to treatment in a typical manner. CPGs are, as the name implies, guides to clinical care, not laws dictating how treatment should be provided.


Some clinicians believe that managed care and insurance companies encourage clinicians to comply with CPGs as a way of reducing costs-often to the detriment of the patient. This assumption mistakenly equates CPGs with the medical necessity criteria used by managed care companies to control costs; these are 2 entirely different sets of guidelines with very different goals. CPGs can actually run counter to the cost-saving goals of managed care medical necessity criteria because CPGs are primarily focused on the provision of care that is defined to be appropriate by clinical experts in the field. CPGs can (but usually do not) explicitly take the cost of care into account in their recommendations.


Some physicians believe that any attempt to convince them to comply with CPGs represents an intrusion into the doctor-patient relationship. Physicians are also concerned that increased emphasis on CPGs will lead to a loss of professional autonomy.

Given these concerns, it is worth remembering why CPGs were developed. Investigators have repeatedly observed significant and unexplained variations in the type and quality of care that clinicians provide in different areas of the country. Moreover, the number of mistakes made in the provision of clinical care in the United States should be sobering to any clinician.

 The Institute of Medicine has referred to these and other problems in care as the "quality chasm."

 No well-intentioned physician would consciously offer poor care to patients, but we can all be misled by our biases and limited knowledge in some areas. CPGs attempt to address these problems by outlining the best medical practices and acceptable parameters of care.

One way to think of a CPG is as a set of treatment principles that fit a significant proportion of patients (perhaps 90% of those with a specific disorder). The remaining patients have a less typical response to treatment. Given this premise, the majority of a physician's patients should fall within the parameters outlined in a specific CPG. A smaller proportion will have to be treated in a manner that deviates from the guidelines. Most physicians will probably discover that they already treat the majority of their patients according to the relevant guidelines. If they find themselves consistently disagreeing with the guidelines, they might want to examine their reasons for doing so.

All clinicians are subject to some regulation by their specialty and by society to ensure that the patients they treat are receiving appropriate care. That is simply a fact of professional life that is unlikely to change. The question is, what is the balance between freedom of action and regulation? The best solution is the one that ensures the most appropriate care for patients while interfering the least in the physician-patient relationship.

Since physicians look to their colleagues the most for consultation and guidance, perhaps this process can be formalized as a method of correcting potential biases and misinformation.

 An effective system might provide some type of real-time online peer review and consultation system that physicians could use both to ask their colleagues for clinical advice and to observe how their colleagues have managed comparable clinical problems. The goal, as always, would be to improve the quality of care.

Dr Fauman is the author of Negotiating Managed Care: A Manual for Clinicians and Study Guide to DSM-IV-TR, both published by American Psychiatric Publishing, Inc. He is adjunct clinical associate professor of psychiatry at the University of Michigan and medical director of Magellan Health Services of Michigan.

 Kane JM, Leucht S, Carpenter D, Docherty JP. Expert consensus guideline series. Optimizing pharmacologic treatment of psychotic disorders. Introduction: methods, commentary, and summary. J Clin Psychiatry. 2003;64(suppl 12):5-19.


 Hampton JR. Guidelines-for the obedience of fools and the guidance of wise men? Clin Med. 2003;3:279-284.


 Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science. 1974;185:1124-1131.


 Kahneman D, Slovic P, Tversky A, eds. Judgment Under Uncertainty: Heuristics and Biases. Cambridge, United Kingdom: Cambridge University Press; 1982.


 Rush AJ, Fava M, Wisniewski SR, et al. Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Control Clin Trials. 2004;25:119-142.


 Rush AJ, Trivedi MH, Wisniewski SR, et al. Bupropion-SR, sertraline, or venlafaxine-XR after failure of SSRIs for depression. N Engl J Med. 2006;354:1231-1242.


 Sachs GS, Thase ME, Otto MW, et al. Rationale, design, and methods of the systematic treatment enhancement program for bipolar disorder (STEP-BD). Biol Psychiatry. 2003;53:1028-1042.


 Hayward RS, Guyatt GH, Moore KA, et al. Canadian physicians' attitudes about and preferences regard-ing clinical practice guidelines. CMAJ. 1997;156:1715-1723.


 Cabana MD, Rand CS, Powe NR, et al. Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999;282:1458-1465.


 Kohn LT, Corrigan JM, Donaldson MS, eds. To Err Is Human: Building a Safer Health System. Washington, DC: The National Academies Press; 2000.


 Committee on Quality of Health Care in America, Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: The National Academies Press; 2001.


 Cranney M, Warren E, Barton S, et al. Why do GPs not implement evidence-based guidelines? A descriptive study. Fam Pract. 2001;18:359-363.  

Articles

Several readers have responded with comments and concerns regarding my column, "Do Physicians Use Practice Guidelines?" Since the issues these readers raised are important and concern many psychiatrists, they merit some discussion.

Concerns About Practice Guidelines

In my last 2 columns, I discussed the problems associated with clinical practice guidelines and described how some physicians use them in practice. In this column, I will review studies on the validation of clinical algorithms, which are 1 of the 2 main types of practice guidelines. Algorithms are detailed, step-by-step flow charts that outline the recommended treatment for patients with a specific disorder.

Initial studies-such as the stepped collaborative care intervention, Texas Medication Algorithm Project (TMAP), and German Algorithm Project (GAP) phase 2-predominantly investigated whether following an expert opinion-based clinical algorithm (irrespective of the content of the algorithm) led to a better outcome than treatment as usual did.

 Subsequent studies, including the Sequenced Treatment Alter na tives to Relieve Depression (STAR*D) and GAP phase 3, focused on comparing the effectiveness of different treatments that were provided at each step in a sequenced algorithm.

 The former studies tell us something about the process of care, or how treatment should be provided. The latter tell us about the content of care, or which treatment should be provided at each stage in the algorithm.


Several studies suggest that patients with a major depressive or bipolar disorder who are treated according to an accepted clinical algorithm (ALGO) have more successful outcomes than those treated as usual (TAU).

 Some of these studies, and their results, are discussed below.

 studied the effect of a stepped collaborative care intervention for primary care patients who had persistent or treatment-resistant depression that did not respond to several weeks of conventional antidepressant treatment. Patients were divided into 2 groups: an ALGO group, treated using a "stepped-care" algorithm that included education and structured clinical interventions, and a TAU group, who were simply told to speak with their primary care physician about treatment for depression. Depression was measured using the 20-question depression subscale of the Hopkins Symptom Checklist-90. Patients in the ALGO group who had moderately severe depression-but not those who had severe depression-were significantly improved compared with patients in the TAU group (P = .004) after several months of treatment.


In the GAP, Adli and colleagues8 developed a standardized stepwise drug treatment regimen (SSTR) for treatment-resistant depression. In a randomized controlled study, they used the Bech-Rafaelsen Melancholia Scale (BRMS) to compare the outcome of patients treated by SSTR (n = 74) and TAU (n = 74).4 SSTR-treated patients had a better outcome than TAU patients (BRMS scores: SSTR = 5.4, TAU = 9.5; P < .01). The SSTR group also had a significantly higher dropout rate (45%) than the TAU group (16%). The authors commented that 33% of the dropouts were due to physician non compliance with algorithm rules.


Researchers in the TMAP developed detailed algorithms for the treatment of major depressive disorder, schizophrenia, and bipolar disorder in the Texas public health system. In a 12-month study of the effectiveness of the algorithm in the treatment of major depressive disorder, Trivedi and associates

 assigned patients with depression to an ALGO group (n = 175) or a TAU group (n = 175). After 3 months of treatment, ALGO patients had significantly fewer depressive symptoms (P = .002), as measured on the Inventory of Depressive Symptomatology-Clinician Rating Scale (IDS-C30), than the TAU group. During the remaining 9 months of treatment, there was no difference in the rate of improvement of the 2 groups (P = .74), but TAU patients never caught up with the initial lead established by the ALGO group.	

ALGO treatment, however, does not always produce a significantly better response than TAU. Suppes and col leagues

 studied the clinical response of a group of patients with bipolar disorder who were treated according to the TMAP bipolar algorithm. In the 12-month study, the ALGO group showed significant improvements (P = .03) on the Brief Psychiatric Rating Scale (BPRS-24) compared with the TAU group in the first 3 months. The differences between the 2 groups narrowed considerably from months 3 to 12 as the TAU group's rate of improvement on the scale increased.

Manic and hypomanic symptoms, measured by the Clinician-Adminis tered Rating Scale for Mania, decreased significantly more in the ALGO group (P = .007) than in the TAU group during the first 3 months of treatment. The ALGO patients continued to do better than the TAU patients from months 3 to 12, but there was no significant difference in the rate of improvement between the 2 groups. ALGO patients demonstrated a significantly greater decrease in psychotic symptoms over TAU patients in the first 3 months of treatment, but the TAU group caught up by month 12. There were no significant differences in the symptoms of depression, measured on the IDS-C30, between the ALGO and TAU groups during the 12 months of the study.

In a second TMAP bipolar study, investigators examined the relationship between the clinical response of patients to treatment and the adherence of their providers to the TMAP bipolar algorithm.

 The authors used a complex formula to calculate the amount of improvement experienced by ALGO-treated and TAU patients. ALG patients who had depression demonstrated statistically significant improvements (P = .005) in symptoms on the IDS-C30 compared with TAU patients during the last 9 months of the 12-month study. The ALGO group also demonstrated greater improvement than the TAU group on the BPRS-24 during that time. ALGO treatment, however, did not produce significant improvements in manic or hypomanic symptoms over TAU. Curiously, providers who had more experience with the TMAP algorithm adhered to it less.


The STAR*D trial was designed to objectively determine an effective 4-level sequence of treatments for outpatients with nonpsychotic, treatment-resistant depression.

 In an initial STAR*D study, 2876 psychiatric and primary care outpatients with depression were treated at level 1 with citalopram (Celexa).

 Depression was mea sured using the 17-item Hamilton De pression Rating Scale (HAM-D) and the 16-item Quick Inventory of Depressive Symptomatology, Self-Report. By the end of the study, 790 (27.5%) patients had achieved remission (de fined as a score of 7 or less on the HAM-D). A large proportion of these patients required 8 or more weeks of treatment with an average dosage of 41.8 mg/d of citalopram.

In a subsequent STAR*D trial, 727 patients who did not respond to level 1 treatment were randomly assigned to level 2 treatment with sustained-release bupropion (Wellbutrin), sertraline (Zoloft), or extended-release venlafaxine (Effexor).

 About one quarter of these patients achieved remission. The difference in response to the 3 medications was not statistically significant. A second group of 851 patients who did not achieve remission with citalopram in the level 1 trial received level 2 augmentations with sustained-release bupropion or buspirone (BuSpar). The proportion of patients who achieved remission was similar for the 2 medications (29.7% for sustained-release bupropion and 30.1% for buspirone).

 treated 235 patients who had not responded to level 1 or 2 treatments in either STAR*D trial with mirtazapine (Remeron) or nor  triptyline (Aventyl, Pamelor) for 14 weeks (level 3). Remission rates were 12.3% for mirtazapine and 19.8% for nortriptyline. The differences in out come between the 2 treatments were not statistically significant.


What conclusions can we draw from these comprehensive, well-designed, and well-executed studies? Like most complex investigations, they resolve some important questions and raise others. The collaborative care, TMAP, and GAP studies repeatedly indicated that ALGO treatment produces a statistically better clinical response than TAU. Unfortunately, it is not exactly clear what this means because we do not know any of the details of how the TAU patients were treated. For example, it would be useful to know whether physicians who treated patients without algorithms made specific types of mistakes that physicians who did use them avoided. Did they routinely treat patients with too little medication for too short a length of time? If so, those mistakes might easily be corrected. In any case, it would be useful to study the TAU group to determine what errors in treatment decreased the potential effectiveness of their care.

Investigators have suggested 2 possible reasons for the improved outcomes seen in patients treated with algorithms: it could be the content of the ALGO treatments or the diligent, organized man ner in which they are provided to patients.

The former seems less likely since the sequence and-to some extent-the content of each step in the TMAP and GAP algorithms were different, yet each algorithm demonstrated superior effectiveness over TAU.

The second and more likely reason for the superiority of ALGO treatment is the consistent and organized manner in which care was provided. Trivedi and colleagues

 referred to this diligent care as "measurement-based care" and defined it as "the routine mea sure ment of symptoms and side effects at each treatment visit" followed by an appropriate adjustment of medication. It is possible that the simple act of following the algorithm, like a to-do list, continually reminds the physician of the necessary components of care. It is also possible that there were more resources devoted to ALGO patients and that the use of these resources was responsible for the patients' higher rate of remission.

The STAR*D studies tell us something interesting about the choice of specific treatments at each level in the care of patients with treatment-resistant depression-the treatments are apparently equivalent. It made little difference in level 2 treatment whether a patient was given sustained-release bupropion, sertraline, or extended-release venlafaxine or whether the initial citalopram treatment was augmented with sustained-release bupropion or buspirone. The same was true in level 3 treatment, where mirtazapine and nortriptyline produced similar responses. The results suggest that clinicians could have given patients sus tained-release bupropion or sertraline as the initial medicine, rather than citalopram, and obtained the same results. This is not surprising given the observation noted earlier that even though the TMAP and GAP algorithms were different, ALGO patients in each study did better than TAU patients.

Assessments of the importance of the STAR*D trials vary. Insel1

 highlighted the importance of the real-world disease management approach of the STAR*D study, its focus on remission rather than response, and the early results showing that patients required higher doses of citalopram for longer periods than previously thought to achieve remission. Menza

 evaluated several STAR*D reports and suggested that the project only incrementally added to our knowledge of how to use antidepressant medications and would probably not have a great impact on how clinicians prescribe these medications. The results do, however, confirm the findings of other studies and the observations of many clinicians about the management of treatment-resistant depression.

It is well worth noting that, despite the comprehensive approach adopted in the STAR*D and GAP phase 3 studies, only half of the patients involved actually achieved remission.

 Clearly, psychiatry has much more to learn about how to treat depression. It is also important to note that even in the ALGO groups, some physicians did not strictly follow the algorithm.

 If this occurs in a controlled clinical study, what will happen in general psychiatric practice?

As a group, current studies of the algorithm-based management of treatment-resistant depression may say as much about the process of care as its content. Perhaps the best conclusion that can be drawn from the research thus far is that conscientious, organized, and consistent measurement-based care is as important as, if not more important than, the specific medications used to manage the disorder.

Dr Fauman is the author of Negotiating Managed Care and Study Guide to DSM-IV-TR, both recently published by American Psychiatric Publishing, Inc. He is adjunct clinical associate professor of psychiatry at the University of Michigan and medical director of Magellan Behavioral of Michigan in Farmington Hills.

 Suppes T, Dennehy EB, Hirschfeld RM, et al. The Texas implementation of medication algorithms: update to the algorithms for treatment of bipolar I disorder. J Clin Psychiatry. 2005;66:870-886.


 Trivedi MH, Rush AJ, Crismon ML, et al. Clinical results for patients with major depressive disorder in the Texas Medication Algorithm Project. Arch Gen Psychiatry. 2004;61:669-680.


 Katon W, Russo J, Von Korff M, et al. Long-term effects of a collaborative care intervention in persistently depressed primary care patients. J Gen Intern Med. 2002;17:741-748.


 Adli M, Rush AJ, Moller HJ, Bauer M. Algorithms for optimizing the treatment of depression: making the right decision at the right time. Pharmacopsychiatry. 2003;36(suppl 3):S222-S229.


 Adli M, Bauer M, Rush AJ. Algorithms and collaborative-care systems for depression: are they effective and why? A systematic review. Biol Psychiatry. 2006;59: 1029-1038.


 Katon W, Von Korff M, Lin E, et al. Stepped collaborative care for primary care patients with persistent symptoms of depression: a randomized trial. Arch Gen Psychiatry. 1999;56:1109-1115.


 Adli M, Berghofer A, Linden M, et al. Effectiveness and feasibility of a standard stepwise drug treatment regimen algorithm for inpatients with depressive disorders: results of a 2-year observational algorithm study. J Clin Psychiatry. 2002;63:782-790.


 Suppes T, Rush AJ, Dennehy EB, et al. Texas Medication Algorithm Project, phase 3 (TMAP-3): clinical results for patients with a history of mania. J Clin Psychiatry. 2003; 64:370-382.


 Dennehy EB, Suppes T, Rush AJ, et al. Does provider adherence to a treatment guideline change clinical outcomes for patients with bipolar disorder? Results from the Texas Medication Algorithm Project. Psychol Med. 2005;35:1695-1706.


 Trivedi MH, Rush AJ, Wisniewski SR, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006;163:28-40.


 Trivedi MH, Fava M, Wisniewski SR, et al. Medication augmentation after the failure of SSRIs for depression. N Engl J Med. 2006;354:1243-1252.


 Fava M, Rush AJ, Wisniewski SR, et al. A comparison of mirtazapine and nortriptyline following two consecutive failed medication treatments for depressed outpatients: a STAR*D report. Am J Psychiatry. 2006; 163:1161-1172.


 Insel TR. Beyond efficacy: the STAR*D trial. Am J Psychiatry. 2006;163:5-7.


 Menza M. STAR*D: the results begin to roll in. Am J Psychiatry. 2006;163:1123.


 Rubinow DR. Treatment strategies after SSRI failure-good news and bad news. N Engl J Med. 2006; 354:1305-1307.

Initial studies-such as the stepped collaborative care intervention, Texas Medication Algorithm Project (TMAP), and German Algorithm Project (GAP) phase 2-predominantly investigated whether following an expert opinion–based clinical algorithm (irrespective of the content of the algorithm) led to a better outcome than treatment as usual did

Algorithm-based Treatment

A clinical practice guideline (CPG) is a set of rules or suggestions for the treatment of a clinical disorder. 

, I asked whether physicians used CPGs. I reviewed several studies of the problems with guidelines, and discussed some of the barriers to their use by physicians.

The studies demonstrated that there are still many questions about how to develop useful guidelines, introduce them into clinical practice, and measure their effectiveness. In this article, I want to discuss a more fundamental question: What does it mean to use a guideline in practice? Do clinicians have to consult it daily or turn to it only when they have a question? Do they follow it exactly or use it only as a guide?

Any discussion of CPGs has to consider the ongoing debate about the relative roles of evidence-based medicine and clinical judgment in the treatment of patients.

 The argument is between evidence-based advocates, who want to model patient care on statistical information derived from therapeutic randomized controlled trials (RCTs) of large groups of patients, and those who think that clinical judgment or common sense gained in the process of caring for individual patients should be sufficient.

CPGs are based on interpretations of RCT results by clinicians who are experts in the treatment of the disorder. Many physicians think that this process produces valid and reliable standards of care that should be followed as closely as possible. They argue that clinical judgment alone can be misleading because humans commonly make cognitive errors, particularly in the assessment of probability, that might bias their clinical decisions about the effectiveness of a specific therapy.

Other physicians are skeptical about the increasing emphasis on RCTs and the CPGs derived from them. They argue that RCTs are not always as scientific, objective, or comprehensive as they are purported to be because researchers can be biased in the way in which they conduct their studies, and experts can be biased in how they interpret the results of those studies.

 Skeptics claim that guidelines are often too simplistic to deal with the variability of expression of an illness in different patients.

Most CPGs state that physicians should use their clinical judgment in applying the guidelines.

 Critics, however, contend that CPGs have the subtle effect of deemphasizing the importance of a physician's judgment in the treatment of a patient. Moreover, they argue that many important clinical decisions are based on common sense and don't require a CPG.

Some have even charged that the evidence-based medicine movement has become a cult that hinders the provision of appropriate care.

There is some truth on each side of the argument. Clinicians make common errors in judgment and experts can be biased in their interpretation of research data. Guidelines are unlikely to explicitly address the variability of expression of illness in every patient. It is also true that many clinical decisions do not require a CPG; few physicians need a guideline to justify suturing a large wound to stop bleeding and promote healing. Yet few CPGs are that simplistic. Most are developed to address more complex treatment issues that contain too many variables to be easily resolved by common sense. At the same time, physicians always have an obligation to use clinical judgment to question the validity of these guidelines for their patients.

The 2 main types of CPGs-comprehensive guidelines and clinical algorithms-differ in the amount and detail of information they provide and the comprehensiveness, specificity, and strength of their treatment recommendations.

Comprehensive guidelines are detailed clinical monographs that discuss the advantages and disadvantages of the various treatment options for a disorder and rate the strength of the scientific evidence for each of the treatments.

 They typically make general recommendations and cede broad discretion to the clinician to decide which of several treatment approaches to prescribe.

Clinical algorithms are narrower in scope and more structured than comprehensive guidelines.

 They usually focus on a specific type of treatment. Medication algorithms, for example, provide step-by-step flowcharts that specify the types of medication, doses to be prescribed, number of treatment sessions, and other factors. They allow patients and physicians to make treatment choices, but these choices are more restricted than those in the comprehensive guidelines. Algorithms try to explicitly outline every step in the provision of care so that treatment can be standardized and data can be collected to monitor physician compliance with the guideline and patient response to treatment.

Once clinicians try to apply these CPGs they soon realize that even the most comprehensive or structured guideline cannot anticipate every variation in a patient's clinical response to treatment. Some patients will respond to medication at a lower than recommended dose. Others will have partial responses to medication, take much longer than expected to respond to treatment, or require higher doses of medication than those specified in the guideline. Still others will have debilitating adverse effects. At each step along the way, physicians will have to make decisions about many factors, including the type and dosage of medication. Given these problems, how do physicians incorporate guidelines into their practices and integrate them with their clinical judgment?

One intriguing answer to this question comes from an ethnographic study by Gabbay and le May

 of how physicians in 2 British general medical practices used CPGs in the course of making clinical decisions. The investigators discovered that the physicians in the study examined CPGs informally (eg, if they came from an authoritative source such as a professional group) to make sure that their own practice was in reasonable compliance with the standards of care. They had access to the Internet and several expert systems, but rarely used information from these sources or from research in their work.

Instead, they relied on what the authors called mindlines-collectively reinforced, internalized tacit guidelines-that were based on some reading, but mainly on interactions with colleagues, especially with trusted sources such as opinion leaders. These mindlines were adjusted over time by experiences with patients, reading, and continuing meetings and discussions with colleagues within their communities of practice. CPGs were only one of the many sources of information that the clinicians used to inform their clinical practice.

These results are consistent with the findings of an earlier study of the diffusion of medical innovation.

 That study of the prescription of a new antibiotic by a large group of general practitioners in the Midwest also showed that discussions with colleagues who had prescribed the antibiotic were one of the major factors influencing physicians to adopt the new drug.

The concept of tacit knowledge used by Gabbay and le May requires some elaboration. Tacit knowledge was defined by Polanyi

 as knowledge gained in practice as opposed to empirical knowledge gained from formal sources, such as research, lectures, or reading. Clinicians usually cannot explain the basis of their tacit knowledge because it often contains hazy ideas that do not follow the rigorous forms of traditional logic. It could be thought of as professional intuition or common sense based on extended clinical experience.

In Gabbay and le May's model, CPGs are digested in the context of a clinician's tacit knowledge and that of his professional community to produce mindlines. The authors caution, however, that the 2 medical practices they studied may not be representative of other clinical practices. Yet the results do raise interesting questions about how clinicians gather and use information and knowledge in their practices.

Other authors have studied the use of brief heuristics, or rules of thumb, in treating patients. Andre and colleagues

 used focus groups to investigate the use of clinical rules of thumb by 23 Swedish general practitioners. The rules were grouped into 2 categories-those used to evaluate potential medical emergencies and those used to evaluate routine somatic and psychosocial problems. Examples included: For me a chest pain in someone whom I've never seen before becomes . . . a quick thought toward the hospital; Flashes in the eye and some spots, it's of course vitreous detachment; and So when I notice that I've gone over the line, I say,'Now maybe you think what I've said has been quite troublesome? Shall we step back a little?'

Some of the rules have the rather inarticulate, fuzzy nature of tacit knowledge. They are often used to make clinical decisions that involve assessments of risk and probability. Gabbay and le May argued that mindlines were similar to, but more complex and comprehensive than, the rules of thumb. Both are forms of clinical judgment; mindlines have the advantage of validation by the peer professional community.

It might be useful to think of a CPG as an intellectual construct analogous to a legal constitution. Both systems represent general rules that govern human behavior. Each has to be interpreted for the individual event. Lawyers use their legal judgment to apply the constitution to specific cases. Physicians use clinical judgment to apply the guidelines to the treatment of specific patients. In that sense, a CPG represents the broad outline of treatment for a specific disorder and defines the limits of acceptable care as determined by the profession. The role of clinical judgment is to finetune this outline for each patient.

The debate between evidence-based medicine and clinical judgment represents a false dichotomy. The 2 approaches address different levels of clinical information. They overlap and periodically encroach on each other's territory. They represent a constant, sometimes contentious dialog about the most appropriate care, but they are not mutually exclusive.

 Negotiating Managed Care: A Manual for Clinicians 

both published by American Psychiatric Publishing, Inc. He is adjunct clinical associate professor of psychiatry at the University of Michigan in Ann Arbor and medical director of Magellan Health Services of Michigan.

1. Sackett DL, Rosenberg WM, Gray JA, et al. Evidence based medicine: what it is and what it isn't. BMJ. 1996;312:71-72. 
2. Jones M, Higgs J, eds. Will Evidence-based Practice Take the Reasoning Out of Practice? Clinical Reasoning in the Health Professions. 2nd ed. Oxford, England: Butterworth-Heinemann; 2000:307-315. 
3. Michelson J. Critique of (im)pure reason: evidencebased medicine and common sense. J Eval Clin Pract. 2004;10:157-161. 
4. Porta M. Is there life after evidence-based medicine? J Eval Clin Pract. 2004;10:147-152. 
5. Miles A, Grey JE, Polychronis A, et al. Developments in the evidence-based health care debate-2004. J Eval Clin Pract. 2004;10:129-142. 
6. Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science. 1974;185: 1124-1131. 
7. Chapman GB, Elstein AS. Cognitive processes and biases in medical decision making. In: Chapman GB, Sonnenberg FA, eds. Decision Making in Health Care: Theory, Psychology, and Applications. New York: Cambridge University Press; 2000:183-210. 
8. Hampton JR. Guidelines-for the obedience of fools and the guidance of wise men? Clin Med. 2003;3:279-284. 
9. Suppes T, Dennehy EB, Hirschfeld RM, et al. Texas Consensus Conference Panel on Medication Treatment of Bipolar Disorder. The Texas Implementation of medication algorithms: update to the algorithms for treatment of bipolar I disorder. J Clin Psychiatry. 2005;66:870-886. 
10. Smith GC, Pell JP. Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials. BMJ. 2003;327:1459-1461. 
11. Clinicians for the Restoration of Autonomous Practice (CRAP) Writing Group. EBM: unmasking the ugly truth. BMJ. 2002;325:1496-1498. 
12. Genuis SK, Genuis SJ. Exploring the continuum: medical information to effective clinical practice. Paper I: the translation of knowledge into clinical practice. J Eval Clin Pract. 2006;12:49-62. 
13. American Psychiatric Association. Practice guideline for the treatment of patients with major depressive disorder (revision). Am J Psychiatry. 2000; 157(suppl 4):1-45. 
14. Dennehy EB, Suppes T, John Rush A, et al. Development of a computerized assessment of clinician adherence to a treatment guideline for patients with bipolar disorder. J Psychiatr Res. 2004;38:285-294. 
15. Gabbay J, le May A. Evidence based guidelines or collectively constructed "mindlines?" Ethnographic study of knowledge management in primary care. BMJ. 2004;329:1013. 
16. Coleman JS, Katz E, Menzel H. Medical Innovation: A Diffusion Study. New York: Bobs-Merrill; 1966. 
17. Polanyi M. The Tacit Dimension. Garden City, New York: Anchor Books, Doubleday & Company, Inc; 1966. 
18. Scott D. Everyman Revived: The Common Sense of Michael Polanyi. Grand Rapids, Mich: William B. Eerdmans Publishing Company; 1995. 
19. Andre M, Borgquist L, Foldevi M, Molstad S. Asking for 'rules of thumb': a way to discover tacit knowledge in general practice. Family Pract. 2002; 19:617-622. 
20. Andre M, Borgquist L, Molstad S. Use of rules of thumb in the consultation in general practice-an act of balance between the individual and the general perspective. Family Pract. 2003;20:514-519.

There are still many questions about how to develop useful guidelines, introduce them into clinical practice, and measure their effectiveness. What does it mean to use a guideline in practice?

How Do Physicians Use Practice Guidelines?

During the last 25 years, clinical practice guidelines (CPGs) have become an accepted part of medical care. A CPG is an authoritative series of rules or suggestions for the treatment of a clinical disorder. Many contemporary CPGs are referred to as evidence-based guidelines, because they are created using objective research information. These guidelines, produced by various professional organizations and academic institutions, are usually considered the best practices or gold standards for clinical care. The increasing amount of time and energy devoted to developing CPGs and implementing them in clinical practice raises a number of important questions about how they are used by physicians and what factors influence their use.


Although CPGs are accepted standards of care, several studies suggest that they have not been as widely adopted by physicians as their developers might have hoped.

 Many clinicians accept the concept of CPGs but, for various reasons, don't effectively incorporate them into their practices. In a survey of 1878 Canadian physicians, only 52% reported that they used guidelines at least once a month, and more than 25% expressed concerns about the source of the guidelines, their rigidity, and the fear that physicians might lose their sense of professional autonomy if they followed the guidelines.

Another survey of 34 general practitioners in England found several barriers to the use of accepted evidencebased hypertension treatment guidelines. 

 Some physicians did not think the data used for the guidelines applied to their patients. Others did not follow the guidelines because they thought they were outdated, they did not feel a sense of ownership toward the guidelines, or they simply didn't know about them. Still others claimed that the time and financial pressures they felt made the treatment of hypertension and the use of guidelines a low priority. Ironically, these seem to be the very practitioners who should have been using the guidelines.

 reviewed 76 articles that investigated barriers to physician use of CPGs. They found 293 barriers that they divided into 3 broad groups, based on physician knowledge (lack of awareness and familiarity with the guidelines), attitudes (resistance to changing prior patterns of practices, lack of agreement with specific guidelines, lack of agreement with guidelines in general, concerns about whether the guidelines would work, and concerns about whether the clinician would be able to implement the guidelines), and external factors (difficulty or complexity of the guidelines, inconvenience of guideline implementation, patient resistance, and lack of time, reminders, and resources). The authors noted that few studies examined the full variety of barriers that may affect the use of practice guidelines.

 commenting on the study, argued that physicians and patients might rationally disagree with the recommendations of a guideline if those recommendations did not represent their outcome value judgments. Suppose, he explained, a guideline stated that antibiotics and tympanostomy tubes should only be used for children with otitis media to prevent long-term damage. A child's physician and parents could rationally disagree with the CPG if they thought that shortterm relief was also an appropriate outcome of treatment.

Several other factors have also been reported to affect the adoption of CPGs in clinical practice including the quality of the guidelines, the characteristics of the clinician and practice setting, the role of incentives, regulation by official organizations, and patient demands and compliance.


Although there have been several investigations of the barriers to the implementation of CPGs, there have been far fewer studies on the quality of the guidelines themselves. Are some of the concerns that practitioners have about CPGs justified? Are there problems with the structure and quality of some guidelines that are independent of their implementation yet influence their adoption by clinicians?

 reviewed 279 CPGs published between 1985 and 1997, using a 25-item scale they developed to evaluate whether the guidelines followed accepted development standards proposed by major medical organizations. On average, the guidelines complied with only 43% (10.77/25) of the standards. The compliance was highest (greater than 60%) for standards related to the statement of the purpose and rationale of the guidelines, the identification of the health problems targeted, the citation of the evidence used, the benefits and harms of the health practices recommended, and the specificity and flexibility of the recommendations.

Compliance was lowest (less than 25%) for several important standards related to the identification and summary of evidence, including the specification of the methods used to identify, extract, grade, classify, and combine the scientific evidence. There was little discussion of the potential professional biases of the individuals who developed the guidelines. The authors noted a gradual but statistically significant improvement in overall compliance with the standards for the survey period from 1985 to 1997. This improvement, however, did not occur for standards related to the identification and summary of evidence.

 commenting on the Shaneyfelt study, discussed the complexity of developing and implementing guidelines and determining their effectiveness. While acknowledging the shortcomings of some guidelines, they questioned whether the standards used in the study applied equally well to all guidelines. They argued that guidelines are developed for different purposes and are based on different types of information. They may, for example, focus on the cost of care, the treatment outcome of individual patients, or the provision of the most effective care to groups of patients. Some guidelines, therefore, may legitimately deviate from the standards.

In a review of the potential benefits, limitations, and harms of CPGs, Woolf and associates

 stated that CPGs have a great potential to improve the consistency and quality of patient care, but they noted that it was not yet clear whether guidelines actually accomplished this in daily practice. They argued that guidelines may be wrong for all patients or for individual patients with special needs and cited 3 reasons why this might be so. The scientific evidence for a recommendation is often "lacking, misleading, or misinterpreted," the recommendations may be affected by the developers' clinical and scientific biases, and economic, policy, or other needs may take the place of the patients' clinical needs as the main guideline priority.

These reports highlight the importance of further studies on both the process of creating effective CPGs and the impact that guidelines have on the outcome of care. Unfortunately, proving that a guideline does have a significant positive impact on patient outcome is one of the most difficult aspects of developing a CPG.


These and other studies demonstrate that there are still many questions about how to develop useful guidelines, how to introduce them into clinical practice, and how to measure their effectiveness. Several authors have examined the process and made useful suggestions about how to resolve these barriers.

CPGs are more likely to be accepted by physicians if they are endorsed by respected professional organizations and colleagues rather than companies and institutions that have a major investment in reducing the cost of care. CPG developers need to test various formats for the guidelines (eg, summaries of recommendations, algorithms, manuals of collected guidelines) before releasing them because physicians apparently prefer some formats over others.

Mailings of CPGs and formal continuing medical education programs have been reported to be minimally effective in changing clinical practice, perhaps because they are too impersonal. This conclusion is supported by reports that some of the more effective methods of ensuring compliance with CPGs are those that involve guideline monitoring, feedback about compliance, and multiple interactions between experts and clinicians using the guidelines.

It is worth noting, however, that the successful implementation of an effective CPG does not necessarily guarantee continued clinician compliance with the guideline. 

 This means that there must be some ongoing system that reinforces compliance with the CPG.

Not surprisingly, the clinical habits that physicians develop over years of practice are resistant to significant change without a generous amount of one-to-one contact with experts who are supportive and mentoring rather than critical. Unfortunately, this is a timeintensive and expensive process. Medicine must therefore develop innovative and effective, but less expensive methods of monitoring guideline adherence and providing reasonable feedback to practitioners.

One obvious method is to use modern information technologies such as online help desks and expert-led forums. The essential component appears to be ongoing feedback from another knowledgeable clinician. Another approach might be confidential online treatment databases, the use of which would enable physicians to immediately compare their treatment decisions with those of their peers.

It is not sufficient to merely disseminate CPGs. If we truly believe that they make a significant difference in the quality of care, we need to develop new methods of encouraging compliance with the guidelines.

 Rutschmann OT, Janssens J, Vermeulen B, Sarasin FP. Knowledge of guidelines for the management of COPD: a survey of primary care physicians. RespirMed. 2004;98:932-937.


 Brouwers MC, Graham ID, Hanna SE, et al. Clinicians' assessments of practice guidelines in oncology: the CAPGO survey. Int J Technol Assess Health Care. 2004;20:421-426. 


 Grol R, Grimshaw J. From best evidence to best practice: effective implementation of change in patients' care. Lancet. 2003;362:1225-1230. 
 

 Hayward RS, Guyatt GH, Moore KA, et al. Canadian physicians attitudes about and preferences regarding clinical practice guidelines. CMAJ. 1997;156: 1715-1723. 


 Cranney M, Warren E, Barton S, et al. Why do GPs not implement evidence-based guidelines? A descriptive study. Fam Pract. 2001;18:359-363.   
 

 Cabana MD, Rand CS, Powe NR, et al. Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999;282:1458-1465. 
  

 Veatch RM. Reasons physicians do not follow clinical practice guidelines. JAMA. 2000;283:1685.   


 Davis DA, Taylor-Vaisey A. Translating guidelines into practice: a systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. CMAJ. 1997;157:408-416.   
  

 Shaneyfelt TM, Mayo-Smith MF, Rothwangl J. Are guidelines following guidelines? The methodological quality of clinical practice guidelines in the peer-reviewed medical literature. JAMA. 1999;281:1900- 1905.   
     

 Cook D, Giacomini M. The trial and tribulations of clinical practice guidelines. JAMA. 1999;281: 1950-1951.   


 Woolf SH, Grol R, Hutchinson A, et al. Clinical guidelines: potential benefits, limitations, and harms of clinical guidelines. BMJ. 1999;318:527-530.   


 Hayward RS. Clinical practice guidelines on trial. CMAJ. 1997;156:1725-1727.   


 Worrall G, Chaulk P, Freake D. The effects of clinical practice guidelines on patient outcomes in primary care: a systematic review. CMAJ. 1997;156:1705-1712.   


 Jamtvedt G, Young JM, Kristoffersen DT, et al. Audit and feedback: effects on professional practice and health care outcomes. Cochrane Database of Syst Rev. 2003;3:CD000259.   


 Brand C, Landgren F, Hutchinson A, et al. Clinical practice guidelines: barriers to durability after effective early implementation. Intern Med J. 2005;35: 162-169. 

Clinical practice guidelines, produced by various professional organizations and academic institutions, are usually considered the best practices for clinical care. But how do physicians use guidelines, and what factors influence their use?

Do Physicians Use Practice Guidelines?

A few years ago, I read an interesting report about a surgeon who performed 120 successive lithotomies--operating for bladder stones--with a mortality rate of only 2.5%. This might not seem so significant, given the low mortality rate for most surgeries today, except for one fact. The operations were performed in the late 18th century by a French surgeon named Pouteau. His surgical approach is discussed in a fascinating book on the history of surgery that was written by Owen Wangensteen, the former chairman of the department of surgery at the University of Minnesota, and his wife Sarah Wangensteen, a medical historian at the same institution.

 The authors reported that the secret to Pouteau's success was his strong emphasis on cleanliness during surgery, his recognition that hospital infections were contagious, and his use of disposable paper bandages for dressing surgical wounds--remarkable innovations for his day.

The obvious question is, Why 			weren't these innovations more widely 			disseminated and adopted by other 			physicians of the time? Why did it take 			almost a century before similar procedures 			of antiseptic and aseptic surgery, 			proposed by Semmelweis and Lister, 			spread throughout medicine and 			became the universal standard of surgical 			care? 		

The diffusion of innovations has been extensively studied by Everett Rogers

 and others. Rogers defines diffusion as "the process in which an innovation is communicated through certain channels over time among members of a social system." The process of adoption usually proceeds through several reasonably well-defined steps, beginning when individuals first learn of the innovation and progressing through stages when they become persuaded of its importance, decide to adopt it, begin implementation, and finally, seek confirmation that they made the correct decision. Various factors influence the rate of adoption of an innovation, including its perceived attributes; how information about it is communicated to members of the society; and the beliefs, customs, and attitudes prevalent in that society.

Rogers identified 5 perceived attributes of an innovation that seem to explain how quickly it will be adopted. These include the relative advantage of the innovation over prior ideas; its compatibility with the beliefs, values, needs, and experiences of the potential adopter; its complexity; how easily a potential adopter can try the innovation; and how visible the results of the innovation are to observers. Rogers refers to the last 2 attributes as the innovation's trialability and observability.

Using Rogers' criteria, we can speculate 			why Pouteau's discoveries in 			aseptic surgery did not immediately 			spread throughout medicine. The relative 			advantage of his technique is obvious 			to us today, but probably was not 			to his contemporaries. Few 18th century 			surgeons compiled outcome statistics 			for their patients. Moreover, Pouteau's 			ideas were not compatible with their 			medical view of the world. They had 			no theory of infection and no reason to 			believe that unsanitary conditions were 			a major cause of patient mortality. 		

One reason for Lister's subsequent 			success is that he postulated a credible 			theory of the cause of wound infection- 			that the small organisms Pasteur 			found in wine might also produce a 			septic fermentation in a patient's 			wound. His process of antiseptic surgery 			was, therefore, consistent with the 			leading scientific knowledge of his day. 			Lister was also living in an age that 			was beginning to accept the process of 			scientific discovery in medicine, and 			that had far more methods of communication 			than those available to Pouteau. 			Although Pouteau's techniques appeared 			simple, his success suggests 			that he must have been exceptionally compulsive about cleanliness during 			and after surgery. Perhaps the average 			surgeons of his day who knew about 			these techniques considered them 			unnecessarily complicated and difficult 			to emulate. In that case, it is unlikely 			that they would have observed the same 			level of success that he did.

A 1953 study by Coleman and his colleagues

 on the adoption of tetracycline by physicians in 4 small Illinois cities demonstrated some of the important elements of diffusion theory in the modern era. The research team interviewed 228 physicians, 64% of the practicing physicians in the 4 cities, about their use of the drug. They discovered that the prescription of tetracycline began with a few innovative or early adopter physicians, was tried by 50% of physicians within 4 months, and was almost universally adopted within 17 months.

They also discovered that the most 			important factor influencing physicians 			to adopt the new antibiotic was the 			personal clinical experience of their 			peers, who reported that tetracycline 			was more effective and better tolerated 			by patients than the standard antibiotics 			of the day. Articles about the drug in 			medical journals and contact with pharmaceutical 			company representatives 			were of lesser importance.

In addition to its increased effectiveness, 			tetracycline also possessed 			several of the other attributes that 			Rogers postulated increased the rate of 			adoption of an innovation. The use of 			the drug was compatible with the 			accepted clinical beliefs, practices, and 			experiences of the average physician; 			it was simple to use; and the results of 			treatment were quite visible.

It should be noted, however, that a few scholars have reanalyzed Coleman's original data and arrived at a slightly different conclusion than he and his colleagues did. The new analyses suggested that the reports in the medical literature and the influence of drug company representatives were as much or more of a factor in a physician's adoption of tetracycline than was the physician's peer network.

 In other words, the social network, although crucially important, was only one of the factors that influenced physicians to adopt the new drug.

In psychiatry, several studies have shown that computer-assisted psychotherapy, an innovative method of delivering cognitive-behavioral therapy using a computer program, may be as effective as treatment by a live therapist. Selmi and associates

 demonstrated that a small group of patients with a diagnosis of major depressive disorder (n = 12) responded as well to 6 sessions of computer-assisted cognitive-behavioral therapy as those who received the same number of treatments administered by a live therapist (n = 12). Both groups did better than controls who were placed on the waiting list and received no therapy.

 reported that both computer-guided (n = 74) and therapist- administered (n = 69) treatments were effective for obsessive-compulsive disorder, but the latter was more effective than the former. Moreover, many patients who did not respond to computer-guided therapy did respond to subsequent clinician-administered therapy. The converse was not true.

 used treatment booklets and a computer-aided interactive voice response telephone system to treat 41 American and British patients with mild to moderate depression in an open, uncontrolled study. All 41 subjects (intent-to-treat group) completed the initial assessment, and 28 subjects (completers group) successfully finished the full 12-week therapy program. All subjects, with the exception of those in the British completers group (n = 6), showed a significant improvement in depression rating scores.

 commenting on these and other studies, questioned why the innovation of computer-assisted psychotherapy was not more widely adopted by the profession. He suggests that psychiatrists have not chosen to adopt computerized methods of treatment because the technique is inconsistent with the values of the social system within which they function. He argues that the use of computers for the diagnosis and treatment of mental illness "might be alien to psychiatrists' selfimage of their role," which is based on the model of "two people in a room, talking."

Yet that self-image has not stopped 			psychiatrists from adopting other nontalking 			therapies, such as electroconvulsive 			therapy for the management of 			severe depression and medication for the 			management of depression, psychosis, 			and anxiety. One could, in fact, argue 			that many psychiatrists are only too 			entrepreneurial in adopting new nontalk 			treatments, such as the latest antiseizure 			medication for treatment of mood instability. 			In the best therapeutic circumstances, 			however, each of these models 			is augmented with talking therapy. There 			is no reason that the same thing cannot 			happen with computer-aided diagnosis 			and treatment.

It is likely that the limited adoption of computer-assisted psychotherapy has as much or more to do with other attributes that influence the rate of diffusion of an innovation than with the concern of psychiatrists about their professional self-image. Rogers

 argued that the relative advantage, compatibility, complexity, trialability, and observability of an innovation affect its rate of diffusion. Although traditional cognitive-behavioral therapy is a wellaccepted mode of treatment, the relative advantage of computer-assisted treatment is unproved. The studies cited earlier suggest that a therapist is unlikely to observe a significant improvement in outcome between the computer-assisted and traditional methods. The former is theoretically compatible with the underlying concept of cognitive-behavioral therapy, but that fact would not necessarily increase its diffusion, since cognitive therapy itself is only practiced by a small proportion of psychiatrists.

Moreover, implementing computer-assisted 			treatment is certainly more 			complex than initiating other forms of 			psychiatric treatment, such as dynamic 			psychotherapy or psychopharmacology. 			Practitioners would have to purchase 			and learn how to run the necessary 			computer equipment and software. That 			is assuming, of course, that they already 			know how to administer the traditional 			form of therapy. Given this complexity, the system cannot easily be tried 			without a considerable investment in 			time and money.

None of these arguments disprove 			the potential usefulness of computer-assisted 			cognitive-behavioral therapy. 			They simply suggest that it is not an 			innovation that provides the same obvious 			benefits as aseptic surgery, CT, or 			lithium in the treatment of patients. 			What it does provide is the potential to 			treat large numbers of patients with a small number of therapists. That may 			be valuable to companies and health 			plans that want to offer cost-effective 			treatment to their employees and 			members. Its effectiveness in that realm, 			however, has yet to be proved.

The Rise of Surgery From Empiric Craft to Scientific Discipline

. Minneapolis: University of Minnesota Press; 1978.
 

 Rogers EM. Diffusion of Innovations. 5th ed. New York: Free Press; 2003.
 

 Selmi PM, Klein MH, Greist JH, et al. Computeradministered cognitive-behavioral therapy for depression. 

 Greist JH, Marks IM, Baer L, et al. Behavior therapy for obsessive-compulsive disorder guided by a computer or by a clinician compared with relaxation as a control. 

 Osgood-Hynes DJ, Greist JH, Marks IM, et al. Self-administered psychotherapy for depression using a telephone-accessed computer system plus booklets: an open U.S.-U.K. study. 

 Freedman J. The diffusion of innovations into psychiatric practice.

In the late 18th century, a French surgeon named Pouteau performed 120 successive lithotomies with an extraordinarily low mortality rate. His secret was the use of innovations used widely today. Why weren't his methods more widely adopted by other physicians in his time?