Dental Decision Simulation (DDSim): Development of a virtual training environment.

PURPOSE: Case-based simulations are powerful training tools that can enhance learning and drive behavior change. This is an overview of the design/development of Dental Decision Simulation (DDSim), a web-based simulation of an electronic dental record (EDR). The purpose was to use DDSim to train dentists to make evidence-based treatment planning decisions consistent with current evidence. This simulated EDR provides case-based information in support of a set of defined evidence-based learning objectives. METHODS: The development of this complex simulation model required coordinated efforts to create several components: identify behavior changes, case authoring mechanism, create virtual patient visits, require users to make treatment plan decisions related to learning objectives, and a feedback mechanism to help users recognize departures from those learning objectives. This simulation was evaluated in a 2-arm, clinic-randomized, controlled pilot study examining the extent to which DDSim changed dentists' planned treatment to conform to evidence-based treatment guidelines relative to change in dentists not exposed to DDSim. Outcomes were measured by comparing preintervention and postintervention patient EDR treatment data. RESULTS: Changes in behavior over time did not favor intervention or control clinics. CONCLUSION: DDSim provides a standardized learning platform that cannot be achieved through the use of live patients. Both live patients and case-based simulations can be used to transfer knowledge and skill development. DDSim offers the advantage of providing a platform for developing treatment planning skills in a low-risk environment. However, further research examining behavior change is needed.

Development, validity and reliability of the Orofacial Esthetic Scale - Spanish version.

PURPOSE: To develop a Spanish version of the Orofacial Esthetic Scale (OES-Sp) and to determine its psychometric properties in dental patients. METHODS: We performed a validation study for OES development and validation into Spanish. In the development phase, eight individuals participated in the translation process (cross-cultural adaptation) following the PROMIS methodology. In the validation phase, 331 dental patients (age mean±sd: 42.9±12.3years, 59 % female) from the HealthPartners dental clinics in Minnesota, USA responded to a survey of Spanish speaking patients (n=540, response rate: 61 %). We first explored dimensionality by means of exploratory factor analysis and scree plot, and then we computed reliability measures with the Cronbach's alpha statistic. Finally, we assessed convergent validity by computing Pearson/Spearman rank correlations between OES-Sp and Oral Health Impact Profile (OHIP)-based orofacial appearance measures. All statistical procedures were performed using Stata v.13 for Windows (StataCorp). RESULTS: Exploratory factor analysis provided evidence that a single factor represents the Spanish OES version. Score reliability was high with Cronbach's alpha statistic of 0.95 (lower limit of the 95 % CI: 0.94). Score validity was sufficient indicated by Pearson and Spearman rank correlations between -0.53 and -0.69 (all 95 % confidence intervals: less than ± 0.10). CONCLUSIONS: OES is a valuable instrument to measure the orofacial appearance construct for Spanish-speaking populations. OES was concluded to be unidimensional with excellent score reliability and sufficient convergent score validity when compared with other Orofacial Appearance measures. Our results suggest that OES could be used in Spanish-speaking patients, an important and growing population around the globe.

Persistence of smoking-cessation decision support use in a dental practice.

INTRODUCTION: A computer-assisted tobacco decision support tool increased dental practitioners' (dentists and dental hygienists) advice to quit smoking and referral to a quitline during a group randomized trial. The purpose of this study is to document the extent to which use persisted after the trial. METHODS: Electronic dental record (EDR) data from 2010 to 2013 were analyzed in 2014 for use of computer-assisted tobacco intervention tool advice scripts and referral to a quitline during four periods: during the trial and post-trial when only intervention clinic dental practitioners had access to the tool, and during full deployment, both before and after an EDR modification. RESULTS: Intervention clinic dental practitioners (18.5 dentist full-time equivalents [FTEs] and 27.8 dental hygienist FTEs practicing in seven clinics) referred 19.0% of 1,368 smokers to a quitline during the trial and referred 15.4% of 4,011 smokers post-trial. After full tool deployment but pre-EDR change, these dental practitioners referred 15.6% of 2,214 intervention clinic smokers, whereas 18.3 dentist FTEs and 29.7 dental hygienist FTEs practicing in eight clinics referred 8.5% of 2,113 smokers. Post-EDR change, dental practitioners referred 12.2% of 2,214 intervention clinic smokers and 8.1% of 2,399 control clinic smokers to a quitline. In the last three quarters of observation, clinic script use ranged from 15.4% to 65.8% and referral to a quitline ranged from 2.0% to 18.7% of visits. CONCLUSIONS: Although EDR design affected rates of referral, dental practitioners persisted in using a computer-assisted tobacco intervention tool to refer smokers to a quitline.

Personalised physician learning intervention to improve hypertension and lipid control: randomised trial comparing two methods of physician profiling.

PURPOSE: To assess the impact of personalised physician learning (PPL) interventions using simulated learning cases on control of hypertension and dyslipidaemia in primary care settings. METHODS: A total of 132 primary care physicians, 4568 eligible patients with uncontrolled hypertension, and 15 392 eligible patients with uncontrolled dyslipidaemia were cluster-randomised to one of three conditions: (a) no intervention, (b) PPL-electronic medical record (EMR) intervention in which 12 PPL cases were assigned to each physician based on observed patterns of care in the EMR in the previous year, or (c) PPL-ASSESS intervention in which 12 PPL cases were assigned to each physician based on their performance on four standardised assessment cases. General and generalised linear mixed models were used to account for clustering and to model differences in patient outcomes in the study arms. RESULTS: Among patients with uncontrolled hypertension at baseline, 49.1%, 46.6% and 47.3% (p=0.43) achieved blood pressure (BP) targets at follow-up. Among patients with uncontrolled dyslipidaemia at baseline, 37.5%, 37.3% and 38.1% (p=0.72) achieved low density lipoprotein cholesterol targets at follow-up in PPL-EMR, PPL-ASSESS and the control group, respectively. Although systolic (BP) (p<0.001) and lipid (p<0.001) values significantly improved during the study, the group-by-time interaction term showed no differential change in systolic BP values (p=0.51) or lipid values (p=0.61) among the three study arms. No difference in intervention effect was noted when comparing the PPL-EMR with the PPL-ASSESS intervention (p=0.47). CONCLUSIONS: The two PPL interventions tested in this study did not lead to improved control of hypertension or dyslipidaemia in primary care clinics during a mean 14-month follow-up period. This null result may have been due in part to substantial overall improvement in BP and lipid control at the study sites during the study. TRIAL REGISTRATION NUMBER: NCT00903071.

Educating resident physicians using virtual case-based simulation improves diabetes management: a randomized controlled trial.

PURPOSE: To test a virtual case-based Simulated Diabetes Education intervention (SimDE) developed to teach primary care residents how to manage diabetes. METHOD: Nineteen primary care residency programs, with 341 volunteer residents in all postgraduate years (PGY), were randomly assigned to a SimDE intervention group or control group (CG). The Web-based interactive educational intervention used computerized virtual patients who responded to provider actions through programmed simulation models. Eighteen distinct learning cases (L-cases) were assigned to SimDE residents over six months from 2010 to 2011. Impact was assessed using performance on four virtual assessment cases (A-cases), an objective knowledge test, and pre-post changes in self-assessed diabetes knowledge and confidence. Group comparisons were analyzed using generalized linear mixed models, controlling for clustering of residents within residency programs and differences in baseline knowledge. RESULTS: The percentages of residents appropriately achieving A-case composite clinical goals for glucose, blood pressure, and lipids were as follows: A-case 1: SimDE = 21.2%, CG = 1.8%, P = .002; A-case 2: SimDE = 15.7%, CG = 4.7%, P = .02; A-case 3: SimDE = 48.0%, CG = 10.4%, P < .001; and A-case 4: SimDE = 42.1%, CG = 18.7%, P = .004. The mean knowledge score and pre-post changes in self-assessed knowledge and confidence were significantly better for SimDE group than CG participants. CONCLUSIONS: A virtual case-based simulated diabetes education intervention improved diabetes management skills, knowledge, and confidence for primary care residents.

Integrating tobacco dependence counseling into electronic dental records: a multi-method approach.

Dentistry has historically seen tobacco dependence as a medical problem. As a consequence, dentistry has not adopted or developed effective interventions to deal with tobacco dependence. With the expanded use of electronic dental records, the authors identified an opportunity to incorporate standardized expert support for tobacco dependence counseling during the dental visit. Using qualitative results from observations and focus groups, a decision support system was designed that suggested discussion topics based on the patient's desire to quit and his or her level of nicotine addiction. Because dental providers are always pressed for time, the goal was a three-minute average intervention interval. To fulfill the provider's need for an easy way to track ongoing interventions, script usage was recorded. This process helped the provider track what he or she had said to the patient about tobacco dependence during previous encounters and to vary the messages. While the individual elements of the design process were not new, the combination of them proved to be very effective in designing a usable and accepted intervention. The heavy involvement of stakeholders in all components of the design gave providers and administrators ownership of the final product, which was ultimately adopted for use in all the clinics of a large dental group practice in Minnesota.

Computer-assisted guidance for dental office tobacco-cessation counseling: a randomized controlled trial.

BACKGROUND: Decreases in smoking prevalence from recent decades have slowed, and national goals to reduce tobacco use remain unmet. Healthcare providers, including those in physician and dental teams, have access to evidence-based guidelines to help patients quit smoking. Translation of those guidelines into practice, however, remains low. Approaches that involve screening for drug use, brief intervention, and referral to treatment (SBIRT) are a promising, practical solution. PURPOSE: This study examined whether dentists and dental hygienists would assess interest in quitting, deliver a brief tobacco intervention, and refer to a tobacco quitline more frequently as reported by patients if given computer-assisted guidance in an electronic patient record versus a control group providing usual care. DESIGN: A blocked, group-randomized trial was conducted from November 2010 to April 2011. Randomization was conducted at the clinic level. Patients nested within clinics represented the lowest-level unit of observation. SETTING/PARTICIPANTS: Participants were patients in HealthPartners dental clinics. INTERVENTION: Intervention clinics were given a computer-assisted tool that suggested scripts for patient discussions. Usual care clinics provided care without the tool. MAIN OUTCOME MEASURES: Primary outcomes were post-appointment patient reports of the provider assessing interest in quitting, delivering a brief intervention, and referring them to a quitline. RESULTS: Patient telephone surveys (72% response rate) indicated that providers assessed interest in quitting (control 70% vs intervention 87%, p=0.0006); discussed specific strategies for quitting (control 26% vs intervention 47%, p=0.003); and referred the patient to a tobacco quitline (control 17% vs intervention 37%, p=0.007) more frequently with the support of a computer-assisted tool integrated into the electronic health record. CONCLUSIONS: Clinical decision support embedded in electronic health records can effectively help providers deliver tobacco interventions. These results build on evidence in medical settings supporting this approach to improve provider-delivered tobacco cessation. TRIAL REGISTRATION: This study is registered at ClinicalTrials.govNCT01584882.

A comparison of dental restoration outcomes after placement by restorative function auxiliaries versus dentists.

OBJECTIVES: To compare the outcomes of restorations placed by restorative function auxiliaries (RFAs) with those placed by dentists. METHODS: Between July 1, 2007, and June 30, 2008, we matched 455 restorations placed by RFAs working at HealthPartners Dental Group with the same number placed by dentists. Restorations were matched by tooth number, American Dental Association procedure code, and patient age-group. RESULTS: Of 910 restorations, 17 (1.9 percent) had problems potentially related to the filling or crown placement during the first year. Problem rates were not significantly different (p = 0.33) for restorations placed by RFAs (1.3 percent, 6 of 455) and those placed by dentists (2.4 percent, 11 of 455). CONCLUSIONS: There was no significant difference in problem rates for restorations placed by RFAs versus those placed by dentists. This finding may free dentists to handle more difficult cases, alleviating some of the pressures of daily practice and meeting the need for improved access.

Cost-effectiveness of an electronic medical record based clinical decision support system.

BACKGROUND AND OBJECTIVE: Medical groups have invested billions of dollars in electronic medical records (EMRs), but few studies have examined the cost-effectiveness of EMR-based clinical decision support (CDS). This study examined the cost-effectiveness of EMR-based CDS for adults with diabetes from the perspective of the health care system. DATA SOURCES/SETTING: Clinical outcome and cost data from a randomized clinical trial of EMR-based CDS were used as inputs into a diabetes simulation model. The simulation cohort included 1,092 patients with diabetes with A1c above goal at baseline. STUDY DESIGN: The United Kingdom Prospective Diabetes Study Outcomes Model, a validated simulation model of diabetes, was used to evaluate remaining life years, quality-adjusted life years (QALYs), and health care costs over patient lifetimes (40-year time horizon) from the health system perspective. PRINCIPAL FINDINGS: Patients in the intervention group had significantly lowered A1c (0.26 percent, p = .014) relative to patients in the control arm. Intervention costs were $120 (SE = 45) per patient in the first year and $76 (SE = 45) per patient in the following years. In the base case analysis, EMR-based CDS increased lifetime QALYs by 0.04 (SE = 0.01) and increased lifetime costs by $112 (SE = 660), resulting in an incremental cost-effectiveness ratio of $3,017 per QALY. The cost-effectiveness of EMR-based CDS persisted in one-way, two-way, and probabilistic sensitivity analyses. CONCLUSIONS: Widespread adoption of sophisticated EMR-based CDS has the potential to modestly improve the quality of care for patients with chronic conditions without substantially increasing costs to the health care system.

Impact of electronic health record clinical decision support on diabetes care: a randomized trial.

PURPOSE: We wanted to assess the impact of an electronic health record-based diabetes clinical decision support system on control of hemoglobin A(1c) (glycated hemoglobin), blood pressure, and low-density lipoprotein (LDL) cholesterol levels in adults with diabetes. METHODS: We conducted a clinic-randomized trial conducted from October 2006 to May 2007 in Minnesota. Included were 11 clinics with 41 consenting primary care physicians and the physicians' 2,556 patients with diabetes. Patients were randomized either to receive or not to receive an electronic health record (EHR)-based clinical decision support system designed to improve care for those patients whose hemoglobin A(1c), blood pressure, or LDL cholesterol levels were higher than goal at any office visit. Analysis used general and generalized linear mixed models with repeated time measurements to accommodate the nested data structure. RESULTS: The intervention group physicians used the EHR-based decision support system at 62.6% of all office visits made by adults with diabetes. The intervention group diabetes patients had significantly better hemoglobin A(1c) (intervention effect -0.26%; 95% confidence interval, -0.06% to -0.47%; P=.01), and better maintenance of systolic blood pressure control (80.2% vs 75.1%, P=.03) and borderline better maintenance of diastolic blood pressure control (85.6% vs 81.7%, P =.07), but not improved low-density lipoprotein cholesterol levels (P = .62) than patients of physicians randomized to the control arm of the study. Among intervention group physicians, 94% were satisfied or very satisfied with the intervention, and moderate use of the support system persisted for more than 1 year after feedback and incentives to encourage its use were discontinued. CONCLUSIONS: EHR-based diabetes clinical decision support significantly improved glucose control and some aspects of blood pressure control in adults with type 2 diabetes.

Simulated physician learning program improves glucose control in adults with diabetes.

OBJECTIVE: Inexpensive and standardized methods to deliver medical education to primary care physicians (PCPs) are desirable. Our objective was to assess the impact of an individualized simulated learning intervention on diabetes care provided by PCPs. RESEARCH DESIGN AND METHODS: Eleven clinics with 41 consenting PCPs in a Minnesota medical group were randomized to receive or not receive the learning intervention. Each intervention PCP was assigned 12 simulated type 2 diabetes cases that took about 15 min each to complete. Cases were designed to remedy specific physician deficits found in their electronic medical record observed practice patterns. General linear mixed models that accommodated the cluster randomized study design were used to assess patient-level change from preintervention to 12-month postintervention of A1C, blood pressure, and LDL cholesterol. The relationship between the study arm and the total of intervention and patient health care costs was also analyzed. RESULTS: Intervention clinic patients with baseline A1C >or=7% significantly improved glycemic control at the last postintervention A1C measurement, intervention effect of -0.19% mean A1C (P = 0.034) and +6.7% in A1C <7% goal achievement (P = 0.0099). Costs trended lower, with the cost per patient -$71 (SE = 142, P = 0.63) relative to nonintervention clinic patients. The intervention did not significantly improve blood pressure or LDL control. Models adjusting for age, sex, and comorbidity showed similar results. PCPs reported high satisfaction. CONCLUSIONS: A brief individualized case-based simulated learning intervention for PCPs led to modest but significant glucose control improvement in adults with type 2 diabetes without increasing costs.

Does diabetes double the risk of depression?

PURPOSE: In this study, we compared the rate of depression diagnoses in adults with and without diabetes mellitus, while carefully controlling for number of primary care visits. METHODS: We matched adults with incident diabetes (n = 2,932) or prevalent diabetes (n = 14,144) to nondiabetic control patients based on (1) age and sex, or (2) age, sex, and number of outpatient primary care visits. Logistic regression analysis was used to assess the association between various predictors and a diagnosis of depression in each diabetes cohort relative to matched nondiabetic control patients. RESULTS: With matching for age and sex alone, patients with prevalent diabetes having few primary care visits were significantly more likely to have a new depression diagnosis than matched control patients (odds ratio [OR] = 1.46, 95% confidence interval [CI], 1.19-1.80), but this relationship diminished when patients made more than 10 primary care visits (OR = 0.95, 95% CI, 0.77-1.17). With additional matching for number of primary care visits, patients with prevalent diabetes mellitus with few primary care visits were more likely to have a new diagnosis of depression than those in control group (OR = 1.32, 95% CI, 1.07-1.63), but this relationship diminished and reversed when patients made more than 4 primary care visits (OR = 0.99, 95% CI, 0.80-1.23). Similar results were observed in the subset of patients with incident diabetes and their matched control patients. CONCLUSIONS: Patients with diabetes have little or no increase in the risk of a new diagnosis of depression relative to nondiabetic patients when analyses carefully control for the number of outpatient visits. Studies showing such an association may have inadequately adjusted for comorbidity or for exposure to the medical care system.

Customized feedback to patients and providers failed to improve safety or quality of diabetes care: a randomized trial.

OBJECTIVE: To assess whether providing customized clinical information to patients and physicians improves safety or quality of diabetes care. RESEARCH DESIGN AND METHODS: Study subjects included 123 primary care physicians and 3,703 eligible adult diabetic patients with elevated A1C or LDL cholesterol, who were randomly assigned to receive customized feedback of clinical information as follows: 1) patient only, 2) physician only, 3) both the patient and physician, or 4) neither patient nor physician. In the intervention groups, patients received customized mailed information or physicians received printed, prioritized lists of patients with recommended clinical actions and performance feedback. Hierarchical models were used to accommodate group random assignment. RESULTS: Study interventions did not improve A1C test ordering (P = 0.35) and negatively affected LDL cholesterol test ordering (P < 0.001) in the 12 months postintervention. Interventions had no effect on LDL cholesterol values (P = 0.64), which improved in all groups over time. Interventions had a borderline unfavorable effect on A1C values among those with baseline A1C >or=7% (P = 0.10) and an unfavorable effect on A1C values among those with baseline A1C >or=8% (P < 0.01). Interventions did not reduce risky prescribing events or increase treatment intensification. Time to next visit was longer in all intervention groups compared with that for the control group (P < 0.05). CONCLUSIONS: Providing customized decision support to physicians and/or patients did not improve quality or safety of diabetes care and worsened A1C control in patients with baseline A1C >or=8%. Future researchers should consider providing point-of-care decision support with redesign of office systems and/or incentives to increase appropriate actions in response to decision-support information.

Simulated physician learning intervention to improve safety and quality of diabetes care: a randomized trial.

OBJECTIVE: To assess two physician learning interventions designed to improve safety and quality of diabetes care delivered by primary care physicians (PCPs). RESEARCH DESIGN AND METHODS: This group randomized clinical trial included 57 consenting PCPs and their 2,020 eligible adult patients with diabetes. Physicians were randomized to no intervention (group A), a simulated case-based physician learning intervention (group B), or the same simulated case-based learning intervention with physician opinion leader feedback (group C). Dependent variables included A1C values, LDL cholesterol values, pharmacotherapy intensification rates in patients not at clinical goals, and risky prescribing events. RESULTS: Groups B and C had substantial reductions in risky prescribing of metformin in patients with renal impairment (P = 0.03). Compared with groups A and C, physicians in group B achieved slightly better glycemic control (P = 0.04), but physician intensification of oral glucose-lowering medications was not affected by interventions (P = 0.41). Lipid management improved over time (P < 0.001) but did not differ across study groups (P = 0.67). CONCLUSIONS: A simulated, case-based learning intervention for physicians significantly reduced risky prescribing events and marginally improved glycemic control in actual patients. The addition of opinion leader feedback did not improve the learning intervention. Refinement and further development of this approach is warranted.

Depression in patients with diabetes: does it impact clinical goals?

INTRODUCTION: To examine whether depressive symptoms are associated with achievement of recommended goals for control of glucose, lipids, and blood pressure among patients with diabetes. METHODS: We used a prospective cohort study of 1223 adults with diabetes that obtained self-reported depression symptoms from a survey. Medication use was obtained from claims data, and pharmacy and clinical data were obtained by manual review of paper medical records. RESULTS: Diabetes patients with depression symptoms were less likely to be at their glucose goal (43% vs 50%; P = .0176) but more likely to be at their blood pressure goal (57% vs 51%; P = .0435). The association between lipids and depression symptoms was related to a lower rate for low-density lipoprotein testing (56% vs 68%; P < .0001). Treatment with antidepressants resulted in a greater percentage achieving glucose and blood pressure goals but not lipid goals. CONCLUSIONS: Depression seems to have a variable impact on achieving these clinical goals, perhaps because the goals have differing measurement logistics and biological profiles. Further research is needed to learn whether better treatment of depressive symptoms leads to improvements in meeting diabetes clinical goals.

Clinical inertia in dentistry: a review of the phenomenon.

AIM: Dentistry has been slow to adopt innovations in dental practice even when they are recommended by national organizations and supported by evidence-based guidelines. The objective of this review is to describe clinical inertia, a concept described frequently in the medical literature, and to use findings from tobacco cessation and dental sealant studies as evidence of its existence. METHODS AND MATERIALS: A review of the literature published during the past 30 years was conducted to determine the state of affairs of two very different areas of dental practice, tobacco cessation intervention and application of sealants, to demonstrate the concept of clinical inertia in dental practice. Factors such as over estimating services provided, unfounded reasons not to act, lack of adequate training, and competing demands that account for the inertia were examined. DISCUSSION: Clinical inertia is a complex concept that needs more attention in dentistry. A variety of strategies will be required to overcome it in order to provide the best care for the public. CONCLUSION: Clinical inertia is a useful paradigm for explaining delays in the incorporation of new knowledge into clinical practice. It offers a model against which the broader dental community can develop and test strategies to reduce the delays in translating best practices into daily practices. CLINICAL SIGNIFICANCE: The path to providing state-of-the-art care for the public is to engage in the discovery, dissemination, and acquisition of new knowledge then transform it into evidence-based best practices to be used in daily clinical practice.

Variation in quality of diabetes care at the levels of patient, physician, and clinic.

INTRODUCTION: We studied variance in glycated hemoglobin (HbA1c) values among adults with diabetes to identify variation in quality of diabetes care at the levels of patient, physician, and clinic, and to identify which levels contribute the most to variation and which variables at each level are related to quality of diabetes care. METHODS: Study subjects were 120 primary care physicians and their 2589 eligible adult patients with diabetes seen at 18 clinics. The dependent variable was HbA1c values recorded in clinical databases. Multivariate hierarchical models were used to partition variation in HbA1c values across the levels of patient, physician, or clinic and to identify significant predictors of HbA1c at each level. RESULTS: More than 95% of variance in HbA1c values was attributable to the patient level. Much less variance was seen at the physician and clinic level. Inclusion of patient and physician covariates did not substantially change this pattern of results. Intensification of pharmacotherapy (t = -7.40, P < .01) and patient age (t = 2.10, P < .05) were related to favorable change in HbA1c. Physician age, physician specialty, number of diabetes patients per physician, patient comorbidity, and clinic assignment did not predict change in HbA1c value. The overall model with covariates explained 11.8% of change in HbA1c value over time. CONCLUSION: These data suggest that most variance in HbA1c values is attributable to patient factors, although physicians play a major role in some patient factors (e.g., intensification of medication). These findings may lead to more effective care-improvement strategies and accountability measures.

Exploring the contributions of components of caries risk assessment guidelines.

OBJECTIVE: To examine the relative contribution of current caries activity, past caries experience, and dentists' subjective assessment of caries risk classifications. METHODS: Administrative data from two dental plans were analyzed to determine dentists' risk classification, as well as current caries activity and previous caries experience at the time of the classification. The performance of these predictors in identifying patients who would experience subsequent caries was then modeled using logistic regression. RESULTS: In both plans, current caries activity alone had relatively low sensitivity and high specificity in identifying patients who would experience subsequent caries. Sensitivity improved, but at the cost of specificity when previous caries experience was included in the models. Further improvement in sensitivity accrued when dentists' subjective assessment was included, but performance was different in the two plans in terms of false-positives. CONCLUSIONS: Consideration of previous caries experience tends to strengthen the predictive power of caries risk assessments. Dentists' subjective assessments also tend to improve sensitivity, but overall accuracy may suffer.

Outcomes associated with dentists' risk assessment.

OBJECTIVE: To examine retrospectively the caries-related restorative experience of at-risk individuals who received fluoride-based preventive interventions to determine if the intervention resulted in fewer caries-related procedures. METHODS: Administrative data from two dental health plans were used to determine the relationship between caries risk assessment (CRA) scores, preventive treatment and caries-related treatment procedures. We identified 45 693 adults who were consecutively enrolled for at least 1 year before and 2.5 years after the CRA. Variables representing the number of teeth with any caries-related treatment procedure and receipt of preventive treatment were created. RESULTS: The outcome variable of interest was having at least one tooth with a caries-related procedure in the 2-year follow-up period. In plan A, the recommendation for home-use fluoride was not significantly related to caries-related treatment procedures in the follow-up period for individuals at low, moderate or high risk (P > 0.300). In plan B, application of in-office fluoride was associated with having at least one tooth with a caries-related treatment procedure in the follow-up period (P < 0.001). CONCLUSIONS: We found incomplete compliance with guidelines for recommendation or administration of preventive treatment for patients at elevated risk for caries. We were also unable to identify any significant reductions in caries-related procedures for individuals receiving a fluoride intervention, compared with those who did not, when stratified by risk level.

Impact of office systems and improvement strategies on costs of care for adults with diabetes.

OBJECTIVE: To assess the impact of organizational features and improvement strategies of primary care clinics on health care costs of adults with diabetes. RESEARCH DESIGN AND METHODS: This study included a prospective cohort study of 1,628 adults with diabetes in a large, health care organization receiving care in 84 clinics within 18 medical groups. Data from surveys of patients, clinic medical directors and managers, and medical record reviews were merged with 3 years of medical claims. Costs were estimated using health plan data on resource use and common Medicare payment methodologies. Generalized linear regression models were used to analyze costs related to clinic characteristics, adjusting for individual patient comorbidity, demographic, and socioeconomic factors. RESULTS: Clinics with regular clinician meetings to discuss patient care problems and clinics that used diabetes registries to prioritize patients based on cardiovascular risk were associated with lower 3-year costs: -$3,962 (P = 0.002) and -$2,916 (P = 0.019), respectively. The use of databases to monitor lab results was associated with higher costs ($2,439, P = 0.038). Quality improvement strategies focused on resource use related to diabetes care (-$2,883, P = 0.017) or heart disease care (-$3,228, P = 0.014) were associated with lowered costs, whereas quality improvement strategies that emphasized pharmacy use for patients with heart disease ($3,059, P = 0.029) or depression ($2,962, P = 0.038) were associated with increased costs. CONCLUSIONS: Several organizational features of primary care offices were significant predictors of future health care costs for adults with diabetes. The mechanism by which such factors affect costs of care and the relationship of costs to clinical outcomes merits further evaluation.