INFLUENCE

OF SOFT LINING

MATERIALS

REFERENCES

7. Lawson WA. The variation of a method used for measuring masticatory forces. J PROSTHET DENT 1960;10:99-111. 8. Anderberg MR. Cluster analysis for applications. New York: Academic Press, 1973.

1. Chase WW. Tissue conditioning utilizing dynamic adaptive stress. J PROSTHET DENT 1961;11:804-15.

2. Craig JF, Gibbons P. Properties of resilient denture liners in simulated mouth conditions. J PROSTHET DENT 1962;12:1043-52. 3. Wilson HJ, Tomlin HR. Osborne J. Tissue condition and functional impression materials. Br Dent J 1966;121:9-16. 4. Wilson HJ, Tomlin HR, Osborne J. The assessment of temporary soft materials used in prosthetics. Br Dent J 1969;126:303-6. 5. Braden M. Tissue conditioners. II. Rheologic properties. J Dent Res 1970;49:496-501. 6. Robinson JG, McCabe JF. Creep and stress relaxation of soft denture liners. J PROSTHET DENT 1982;48:135-40.

Decision making in dentistry. decision methods Ann M. McCreery, University

of Washington,

Ph.D.,*

and Edmond

School of Dentistry,

Reprint requests to: DR. FUMIAKI KAWANO SCHOOL OF DENTISTRY UNIVERSITY OF TOKUSHIMA 3.KURAMOTO-CHO TOKUSHIMA 770 JAPAN

Part II: Clinical

Truelove,

D.D.S.,

applications

of

M.S.D.**

Seattle, Wash.

The study of clinical decision making provides a common model on which to base dental practice and thus promotes standardization of care and treatment. Decision analysis can assist in identifying missing data in clinical problems and thus generate clinically relevant research agendas. Part II reviews the current literature focusing on three areas of decision making in dentistry: diagnosis, treatment planning, and disease prediction. The growing body of literature indicates that wide variation exists among the treatment plans made by dentists. Considerable bias arises from many sources of uncertainty in decision making, including the limitations of human memory and judgment. Literature pertaining to computer application of decision analysis in dentistry and to policy making are reviewed. (J

PROSTEETDENT~~~~;~~:S~~-~~.)

T his is a review of dental research in which formal decision-making methods have been applied. Decision studies have been reported in the spheres of dental education, diagnosis, treatment planning, computers, practice management, and cost control. This review focuses on the critical issue of how dentists make decisions related to diagnosis, treatment planning, and disease prediction. In addition, articles related to policy and applications of decision making methods to computers are reviewed. Articles were included if the authors used formal decisionanalysis methods, other statistical methods appropriate for decision analysis, or if they used descriptive methods in such a way as to extend the application of decision making to dentistry. Articles related to the important areas of dental education and practice management were not included.

*Assistant Professor, Department of Prosthodontics. **Professor and Chairman, Department of Oral Medicine.

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DIAGNOSIS In this review 31 studies were identified that applied formal decision-making methods to problems of diagnosis in dentistry (Table I). Fifteen studies addressing diagnosis of dental problems focused on the use of radiographs in diagnosing caries. In addition, this literature addressed diagnoses in the subspecialties of endodontics, periodontics, orthodontics, oral pathology, and oral medicine, as well as general issues of diagnosis, differential diagnosis, and selection of diagnostic procedures. Seven of the decision studies of caries detection published between 1979 and 1986 were published by the same author or group of authors. In the studies that addressed the use of radiographs and their relative value in caries detection, significant differences were identified among different groups of examiners. l-5 In several of the studies, widely differing caries rates were diagnosed, depending on conditional information given to clinicians before the interpretation of radi0graphs.b 4,6l7 Significant variation in lesion diagnosis was found, even among experienced examiners. A decision tree including utility assessment was developed by Mileman et al5 who suggested there is a need

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Table I. Publications Dental

Method

Radiography Radiography Restorative dentistry

ROC curve Wilcoxon’s rank Correlation

Restorative dentistry

Decision tree

Radiography Restorative dentistry Radiography Radiography Radiography Orthodontics Prosthodontics Restorative dentistry

Bayes’ theorem Correlation Correlation Utilities assessment ROC curve/Bayes’ Descriptive/opinion Process description Correlation

Endodontics Endodontics

ROC curve Utilities assessment

Endodontics Endodontics Endodontics

Decision-flow diagram Bayes’ theorem Review

Periodontics

Sensitivity ratios

Periodontics

ROC curve

Periodontics Periodontics

Analysis of variance Sensitivity/specificity

Periodontics Periodontics

ROC curve Multiple regression

Periodontics Oral medicine

ROC curve Discriminant analysis

Oral medicine Oral pathology

Markovian model Bayes’ theorem

General

Description of system

Radiography

Decision tree/ROC curve Decision tree ROC curve

for more information on the diagnostic process before recommendations about changes in the process can be made. These studies used radiographs in ways that are potentially valuable in assisting clinicians to diagnose caries. In

addition, they focused on differences in examiner reliability in reading radiographs and offered insights as to why such variation is seen in dental treatment planning. Two recent studies examined another basic clinical decision: how often bite-wing radiographs should be 576

TRUELOVE

on diagnosis

domain

Endodontics Radiography

AND

Topic

(Reference)

Decision to restore carious lesions (1) Comparison of diagnoses of caries between groups (2) Variation in caries diagnosis and treatment decisions (394) Application of decision analysis to diagnosis of approximal caries (5) Caries diagnosis and treatment decisions (6) Variation in caries treatment decision (7) Limiting number of radiographs (8) How often to perform bite-wing radiographs (9) Application of methods to evaluate radiographs (10) Need for differential diagnostic decisions (11) Description of diagnosis as a decision process (12) Longitudinal radiographic study of caries increment (13) Diagnosis of periapical lesions (14,15) Approach to lack of research-based data using decision analysis (16) Analysis of radiographs to diagnose pulpitis (17) Differential diagnosis of pulpal disease (18) Determination of the presence of pathology in order to treat (19) Measures diagnostic sensitivity and predictability of clinical indicators (20) Testing reliability of diagnostic procedures to measure patient status (21) Thresholds for measuring attachment loss (22) Use of fluid prostaglsndin levels to predict perioattachment loss (23) Reliable application of laboratory tests (24) Capability of specific bacteria to indicate progressive periodontitis (25) Problems in identifying bursts of attachment loss (26) Automated diagnostic classification of craniofacial pain (27) Automated diagnosis of craniofacial pain (28) Differentiation between possible alternative causes of disease (29) Application of decision making to diagnosis of disease (30,31) Extent of radiolucent area and decision to treat (38) Interexaminer variation in periapical lesions (39) Evaluation of endodontically treated teeth (67)

taken.s g Brooks* evaluated methods for using formal decision systems to reduce the number of radiographs as part of new and returning patient evaluations. He concluded that with the use of formal, clinical selection criteria, the number of films per patient could be reduced and replaced

by observations of disease prevalence and risk factors. Plislcm et a1.gassessedinclusion of patient preference attributes in making formal decisions about the rate of radiographic follow-up and reexamination. They suggested that the inclusion of patient preferences in the decision-

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making process could result in fewer radiographs, without significant advances in disease due to inadequate monitoring. Douglas and McNeillO examined the possible applications of clinical decision analysis for weighing the costs and the effectiveness of alternative dental radiographs and strategies for their use. The questions raised by these authors have implications for radiologists. They carry decision methods a step further by applying them to such questions: How much better is one particular radiograph? What are the increased financial costs associated with finding an additional diseased patient when one particular diagnostic strategy is compared with another? How much more reliable are diagnostic decisions based on dental radiographs when compared with decisions based on the clinical examination alone? They reviewed the use of the decision matrix and the receiver operating characteristic (ROC) curve to evaluate radiographic projections, taking into consideration the prior prevalence of disease in the patient. In a 1972 study, Graberli discussed diagnostic decision systems in orthodontic diagnosis, describing specific methodologies. Owen and Rayson,r2 also in 1972, outlined the use of formal, sequential decisions in the diagnosis of denture problems. Both of these studies are theorybased and are not supported by specific data except in the most general terms. A number of the earlier studies of diagnostic decision making, such as the two cited here, represent a less formal method of thinking about decision making. Five of the radiology studies focused on the field of endodontics. Three studies investigated differences in diagnoses of periapical lesions among several different examiners, and attributed the differences to variations in examiner criteria for periapical disease detection instead of failure in detecting differences in radiographs.13*14,l5 Reit and GrondahP4 concluded in the second of these studies that the best method for ensuring examiner reliability is to define strict criteria for disease presence and to limit reporting of disease detection to only those cases where the examiner is certain of its presence. Another study examined endodontic decisions that must be made under uncertainty, a common problem in all clinical disciplines. Reit and Grondahl16 determined utility values for two endodontic situations in which probability values were provided by two groups of examiners. They proposed that the utility values act as guidelines for the selection of optimal decision strategies, and that the uncertainties set the direction for future research projects. Tzukert,” in a study of decision making in casesof acute pulpitis, using historical data and decision flow systems, determined that radiographs were of mixed value and should not be used routinely for diagnosis, because clinical data can detect better the presence of pulpitis. Hyman and Doblecki18 reviewed their computer program for differen-

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tial diagnosis of pulpal disease and concluded that it can serve as a readily available second opinion. Smulsonls reviewed some concepts regarding the physiology and hi&pathology of the pulpodentinal complex and language of classification. He indicated that knowledge and appreciation of the ideas presented could be helpful in determining the presence of a disorder for the ultimate purpose of clinical decision making about whether to treat it. A number of studies have been published in recent years on periodontal diagnosis using decision analysis, and several of them evaluated the usefulness of clinical measurements in predicting destructive periodontal disease activity by using measures of sensitivity and specificity. Htiajee et al.zo concluded that the clinical parameters were useful to predict disease activity at individual sites. Badersten et a1.2*found similar results using a larger sample of patients. Aeppli et al.22 calculated sensitivities and specificities of probing depth and attachment lOS8, and they concluded that observing an increase of probing depth greater than 1 mm can serve as a diagnostic test with high sensitivity and specificity. These studies were followed by a longitudinal study in which cervicular fluid levels to predict attachment loss were shown to have a high degree of sensitivity and specificity.23 In 1986, Listgarten24 concluded that for a laboratory test to be reliably applied to the diagnosis of a clinical condition, it is essential that an absolute criterion of the clinical disease first be established and the sensitivity and specificity determined using experimental designs. The statistical association of Actinobacillus actinomycetemcomitans, Bacteroides gingivalis, and Bacteroides intermedius with progressive periodontitis was evaluated in 1987 by use of multiple regression analysis in 1987.25 The authors concluded that, with sensitivities between 83 % and 95 % and specificities between 86 % and 99%) these three bacterial species might serve as valuable components of a periodontitis activity test based on microbiological variables. Finally, Ralls and Cohe# reexamined the experimental data that have been used to support the “burst” model of periodontal attachment loss by using (ROC) analysis. They questioned the validity of evidence supporting this model. Several studies have addressed the problems of computer approaches to diagnosis. Leonard et a1.27,28in two earlier studies, discussed the use of automated modes to diagnose craniofacial pain in oral medicine. Kramerzg reviewed examples of the types of approaches that had been used until 1980 to diagnose conditions such as jaundice or to differentiate between lichen planus and leukoplakia. He showed that such programs give results at least as accurate as those of the most skilled clinician. Two recent studies by Ralls et a130,31applied computer technology to computer-assisted diagnosis of dental emergencies. They concluded that the data-handling capabilities of the computer can be used to produce diagnostic outcomes of 577

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Table

II.

AND

TRUELOVE

Publications on treatment planning Dental

domain

Method

Topic (Reference)

Restorative dentistry

Correlation

Restorative dentistry

Decision tree

Restorative dentistry Endodontics

Correlation Correlation

Oral medicine

Markovian model

Restorative dentistry Restorative dentistry Restorative dentistry Restorative dentistry

Descriptive Descriptive Correlation Descriptive

Radiography

Progression

Radiology

Descriptive

Radiography Endodontics Endodontics

Decision tree/ROC curve Decision tree General

Endodontics

Bayes’ theorem

Oral surgery Oral surgery

Sensitivity analysis Decision tree

Oral medicine

Oral medicine

Probability flowchart Decision tree

Geriatric dentistry Orthodontics

Flow chart Descriptive

General

Multivariate

Restorative dentistry

Descriptive

Restorative dentistry

Descriptive

Pediatric dentistry

Chi-square

Variation in caries diagnosis and treatment decisions (3,4) Application of decision analysis to diagnosis of approximal caries (5) Variation in caries treatment decision (7) Individual variations in radiographic diagnosis-periapical lesions (16) Automated treatment planning for craniofacial pain

(28)

analysis

equal or greater accuracy than those made by accepted experts.

TREATMENT

PLANNING

Formal decision-making methods and techniques have been applied to questions of treatment planning and choice of treatment (Table II). These decision studies have addressed radiology, caries prevention and treatment, variation in decisions among dentists, factors that influence dentists’ decisions, extraction of third molars, and the specialties of geriatrics, endodontics, orthodontics, oral medicine, and pedodontics. 578

Variation in treatment planning (32) Analysis of treatment planning (33) Nature of clinical judgments (34) Interexaminer reliability in clinical trials of caries prevention (35) Disparity in criteria for decision to treat approximal caries (36) Interexaminer agreement on reading serial radiographs (37) Extent of radiolucent area and decision to treat (38) Interexaminer variation in periapical lesions (39) Decision theory used to explain variations in treatment decision (41) Computation of odds on success or failure in treatment (42) Identification risk minimizing options (43) Calculation of expected value of alternative treatment of third molars (44) Prevention of infective endocarditis (45) Decision analysis to evaluate treatment of cleft palate (46) Model of decision making for care of elderly (47) Interactive system-clinician interfaces with treatment decisions (48) Survey to determine patient influence in clinical decisions (49) Multiple factors to consider in making decision to restore (51) Discussion of whether patient or dentist makes treatment decision (52) Factors in decisions to use or not use sealants-national survey (60)

Of the 11 decision studies of planning for caries prevention and treatment included in this review, 10 are concerned with the variation among dentists in planning treatment. Two studies characteristic of this literature were done by Elderton and Nuttall,a2, 33who examined the agreement between 15 dentists when they planned treatment for the same group of 18 young adults. One study found wide variation among the dentists in their determinations of which tooth surfaces required restoration. Merrett and Elderton34 followed up this work with a study in which nine dentists examined 228 extracted restored teeth and determined whether caries was present. When the

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on disease prediction

Dental domain

Method

Radiography/pediatric dentistry Restorative dentistry Epidemiology Radiography/restorative dentistry

Kaplan-Meier

Progression of carious lesions in children (53)

Bayes’ theorem

Estimates of probability of new carious Iesions in a year (54) Estimates of progression of carious lesions (55) Estimates of the rate of caries progression (56)

Time series Kaplan-Meier

teeth were sectioned, a considerable lack of correlation was

found between the teeth in which caries had been identified in the clinical examinations and those in which caries was identified in the laboratory after sectioning. The authors concluded that there is an urgent need for improved criteria for assessment of caries. Further difficulties in the reliability of caries treatment planning were identified by Mileman et al.3,4,7 in three studies of variation in radiographic caries diagnosis and treatment among university faculty. Dental clinic teachers were asked to assessa selected set of radiographs for the presence of caries. As with other studies, a significant variation in detection and treatment decisions was observed among the participants. In Horowitz’s studg5 of differences in detection of new caries and judgement of cariostatic effects, two dentists were evaluated and found to have poor agreement about the presence of new lesions. They agreed in assessment of cariostatic effect but showed significant variation in which teeth had been protected, demonstrating an artificial agreement in overall beneficial effect. Evaluation of decisions to treat proximal caries was done for different groups of practitioners using the same clinical records in a study by Espelid et al.36 The study found significant variation among the groups in deciding whether teeth required treatment. Only 20% of the variance was explained by differences in the dentists’ criteria for what constituted a radiographic image indicating treatable caries. Finally, a study by Pliskin et al.37directly assessedinterexaminer agreement with serial bite-wing radiographs used to determine the presence and depth of carious lesions. Agreement in this study ranged from 59% to 90%) indicating less significant variation than in some other studies. Grondahl’s study38 of radiographic diagnosis and treatment decisions, instead of focusing on examiner agreement, determined that in populations with a low caries rate the risk of overtreating was significant, whereas the significant risk in caries-active populations is undertreatment. He concluded that undertreatment was a more desired option, because not only can caries progression be followed, but it progresses much more slowly than expected by most clinicians. Four studies applying decision-making methods to prob-

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Topic (Reference)

lems of choosing among endodontic treatments have been reported. In three studies of decisions to treat periapical lesions in endodontically treated teeth, Reit, Grondahl, and Engstrom3g-41used a decision tree format, with utility values based on expert opinion. They discovered large interexaminer variations in attitudes toward treatment and retreatment. The high range of interexaminer variability in decisions to treat suggested that the decision process in clinical dentistry may not be adequately scientifically based. Van Velzen et al.42 described Bayes’ theorem, and gave examples of the use of the theorem for computing the odds on successor failure of the endodontic treatment of a tooth, given a particular preoperative status and the results of the preparation and filling of the root canal of the tooth. They concluded that such an application, by use of data collected, can affect the ultimate treatment result. Asymptomatic, impacted third molars in young patients are generally extracted in the belief that this action will avoid future pathosis. However, no extensive studies have been completed that demonstrate the risks when the decision is made to delay extraction. Two treatment decision studies pertain to determining the validity of extracting impacted, asymptomatic third molars to avoid patholosis.43*44Both studies used the decision tree model and determined utility values for each possible branch of the tree. Different decision outcomes could be seen clearly as the hypothetical utility values changed. The outcomes of these studies indicated that additional research is needed to assessthe values and risks of impacted molar extraction. Three treatment decision studies have been done in oral medicine. Leonard et al.28used automated modes in their study of treatment planning for craniofacial pain, Tzukert et al.45conducted a study using probability assessment to determine the relative value and risk of following the American Heart Association guidelines for antibiotic use in patients with heart disease. The authors used a strict outcome measure of mortality from infective endocarditis or antibiotic reactions, and they concluded that the Heart Association protocol should be used only in high-risk patients, because little protective value could be projected for other risk categories. Krischer46 presented a decision analysis method for the evaluation of treatment alternatives in cleft palate pa-

579

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Table IV. Publications on computer applications Method

Dental domain

Differential diagnosis of pulpal disease (18)

Oral medicine

Applesoft Basic/ Bayes’ Theorem Discriminant analysis

Oral medicine

Markovian model

Automated diagnosis and treatment of craniofacial

Oral pathology

Bayes’ theorem

General

Description of system

Orthodontics

Descriptive

Endodontics

Literature review General

Review

Orthodontics General

Review Correlation

Oral medicine

Orthodontics

Descriptive Descriptive

Orthodontics

Decision tree

Orthodontics

Boolean operations

tients. Results indicated that treatment alternatives can be preferentially ordered by use of analytic methods that incorporate costs, benefits, and probabilities of successful and unsuccessful outcomes. Ettinger47 evaluated the principles of problem solving and decision making in general and applied them to dental treatment planning for the elderly. He presented a model and discussed its component parts. In his 1984 review he suggested that “ . . . we do not know very much about how dentists make decisions, nor do we have very much information on measuring the outcomes of treatment; both of which affect clinical decision making.” He concluded that this situation will have grave implications for the care of the elderly in the future because of the increasing complexity of related clinical decisions. Faber et aL4s addressed the problem of orthodontic treatment planning based on a computerized data base. They concluded that the computer had the potential to be an aid in organizing and displaying information required for a complete treatment plan, rather than a dictator of treatment, but that at the time of publication (1978), there had been only limited success in achieving these goals. Two recent studies examined the factors that affect dentists’ clinical decisions. Grembowski et al.4g used descriptive analyses in combination with multivariate analyses to address the relative influence of patient and technical fac580

Topic (Reference)

Automated diagnostic classification of craniofacial pain (27) pain (28) Differentiation between possible alternative causes of disease (29) Application of decision making to diagnosis of disease (36,311 Interactive system-clinician interfaces with treatment decisions (48) Computer applications in practice, including decision making (58) Computer applications in dental office, including decision making (59) Applications of computer technology (61) Effect of simulation on decision making of practitioners (62) Application of expert system (63) Description of system and recommendations for diagnostic applications (64) Expert system for orthodontic advice to general practitioners (65) Registration and analysis of malocclusion prevalence (66)

tors on dentists’ clinical decisions. They surveyed 156 general dentists in one state regarding the top three factors that influence their choice of service in four specific treatments. The results showed that technical factors dominated over patient concerns such as cost and patient preference. Romberg et aL50 examined the professional, scientific, social, and economic factors that might influence the pedodontist’s decision to use sealants. A national mail survey was sent to 591 practicing pedodontists across the United States. Descriptive statistics were used along with statistics to test for the relationships among sealant use and other selected factors. The authors found that research, specialty association endorsement, colleagues, and nationally known clinicians were influential in the decision making of these dentists, and they drew implications for education aimed at practitioners and consumers. Finally, two descriptive decision studies, done approximately a decade apart, present additional perspectives on patient choice in dental care decisions. Mounts1 described the multiple factors to be considered in making a decision about restorations. He concluded that dentists must be prepared to advise patients on the indications and contrindications for the various materials and designs. Odom52 examined the issue of paternalism

versus patient autonomy

with respect to treatment decisions. He also recommended

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providing patients with adequate information and raised the types of questions relevant for utilities analysis.

DISEASE

PREDICTION

One group of dental researchers has examined the implications of decision analysis for predicting disease (Table III). Schwartz et a1.53-56 published a series of articles examining the progression of dental caries over time. Applications of several decision methods, including the Kaplan-Meier estimate, Bayes’ theorem, and time series analysis, are useful products of this series of studies. In the first of these studies published by the same group of authors, progression of approximal caries through the dental enamel of 700 children was measuredF3 Of the lesions, 10% were found to have progressed through the enamel in 1 year, 25 % in 2 years, but more than 40 % of the lesions had not progressed in 4 years. In the remaining three studies, Shwartz et a1.54developed a model to generate an individual’s probability distribution for new carious lesions in a year and designed a method to reduce the bias of examiners in estimating the progression of lesions.55*56 They suggested using three examiners to minimize bias in research protocols. Continued research in the area of disease prediction can begin to elucidate and offset the effects of interexaminer variation. This research has implications for the individual clinician and for those concerned with policy-based strategies.

COMPUTER APPLICATIONS DECISION METHODS

OF

Sixteen articles in which computer technology was combined with decision making are included in this section of the dental literature (Table IV). These publications focus on diagnostic and treatment decisions in the specialties of orthodontics, endodontics, oral pathology, and oral medicine. In addition, general reviews or discussions of potential computer applications for dental practice have been published. Computer-assisted decision making is a relatively new focus for dentistry, dating back approximately 15 years. It is built on a wealth of information from previous decades, particularly in orthodontics, that described the development of hardware, software, and databases for use with diagnostic systems, and which have resulted in a few sophisticated dental applications. For example, in 1975 Poulsen et a1.57described the basic principles underlying the development of a computer program for the analyses of dental caries data. Although this and similar efforts were indispensable in the development of expert decision systems, they preceded the application of decision methodology and thus are not included in this review. Some dental journals publish articles every few years to review or update applications of computers in the management of dental offices. Until relatively recently, few of these articles mentioned the potential for applications of com-

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puterized diagnostic and treatment decision-making systems, or “expert systems.” CrandelI’s 1980 review58 of literature pertaining to computer applications in dental office management included articles concerned with clinical diagnosis and indicated what he perceived to have limited potential for application. Ven der Stelt’s 1985 reviedg continued the tradition of reviewing computer applications to dental office procedures and accounting. In addition, he described the applications of computers for diagnostic systems and gave examples of how decision-making, or expert systems, will be applicable in dentistry. He concluded that the future efficacy of such systems is promising. Ricketts’ 1969 review60 of the evolution of orthodontic diagnosis was based on his numerous studies in the development of cephalometrics. He suggested that cephalometric computation had combined with expert individual judgment to move the decision making of the orthodontic clinician into the realm of a science. In 1980 Sloan”’ reviewed the large body of computer literature in orthodontics and determined that the most valuable contribution of computers in orthodontics will be to forecast growth from serial cephalograms. He indicated the importance of the role of computers in managing patient information for interdisciplinary health teams. A number of publications have focused primarily on clinical applications of computers. The usefulness of computers to practitioners has been investigated in other medical fields, but has received limited attention in dentistry, except as a teaching tool for dental students. Lipson et al.62 evaluated the effectiveness of computer simulations in improving decision-making skills of dental practitioners. They found that the simulations affect the “critical” decisions, as opposed to routine decisions, and concluded that decision making is improved with the use of simulations. Literature describing the development of interactive computer systems based on decision analysis has increased in the past 15 years, and is becoming increasingly sophisticated. 63Some of these reports include recommendations for applications of hardware and software for diagnostic decision making in various areas of dental practice. This approach has been used in diagnosing pulpal disease, craniofacial pain, laboratory diagnosis, dental disease and the initial treatment of emergency diagnosis, and orthodontic problems. 18,27-2g* 30,31~s4This literature represents a more advanced stage in the computer application of decision analysis to dental care. The information collected in the orthodontic discipline, for example, is now beginning to lead to systems such as that developed by Sims-Williams et a1.65reported in 1987. They combined previous research with collected data to develop an expert system. Using a decision tree, they developed a system to give advice closely matching that given by the clinicians whose knowledge provided the necessary clinical data. They proposed that general practitioners eventually can use the system to diagnose and treat simple orthodontic problems. 581

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Table V. Publication on policy Dental

domain

Method

General Pedodontics General Prosthodontics Caries prevention General Pedodontics

Multivariate analysis Chi-square Historical review Descriptive Cost/benefit analysis Descriptive Multiple regression

General General Periodontics Restorative dentistry

Regression analysis Utilities assessment Cost effectiveness Cost-effective analysis

General

Decision tree

General

Review

General

Review

Topic

(Reference)

Patient influence in dentists’ decisions (49) National survey of sealant use (50) History of policy decision making in dentistry (68) How consumers choose dentists (69) Alternative caries prevention programs (70) Decision making among insurance consultants (71) Critical components of oral health for population of children (72) Issues of consumer choice of dental plans (73) Definitions of “need” for setting policy (74) Analysis of perio disease control (75) Lifetime restorative needs of adult posterior tooth

6’6)

The step beyond applying computers to diagnosis is to apply them to treatment planning. Again, orthodontic systems supply the best examples of the more advanced applications in dentistry. Solowec presented a system for registration and analysis of malocclusion prevalence, with the ultimate goal to determine the need for treatment. Faber et a1.4saddressed the problem of orthodontic treatment planning based on a computerized data base. In a 1973 editorial, Salzman67 warned orthodontists of the danger of using computers to replace the personal contribution of the competent orthodontist in the treatment of patients. This fear appears to have been common in initial reactions to use computers to make clinical decisions. It has since become apparent that this early threat from computer applications will not materialize. Decision systems now being developed cannot replace practitioners, but instead are designed to support the highest level of clinical competence by supplying accessto current knowledge and rapid computation of complicated decision analyses.

POLICY The aspect of decision analysis concerned with questions of policy

development

(decisions that have implications

for, or are performed at the group or greater level of analysis) includes a variety of topics such as consumer choice, the relationship of need to treatment decisions, using dentists’ judgments to identify the components of oral health, identifying the factors affecting dentists’ treatment decisions, dental insurance issues, and assessing the cost-effectiveness of various treatment and preventive strategies (Table V). A common theme is the increasing demand for dental professionals to attend to the concerns of others, such as consumers and third-party payors, thereby de-

582

Model to consider factors in dentists’ acceptance of Medicaid patients (77) Group and behavioral decision-making techniques (78) Application of methods to malpractice (79)

creasing the more traditional paternalistic decision model that has prevailed in the past for many types of health care professionals. A 1975 study by Howe6s represents the thinking of many individuals about the nature of decision making before the development of more structured decision analysis methods. Howe reviewed the history of policy decision making in dentistry and discussed the need for committees and how to make them work. He described decision making as the basis for setting dental policy in the future, but his work was of little benefit for the structured

study of policy de-

cision making. Patients’ choice of dentists is another aspect of policy studied by dental decision researchers. Garfunkel’seg 1980 study reported the results of a survey indicating that the most important reason for clients’ choice of dentists is the willingness

to talk with patients about all problems, and to

discuss alternative treatments. By contrast, Grembowski et al’s 1988 study49 indicated that among the 156 general dentists surveyed in one state, technical factors instead of patient concerns dominated the dentists’ treatment decisions. The tension revolving

about who will influence

treat-

ment decisions is evident in other articles in this section of the dental decision literature. Horowitz and Heifetz70 defined cost/benefit analysis as applied to preventive programs and described their method as a means of enabling dentists to identify the issues that should affect their decisions about treatment and the prevention of caries. Hoffman’l described how a dental insurance consultant arrived at a decision to deny requests for treatment. Ger-

shen et a1.72described their preliminary findings from the paired-preference methodology, using dentists’ decisions

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to identify the critical components of oral health for populations of children. They concluded that although both the generalists and: the specialists in their study agreed on the constituents of good oral health in children, they disagreed on pedodontic treatment decisions and philosophy of care. StreveP3 attempted to identify the issues affecting the consumers’ choice of dental plan. He indicated that the future balance between prepaid group plans and feefor-service plans will depend on the consumers’ preferences. Yule74 raised the issue of alternative definitions of need and identified dentists, patients, and society as the three assessorsof need for the future. Four recent stud.ies have drawn policy implications from the analysis of clinical questions. In 1987, AntczakBoukoms and Weinstein75 applied cost-effectiveness analysis to the evaluation of alternative methods of periodontal disease control.. The 1988 study by Romberg et a1.,50 conducted to determine the professional, scientific, social, and economic factors that might influence the pedodontist’s decision to use sealants, suggested implications for insurance coverage, the development of professional guidelines, and education. Maryniuk et a1.76developed a computer model to analyze the cost-effectiveness of placing large amalgams versus crowns as a method to determine the optimum strategy for replacing failed amalgams. They were able to identify alternatives with potential lifetime savings of 11% to 24 % , and concluded that the technique of decision analysis provides the dental community with an effective evaluation tool for the study of clinical decision making. Also in 1988, Capilouto77 used an expected benefits model to calculate the expected benefits in dollars for comparable treatment of Medicaid and non-Medicaid patients. He found that the strategy to schedule and treat non-Medicaid patients dominated alternative strategies. He discussed the implications of the findings for policy options such as raising Medicaid fee levels, linking licensure to agreements to accept Medicaid patients, and providing more favorable income tax rates for practices that increase their Medicaid populations. He concluded that it is important to improve access to care for the benefit of society. Some of the dental studies that have examined choice of treatments or diagnostic methods such as radiographs, also have evaluated various methods of decision analysis. Some described decision methods, such as Simpson et al.% article,78 based on the premise that “effective decision making technique,3 are useful to the dentist.” These authors defined basic group decision-making techniques and identified two major models as “normative” and “descriptive.” They proposed a “facilitative” model stressing the operational aspects of the decision process. Finally, Narasin~han,7g an attorney, explored the application of decision rnethods in dentistry. He pointed out the similarity in all decision analyses and focused on the transferability of the general structure of decision methods across disciplines. He outlined decision-making variables,

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discussed decision trees, described the selection of decision strategies, and then applied these concepts to a dental malpractice suit. The author concluded that the purpose of formal analysis is to enhance understanding and description of a problem and to aid the decision maker in being more explicit and comprehensive in searching for a preferred course of action.

CONCLUSIONS The decision-making literature in dentistry shows good evidence of providing useful information and methods to assist the dental profession to face the impact of changing epidemiology, shifting demographics, and the rapid development of science and technology. This body of literature is limited but is growing rapidly and becoming more sophisticated. Important areas for clinical applications of decision approaches have focused on diagnosing, predicting, and planning for treatment of dental diseases. Studies have demonstrated the effectiveness of decision methods to extend clinical knowledge, but as the important questions have been addressed only partially, they remain promising arenas for further research. A number of studies in diagnosis and treatment planning have been concerned with interexaminer variation, indicating a sometimes startling amount of variance among clinicians treating the same patients. The attempt to examine and establish the existence of discrepancies in interexaminer agreement is useful in identifying the need for improved criteria or standards for treatment. However, as a direction for the study of clinical decision making, its usefulness is less clear. Once the problem of the prevalence of examiner disagreement has been been established, decision methods can he more usefully applied to establishing diagnoses, criteria for treatment, cost-benefit analysis, and integration of patient preferences. The development of computer applications and policy are perhaps the most rapidly expanding areas for decision making. Automated clinical decision support, cost-benefit analysis, and utilities analysis will benefit by the application of decision methods. Although computers cannot replace the skill and knowledge of outstanding dental clinicians, there is no doubt that they will become an integral part of the dental operatory in 2000 AD. REFERENCES 1. Grondahl HG. Decision strategies in radiographic caries diagnosis. Swed Dent J 19’79;3:173-80. 2. Grondahl HG. Some factors influencing observer performance in radiographic caries diagnosis. Swed Dent J 1979;3:157-72. 3. Mileman P, Purdell-Lewis D, van der Weele L. Effect of variation in caries diagnosis and degree of caries on treatment decisions by dental teachers using bitewing radiographs. Corn Dent Oral Epid 1983;11:35662. 4. Mileman P, Purdell-Lewis DJ, Dummer P, van der Week L. Diagnosis and treatment decisions when using bite-wing radiographs-A comparison between two dental schools. J Dent 1985;13:140-51. 5. Mileman PA, Vissers T, Purdell-Lewis DS. The application of decision

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making analysis to the diagnosis of approximal caries. Community Dent Health 1986;3:65-81. 6. Grondahl HG. Radiographic caries diagnosis; a study of caries progression and observer performance. Swed Dent J 1979;3:1-32. 7. Mileman P, Purdell-Lewis D, van der Weele L. Variation in radiographic caries diagnosis and treatment decisions among university teachers. Community Dent Oral Epidemiol 1982;10:329-34. 8. Brooks SL. A study of selection criteria for intraoral dental radiogr8phy. Oral Surg 1986;62:234-9. 9. Pliskin JS, Shwartz M, Grondahl H-G, Boffa J. Incorporating individual patient preferences in scheduling bite-wing radiographs. Methods Inf Med 1985;24:213-7. 10. Douglas CW, McNeil BJ. Clinical decision analysis methods applied to diagnostic tests in dentistry. J Dent Ed 1983;47:708-12. 11. Graber TM. Orthodontic therapy: an exercise in decision making. Trans Eur Ortho Sot 1972; :215-30. 12. Owen WD, Rayson JM. Decision making in common denture complaints. J Acad Gen Dent 1972;20:14-6. 13. Grondahl H-G, Hollender L, Mahncrona E, Sundquist B. Dental caries and restorations in teenages: II. A longitudinal radiographic study of the caries increment of proximal surfaces among urban teenagers in Sweden. Swed Dent J 1977;1:51-7. 14. Reit C, Grondahl H-G. Application of statistical decision theory to radiographic diagnosis of endodontically treated teeth. Stand J Dent Res 1983;91:213-8. 15. Reit C. On decision making in endodontics: a study of diagnosis and management of periapical lesions in endodontically treated teeth. Swed Dent J Suppl41,1986:2-30. 16. Reit C, Grondahl H-G. Endodontic decision making under uncertainty: a decision analytic approach to management of periapical lesions in endodontically treated teeth. Endo Dent Trauma 1986; :l-10. 17. Tzukert AA. Pulpitis and root canal therapy: is a diagnostic radiograph of value? Oral Surg Oral Med Oral Path01 1986;61:284-8. 18. Hyman JJ, Doblecki W. Computerized endodontic diagnosis. J Am Dent Assoc 1983;107:755-8. 19. Smulson MH. Classification and diagnosis of pulpal pathoses. Dent Clin North Am 1984,28:699-723. 20. H&ajee AD, Socransky SS, Goodson JM. Clinical parameters as predictors of destructive periodontal disease activity. J Clin Periodontol 1983;10:257-65. 21. Badersten A, Nilvens R, Egelberg J. Effect of nonsurgical periodontal therapy. J Clm Periodontol 1985;12:432-40. 22. Aeppli DM, Boen JR, Bandt L. Measuring and interpreting increases in probing depth and attachment loss. J Clin Periodontol 1984,56:262-4. 23. Offenbacher S, Odle BM, Van Dyke TE. The use of crevicular fluid prostaglandin Ez levels as a predictor of periodontal attachment loss. J Periodont Res 1986,21:101-12. 24. Listgazten MA. A perspective on periodontal diagnosis. J Clin Periodonto1 1986;13:175-81. 25. Dahlen BL, Wikstrom M, Slots J. The capability of Actinobacilllls Actinomycetemcomitans, Bacteroides gingiualis and Bacteroides intermedius to indicate progressive periodontitis; a retrospective study. J Clin Periodontol 1987;14:95-9. 26. Ralls SA, Cohen ME. Problems in identifying “bursts” of periodontal attachment loss. J Clin Periodontol 1986;57:746-52. 27. Leonard MS, Roberts ST, Fast TB, Mahan PE. Automated diagnosis of craniofacial pain. J Dent Res 1973;52:1297-1302. 28. Leonard MS, Kilpatrick KE, Fast TB, Mahan PE, Mackenzie RS. Automated diagnosis and treatment planning for craniofacial pain. J Dent Res 197$53:1155-g. 29. Kramer IR. Computers in clinical and laboratory diagnosis. Int Dent J 1980;30:214-25. 30. Rails SA, Cohen ME, Southard TE. Computer-assisted dental diagnosis. Dent Clin North Am 1986;30:695-712. 31. Rails SA, Southard TE. A system for computer-assisted dental emergency diagnosis. Milit Med 1986. 32. Elderton RJ, Nuttal NM. Variation among dentists in planning treatment. Br Dent J 1983;154:201-6. 33. Nuttall NM, Elderton R.J. The nature of restorative dental treatment decisions. Br Dent J 1983;154:363-5. 34. Merrett MCW, Elderton RJ. An in vitro study of restorative dental treatment decisions and dental caries. Br Dent J 1984;157:128-33. 35. Horowitz HS. Evaluation of the effect of interexaminer reliability in

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clinical trials of dental caries prevention. Community Dent Oral Epidemiol 1983;11:384-5. 36. Espelid I, Tveit A, Haugejorden 0, Riordan PJ. Variation in radiographic interpretation and restorative treatment decisions on approximal caries among dentists in Norway. Community Dent Oral Epidemiol 1985:13:26-g. 37. Pliskin JS, Shwartz M, Grondahl HG, Boffa J. Reliability of coding depth of approximal carious lesions from non-independent interpretation of serial bite-wing radiographs. Community Dent Oral Epidemiol 19&1;12:366-70. 38. Grondahl H-G. Radiographic caries diagnosis and treatment decisions. Swed Dent J 1979;3:109-17. 39. Reit C, Grondahl H-G. Management of periapical lesions in endodontically treated teeth: a study on clinical decision making. Swed Dent J 1984;8:1-7. 40. Reit C, Grondahl H-G. Endodontic retreatment decision making among a group of general practitioners. Stand J Dent Res 1988;96:112-7. 41. Reit C, Grondahl H-G, Engstrom B. Endodontic treatment decisions: a study of the clinical decision making process. Endo Dent Trauma 1985;1:102-7. 42. van Velsen TSK, Duivenvoorden HJ, Schuurs AH. Probabilities of success and failure in endodontic treatment: a bayesian approach. Oral Surg Oral Med Oral Path01 1981;52:85-90. 43. Tulloch TF, Antczak-Bouckoms AA. Decision analysis in the evaluation of clinical strategies for the management of mandibular third molars. J Dent Educ 1987;5:652-60. 44. Tulloch JFC, Antczak AA, Wilkes JW. The application of decision analysis to evaluate the need for extraction of asymptomatic third molars. J Oral Maxillofac Surg 1987;45:855-63. 45. Tzukert AA, Leviner E, Benoliel R, Katz J. Analysis of the American Heart Association’s recommendations for the prevention of infective endocarditis. Oral Surg Oral Med Oral Path01 198%64:276-g. 46. Krisher JP. A decision analytic approach to cleft palate treatment evaluation. Cleft Palate J 1980$7:319-25. 47. Ettinger RL. Clinical decision making in the dental treatment of the elderly. Gerodontology 1984;3:1457-65. 48. Faber RD, Burstone CJ, Solonche DJ. Computerized interactive orthodontic treatment planning. Am J Orthod 1978;73:36-46. 49. Grembowski D, Milgrom P, Fiset L. Factors influencing dental decision making. J Public Health Dent 1988,&159-67. 50. Romberg E, Cohen LA, LaBelle AD. A national survey of sealant use by pediatric dentists. J Dent Children 1988; :257-64. 51. Mount GJ. Methods of restoration-a decision. New Zealand Dent J 1977;73:135-42. 52. Odom JG. Who makes the treatment decision? J Dent Prac Admin 1986;3:57-60. 53. Shwartz M, Grondahl H-G, Pliskin JS, Boffa J. A longitudinal analysis from bite-wing radiographs of the rate of progression of approximal carious lesions through human dental enamel. Arch Oral Biol 1984;29:529-36. 54. Shwarts M, Pliskin JS, Grondahl H-G, Boffa J. A deep model of the incidence of dental caries on proximal surfaces. Med Decis Making 1986;6:42-6. 55. Shwartz M, Pliskin JS, Grondahl H-G Boffa J. Study design to reduce biases in estimating the percentage of carious lesions that do not progress within a time period. Community Dent Oral Epidemiol 1984;12:109-13. 56. Shwartz M, Pliskin JS, Grondahl H-G, Boffa J. Use of the Kaplan-Meier estimate to reduce biases in estimating the rate of caries progression. Community Dent Oral Epidemiol 1984;12:103-8. 57. Paulsen S, Lind OP, Gadegaarde E. A computer program for analysis of dental caries data. Int J Bio-Med Comput 1975;6:221-7. 58. Crandell CE. Use of computers in dental office management. Int Dent J 1980;30:226-33. 59. van der Stelt PF. The microcomputer in the dental office: a new diagnostic aid. Int Dent J 1985;35:103-8. 60. Ricketta RM. The evolution of diagnosis to computerized cephalometrics. Am J Orthod 1969;55:795-803. 61. Sloan RF, Computer applications in orthodontics. Int Dent J 1980;30:189-99. 62. Lipson LF, Keith KD, Rothmeier R, Jones GL. Evaluating effects of computerized patient simulations on dental decision making. J Dent Res 1980;59:418.

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63. Abbey LM. An expert system for oral diagnosis [Abstract]. J Dent Educ 1987;51:475-80. 64. Bhatia SN. A comprehensive interactive on-line computer system for research and clinical practice in orthodontics. Br J Orthod 1985;12:1526. 65. Sims-Williams JH, Brown ID, Matthewman A, Stephens CD. A computer-controlled expert system for orthodontic advice. Br Dent J 1987;163:161-6. 66. Solow B. Computer analysis of malocclusion prevalence. Int Dent J 1970;20:633-42. 67. Salzman JA. The computerization of the orthodontic patient. Am J Orthod 1973;63:539-40. 68. Howe GL. Decision making in dentistry. Br Dent J 1975;138:105-8. 69. Garfunkel E. The consumer speaks: how patients select and how much they know about dental health care personnel. J PROSTH~ DENT 1980;43:380-4. 70. Horowitz HS, Heifetz SB. Methods of assessing the cost-effectiveness of caries preventive agents and procedures. Int Dent J 1979;29:106-17. 71. Hoffman GA. Why most claims are rejected. Dent Econ 1980,70:59-61. 72. Gershen JA, Marcus M, Koch A. Using dentists’ judgments to identify the components of children’s oral health. J Dent Child 1980;47:419-24.

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73. Strevel DW. Consumer choice between dental delivery systems. J Am Dent Assoc 1982;104:157-63. 74. Yule BF. Need and decision making in dentistry-an economic perspective. Int Dent J 1984;34:219-23. 75. Antczak-Bouckoms AA, Weinstein MC. Cost-effectiveness analysis of periodontal disease control. J Dent Res 1987;66:1630-5. 76. Maryniuk GA, Schweitzer SO, Braun RJ. Replacement of amalgams with crowns: a cost-effectiveness analysis. Community Dent Oral Epidemiol 198&l&163-267. 77. Capilouto E. The dentist’s role in accessto dental care by Medicaid recipients. J Dent Educ 198852647-52. 78. Simpson R, Hall D, Crabb L. Decision-making in dental practice. J Am Co11Dent 1981;48:238-45. 79. Narasimhan R. Decision analysis and dental malpractice suits. Dent Clin North Am 1982;26:411-21. &print P2QUeStS to.’ DR. ANN M. MCCREERY 6531 254~~ ST., N.E. ARLINGTON.WA 98223

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